Factors influencing Industry 4.0 adoption

Abstract

Purpose
The digital transformation towards Industry 4.0 (I4.0) has become imperative for manufacturers, as it makes them more flexible, agile and responsive to customers. This study aims to identify the factors influencing the manufacturing firms’ decision to adopt I4.0 and develop a triadic conceptual model that explains this phenomenon.

Design/methodology/approach
This study used a qualitative exploratory study design based on multiple case studies ( n = 15) from the manufacturing industry in Malaysia by conducting face-to-face interviews. The data were analysed using NVivo. The conceptual model was developed based on grounded theory and deductive thematic analysis.

Findings
Results demonstrate that driving, facilitating and impeding factors play influential roles in a firms’ decision-making to adopt I4.0. The major driving factors identified are expected benefits, market opportunities, labour problem, customer requirements, competition and quality image. Furthermore, resources, skills and support are identified as facilitating factors and getting the right people, lack of funding, lack of knowledge, technical challenges, training the operators and changing the mindset of operators to accept new digital technologies are identified as impeding factors.

Research limitations/implications
Due to its qualitative design and limited sample size, the findings of this study need to be supplemented by quantitative studies for enhanced generalizability of the proposed model.

Practical implications
Knowledge of the I4.0 decision factors identified would help manufacturers in their decision to invest in I4.0, as they can be applied to balancing advantages and disadvantages, understanding benefits, identifying required skills and support and which challenges to expect. For policymakers, our findings identify important aspects of the ecosystem in need of improvement and how manufacturers can be motivated to adopt I4.0.

Originality/value
This study lays the theoretical groundwork for an alternative approach for conceptualizing I4.0 adoption beyond UTAUT (Unified Theory of Acceptance and Use of Technology). Integrating positive and negative factors enriches the understanding of decision-making factors for I4.0 adoption.

Full text

Factors influencing Industry
4.0 adoption
Sabai Khin and Daisy Mui Hung Kee
School of Management, Universiti Sains Malaysia, Penang, Malaysia
Abstract
Purpose –The digital transformation towards Industry 4.0 (I4.0) has become imperative for manufacturers, as
it makes them more flexible, agile and responsive to customers. This study aims to identify the factors
influencing the manufacturing firms’decision to adopt I4.0 and develop a triadic conceptual model that
explains this phenomenon.
Design/methodology/approach –This study used a qualitative exploratory study design based on multiple
case studies (n515) from the manufacturing industry in Malaysia by conducting face-to-face interviews. The
data were analysed using NVivo. The conceptual model was developed based on grounded theory and
deductive thematic analysis.
Findings –Results demonstrate that driving, facilitating and impeding factors play influential roles in a firms’
decision-making to adopt I4.0. The major driving factors identified are expected benefits, market opportunities,
labour problem, customer requirements, competition and quality image. Furthermore, resources, skills and
support are identified as facilitating factors and getting the right people, lack of funding, lack of knowledge,
technical challenges, training the operators and changing the mindset of operators to accept new digital
technologies are identified as impeding factors.
Research limitations/implications –Due to its qualitative design and limited sample size, the findings of
this study need to be supplemented by quantitative studies for enhanced generalizability of the
proposed model.
Practical implications –Knowledge of the I4.0 decision factors identified would help manufacturers in their
decision to invest in I4.0, as they can be applied to balancing advantages and disadvantages, understanding
benefits, identifying required skills and support and which challenges to expect. For policymakers, our findings
identify important aspects of the ecosystem in need of improvement and how manufacturers can be motivated
to adopt I4.0.
Originality/value –This studylays the theoreticalgroundwork for an alternative approachfor conceptualizing
I4.0 adoption beyond UTAUT (Unified Theory of Acceptance and Use of Technology). Integrating positive and
negative factors enriches the understanding of decision-making factors for I4.0 adoption.
Keywords Industry 4.0, Adoption, Driving factors, Facilitating factors, Impeding factors
Paper type Research paper
1. Introduction
Manufacturing firms face increasing challenges in the current digital era, such as market
volatility and shorter product life cycles due to rapidly changing digital needs, new
requirements of buyers and consumers and the pressing need to manufacture smarter and
more innovative products (Aytac and Wu, 2013). In addition, the current Covid-19 pandemic
imposes pressure on them to considera digital operating systemthat can be controlled remotely
to reduce onsite human intervention. These challenges have driven manufacturers to seek more
flexible and efficient operational processes now possible due to therecent and innovative digital
technologies collectively known as Industry 4.0 (I4.0) or Smart Manufacturing.
Industry 4.0 refers to the idea of smart factories where machines are augmented through
web connectivity and become a system that can visualize the entire production chain and make
decisions on its own (Nardo et al., 2020). The new digital technologies of I4.0 allow increased
automation, improved communication and self-monitoring of machineries. The trend toward
Industry 4.0
adoption
The authors gratefully acknowledge the funding support of Malaysian Technology Development
Corporation for this research project. The authors thank the informants from the companies involved in
the interviews and the reviewers and the editors who have given constructive suggestions to improve
the paper.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1741-038X.htm
Received 29 March 2021
Revised 21 July 2021
1 November 2021
3 December 2021
Accepted 4 December 2021
Journal of Manufacturing
Technology Management
© Emerald Publishing Limited
1741-038X
DOI 10.1108/JMTM-03-2021-0111

