Determinants of big data analytics adoption in small and medium- sized enterprises (SMEs)

Abstract

Purpose
The study challenges the assumption of independence among Technological, Organizational and Environmental (TOE) factors and investigates the influence of TOE factors on Big Data Analytics (BDA) adoption among Small and Medium Enterprises (SMEs). Top management support was proposed as a mediator between technological and organizational factors and BDA adoption. Furthermore, the moderating effect of environmental factors on the association between relative advantage, compatibility, competitiveness, organizational readiness and BDA adoption was evaluated.

Design/methodology/approach
Data were collected from 171 SME manufacturing firms and analyzed using the partial least squares technique.

Findings
The findings confirmed the interrelationships among the TOE factors. The effects of compatibility, competitiveness and organizational readiness on BDA adoption were mediated by top management support. Furthermore, environmental factors moderate the influences of compatibility and organizational readiness on top management support.

Originality/value
The findings contribute to the TOE model by challenging the assumption of independence among TOE factors, and future studies should use this model with more caution and consider the potential relationships between TOE factors.

Full text

Determinants of big data analytics
adoption in small and medium-
sized enterprises (SMEs)
Parisa Maroufkhani
Zhejiang University City College, The University of Waikato Joint Institute,
Hangzhou, China
Mohammad Iranmanesh
School of Business and Law, Edith Cowan University, Joondalup, Australia, and
Morteza Ghobakhloo
School of Economics and Business, Kaunas University of Technology,
Kaunas, Lithuania and
Graduate School of Business, Universiti Sains Malaysia, Penang, Malaysia
Abstract
Purpose –The study challenges the assumption of independence among Technological, Organizational and
Environmental (TOE) factors and investigates the influence of TOE factors on Big Data Analytics (BDA)
adoption among Small and Medium Enterprises (SMEs). Top management support was proposed as a mediator
between technological and organizational factors and BDA adoption. Furthermore, the moderating effect of
environmental factors on the association between relative advantage, compatibility, competitiveness,
organizational readiness and BDA adoption was evaluated.
Design/methodology/approach –Data were collected from 171 SME manufacturing firms and analyzed
using the partial least squares technique.
Findings –The findings confirmed the interrelationships among the TOE factors. The effects of compatibility,
competitiveness and organizational readiness on BDA adoption were mediated by top management support.
Furthermore, environmental factors moderate the influences of compatibility and organizational readiness on
top management support.
Originality/value –The findings contribute to the TOE model by challenging the assumption of
independence among TOE factors, and future studies should use this model with more caution and consider the
potential relationships between TOE factors.
Keywords TOE model, Big data analytics, Technology adoption, Environmental factors, Top management
support, Small and medium enterprises
Paper type Research paper
1. Introduction
In the recent digital age, Small and Medium-sized Enterprises (SMEs) use disrupting
technologies to nurture their businesses and progress their operational activities (Akpan
et al., 2020). The need for implementing digital technologies by SMEs has been intensified by
the digitalization race pushed by the fourth industrial revolution (Ghobakhloo and
Iranmanesh, 2021;Gupta et al., 2020). Digitalization delivers great chances and prospects
for enterprisers who lead and run SMEs to make a difference in the market and thereby
develop their business (Ching et al., 2022;Ghoabkhloo et al., 2021). There is a robust demand
to embrace emerging technologies in order to flourish faster and stay competitive with limited
monetary and non-monetary resources (El-Haddadeh et al., 2021;Pappas et al., 2021). Under
the Industry 4.0 phenomenon, emerging technologies, especially Big Data Analytics (BDA),
are flooring the SMEs’path by making them able to be more optimized (Liu et al., 2020;
Mangla et al., 2021;Sun et al., 2020). In particular, BDA can enable SMEs to move toward
servitization and adopt innovative business models. Currently, the revenue from BDA
Determinants
of BDA
adoption in
SME
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0263-5577.htm
Received 15 November 2021
Revised 19 January 2022
Accepted 30 January 2022
Industrial Management & Data
Systems
© Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-11-2021-0695

worldwide is estimated to be 215 billion USD and is expected to grow to over 270 billion USD
in 2022 (Statista, 2021). As an evolving technology that possibly increases operational,
strategic and other firms’performances, BDA is yet to recognize substantial rates of adoption
within the companies across different sectors and industries, particularly SMEs (Mikalef
et al., 2020). While the substantial function and applicability of BDA in SMEs and the vital
role of SMEs in the constant development of national economies have been shown by scholars
(Coleman et al., 2016;Liu et al., 2020), the adoption of such disruptive technology is practically
missing by SMEs (Liu et al., 2020;Maroufkhani et al., 2020). Recently, a few studies have been
evolved to empirically investigate the adoption of BDA within SMEs (Liu et al., 2020;Mangla
et al., 2021;Maroufkhani et al., 2020;Saleem et al., 2020). However, there is still a need for more
investigation on the role of emerging technologies (Youssef et al., 2022) and, particularly,
BDA on SMEs (Liu et al., 2020).
As such, thisstudy precisely focuses on SMEs and theirpotential provocations for adopting
BDA as this is claimed that it is more remarkable for SMEs than for large organizations to
adopt and take advantage of BDA adoption (Dong and Yang, 2020). Apart from the need for
BDA implementation among SMEs, identifying the possible factors that may influence its
adoption is a prerequisite for the value delivery of BDA usage (Maroufkhani et al., 2020). Given
that, the Technological-Organizational-Environmental (TOE) model has gained more attention
from scholars to explain the antecedents of technology adoption. The TOE model argues that
TOE conditions play a critical role in shaping firms’decisions to adopt technology and that
each factor is independent of the other. This questionable assumption may have influenced the
findings of the previous studies on the determinants of BDA adoption among SMEs, which
leads to underestimating the role of factors in technology adoption.
In this line, Chen et al. (2015) found that top management support mediates the effect of
competitive pressure and organizational readiness on BDA adoption. Top management
support as an independent variable takes the direct impact of organizational readiness and
competitive pressure on BDA adoption and may lead to biased findings. The insignificant
effect of competitive pressure and low impact of organizational readiness in the study of
Maroufkhani et al. (2020) on BDA adoption can be the consequence of considering top
management support as an independent variable. El-Haddadeh et al. (2021) confirmed the
effect of organizational and environmental aspects on top management support. In another
study, Lai (2018) found that environmental factors moderate the impacts of organizational
and technological factors on BDA adoption. Oliveira et al. (2019) found support for the
moderating effect of environmental factors in their study on Software-as-a-Service (SaaS)
adoption.
Against this backdrop, the present study challenges the assumption of independence
among TOE factors. This study proposes that the TOE factors should not be considered as
independent predictors of BDA adoption. The authors propose top management support
mediates the influences of technological and organizational factors on BDA adoption.
Further, the authors contend that environmental factors act as a moderator on the impacts of
technological and organizational factors on top management support. It is essential to
mention that within the present study, the BDA adoption level demonstrates the BDA usage
intensity. In summary, this study aims to address two objectives:
(1) To examine the mediating role of top management support in the relationship
between technological and organizational factors and BDA adoption.
(2) To investigate the moderating role of environmental factors on the effects of
technological and organizational factors on top management support.
The outcome of this research contributes to the literature on the TOE model by challenging
the assumption of independence among TOE factors. By testing the mediating impact of top
IMDS

