Research on the Improvement Path of Total Factor Productivity in the Industrial Software Industry: Evidence from Chinese Typical Firms

Abstract

The high-quality development of the industrial software industry is of strategic significance to enhancing the core competitiveness of the manufacturing industry and promoting the high-quality development of China’s industrial economy. By integrating the “capital-technology-environment-human” production factor theory and configuration perspective, this paper constructs a comprehensive analysis framework that drives the total factor productivity (TFP) of the industrial software industry. This paper uses 40 typical industrial software firms in 2018–2020 as case samples and uses fuzzy set Qualitative Comparative Analysis (fsQCA) to empirically explore the influencing factors and complex mechanisms that achieve high-quality development of the industrial software industry. It is found that: (1) a single industrial factor is hardly a necessary condition to drive the industrial software industry; (2) there are four paths to achieving high TFP, which are summarized as “technical-human-environmental” balanced driving type, “capital-human-environmental” balanced driving type, “technical-capital” dual driving type, and “capital” single driving type. There are four driving mechanisms. There are also four not-high TFP configurations with asymmetric characteristics; (3) under certain conditions, the combination of capital factors, technical factors, environmental factors, and human factors can drive TFP in an “all roads lead to Rome”. In this process, the government’s attention plays a more universal role. The study not only expands the application scenarios of fsQCA but also provides decision guidelines for the practice of strategic emerging industrialization represented by the industrial software industry.

Full text

Citation: Wang, X.; Wu, S.; Zhao, L.
Research on the Improvement Path of
Total Factor Productivity in the
Industrial Software Industry:
Evidence from Chinese Typical Firms.
Mathematics 2023,11, 4944. https://
doi.org/10.3390/math11244944
Academic Editors: Jorge de Andres
Sanchez and Laura González-Vila
Puchades
Received: 21 November 2023
Revised: 9 December 2023
Accepted: 10 December 2023
Published: 13 December 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
mathematics
Article
Research on the Improvement Path of Total Factor Productivity
in the Industrial Software Industry: Evidence from Chinese
Typical Firms
Xiaoxiang Wang 1,2,* , Songling Wu 3and Lixiang Zhao 1
1College of Economics and Management, Beijing University of Technology, Beijing 100124, China;
b201811002@emails.bjut.edu.cn
2School of Economics and Management, Wenzhou University of Technology, Wenzhou 325035, China
3
Business School, Henan University of Science and Technology, Luoyang 471023, China; 9901658@haust.edu.cn
*Correspondence: wangxiaoxiang626@163.com
Abstract:
The high-quality development of the industrial software industry is of strategic signif-
icance to enhancing the core competitiveness of the manufacturing industry and promoting the
high-quality development of China’s industrial economy. By integrating the “capital-technology-
environment-human” production factor theory and configuration perspective, this paper constructs a
comprehensive analysis framework that drives the total factor productivity (TFP) of the industrial
software industry. This paper uses 40 typical industrial software firms in 2018–2020 as case samples
and uses fuzzy set Qualitative Comparative Analysis (fsQCA) to empirically explore the influencing
factors and complex mechanisms that achieve high-quality development of the industrial software
industry. It is found that: (1) a single industrial factor is hardly a necessary condition to drive the
industrial software industry; (2) there are four paths to achieving high TFP, which are summarized
as “technical-human-environmental” balanced driving type, “capital-human-environmental” bal-
anced driving type, “technical-capital” dual driving type, and “capital” single driving type. There
are fo
ur driv
ing mechanisms. There are also four not-high TFP configurations with asymmetric
characteristics; (3) under certain conditions, the combination of capital factors, technical factors,
environmental factors, and human factors can drive TFP in an “all roads lead to Rome”. In this
process, the government’s attention plays a more universal role. The study not only expands the
application scenarios of fsQCA but also provides decision guidelines for the practice of strategic
emerging industrialization represented by the industrial software industry.
Keywords: industrial software industry; total factor productivity; fsQCA; path
MSC: 03E75; 91B86
1. Introduction
At present, many world manufacturing powers, such as “Made in China 2025” in
China, “Industrial Internet” in the United States, and “Industry 4.0” in Germany, have
implemented the development strategy of “intelligent manufacturing” at the national level.
Although China’s industrial capacity and export scale have been at the world’s leading
level, and its industrial added value as the world’s largest manufacturing country accounts
for about 30% of the world’s total, China is the only country with all industrial categories
(41 major categories, 207 medium categories, and 666 subcategories) in the United Nations
Industrial Classification, and has been leading other manufacturing economies in the world
for many consecutive years. However, even though China has such a huge industrial
scale, it is still facing “industrial software”, which cannot be bypassed by “intelligent
manufacturing” so far [
1
]. In 2021, at the General Assembly of the China Association for
Science and Technology and the General Assembly of the academicians of the Chinese
Mathematics 2023,11, 4944. https://doi.org/10.3390/math11244944 https://www.mdpi.com/journal/mathematics

Mathematics 2023,11, 4944 2 of 26
Academy of Sciences and the Chinese Academy of Engineering, China’s scientific and
technological breakthroughs should be based on the urgent needs of current and long-term
development, and focus on breakthroughs in core technologies in high-end chip fields and
industrial software. Industrial software is considered to be the most urgent problem to
be solved today, which is related to the country’s current economic development needs
and highlights the important strategic value of industrial software [
2
]. In 2021, industrial
software was included in the “National Key Research and Development Plan—the first
batch of key special research plan” of the Ministry of Science and Technology for the first
time, which represents that it has become the highest level of strategic deployment in the
domestic science and technology field, and also marks that China’s domestic industrial
software will step into a new stage of vigorous development. Academite Ni Guangnan
(2019) pointed out that China is facing the transformation from a “manufacturing power” to
a “manufacturing power”, and vigorously developing industrial software is the key support
for “intelligent manufacturing” and an important foundation for realizing high-quality
development of manufacturing [
3
]. Promoting the development of China’s “intelligent
manufacturing” to industrial “intelligent digitalization” can not only give priority to the
layout of the transformation and upgrading of “manufacturing power”, but also achieve
the purpose of rapidly occupying the middle- and high-end global market [4].
According to the statistics of the White Paper on China’s Industrial Software Indus-
try (2020), the market size of China’s industrial software in 2020 was 197.4 billion CNY,
accounting for only 6% of the global industrial software market size, with a year-on-year
growth rate of up to 15% (see Figure 1). In 2020, for example, China’s industrial-added
value exceeded 31.3 trillion CNY, accounting for nearly 30% of the world’s total. Although
the scale of the industrial added value industry is huge, the proportion of China’s industrial
software market in the world is too low, resulting in a very strong domestic demand for
industrial software [
5
,
6
]. Since 2020, with the continuous spread of COVID-19 worldwide
and the increased risk of anti-globalization in the international situation, China’s economic
development environment has undergone great changes, and the policy of “new pattern of
internal circulation” has become the main line of our future economic and social develop-
ment. In the process of high-quality development into a “manufacturing power”, China
is also facing a series of problems from the “bottleneck” of industrial software in Europe
and the United States. As shown in Figure 2, such as the United States banned ZTE and
Huawei from using industrial software related to chip design of integrated circuit Electronic
Design Automation (EDA), and then prohibited some domestic universities from using
MATLAB software (MATLAB Campus Edition) in course teaching [
7
]. Based on the above
research background, we find that the state attaches special importance to the development
of the industrial software industry and has formulated promotion policies through relevant
departments, which also shows that the government recognizes the important role of indus-
trial software in promoting the growth of the industrial economy. In October 2022, China’s
“Report to the 20th National Congress” emphasized the need to “accelerate the construction
of a modern economic system, strive to improve total factor productivity, and strive to
improve the resilience and safety level of industrial and supply chains. In addition, the
“14th Five-Year Plan for the Development of Software and Information Technology Service
Industry” issued by the Ministry of Industry and Information Technology of China in
November 2021 has a more detailed plan for the future development of industrial software.

Mathematics 2023,11, 4944 3 of 26
Mathematics 2023, 11, x FOR PEER REVIEW 3 of 27
(a) (b)
Figure 1. (a) 2012–2022 Global industrial software market size and growth rate; (b) 2012–2020 China
industrial software market size and growth rate.
Figure 2. Industrial software disablement event.
Why study total factor productivity (TFP) and improvement paths in the industrial
software industry? First, China’s industrial software affects the high-quality development
of intelligent manufacturing. The way to achieve high-quality development of the indus-
trial software industry is to optimize the allocation efficiency of the industry. Therefore,
improving the total factor productivity (TFP) of the industrial software industry is an ef-
fective means to achieve development of the industrial software industry. The total factor
productivity (TFP) of the industrial software industry is a measure of the high-quality
development of the industrial software industry. Second, when analyzing the influencing
factors of the industrial software industry, capital, technology, environment, and human
factors cannot fully explain the impact on the industrial software industry. Therefore, it is
necessary to study the path to improving the total factor productivity (TFP) of the software
industry through the configuration and combination of production factors. Finally, in the
process of China’s intelligent manufacturing transformation, a considerable number of
285.0 300.0 317.5 336.3 353.1 370.1 389.3 410.7 435.8 456.1 477.9
5.30%
5.80% 5.90%
5.00% 4.80% 5.20% 5.50%
6.10%
4.70% 4.80%
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
0.0
100.0
200.0
300.0
400.0
500.0
600.0
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022E
2012-2022 Global Industrial Software Market Size and Growth Rate
Market size（billion dollars）Growth rate（%）
728.6 810.2 939.9 1082.5 1194.1 1299.4 1477.0 1720.0 1974.0
2414.0
11.20%
16.00%15.20%
10.30% 8.80%
13.70%
16.50%
14.80%
22.30%
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
0.0
500.0
1000.0
1500.0
2000.0
2500.0
3000.0
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
2012-2021 China Industrial Software Market Size and Growth Rate
Market Size（billion RMB）Growth rate（%）
Figure 1.
(
a
) 2012–2022 Global industrial software market size and growth rate; (
b
) 2012–2020 China
industrial software market size and growth rate.
Mathematics 2023, 11, x FOR PEER REVIEW 3 of 27
(a) (b)
Figure 1. (a) 2012–2022 Global industrial software market size and growth rate; (b) 2012–2020 China
industrial software market size and growth rate.
Figure 2. Industrial software disablement event.
Why study total factor productivity (TFP) and improvement paths in the industrial
software industry? First, China’s industrial software affects the high-quality development
of intelligent manufacturing. The way to achieve high-quality development of the indus-
trial software industry is to optimize the allocation efficiency of the industry. Therefore,
improving the total factor productivity (TFP) of the industrial software industry is an ef-
fective means to achieve development of the industrial software industry. The total factor
productivity (TFP) of the industrial software industry is a measure of the high-quality
development of the industrial software industry. Second, when analyzing the influencing
factors of the industrial software industry, capital, technology, environment, and human
factors cannot fully explain the impact on the industrial software industry. Therefore, it is
necessary to study the path to improving the total factor productivity (TFP) of the software
industry through the configuration and combination of production factors. Finally, in the
process of China’s intelligent manufacturing transformation, a considerable number of
285.0 300.0 317.5 336.3 353.1 370.1 389.3 410.7 435.8 456.1 477.9
5.30%
5.80% 5.90%
5.00% 4.80% 5.20% 5.50%
6.10%
4.70% 4.80%
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
0.0
100.0
200.0
300.0
400.0
500.0
600.0
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022E
2012-2022 Global Industrial Software Market Size and Growth Rate
Market size（billion dollars）Growth rate（%）
728.6 810.2 939.9 1082.5 1194.1 1299.4 1477.0 1720.0 1974.0
2414.0
11.20%
16.00%15.20%
10.30% 8.80%
13.70%
16.50%
14.80%
22.30%
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
0.0
500.0
1000.0
1500.0
2000.0
2500.0
3000.0
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
2012-2021 China Industrial Software Market Size and Growth Rate
Market Size（billion RMB）Growth rate（%）
Figure 2. Industrial software disablement event.
Why study total factor productivity (TFP) and improvement paths in the industrial
software industry? First, China’s industrial software affects the high-quality development
of intelligent manufacturing. The way to achieve high-quality development of the indus-
trial software industry is to optimize the allocation efficiency of the industry. Therefore,
improving the total factor productivity (TFP) of the industrial software industry is an
effective means to achieve development of the industrial software industry. The total factor
productivity (TFP) of the industrial software industry is a measure of the high-quality
development of the industrial software industry. Second, when analyzing the influencing
factors of the industrial software industry, capital, technology, environment, and human
factors cannot fully explain the impact on the industrial software industry. Therefore, it is
necessary to study the path to improving the total factor productivity (TFP) of the software
industry through the configuration and combination of production factors. Finally, in the
process of China’s intelligent manufacturing transformation, a considerable number of