automation and data exchange in manufacturing technologies includes cyber-physical
systems, the Internet of things (IoT), artificial intelligence, robotics and cloud computing,
allowing firms to react faster to demand changes and implement new configurations easier or
re-plan production (Kayikci, 2017). For example, IoT allows devices to do most of the work
without human intervention, although people can interact with the devices. I4.0 has numerous
positive impacts on manufacturing firmsand economic development. The most cited benefits of
I4.0 include increased flexibility in manufacturing, mass customization, smart products, better
quality and improved productivity (Agostini and Nosella, 2019;Kiel et al., 2017).
Despite the numerous benefits offered by I4.0 and its growing importance along the
supply chain, manufacturing firms, in general, are still hesitant to embrace I4.0 to transform
their operations digitally. It could be because of many other factors that should also be
considered before investing in the costly project. However, it is still unknown what factors are
important to consider in deciding to embrace I4.0 due to the scant literature available on
determinants of I4.0 adoption. This raises our major research question –“What are the factors
influencing the decision of manufacturing firms to invest in I4.0?”. Understanding the factors
important to consider would help manufacturing firms make the right decision to invest in
I4.0 because they will be more encouraged to adopt I4.0 if they are well-convinced by the
positive factors of I4.0 rather than getting discouraged by the negative factors. Hence, the
knowledge of positive and negative aspects of I4.0 will allow them to weigh the pros and cons
in embracing I4.0. In view of the research question, this study was conducted with the first
objective of identifying the influencing factors underlying I4.0 adoption and the second
objective of presenting a triadic conceptual model that explains I4.0 adoption more
holistically. To achieve the objectives, a literature review was carried out to identify relevant
factors, theory and knowledge gaps to develop interview questions and conduct semi-
structured interviews with manufacturing firms in Malaysia.
The purpose of conducting interviews is to identify driving, impeding and facilitating
factors influencing I4.0 adoption and explore the relationship between these factors and
decision-making. Based on interview findings, we developed a conceptual model that
integrates both positive and negative influencing factors to enrich the understanding of
decision-making for digital transformation. As a contribution, the proposed model provides a
new research direction and a more holistic perspective that allows manufacturing firms to
understand the important factors for effective decision-making to adopt I4.0. Moreover, the
findings of the study offer valuable insights to manufacturing firms hesitant to adopt I4.0 in
weighing the pros and cons to make the right decision. The influencing factors identified in
this study are useful to policymakers in improving the policies and initiatives to expedite the
digitalization of manufacturing firms.
The remainder of the paper is organized as follows: Section 2 describes the background of
the study; a brief literature overview on the factors related to I4.0 adoption are presented in
Section 3; the methodology, findings, discussion and development of a triadic conceptual
model are explained in Sections 4–7. Finally, implications, contributions and limitations are
discussed in Sections 8–10.
2. Background
2.1 Industry 4.0 (I4.0)
Industry 4.0 is widely seen as a subset of the Fourth Industrial Revolution. Klaus Schwab, the
founder of the World Economic Forum, argued that we are at the beginning of a revolution
fundamentally changing the way we live, work and relate to one another (Schwab, 2015).
According to Germany Trade and Invest (2018) in “Industry 4.0: Germany Market Report and
Outlook”, the term originates from “Industrie 4.0”, the name given to the strategic initiative to
establish Germany as a lead market and provider of advanced manufacturing solutions. The
goal of the initiative is the transformation of industrial manufacturing through digitalization
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and exploitation of new technologies (Rojko, 2017). I4.0 proposes that if interconnected
computers, smart materials and intelligent machines communicate with one another, they can
interact with the environment and make decisions with minimal human involvement
(Gilchrist, 2016). Therefore, what distinguishes Industry 4.0 from Industry 3.0 is the
connectivity between digital technology, humans and other physical systems, and the
integration of the digital and physical world through cyber-physical systems and the IoT
(€
Oberg and Graham, 2016). Wang and Wang (2016) suggest that transformation to I4.0 is
dependent on technological advances: adaptive robotics, data analytics, artificial intelligence,
cloud systems, additive manufacturing and virtualization technologies.
2.2 Theoretical background for Industry 4.0 adoption
Among the theories used to conceptualize new technology adoption, Unified Theory of Acceptance
and Use of Technology (UTAUT) was extensively used by researchers in IS, IT and new
manufacturing technologies. UTAUT suggests that four core constructs (performance expectancy,
effort expectancy, social influence and facilitating conditions) are direct determinants of
behavioural intention and, ultimately, behaviour (Venkatesh et al., 2003). UTAUT has been
supported by numerous technologyadoptionstudies.Forexample,Ronaghi and Forouharfar
(2020) empirically proved the positive impacts of performance expectancy, effort expectancy, social
influence, individual factors and facilitating conditions, on the intention to use IoT technology.
While performance expectancy, effort expectancy and social influence could drive firms to consider
I4.0, facilitating conditions such as government support could expedite their decision. A study of
small and medium enterprises (SMEs) in the USA by Bosman et al. (2019) found that a firm’s access
to financial capital can significantly impact its attitude toward, behavioural intentions toward and
decision to implement I4.0 technologies. Thus, UTAUT is the suitable underpinning theory to
conceptualize driving and facilitating factors as possible determinants of I4.0 adoption. Although
UTAUT explains important factors for new technology adoption, Dwivedi et al. (2019) pointed out
that the model excluded some constructs that may be crucial for explaining IT acceptance and use.
Although several theories, including UTAUT, account for factors that positively influence the
adoption of new technologies, none have considered factors with a negative influence. In addition,
facilitating conditions in UTAUT is defined as the degree to which an individual believes that an
organizational and technical infrastructure exists to support the use of the system (Venkatesh et al.,
2003). It does not consider external support from the government. Hence, this study addresses this
theoretical gap by exploring positive and negative factors, together with facilitating factors that
could influence I4.0 adoption.
3. Literature review
Extant literature in new technology adoption has shown that many manufacturing firms
consider adopting new manufacturing technologies because of their benefits and
opportunities (e.g. Kharuddin et al.,2015). Benefits of implementing I4.0, such as
productivity and efficiency, are the important driving factors to implement I4.0 (Horvath
and Szavo, 2019). Besides operational benefits, market and business opportunities are also
regarded as why manufacturing firms consider adopting I4.0 (e.g. M€
uller et al., 2018).
Although promising advantages compel the firms to adopt I4.0, many firms face
challenges in embracing Industry 4.0 (Rajput and Singh, 2019). Stentoft et al. (2021) found that
Industry 4.0 is a nascent research area where extant academic literature lacks adequate
drivers and barriers for I4.0. Several studies have shown that possible challenges and barriers
could hamper firms’interest to initiate the digital transformation (e.g. Moktadir et al., 2018;
Masood and Sonntag, 2020). Likewise, Stentoft et al. (2021) demonstrated that perceived
barriers could directly lead to decisions not to invest in the new technologies.
Industry 4.0
adoption