management support, this study investigates whether previous studies on BDA adoption
among SMEs have underestimated the impact of technological and organizational factors on
technology adoption. Besides, by testing the moderating effect of organizational readiness,
this study illustrates the potential interactions among TOE factors. From a practical point of
view, the present study is expected to provide managers of SMEs, policymakers and BDA
service providers with a more precise understanding of the role of TOE elements in
technology adoption.
2. Adoption of BDA by SMEs
Generally, the notion of BDA refers to a term that has lied in using advanced analytics
techniques to analyze the large data set of firms (Dubey et al., 2019a;Liu et al., 2020). The term
“big data”refers to the massive amounts of data, including both unstructured and structured,
which is accessible in real-time (Ghasemaghaei and Calic, 2020). Several academicians and
practitioners have explained big data using the notion of “Vs”.Russom (2011) proposed
volume, variety and velocity as three unique attributes of big data. The volume describes a
massive amount of data generated and collected in the current business environment
(Ghasemaghaei, 2020). Variety refers to various data models in terms of structured, semi-
structured and unstructured, which is tough to be analyzed via traditional analytic systems
(Mohapatra and Mohanty, 2020). Velocity refers to data generation speed and real-time data
analysis (Shukla et al., 2020). Table 1 briefly reviews the most prominent works within the
SME-BDA adoption literature.
Currently, BDA has gained remarkable attention from researchers (Dong and Yang, 2020).
The business values and expository insights that can be captured out of BDA in shaping
organizational decision-making have made firms keenly interested in the deployment of this
technology (Dong and Yang, 2020;Mikalef et al., 2020). BDA can be implemented for three key
schemes, which are predictive, descriptive and prescriptive (Kamble et al., 2020). BDA
adoption by SMEs enables them to have the most accurate forecasting on consumers’
behavior and market, therefore reducing the firm’s insecurity and time consumption for
adopting changes in the organizational system (Dong and Yang, 2020;Mangla et al., 2021).
Furthermore, it will help minimize production costs in the process of production (Saleem et al.,
2020). Maroufkhani et al. (2020) discovered that the adoption of BDA would improve the
market and financial performance of SMEs. Coleman et al. (2016) believed that BDA boosts
the knowledge abilities of SMEs as well as their business strategies, data management and
data governance. In this vein, knowledge management has been reported as one of the best
strategies to help SMEs increase their abilities to adopt BDA (Mangla et al., 2021). Akpan et al.
(2020) found out that great potential benefits of BDA for SMEs are digitalizing the processes
and internal operations, increasing effectiveness and efficiencies of the performance,
redesigning business models and assuring business sustainability. Saleem et al. (2020)
indicated that BDA is one of the most applicable and useful technologies for SMEs to make
effective strategies and eventually deliver the best organizational performance.
Considering BDA benefits, determinants of BDA adoption have received special attention
from scholars. However, most of the studies have focused on large companies (Bag et al., 2021;
El-Haddadeh et al., 2021). Previous scholars have abundantly shown that SMEs differ from
large corporations in terms of resources, size, flexibility and hierarchy (Dong and Yang, 2020;
Maroufkhani et al., 2020). For example, SMEs’resource and budget limitations are the main
barriers to value creation from BDA adoption (Akpan et al., 2020;Coleman et al., 2016;Mangla
et al., 2021). SMEs perceive BDA adoption as a costly, tricky, insecure and complex process
(Dong and Yang, 2020;Mangla et al., 2021;Maroufkhani et al., 2020). However, SMEs would
be able to take prompt actions in adopting BDA as their high flexibility and less hierarchical
system enable them to make new decisions and implement strategies faster than large
Determinants
of BDA
adoption in
SME

Study Theoretical background Methodology Major findings
Verma and
Bhattacharyya
(2017)
Technology-organization-
environment framework
Face-to-face semi-structured
interviews with 22 small to
large enterprises
Various organizational,
technological, and
environmental factors such as
industry type or
implementation costs
determine the perceived
strategic value of BDA. BDA
strategic value, in turn,
determines BDA adoption of
Indian firms
Lai et al. (2018) Technology-organization-
environment framework
Survey of 210 small to large
Chinese organizations
Perceived benefits and top
management support
determine the BDA adoption
intention. Government
support, supply chain
connectivity, and competitors
mediate how determinants
impact BDA adoption
Maroufkhani et al.
(2020)
Diffusion of innovation,
resource-based view
(RBV), and technology-
organization-environment
framework
A cross-sectional survey of
171 Iranian manufacturing
SMEs
Complexity, uncertainty and
insecurity, trialability,
observability, top
management support, and
organizational resources
determine BDA adoption
extent. BDA adoption leads to
business performance
improvement
Wang and Wang
(2020)
Knowledge management
literature
Qualitative analysis of eight
case research
Identifies the synergistic
relationship between big data
and knowledge management
among SMEs
Yadegaridehkordi
et al. (2020)
Technology-organization-
environment framework
and HOT-fit theory
A questionnaire-based
survey of 418 Malaysian
SMEs in the hotel sector
Various organizational,
technological, environmental,
and human-based factors
determine BDA adoption
intention. BDA adoption, in
turn, impacts business
performance
El-Haddadeh et al.
(2021)
Technology-organization-
environment framework
A questionnaire-based
survey of 320 managers from
SME and larger UK firms
Perceived benefits,
technological complexity, and
management support
determine BDA adoption.
Organizations can contribute
to promoting sustainable
development goals via BDA
adoption
Lei et al. (2021) Information system
theories such as the theory
of reasoned action and
diffusion of innovation
theory
Real case survey of 54 BDA
platforms in China
The BDA adoption level is
generally low among Chinese
firms regardless of business
size. BDA adoption behavior
significantly depends on the
industry type
Mangla et al. (2021) BDA and project
management literature
Survey of 106 Indian
manufacturing SMEs
BDA adoption leads to
improved project
performance among
manufacturing SMEs. Project
knowledge management,
project operational capability,
and green purchasing
determine BDA adoption in
SMEs
(continued )
Table 1.
Previous studies on the
adoption of BDA
within SMEs
IMDS