Mathematics 2023,11, 4944 4 of 26
industrial enterprises have low efficiency due to low intelligence and informatization, and
the industrial software industry directly affects the transformation and upgrading of the
manufacturing industry. Therefore, it is necessary to study whether China’s industrial
software industry can develop with high quality. The high-quality development of the
industrial software industry is measured by the total factor productivity (TFP) of the indus-
trial software industry. The improvement of the total factor productivity (TFP) of China’s
industrial software industry depends to a large extent on the R&D investment of domestic
industrial software companies, government support, and corporate R&D personnel invest-
ment. How to improve the total factor productivity (TFP) of the industry has become an
extremely realistic issue.
In summary, this paper starts with the total factor productivity (TFP) of the industrial
software industry, quantitatively measures the total factor productivity of my country’s
industrial software industry, analyzes the influencing factors of the total factor productivity
of the industrial software industry, and uses the fsQCA method to study its improvement
path. From the perspective of the overall analysis, we explore the best path for different
factors to improve the total factor productivity of the industrial software industry [8].
The main purposes of this study mainly include the following aspects: Firstly, using
fsQCA to study the improvement of TFP in China’s industrial software industry is to enrich
the literature and efficiency methods of China’s industrial software industry, especially
for the specific path to improve TFP in the industrial software industry [
9
,
10
]. Secondly,
explore the relationship between influencing factor variables and outcome variables from a
system perspective and use fsQCA to explain the outcome achieved by multiple different
path configurations. The research results of this paper not only conform to fuzzy logic
but are also more critical to the high-quality development of China’s industrial software
industr
y [9,11]
. Thirdly, the fsQCA method is used to focus on the asymmetric causal
relationship between cause and effect, which makes up for the limitations of symmet-
ric thinking based on correlation coefficients in traditional quantitative linear regression
research [9,12,13].
The rest of the paper is structured as follows: This article continues with Section 2,
which presents the relevant theoretical basis and analytical framework. In Section 3, we
discuss the materials and methods. Section 4exhibits the empirical analysis of the TFP
of the Chinese industrial software industry and also conducts a fsQCA to explore the
configurations of TFP improvement [
14
]. Conclusions and discussion are presented in
Section 5.
2. Theoretical Basis and Analytical Framework
Existing research has conducted preliminary and useful explorations on the topic of in-
dustrial software industry development, which has laid a certain foundation for subsequent
related research. However, there is still room for improvement in existing research. On the
one hand, existing research still lacks direct empirical evidence to explore the relationship
between relevant industry factors and industrial software industrialization. Compared
with traditional industries, the industrial software industry is a strategic emerging industry.
The differences in industrial attributes may lead to certain differences in the elements of
its industrialization. Therefore, it is very necessary to summarize the supporting factors
related to the industrial software industry with the help of theory. On the other hand, exist-
ing industrialization research is mostly one-dimensional and prefers to explore the single
impact of industrial factors on the level of industrialization. However, the industrialization
of industrial software itself is a highly complex phenomenon. During the development
process, there will definitely be the joint action of multiple factors and the coordinated
interaction of multiple factors. At the same time, there may also be situations where the
combination of factors is equivalent. In this context, multi-dimensional research is more in
line with the reality of high-quality development of industrial software. In addition, there
are certain limitations and mismatches in the methods adopted by existing studies. First,
quantitative methods based on regression ideas can only explain linear relationships or net

Mathematics 2023,11, 4944 5 of 26
effects but cannot reveal the nonlinear relationship between industrial factors, the level of
industrial software industrialization, or the underlying mechanism. Second, the application
of qualitative methods represented by case analysis has always faced doubts about the
representativeness of the sample and the validity of the generalization of the results.
In view of this, this paper attempts to creatively introduce the production factor theory
of “capital-technical-environmental-human” into the field of industrial development and
combines the configuration perspective to build an integrated analysis framework. It
uses the fuzzy set qualitative comparative analysis method to analyze China’s typical
industrial software companies, which are used as case samples to empirically analyze the
influencing factors and implementation mechanisms of industrial software industrialization.
Therefore, on the basis of this theory, this paper introduces the configuration perspective for
modification and finally forms an integrated analysis framework including six influencing
factors at the four levels of technology, capital, human resources, and environment (as
shown in Figure 3).
Mathematics 2023, 11, x FOR PEER REVIEW 5 of 27
by existing studies. First, quantitative methods based on regression ideas can only explain
linear relationships or net effects but cannot reveal the nonlinear relationship between
industrial factors, the level of industrial software industrialization, or the underlying
mechanism. Second, the application of qualitative methods represented by case analysis
has always faced doubts about the representativeness of the sample and the validity of the
generalization of the results.
In view of this, this paper aempts to creatively introduce the production factor the-
ory of “capital-technical-environmental-human” into the field of industrial development
and combines the configuration perspective to build an integrated analysis framework. It
uses the fuzzy set qualitative comparative analysis method to analyze China’s typical in-
dustrial software companies, which are used as case samples to empirically analyze the
influencing factors and implementation mechanisms of industrial software industrializa-
tion. Therefore, on the basis of this theory, this paper introduces the configuration per-
spective for modification and finally forms an integrated analysis framework including
six influencing factors at the four levels of technology, capital, human resources, and en-
vironment (as shown in Figure 3).
Figure 3. Analysis framework of total factor productivity driving mechanism in the industrial soft-
ware industry.
2.1. In Terms of Capital Factors
The development of the industrial software industry requires a large amount of cap-
ital investment, and capital investment plays an important role in the production process
of the industrial software industry. Capital investment is support for the stable develop-
ment of the industrial software industry, which is conducive to improving the develop-
ment environment of the industrial software industry and the infrastructure level of the
industrial software industry. The intensity of fixed asset investment can reflect the devel-
opment in the industrial software industry, so the amount of fixed asset investment is also
closely related to the total factor productivity of the industrial software industry. When
Bai Wen studied the factors affecting the efficiency of my country’s provincial software
industry through Tobit model regression analysis, he found that increasing fixed asset
investment is conducive to improving industrial efficiency [15]. He Xiong used empirical
analysis of soft packaging industry data from 28 provinces in China and found that in-
dustry scale, human input level, capital investment level, etc. are the main factors affecting
total factor productivity, and believed that the level of capital investment can improve soft
packaging industrial efficiency [16]. Based on the availability of data on China’s software
industry from 2000 to 2015, Guo Rengui and Qiao Yongzhong believe that the software
industry is affected by factors such as the intensity of copyright protection, income quota,
fixed asset investment, number of employees, export quota, etc. The intensity of copyright
Figure 3.
Analysis framework of total factor productivity driving mechanism in the industrial
software industry.
2.1. In Terms of Capital Factors
The development of the industrial software industry requires a large amount of capital
investment, and capital investment plays an important role in the production process of
the industrial software industry. Capital investment is support for the stable development
of the industrial software industry, which is conducive to improving the development
environment of the industrial software industry and the infrastructure level of the industrial
software industry. The intensity of fixed asset investment can reflect the development in
the industrial software industry, so the amount of fixed asset investment is also closely
related to the total factor productivity of the industrial software industry. When Bai Wen
studied the factors affecting the efficiency of my country’s provincial software industry
through Tobit model regression analysis, he found that increasing fixed asset investment
is conducive to improving industrial efficiency [
15
]. He Xiong used empirical analysis of
soft packaging industry data from 28 provinces in China and found that industry scale,
human input level, capital investment level, etc. are the main factors affecting total factor
productivity, and believed that the level of capital investment can improve soft packaging
industrial efficiency [
16
]. Based on the availability of data on China’s software industry
from 2000 to 2015, Guo Rengui and Qiao Yongzhong believe that the software industry is
affected by factors such as the intensity of copyright protection, income quota, fixed asset
investment, number of employees, export quota, etc. The intensity of copyright protection
A significant negative impact occurs, fixed asset investment has a significant negative
impact, and other remaining influencing factors are not significant [17].