On the other hand, Raj et al. (2020) suggested that improvements in government regulation
and technological infrastructure could facilitate the adoption of Industry 4.0 technologies.
Similarly, Jain and Ajmera (2020) showed that Internet facility, financial support and skill
training were the major enablers of I4.0. Hence, facilitating factors as such may play a
significant role in the decision to start the digital transformation, as they may expedite the
acquisition of required resources. For example, a firm keen to invest in I4.0 for its benefits yet
financially incapable of investing in I4.0 may still consider doing so if it gets access to funding
support.
Despite the multiple factors affecting the decision of manufacturing firms to embrace I4.0,
they have only been addressed separately by limited studies. For example, only driving
factors of I4.0 implementation were discussed by Horvath and Szavo (2019), while driving
factors and challenges were studied by M€
uller et al. (2018). In sum, no attempt was found to
have integrated driving, impeding and facilitating factors into a single model predicting the
major influencing factors of manufacturing firms’decision to adopt I4.0. This study fills this
literature gap.
3.1 Driving factors of Industry 4.0
Stentoft et al. (2021) have recently claimed that the determining factors of I4.0 adoption have
not received enough attention. They suggested that companies should focus on drivers
instead of barriers in order to improve I4.0 implementation since opportunities outweigh
constraints, and barriers can be neutralized by drivers. They identified market factors
(customer requirements, competitors) and benefits (cost reduction and time-to-market) as
drivers of I4.0 implementation similar to Horvath and Szavo (2019). On the other hand, M€
uller
et al. (2018) showed that strategic, operational, environmental and social opportunities and
opportunities for business model innovation were positive drivers of I4.0 implementation by
manufacturing firms in Germany. Different findings of these studies indicate that research in
driving factors of I4.0 adoption is not exhaustive enough, and there is a need to explore
further other driving factors that influence I4.0 adoption.
3.2 Facilitating factors of Industry 4.0
Although several studies have shed light on drivers of I4.0 adoption, facilitators of I4.0 have
been under-researched. Facilitators such as support from a government may positively
influence the decision to embrace I4.0 for the firms that have access to more resources
required to implement I4.0. In the context of new technology adoption, facilitating factors
identified are external to companies, such as governmental support in terms of funding,
training and technological advice, while others are internal or organizational such as
technological skills, capable workforce and infrastructure. In the I4.0 context, Havle and Ucler
(2018) proposed three dimensions of enablers: “Human”,“Organization”and “Technology”.
Veile et al. (2019) suggested that financial resources, employees’skills, education and training
support are important factors to be considered for I4.0. On the other hand, a few studies have
cited technological skills and resources as facilitators of digital transformation (e.g.
Fatorachian and Kazemi, 2018;Hsu et al., 2014;Biahmou et al., 2016) ignoring the importance
of financial support. This inconsistency warrants this study to explore both external and
internal factors that would facilitate manufacturing firms in digital transformation.
3.3 Impeding factors of Industry 4.0
Awareness of impeding factors in I4.0 adoption is crucial to firms before making the right
decision to embark on I4.0 journey. The challenges could be perceived as impeding factors
that can directly lead to a decision not to invest in the new technologies. Although a couple of
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studies have identified I4.0-related challenges, only a few studies have shown their impact on
I4.0 adoption. Moktadir et al. (2018) showed that “lack of technological infrastructure”
represented the most pressing challenge hindering I4.0, while Herceg et al. (2020) found that
lack of competencies and financial resources represent the greatest barriers. However, their
findings are not consistent with the findings of Stentoft et al. (2021), who suggested that SME
managers’perceptions of barriers do not affect the adoption of I4.0 technologies. Moreover,
other studies have identified different conclusion about different challenges of I4.0 that needs
further verification. For example, M€
uller et al. (2018) showed that the challenges regarding
competitiveness and future viability may significantly prevent initiatives toward I4.0. Where
certain studies have identified some challenges, there is a need to holistically develop further
knowledge on other possible impeding factors of I4.0 adoption. This study fills this need.
4. Methodology
The purpose of this explanatory descriptive study is to explore and explain how firms make a
decision for I4.0 adoption and also to describe the influencing factors. A qualitative design
based on multiple case studies (N515) from the manufacturing industry in Malaysia to
identify factors contributing to the adoption of I4.0. The qualitative technique was chosen
instead of the quantitative because the former gives a unique depth of understanding which
is difficult to gain from a closed question survey. Moreover, informants are able to freely
disclose their experiences, thoughts and feelings and clarify the questions in a qualitative
interview.
The development of the conceptual model in this study was based on the grounded
theory (Corbin and Strauss, 1990). Based on the grounded theory, we identified concepts
and build a theory based on qualitative data collection because existing theories do not
explain all important factors that influence the firms’decisions to transform digitally. To
develop our model, we used deductive thematic analysis to confirm pre-determined
themes, identify new sub-themes based on collected data and qualify coded data instead of
quantifying frequencies. Thematic analysis is a method for identifying, analysing,
organizing, describing and reporting themes identified by a data set (Braun and Clarke,
2006). The main goal of this approach is to provide a description and understanding of
answers. We used a deductive approach because I4.0 is a new concept to many firms,
requiring interview questions to be more specific. In deductive analysis, coding and theme
development are directed by existing theories, concepts, or ideas. Moreover, interview
questions were developed based on literature findings and research objectives to predict
existing themes and sub-themes, and at the same time, explore new sub-themes. This
approach is particularly useful when specific research questions can already identify the
main themes or categories used to group the data (Braun and Clarke, 2006). Table 1 shows
the themes, sub-themes identified by literature review and research objectives, together
with key interview questions.
In the first stage, a literature review was carried out to identify knowledge gaps, relevant
theories and related factors of new technology and I4.0 adoption. To conduct a literature
review, we searched five databases using several key phrases for peer-reviewed scholarly
articles solely written in English. The databases were EBSCO, Web of Science, Emerald,
SAGE and Science Direct. The main search phrases were Industry 4.0, Industry 4.0 adoption,
technology adoption, adoption factors, digital transformation, driving factors, facilitating
factors, impeding factors, benefits, opportunities, support, case study and qualitative. The
whole research team was involved in preparing the meta-analysis of literature findings and
each researcher screened through each group of articles with the aim to identify the most
prominent factors that were empirically proven to be related to technology adoption. Next,
Industry 4.0
adoption

