Content uploaded by Johannes W. Veile
Author content
All content in this area was uploaded by Johannes W. Veile on May 03, 2018
Content may be subject to copyright.
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 1 of 19
WHAT DRIVES INDUSTRY 4.0 ADOPTION? AN EXAMINATION OF TECHNOLOGICAL,
ORGANIZATIONAL, AND ENVIRONMENTAL DETERMINANTS
CHRISTIAN ARNOLD
Friedrich-Alexander University Erlangen-Nürnberg (FAU), Chair of Industrial Management,
Lange Gasse 20, 90403 Nürnberg, Germany
christian.arnold@fau.de (Corresponding)
JOHANNES W. VEILE
Friedrich-Alexander University Erlangen-Nürnberg (FAU), Chair of Industrial Management,
Lange Gasse 20, 90403 Nürnberg, Germany
johannes.veile@fau.de
KAI-INGO VOIGT
Friedrich-Alexander University Erlangen-Nürnberg (FAU), Chair of Industrial Management,
Lange Gasse 20, 90403 Nürnberg, Germany
kai-ingo.voigt@fau.de
ABSTRACT
Industry 4.0 refers to a novel production approach characterized by an entirely digitized and
connected industrial value creation (Kagermann et al. 2013). This is associated with several benefits,
e.g., increased resource efficiency, higher degrees of customization, and novel business models
(Bauernhansl 2014; Kagermann et al. 2013; Rehage et al. 2013). Nevertheless, manufacturing
companies show differences regarding adoption intensity and extent (Schmidt et al. 2015). Hence, it
is the purpose of this paper to examine, which factors determine the adoption of Industry 4.0 in
German manufacturing companies.
This study relates to technology adoption literature. Previous studies in this field revealed significant
differences between different companies regarding the adoption of new technologies (Schmidt et al.
2015). In this context, renowned literature identified various factors that determine the adoption of
different technologies by manufacturing companies. Since to date, there are no academic studies that
examined relevant factors determining the adoption of Industry 4.0 as a comprehensive concept, this
represents a clear research gap.
To address this gap, the technology-organization-environment framework of DePietro et al. (1990),
which has been successfully employed in several previous IT adoption studies, is applied. In total, the
applied model contains nine constructs. A quantitative study was carried out among German
manufacturing companies using a survey questionnaire as an instrument for gathering data. The final
sample consists of 197 usable responses. The developed hypotheses were tested using a logistic
regression.
This study reveals valuable insights with regard to relevant adoption factors in the context of Industry
4.0. In particular, the results show that relative advantage associated with Industry 4.0, support by a
company’s top management, and high levels of competition positively influence Industry 4.0 adoption,
while environmental uncertainty is the only determinant that negatively affects Industry 4.0 adoption.
The study further reveals no significant influence of perceived challenges, firm size, absorptive
capacity, and perceived outside support. In doing so, this study provides the first analysis of factors
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 2 of 19
that determine the adoption of Industry 4.0. Further research is still recommended as only German
manufacturers were analyzed and there was no differentiation between small and large companies.
Additionally, there are still various other potentially influencing factors, which are not addressed by
this study.
The results show important managerial implications for companies at the threshold of implementing
Industry 4.0 in their company. By revealing relevant factors that influence the adoption of Industry
4.0, this paper provides managers with valuable insights regarding important aspects to look at. This
study is among the first to analyze relevant adoption determinants especially for Industry 4.0. In this
course, several factors stemming from three perspectives, i.e., technology, organization, and
environment, are examined in one study. The results give a first indication about relevant
determinants influencing Industry 4.0 adoption behavior of manufacturing companies.
Key words: Industry 4.0; Industrial Internet of Things; German manufacturing companies; Technology
adoption; Quantitative research
INTRODUCTION
Industry 4.0 represents a novel paradigm of industrial value creation that aims at addressing the
arising challenges that manufacturing enterprises have to deal with. Among others, those companies
have to face shortened technology and innovation cycles, the necessity of bringing highly customized
products in accordance with the cost of a large-scale production, and intensified competition
originating in Asia, in particular (Bauer et al., 2014; Bauernhansl, 2014; Dais, 2014). At its core, Industry
4.0 enables the real-time capable, intelligent, horizontal, and vertical connection of people, machines,
and objects by employing cyber-physical systems and the internet (Bauer et al., 2014). The equipment
of machines and products with embedded systems like actuators, sensors, and microcomputers
provides them with intelligence, resulting in a so-called smart factory. This autonomous factory
enables a flexible and efficient execution of production and results in increased resource efficiency
(Rehage et al., 2013), higher degrees of customization (Kagermann et al., 2013), highly profitable
business models (Bauernhansl, 2014), and job designs suitable for future employee requirements
(Hirsch-Kreinsen and Weyer, 2014; Spath et al., 2013).
Although Industry 4.0 is associated with the aforementioned benefits, several studies have revealed
differences regarding adoption intensity and the extent to which it is adopted (McKinsey, 2015;
Schmidt et al., 2015). Considering previous research in the field of technology management, similar
observations are conspicuous for various technologies, since companies’ technology adoption
depends on several determinants. For instance, Chang et al. (2008) reveal factors that influence the
adoption of enterprise resource planning (ERP) systems, Peltier et al. (2012) examine, which factors
determine the decision of small businesses to implement technologies, and Sila (2013) analyzes the
implementation factors of e-commerce technologies. With reference to Industry 4.0, Arnold et al.
(2015) examine adoption factors of embedded systems, Reyes et al. (2016) study radio-frequency
identification (RFID) adoption, and Oettmeier and Hofmann (2017) ascertain determinants of additive
manufacturing technology adoption. Nevertheless, to date, there are no academic studies on the
relevant factors that determine the adoption of Industry 4.0 as a comprehensive concept.
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 3 of 19
Hence, the present paper aims to address this research gap by pursuing the following research
question: Which factors determine the adoption of Industry 4.0 in manufacturing companies? To
answer this question, we applied the technology-organization-environment (TOE) framework of
DePietro et al. (1990) and conducted a quantitative research design based on a sample of 197 German
manufacturing companies.
By doing so, we identified four significant Industry 4.0 adoption determinants from all three
dimensions of our framework, i.e., factors referring to Industry 4.0 itself, the respective organization,
and the organization’s environment. To be more precise, the significant factors are: relative
advantage, top management support, competition, and environmental uncertainty.
The remainder of the paper is structured as follows: In the next section, we propose the chosen
research framework and our hypotheses. Afterwards, the employed methodology is described before
the results are presented. Then, the findings are discussed, and finally, the conclusion contains
limitations, perspectives for future research, and managerial implications.