Mathematics 2023,11, 4944 6 of 26
2.2. In Terms of Technical Factors
Li Xu analyzed the relationship between the technological innovation of software
companies and the performance of listed companies. The use of R&D funds and personnel
investment levels can reflect technological innovation capabilities. The higher the level
of R&D investment, the higher the technological innovation capabilities and corporate
performance levels [
18
]. Shao Jinju and Wang Pei measured the input and output efficiency
of China’s domestic software service industry. The key influencing factor, R&D investment,
is significantly positively correlated with the efficiency of the software service industry [
19
].
Jiao Yunxia [
20
] used the SFA method to analyze the factors that affect the efficiency of
China’s software industry. The influencing factors include the level of informatization (rep-
resented by the informatization development index), the level of specialization (represented
by the proportion of R&D personnel), and the level of R&D investment (represented by the
proportion of R&D funds) (represented by ratio), government support level (represented
by the proportion of government funding), and enterprise size (represented by the ratio of
total business income to the number of regional enterprises). Among them, investment in
R&D personnel can improve the efficiency level of the software industry, but the level of
R&D investment has a negative impact on the efficiency of the software industry. Chen
Guanju (2015) used the SFA method to study relevant data from 31 national-level software
industry bases from 2008 to 2012. The results showed that science and technology funding
can promote efficiency improvement, and science and technology funding is a key factor
affecting innovation efficiency [
21
]. Jiao Yunxia [
22
] used the SFA method to analyze the
factors that affect the efficiency of the software industry. The influencing factors include the
level of specialization (represented by the proportion of R&D personnel), the level of R&D
investment (represented by the proportion of R&D funds), and the degree of industrial
trade openness (represented by the proportion of export revenue) (represented by ratio),
enterprise size (represented by the ratio of total business revenue to the number of regional
enterprises), and these factors have a very significant impact. Among them, the level of
R&D personnel investment can improve the efficiency of the software industry, and the
level of R&D investment has a negative impact on the efficiency of the software industry.
Du Qiaoqiao (2019) analyzed the dimensions of production factors (indicating human
capital and innovation capabilities), industry dimension (indicating the development in
related industries), urban dimension (indicating city scale), and institutional dimension (in-
dicating government support) that affect the agglomeration level of the information service
industry. Five-dimensional factors include intensity (indicating intensity) and international
dimension (indicating the level of opening up to the outside world) [
23
]. Ye Hongyun
(2020) obtained two main factors that affect the performance of the industrial software in-
dustry through a literature review: technological innovation capability factors and resource
integration factors. The study also found that technological innovation has a significant
positive impact on the performance of enterprises, and resource integration also has a
significant positive impact on performance [
24
]. When Guo Chaoxian, Miao Yufei, et al.
(2022) analyzed the current competitiveness level of China’s industrial software industry,
the study believed that increasing R&D investment can improve the development level of
the industrial software industry [
25
]. Dai Xiaolong (2022) believes that industrial software
technology innovation and R&D investment are the keys to the development of industrial
software companies and are the key factors that promote high-quality development of
industrial software companies [26].
2.3. In Terms of Environmental Factors
When Chen Na (2013) analyzed the operating performance of China’s listed software
companies, she found that the company’s performance was positively correlated with the
proportion of the top five shareholders’ shareholdings to the company’s total shares [
27
].
When Zhiguang Li (2020) analyzed industrial ownership concentration, he believed that
there was a negative relationship between company performance and the shareholding
ratio of the company’s largest shareholder [
28
]. When Liao Mingyan et al. (2018) studied

Mathematics 2023,11, 4944 7 of 26
the efficiency of software industry clusters, they used the four-stage DEA method to mea-
sure the decomposition of TFP. Their study found that environmental factors are the key
influencing factors that limit the improvement of cluster efficiency [
29
]. Yan Xiaochang and
Huang Guitian (2019) used the software industry base as a research data sample and used
a panel regression model to measure the influencing factors on the development of the
software industry. The results concluded that enterprises and central government funds, tax
incentives, land incentives, and the preferential policies available to talents are significantly
positive. Therefore, they believe that government support policies are the main influencing
factor [
30
]. Tao Zhuo and Huang Weidong (2021) sorted out a series of relevant policies at
the national level and major provinces and cities regarding the domestic industrial software
industry, analyzed the specific current situation of the industrial chain, R&D chain, and
market chain, and distinguished between foreign and domestic representative provinces
(Jiangsu, Guangdong) industry development trends. It is proposed to improve the govern-
ment support environment (policies, tax incentives) [
31
]. Long Yuntao, Huang Tingting,
and others (2021) analyzed the root causes of bottlenecks that restrict the development
of domestic industrial software in China and proposed that improving the innovative
ecological environment (intellectual property protection, government tax exemptions) can
improve the development of industrial software [
32
]. When Zhou Yong, Zhao Dan, et al.
(2022) analyzed the development of China’s industrial software industry, they believed
that preferential tax policies, support for software trade, and other forms could enhance
the development of China’s industrial software [
33
]. When Guo Chaoxian, Miao Yufei,
and others (2022) analyzed the current competitiveness level of China’s industrial software
industry, they proposed ways to increase government loan support, insurance subsidy
support, application reward and subsidy support, and intellectual property protection to
improve industrial software industrial development [25].
2.4. In Terms of Human Factors
Shao Jinju and Wang Pei (2013) used the SFA method to measure the input and output
efficiency of China’s domestic software service industry and the Tobit model to empiri-
cally test the key factors affecting the efficiency of the software service industry. The key
influencing factors include scientific and technological innovation capabilities (represented
by R&D investment), urbanization level (represented by the proportion of the tertiary
industry to GDP and the proportion of non-agricultural population), human resource levels
(represented by the cost of employees with college or above and labor costs), infrastructure
level (represented by the number of Internet accounts), and industrial accumulation degree
(represented by location entropy). However, the results found that human capital has a
positive but not significant impact on efficiency [
19
]. Wu Lei et al. (2013) studied 12 soft-
ware industry cities in China. They believed that factors such as the number of high-tech
talents, R&D investment, and government support policies were important influencing
factors. Among them, the number of high-tech talents as a human capital factor can im-
prove efficiency levels [
34
]. Chen Guanju (2015) used the SFA method to study relevant
data from 31 national-level software industry bases from 2008 to 2012. The results showed
that human capital stock can promote efficiency improvement; human capital structure
is a key factor affecting innovation efficiency [
21
]. Tao Zhuo and Huang Weidong (2021)
sorted out a series of relevant policies at the national level and major provinces and cities
(Beijing, Guangdong, Shanghai, Jiangsu) about the domestic industrial software industry
and analyzed the specific status of the industrial chain, R&D chain, and market chain.
The development trend of the industry in foreign and domestic representative provinces
(Jiangsu and Guangdong) was analyzed, and it was pointed out that the talent structure of
industrial software practitioners is a key factor affecting the development of the industrial
software industry [
31
]. When Zhou Yong, Zhao Dan, et al. (2022) analyzed the break-
through path of industrial software development, based on the development situation of
China’s industrial software industry, they believed that supporting the training of industrial
software talents could improve the development of China’s industrial software [
33
]. Guo

Mathematics 2023,11, 4944 8 of 26
Chaoxian, Miao Yufei, and others (2022) found that the competitiveness level of China’s
industrial software industry needs to be improved compared with European and American
countries; they believe that paying attention to the talents of industrial software companies
can improve the development level of the industrial software industry [25].
3. Materials and Methods
3.1. Data Collection
After combing through the relevant literature of domestic scholars in China, it was
found that there is currently no sample data for the industrial software industry, and no
scholars have empirically studied how to obtain it because there is no statistical yearbook or
related database on the industrial software industry in China. Therefore, this paper refers to
the data acquisition methods of Ma Hong and Wang Yuanyue, Chu Deyin et al. (2016) [
35
],
and Ye Hongyun (2020) [
24
]. This paper considers the development of listed firms in the
industrial software industry or firms that have received IPO GEM acceptance. It is relatively
good. These typical firms are basically within 100 in the industrial software ranking list
and can represent the current development level of China’s domestic industrial software
industry. Therefore, this paper conducts sample screening and analysis of 848 domestic
industrial software firms in the “Directory of Chinese Industrial Software and Service
Firms” and collects and compiles available relevant data on industrial software firms.
As shown in Figure 4above, judging from the distribution of industrial software firms
in various provinces in China, the development of China’s industrial software industry is
extremely uneven. About 80% of the total number of industrial software firms is concen-
trated in five provinces, namely Beijing, Shanghai, Guangdong, Jiangsu, and Zhejiang. The
industry in these provinces is relatively developed, with a relatively large number of firms
in the industrial field, and the operating efficiency of industrial firms is relatively good.
Compared with other regions, they pay more attention to digital transformation. These
regions have a greater demand for industrial software. Therefore, this paper chooses to
study the basic situation of typical enterprises in the industrial software industry at the
micro-level.
This paper collects and collates the relevant data of industrial software firms and
takes the listed firms or IPO firms accepted by GEM among 848 industrial software firms
as samples, mainly including R&D and design industrial software firms, operation and
management industrial software firms, production control industrial software firms, indus-
trial Internet platform, and industrial APP industrial software firms. For the purpose of
empirical research, the data of the above firms is processed:
•
First of all, the input-output data of the DEA model cannot be negative. However, due
to the characteristics of some indicators, there may be situations where the original
data of some indicators is negative. This requires dimensionless processing of these
indicator data so that the processed original data is scaled to be within the positive
range.
•
Secondly, firms with missing enterprise indicator data or input-output indicators of
0 are eliminated. In order to ensure the research sample size, industrial software
accounts for the main business firms or certain comparable firms in the listed annual
reports of category firms are also used as supplementary samples.
•
Finally, after processing, relevant data of 40 typical industrial software firms from 2018
to 2020 were obtained (because the listing of typical industrial software firms in the
R&D and design category or the platform category is relatively late; even ZW Software
will be launched in 2021, resulting in a short time interval for data acquisition, so only
data from 2018 to 2020 can be selected in a limited manner).