semi-structured interview themes and questions were developed based on literature findings
and research objectives.
4.1 Sample and data collection
The study sample included Malaysian-owned manufacturing firms operating in Malaysia.
The reasons why we chose Malaysian manufacturing firms are that Malaysia is facing
increased competition from emerging economies and it is critical for them to move up the
value chain towards a higher-end manufacturing base. Thus, it is imperative for Malaysian
manufacturing firms to embrace I4.0 to sustain their future manufacturing competitiveness.
Although the Malaysian government has launched initiatives to assist the technological
development of manufacturing firms, digital adoption, especially among SMEs, is still at
around 20%, and most manufacturing firms apply less than 50% automation (Yatid, 2019).
Yatid also reported that the drive of industry players to transform themselves digitally fell
short. Understanding why firms are slow on the uptake of I4.0 and what encourages or
discourages their decision to adopt is thus crucial to assist policymakers in convincing the
reluctant manufacturers and boosting their digital transformation. Moreover, most of the
studies on I4.0 were conducted in developed western countries and research related to I4.0
adoption in eastern countries is still limited. Hence, it is one of the interests of this study to
explore whether findings would be different in eastern context.
Ethical approval for data collection was obtained from the Ethics Committee at the
Universiti Sains Malaysia. We used the directory of FMM (Federation of Malaysian
Manufacturers) to contact and invite over 60 Malaysian-owned manufacturing firms in
various manufacturing sectors to participate in the study until 15 firms agreed to be
interviewed. The interviewed informants were owners or managers, including CEOs,
managing directors, general managers and production or operation managers. We conducted
a total of 15 interviews, lasting between 45 min and 90 min, each. These spread over the entire
three months of fieldwork from August to October 2019.
Bias may be introduced in any study because of particular responses or characteristics of
the informants. To avoid informant bias, we made sure firstly that informants are very clear
on the nature and topic of the research and how the interview will be conducted; and secondly,
Themes Sub-themes Supporting articles Key interview questions
Driving
factors
Expected
benefits
Horvath and Szavo (2019);Stentoft et al.
(2021);Muller et al. (2018);Kiel et al.
(2017);Vrchota et al. (2019)
- Why would you make the
decision to adopt I4.0?
- What are the driving factors
that motivate you to adopt it?
- What are the benefits you
expect from I4.0 adoption?
- What opportunities do you
foresee by adopting I4.0?
Expected
opportunities
Facilitating
factors
Resources Veile et al. (2019);Hsu et al. (2014);Havle
and Ucler (2018);Fatorachian and
Kazemi (2018);Biahmou et al. (2016);
Jain and Ajmera (2020)
- What are major resources do
you need to adopt I4.0?
- What are the major skills
and capabilities required for
I4.0 adoption?
- What kind of support do you
need for I4.0?
Capabilities
Support
Impeding
factors
Expected
challenges
Moktadir et al. (2018);Masood and
Sonntag (2020);Herceg et al. (2020);
Stentoft et al. (2021)
- What kind of challenges do
you expect along the process?
Table 1.
Themes, sub-themes
and interview
questions
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by confirming the findings as suggested by Brink (1993). Moreover, we interviewed only the
senior management level of SMEs and it reduces the bias caused by a high degree of
difference in opinions of informants from various levels (Phillips, 1981;Seidler, 1974). This
study used purposive sampling, which involved selecting the cases that best guarantee
comprehension of the studied phenomenon. In qualitative research, purposive sampling has
advantages when compared to convenience sampling in which bias is reduced because the
sample is constantly refined to meet the study aims (Smith and Noble, 2014). Moreover, we
asked questions that asked relatively objective, observable phenomena which is less
demanding to reduce measurement error as advised by Phillips (1981).
4.2 Profile of participating firms
Firm profiles and their digital transformation status are shown in Table 2. Eight SMEs and
seven LLCs (Large Local Firms) participated in the study. They were from various
manufacturing sectors such as food, pharmaceutical, automotive, cosmetic, tin can, chemical,
electronics and electrical, construction and material sectors. All the 15 firms showed interest
in and awareness of I4.0 as an emerging topic. Seven firms have already decided to embrace
I4.0, five firms have started to explore the feasibility and requirements and two firms have not
considered adopting I4.0.
4.3 Data analysis
We used NVivo for data analysis because it has features such as character-based coding, rich
text capabilities and multimedia function, which are essential for qualitative data
management (Zamawe, 2015). All interviews were transcribed and entered into NVivo
from a Word document with no modifications made to the transcript documents. The
examined material was then classified into various pre-constructed coding units, which
involved identifying particular words, characters or items repeatedly in the open-ended
responses. The data were then coded and entered into “nodes”in NVivo. The nodes represent
topics, themes or categories that emerged from the data and convey “facts”about the text and
represent relationships within the data (Richards and Richards, 1994). Next, “data trees”
containing related nodes that branched from the “trees”and stored related information were
built. Three pre-defined main themes (driving factors, facilitating and impeding factors)
Firm Sector Size Staff Location Age (years) I4.0 status
Firm A Food SME 70þSelangor 12 Getting ready
Firm B Pharmaceutical SME 60þPenang 26 Exploring
Firm C Food SME 10þPenang 5 Not yet considered
Firm D Chemical SME 50þPenang 19 Exploring
Firm E Plastic SME 40þSelangor 15 Getting ready
Firm F Electronics and electrical SME 80þPenang 13 Interested
Firm G Food SME 50þPenang 39 Exploring
Firm H Cosmetics SME 140þPenang 27 Exploring
Firm I Tin can LLC 4,000þSelangor 63 Started first trial line
Firm J Automotive LLC 320þPenang 41 Started first trial line
Firm K Electronics and electrical LLC 1,000þPenang 12 Exploring
Firm L Plastic LLC 750þSelangor 25 Getting ready
Firm M Electronics and electrical LLC 500þPenang 20 Implemented
Firm N Construction material LLC 4,000þSelangor 35 Getting ready
Firm O Chemical LLC 200þPenang 18 Not yet considered
Note(s): SME: small and medium enterprises, LLC: local large firms
Table 2.
Profile of
participating firms
Industry 4.0
adoption