RESEARCH FRAMEWORK AND HYPOTHESES
Technology-organization-environment framework
The research framework applied for our study, i.e., the TOE framework, considers three dimensions
that impact technology adoption simultaneously. Firstly, the framework includes characteristics
specific to the technology under examination. Secondly, it incorporates aspects concerning the
adopting organization. Thirdly and lastly, it takes the organization’s external environment into
account. The TOE is among the most frequently employed frameworks in the context of technology
adoption research (Baker, 2012; Sila, 2013). It has been successfully employed in several IT adoption
studies, including RFID adoption (Wei et al., 2015), e-business adoption (Zhu and Kraemer, 2005; Zhu
et al., 2003), e-commerce adoption (Rodríguez-Ardura and Meseguer-Artola, 2010), electronic data
interchange (Iacovou et al., 1995; Kuan and Chau, 2001), and information systems (IS) (Thong, 1999).
In addition to the strong empirical support of the TOE framework, it allows for the integration of other
popular adoption theories like Rogers’ (1995) innovation diffusion theory or the technology
acceptance model of Davis et al. (1989) (Baker, 2012; Oettmeier and Hofmann, 2017; Wei et al., 2015;
Zhu et al., 2003). Furthermore, the TOE framework is rather generic and allows for the utilization of
various factors, thereby making it highly adaptable to different research contexts. This is a valuable
advantage over other models since unified theories consider specific technology characteristics only
in an insufficient manner (Baker, 2012; Vilaseca-Requena et al., 2007). For these reasons, we apply
the TOE framework in our study to examine our object of investigation.
Predicting factors related to the technological context
In this section, we introduce Industry 4.0 adoption determinants referring to the technological
perspective of the TOE framework. They include relative advantage, perceived challenges, and
compatibility. In the following, the reasons for including these three factors are discussed.
Relative advantage
A variety of studies have used relative advantage to explain technology adoption. Relative advantage
is “the degree to which an innovation is perceived as being better than the idea it supersedes” (Rogers,
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 4 of 19
1995, p. 212). Vowles et al. (2011) examine the determinants of voice over internet protocol (VoIP)
adoption in Australian companies and found relative advantage to be a significant predictor. Jurison
(2000) conducted a longitudinal study on adoption of office IS. According to his results, the perceived
relative advantage is positively associated with a rapid adoption and diffusion. Although many scholars
found relative advantage to be significantly positive related to technology adoption, some authors
could not find a significant correlation. Wei et al. (2015), for instance, studied the adoption of RFID
technology by Chinese companies and found relative advantage to be positively but not significantly
related to RFID implementation. Nevertheless, in a literature review of 51 studies on organizational IT
adoption, Jeyaraj et al. (2006) revealed that relative advantage is among both the most frequently
used predictors and the best predictors in terms of significance. We argue that relative advantage is
also a motivating factor for Industry 4.0 adoption as companies only implement new technologies if
benefits exceed potential negative effects. In this context, Industry 4.0 is, among others, associated
with increased resource efficiency, flexibility, and customization (Kagermann et al., 2013). Therefore,
we propose the following hypothesis:
H1. Relative advantage is positively related to Industry 4.0 adoption.
Perceived challenges
In addition to potential benefits for implementing companies, Industry 4.0 comes with several
challenges that deter companies from implementing this new value creation paradigm (Erol et al.,
2016). In particular, manufacturing companies have to adequately qualify employees, ensure IT
security, adjust their business models, adapt internal and external communication, and deal with an
unsure legal framework and missing standards (Bradley et al., 2014; Chen, 2012; Kagermann et al.,
2013). Consequently, we argue that perceived challenges associated with Industry 4.0 have the
potential to prevent manufacturers from adoption and are therefore of particular importance. Thus,
we add perceived challenges to our research framework and propose the following hypothesis:
H2. Perceived challenges are negatively related to Industry 4.0 adoption.
Compatibility
Compatibility is defined as “the degree to which an innovation is perceived as consistent with the
existing values, past experiences, and needs of potential adopters” (Rogers, 1995, p. 224).
Implementing IS, to which Industry 4.0’s underlying systems can be assigned, might easily fail, if they
are not compatible with the company’s culture (Yusuf et al., 2004). Compatibility is accordingly
included in various technology adoption studies. Chang et al. (2008) examined the adoption of ERP
systems by analyzing the usage behavior. They found compatibility to have a significant positive effect
and explain this by the fact that people within an organization are more likely to use a new technology,
if it fits to the organization. Waarts et al. (2002) also studied relevant factors determining the adoption
of ERP systems. They likewise revealed a high significance of the compatibility of the new ERP system
with already existing systems and equipment. We argue that this positive relationship between a
technology’s compatibility and its adoption also holds true for Industry 4.0. Companies may be more
likely to implement this new value creation approach and its underlying systems and processes if they
fit the existing processes. Therefore, we propose the following hypothesis:
H3. Compatibility is positively related to Industry 4.0 adoption.
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 5 of 19
Predicting factors related to the organizational context
In addition to technology-related factors, the TOE framework considers adoption determinants that
originate in the adopting organization itself. Therefore, we include three factors related to the
organizational perspective, i.e., firm size, top management support, and absorptive capacity.
Firm size
The size of a company has been proven to influence the adoption of new technologies in various
studies. This is evidenced by Jeyaraj et al.’s (2006) review of relevant technology adoption studies.
They learned that an organization’s size is among the most often utilized IT adoption factors and
represents a significant predictor in most cases. Gomez and Vargas (2009), who analyzed the adoption
of multiple process technologies by Spanish manufacturers, found firm size to have a significantly
positive impact on the decision to adopt new technologies. They argue that large companies have
more resources available, resulting in a higher ability to both finance an investment and absorb losses
associated with risky investments. Similarly, Ko et al. (2008) and Patterson et al. (2003) follow the line
of reasoning that larger organizations do not only have more financial resources than smaller
companies, but that they also have a higher risk capacity, which is required for investments in new,
risky technologies. Since the implementation of Industry 4.0 is associated with novel intelligent and
connected facilities as well as the adaptation of processes, large investments in IT and machines are
required. Consequently, we propose the following hypothesis:
H4. Firm size is positively related to Industry 4.0 adoption.
Top management support
In his examination of factors affecting the adoption of B2B e-commerce technologies, Sila (2013)
included the independent variable top management support since a positive commitment toward a
new technology is critical for a successful implementation. This is particularly true for
interorganizational systems (Grover, 1993), including Industry 4.0. Reyes et al. (2016) are in line with
Sila (2013) arguing that the adoption of RFID requires large investments. This in turn requires
involvement and support of top management as a prerequisite for a successful implementation.