Mathematics 2023,11, 4944 9 of 26
Mathematics 2023, 11, x FOR PEER REVIEW 9 of 27
Figure 4. Distribution of Chinese industrial software firms in 2021.
This paper collects and collates the relevant data of industrial software firms and
takes the listed firms or IPO firms accepted by GEM among 848 industrial software firms
as samples, mainly including R&D and design industrial software firms, operation and
management industrial software firms, production control industrial software firms, in-
dustrial Internet platform, and industrial APP industrial software firms. For the purpose
of empirical research, the data of the above firms is processed:
• First of all, the input-output data of the DEA model cannot be negative. However,
due to the characteristics of some indicators, there may be situations where the orig-
inal data of some indicators is negative. This requires dimensionless processing of
these indicator data so that the processed original data is scaled to be within the pos-
itive range.
• Secondly, firms with missing enterprise indicator data or input-output indicators of
0 are eliminated. In order to ensure the research sample size, industrial software ac-
counts for the main business firms or certain comparable firms in the listed annual
reports of category firms are also used as supplementary samples.
• Finally, after processing, relevant data of 40 typical industrial software firms from
2018 to 2020 were obtained (because the listing of typical industrial software firms in
the R&D and design category or the platform category is relatively late; even ZW
Software will be launched in 2021, resulting in a short time interval for data acquisi-
tion, so only data from 2018 to 2020 can be selected in a limited manner).
3.2. Variable Selection: Input-Output
Figure 4. Distribution of Chinese industrial software firms in 2021.
3.2. Variable Selection: Input-Output
The investment indicators selected by the industrial software industry are in line
with Bai Wen (2015) [
15
], Wang Zhen et al. (2016) [
36
], Wang Zhe et al. (2017) [
37
], and
Wang Huanfang et al. (2020) [
35
]. For research and analysis in the field, the number of
industry personnel at the end of the year is selected to represent the labor input index; in
line with the research of Zhou Jing (2011) [
38
], Liao Jing (2016) [
39
], and others, fixed asset
investment is taken as a capital investment; fixed asset investment can indirectly reflect
the scale of the firm and its development, etc. It is generally considered to be the material
guarantee for innovation and development; it is in line with the views of Ren Yousheng
and Qiu Xiaodong (2017) [
40
], Wang Huanfang et al. (2020) [
35
], etc., who choose R&D
investment as the capital investment indicator of the industrial software industry.
This paper takes the main business income and net profit of industrial software firms
as output indicators. Refer to the research of Li Zhifeng (2018), Yang Ruoxia (2018) [
41
],
and Wang Huanfang et al. (2020) [35]. Corporate performance can generally be measured
by corporate net profit indicators. Obtaining net profits is the purpose of corporate partic-
ipation in economic activities. Drawing on the research of Bai Wen (2015) [
15
], Liao Jing
(2016) [
39
], and others, they classified each enterprise into software. The main operating
income is used as an output indicator to measure the efficiency of software firms.
Based on the above analysis, according to the characteristics of the industrial software
industry and the common ground of industrial development in related fields, and referring
to the empirical research of scholars in related fields, the final selected input indicators are
the number of employees at the end of the year, investment in fixed assets, and investment
in R&D funds. The output indicators are the operating income and net profit of each

Mathematics 2023,11, 4944 10 of 26
software business owner (see Table 1). By processing the relevant data collected and
collated, this paper selects the input-output indicators of data. First of all, it fully considers
their availability, and second, it also refers to the index selection of other scholars to verify
the effectiveness of the index selection of the paper.
Table 1. Input-output indicators of typical industrial software firms.
Category Indicators Indicators Meaning
Input
Labor input
Number of employees at the end of the year (person)
Capital input R&D expenditure (10,000 CNY)
Investment in Fixed Assets (10,000 CNY)
Output Industry revenue Operating income of each business owner
(10,000 CNY)
Total net profit Total net profit of each firm (10,000 CNY)
3.3. Measurement Methods of TFP in China’s Industrial Software Industry
This paper chooses to use the DEA-Malmquist index method to measure the TFP of
typical firms in China’s industrial software industry for the following reasons:
•
First, this method does not require certain constraints or specific forms for the function.
The real development time of China’s industrial software industry is not long, and
the specific development situations of each category of industrial software are also
different, which makes it difficult to set a consistent production function suitable for
different types of industrial software. In this field, using the DEA-Malmquist index
method can avoid measurement deviations caused by setting different functional
forms to the greatest extent possible.
•
Second, the study uses relevant data from typical industrial software firms in China
from 2018 to 2020, which can analyze the overall TFP changes of the industrial soft-
ware industry and the TFP changes of sub-categories from the perspective of time
and category.
•
Third, this method is not affected by the selected input-output data unit, and it can
incorporate multiple input and output indicators.
•
Fourth, the TFP change index obtained by this method is the product of the technical
efficiency change index (EFFCH) and the technological progress rate change index
(TECHCH), and EFFCH is the change index of pure technical efficiency (PECH) and
the scale efficiency change index. EFFCH can be used to empirically analyze TFP from
the aspects of technological innovation level, capital investment status, R&D funds,
and changes in the number of employees in related industries, and explore the sources
of dynamic changes in TFP and the internal influencing mechanisms.
This section discusses methods of measuring TFP. Malmquist proposed the Malmquist
index for analyzing the consumption domain, and then the application of the Malmquist
index was extended to the production domain and combined with the data envelopment
method (DEA) to calculate the TFP [
9
,
42
]. At present, the DEA-Malmquist index method
based on output constructed by Fare et al. [
9
] is generally adopted to measure TFP. Its
formula is expressed as follows:
TF PC H =M0(xt+1,yt+1,xt,yt) = v
u
u
t
Dt
0(xt+1,yt+1)
Dt
0(xt,yt)×Dt+1
0(xt+1,yt+1)
Dt+1
0(xt,yt)(1)
Among them,
Dt
0(xt
,
yt)
,
Dt+1
0(xt+1
,
yt+1)
, respectively, represent the production ef-
ficiency distance function of period twith the technology of period tas a reference and
the production efficiency distance function of period t+ 1 with the technology of period
t+ 1
as a reference;
(xt
,
yt)
,
(xt+1
,
yt+1)
represents the input-output combination of period t
and t+ 1 [
9
]. Formula (1) expresses the change in total factor production efficiency of the
input-output combination of the software industry in period t to the input-output mix of

Mathematics 2023,11, 4944 11 of 26
the software industry in period t+ 1 [
9
,
42
]. When M
0
< 1, it means that the total factor
productivity from period tto period t+ 1 is decreasing; when M
0
= 1, it means that the
TFP from period tto period t+ 1 remains unchanged; when M
0
> 1, it means that the total
factor productivity from period tto period t+ 1 is increasing [
9
,
42
]. Formula (1) can be
further decomposed into the following:
M0(xt+1,yt+1,xt,yt) = Dt+1
0(xt+1,yt+1)
Dt
0(xt,yt)×v
u
u
t
Dt
0(xt,yt)
Dt+1
0(xt,yt)×Dt+1
0(xt+1,yt+1)
Dt
0(xt+1,yt+1)(2)
In Formula (2), the first term
Dt+1
0(xt+1,yt+1)
Dt
0(xt,yt)
on the right side of the equal sign represents
the change index of technical efficiency from period tto period t+ 1, denoted as EFFCH;
The second term
rDt
0(xt,yt)
Dt+1
0(xt,yt)∗Dt+1
0(xt+1,yt+1)
Dt
0(xt+1,yt+1)
represents the change index of technological
progress from period tto period t+ 1, denoted as TECHCH [9,42].
It can be seen that, under the condition of constant returns to scale [
9
], the equation of
TFP is as follows:
TF PC H =EFFC H ×TEC HCH (3)
3.4. Methodology: Apply fsQCA to Improvement Paths
This paper uses qualitative comparative analysis (QCA) to analyze the factors and
mechanisms that drive total factor productivity in the industrial software industry. There
are three main reasons: First, the improvement of TFP in industrial software is a complex
issue caused by multiple concurrent causes and effects. QCA can use configuration thinking
to test the linkage-matching effect of multiple factors, identify multiple equivalent paths
that drive the improvement of total factor productivity in the industrial software industry,
and explore potential substitute relationships between various factors. Second, the QCA
method can accurately locate The typical enterprise cases covered by each equivalent path
helping this article provide an in-depth explanation of the industrial development paths
of different types of industrial software enterprises. At the same time, QCA follows the
assumption of causality asymmetry, which can help this paper discover the differences and
reasons for the combination of conditions that produce high and non-high levels of total
factor productivity in the industrial software industry. Third, the variables selected in this
study are all continuous variables, and it is more suitable to adopt fuzzy set qualitative
comparative analysis (fsQCA) to reflect the changes in the degree and level of variables [
42
].
The QCA method set operation logical relationship is expressed in the form of Boolean
algebra, stipulating that the ~ symbol represents “not”, the * symbol represents “and”,
and the + symbol represents “or”. This method is to obtain different paths with strong
explanatory power for the outcome variables by screening and optimizing the consistency
value and coverage level of the antecedent condition configuration [
43
]. The consistency
value represents the similarity between the corresponding sample configuration combi-
nation and the original data, and the coverage represents the extent to which the sample
result variable can be explained by a specific configuration. The following are formulas
representing consistency value and coverage, respectively:
Consistency(Y≤X)=∑min(xi,yi)/∑xi(4)
Converage(Y≤X)=∑min(xi,yi)/∑yi(5)
Research steps of fsQCA method:
•Step 1: Select research case objects. Based on determining the content of the research,
delineate a scope according to attributes, such as category or subdivision level, and
then select the case objects to be studied based on the standards.

Mathematics 2023,11, 4944 12 of 26
•
Step 2: Determine the antecedent conditions and outcome variables. The outcome
variable of the research content is the core point, and the antecedent condition variables
are selected from the influencing factors involved in the previous research by scholars
to further construct the antecedent condition variable configuration. In the fsQCA
antecedent condition variable selection process, the range of the number of antecedent
condition variables is usually relatively small; generally, 4–6 are selected. Too many
antecedent condition variables will make the case objects “individualized,” which
cannot fully explain the regularity and integrity of cross-case objects [44].
•
Step 3: Quantify each variable and obtain case data. Based on the clarified variables,
combined with available case data, each variable is quantified, and relevant data
values are obtained using databases, corporate yearbooks, survey prospectuses, etc.
•
Step 4: Variable data calibration. Three calibration anchor points are set for each
variable to transform the original case data into a membership value between 0
and 1. The membership includes complete membership (membership value = 1),
fuzzy intersection point (membership value = 0.50), and complete non-membership.
(Membership value = 0), drawing on the research experience of relevant scholars,
95% is selected as the complete membership point, 5% as the incomplete membership
point, and 0.5 as the fuzzy intersection point. The original case data for each variable
is calibrated to fuzzy membership values [45].
•
Step 5: Test the sufficiency and necessity of a single variable. The adequacy test of
fsQCA can tell whether a single factor as an antecedent condition variable is a subset
of the outcome variable. If the test is not ideal, it means that improving total factor
productivity is the result of the interaction of multiple factors. Multiple different
antecedent condition variables are important for improving total factor productivity.
There is a complex relationship between factor productivity. fsQCA analysis tests
the necessity of a single factor and can determine whether the outcome variable is a
subset of the antecedent condition variables. The fsQCA method explores the impact
of different configurations of antecedent condition variables on outcome variables
under non-essential conditions. The antecedent condition variables are selected by
eliminating variables that pass the necessity test. According to scholars’ research,
if the consistency value exceeds 0.9, it is deemed that the test result is sufficient or
necessary [45].
•
Step 6: Construct a truth table. The calibrated case sample data is converted into a set
membership value, and a 2krow truth table can be generated, where krepresents the
number of antecedent conditions, and the antecedent condition variable configuration
in each row is a path that promotes the outcome variable. Set reasonable case sample
frequencies and consistency threshold values, eliminate configurations that do not
meet the set conditions, and finally build a truth table. Considering that the sample
size of domestic industrial software enterprise cases is relatively small, the frequency
threshold is set to 1 and the consistency threshold is 0.85 in this paper, which also
satisfies the requirement that the selected configuration samples account for more than
75% of the total case samples [46].
•
Step 7: Conditional combination configuration analysis. After calibration and analysis
of this method, complex solutions, simple solutions, and intermediate solutions can
be obtained. The complex solution does not consider the logical remainder, and
its analysis is more complicated and cumbersome. The simple solution completely
takes into account all the logical remainders, and it is definitely inconsistent with
the actual situation. The intermediate solution is to add the consistent part of the
logical remainder to the configuration without removing the necessary conditions for
the outcome variable. Researchers generally believe that the intermediate solution
is better than the other two solutions. The analysis of the paper is an intermediate
solution adopted to obtain the consistency value, original coverage, and unique
coverage values under each configuration. At the same time, this method also needs
to judge and analyze the antecedent condition variables. If the antecedent condition

