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Article

Embracing Industry 4.0: Empirical Insights from Malaysia

1
Faculty of Economics and Management, Universiti Kebangsaan Malaysia (The National University of Malaysia), UKM Bangi, Selangor 43600, Malaysia
2
UKM Graduate School of Business, Universiti Kebangsaan Malaysia (The National University of Malaysia), UKM Bangi, Selangor 43600, Malaysia
3
Faculty of Applied and Human Sciences, Universiti Malaysia Perlis, Kangar, Perlis 01000, Malaysia
*
Author to whom correspondence should be addressed.
Academic Editors: Luca Romeo, Marina Paolanti, Emanuele Frontoni and Tapio A. Heikkilä
Received: 24 March 2021 / Revised: 17 April 2021 / Accepted: 20 April 2021 / Published: 22 April 2021

Abstract

Industry 4.0 revolution, with its cutting-edge technologies, is an enabler for businesses, particularly in reducing the cost and improving the productivity. However, a large number of organizations are still too in their infancy to leverage the true potential of Industry 4.0 and its technologies. This paper takes a quantitative approach to reveal key insights from the companies that have implemented Industry 4.0 technologies. For this purpose, 238 technology companies in Malaysia were studied through a survey questionnaire. As technology companies are usually the first in line to adopt new technologies, they can be studied better as leaders in adopting the latest technologies. The findings of this descriptive study surfaced an array of insights in terms of Industry 4.0 readiness, Industry 4.0 technologies, leadership, strategy, and innovation. This research paper contributes by providing 10 key empirical insights on Industry 4.0 that can be utilized by managers to pace up their efforts towards digital transformation, and can help the policymakers in drafting the right policy to drive the digital revolution.
Keywords: Industry 4.0; Industry 4.0 readiness; Industry 4.0 technologies; fourth industrial revolution; leadership; strategy; innovation Industry 4.0; Industry 4.0 readiness; Industry 4.0 technologies; fourth industrial revolution; leadership; strategy; innovation

1. Introduction

Industry 4.0 is a phenomenon whereby the physical world and the virtual world have merged into one as Cyber-Physical Systems (CPS) [1]. The conception of Industry 4.0 started in Germany in an economic debate in 2011 [2]. In the same year, 2011, the United States (US) started the “Advanced Manufacturing Partnership (AMP)” which is the Industry 4.0 version of manufacturing. In 2012, the German government crafted “Industrie 4.0” for the manufacturing sector. In 2013, the French government started “La Nouvelle France Industrielle”. In the same year, 2013, the United Kingdom (UK) government presented the “Future of Manufacturing for 2050”. In 2014, the European Commission initiated “Factories of the Future (FOF)”. In 2014, the South Korea government publicized the “Innovation in Manufacturing 3.0” for Korean manufacturing [3]. In 2015, the Chinese government initiated the “Made in China 2025” to accelerate the informatization and industrialization in China. In the same year, 2015, the Japanese government revealed “Super Smart Society”. In 2016, the Singapore government revealed the “Smart Readiness Index” to capture Industry 4.0 opportunities. In 2018, the Malaysian government launched “Industry4WRD” to take advantage of Industry 4.0 initiatives [4]. A summary of notions used for Industry 4.0 in different countries is presented in Table 1.
Around the globe, manufacturing companies have taken the first step in adopting Industry 4.0 technologies [5]. By digitizing their businesses, manufacturers in all sectors are finding innovative and cost-effective ways to run their production and serve their customers (Industry Week Magazine, 2020). The benefits of Industry 4.0 are immense in terms of improved quality, reduced turnaround time, and overall optimization. Despite this, according to the World Economic Forum (WEF), only 29 percent of the companies globally are deploying Industry 4.0 technologies at scale, granting them opportunities to realize game-changing impacts. The challenges faced in the process include awareness of new technology roadmaps, customization of existing processes, and existing technology upgradation [6]. To overcome this, companies from both manufacturing and service sectors, companies of small, medium, and large size, have to challenge some of their assumptions that are holding them back. This includes a belief that a massive overhaul of existing equipment will be needed and immense additional employee training will be required [7]. In fact, there are many small steps that companies can take to adapt their existing systems and processes for Industry 4.0 suitability [8].
In the context of Malaysia, the government expects to undergo a paradigm shift in companies resulting from the adoption of Industry 4.0 technologies, particularly after seeing the lost opportunities and slow growth due to the ongoing COVID-19 pandemic (Star Newspaper, 2020). However, there is little information available on the profiling of companies that have successfully adopted Industry 4.0. Therefore, this paper explores such key empirical insights from technology companies in Malaysia that have implemented Industry 4.0. In terms of research objectives, this paper focuses on five research questions considering the context of Malaysia. Here, two factors have been considered: organization age (3–5 years, 6–10 years, and more than 10 years) and organization type (manufacturing large, manufacturing medium, manufacturing small, services large, services medium, and services small). These two factors are important as they are often cited in other studies in literature on Industry 4.0 [9,10,11]. Furthermore, in terms of industry prominence, organization age is important as it compares older with newer organizations, and organization type is important as it shows the contrast of manufacturing and service firms. Furthermore, this paper considered three critical non-technology factors that help organizations in preparing for Industry 4.0: leadership, strategy, and innovation.
This paper aims to address the following five important research questions:
Research Question 1:
 What are the differences of Industry 4.0 readiness among companies according to the age and type of the organization?
Research Question 2:
 What are the differences of Industry 4.0 technologies employed among companies according to the age and type of the organization?
Research Question 3:
 What are the differences of leadership in embracing Industry 4.0 according to the age and type of the organization?
Research Question 4:
 What are the differences of strategy in embracing Industry 4.0 according to the age and type of the organization?
Research Question 5:
 What are the differences of innovation in embracing Industry 4.0 according to the age and type of the organization?
The remaining paper follows this sequence: Section 2 states the theoretical background. Section 3 presents the research methods, and Section 4 states the results and discussion. Lastly, Section 5 summarizes this paper with the conclusions and study contributions.