Vowles et al. (2011) analyzed VoIP adoption and found a significant positive influence of a so-called
champion, who uses his/her power to support an innovation. Jeyaraj et al. (2006) also reveal that top
management support is among the best IT adoption predictors in terms of significance. In our opinion,
since the implementation of Industry 4.0 is accompanied by extensive organizational consequences
and substantial investments (Kagermann et al. 2013), support of top management is an important
factor for successful Industry 4.0 adoption. Therefore, we propose the following hypothesis:
H5. Support of top management is positively related to Industry 4.0 adoption.
Absorptive capacity
The absorptive capacity of a company is defined as its “ability to recognize the value of new
information, assimilate it, and apply it to commercial ends” (Cohen and Levinthal, 1990, p. 128). Cohen
and Levinthal (1990) emphasize the particular importance of absorptive capacity for successful
innovation implementation in an environment characterized by uncertainty. Since Industry 4.0
represents a new paradigm of value creation for manufacturing companies with substantial
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 6 of 19
uncertainty, absorptive capacity is a critical success factor in the context of Industry 4.0 adoption.
Previous studies examined absorptive capacity as a predicting factor for technology adoption. Wei et
al. (2015) revealed a significant positive relationship between a company’s absorptive capacity and
RFID adoption in China. According to them, prior experiences of related technologies foster the early
recognition of the value of new technologies. Vowles et al. (2011), who analyzed VoIP adoption, found
that the capability to absorb information about innovations is significantly positively related to the
VoIP adoption. Arvanitis and Hollenstein (2001) showed that absorptive capacity is a significant
determinant for the application of advanced manufacturing technologies. As Industry 4.0 can be
regarded as a combination of several advanced manufacturing technologies, we propose the following
hypothesis:
H6. Absorptive capacity is positively related to Industry 4.0 adoption.
Predicting factors related to the environmental context
Lastly, the TOE framework comprises determinants related to an organization’s environment.
Consequently, we included three respective factors in our research framework, i.e. competition,
environmental uncertainty, and perceived outside support.
Competition
With regard to IT adoption determinants involving the environment, competition is one of the best
predicting factors (Jeyaraj et al., 2006). In most studies, a positive relationship between competition
and technology adoption has been revealed. Zhu et al. (2003) argue that the adoption and
implementation of new technologies allows companies to change the rules of competition and
outperform rivals. Therefore, high levels of competition lead to an increased adoption of new
technologies. According to Gatignon and Robertson (1989), strong competition motivates companies
to watch narrowly competitive moves of rivals. Consequently, innovations are quickly adopted to
avoid falling behind. Vilaseca-Requena et al. (2007) analyzed the adoption of e-commerce in Spain and
reason that there is a positive influence of a more complex competitive environment because of the
perceived pressure to change the rules of competition. Thus, companies increasingly deploy
e-business to gain advantage over competitors. On the contrary, Rodríguez-Ardura and Meseguer-
Artola (2010) revealed a significant negative correlation between the pressure from a company’s
competitive environment and the adoption of e-commerce technologies. They explain this result by
the fact that companies operating in a business environment characterized by low competition can
allocate more resources for the development of innovations like e-commerce. Despite this opposing
finding, we follow the majority and argue that competition leads to Industry 4.0 adoption. This is
reasonable because manufacturing companies currently have to face increased competition and
Industry 4.0 is regarded as a possible response to this challenge (Bauer et al., 2014). Consequently, we
propose the following hypothesis:
H7. Competition is positively related to Industry 4.0 adoption.
Environmental uncertainty
Environmental uncertainty is characterized by “fluctuating prices, unpredictable competitor actions,
unreliability of inbound supplies, rapid change in production processes, rapid change in customer
preferences, volatile levels of demand, and/or quick product obsolescence” (Dröge and Germain,
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 7 of 19
1998, p. 28). Wei et al. (2015) find a significant negative relationship between environmental
uncertainty and the adoption of RFID technology. With regard to their results, they point out that a
positive relationship would be expected, based on previous studies. The deviation of their results can
be explained by the fact that they examined Chinese companies, which are more risk averse than
Western companies and tend to avoid high investments. Contrary to that and in accordance with the
prevailing view, Patterson et al. (2003) argue that companies facing high environmental uncertainty
have a greater motivation to adopt advanced, value chain spanning information technologies that
foster a fast and reliable share of data and production schedules. As a result, these companies improve
the exchange of information and data and are able to manage uncertainty between organizations.
Although they focus on supply chain technologies, their line of reasoning applies to Industry 4.0 as
well since the interconnection of entire supply chains is an inherent part of Industry 4.0 (Obermaier,
2016). Therefore, we propose the following hypothesis:
H8. Environmental uncertainty is positively related to Industry 4.0 adoption.
Perceived outside support
With perceived outside support, we understand it to include every activity conducted by organizations
external to the company that helps it to decide whether or not to adopt Industry 4.0. Previous studies
have showed the relevance of perceived outside support for innovation and technology adoption.
Cragg and King (1993), for instance, analyzed motivators for implementing IS in small firms and came
to the conclusion that expert consultations have a strong influence. Yap et al. (1992) similarly showed
that external expertise in terms of consultations and vendor support has a significantly positive effect
on the successful implementation of IS in small businesses. Waarts and van Everdingen (2005) and
Waarts et al. (2002) examined the adoption of ERP systems. In both studies, supplier activities proved
to be a significant adoption predictor. Likewise, Vowles et al. (2011) argue that suppliers can influence
VoIP adoption by offering information, education, and trials. According to Oettmeier and Hofmann
(2017), such outside support facilitates a company’s efforts to reduce uncertainties about a new
technology and therefore to better assess its potential. As Industry 4.0 is still subject to uncertainty at
this juncture, we argue that this rationale is also suitable for Industry 4.0. Thus, we propose the
following hypothesis:
H9. Perceived outside support is positively related to Industry 4.0 adoption.
Dependent variable
The dependent variable of our model is Industry 4.0 adoption. Adoption is part of the innovation-
decision process, which consists of five steps: (1) obtaining knowledge about an innovation or
technology, (2) building an attitude toward the technology, (3) deciding whether to adopt the
technology or not, (4) implementing the technology in the company, and (5) confirming the
technology during future use (Rogers, 1995). In relation to adoption, diffusion has to be differentiated.
While technology adoption refers to the decision and initial implementation, technology diffusion
concerns the subsequent spread of a technology within an organization (Hall and Khan, 2003). Hence,
Industry 4.0 adoption refers to the first implementation of this novel production approach into a
company, but does not require the entire production to be changed subsequently.