became nodes. The nodes were fleshed out, as data were extracted from each interview and
the words or factors that answered research questions were coded as sub-themes and child-
themes.
The analysis aims at the identification of relations between main themes, sub-themes and
their properties in the data to ensure that these conceptual relationships are grounded in the
data. In the analysis stage, the constant comparison method was used to find emerging sub-
themes and child-themes (related elements) for uncovering their relationships. Constant
comparison is the data-analytic process whereby each finding is compared with existing
findings, as it emerges from the data analysis (Lewis-Beck et al., 2004). Sub-themes and their
related child-themes were identified by grouping the codes by topics clustered together. This
allowed the organization of the codes into patterns of shared meaning across the data. As
each interview was analysed, themes, sub-themes and child-themes (related elements) and
their conceptual relationships were identified. For example, in Figure 1, codes such as
“operational benefits”,“market opportunities”,“labor problem”,“customer requirements”,
“competition”and “quality image”emerged as sub-themes for the main theme –“driving
factors of I4.0”. Since the first two factors (operational benefits and market opportunities)
have been identified by literature review, the rest four factors emerged from the data as new
driving factors. Furthermore, “ecosystem factors”emerged as a new impeding factor besides
challenges. Related elements of sub-themes such as operational benefits, market
opportunities, resource, skill, support, challenges and ecosystem factors were also identified.
In the final stage, a mind-mapping tool in NVivo was used to illustrate a hierarchy of main
themes and sub-themes to build a conceptual model (Figure 6). Next, relationships between
themes, sub-themes and related elements were illustrated as presented in Figure 1–5.
5. Findings
5.1 Driving factors
To identify the factors that drive the firms to consider or adopt I4.0, they were asked why they
would consider or decide to embrace I4.0 and what benefits and opportunities they expect
from digital transformation. The six major driving factors that emerged as sub-themes based
on thematic analysis were operational benefits, market opportunities, labour problem,
customers’requirements, competition and company image (Figure 1).
We have to embrace automation because we cannot continue depending on labour from foreign
countries. Wages and levies are also going up. It is getting more difficult to bring them in and also to
get the right skill sets. (Firm K)
Operational
Benefits
Market
Opportunities
Labour problem
Driving Factors
of Industry 4.0
Customers’
Requirement
Competition
Quality Image
Figure 1.
Driving factors of I4.0
adoption
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Both benefits and opportunities I4.0 can offer have driven us to go for I4.0. (Firm M)
We believe in the benefits of Industry 4.0. We have to move with the changes so that we are not left
out. (Firm N)
Our competition demands digitalization, therefore we want to modernize the factory to attract young
talents to join us. We have many overseas customers and they all require modern standards. We
need to follow the market changes. (Firm L)
Most informants mentioned perceived benefits as the major factor driving I4.0 adoption,
including productivity, efficiency, flexibility and cost-saving, among others (Figure 2). Some
other informants believe that I4.0 is the way to remain competitive as the process is changing
the industries and economy of Malaysia.
We feel that for a food company, food safety and quality is our priority. To tackle that benefit, we
need that kind of technology and reduce wastage as well. (Firm A)
The first very important consideration is that we are expecting to reduce wastage and increase
productivity. The new system is also important to us. Without it, nobody could give us production
updates when we were overseas. Access to production updates will help us in ensuring that no
Benefits
Production
efficiency
Cost
efficiency
Traceability
Productivity
Flexibility
Better
quality
Better
hygiene
Less human
contact
Less labor
dependency
Less Defect
Reduced
Wastage
Time saving
Opportunities
New market
with better
margin
More export
market
More high-end
customers
More supply
to meet higher
demand
Bigger market
share
Improved
people & culture
Better-imaged
products
Customers’
confidence in
quality
Figure 2.
Expected benefits of
I4.0 adoption
Figure 3.
Expected opportunities
of I4.0 adoption
Industry 4.0
adoption

matter where we go, staff will still be able to do their job well, knowing that they are being monitored.
This will reduce productivity losses Moreover, I4.0 will reduce our labour and costs. If we buy one
robot, we eliminate 3 or 4 jobs. (Firm E)
We saw a lot of benefits in terms of monetary, cost effectiveness, productivity growth, lean
management. We have seen a lot. We have achieved 40% of expected benefits. (Firm M)
We are expecting benefits such as cost saving, reduction of wastage, flexibility, traceability, and
many more. (Firm B)
Facilitating
Factors
Resource
Finance Human Support
Technology
Funding
Training Technology
Guidance
Skill
System
Integration
Data
Analytics
Engineering
IT & Digital
Getting the
right people
Lack of
Funding
Technical
challenges
Lack of
knowledge
Training
operators
Changing
people’s
mindset
Challenges Impeding
Factors
Ecosystem
Factors
Insufficient
service
providers
Insufficient
digital-ready
graduates
Lack of one-
stop centre
Lack of info
sharing
platforms
Figure 4.
Facilitating factors of
I4.0 adoption
Figure 5.
Impeding factors of
I4.0 adoption
JMTM

Some of the firms mentioned market opportunities as a driver of I4.0 (Figure 3). Most of the
SMEs and LLCs mentioned that increased productivity as benefit of I4.0 can boost their sales
since there are many overseas markets, not yet been tackled due to production constraints.
In the long run, I see that our clients will be impressed with what we are implementing. If we manage
to get this I4.0 done, we will be rewarded with big projects and our sales will jump to 20 million per
month, not per year. (Firm E)
If you are talking about I4.0, we will eliminate the quality issue due to manual work because robotics
will provide better quality ... the customer will be happier ... right now, our customers do not
require I4.0 products. But one day they will, because we are moving towards a new market for the
automotive industry and new customers will need this kind of process or product. We also have
plans for aerospace and medical industries. (Firm F)
The informant from Firm B, a pharmaceutical company, stated that digitalization allows
them to meet increased demand and target a new export market with better margins.
Moreover, the informant added that digitalized products represent a significant business
opportunity in high-tech or developed countries but not in price-sensitive countries.
Therefore, I4.0 is the right choice for companies targeting foreign markets with strict
requirements or higher value products.
Interestingly, firm D and firm E mentioned international competitiveness and the
importance of catching up with technological developments as the driving factors to embrace
I4.0 as follow.
We want to try everything including new I4.0 technologies. If successful, we can transfer this model
to other countries for international expansion. Our management understands that we will be left
behind our competitors if we do not transform to upgrade ourselves. (Firm D)
People are talking about I4.0. We do not want to be left out. Next year, our business plan is to become
the global player in injection moulding, and we want to match our competitors. Our I4.0 project is
preparing us for that. (Firm E)
Alternatively, some of the firms shared the view that customer requirements for I4.0 products or
processes are also compelling them to consider I4.0, as many global companies have embraced
Industry 4.0
Adoption
Operational
benefits
Market
opportunities
Competitiveness
Customers
requirement
Labour
problem Quality
image
Impeding
Factors
Driving
Factors
Challenges Ecosystem
factors
Resource
Facilitating
Factors Skill
Support
Figure 6.
Triadic conceptual
model: factors
influencing I4.0
adoption
Industry 4.0
adoption