companies (Dong and Yang, 2020). Considering the lack of attention to BDA adoption among
SMEs and the significant difference between environmental situations, technological
infrastructures and resource availability of large companies and SMEs, this study aims to
investigate the influence of TOE factors on BDA adoption among SMEs.
3. Theoretical background and hypotheses development
Scholars have explained a firm’s decision to adopt technology using various theories,
including the diffusion of innovations (DOI) model (Sun et al., 2018), institutional theory
(Dubey et al., 2019b), RBV theory (Bag et al., 2021) and TOE model (Khayer et al., 2020).
Among them, the TOE model has been highlighted as the most flexible and strongest theory
in explaining the firms’decision to adopt a technology (Grant and Yeo, 2018). Besides that, the
TOE model is a flexible and contextual theory (Grant and Yeo, 2018), and unlike DOI, which
only focuses on technological factors, the TOE model covers all internal and external
elements for adopting technology within firms (Zhu et al., 2006). Accordingly, this study has
applied the TOE model as the main underpinning theoretical model.
3.1 TOE model
Scholars have commonly used the TOE model in explaining technology adoption at the firm
level. The model has been tested in various contexts, including customer relationship
management (CRM) (Cruz-Jesus et al., 2019), cloud computing (Hiran and Henten, 2020),
blockchain (Clohessy and Acton, 2019), social commerce (Abed, 2020), social media marketing
(Abbasi et al., 2022), internet of things (Asadi et al., 2021) and BDA (Maroufkhani et al., 2020).
Many studies have confirmed its ability to clarify the adoption of various technologies in an
organizational setting (El-Haddadeh et al., 2021;Gupta et al., 2020;Oliveira et al., 2019). In
comparison to other theories that explain technology adoption, including RBV, institutional
theory, knowledge-based view and technology structuration theory, the main strength of
TOE lies in its consideration of both internal and external factors at a single model (Xu et al.,
2017). Despite being widely used, the limitation of the existing studies is that following the
original TOE model, the technological, organizational, and environmental are considered as
independent factors. This approach overlooks the interaction among factors, which
underestimates the influence of the factors and consequently misinterpretation of the
results. Thus, we propose top management support as a mediator of the relationship between
technological and organizational factors and BDA adoption. In contrast, environmental
factors act as a moderator between technological and organizational factors and top
management support.
Study Theoretical background Methodology Major findings
Park and Kim (2021) Technology-organization-
environment framework
Interview of 50 experts and
survey of 226 SMEs in the
Republic of Korea
Different organizational,
technological, and
environmental factors such as
government policy or system
usage simplicity impact the
adoption of BDA within
Korean SMEs
Youssef et al. (2022) Diffusion of innovation,
institutional theory and
technology-organization-
environment framework
A questionnaire-based
survey of 2,278 responses
from small to large
organizations from Egypt,
the UK and the UAE
Security concerns and
rational decision-making
culture are among the
essential determinants of
BDA adoption. The BDA
adoption behavior varies
among businesses of different
sizes and regions Table 1.
Determinants
of BDA
adoption in
SME

3.2 TOE factors emerging from the literature
The TOE factors are dynamic depending on the type of technology and organization
(Maroufkhani et al., 2020). Following the previous studies that have used TOE, we select the
factors in a two-step process (Oliveira et al., 2019). Firstly, the most significant factors were
identified from well-cited articles. Secondly, we reviewed prior studies on BDA, and based on
the BDA characteristics, selected the relevant factors to BDA context from the identified
factors in the first step. Compatibility and complexity represent the most relevant factors
from the technological factor cluster (Gangwar, 2018;Lai et al.,2018;Verma and
Bhattacharyya, 2017). Among the organizational factors, top management support and
organizational readiness were consistently significant constructs (El-Haddadeh et al., 2021;
Ramanathan et al., 2017;Wang et al., 2016c). Finally, among the environmental factors,
external support, competitive pressure and government regulations were selected for their
relevance in the context of BDA (Lai et al., 2018).
3.3 Conceptual model
Drawing on the TOE model and the literature, we propose top management support mediates
the influences of compatibility, complexity and organizational readiness on BDA adoption
(Figure 1). As a composite construct, environmental factors act as a moderator that influences
the impacts of technological and organizational factors and top management support.
3.3.1 Top management support. Top management support is “the degree to which
managers comprehend and embrace the technological capabilities of a new technology
system”(Maroufkhani et al., 2020, p. 4) hence a critical factor for the successful adoption of a
technology (El-Haddadeh et al., 2021;Maroufkhani et al., 2020). On the other hand, it can be a
considerable obstacle to business analytics adoption (LaValle et al., 2011). The influence of top
management support on the adoption of various technologies such as cloud (Khayer et al.,
2020), CRM system (Cruz-Jesus et al., 2019), SaaS (Oliveira et al., 2019) and big data (Talwar
et al., 2021) has been supported. Top managers play a critical role in creating a suitable
environment for adopting BDA, where sufficient resources are available for technology
adoption (El-Haddadeh et al., 2021). The support of top managers is essential during the
technology adoption process; thus, they would have a positive effect on BDA adoption (Wang
et al., 2016a;Youssef et al., 2022). Given this, top management support works as an agent for
accelerating the process of business transformation and, consequently, adopting BDA (Chen
et al., 2015;Lai, 2018;Verma and Bhattacharyya, 2017). Accordingly, the following
hypothesis was proposed:
H1. Top management support positively influences BDA adoption.
Compatibility
Complexity
Organizational
Read iness
Top Management
Support
BDA adoption
Environmental Factors
H3
H2
H6
H5
H1
H9
H8
H11
Figure 1.
The research model
IMDS