The following Figure 1 shows our research framework consisting of the three dimensions of
technology, organization, and environment. Additionally, the illustrated framework includes the
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 8 of 19
positive/negative relationship between each determinant and the adoption of Industry 4.0, as
proposed above.
Figure 1: Research Framework
METHODOLOGY
A cross-sectional field survey was conducted to collect data about Industry 4.0 adoption among
German manufacturing companies. The German market was chosen for several reasons. Firstly, the
term ‘Industry 4.0’ originally appeared in Germany. Secondly, the German manufacturing industry is
a global leader and has always been a pioneer in terms of implementing innovative technologies
(Breznitz, 2014). Lastly, the manufacturing sector is the most important one for the German economy
in terms of employees and contribution to the GDP (Federal Bureau of Statistics, 2016), which justifies
examining this industry in particular.
Data were gathered by means of an electronic survey based on ‘Unipark’, an academic online survey
software. Therefore, a list of German manufacturing companies was obtained from the bisnode
database that comprises German companies of all industries and sizes. Manufacturing companies
were identified and subsequently contacted via e-mail and telephone from January until March 2017.
The applied questionnaire consists of closed-ended questions. Section A contains demographics about
the respective company, followed by a dichotomous question about the dependent variable, i.e.,
whether the company has already adopted Industry 4.0 or not. Non-adopters were additionally asked
if an adoption is planned in the medium term. Section B comprises questions regarding the items that
measured the eight factors hypothesized in order to determine Industry 4.0 adoption in areas other
than firm size. They all consist of a Likert-Scale reaching from 1 (strongly disagree) to 5 (strongly agree).
Section C emphasizes personal data about the respondent. The items applied to measure the
constructs were borrowed from prior technology adoption studies and were modified to fit our
Or ganization
Fi r m size
Top mana gemen t support
Absorptive capacity
Technology
Rel a tive advantage
Per c ei ved cha l l enges
Compatibility
Environment
Competi ti on
Environmental uncer ta i nty
Per c ei ved outsi de support
Industry 4.0
adoption
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 9 of 19
purpose where appropriate. This procedure increases content validity (Ramsey et al., 2008). Table 1
shows the constructs, related items, if they are directly adopted or modified, and their sources.
Table 1: Constructs and items of this study
Constructs
Items
Origin
Source
Relative
advantage
(1) Industry 4.0 adoption is associated with cost reduction.
Modified
Chau and
Tam, 2000
(2) Industry 4.0 adoption is associated with increased
resource efficiency.
Modified
(3) Industry 4.0 adoption is associated with increased
production flexibility.
Modified
(4) Industry 4.0 adoption is associated with job design
oriented toward employee needs.
Modified
(5) Industry 4.0 adoption is associated with the offering of
customized solutions.
Modified
Compatibility
(1) Industry 4.0 fits our company well.
Adopted
Oettmeier
and
Hofmann,
2017
(2) The implementation of Industry 4.0 technologies would
require few firm-specific adaptations.
Adopted
(3) The physical integration of Industry 4.0 technologies
into our company would be unproblematic.
Adopted
(4) We could integrate the software necessary for Industry
4.0 with little effort into our existing IT landscape.
Adopted
Perceived
challenges
(1) Industry 4.0 adoption is associated with adequate
employee qualification.
Modified
Chau and
Tam, 2000
(2) Industry 4.0 adoption is associated with unsure legal
circumstances.
Modified
(3) Industry 4.0 adoption is associated with Industry 4.0-
specific business model adaptations.
Modified
(4) Industry 4.0 adoption is associated with necessary
guaranteeing of IT security.
Modified
(5) Industry 4.0 adoption is associated with internal
communication and coordination among departments
and locations.
Modified
(6) Industry 4.0 adoption is associated with supply chain
spanning communication with external organizations.
Modified
(7) Industry 4.0 adoption is associated with establishment
of standards.
Modified
Top
management
support
(1) Our top management is likely to invest funds in Industry
4.0.
Adopted
Sila, 2013
(2) Our top management is willing to take risks involved in
the adoption of Industry 4.0.
Adopted
(3) Our top management is likely to be interested in
adopting Industry 4.0 in order to gain competitive
advantage.
Adopted
(4) Our top management is likely to consider the adoption
of Industry 4.0 as strategically important.
Adopted
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 10 of 19
Table 1: Continued
Constructs
Items
Origin
Source
Absorptive
capacity
(1) Searching for relevant information regarding our
industry is part of employees’ daily tasks.
Adopted
Flatten et
al., 2011
(2) Corporate management motivates employees to
employ information sources internal to our industry.
Adopted
(3) Corporate management expects employees to deal
with information external to our industry.
Adopted
Competition
(1) Competition in our sector is "cut throat".
Modified
Ramsey et
al., 2008
(2) Our customers tend to look for new services all the time.
Adopted
(3) In our kind of business, customers' preferences for
services and products change quite a bit over time.
Modified
(4) An industry move to utilize Industry 4.0 would put
pressure on my firm to do the same.
Modified
Environmental
uncertainty
(1) The evolution of Industry 4.0 is unpredictable.
Adopted
Wei et al.,
2015
(2) The net payoffs of using Industry 4.0 remain uncertain.
Adopted
Perceived
outside
support
(1) There is a sufficient number of experts that could help
us to implement Industry 4.0.
Adopted
Mole et al.,
2004
(2) We could get outside support to help us
troubleshooting with little effort.
Adopted
Ten companies participated in a pilot test of the survey to assure comprehensibility and content
validity (Cooper and Schindler, 1998). Feedback resulted in slight adjustments of the phrasing of a few
questions. The final survey link was sent to 2,750 German manufacturing companies. As a result, we
received 362 questionnaires, representing a response rate of 13.2%. After a data-cleaning procedure,
which eliminated incomplete values, a total of 197 usable questionnaires were left, constituting a final
7.2% response rate.
Since non-response is a potential source of bias that has to be addressed (Fowler, 1993), we compared
data between early and late respondents. For this test, we employed size in terms of revenues and
ten randomly selected items. The results of the Mann-Whitney test shows no significant difference
between early and late respondents, indicating the absence of non-response bias (Ramsey et al.,
2008).
RESULTS
Results indicate that most respondents are at least at a senior manager level (60.4%) and are well
educated, as 70.1% graduated from university and 9.1% even hold a PhD. Further, most companies
operate in the machine engineering sector (26.4%), reflecting its strong position in the German
economy. Regarding Industry 4.0 adoption, 48 companies (24.4%) had already adopted this new
manufacturing paradigm, while the remaining 149 companies have not yet adopted it. Among non-
adopters, more than half of them (n = 82) intend to adopt Industry 4.0 in the medium term. Table 2
summarizes demographics of the sample.