2. Theoretical Background

2.1. Industry 4.0

Industry 4.0 is largely about digitalization [12]. In that respect, Industry 4.0 is defined as “the digitalization transformation driven by connected technologies to build a cyber-physical entity” [13]. There are various connotations to Industry 4.0, which lead to a variety of definitions. Combining all these technologies and concepts, the consulting firm called Boston Consulting Group (BCG) identified nine pillars of Industry 4.0. The nine pillars of Industry 4.0 are industrial internet, advanced manufacturing, additive manufacturing, simulation, horizontal/vertical integration, cloud, cyber-security, augmented reality, and big data analytics [14]. The popularity of these nine pillars of Industry 4.0 has increased in recent times, but several firms are still struggling to make use of them [15]. Sharma and Gandhi observed risks in adopting these technology pillars in the areas of data security and job loss [16]. Digital technology is the driving force for Industry 4.0. Nearly all the innovations of Industry 4.0 come through digital power [17]. Four aspects usually covered are Internet of Things (IOT), artificial intelligence, cloud computing, and big data. Moreover, IOT can serve as a technology enabler for Industry 4.0 vision [18].
The most recent extension of Industry 4.0 technologies is the concept of Industrial Internet of Things (IIOT). IIOT is primarily about converting traditional factory to Factory of the Future [19]. There is no universally accepted definition of IIOT as well. According to Bauer, IIOT is horizontal and vertical connection of people and machines [20]. It is also known as IOT plus Cyber-Physical Systems (CPS). IIOT’s reference with Industry 4.0 is relatively new as compared to IOT. Hence, Industry 4.0 readiness is defined as “the degree to which organizations are able to exploit and derive benefits from Industry 4.0 technologies” [21]. Hence, it is about getting companies ready for Industry 4.0. Though Industry 4.0 technologies have convergence which helps in integration within the organization, it brings its own set of challenges [22].
Similar to change readiness, Industry 4.0 can be implemented in companies on three prime levels [23]. At “operations” level, reconfiguration of Industry 4.0 technologies is needed. Next, at “organization” level, organizational processes require realignment to match with technicalities of Industry 4.0. Finally, at “customers” level, customer demands with respect to Industry 4.0 are to be anticipated, planned, and served. Overall, Industry 4.0 technologies are potent enough to cause disruption inside the organizations and across the markets [24,25].