the trend and expect their suppliers to supply raw materials satisfying the same standard.
Informants stated that if they did not follow digital wave, their competitors would, and they
would face a high risk of their existing customers switching to competitors with I4.0 status.
Our products are customer-driven. If customers need that kind of process, we must consider I4.0.
(Firm O)
When our major buyer merged with car maker from China, there was a requirement from China
partner to encourage its suppliers to go for I4.0. (Firm J)
Informants also demonstrated the need to adopt I4.0 improve their quality image and
customer confidence in the quality of their products as follows. Firm D stated that its potential
buyers bought from MNCs because of quality consistency due to automation and therefore,
they also needed to consider I4.0 to offer the same quality image to compete with MNCs.
We are now expanding to other countries. Those potential buyers sometimes come and visit us. We
have nothing to impress them. Because our product is adhesive or glue, there is nothing for us to
show to raise their interest. It is just white colour liquid. We can only talk about achieving consistent
quality, but we cannot show how we ensure it. But to them, consistent quality is quite critical as one
of our customers produces cigarettes and use our glue. They make 10,000 cigarettes per minute. If
our glue fails, their whole process, timing, and product quality are affected. That’s why they are quite
concerned about the consistency of our product quality. The fraction of glue in their product is very
small, so they do not bother about cost. That’s why most of the time they buy from MNCs because we
cannot impress them. They always ask: “How do you keep your consistency and quality?”MNCs are
all automated so they can show how they control quality and consistency (Firm D)
5.2 Facilitating factors
To understand the facilitating factors behind I4.0 adoption, the informants were asked about
the three sub-themes –resources, skills and support they need for I4.0 (Figure 4). Three sub-
themes were used as “a priori codes”that are derived from literature and observation. All
informants stated that human, financial and knowledge resources are essential for the
adoption of I4.0.
We need operational resources such as skilled workers who can handle I4.0, technical knowledge,
and better machinery, etc. (Firm N)
We need a qualified person with experience and who understands connectivity, communication,
engineering and IT. (Firm B)
We need experts with general knowledge of I4.0. It is not fair to obtain info only from suppliers to
make our decisions. The government should offer some course to equip us with the knowledge
required to talk to those people who are one step above us. (Firm D)
Digital skills both in IT and Engineering disciplines are also cited as required, mainly because
they are not happy with their current standards digital services and technology providers
such as SI (System Integration) companies and have to train their own staff to manage
digitalized operations.
We need people who are capable to bring in the automation and digitalize our system. (Firm K)
We need talented workers who are skilled enough to handle system integration. (Firm L)
As for support, most of the informants mentioned that they needed financial support,
technological support, guidance and training.
We are not ready to adopt I4.0 because we need support from government in terms of technology and
funding to start this project. We need to recruit more staffs and need to train them well. (Firm C)
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We need good infrastructure, skilled workers, funding, and training support. (Firm J)
Firm C and D mentioned that they need funding support to adopt I4.0.
Without getting any funding and technology support, we cannot start this project because we are
still new to digital technologies. (Firm C)
For everything we invest, we need to get a return. There is uncertainty or risk if we invest in I4.0. If
the government provides funding, we feel that it is worth to start it and we can become pioneers in
this technology (Firm D)
Interestingly, firm A and firm E which are SMEs stated that they are committed to initiate I4.0
even without getting financial support.
At this stage, we are using our own fund to invest in this I4.0 project because it takes time to get
government’s funding and we do not want to wait for something we are not sure to get. We believe
that it is worthy to invest because it makes business sense and technology is affordable. (Firm A)
We are still waiting for funding from the government. To show our commitment to I4.0, we have
already invested in the IoT project while waiting. It is about 25% of the whole investment. We have
to look at it from the positive side. If we do not get the grant from the government, we still need to
implement this project because the new system will give us benefits. If we get a grant from the
government, it will be a bonus for us. (Firm E)
Our management is committed to this I4.0, so they have approved the required budget for the
implementation so far. (Firm M)
On the other hand, some informants revealed that support from academic institutions could
facilitate their digital transformation. Firm I mentioned that one of their staff had been
attached to a college for a master’s degree that focused on developing software required for
the I4.0 system.
Digitalization is very costly, as well as engaging external software providers to assist the current
company operations. Therefore, our collaboration with external organizations such as universities
supporting software development reduces our cost. (Firm I)
5.3 Impeding factors
Informants were asked about two impeding factors: expected challenges and problems
related to the ecosystem. Some of the informants have started to explore I4.0 and the
challenges they mentioned have not halted their digital transformation. The major challenges
mentioned by most of the informants are getting the right people to handle the I4.0 project,
lack of funding, lack of knowledge, technical challenges, training operators and changing the
mindset of operators. Many informants shared concerns about the financial constraints
associated with the digital transformation:
I think finance is a challenge because we have spent a lot on the recent purchase of new machines. I
think it is going to be difficult for us to make another investment any time soon. (Firm F)
Different from other informants, Firm B stated the challenge of recruiting the right people to
work with new technologies and processes:
The major challenge is to get the right people keen to engage with I4.0. Even if we offer more money,
they think of more work or pressure. (Firm N)
Other firms mentioned lack of knowledge and technical challenges as follow:
Lack of knowledge I would say. We are not equipped with technical knowledge and information
about all these new technologies. (Firm D)
Industry 4.0
adoption