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 11 of 19
Table 2: Demographics of respondents (n = 197)
Variables
Frequency
Variables
Frequency
Industry 4.0 Adoption
Gender
Adopted
24.4%
Male
86.3%
Not adopted
75.6%
Female
7.6%
Revenues (in million EUR)
Year of birth
0-10
30.5%
1940s
0.5%
10-50
30.5%
1950s
14.5%
50-100
10.7%
1960s
35.2%
100-250
6.6%
1970s
22.2%
250-500
4.6%
1980s
16.1%
500-1,000
1.0%
1990s
2.0%
1,000-5,000
4.1%
Educational level
5,000-10,000
0.5%
Apprenticeship
12.7%
10,000-50,000
4.6%
University degree
70.1%
>50,000
4.1%
PhD degree
9.1%
Industry sector
Length of service with company
Machine engineering
26.4%
1-10 years
49.7%
Plant engineering
7.6%
11-20 years
19.2%
Automotive
8.6%
21-30 years
17.1%
Electrical equipment
11.2%
31-40 years
5.0%
Metal products
14.7%
Job position
Electronics
10.7%
Executive board
13.7%
Rubber & plastics
5.1%
Top management
21.8%
Chemical products
5.1%
Senior management
24.9%
Middle management
17.7%
Employees with managerial responsibility
7.6%
Employees without managerial responsibility
11.2%
In order to test our model, three steps were conducted. Firstly, the validity of the applied constructs
was assessed by applying a factor analysis. Secondly, Cronbach’s alpha was calculated for each
construct to evaluate the reliability of each construct included in our research framework. Lastly, a
logistic regression was conducted to assess the impact of the independent factors on Industry 4.0
adoption.
A confirmatory factor analysis was conducted with SPSS 24. An initial run resulted in all items loading
on their intended factors. The only exception was compatibility, in which items did not load as
expected. Consequently, these items were dropped. As shown in Table 3, all items in the final analysis
loaded perfectly on predicted factors with values higher than 0.5 as suggested in the literature
(Hatcher, 1994). In order to assess construct reliability, Cronbach’s alpha was calculated. As shown in
Table 3, all constructs meet the required cut-off value of 0.7 (Nunnally, 1978). The only exception is
competition with a value of 0.66, which is also acceptable in explorative studies (Nunnally, 1978).
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 12 of 19
Table 3: Rotated component matrix
Relative
advantage
(RA)
Perceived
challenges
(PC)
Top
management
support
(TMS)
Absorptive
capacity
(AC)
Competition
(COM)
Environmental
uncertainty
(EU)
Perceived
outside
support
(POS)
Cron-
bach’s α
0.86
0.80
0.92
0.81
0.66
0.74
0.79
RA1
0.762
RA2
0.756
RA3
0.722
RA4
0.708
RA5
0.701
PC1
0.789
PC2
0.716
PC3
0.698
PC4
0.685
PC5
0.646
PC6
0.548
PC7
0.536
TMS1
0.803
TMS2
0.799
TMS3
0.797
TMS4
0.769
AC1
0.807
AC2
0.802
AC3
0.801
COM1
0.734
COM2
0.663
COM3
0.661
COM4
0.512
EU1
0.795
EU2
0.696
POS1
0.883
POS2
0.851
For the last step of data analysis, a logistic regression was conducted. We preferred a logistic
regression over a discriminant analysis, because the dependent variable is dichotomous, fewer
assumptions are required, and logistic regressions are more robust than discriminant analyses
(Dattalo, 1995). The chi-square test was significant (Omnibus χ² = 54.264, df = 8, p = 0.000) and
Nagelkerke R2 (= 0.467) proved satisfactory. Furthermore, the Hosmer and Lemeshow test showed a
satisfactory goodness-of-fit (χ² = 6.189, df = 8, p = 0.626), indicating no significant difference of the
proposed model compared to a perfect one (Chau and Tam, 1997).
Regarding hypothesized adoption factors, the results are displayed in Table 4 and show four significant
determinants. Relative advantage, top management support, and competition each have a significant
positive effect on Industry 4.0 adoption, which are all in line with our expectations. Hence, hypotheses
1, 5, and 7 are supported. Environmental uncertainty also has a significant effect, which is negative
and therefore opposite our proposed positive correlation. Thus, hypothesis 8 cannot be supported.
No significance can be revealed for perceived challenges, firm size, absorptive capacity, and perceived
outside support. Consequently, hypotheses 2, 4, 6, and 9 have to be rejected. Additionally, we
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 13 of 19
controlled for industry sector. As we did not find a significant correlation between any industry sector
and Industry 4.0 adoption, we can assume that industry affiliation does not play a role regarding the
adoption of Industry 4.0.
Table 4: Results of logistic regression
Variables
B
Exp(B)
S.E.
Wald
Sig.
Hypothesis
Relative advantage
1.535
4.641
0.378
16.495
0.000
supported
Perceived
challenges
-0.118
0.889
0.295
0.159
0.690
rejected
Firm size
0.109
1.116
0.095
1.338
0.247
rejected
Top management
support
0.656
1.927
0.291
5.068
0.024
supported
Absorptive capacity
0.482
1.620
0.307
2.463
0.117
rejected
Competition
0.772
2.164
0.322
5.737
0.017
supported
Environmental
uncertainty
-0.816
0.442
0.275
8.808
0.003
opposite the prediction
Perceived outside
support
0.187
1.205
0.270
0.479
0.489
rejected
DISCUSSION
In the following section, the results are discussed in detail. Regarding technology-related factors, our
study shows that relative advantage of Industry 4.0 has a highly significant positive effect on the
adoption decision. Moreover, this factor represents the most influential determinant. This indicates
that benefits associated with Industry 4.0 play the most important role when deciding whether or not
to adopt this new production approach. The significance of this construct is consistent with our
expectation and confirms the findings of previous studies that examined the effect of a technology’s
benefits on technology adoption (Jurison, 2000; Vowles et al., 2011; Waarts et al., 2002).
The second technology-related factor, perceived challenges, has no significant influence on the
adoption of Industry 4.0. This contradicts our expectation but reflects the fact that we did not find any
technology adoption study identifying challenges to have an impact on the adoption decision. This
indicates that companies tend to have faith in Industry 4.0’s benefits rather than being deterred by
potential challenges. One explanation might be the fact that German manufacturers have always been
pioneers in implementing innovative technologies (Breznitz, 2014). Consequently, they are used to
facing associated challenges and and have learned to overcome them.