2.2. Technology Acceptance Model (TAM)

Amongst the most popular models of technology is the Technology Acceptance Model (TAM) conceived by Davis in 1989 [26]. It is also the most cited and replicated empirically. The TAM is ranked high in the domain of information systems, where its conception of theory originated. The theory received prominence on findings and insights that clarified the patterns on acceptance of technology. The model is based on two primary factors that promote the use of new technology: perceived ease of use and perceived usefulness. Perceived usefulness (PU) implies that technology would enhance job performance, and perceived ease of use (PEOU) implies that technology would be hassle-free and less complicated [27].
The TAM is based on Theory of Reasoned Action (TRA) [28]. The study by the author Lin in 2007 combined technology readiness (TR) with TAM, and developed an integrated Technology Readiness and Acceptance Model (TRAM), which is an extension of TAM [29]. Subsequently, another interesting study by the author Chimay mentions the perspectives on e-readiness [9]. The author mentions that e-readiness is the ability to adopt information and communication technologies (ICTs). In the last few years, a good number of readiness assessment tools have been developed [30]. Harvard University’s tool “Networked Readiness Index” is a similar measure but measures a country’s capacity to make use of its ICT resources [31]. Despite this, the TAM remains the prime model for technology-related revolution such as Industry 4.0. It is also important to note that in addition to perceived usefulness and perceived ease of use influencing technology adoption, there are external variables involved as well, such as training [32] and social influence processes [33]. It is also well-known that people in organizations are often uneasy and unwilling to implement the latest technologies such as Industry 4.0, hence a push of management support and leadership intervention is needed [34].

3. Methods

3.1. Scope and Respondents

This paper focused on technology companies as Industry 4.0 technologies are more relevant and needed by technology companies. Thereon, the key insights can be better understood first by studying technology companies. A technology company is one that uses technology as an advantage in its internal and external operations [35]. Precisely, 238 technology companies in the scope of this study included: (i) manufacturing technology companies; (ii) services technology companies; (iii) local technology companies; and (iv) foreign technology companies operating in Malaysia. Respondents were selected on the basis of two criteria: (i) he or she should be at least a manager working in that company; and (ii) he or she should have worked with that company for at least one year. These two conditions are important as this will filter professionals that are senior and have greater decision-making abilities [36,37]. Likewise, two filter questions were used for screening of companies: (i) the company should be a technology company; and (ii) the company should have implemented at least one of the Industry 4.0 technologies.

3.2. Questionnaire

In terms of questionnaire, data was collected through a cross-sectional survey using the 5-point Likert scale, 1 (Do not use) to 5 (Use to a very high degree). A 5-point Likert scale is better as it is adequate, and includes a middle option which does not force the respondent to take a leading side [38,39,40]. The data analysis was performed through analysis of variance (ANOVA) in Statistical Package for the Social Sciences (SPSS). It is important to note that authors in this study while performing the data analysis through ANOVA did not investigate the convergence of the results obtained, and the results were hence concluded on the basis of 238 surveyed companies. The questionnaire had 5 sections and 40 overall items/questions: Industry 4.0 Readiness (7 items), Industry 4.0 Technologies (12 items), Leadership (6 items), Strategy (7 items), and Innovation (8 items). As the questionnaire used in this study is under copyright with Malaysian Technology Development Corporation (MTDC), only selected items from the questionnaire are presented in this article, in Table 2.

4. Results and Discussion

This study was based on 238 technology companies in Malaysia. In terms of organizational profiling, 62 percent of the organizations were services companies and the remaining 38 percent were manufacturing companies. In terms of respondent profiling, 37 percent of participating respondents were part of senior management, 53 percent represented middle management, and the remaining 10 percent were first-line management. This section presents the findings on embracing Industry 4.0 in terms of five areas: Industry 4.0 readiness, Industry 4.0 technologies, leadership, strategy, and innovation. These five areas are further supplemented with discussion based on the research questions that were established in the introduction of the paper.