Different from literature, Firms B and E mentioned the challenge of changing workers’
mindset due to their fear of unemployment:
Management would want to embrace I4.0, but not the lower-level staff due to fear of losing their jobs.
We have to educate them and show that they are not going to lose their job, but instead life is going to
get much better with the help of technology. It takes some time to change their mentality (Firm B).
When we introduced something good or improvements, our production workers were still reluctant.
Therefore, we have to change their attitude. I had the same problem when we implemented a lean
system. They were like “oh, the system is already good ...why does management want to change the
system? Last time we did one job, and now we have to do more jobs”. That’s their mentality (Firm E).
Some other challenges mentioned by informants were the current problems related to the
ecosystem for I4.0. These challenges include insufficient and inexperienced digital tech and
service providers, an insufficient supply of digital-ready graduates, lack of one-stop centres
and lack of info-sharing avenues or platforms:
We do not really talk to them. I know that they cannot solve our problems because what we feel is
that they look at the problem at different dimensions. Their idea and thinking is very much different.
So, SMEs require their own solutions. The solution or methodology they have for big companies
cannot be used for small companies. A lot of solutions are tailored to suit big enterprises (Firm A).
Service providers are not fully confident about their solutions. They are not fully equipped and
depend on someone else. If there are no professionals to solve problems with digitalization, we may
have to revert to our original production process (Firm B).
It would be good if all the services and support can be provided by one agency or one-stop centre so
that we can get all the information at one stop (Firm E).
We are willing to share our experience with SMEs who are our suppliers. So, we need an avenue or
platform to share knowledge and experience with new comers for I4.0 (Firm I).
6. Discussion
This study was set off with the objectives to identify the factors that influence the
manufacturing firms’decision to adopt I4.0 and develop a holistic conceptual model that
explains I4.0 adoption. The six major driving factors which emerged as sub-themes were
expected benefits, market opportunities, labour problem, customer requirements,
competition and quality image. Among these factors, labour problem, customer
requirements and quality image are new findings that have not been recognized by
previous studies (e.g. Stentoft et al. (2021);Mogos et al., 2019;M€
uller et al., 2018), which were
conducted in the manufacturing industry of western countries such as Denmark, Norway and
Germany, respectively. Similarly, in the eastern context, Jain and Ajmera (2020) and
Subramanian et al. (2021) did not report these factors. Hence, these particular driving factors
could be due to the country context of the study. In Malaysia, labour problem could push the
manufacturers to adopt I4.0 because the problem of labour shortage has been countered by
hiring foreign workers and the cost of foreign workers has risen recently. Similarly in other
countries with labour problems, the benefit of I4.0 in replacing humans with machines is
crucial to be considered in I4.0 adoption as it solves labour problems.
In accordance with the related literature for facilitating factors, findings highlight the
importance of resources, skills and support in considering I4.0 adoption. Findings show that
skilled workers, qualified staff with knowledge of I4.0 and financial resources are the most
important resources required to implement I4.0, supporting the findings of Veile et al. (2019) in
Germany and Bosman et al. (2019) in the context of Germany and USA, respectively. As for
the skills, most of the participating firms need engineering, IT, digital skills and skills
JMTM

required for I4.0, as suggested by Hsu et al. (2014) based on a study in Taiwan and Kipper et al.
(2021) based on a literature survey. Hence, the findings of important resources and skills are
consistent in different regions.
With regard to financial resources and support, two interesting findings emerged. First,
findings suggest that financial support is needed to facilitate SMEs with limited financial
capability, such as Firms B, C, D and F to invest in I4.0. This finding is consistent with that of
Veile et al. (2019) and Jain and Ajmera (2020). On the other hand, Firm A and Firm E, which are
SMEs are also going ahead with preparation for I4.0 without any funding support from the
government. Since I4.0 studies have not highlighted the impact of funding support, this
finding adds new knowledge to existing research by revealing that lack of funding support is
not a barrier for certain SMEs to adopt I4.0 if they are driven by a strong belief in the benefits
of I4.0 and are financially capable.
Another new knowledge the findings offer is that support from academic institutions in
developing the technology or solutions required could facilitate their digital transformation.
By having collaboration with universities, firms could save the cost of buying the solutions
required. At the same time, universities could facilitate the firms in the skill development of
the employees by offering new courses designed for I4.0-related skills and knowledge. Thus,
more studies need to probe into the role of collaboration between academic institutions and
industries in expediting digital transformation.
As for impeding factors, major challenges informants mentioned such as getting the right
people to handle the I4.0 project, lack of funding, lack of knowledge and technical challenges are
consistent with existing research findings of Herceg et al. (2020) and Stentoft et al. (2021).Anew
challenge that has not been reported by previous studies is the fear of workers for the change due
to the possibility of unemployment that may come with digital transformation. This finding
contradicts the finding of Herceg et al. (2020) who found in the manufacturing industry of Serbia
that resistance to change caused by Industry 4.0 implementation is not seen as an important
barrier. This new finding could be country-specific because it is more likely for workers to have
this kind of fear or worry in a country like Malaysia, where workers have a fear of job loss and
difficulty to find another job (Sharudin, 2020). Hence, job security and job availability of the
workforce in the country could be the possible factors that could determine the impact of
workers’fear on digital transformation. Future studies could affirm this.
Another impeding factor that emerged from the data was “ecosystem factors”which has
not been reported by previous studies. Some challenges mentioned by informants are related
to the ecosystem for digital transformation such as inexperienced technology and service
providers for I4.0, an insufficient supply of digital-ready graduates, lack of one-stop centres
and lack of info-sharing avenues or platforms. Although most of the impeding factors
identified in this study are consistent with other studies, ecosystem factors and workers’fear
to change are somewhat different from findings of other studies conducted in both western
and eastern context. While M€
uller et al. (2018),Herceg et al. (2020) and Stentoft et al. (2021)
conducted their studies in western context, Wagire et al. (2021) and Moktadir et al. (2018)
conducted theirs in eastern context. Since studies in both regions had not discussed these two
new factors, we conclude that the new factors identified in this study are more likely to be
country-specific rather than regional context. Apparently, more studies need to be conducted
in both western and eastern regions to see more differences in influencing factors.
7. Development of triadic conceptual model
Since the literature review revealed the absence of a holistic model that addresses major
factors that influence the decision to adopt I4.0, we developed an integrated conceptual model
that would be valuable to manufacturing firms and policymakers. The model is supported by
a literature review to a certain extent and was developed based on interview findings. Thus,
Industry 4.0
adoption