3.3.2 Compatibility. Compatibility examines “the degree to which a new system is consistent
with the current system within the company”(Maroufkhani et al., 2020, p. 3). Compatibility
has been a frequently cited driver of technology adoption (Bian et al., 2020). Alsetoohy et al.
(2019) argued that firms select and adopt technologies that conform to their internal culture
and values and consequently need minimal changes and adjustments. The influence of
compatibility on the adoption of various technologies such as mobile reservation systems
(Wang et al., 2016c), intelligent agent technology (Alsetoohy et al., 2019) and cloud computing
(Oliveira et al., 2014) has been supported in the previous studies. In the context of BDA, the
association between compatibility and BDA adoption has been confirmed (Chen et al., 2015;
Verma and Bhattacharyya, 2017). Thus, the following hypothesis was developed:
H2. Compatibility positively influences BDA adoption.
Compatibility of technology with the existing firm’s business practices and culture is a factor
that managers consider while deciding to adopt a technology. If a new technology is
incompatible, the firms should make some adjustments in the processes and invest in training
(Duan et al., 2019). Accordingly, top management will be more supportive when the new
technology is well-matched with the existing system and culture. Alsetoohy et al. (2019) found
compatibility as a significant driver of managers’attitude toward intelligent agent
technology adoption. A qualitative study by Das and Dayal (2016) on determinants of
managers’decision to support the adoption of Cloud-based Enterprise Resource Planning
(CLERP) system showed compatibility of CLERP systems with values, culture, operations
and business pre-conditions of a firm has a positive effect on the managers’decision to
support technology adoption. Previous studies have shown the positive influence of
compatibility on managers’attitudes and decisions to adopt new technologies (Lian et al.,
2014;Vagnani and Volpe, 2017). Accordingly, this study proposes the following hypothesis.
H3. Compatibility has a positive influence on top management support.
When managers of a firm believe technology is incompatible with the current values, culture
and practices of the firm, they will not support its adoption due to considerable learning and
adjustment in processes are required (Low et al., 2011). Considering the findings of the
previous studies on the influence of compatibility on top management attitude (Lian et al.,
2014;Vagnani and Volpe, 2017) and the association between top management support and
technology adoption (El-Haddadeh et al., 2021;Maroufkhani et al., 2020), this study proposed
top management support as an explanation for the impact of compatibility on the
implementation of BDA. The finding of Vagnani and Volpe’s (2017) study supported our
proposition, and they found that attitudes of managers and decision-makers mediate the
association between compatibility and the decision to adopt the technology. Hence, this study
develops the following hypothesis.
H4. Top management support mediates the association between compatibility and BDA
adoption.
3.3.3 Complexity. Kapoor et al. (2014, p. 83) defined complexity as “the degree to which an
innovation is considered as difficult to understand and use.”Previous studies have illustrated
the negative impact of complexity on adopting new technologies (Alkhatib et al., 2019;
Alsetoohy et al., 2019). The chance of adopting technology is higher if its integration into
business operation is easy (Oliveira et al., 2014). According to Alsetoohy et al. (2019), the
complexity of technology is the most critical obstacle in its implementation. Studies have
shown the negative effect of complexity on the adoption of various technologies such as
intelligent agent technology (Alsetoohy et al., 2019), cloud computing (Oliveira et al., 2014),
blockchain (Wong et al., 2020) and big data (Chen et al., 2020). Previous studies have
Determinants
of BDA
adoption in
SME

confirmed the negative effect of complexity on BDA adoption (Gangwar, 2018;Lai et al., 2018;
Maroufkhani et al., 2020). Accordingly, the following hypothesis is proposed.
H5. Complexity has a negative impact on BDA adoption.
Baig et al. (2019) argued that the complexity of BDA prevents its diffusion because firms are
less likely to proceed with adopting complex technology. In this regard, if the managers
perceive that BDA adoption requires considerable effort, they are less likely to support its
adoption (Maroufkhani et al., 2020). Further, Vagnani and Volpe (2017) showed that
complexity negatively influences managers’attitudes toward adopting new technology.
Tashkandi and Al-Jabri (2015) argued that the management’s decision to adopt technology is
influenced by complexity. Accordingly, a negative association between complexity and top
management support is expected; hence we propose the following hypothesis.
H6. Complexity negatively influences top management support.
The negative perception of top managers on BDA adoption due to their initial view of the
complexity and difficulty of adopting BDA negatively affects the managers’intention to
support BDA and may also hinder its adoption. Gangwar (2018),Lai et al. (2018) and
Maroufkhani et al. (2020) illustrated that complexity has a negative influence on BDA
adoption. As successful adoption of technology depends on management support (Cruz-Jesus
et al., 2019;Khayer et al., 2020), and complexity of technology integration and implementation
plays a critical role in the decision of top management to adopt the technology (Vagnani and
Volpe, 2017), we expect that top management support mediates the relationship between
complexity and BDA adoption. Vagnani and Volpe (2017) showed complexity has an indirect
effect on technology adoption decisions through the attitudes of managers and decision-
makers. Hence, the following hypothesis was propositioned:
H7. Top management support mediates the association between complexity and BDA
adoption.
3.3.4 Organizational readiness. Organizational readiness is defined as the extent to which
financial, technological and skilled human resources are accessible to an organization
wishing to embrace a technology (Maroufkhani et al., 2020). Chen et al. (2015, p. 18) defined
organizational readiness as “the availability of the necessary organizational resources for
using BDA.”Previous studies have shown that organizational readiness plays a significant
role in the adoption of technologies such as social commerce (Abed, 2020), intelligent agent
technology (Alsetoohy et al., 2019), e-commerce (Hajli et al., 2014), mobile commerce (Chau
et al., 2020), blockchain (Wang et al., 2016b) and big data (Hajiheydari et al., 2021).
Besides, monetary funds, IT infrastructure, analytics capabilities and skilled capital are
critical for successful technology adoption (Raut et al., 2021;Zailani et al., 2014) Consistently,
organizational readiness in the present work refers to the SMEs’overall access to financial
resources/capital, skilled employees, knowledge resources, analytical competencies and
necessary infrastructure that enable them to exploit BDA fully. If a firm cannot reap the
benefits of technology due to insufficient resources and capabilities, investment in that
technology is meaningless despite its tremendous advantages (Alsetoohy et al., 2019).
Previous researchers have shown the influence of organizational resources on BDA adoption
(Gangwar, 2018;Maroufkhani et al., 2020;Ramanathan et al., 2017). Hence, the following
hypothesis was developed.
H8. Organizational readiness positively influences BDA adoption.
Previous studies have suggested that the extent of top management support depends on
organizational conditions (Chen et al., 2015). El-Haddadeh et al. (2021) argued that organizational
readiness is an essential factor in shaping the attitude of top management toward BDA adoption.
IMDS