4.1. Industry 4.0 Readiness

Industry 4.0 readiness is the degree to which organizations are able to exploit and derive benefits from Industry 4.0 technologies [41]. This section highlights the importance of Industry 4.0 readiness in embracing Industry 4.0 in terms of organization age and organization type.
First, considering Industry 4.0 readiness as the dependent variable, ANOVA analysis was conducted with age of the organization. The output of the ANOVA analysis in Table 3 shows that there is a statistically significant difference between the group means. As the significance value is p = 0.000 (which is below 0.05), there is a statistically significant difference among the groups reported with F(2235) = 8.623. Furthermore, the mean and standard deviation of the five groups shows the highest mean (M = 3.56) for more than 10 years and highest standard deviation (SD = 1.081) for six to ten years. This implies that older organizations (over 10 years) have a better approach to embracing Industry 4.0.
Second, considering Industry 4.0 readiness as the dependent variable, ANOVA analysis was conducted with organization type. The output of the ANOVA analysis in Table 4 shows that there is a statistically significant difference between the group means. As the significance value is p = 0.032 (which is below 0.05), there is a statistically significant difference among the groups reported with F(5232) = 2.487. Furthermore, the mean and standard deviation of the six groups show the highest mean (M = 4.02) for services large and highest standard deviation (SD = 0.705) for services small. Thereby, large size service organizations are more advanced in embracing Industry 4.0.
Research Question 1:
 What are the differences of Industry 4.0 readiness among companies according to the age and type of the organization?
The findings presented in this section reflect that Industry 4.0 readiness is important for embracing Industry 4.0 in two ways: organization age and organization type. In terms of organization age, older organizations (over 10 years) have better approach to embracing Industry 4.0. This yields that established organizations can gear up better and faster with new revolutions such as Industry 4.0. This can be due to multiple factors, including the strong processes and reputation [30]. Additionally, the older the organization, the more resilient the organization is, which is much needed in gearing up for Industry 4.0. Secondly, with respect to Industry 4.0 readiness and in terms of organization type, large size service organizations are more advanced in embracing Industry 4.0. This can possibly be resulting from the intangibility of service offerings which gives an advantage over manufacturing companies [42]. It is relatively expensive for manufacturing organizations to develop Industry 4.0 readiness, as machinery and equipment is involved.

4.2. Industry 4.0 Technologies

Industry 4.0 technologies are technologies that build a cyber-physical organization [43]. As per the Boston Consulting Group (BCG), they mostly include these nine technologies: (1) Big Data, (2) Simulation, (3) Horizontal and Vertical Integration, (4) Industrial Internet of Things, (5) Autonomous Robots, (6) The Cloud, (7) Cyber Security, (8) Augmented Reality, and (9) Additive Manufacturing. This section highlights the prominence of Industry 4.0 technologies in embracing Industry 4.0 in terms of organization age and type.
First, considering Industry 4.0 technologies as the dependent variable, ANOVA analysis was conducted with organization age. The output of the ANOVA analysis in Table 5 shows that there is a statistically significant difference between the group means. As the significance value is p = 0.071 (which is below 0.10), there is a statistically significant difference among the groups reported at 90 percent significance. In social sciences, 90 percent significance level is also considered important particularly in new domain or subjects, such as Industry 4.0 [44,45]. Here, the mean (M = 3.32) also indicates that older organizations (more than 10 years) have better technologies. This implies that there are significant differences in Industry 4.0 technologies in terms of organization age in embracing Industry 4.0.
Second, considering Industry 4.0 technologies as the dependent variable, ANOVA analysis was conducted with organization type. The output of the ANOVA analysis in Table 6 shows that there is a statistically significant difference between the group means. As the significance value is p = 0.001 (which is below 0.05), there is a statistically significant difference among the groups reported with F(5232) = 4.169. Furthermore, the mean and standard deviation of the six groups show the highest mean (M = 3.45) for manufacturing large and highest standard deviation (SD = 0.958) for manufacturing small. Thereby, large size manufacturing organizations, in contrast to the large size service organizations, are more advanced in embracing Industry 4.0.
Research Question 2:
 What are the differences of Industry 4.0 technologies employed among companies according to the age and type of the organization?
The results presented showcase that the adoption of various Industry 4.0 technologies is imperative for embracing Industry 4.0 in two ways: organization age and type. In terms of Industry 4.0 technologies, the more the merrier, as it gives lessons on failure and success, and overall gives confidence to the managers in organizations to implement the right fit of Industry 4.0 technologies [46]. The results here show that there is significant difference in Industry 4.0 technologies in terms of organization age in embracing Industry 4.0, inferring that more than 10 years organizations adopt Industry 4.0 technologies more effectively. With respect to Industry 4.0 technologies, and in terms of organization type, large size manufacturing organizations, in contrast to the large size service organizations, are more advanced in embracing Industry 4.0. It is a double advantage for adoption of Industry 4.0 technologies if the company is large sized and the company is a manufacturing concern as well [47]. As most of the Industry 4.0 technologies were designed originally for manufacturing concerns, it is more relevant and pragmatic to gain control and efficiency on Industry 4.0 technologies to embrace Industry 4.0.