Mathematics 2023,11, 4944 13 of 26
variables in the configuration all appear in the configuration of the intermediate and
parsimonious solutions, then this variable is considered to be the core variable, which
has an important influence on the outcome variable. It has a super strong influence; if
the antecedent condition variable only appears in the intermediate plan configuration,
then the variable is considered a non-core variable, and its impact on the outcome
variable is relatively weak [47].
4. Results
4.1. Results from the Measurement Model
From the perspective of the industrial software industry as a whole, the DEA-Malmquist
index analysis was conducted on the relevant data of listed companies or IPO GEM-
accepting companies in China’s industrial software industry from 2018 to 2020 to measure
the total factor productivity change index and its decomposition of typical companies
in the industrial software industry. The summary of results shows (see Table 2) that the
average annual total factor productivity of typical enterprises in China’s industrial soft-
ware industry is 0.965 and the average annual growth rate is
−
3.5%. After decomposing
the average total factor productivity of typical enterprises in China’s industrial software
industry, we get the annual average technical efficiency is 0.793, the annual average growth
rate of technical efficiency is
−
20.7%, the annual average technical progress rate is 1.216,
and the annual average growth rate is 21.6%, which shows the annual average growth rate
of typical enterprises in China’s industrial software industry. The reason for the decline in
total factor productivity comes from the decline in the annual average technical efficiency
growth rate. Further decomposing the technical efficiency of typical industrial software
enterprises in China, it can be seen that the annual average growth rate of pure technical
efficiency and scale efficiency has declined. The annual average growth rate of pure techni-
cal efficiency is 0.814, and the average annual growth rate is
−
18.6%. The annual average
value of scale efficiency is 0.975, and the average annual growth rate is
−
2.5%. From the
above analysis, it can be seen that the decline in the annual average growth rate of technical
efficiency is due to the decrease in the annual average value of pure technical efficiency
and the annual average value of scale efficiency. As a result, the total factor productivity
of China’s industrial software industry has declined, resulting in a low-end development
trend. This is due to the low level of optimal allocation efficiency of typical industrial
software enterprises; that is, the scale of the enterprise is too small, the daily management
capabilities of the enterprise are too weak, and the utilization of enterprise resources is too
low. Problems such as low levels are the main bottlenecks in improving the total factor
productivity of the industrial software industry.
Table 2. 2018–2020 Industrial software TFP change index and its decomposition.
Year EFFCH=PECH ∗SECH TECHCH PECH SECH TFPCH
2018–2019 0.612 1.473 0.683 0.896 0.901
2019–2020 1.029 1.004 0.969 1.061 1.033
Mean value 0.793 1.216 0.814 0.975 0.965
The analysis of the measurement model was done in Appendix Aand quantitative
results are summarized in Tables A1 and A2 of Appendix A.
From the index change from 2018 to 2019 (see Table A1), the technological progress rate
index is 1.473, and the technical efficiency, pure technical efficiency, and scale efficiency are
0.612, 0.683, and 0.896, respectively, indicating that the decline in TFP of typical enterprises
in China’s industrial software industry from 2018 to 2019 is mainly caused by the decline
in technical efficiency. Although enterprises have improved in technology update and
iteration, technology introduction, and other aspects, the utilization efficiency of production
factors in industrial software enterprises has been greatly reduced. From the index change
from 2019 to 2020 (see Table A2), the TFP of typical industrial software enterprises is 1.033,

Mathematics 2023,11, 4944 14 of 26
which indicates that the TFP of typical industrial software enterprises has increased by
3.3%, and its technical efficiency, pure technical efficiency, scale efficiency, and technological
progress rate are 1.029, 0.969, 1.061, and 1.004, respectively. Technical efficiency and
technological progress rates have changed significantly. It can be seen that although the
technological innovation and technological progress of industrial software enterprises have
not improved much from 2019 to 2020, the scale efficiency and daily management level of
industrial software enterprises have greatly improved from 2019 to 2020. This shows that
the combined effect of technical efficiency and technological progress rate promotes the
positive growth of TFP in the industrial software industry.
4.2. Results from fsQCA
4.2.1. Variable Selection and Descriptive Statistics
Wang and Jiang et al. [
48
] pointed out that the sample size of the fsQCA method
should be at least greater than or equal to 10. In this paper, the DEA-Malmquist index
analysis method is used to measure the TFP of the industrial software industry, and the
TFP of the industrial software industry in 2020 is used as the outcome variable of fsQCA [
9
].
Considering the time lag of the input and output of the industrial software industry, six
variables under the four dimensions that affect the TFP of the industrial software industry
in 2019 are selected, and the level of government support, fixed asset investment intensity,
R&D investment level, R&D personnel investment level, ownership concentration, and
education level are the antecedent condition variables (see Table 3).
Table 3. Descriptive statistics.
Variable Measurement Variables Variable Mean SD Max Min
Outcome Total factor productivity of industrial software industry TFP 1.134 0.510 3.004 0.136
Condition
Capital factors Fixed asset intensity (%) FIX 8.781 8.410 38.300 0.100
Technical factors R&D intensity (%) RD 16.644 12.227 54.550 0.330
R&D personnel intensity (%) RDP 41.857 19.593 90.280 11.400
Environmental factors Government support (‰) GOV 50.190 64.560 287.400 0.100
Ownership concentration (%) OC 56.145 19.325 97.750 23.330
Human factors Higher education (year) HE 16.349 0.619 18.329 15.332
4.2.2. Calibration of Variables
Unlike traditional variables, the dataset must be calibrated before it can be analyzed
by fuzzy set software. In the current version of the fsQCA 3.0 software, the calibration
is automatic and easy to perform once the three qualitative anchors are defined: full
membership, full non-membership, and crossover point [
9
]. This paper uses fsQCA to
analyze the relationship between the causal conditions (namely, the intensity of fixed
asset investment, the level of R&D investment, the level of R&D personnel investment,
the level of government support, and the level of education) and the outcome (TFP of
industrial software firms). In this paper, fsQCA is used to set the three qualitative anchors
of fuzzy sets of outcome variables and condition variables as full membership (95%), full
non-membership (5%), and crossover point (0.50) [
49
]. All variable calibration anchors
are shown in Table 4. Through qualitative anchors of outcome variables and condition
variables, the original values of all variables are transformed into fuzzy membership scores
(values between 0 and 1) by using the “calibrate” calibration command in fsQCA 3.0
software. However, there is a problem with the calibration in that it can produce a fuzzy
set membership score of exactly 0.5, which makes it difficult to analyze this situation due to
the ambiguity of the case member set. Therefore, the use of an exact membership score of
0.5 for causal conditions should be avoided. According to the research practices of previous
scholars, this paper adds a constant of 0.01 to the score of all fuzzy set members. Doing so
ensures that no cases are removed from the fuzzy set analysis [
50
]. Finally, the membership
scores of fuzzy sets are obtained.

Mathematics 2023,11, 4944 15 of 26
Table 4. Summary of the calibration of all variables.
Variable Measurement
Variables
Calibration Anchors
Full
Membership Crossover Full Non-
Membership
Outcome TFP 2.09 0.99 0.58
Condition
Capital factors FIX 17.40 6.95 0.30
Technical factors RD 40.45 13.26 2.31
RDP 78.08 38.49 12.86
Environmental
factors
GOV 211.10 29.35 0.40
OC 93.4 52.11 26.26
Human factors HE 17.44 16.22 15.56
4.2.3. Analysis of Necessity Conditions
Although the analysis of sufficient condition combinations is the most critical part of
the fsQCA study, the necessity of each condition must be tested before constructing the
truth table [
9
]. As suggested by researchers such as Xie, X., Wang, H. (2020) [
49
], and Ragin,
C. C. (2008) [
51
], if a single condition variable is required, the consistency and coverage of
each condition variable must be above the recommended threshold of 0.9; otherwise, it is
not a requirement. This study analyzes several condition variables of production factors
such as FIX, RD, RDP, and GOV, as well as the prerequisites for OC and HE to produce TFP
in the industrial software industry. In order to determine whether any of these 6 conditions
are required for total factor productivity in the industrial software industry, this paper
analyzes whether this antecedent condition variable always exists (does not exist) in all
cases where the outcome variable exists (does not exist). The results in Table 5show that
the necessary consistency of all individual variables is less than 0.9, which is not enough to
constitute a necessary condition for TFP in the industrial software industry. No antecedent
condition variable can independently improve the TFP of the industrial software industry.
One possible reason is that TFP in the industrial software industry is caused by multiple
factors, and therefore, no single factor is necessary for high or not-high TFP in the industrial
software industry [9].
Table 5. Necessity analysis on TFP and ~TFP.
Outcome/Condition
TFP ~TFP
Consistency Coverage Consistency Coverage
FIX 0.678659 0.701478 0.588534 0.590932
~FIX 0.604238 0.601865 0.702689 0.679922
RD 0.719073 0.752450 0.651953 0.662713
~RD 0.677674 0.667152 0.756469 0.723435
RDP 0.650074 0.666835 0.714865 0.712336
~RDP 0.719566 0.722057 0.665652 0.648863
GOV 0.643174 0.769004 0.592085 0.687684
~GOV 0.738787 0.650890 0.801116 0.685627
OC 0.705274 0.704926 0.714865 0.694089
~OC 0.693938 0.714721 0.696093 0.696447
HE 0.676195 0.701432 0.705226 0.710634
~HE 0.721045 0.715753 0.703704 0.678571
4.2.4. Constructing the Truth Table
In order to identify combinations of conditions that are logically sufficient for the
existence of an outcome, it is necessary to construct a truth table. The truth table needs to
be preliminary refined according to three criteria of frequency threshold, original consis-
tency, and proportional reduction in inconsistency (PRI) consistency before analysis [
9
,
49
].