Top management is more supportive if they believe that sufficient capabilities and resources for
adopting BDA exist in the firm. In a study on radio frequency identification (RFID) adoption,
Lu et al. (2013) used the Multiple Criteria Decision Making (MCDM) method and found that
organizational readiness is an essential factor that managers consider in deciding whether to
adopt the technology. Accordingly, this study expects that organizational readiness influence
positively on top management support. Thus, we propose the following hypothesis.
H9. Organizational readiness has a positive effect on top management support.
The previous studies have argued that organizational readiness does not influence
technology adoption directly, and the relationship between these two concepts should be
mediated by top management awareness and evaluation of the technology in the initiation
stage of adoption (Zhu et al., 2006). To evaluate this argument, Chen et al. (2015) proposed that
adopting a technology depends on managers’perceived availability of the necessary
resources and capabilities for the successful adoption of the technology. They empirically
tested the mediating effect of top management support and found that the influence of
organizational readiness on BDA adoption could be mediated via top management support.
As such, we proposed that organizational readiness has an indirect effect on BDA adoption
through top management support, and the following hypothesis is proposed.
H10. Top management support mediates the association between organizational
readiness and BDA adoption.
3.3.5 Moderating effect of environmental factors. In the TOE framework, environmental
factors are referred to as the domain wherein a firm manages its business activities (Maduku
et al., 2016). The external elements affecting an organization’s operation would be considered
environmental factors such as competitive pressure, external support and government
regulation (Maroufkhani et al., 2020). Ramanathan et al. (2017) and Wamba et al. (2020) stated
that environmental factors stipulate opportunities and barriers to a firm for technological
adoption, which may consist of competitor pressures, business partners and government. These
factors can play a dual role, promoter, or stoppage, in the decision of managers and owners of
organizations for adopting a new information system technology (Lai, 2018). SMEs are more
vulnerable to the influence of external factors. Ghobakhloo et al. (2011) and Wamba et al. (2016)
stated that pressure from competitors would promote the adoption of technology among SMEs.
Imposing favorable regulations and pressure from the government would positively boost
SMEs’top managers’inclination to facilitate the process of BDA adoption (Lai, 2018;
Maroufkhani et al., 2020). External support, as the extended support from a service provider or
third party, is another vital external factor in firms’decision to adopt a technology (Gangwar,
2018;Ghobakhloo et al., 2011). Receiving financial and non-monetary support from external
parties is an influential attribute for BDA adoption, as it accentuates SMEs in promoting their
learning capabilities from service providers (Gangwar, 2018;Maroufkhani et al., 2020).
Firms operate in a specific environment, and the management’s decisions are affected by it
(Oliveira et al., 2019;Wang et al., 2016a). For instance, seeing competitors adopting BDA
motivates the managers to facilitate and support the process of BDA adoption as the firm is at
risk of losing its competitive advantage. The dependency on receiving support from the
government may trigger the management’s decision to adopt BDA. Accordingly, the
moderating impact of environmental aspects on the influences of technological and
organizational factors on top management support is expectable. Therefore, in the lack of
government support, external support and competitor pressure, the management may delay
adopting BDA even though they found it helpful and compatible with the firm’s current
practices and culture. Researchers have demonstrated how environmental factors moderate
the relationship between technological and organizational factors (Lai et al., 2018;Oliveira
et al., 2019); hence, the following hypotheses:
Determinants
of BDA
adoption in
SME

H11. Environmental factors moderate the associations between (a) compatibility, (b)
complexity, (c) organizational readiness and top management support.
4. Method
4.1 Measurements
We used a survey questionnaire to test the revised TOE model and adapted it from the
previous studies with valid and reliable constructs. Following the literature (Maroufkhani
et al., 2020;Shareef et al., 2017;Sharma and Sharma, 2019), all the items were measured using
a five-point Likert scale with anchors ranging from “Strongly Disagree”(1) to “Strongly
Agree”(5). Three scholars and two BDA experts from the industry pretested the
questionnaire. Minor changes were made based on the comments and inputs of the
scholars and experts. A professional translator translated the revised questionnaire into
Persian to accommodate the respondents. Later, another translator independently translated
the items back to English to validate the translation. Two experts compared the translated
version of the measurement with the original one and confirmed that both versions are
equivalence. Data were collected from 32 SMEs’owners/managers in Iran to pilot the
measurement. All the Cronbach’s alpha values were higher than 0.7, indicating the reliability
of the developed constructs (Hair et al., 2019). The questionnaire and sources of items were
provided in Table A1.
4.2 Sampling and data collection
SME manufacturing firms in Iran form the population of the study. The sampling frame was
obtained from Iran Small Industries and Industrial Parks Organization (ISIPO). The total
number of 10,931 SMEs in manufacturing sectors in Tehran, Iran, was listed in the ISIPO.
Data were collected from the SME owners/managers because they have adequate knowledge
regarding the firm strategic direction. We contacted the firms and explained the meaning of
BDA and the aim of the study. We then collected the email addresses of the owners/managers
who agreed to participate and emailed the questionnaire. The meaning of BDA and the aim of
the study were explained in the attached cover letter. To ensure that participating firms have
experience with BDA, the participants first respond to the filtering question of whether their
firm uses BDA (outsourced included). Only firms that answered “Yes”were included in the
study. Data were collected from June to September 2019. Of the 500 firms we sent the
questionnaire to, 182 firms submitted their responses after follow-up calls at two-week
intervals. Of the total collected responses, 11 were excluded as they were incomplete or did
not meet the inclusion criteria. As the response rate of the study was 34.2%, non-response rate
bias was evaluated by comparing early and late responses using a t-test. Non-response bias
was not a concern as there was no significant difference between early and late responses
(King and He, 2005). The profile of respondents is shown in Table 2.
As a self-reported questionnaire was used to collect data from a single, the data are subject
to common method bias (CMB), which may inflate the relationships in the model.
Accordingly, we tested CMB using two common techniques, namely Harman’s single-
factor approach (Podsakoff et al., 2003) and the statistical remedies technique (Lindell and
Whitney, 2001), to evaluate the validity and reliability of the underlying constructs and
relationships (Fuller et al., 2016). The result of Harman’s single factor demonstrated that CMB
is not the concern as the first factor explains less than 50% of the total variance (37.62%)
(Fuller et al., 2016). Furthermore, the correlations between the constructs and the marker
variable (attitude toward buying green products) were assessed following the statistical
remedies technique. As the correlations were low and insignificant, CMB is not an issue
(Lindell and Whitney, 2001).
IMDS

4.3 Data analysis
The conceptual framework of the study was tested using partial least squares structural
equation modeling (PLS-SEM). This technique was selected due to the study’s model and
sample characteristics. PLS-SEM is preferred over covariance-based structural equation
modeling (CB-SEM) because the model is complex and exploratory, the sample size is
relatively small and the data are non-normal (Hair et al., 2019). Following the two-step
approach, we first evaluated the reliability and validity of the constructs. In the second step,
we ran the bootstrapping analysis to test the hypotheses of the study.
5. Results
5.1 Measurement model
Factor loadings, composite reliability (CR), and average variance extracted (AVE) were
evaluated to test the convergence of the first-order constructs. The values of factor loadings,
CR and AVE must be greater than 0.7, 0.5 and 0.7, respectively (Hair et al., 2019). All the
Characteristics Frequency Percentage
Education Primary qualification 8 4.7
Secondary qualification 12 7.0
Diploma 56 32.7
Undergraduate degree 90 52.6
Postgraduate degree (Master/PhD) 5 2.9
Age 18–25 years old 9 5.3
26–33 years old 55 32.2
34–41 years old 63 36.8
42–49 years old 33 19.3
50 years old or older 11 6.4
Gender Male 155 90.6
Female 16 9.4
Number of employees 1–10 employees 26 15.2
11–49 employees 99 57.9
50–99 employees 34 22.2
100–149 employees 8 4.7
Sector type Food and beverages 24 14.0
Wood and wood products (except furniture) 2 1.2
Chemical 16 9.4
Rubber and plastic 14 8.2
None-metal minerals 30 17.5
Basic metals 19 11.1
Fabric metals 16 9.4
Machinery and equipment 24 14.0
Office equipment 3 1.8
Electrical machineries and equipment 11 6.4
Radio, TV communication tools 3 1.8
Optical and medical instrumental and watch 9 5.3
Position Executive/senior manager 70 40.9
Chief executive manager/owner 101 59.1
Big data experience <1 year 14 8.2
1–2 years 78 45.6
2–3 years 56 32.2
3–4 years 18 10.5
4þyears 5 2.9
Table 2.
Demographic
information of
respondents
Determinants
of BDA
adoption in
SME