the interview findings of this study further validate the importance of these three factors by
interconnecting the three major themes, sub-themes and elements of the sub-themes, creating
a triadic conceptual model (Figure 6). The model exhibits three key factors to be considered
when manufacturing firms deliberate over I4.0 adoption. It suggests that the driving factors
identified in this study could trigger them to consider I4.0 technologies, access to facilitating
factors could push them to go ahead and impeding factors should be evaluated to ensure that
the benefits of I4.0 adoption outweigh challenges and that the latter can be addressed.
Moreover, understanding impeding factors allows firms to prepare both mentally and
technically for the process of digital transformation. By integrating driving, facilitating and
impeding factors, the proposed model offers a new approach to conceptualize decision-
making process for I4.0 adoption from three perspectives.
8. Implications
This study offers a broader perspective of important factors influencing the decision-making
for I4.0 adoption. The first implication of our findings on driving factors is that firms need to
be aware and convinced of various benefits and opportunities associated with I4.0 and
possible expectations of customers and competitors’threats as these could drive them toward
I4.0 adoption. Therefore, authorities should organize more awareness programs discussing
the benefits and opportunities of I4.0 including improvement in the product image and higher
technology standards, in parallel with increased funding opportunities. The firms will be
more compelled to adopt I4.0 if they are convinced by the advantages of I4.0 and realize the
importance of meeting customers need for digitally produced products. Moreover, the new
finding of meeting customer requirements and improving quality image by upgrading their
technologies sheds light on the importance of paying attention to changing needs of
customers and improving the company image by adopting I4.0 as some customers are more
impressed by the image of a company that operates using I4.0 technologies.
Moreover, findings suggest that many manufacturing firms need resources, skills and
support to initiate the transition toward I4.0. It highlights that manufacturing firms with
tangible resources such as qualified human resource, machinery and equipment and
intangible resources such as skill and capabilities are more likely to adopt I4.0. Since LLCs
usually have a higher level of resources, SMEs with limited resources should be given a
priority in getting the access to government’s support to procure required resources. Hence,
policymakers should identify which resources and what support SMEs need, since it is one of
the authorities’important national agendas to accelerate I4.0. Importantly, authorities should
provide more funding support which allows firms to obtain required resources and skills.
Since technological support is an important facilitator, institutions should set up agencies to
provide technological guidance and advice as consultants.
With respect to impeding factors, we found that challenges such as the difficulty to recruit
digitally skilled staff or lack of finance prevent many firms from adopting digital
technologies. It implies that they would not adopt I4.0 unless they have enough funds to get
the required human resource. To reduce the hesitation due to those concerns, governmental
institutions should accelerate human resource development in collaboration with academic
institutions or setting up centres for free digital skill trainings in line with I4.0. The enhanced
digital skill and confidence may encourage the firms to adopt I4.0 even without funding from
the government. Moreover, the new finding of workers’fear to change highlights the
importance of preparing the mindset of workers to get the right understanding about digital
transformation and positive job prospects for them to get upskilled for different job positions
so that they will be more positive about I4.0.
The findings regarding ecosystem-related problems call for more qualified and
experienced technological service providers, as informants mentioned that their solutions
JMTM

are not suitable for SMEs. The technological service providers play an important role in I4.0
transformation as manufacturing firms depend on them. Thus, the government should
consider funding technological service providers to enhance their service quality. Since they
are also at the beginning of the I4.0 journey, they also need funding support to upgrade
themselves by learning from experienced I4.0 experts from other countries. Other
implications include the need for one-stop solution centres providing a complete guide to
beginners in the transition to I4.0. A conducive ecosystem including supportive agencies,
required human and financial resources, digital training and qualified technology providers
is crucial to convince firms to invest in a costly I4.0 project.
9. Contributions
This study provides both theoretical and practical contributions. From a theoretical perspective,
this study lays the theoretical groundwork for an alternative conceptualization of I4.0 adoption
by extending UTAUT. Integrating both positive and negative factors enriches the
understanding of decision-making factors for digital transformation and underscores that
firms should consider both advantages and disadvantages when making decisions to invest in
costly digital transformation. By doing so, the proposed model provides a new research direction
and perspective to understanding new technology adoption as an important theoretical
contribution. This study also reveals under-researched factors such as governmental support,
firms’capabilities and the ecosystem of the country for digital transformation by adding new
knowledge to existing literature and offering a future research arena.
As a practical contribution, knowledge of the influencing factors of I4.0 adoption revealed
by our model would help manufacturers in their decision to invest in I4.0, as they can be
applied for weighing pros and cons, understanding potential benefits, identifying required
skills and support and which challenges to expect. In addition, new knowledge of benefits and
opportunities identified by this study may encourage firms to implement I4.0, tackle new
opportunities and achieve I4.0 status earlier than their competitors. For policymakers, our
findings identify important aspects of the ecosystem in need of improvement, and how
manufacturers can be motivated to adopt I4.0. Policymakers should apply three categories of
factors identified by our study to improve policies and initiatives and to expedite the
digitalization of manufacturing firms to ensure that the national agenda for I4.0 is fulfilled.
In sum, this study can be distinguished from previous studies on technology adoption in a
few ways. First, to the best of our knowledge, this study is the first to propose an integrated
model of three important influencing factors to consider before adopting I4.0. Second, it is the
first study to map out more driving, facilitating and impeding factors of I4.0 adoption
exhaustively based on qualitative data. It is important for manufacturers to understand all
these factors in detail to make the right decision. Third, it fills literature and theoretical gaps
unfilled by existing studies and theories for new technology adoption. Currently, no holistic
appraisal of factors influencing the decision to adopt I4.0 can be found in the literature.
Existing models, UTAUT for example, consider only positive factors, ignoring factors with a
negative impact on decision-making. Moreover, previous studies have failed to consider
facilitating factors as determinants of new technology adoption. Considering facilitating
factors together with driving and impeding factors make the model more comprehensive and
holistic. Moreover, facilitating factors add more positive influence to decision-making. Failure
to consider these may result in wrong decision-making.
10. Limitations and future direction for research
Due to its qualitative design and limited sample size, the findings of this study need to be
supplemented by quantitative studies, thus allowing proper testing and generalization of our
Industry 4.0
adoption

proposed model. Despite its limitations, this study offers a new technology adoption model
that could be empirically tested by future researchers in the context of different countries,
sectors and technologies. Although this study focuses on three influencing factors, future
research should explore other factors. Overall, it offers valuable findings that may help
manufacturing firms and governments in their efforts to promote a faster transition towards
I4.0 paradigm. It may also help manufacturing firms realize that digital transformation is not
just a threat or cost to the firms as it is an opportunity for early adopters who considers three
important factors before making the decision.
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About the authors
Sabai Khin, PhD, is Lecturer at the School of Management, Universiti Sains Malaysia (USM). She
received her MBA and PhD from USM and was awarded USM Fellowship by USM during her PhD
candidature. Her research and teaching interests include industry 4.0, innovation management,
technology readiness, technopreneurship, start-up ecosystem and international business. She has
earned many years of industry experience in international business, new product development,
commercialization, customer and export management and business planning in the manufacturing,
trade, and hospitality industries. Sabai Khin is the corresponding author and can be contacted at:
vykino@gmail.com
Daisy Mui Hung Kee, PhD, MBA, is Associate Professor at the School of Management, Universiti
Sains Malaysia in Penang, Malaysia. Her areas of interest are human resource management,
organizational behaviour, work values, leadership, psychosocial safety climate, entrepreneurship and
small and medium enterprises. She earned her Master of Business Administration degree from the
School of Management, Universiti Sains Malaysia and her doctoral degree in business and management
from the International Graduate School of Business of the University of South Australia. In 2006, she
received the Merdeka Award from the Australia Malaysia Business Council of South Australia.
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