With reference to the organizational perspective, firm size is not a significant predictor of Industry 4.0
adoption. This is surprising, as in previous technology adoption studies, size usually proved to be a
significant determinant (Gomez and Vargas, 2009; Ko et al., 2008; Mole et al., 2004). An explanation
for this deviation might be rooted in our sample. As shown in Table 2, the majority of the respondents
are rather small companies and only very few can be characterized as large. Although this reflects the
distribution among German manufacturers, which are typically small and medium-sized enterprises
(Federal Bureau of Statistics, 2016), the comparably small portion of large companies in the sample
could lead to the result that size has no significant influence on Industry 4.0 adoption.
The second significant organizational adoption determinant is top management support. Although this
factor has the weakest positive influence, the results emphasize that involvement and active support
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 14 of 19
of a company’s executives is of particular importance regarding Industry 4.0 adoption. This finding is
in accordance with our expectation and previous findings. Vowles et al. (2011) identified influential
persons within a company, like top managers, who make use of their power to push technology
implementation. Similarly, Reyes et al. (2016) came to the same conclusion. According to them,
support of top management ensures that required financial resources are appropriately allocated to
successful implementation of new technologies in a company.
Absorptive capacity did not have a significant influence on Industry 4.0 adoption. This contradicts both
our proposed hypothesis and prior studies (Ramsey et al., 2008; Wei et al., 2015). Nevertheless,
Vowles et al. (2011) found at least partial insignificance on condition that the dependent variable was
differentiated between respondents, which implemented VoIP and respondents who had never heard
of VoIP. Since this is the only study we found, which finds no significant correlation, our results are a
little bit surprising as we assume that all respondents who filled out the questionnaire have heard
about Industry 4.0. A reason for this might be the fact that, due to their pioneering position, German
manufacturing companies apply comprehensive technology scouting, independently from being an
Industry 4.0 adopter or non-adopter (Kiel et al., 2015). Hence, our sample companies might all possess
a comparably high absorptive capacity, which therefore cannot act as a differentiator between
adopters and non-adopters.
Regarding the third dimension of our research model, i.e., environment, the companies’ competition
has a significant positive impact on Industry 4.0 adoption, supporting our proposed hypothesis. This
finding is in line with most previous technology adoption studies, which argue that competitive
pressure urges companies to apply new technologies in order to gain a competitive advantage
(Vilaseca-Requena et al., 2007; Zhu et al., 2003). A similar line of reasoning can be given for
Industry 4.0. Manufacturers face increased competition, particularly from new entrants from Asia
(Kagermann et al., 2013). As Industry 4.0 is supposed to be a new production approach to cope with
this challenge (Bauer et al., 2014), competitive pressure might lead to increasing Industry 4.0
adoption.
Environmental uncertainty also has a significant influence on Industry 4.0 adoption. Contrary to the
previous factors, it has a negative effect and contradicts our proposition. This is surprising since the
line of reasoning is similar to that of competition, which has a positive effect. We expected increasing
environmental uncertainty to foster Industry 4.0 adoption in order to improve information flows and
therefore reduce uncertainty. It is probable that the uncertainty perceived by the sample companies
cannot be reduced by an enhanced flow of information. The source of uncertainty might instead be
the unknown further development of Industry 4.0, e.g., regarding standards. Hence, the less
companies feel sure about future standards, the less likely they are to engage in Industry 4.0 adoption.
Lastly, perceived outside support was tested and found to have no significant effect on Industry 4.0
adoption. Either manufacturers seem not to be willing to draw on external support or such support
does not exist. In particular, the former reason corresponds to previous research revealing that
German manufacturers rarely build on external organizations. Arnold et al. (2016) show that external
partners play a subordinate role in terms of Industry 4.0-triggered adjustments of a company’s
business model. This aversion to integrating external organizations into the business model seems to
apply to the support from outside with reference to Industry 4.0 adoption as well.
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 15 of 19
CONCLUSION AND IMPLICATIONS
In summary, the results indicate that factors from all three perspectives, i.e., technology, organization,
and environment, have a significant influence on the adoption of Industry 4.0. In particular, relative
advantage, top management support, and competition positively affect Industry 4.0 adoption.
Environmental uncertainty, in contrast, has a negative effect. Among these determinants, relative
advantage has the strongest impact. This is particularly interesting against the background that
perceived challenges associated with Industry 4.0 have no significant influence, indicating that
benefits play a substantially more important role for the adoption decision.
This study enhances existing research on technology adoption as well as on Industry 4.0 in several
ways. Various factors that already proved to be significant in previous examinations were assessed by
applying the pervasive TOE framework of DePietro et al. (1990). In this course, we were able to extend
the validity of earlier results. The three determinants of relative advantage, top management support,
and competition, which proved to have a significant positive effect on the adoption of a multitude of
technologies so far, affect Industry 4.0 adoption in the same way. Moreover, environmental
uncertainty, which proved to have also a significant positive influence in prior studies, shows a
negative impact on Industry 4.0 adoption. Firm size, absorptive capacity, and perceived outside
support do not seem to affect the adoption of Industry 4.0, although they have proven to do so in the
context of other technologies. Perceived challenges were tested for the first time as a potential
adoption predictor but do not play a noteworthy role, at least for our sample. Especially with regard
to Industry 4.0 research, our findings provide novel insights. Previous research dealt with potential
effects of Industry 4.0 implementation from several perspectives, but failed to examine relevant
factors influencing the adoption decision.
The findings also provide valuable insights for managers. Firstly, top management support shows a
significant positive influence on Industry 4.0 adoption. Therefore, companies that plan to implement
Industry 4.0 in their industrial value creation should involve the executive board and ensure
appropriate support. Secondly, the absence of significance with regard to perceived challenges
associated with the adoption of Industry 4.0 should not lead to a negligence of those challenges.
Nevertheless, they should also not be overemphasized, and should be assessed appropriately,
particularly against the background of potential benefits. Thirdly, when facing increasing competitive
pressure, manufacturers are well advised to consider adopting Industry 4.0. Otherwise, these
companies might fall behind competitors who invest in Industry 4.0 in order to achieve a competitive
advantage. Lastly, environmental uncertainty, particularly in terms of lacking standards, prevents the
adoption of Industry 4.0. Therefore, we recommend manufacturers to participate in respective
committees and organizations to drive the establishment of standards.
Despite this study’s contributions, there are some limitations. The sample consists only of German
manufacturing companies. Since Industry 4.0 is also relevant for other companies, e.g., service
providers and companies from other countries, future studies should consider respective companies.