first-order constructs of this study met the threshold prerequisites and indicated acceptable
convergent validity (Table 3). We applied a two-stage approach to evaluate the two second-
order constructs, namely BDA adoption and environmental factors. As a reflective-reflective
second-order construct, the validity and reliability of BDA adoption were evaluated in the
same way as for first-order constructs. To assess environmental support as a composite, we
evaluated and found significant weights of external support, competitive pressure and
government regulations. Following Riel et al. (2017), the model fit of the second-order
composite was evaluated. The test for the overall model fit was not rejected, and the SRMR
was below the threshold of 0.08, indicating an acceptable model fit. As such, environmental
factors were appropriately modeled as a composite.
The Heterotrait–Monotrait (HTMT) criteria were evaluated to assess the discriminant
validity (Henseler et al., 2015). All HTMT values were less than 0.85 (Table 4), indicating a
satisfactory level of discriminant validity (Kline, 2016).
5.2 Structural model
The predictive accuracy of the conceptual model of the study was evaluated using the
proportion of variance (R
2
)(Hair et al., 2019). The R
2
values of BDA adoption and top
management were 0.658 and 0.520, respectively. The structural model was analyzed using
non-parametric bootstrapping with 5,000 replications (Hair et al., 2019). The results showed
that compatibility and complexity, directly and indirectly, affect top management support on
BDA adoption (Table 5). Although organizational support has no direct effect on BDA
adoption, it has an indirect effect through top management support. As such, out of 10
hypotheses in the main model, only one hypothesis was rejected (H8).
We applied the two-stage approach in testing the moderating effect of environmental
factors. The results showed that environmental factors positively moderate the relationship
between compatibility and top management support (β50.170; p< 0.05) while negatively
moderating the relationship between organizational readiness and top management support
(β50.171; p< 0.01). The moderating effect of environmental factors on the influence of
Constructs Types
Number of
items
Loadings/
weights CR AVE
Compatibility (CMP) Reflective 3 0.879–0.890 0.915 0.783
Complexity (CPX) Reflective 3 0.880–0.923 0.924 0.802
Organizational readiness (OR) Reflective 4 0.813–0.876 0.900 0.692
Top management support
(TMS)
Reflective 4 0.856–0.891 0.929 0.765
Environmental factors (EF) Second-order
(composite)
3 0.434–0.459 NA NA
External support (ES) Reflective 3 0.904–0.927 0.941 0.842
Competitive pressure (CP) Reflective 3 0.855–0.892 0.903 0.756
Government regulation (GR) Reflective 3 0.872–0.881 0.909 0.770
Big data analytics adoption
(BDAA)
Second-order
(reflective-reflective)
4 0.824–0.891 0.928 0.762
Informational value (IV) Reflective 3 0.915–0.921 0.942 0.844
Transformational value (TRF) Reflective 4 0.828–0.889 0.923 0.750
Transactional value (TV) Reflective 3 0.911–0.944 0.946 0.854
Strategic value (SV) Reflective 3 0.897–0.929 0.941 0.841
Note(s): CR: Composite Reliability; AVE: Average Variance Extracted; NA: Not Applicable
Table 3.
Assessment of
reflective measurement
and composite models
IMDS

complexity on top management supported is rejected. As such, H11a was supported, whereas
H11b and H11c were rejected.
Figure 2a shows that even though compatibility sounds like a vital element in shaping top
management support when the environmental pressure or support is high, it has almost no
effect on top management support when the environmental pressure or support is low. Based
on Figure 2b, the positive influence of organizational readiness on top management support is
significantly higher in the business environment with low support/pressure compared to the
environment with high support/pressure.
6. Discussion
The two technological factors, namely compatibility and complexity, have both direct
and indirect effects through top management support on BDA adoption. Verma and
Bhattacharyya (2017) and Gangwar (2018) confirmed the effect of compatibility
and complexity on BDA adoption. Our findings indicate that SME owner/manager’s
decision to support BDA depends on its ease of use as well as its consistency with the current
firm’s business practices and culture. However, the significant direct effects of compatibility
CMP CPX OR TMS ES CP GR IV TRF TV SV
CMP
CPX 0.560
OR 0.577 0.789
TMS 0.547 0.690 0.771
ES 0.485 0.498 0.412 0.395
CP 0.077 0.124 0.186 0.080 0.279
GR 0.069 0.075 0.216 0.057 0.284 0.589
IV 0.613 0.678 0.664 0.641 0.706 0.121 0.133
TRF 0.631 0.735 0.754 0.785 0.558 0.072 0.053 0.831
TV 0.548 0.707 0.643 0.703 0.607 0.019 0.096 0.802 0.762
SV 0.420 0.675 0.552 0.572 0.611 0.063 0.194 0.671 0.681 0.767
Hypotheses Relationships Path coefficients tvalues pvalues Decision
Main model
H1 TMS - > BDAA 0.327 5.036 0.000*** Supported
H2 CMP - > BDAA 0.179 3.410 0.000*** Supported
H3 CMP - > TMS 0.120 1.919 0.028* Supported
H4 CMP - > TMS - > BDAA 0.035 1.863 0.031* Supported
H5 CPX - > BDDA 0.347 5.601 0.000*** Supported
H6 CPX - > TMS 0.210 3.065 0.001*** Supported
H7 CPX - > TMS - > BDAA 0.061 2.792 0.003** Supported
H8 OR - > BDAA 0.117 1.612 0.054 Not supported
H9 OR - > TMS 0.462 7.292 0.000*** Supported
H10 OR - > TMS - > BDAA 0.134 3.695 0.000*** Supported
Moderating effect of environmental factors
–EF - > TMS 0.095 1.471 0.071 –
H11a EF*CMP - > TMS 0.170 2.315 0.010* Supported
H11b EF*CPX - > TMS 0.103 1.262 0.103 Not supported
H11c EF*OR - > TMS 0.171 2.548 0.005** Not supported
Note(s):*p< 0.05; **p< 0.01; ***p< 0.001
Table 4.
Discriminant validity
assessment (HTMT
0.85
)
Table 5.
Results of the
structural model
Determinants
of BDA
adoption in
SME