Mathematics 2023,11, 4944 16 of 26
Although some recent scholars have shown that the fsQCA method is a very useful tool
for analyzing large N (i.e., more than 50 cases) case situations, most previous scholars’
studies using the fsQCA method mostly involve relatively small N case situations (i.e.,
10–50 cases) [
9
,
52
]. Ragin (2008) [
51
] and Jin et al. (2020) [
50
] suggested that for the case of
small N, the frequency cutoff of 1 is the most appropriate. However, for case scenarios with
large N, the frequency cutoff should be set higher with the number of cases. This paper
studies 40 cases of typical Chinese industrial software companies, which are consistent
with the situation of small N. Therefore, the frequency cutoff value is set to 1 in this paper.
In addition, the main representative scholar studies of the fsQCA approach suggest [9,53]
that at least 75–80% of all empirical cases should be included as part of the analysis [9,54].
In the study presented in this paper, we rely on both original consistency and PRI
consistency. This paper adopts the two rules suggested by Park (2020) [
55
] and other
scholars on the QCA method to determine the critical value of original consistency. Firstly,
the raw consistency should be higher than 0.85 for combinations/rows that reliably produce
high or non-high TFP [
52
]. Second, if there is a breakpoint in which agreement between
two rows decreases significantly from the row with a high level of raw consistency to the
row with the next level of raw consistency, then the breakpoint can be either high TFP or
not-high TFP [
9
]. For example, in the high TFP of the industrial software industry, there
is a significant decrease in consistency from line 29 with a consistency of 0.851163 to line
30 with a consistency of 0.845343 at the next level (see Table 6). For the not-high TFP of
the industrial software industry, there is a breakpoint between the consistency of 0.852252
in line 27 and 0.846875 in line 28 (see Table 6); therefore, we can decide to use 0.85 as the
original consistency cutoff. Therefore, the critical value selection to determine the original
consistency of the result column values in the truth table ultimately depends on the context,
and researchers should consider some decision criteria to determine the critical value cutoff
value based on their knowledge of the case and context [
55
]. In fsQCA fuzzy set analysis, it
is also important to consider PRI consistency scores. PRI consistency scores should be high
and ideally not too far from raw consistency scores (e.g., 0.75), Current best practice further
recommends that each solution meet a PRI consistency cutoff of 0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [
9
]. Tables of truth values are shown in Appendix B
(see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the attribute
combination that is always associated with the outcome and can obtain the complex
solution, parsimonious solution and intermediate solution of TFP and ~TFP, respectively.
Generally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solution
in the configuration mode.

Mathematics 2023,11, 4944 17 of 26
Table 6. Path configurations for achieving a high TFP.
Condition
Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
RD
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
RDP
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
GOV
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
OC
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
HE
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermediate
solution configuration and the simple solution configuration as core conditions, recorded
as “
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
” or “
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
” [
9
]; the antecedent conditions and parsimonious solutions that appear in
the intermediate solution are excluded the antecedent condition is defined as a peripheral
condition, represented by a small “
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
” or “
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
”, and a blank indicates that the condition
variable is insignificant [
45
]. Under the conditions of satisfying the consistency and cov-
erage of path configurations, the results show that there are 4 path configurations with
core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4 path
configurations with core conditions that can be used to evaluate the not-high TFP (i.e.,
paths L1–L4). The specific path configuration of TFP in China’s industrial software industry
is shown in Tables 6and 7[9].
Table 7. Path configurations for achieving a not-high TFP.
Condition
Outcome = ~TFP
L1a L1b L1c L1d L2a L2b L3 L4
FIX
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
RD
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
RDP
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
GOV
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
OC
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
HE
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Mathematics 2023, 11, x FOR PEER REVIEW 17 of 27
the original consistency of the result column values in the truth table ultimately depends
on the context, and researchers should consider some decision criteria to determine the
critical value cutoff value based on their knowledge of the case and context [55]. In fsQCA
fuzzy set analysis, it is also important to consider PRI consistency scores. PRI consistency
scores should be high and ideally not too far from raw consistency scores (e.g., 0.75), Cur-
rent best practice further recommends that each solution meet a PRI consistency cutoff of
0.65 [9].
In summary, this paper excluded from the subsequent analysis the leading combina-
tions that did not satisfy the frequency (1 or above), raw consistency (above 0.85), and PRI
consistency (above 0.60) criteria. As a result, the retained truth table contains 31 rows of
high TFP and 31 rows of not-high TFP [9]. Tables of truth values are shown in Appendix
B (see Tables A3 and A4).
4.2.5. Path Configuration Analysis
After obtaining the truth table in the previous section, this paper uses Ragin’s truth
table algorithm to conduct sufficiency analysis in this step so as to identify the aribute
combination that is always associated with the outcome and can obtain the complex solu-
tion, parsimonious solution and intermediate solution of TFP and ~TFP, respectively. Gen-
erally speaking, most researchers use an intermediate solution that is both general and
heuristic. This paper uses the intermediate solution to analyze the specific configuration
and combination model to improve the TFP of the industrial software industry, including
the configuration of each path, the raw coverage, unique coverage, consistency value, as
well as the coverage of the overall solution and the consistency value of the overall solu-
tion in the configuration mode.
Table 6. Path configurations for achieving a high TFP.
Condition Outcome = TFP
H1a H1b H1c H2a H2b H3 H4
FIX ● ● ● ● ● ●
RD
● ●
RDP ⊕ ⊕ ⊕
● ⊕
GOV ● ●
OC ⊕ ⊕ ⊕ ● ● ● ●
HE
● ● ●
Raw Coverage 0.4032 0.3800 0.3849 0.2765 0.2666 0.3760 0.2755
Unique Coverage 0.0276 0.0079 0.0039 0.0074 0.0030 0.1774 0.0237
Consistency 0.9317 0.9256 0.9029 0.9525 0.9508 0.8760 0.9459
Overall solution
Coverage 0.723016
Overall solution
Consistency 0.830221
Fiss (2011) defined the antecedent conditions for the overlap between the intermedi-
ate solution configuration and the simple solution configuration as core conditions, rec-
orded as “●” or “⊕” [9]; the antecedent conditions and parsimonious solutions that ap-
pear in the intermediate solution are excluded the antecedent condition is defined as a
peripheral condition, represented by a small “●” or “ ”, and a blank indicates that the
condition variable is insignificant [45]. Under the conditions of satisfying the consistency
and coverage of path configurations, the results show that there are 4 path configurations
with core conditions that can be used to evaluate the high TFP (i.e., paths H1–H4) and 4
path configurations with core conditions that can be used to evaluate the not-high TFP
(i.e., paths L1–L4). The specific path configuration of TFP in China’s industrial software
industry is shown in Tables 6 and 7 [9].
Raw Coverage
0.3415 0.3333 0.3699 0.3338 0.3470 0.3409 0.3125 0.2268
Unique Coverage
0.0178 0.0091 0.0036 0.0091 0.0223 0.0213 0.0036 0.0320
Consistency
0.9479 0.9467 0.9251 0.9777 0.9072 0.9573 0.9319 0.9293
Overall solution
Coverage 0.645358
Overall solution
Consistency 0.888268
The path configuration of China’s industrial software industry with high TFP as the
outcome variable is shown in Table 6. Through the analysis of intermediate solutions and
parsimonious solutions, four path configurations with core conditions were obtained to
improve the TFP of China’s industrial software industry. The overall solution consistency
score of the high TFP improvement path configuration of the industrial software industry
is 0.830221. The consistency scores of specific path configurations are 0.9317 (for path
configuration H1a), 0.9256 (for path configuration H1b), 0.9029 (for path configuration

Mathematics 2023,11, 4944 18 of 26
H1c), 0.9525 (for path configuration H2a), 0.9508 (for path configuration H2b), 0.8760 (for
path configuration H3), 0.9459 (for path configuration H4) [
9
]. Therefore, it can be seen that
the consistency value of each path configuration exceeds 0.85, and the consistency value of
the overall solution exceeds 0.80. This shows that the four path configurations have a good
explanation for the industrial software industry with high TFP. The TFP of the industrial
software industry can be improved through these four paths. The overall solution coverage
is 0.723016. Among the four path configurations, path configuration H1 (H1a raw coverage
value 0.4032, H1b raw coverage value 0.3800, H1c raw coverage value 0.3849) achieved
better performance than other path configurations (H2a raw coverage value 0.2765, H2b
raw coverage value 0.2666, H3 raw coverage value 0.3760, H4 raw coverage value 0.2755),
which indicates a higher relative empirical correlation [
56
]. Among them, H1 has higher
coverage, and most industrial software firms with high TFP achieve TFP improvement
through H1 path configuration. The above is in line with the qualitative comparative
analysis standards proposed by Woodside (2017) [53].
The detailed analysis of these four path configurations is as follows:
•
The capital path takes high fixed asset investment intensity, low R&D personnel
investment, and low equity concentration as the main adjustment means. High TFP
path configuration H1 includes path H1a (FIX*~RDP*~GOV*~OC*~HE), path H1b
(FIX*~RD*~RDP*~OC*~HE), and path H1c (FIX*~RD*~RDP*~GOV*~OC). Paths H1a,
H1b, and H1c show that high fixed asset investment intensity, low R&D personnel
investment, and low equity concentration are the core conditions for improving the
TFP efficiency of the industrial software industry. The auxiliary conditions of path
H1a are low government support and low education level. In path H1b, the other
two auxiliary conditions are a low R&D investment level and a low education level.
In path H1c, the other two auxiliary conditions are a low R&D investment level and
low government support. Path H1a describes that when the level of R&D personnel
investment is low, the degree of ownership concentration is low, but the fixed asset
investment intensity of industrial software companies is high, the TFP of the industrial
software industry can be improved even if there is a lack of high government support
and good education. Path H1b describes that when the level of R&D personnel
investment is low, the degree of ownership concentration is low, but when the fixed
asset investment intensity of industrial software companies is high, the TFP of the
industrial software industry can be improved even if there is a lack of high R&D
investment levels and good education levels. Path H1c describes that when the level
of R&D personnel investment is low, the degree of equity concentration is low, but
the fixed asset investment intensity of industrial software companies is high, the
TFP of the industrial software industry can be improved even if there is a lack of
high R&D investment levels and higher levels of government support. High TFP
path configuration H1 includes path H1a, path H1b, and path H1c. The case firms
represented by paths H1a, H1b, and H1c are Runhe Software firm, Jinzhi Technology
firm, and Dingjie Software firm, respectively.
•
The capital-human-environmental path takes high fixed asset investment intensity,
high equity concentration, and high educational attainment as the main adjustment
means. The high total factor productivity path configuration H2 includes path H2a
(FIX*~RDP*~GOV*OC*HE) and path H2b (FIX*~RD*~RDP*OC*HE). Paths H2a and
H2b show that high fixed asset investment intensity, high ownership concentration,
and high educational level are the core conditions for improving the total factor
productivity efficiency of the industrial software industry. The auxiliary conditions
for path H2a are low government support and low R&D personnel investment. In
path H2b, the other two auxiliary conditions are low R&D investment level and low
R&D personnel investment level. Path H2a describes that when the intensity of fixed
asset investment, ownership concentration, and educational level are high, the TFP
of the industrial software industry can be improved even if there is a lack of high
government support and R&D personnel investment. Path H2b describes that when