Especially a sample with foreign companies would allow for the analysis of potential cultural
influences. Furthermore, this study includes firm size as an independent variable but does not
differentiate between small and large companies. Since it is probable that different factors determine
Industry 4.0 adoption in small and large companies, future research should differentiate between
companies’ sizes. Regarding the influencing factors analyzed in this study, they represent only a small
portion of all potential adoption determinants. Therefore, future studies should consider other factors
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 16 of 19
that proved to be significant in previous studies as well, e.g., organizational structure, or factors that
are newly defined specifically for the Industry 4.0 context.
REFERENCES
Arnold, C., Kiel, D., Baccarella, C., Voigt, K.-I., and Hoffmann, D., (2015), Technology Adoption with
Reference to Embedded Systems. In Proc. 2nd Int. Conf. Advances in Management, Economics and
Social Science, theIRED (ed.), pp. 119-127. Rome: Italy.
Arnold, C., Kiel, D., and Voigt, K.-I., (2016), How the Industrial Internet of Things Changes Business
Models in Different Manufacturing Industries. International Journal of Innovation Management,
20(8), 1640015-1-1640015-25.
Arvanitis, S., and Hollenstein, H., (2001), The determinants of the adoption of advanced
manufacturing technology. Economics of Innovation and New Technology, 10(5), 377–414.
Baker, J., (2012), The Technology–Organization–Environment Framework. In Integrated Series in
Information Systems. Information Systems Theorie. Explaining and Predicting Our Digital Society, YK
Dwivedi, MR Wade, and SL Schneberger (eds.), pp. 231-245. New York: Springer.
Bauer, W., Schlund, S., Marrenbach, D., and Ganschar, O., (2014), Industrie 4.0 –
Volkswirtschaftliches Potenzial für Deutschland. Berlin and Stuttgart: BITKOM and Fraunhofer IAO.
Bauernhansl, T., (2014), Die Vierte Industrielle Revolution – Der Weg in ein wertschaffendes
Produktionsparadigma. In Industrie 4.0 in Produktion, Automatisierung und Logistik – Anwendung,
Technologien, Migration, T Bauernhansl, M ten Hompel, and B Vogel-Heuser (eds.), pp. 5-35.
Wiesbaden: Springer.
Bradley, T., Thibodeau, P., and Ng, V., (2014), The Internet of Things - threats and challenges.
NetworkWorld Asia, 11(1), 16–18.
Breznitz, D., (2014), Why Germany Dominates the U.S. in Innovation. Available at:
https://hbr.org/2014/05/why-germany-dominates-the-u-s-in-innovation (accessed 8 April 2017).
Chang, M.-K., Cheung, W., Cheng, C.-H., and Yeung, J.H.Y., (2008), Understanding ERP system
adoption from the user’s perspective. International Journal of Production Economics, 113(2), 928-
942.
Chau, P.Y.K., and Tam, K.Y., (2000), Organizational adoption of open systems: A 'technology-push,
need-pull' perspective. Information & Management, 37, 229-239.
Chau, P.Y.K., and Tam, K.Y., (1997), Factors affecting the adoption of open systems: an exploratory
study. MIS Quarterly, 21(1), 1-21.
Chen, Y.-K., (2012), Challenges and opportunities of internet of things. In Proc. 17th Asia and South
Pacific Design Automation Conf., pp. 383-388. Sydney: Australia.
Cohen, W.M., and Levinthal, D.A., (1990), Absorptive Capacity: A New Perspective on Learning and
Innovation. Administrative Science Quarterly, 35(1), 128-152.
Cooper, D.R., and Schindler, P.S., (1998), Business Research Methods. Burr Ridge: Irwin/McGraw-Hill.
Cragg, P.B., and King, M., (1993), Small-Firm Computing: Motivators and Inhibitors. MIS Quarterly,
17(1), 47-60.
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 17 of 19
Dais, S., (2014), Industrie 4.0 – Anstoß, Vision, Vorgehen. In Industrie 4.0 in Produktion,
Automatisierung und Logistik – Anwendung, Technologien, Migration, T Bauernhansl, M ten Hompel,
and B Vogel-Heuser (eds.), pp. 625-634. Wiesbaden: Springer.
Dattalo, P.A., (1995), A comparison of discriminant analysis and logistic regression. Journal of Social
Service Research, 19(3), 121-144.
Davis, F.D., Bagozzi, R.P., and Wrshaw, P.R., (1989), User acceptance of computer technology: A
comparison of two theoretical models. Management Science, 35(8), 892-1003.
DePietro, R., Wiarda, E., and Fleischer, M., (1990), The context of change: Organization, technology,
and environment. In The process of technological innovation, LG Tornatzky, and M Fleischer (eds.),
pp. 151-175. Lexington: Lexington Books.
Dröge, C., and Germain, R., (1998), The design of logistics organizations. Transportation Research
Part E, 34(1), 25-37.
Erol, S., Schumacher, A., and Sihn, W., (2016), Strategic guidance towards Industry 4.0 – a three-
stage process model. In Proc. Int. Conf. Competitive Manufacturing, pp. 495-502. Stellenbosch:
South Africa.
Federal Bureau of Statistics, (2016), Statistisches Jahrbuch 2016. Wiesbaden: Federal Bureau of
Statistics.
Flatten, T.C., Engelen, A., Zahra, S.A., and Brettel, M., (2011), A measure of absorptive capacity: Scale
development and validation. European Management Journal, 29, 98-116.
Fowler, F.J., (1993), Survey Research Methods (2nd. ed.). Thousand Oaks: Sage.
Gatignon, H., and Robertson, T.S., (1989), Technology diffusion: An empirical test of competitive
effects. Journal of Marketing, 53(1), 35-49.
Gomez, J., and Vargas, P., (2009), The effect of financial constraints, absorptive capacity and
complementarities on the adoption of multiple process technologies. Research Policy, 38(1), 106-
119.
Grover, V., (1993), An Empirically Derived Model for the Adoption of Customer-based
Interorganizational Systems. Decision Science, 24(3), 603-640.
Hall, B., and Khan, B., (2003), Adoption of New Technology. Cambridge: National Bureau of Economic
Research.
Hatcher, L., (1994), A step-by-step approach to using the SAS system for factor analysis and
structural equation modelling. Cary: SAS Institute.
Hirsch-Kreinsen, H., and Weyer, J., (2014), Wandel von Produktionsarbeit – ‚Industrie 4.0‘, working
paper. Dortmund: Technische Universität Dortmund.