and complexity on BDA adoption reveal that top management support is not the only reason
these two factors are important in the extent of BDA adoption. Another potential reason is the
influence of compatibility and complexity on employees’perceived ease of use (Park et al.,
2019). When employees feel that BDA is considerably compatible with their current practices
and learning and using it are uncomplicated, they perceive it is easy to use and consequently
more willing to adopt BDA. Thus, we recommend that future studies test the mediating role of
perceived ease of use undertaking multi-level data collection.
Although organizational readiness indirectly affects BDA adoption through top
management support, it has no direct effect on BDA adoption. Gangwar (2018) and
Ramanathan et al. (2017) found support linking organizational readiness and BDA adoption.
The finding of our study introduces top management support as an explanation for the
relationship between organizational readiness and BDA adoption. The managers’perception
of organizational readiness to embrace BDA plays a critical role in how managers support
BDA adoption. The significant indirect effect of organizational readiness on BDA adoption
through top management support and its insignificant direct effect on BDA adoption indicate
the key role of available skilled employees, IT infrastructure and financial resources. The
perceived existence of those resources enhances managers’confidence that the company has
the required resources and capabilities to implement BDA successfully.
We could not establish evidence for the moderating effect of environmental factors on the
association between complexity and top management support. This finding indicates that
regardless of the level of government regulation, external support and competitive pressure,
complexity negatively influences top management support. The dependency of top management
support on organizational readiness can be the potential explanation for this finding. When the
owner or manager of an SME believes that the organization does not have sufficient resources
and capabilities to adopt BDA successfully, he or she will not support the adoption of BDA
regardless of the extent of pressures or assistance from the government, service providers and
competitors. Furthermore, as most SMEs struggle with a lack of resources and capability, the
complexity of the BDA can cast doubt among managers about BDA investment.
As proposed, environmental factors positively moderate the influence of compatibility on
top management support. Figure 2a illustrates that although compatibility substantially
influences BDA adoption in the business environment with high support and pressure, it does
Low Compatibility High Compatibility
Top Management
Support
Low
Environmental
Factors
High
Environmental
Factors
Low Organizational Readiness High Organizational Readiness
Top Management
Support
Low
Environmental
Factors
High
Environmental
Factors
(a)
(b)
Figure 2.
Moderating effect of
environmental factors
IMDS

not affect BDA adoption when the pressure and support are low. When the level of BDA
adoption is low among competitors and government and service providers do not support
SMEs, the compatibility of the BDA cannot assure support from the manager or owner of the
SME. Against our expectations, environmental factors negatively moderate the influence of
organizational readiness on top management support. It means organizational readiness has
a less tense function in the top management’s inclination to support BDA adoption when
competitors adopted BDA and government and service providers support the process of
BDA. This finding is expected because assistance from government and service providers
can offset the risk of adoption failure due to a lack of resources and capability. Furthermore,
in a situation where competitors have adopted BDA, the SMEs should follow suit to remain
competitive. In the event of resources limitation, SMEs can rely on government and service
provider supports, hence the less important role of organizational resources.
6.1 The international comparison
Data in the present study came from Iranian manufacturing SMEs. The study primarily
highlighted the critical role of top management support in increasing the BDA adoption
extent. This salient role of top management involved directly enabling BDA adoption by
making it a strategic priority and mediating the associations between other adoption
determinants and BDA adoption. This finding is largely in agreement with previous studies
highlighting the critical role of top management support for BDA adoption within Malaysian
hotel SMEs (Yadegaridehkordi et al., 2020), UK SMEs (El-Haddadeh et al., 2021) and Indian
firms (Verma and Bhattacharyya, 2017). This finding, however, contradicts Mangla et al.
(2021), who could not support the association of top management support and BDA adoption.
Results further revealed that compatibility has a direct and an indirect effect through top
management support on the extent of BDA adoption with Iranian SMEs. This finding is in
line with the study of Yadegaridehkordi et al. (2020), who found a similar pattern for
Malaysian SMEs. Nonetheless, Maroufkhani et al. (2020) could not support this association in
their study of SMEs. Assuming compatibility and top management support as independent
variables may cause underestimation.
The present study further revealed that Iranian SMEs who struggled with the complexity
of BDA technologies had been less successful in reaping benefits from implementing and
using BDA. Furthermore, we found that top management support mediates the association
between complexity and BDA adoption. Previous studies have provided mixed insights on
the role of complexity. While El-Haddadeh et al. (2021) supported the negative impact of
complexity on BDA among UK SMEs, such negative association was not statistically
supported within Chinese (Lai et al., 2018) and Egyptian/UAE firms (Youssef et al., 2022).
Results also revealed that although organizational readiness has no direct effect on BDA
adoption, it significantly affects BDA adoption through top management support. This
finding extended the findings of Verma and Bhattacharyya (2017) and Yadegaridehkordi
et al. (2020), who found that readiness for having the necessary resources and infrastructure is
an essential enabler of BDA adoption. It means we introduced top management support as a
reason that organizational readiness leads to BDA adoption. Furthermore, this finding
challenged the findings of Lai et al. (2018), who found an insignificant relationship between
organizational readiness and BDA adoption. We believe, as the results of considering top
management support and organizational readiness as independent factors in the study of Lai
et al. (2018), the influence of organizational readiness on top management support was
underestimated in this study. Finally, yet importantly, the study highlighted the critical
moderating role of environmental factors for the association between independent variables
and top management support, which extends the work of Lai et al. (2018) that highlighted the
need for assessing the moderating roles of environmental circumstances.
Determinants
of BDA
adoption in
SME

6.2 Theoretical contributions and implications
The present study and the underlying findings contribute to the literature on the TOE model
and BDA adoption among SMEs in various ways. Firstly, the findings provide a richer
insight into the literature on the TOE model, which generally assumed the independence of
TOE factors. We find that top management support mediates the influences of compatibility,
complexity and organization resources on BDA adoption. Chen et al. (2015) and El-Haddadeh
et al. (2021) showed top management support mediates the influences of organizational and
environmental factors, however, technological factors and top management support remain
independent within both studies. Our findings show that technological factors and top
management support are not independent, and top management support can explain the
association between technological factors and BDA adoption. The mediating role of top
management support suggests that previous studies underestimated the influences of
technological and organizational factors on technology adoption. Therefore, caution is
advised when drawing on the results of prior studies, given the potential tendencies for
underestimating the role of technological and organizational factors. Secondly, our findings
showed that environmental factors moderate the influences of compatibility and
organizational readiness on top management support. This finding confirms our
proposition regarding the interactivity among TOE factors while the effects of
technological and organizational factors on top management support depend on the
business environment. In summary, this study challenges the assumption of independence
among TOE factors and shows TOE factors are interrelated and interact with each other.
6.3 Implications for practice
The findings present several practical implications for managers of SMEs, policymakers and
BDA service providers. This study finds that top management performs a remarkable role in
the process of BDA adoption. The support of the top management is particularly important
among SMEs because smaller businesses generally have a more straightforward
management structure. In most SMEs, owners hold the role of CEO, which represents less
complicated and centralized decision-making processes. Furthermore, as SMEs are less likely
to have formal and established protocols and policies to steer the employees’functions, the
managers should play an extensive role in adopting new technology. This finding
recommends that managers of SMEs should actively support the technology adoption
process and provide an enabling environment. This study investigated the drivers of top
management support, considering the importance of top management support in successful
technology adoption. The findings showed that BDA compatibility with the current practices
and culture of the firms, complexity and difficulty of adopting BDA, and the accessibility of
monetary and skilled human capitals and IT infrastructures are the factors instrumental to
top management’s decision of whether to adopt BDA.
Accordingly, BDA service providers should communicate with the managers of SMEs and
emphasize the compatibility of the BDA system and assure them that they support SMEs in
the process of BDA adoption. With the supports of the service providers, the adoption of BDA
is not an unreasonably complex and challenging process. Further, providing a trial version,
adequate technical support and training programs can reduce the managers’perception of
the complexity of BDA adoption and their concerns regarding organizational readiness. Per-
user pricing plans can make BDA affordable to SMEs and induce investment in technology.
Policymakers can play a pivotal role in addressing organizational readiness concerns. The
government incentives for BDA investment and the availability of technical support and
data-analytics related training programs help SMEs’managers overcome the challenges
faced due to lack of resources and capabilities and consequently boost their willingness to
adopt BDA.
IMDS