Mathematics 2023,11, 4944 19 of 26
the intensity of fixed asset investment, ownership concentration, and educational level
are all high, the TFP of the industrial software industry can be improved even if there
is a lack of higher R&D investment levels and R&D personnel investment levels. The
high TFP path configuration H2 includes path H2a and path H2b. The case firms
represented by paths H2a and H2b are Qingyun Technology firm and Baoxin Software
firm, respectively.
•
The technical-human-environmental path is based on high R&D investment lev-
els, high R&D personnel investment levels, high equity concentration, and high
education levels as the main adjustment means. High TFP path configuration H3
(RD*RDP*GOV*OC*HE). Path H3 shows that high R&D investment levels, high R&D
personnel investment levels, high ownership concentration, and high education levels
are the core conditions for improving the total factor productivity efficiency of the
industrial software industry. The auxiliary condition of path H3 is higher government
support. Path H3 describes that when there is a high level of R&D investment, a high
level of R&D personnel investment, a high degree of equity concentration, and a high
level of education, the TFP of the industrial software industry can be improved even if
there is a lack of high government support. The case firm represented by path H3 is
Zhongwang Software firm.
•
The technical-capital path is based on high fixed asset investment intensity, high R&D
investment level, and low R&D personnel investment level as the main adjustment
means [
9
]. High TFP path H4 (FAI*RD*~RDP*GOV*OC*~HE). Path H4 shows that
high fixed asset investment intensity, high R&D investment level, and low R&D
personnel investment level are the core conditions for improving the total factor
productivity efficiency of the industrial software industry. The auxiliary conditions of
path H4 are higher government support, higher ownership concentration, and lower
educational level. Path H4 describes when there is high fixed asset investment intensity,
high R&D investment level, and low R&D personnel investment level [
9
]. Even in the
absence of higher government support, higher ownership concentration, and lower
education levels, the TFP of the industrial software industry can be improved. The
case firm represented by path H4 is Yonyou Software firm.
The path configuration of China’s industrial software industry with not-high TFP as
the outcome variable is shown in Table 7. The overall solution consistency score of not-high
TFP path configuration in the industrial software industry is 0.888268. It can be seen that
the consistency value of each path configuration exceeds 0.85 and the overall solution
consistency value exceeds 0.80, which indicates that there are 4 path configurations with a
good explanation for the industrial software industry with low TFP, and the analysis of the
not-high TFP of the industrial software industry can be realized through these four paths.
The overall solution coverage is 0.645358, and most industrial software companies without
high TFP are configured for the L1c path.
The detailed analysis of these four path configurations is as follows:
•
External Environmental constrained path. The not-high TFP path configuration L1
includes L1a “FIX*~RD*RDP*~GOV*~HE”, L1b “FIX*~RD*RDP*~GOV*~OC”, and
L1c “RD*RDP*~GOV*~OC*~HE”, L1d “~FIX*RD*RDP*~GOV*~OC”. Paths L1a, L1b,
L1c, and L1d show that high levels of personnel investment and low levels of govern-
ment support are core conditions that are not conducive to improving the TFP of the
industrial software industry.
•
Capital-technical constrained path. The not-high TFP path configuration L2 includes
path L2a (~FIX*~RD*~GOV*OC*HE), and (~FIX*~RD*RDP*OC*HE). Paths L2a and
L2b show that low fixed asset investment intensity, low R&D investment level, and
high education level are core conditions that are not conducive to improving the TFP
of the industrial software industry.
•
Internal environmental-constrained path. Not-high TFP path configuration L3 (RD*RDP*
GOV*~OC*HE). Path H3 shows that high R&D investment levels, low ownership

Mathematics 2023,11, 4944 20 of 26
concentration, and high education levels are core conditions that are detrimental to
the TFP of the industrial software industry.
•
Environmental-human-constrained path. Not-high total factor productivity path L4
(~FIX*RD*~RDP*~GOV*OC*~HE). Path L4 shows that high R&D investment levels,
low government support, high ownership concentration, and low education levels are
core conditions that are unfavorable to the TFP of the industrial software industry.
5. Discussion and Conclusions
There are not many empirical studies on the TFP of the industrial software industry at
the micro-level, which provides a new perspective for studying the TFP of the industrial
software industry. Apply fsQCA to the analysis of the high-quality development path of
industrial software in the field of economics and obtain the synergistic path of multiple
variable factors, providing reference suggestions for industrial software companies to
choose a higher TFP path. Scholars’ current research using the fsQCA method is mostly
applied in management, sociology, and other fields. In recent years, some researchers have
begun to extend the application of the fsQCA method to the field of economics. Based on
the analysis of the configuration principle and the applicability of this method, this paper
uses the fsQCA method to obtain the path configuration of each factor to improve the TFP
of the industrial software industry [57].
This study has four findings: first, the necessity test finds that the six factors, includ-
ing technological innovation, cannot constitute the necessary conditions for promoting
high-quality development of the industrial software industry alone. Secondly, the config-
uration analysis finds that there are four paths to drive high-quality development in the
industrial software industry, which can be summed up as four driving modes: “technical-
human-environmental” balanced driving type, “capital-human-environmental” balanced
driving type, “technical-capital” dual driving type, and “capital” single driving type. These
fo
ur con
figurations and four modes reflect the multiple implementation methods of typical
enterprises in different industrial software industries. In addition, there are four paths
that produce non-high industrialization, and there is an obvious asymmetric relationship
between the two types of configurations. Finally, the analysis of the potential substitution
relationship finds that under specific objective endowment conditions, the combination
of technology, capital, human resources, and environmental factors can promote high-
quality development of the industrial software industry through equivalent substitution.
Among them, the government attaches importance to the significance of more important
values. Based on industrial development theory, the balanced drive of “technical -capital-
environmental-human” is an ideal implementation model. Industrial economics points out
that industrial development is a process of absorbing and integrating resource elements.
The balanced driving model of ideality means that the intensity of fixed asset investment,
the level of R&D investment, the level of R&D personnel investment, the degree of gov-
ernment support, the degree of ownership concentration, and the level of education in the
path allocation, as the production demand factors supporting the development of relevant
industries, together become the influencing factors to promote the improvement of total
factor productivity [58].
Based on the above conclusions, this paper makes four suggestions:
•
Implementing the technological innovation-driven strategy and implementing the
classification policy: Increase investment in R&D funds and human capital in the in-
dustrial software industry, implement a strategy centered on technological innovation,
and improve the utilization of R&D funds and human capital based on technological
innovation. In the early stages of technological innovation, a large amount of human
capital, R&D funds, etc. are required to be invested. Since the transformation of
technological innovation results is extremely slow, a long-term mechanism must be
established to ensure the sustainable operation of technological innovation. In October
2021, the 34th collective study session of the Political Bureau of the Chinese Central
Committee pointed out that it is necessary to comprehensively promote industrializa-

Mathematics 2023,11, 4944 21 of 26
tion and large-scale application, focus on breakthroughs in key software, promote the
software industry to become bigger and stronger, and enhance key software technology
innovation and supply capabilities.
•
Increase government support and accurately formulate government support policies:
The government and relevant industry participants should follow the development
rules of strategic emerging industries, gain insight into the internal correlations and
conflicts between various factors that affect industrial development, explore the key
factors and paths that restrict industrial development, and use information and intelli-
gent means for good industry whole-process management.
•
Coordinate efforts to support the training of industrial software talents through multi-
ple channels: Give full play to the open nature of the open source community, based
on national conditions, gather talents from multiple parties, promote the construc-
tion of industrial software open-source ecosystem, technical community construction,
open-source project cultivation, open-source group standard formulation, open source
technology promotion and application, open-source talent training, etc., and explore
the formation of an Internet environment. A new model for open source development
of industrial software. Provide policy guidance, intellectual property protection, open
source community construction, relevant standard formulation, data asset protection,
and other services for talent targets at all levels. It is necessary to improve the industrial
innovation distribution system and incentive mechanism, improve the development
evaluation system that is consistent with the characteristics of various talents, and
fully stimulate the motivation of talents to innovate. Respect human input and wis-
dom output, reasonably ensure personnel treatment, and increase the proportion of
personnel costs in project implementation. Promote the “industry-university-research-
application” coordination mechanism and encourage industrial software companies
to collaborate with universities and scientific research institutions to cultivate the
industry. Add industrial software courses in colleges and universities, strengthen the
construction of domestic industrial software training systems, and improve the level
of human-related industrial software applications.
•
The promotion of industrial software classification creates a good environment for
the development of China’s domestic industrial software industry. Promote the com-
bination of effective markets and promising governments around the industrial soft-
ware development environment, start from the market demand driven by industrial
enterprise software products and industrial enterprise application scenarios, imple-
ment policies by coordinating and integrating the policy resources of all parties, and
rationally allocate taxation and finance in the domestic manufacturing market, fi-
nancial support, and other resource support, forming an “internal circulation” and
“internal and external dual circulation” pattern for the development of the industrial
software industry.
This research had some limitations. This paper has shortcomings and issues worthy of
future research. This paper only uses relevant data from 40 industrial software firms, which
may lead to less than ideal accuracy of TFP and its decomposition indicators. Since the
development cycle of industrial software is relatively long and may be interfered with by
random factors, this paper uses the DEA-Malmquist index method, which is only suitable
for non-parametric estimation. This method ignores the impact of random factors on TFP
and attempts to use the SFA method to explore these factors.
Author Contributions:
Conceptualization, X.W., S.W. and L.Z.; Methodology, X.W.; Software, X.W.;
Validation, X.W. and L.Z.; Formal analysis, X.W.; Investigation, X.W.; Resources, L.Z.; Data curation,
X.W.; Writing, original draft preparation, X.W.; Writing, review and editing, X.W., S.W. and L.Z.;
Supervision X.W. and L.Z.; Project administration, X.W., S.W. and L.Z.; Funding acquisition, L.Z. All
authors have read and agreed to the published version of the manuscript.
Funding:
This research was supported by the National Social Science Foundation of China (Grant
No. 20BJY097).