Iacovou, C.L., Benbasat, I., and Dexter, A.S., (1995), Electronic data interchange and small
organizations: adoption and impact of technology. MIS Quarterly, 19(4), 465–485.
Jeyaraj, A,. Rottmann, J.W., and Lacity, M.C., (2006), A review of the predictors, linkages, and biases
in IT innovation adoption research. Journal of Information Technology, 21(1), 1-23.
Jurison, J., (2000), Perceived Value and Technology Adoption Across Four End User Groups. In
Information Systems Evaluation Management, W van Grembergen (ed.), pp. 1-16. Antwerp:
University of Antwerp.
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 18 of 19
Kagermann, H., Wahlster, W., and Helbig, J., (2013), Recommendations for implementing the
strategic initiative Industrie 4.0 – Final report of the Industrie 4.0 Working Group. Frankfurt am
Main: Communication Promoters Group of the Industry-Science Research Alliance, acatech.
Ko, E., Kim, S.H., Kim, M., and Woo, J.Y., (2008), Organizational characteristics and the CRM adoption
process. Journal of Business Research, 61(1), 65-74.
Kuan, K.K.Y., and Chau, P.Y.K., (2001), A perception-based model for edi adoption in small businesses
using a technology-organization-environment framework. Information & Management, 38(8), 507-
521.
Kiel, D., Arnold, C., Baccaralla C.V., Voigt, K.-I., and Hoffmann, D., (2015), Technology Identification in
Relation to Embedded Systems. In Proc. Int. Association for Management of Technology (IAMOT)
Conf. Cape Town: South Africa.
McKinsey, (2015), Industry 4.0: How to navigate digitization of the manufacturing sector. McKinsey
Digital.
Mole, K.F., Ghobadian, A., O’Regan, N., and Liu, J., (2004), The Use and Deployment of Soft Process
Technologies within UK Manufacturing SMEs: An Empirical Assessment Using Logit Models. Journal
of Small Business Management, 42(3), 303-324.
Nunnally, J., (1978), Psychometric Theory. New York: McGraw-Hill.
Obermaier, R., (2016), Industrie 4.0 als unternehmerische Gestaltungsaufgabe.
Betriebswirtschaftliche, technische und rechtliche Herausforderungen. Wiesbaden: Springer Gabler.
Oettmeier, K., and Hofmann, E., (2017), Additive manufacturing technology adoption: an empirical
analysis of general and supply chain-related determinants. Journal of Business Economics, 87(1), 97-
124.
Patterson, K.A., Grimm, C.M., and Crsi, T.M., (2003), Adopting new technologies for supply chain
management. Transportation Research Part E, 39(2), 95-121.
Peltier, J.W., Zhao, Y., and Schibrowsky, J.A., (2012), Technology adoption by small businesses: An
exploratory study of the interrelationships of owner and environmental factors. International Small
Business Journal, 30(4), 406-431.
Ramsey, E., Ibbotson, P., and McCole, P., (2008), Factors that impact technology innovation adoption
among Irish professional service sector. International Journal of Innovation Management, 12(4), 629-
654.
Rehage, G., Bauer, F., Gausemeier, J., Jurke, B., and Pruschek, P., (2013), Intelligent Manufacturing
Operations Planning, Scheduling and Dispatching on the Basis of Virtual Machine Tools. In Digital
Product and Process Development, GL Kovács, and D Kochan (eds.), pp. 391-400. Berlin: Springer.
Reyes, P.M., Li, S., and Visich, J.K., (2016), Determinants of RFID adoption stage and perceived
benefits. European Journal of Operational Research, 254(3), 801-812.
Rodríguez-Ardura, I., and Meseguer-Artola, A., (2010), Toward a Longitudinal Model of e-Commerce:
Environmental, Technological, and Organizational Drivers of B2C Adoption. The Information Society,
26(3), 209-227.
Rogers, E.M., (1995), Diffusion of Innovations (4th ed.). New York: The Free Press.
International Association for Management of Technology
IAMOT 2018 Conference Proceedings
Page 19 of 19
Schmidt, R., Möhring, M., Härting, R.-C., Reichstein, C., Neumaier, P., and Jozinvic, P., (2015),
Industry 4.0 – Potentials for Creating Smart Products: Empirical Research Results. In Proc. Int. Conf.
Business Information Systems, pp. 16-17. Poznań: Poland.
Sila, I., (2013), Factors affecting the adoption of B2B e-commerce technologies. Electronic Commerce
Research, 13 (2), 199-236.
Spath, D., Ganschar, O., Gerlach, S., Hämmerle, M., Krause, T., and Schlund, S., (2013),
Produktionsarbeit der Zukunft – Industrie 4.0. Stuttgart: Fraunhofer Verlag.
Thong, J.Y.L., (1999), An integrated model of information systems adoption in small business. Journal
of Management Information Systems, 15(4), 187–214.
Vilaseca-Requena, J., Torrent-Sellens, J., Meseguer-Artola, A., and Rodríguez-Ardura, I., (2007), An
Integrated Model of the Adoption and Extent of E-Commerce in Firms. International Advances in
Economic Research, 13(2), 222-241.
Vowles N., Thirkell, P., and Sinha, A., (2011), Different determinants at different times: B2B adoption
of a radical innovation. Journal of Business Research, 64(11), 1162-1168.
Waarts, E., and van Everdingen, Y., (2005), The Influence of National Culture on the Adoption Status
of Innovations: Am Empirical Study of Firms Across Europe. European Management Journal, 23(6),
601-610.
Waarts, E., van Everdingen, Y.M., and van Hillegersberg, J., (2002), The dynamics of factors affecting
the adoption of innovations. The Journal of Product Innovation Management, 19(6), 412-423.
Wei, J., Lowry, P.B., and Seedorf, S., (2015), The assimilation of RFID technology by Chinese
companies. Information & Management, 52(6), 628-642.
Yap, C.S., Soh, C.P.P., and Raman, K.S., (1992), Information Systems Success Factors in Small
Business. OMEGA, 20(5-6), 597-609.
Yusuf, Y. Gunasekaran, A., and Abthorpe, M.S., (2004), Enterprise information systems project
implementation: A case study of ERP in Rolls-Royce. International Journal of Production Economics,
87(2), 251-266
Zhu, K., and Kraemer, K.L., (2005), Post-adoption variations in usage and value of e-business by
organizations: Cross-country evidence from the retail industry. Information Systems Research, 16(1),
61–84.
Zhu, K., Kraemer, K., and Xu, S., (2003), Electronic business adoption by European firms: a cross-
country assessment of the facilitators and inhibitors. European Journal of Information Systems, 12,
251-268.