The positive moderating effect of environmental factors on the relationship between
compatibility and top management support indicates that when the adoption level of BDA in
an industry sector is low, service providers can put less priority on communicating the
compatibility of BDA with the current process of the SMEs. However, in the sectors that the
BDA adoption rate is high, the service providers should highlight its compatibility to boost
top management support. The negative influence of the interaction between organizational
readiness and environmental factors on top management support confirms the critical role
of policymakers and BDA service providers. In a supportive environment, a lack of
organizational readiness has a lower power to restrict BDA adoption as the support of
government and BDA service providers reassure SMEs’managers that the support is
available when they need it. Accordingly, supports from government and BDA service
providers can offset the negative influence of the lack of organizational readiness on top
management support.
6.4 Limitations and future research direction
Although the objectives of the study are met, like any other study, this study has some
limitations. Firstly, the restructured TOE model was tested in the context of BDA adoption,
and the sample of the study was limited to SMEs. Future studies are needed to test the
mediating influence of top management support and the moderating effect of environmental
elements in adopting other technologies. Future researchers should test the model of this
study by collecting data from large firms. Secondly, the study conducted a detailed literature
review from the lens of the TOE framework to identify possible determinants of BDA
adoption among SMEs. While this is a widely accepted and reliable procedure for identifying
the determinants and constructing the research model, it is limited in the sense of merely
relying on the literature. Alternative approaches such as interpretive structural modeling
that rely on literature and expert opinion can complement the present study by providing a
more holistic overview of the BDA adoption phenomenon and its determinants. Third, and
although the SME-BDA literature falls short in highlighting the critical role that
cybersecurity might play in BDA adoption, the Industry 4.0 literature, such as the recent
work of Ghobakhloo and Iranmanesh (2021), propose that cybersecurity maturity is an
important enabler of Industry 4.0 technology implementation. Since BDA is among the
essential technological constituents of Industry 4.0, we expect cybersecurity issues to act as
crucial determinants of SMEs’BDA adoption behavior. Consistently, future studies are
invited to assess the critical role of cybersecurity risks and capabilities in SMEs’BDA
adoption and implementation processes. Finally, the significant direct influences of
compatibility and complexity indicate that other mediators exist. Researchers may
investigate the mediating effect of perceived ease of use (Park et al., 2019). Finally, further
studies can extend the literature on the TOE model by testing the moderating effect of
organizational factors.
7. Conclusion
This study challenges the assumption of independence among TOE factors. We proposed
that top management support mediates the influences of technological and environmental
factors on BDA adoption. We also investigated the moderating effects of environmental
factors on the influences of technological and organizational factors on top management
support. The findings confirmed that the independence assumption is questionable,
underestimating the strength of associations between technological and environmental
factors and BDA adoption. According to the results, environmental factors moderate the
influences of compatibility and organizational readiness of top management supports.
Determinants
of BDA
adoption in
SME

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Appendix
BDA adoption (Raguseo and Vitari, 2018)
Strategic value
My company has used BDA to ... ... ....
Respond more quickly to change
Create competitive advantage
Improve customer relations
Transactional value
My company has used BDA to ... ... ....
Enhance savings in supply chain management
Reduce operating costs
Reduce communication costs
Enhance employee productivity
Transformational value
My company has used BDA to ... ... ....
Improve employees’skill level
Develop new business opportunities
Expand capabilities
Improve organizational structure and processes
Informational value
My company has used BDA to ... ... ....
Enable faster access to data
Improve management data
Improve data accuracy
Technological factors
Relative advantage (Chen et al., 2015;Ghobakhloo et al., 2011;Premkumar and Roberts, 1999)
BDA improves the quality of work
BDA makes work more efficient
BDA lowers costs
BDA improves customer service
BDA attracts new sales to new customers or new markets
(continued )
Table A1.
Questionnaire items
Determinants
of BDA
adoption in
SME

Corresponding author
Mohammad Iranmanesh can be contacted at: m.iranmanesh@ecu.edu.au
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BDA adoption (Raguseo and Vitari, 2018)
BDA adoption identifies new product/service opportunities
Compatibility (Chen et al., 2015;Ghobakhloo et al., 2011;Thong, 1999;Tornatzky and Klein, 1982)
Using BDA is consistent with our business practices
Using BDA fits our organizational culture
Overall, it is easy to incorporate BDA into our organization
Complexity (Lai et al., 2018;Xu et al., 2017)
Learning to use the BDA is difficult for employees
BDA is difficult to maintain
BDA is difficult to operate
Organizational factors
Top management support (Chen et al., 2015;Lai et al., 2018;Priyadarshinee et al., 2017)
Our top management promotes the use of BDA in the organization
Our top management creates support for BDA initiatives within the organization
Our top management promotes BDA as a strategic priority within the organization
Our top Management is interested in the news about using BDA adoption
Organizational readiness (Chen et al., 2015)
Lacking capital/financial resources has prevented my company from fully exploit BDA
Lacking needed IT infrastructure has prevented my company from exploiting BDA
Lacking analytics capability prevent the business fully exploit BDA
Lacking skilled resources prevent the business fully exploit BDA
Environmental factors
Competitive pressure (Lai et al., 2018)
Our choice to adopt BDA would be strongly influenced by what competitors in the industry are doing
Our firm is under pressure from competitors to adopt BDA
Our firm would adopt BDA in response to what competitors are doing
External support (Ghobakhloo et al., 2011;Li, 2008)
Community agencies/vendors can provide required training for BDA adoption
Community agencies/vendors can provide
Effective technical support for BDA adoption
Vendors actively market BDA adoption
Government regulation (Agrawal, 2015;Gupta and Barua, 2016;Lai et al., 2018;Li, 2008)
The governmental policies encourage us to adopt new information technology (e.g. BDA
The government provides incentives for using BDA in government procurements and
contracts such as offering technical support, training, and funding for BDA us
There are some business laws to deal with the security and privacy concerns over the BDA technology
Table A1.
IMDS