Mathematics 2023,11, 4944 22 of 26
Data Availability Statement:
Survey supporting the study can be obtained by demanding it from
any author.
Acknowledgments: Authors acknowledge the helpful comments of anonymous reviewers.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. 2018–2019 Industrial software industry TFP change index and its decomposition.
Category Firm Name EFFCH TECHCH PECH SECH TFPCH
R&D and design
ZWsoft 0.612 1.449 0.753 0.812 0.886
Glodon 0.476 1.557 0.231 2.055 0.741
General electron 0.056 1.687 0.507 0.110 0.094
Gstarsoft 0.258 1.254 0.649 0.398 0.324
Anwise 0.462 1.634 0.942 0.490 0.754
S2C 0.802 1.217 1.000 0.802 0.977
Empyrean 0.706 1.362 0.820 0.861 0.962
Semitronix 1.289 1.634 0.933 1.381 2.106
YJK Building 1.000 1.139 1.000 1.000 1.139
Hollywave 0.668 1.417 1.000 0.668 0.946
Business
management
Yonyou Network 0.942 1.404 1.430 0.658 1.323
Neusoft 0.568 1.581 0.384 1.478 0.898
Dahua Technology 0.763 1.534 1.000 0.763 1.171
BMsoft 0.675 1.307 0.499 1.351 0.882
YGsoft 0.805 1.248 0.519 1.551 1.005
QM information 1.087 1.268 1.346 0.808 1.378
DHC Software 0.562 1.692 0.577 0.975 0.952
HAND Enterprise 0.168 1.615 0.081 2.072 0.272
HopeRun 0.140 1.452 0.112 1.253 0.203
DigiwinSoft 0.979 1.284 1.092 0.896 1.257
Production
control
Baosight 0.787 1.501 0.998 0.789 1.182
Taiji Computer 0.624 1.713 0.670 0.933 1.070
Supcon 0.834 1.239 0.865 0.964 1.034
Friendess Electronic 1.000 1.344 1.000 1.000 1.344
Wiscom System 0.716 1.713 0.776 0.922 1.226
Sifang Automation 0.564 1.703 0.498 1.132 0.961
Integrated Electronic 0.518 1.618 0.695 0.745 0.839
HITE 0.698 1.830 0.756 0.924 1.277
SCIYON 0.738 1.253 0.838 0.882 0.925
HuazhongCNC 0.599 1.861 0.779 0.769 1.115
Industrial
Internet and
industrial app
Nancal 0.800 1.486 0.874 0.916 1.189
Yonyou auto 0.793 1.331 0.578 1.373 1.056
QingCloud Tech 0.585 1.861 0.860 0.680 1.089
Thunder Soft 0.746 1.550 0.548 1.362 1.157
Autel 0.590 1.243 0.594 0.994 0.734
Seeyon 0.833 1.468 0.879 0.947 1.222
BONC 0.595 1.248 0.553 1.076 0.743
FII 0.763 1.474 1.000 0.763 1.125
GUOLIAN 1.000 1.581 1.000 1.000 1.581
UNIS 0.606 1.681 1.000 0.606 1.019
mean value 0.612 1.473 0.683 0.896 0.901

Mathematics 2023,11, 4944 23 of 26
Table A2. 2019–2020 Industrial software industry TFP change index and its decomposition.
Category Firm Name EFFCH TECHCH PECH SECH TFPCH
R&D and design
ZWsoft 1.219 0.828 1.120 1.089 1.010
Glodon 0.763 1.471 0.715 1.067 1.122
General electron 2.944 0.867 1.181 2.493 2.552
Gstarsoft 2.285 0.916 1.131 2.020 2.093
Anwise 1.175 0.492 1.714 0.686 0.578
S2C 0.179 0.760 0.835 0.215 0.136
Empyrean 1.034 1.242 0.994 1.040 1.284
Semitronix 1.337 1.208 0.958 1.396 1.615
YJK Building 1.000 0.925 1.000 1.000 0.925
Hollywave 0.770 1.139 1.000 0.770 0.877
Business
management
Yonyou Network 1.061 0.963 0.926 1.146 1.021
Neusoft 0.535 1.394 0.348 1.539 0.746
Dahua Technology 0.784 1.263 1.000 0.784 0.990
BMsoft 0.897 1.077 0.684 1.312 0.966
YGsoft 1.454 0.763 1.480 0.982 1.109
QM information 0.759 1.038 0.728 1.042 0.788
DHC Software 1.533 0.601 0.758 2.021 0.921
HAND Enterprise 1.947 0.491 1.761 1.106 0.956
HopeRun 2.958 1.015 1.872 1.580 3.004
DigiwinSoft 0.985 1.052 1.083 0.910 1.036
Production
control
Baosight 1.063 1.262 1.120 0.949 1.341
Taiji Computer 0.640 1.538 0.372 1.722 0.984
Supcon 1.190 0.828 0.750 1.587 0.986
Friendess Electronic 1.000 0.954 1.000 1.000 0.954
Wiscom System 0.544 1.731 0.773 0.704 0.942
Sifang Automation 1.131 1.455 1.129 1.002 1.646
Integrated Electronic 0.889 1.097 1.028 0.864 0.975
HITE 1.424 1.285 1.473 0.967 1.830
SCIYON 0.835 1.085 0.931 0.897 0.906
HuazhongCNC 0.754 1.952 1.053 0.717 1.472
Industrial
Internet and
industrial app
Nancal 0.805 1.358 0.940 0.856 1.094
Yonyou auto 1.314 0.725 1.329 0.989 0.952
QingCloud Tech 0.491 2.073 1.166 0.421 1.019
Thunder Soft 1.419 0.771 1.899 0.748 1.094
Autel 0.985 0.865 0.737 1.335 0.851
Seeyon 1.383 0.736 1.341 1.032 1.018
BONC 0.748 0.788 0.278 2.691 0.590
FII 1.447 0.647 1.000 1.447 0.936
GUOLIAN 1.000 0.998 1.000 1.000 0.998
UNIS 1.345 0.777 1.000 1.345 1.045
mean value 1.029 1.004 0.969 1.061 1.033
Appendix B
Table A3. Truth table (Outcome = TFP).
FIX RD RDP GOV OC HE NUMBER TFP RAW
CONSIST
PRI
CONSIST
SYM
CONSIST
1 1 1 1 1 1 1 1 0.966229 0.810527 0.810527
1 1 0 0 1 1 1 1 0.965251 0.766233 0.766234
1 0 0 1 1 1 1 1 0.961621 0.766234 0.766234
1 1 0 0 0 0 1 1 0.951563 0.75969 0.75969
1 0 0 0 1 1 1 1 0.949549 0.745454 0.745455
1 1 0 1 1 0 2 1 0.945854 0.776223 0.776224

Mathematics 2023,11, 4944 24 of 26
Table A3. Cont.
FIX RD RDP GOV OC HE NUMBER TFP RAW
CONSIST
PRI
CONSIST
SYM
CONSIST
1 0 0 0 0 1 1 1 0.937407 0.65 0.661018
1 0 0 1 0 0 1 1 0.937288 0.637255 0.637255
1 0 0 1 0 0 2 1 0.93426 0.727749 0.727749
0 0 0 1 1 0 1 0 0.930328 0.507247 0.507247
0 0 0 0 0 7 2 0 0.927565 0.5 0.571429
0 0 0 0 0 0 1 0 0.915398 0.468086 0.468085
0 1 0 1 1 1 1 0 0.913481 0.537634 0.537635
0 0 0 0 1 0 1 0 0.912162 0.469389 0.469389
1 1 1 1 1 0 1 0 0.910985 0.548076 0.548077
1 1 1 1 0 0 1 0 0.907873 0.59854 0.59854
1 0 0 1 1 0 1 0 0.906621 0.583333 0.583334
1 1 1 0 0 0 2 0 0.902208 0.392157 0.392157
1 0 1 0 0 0 1 0 0.894188 0.342593 0.342593
1 1 1 1 0 1 1 0 0.893855 0.366667 0.366666
0 0 1 1 1 1 2 0 0.886364 0.076923 0.086206
0 1 0 0 1 0 1 0 0.883576 0.377779 0.377778
1 0 1 0 1 0 1 0 0.879859 0.352381 0.352381
0 1 1 1 1 1 4 1 0.879357 0.623431 0.680365
1 0 1 0 0 1 1 0 0.877388 0.197916 0.197916
0 1 0 0 0 0 1 0 0.872381 0.417392 0.417392
0 1 1 1 0 1 1 0 0.86907 0.233334 0.233334
0 1 1 0 0 0 1 0 0.859247 0.148514 0.148515
0 0 1 0 1 1 2 0 0.851163 0.119266 0.131313
0 0 0 0 1 1 1 0 0.845343 0.278689 0.278689
0 1 1 0 0 1 1 0 0.84168 0.066667 0.066667
Table A4. Truth table (Outcome = ~TFP).
FIX RD RDP GOV OC HE NUMBER TFP RAW
CONSIST
PRI
CONSIST
SYM
CONSIST
1 1 1 1 1 1 1 1 0.988691 0.933333 0.933333
1 1 0 0 1 1 1 1 0.977273 0.815384 0.913793
1 0 0 1 1 1 1 1 0.97545 0.851484 0.851485
1 1 0 0 0 0 1 1 0.969745 0.802083 0.802084
1 0 0 0 1 1 1 1 0.964341 0.788991 0.868687
1 1 0 1 1 0 2 1 0.960152 0.766666 0.766666
1 0 0 0 0 1 1 1 0.944858 0.657407 0.657407
1 0 0 1 0 0 1 1 0.940246 0.721312 0.721311
1 0 0 1 0 0 2 1 0.938548 0.633334 0.633334
0 0 0 1 1 0 1 1 0.936909 0.607843 0.607843
0 0 0 0 0 7 2 1 0.934629 0.647618 0.647619
0 0 0 0 0 0 1 1 0.929314 0.622223 0.622222
0 1 0 1 1 1 1 0 0.928279 0.492754 0.492753
0 0 0 0 1 0 1 0 0.92555 0.531914 0.531915
1 1 1 1 1 0 1 0 0.922297 0.530611 0.530611
1 1 1 1 0 0 1 0 0.909457 0.375 0.428571
1 0 0 1 1 0 1 0 0.908571 0.582609 0.582608
1 1 1 0 0 0 2 0 0.899396 0.462365 0.462365
1 0 1 0 0 0 1 0 0.892045 0.451923 0.451923
1 1 1 1 0 1 1 0 0.889831 0.362745 0.362745
0 0 1 1 1 1 2 0 0.8861 0.233766 0.233766
0 1 0 0 1 0 1 0 0.880775 0.333333 0.338983
1 0 1 0 1 0 1 0 0.8742 0.233766 0.233766
0 1 1 1 1 1 4 0 0.86927 0.416666 0.416667
1 0 1 0 0 1 1 0 0.862647 0.40146 0.40146
0 1 0 0 0 0 1 0 0.855535 0.189473 0.189473
0 1 1 1 0 1 1 0 0.852252 0.254545 0.254545
0 1 1 0 0 0 1 0 0.846875 0.24031 0.24031
0 0 1 0 1 1 2 0 0.824273 0.272251 0.272251
0 0 0 0 1 1 1 0 0.812183 0.223776 0.223776
0 1 1 0 0 1 1 0 0.773458 0.292887 0.319635

Mathematics 2023,11, 4944 25 of 26
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