4.3. Leadership

Leadership is defined as the role of the organization’s management team in communicating and reinforcing the organization’s vision [48]. This section focuses on the importance of leadership in embracing Industry 4.0 in terms of organization age and organization type.
First, considering leadership as the dependent variable, ANOVA analysis was conducted with organization age. The output of the ANOVA analysis in Table 7 shows that there is a statistically significant difference between the group means. As the significance value is p = 0.017 (which is below 0.05), there is a statistically significant difference among the groups reported with F(2235) = 4.141. Furthermore, the mean and standard deviation of the three groups show the highest mean (M = 4.09) for more than 10 years and highest standard deviation (SD = 0.722) for six to ten years. This implies that older organizations (over 10 years) have a better approach in terms of leadership to embracing Industry 4.0.
Second, considering leadership as the dependent variable, ANOVA analysis was conducted with type of the organization. The output of the ANOVA analysis in Table 8 shows that difference between the group means is not significant. As the significance value is p = 0.182 (which is not below 0.05), there is a statistically insignificant difference among the groups reported. This implies that there are no significant differences of leadership in terms of organization type in embracing Industry 4.0.
Research Question 3:
 What are the differences of leadership in embracing Industry 4.0 according to the age and type of the organization?
Leadership in embracing Industry 4.0 is associated with age of the company. Older companies (10 years and above) have better leadership than younger companies (3 to 5 years) in embracing Industry 4.0. Leadership skills are developed and tested faster and better in old and large organizations, as the business complexity and stakes are higher [49,50]. Another factor of contribution is that old and large organizations have more staff and naturally more leaders. This creates more chance of preparing a leadership pipelines in terms of Industry 4.0 skillset and expertise [51]. Hence, older organizations (over 10 years) have a better approach in terms of leadership to embracing Industry 4.0. However, the findings reflect that there is no significant difference of leadership in terms of organization type in embracing Industry 4.0. This implies that there is no major difference between manufacturing and services companies in terms of leadership.

4.4. Strategy

Strategy is the creation of a unique and valuable position, involving a set of activities [52]. This section highlights the importance of strategy in embracing Industry 4.0 in terms of organization age and organization type.
First, considering strategy as the dependent variable, ANOVA analysis was conducted with age of the organization. The output of the ANOVA analysis in Table 9 shows that there is a statistically significant difference between the group means. As the significance value is p = 0.074 (which is below 0.10), there is a statistically significant difference among the groups reported at 90 percent significance. In social sciences, 90 percent significance level is also considered important particularly in new domain or subjects such as Industry 4.0 [44,45]. Here, the mean (M = 3.76) also indicates that older organizations (more than 10 years) have better strategy. This implies that there are significant differences of strategy in terms of organization age in embracing Industry 4.0.
Second, taking strategy as the dependent variable, ANOVA analysis was conducted with organization type. The output of the ANOVA analysis in Table 10 shows that there is a statistically significant difference between the group means. As the significance value is p = 0.048 (which is below 0.05), there is a statistically significant difference among the groups reported with F(5232) = 2.276. Furthermore, the mean and standard deviation of the five groups show the highest mean (M = 3.92) for services large and highest standard deviation (SD = 0.833) for manufacturing large. Thereby, large size service organizations are more advanced in terms of strategy in embracing Industry 4.0.
Research Question 4:
 What are the differences of strategy in embracing Industry 4.0 according to the age and type of the organization?
Strategy in embracing Industry 4.0 was studied with the age and type of the company. The findings reflect that organization age is material to establish better strategy-making in organizations. So, there is significant difference of strategy in terms of organization age in embracing Industry 4.0. Likewise, organization type differences are material in terms of strategic management. Large services companies have better strategy than medium and small companies in embracing Industry 4.0. Strategy is an extensive exercise which requires conducting scenario-planning and building roadmaps [53,54]. In terms of Industry 4.0, the minority of the companies have been successful in preparing and executing Industry 4.0 roadmaps, as the technologies involved are mostly disruptive and have high pace of upgradation and obsolesce. Furthermore, small and medium size organizations often work on the most pressing and immediate business investments, hence the exercise of strategic planning seems a burden for them. Therefore, large size service organizations are more advanced in terms of strategy in embracing Industry 4.0.

4.5. Innovation

Innovation is about openness to new ideas as an aspect of an organization [55]. Finally, this section focuses on the importance of innovation in embracing Industry 4.0 in terms of organization age and organization type.
First, considering innovation as the dependent variable, ANOVA analysis was conducted with organization age. The output of the ANOVA analysis in Table 11 shows that there is a statistically significant difference between the group means. As the significance value is p = 0.097 (which is below 0.10), there is a statistically significant difference among the groups reported at 90 percent significance. As mentioned earlier as well, in social sciences, 90 percent significance level is also considered important particularly in new domain or subjects such as Industry 4.0 [44,45]. Here, the mean (M = 3.91) also indicates that older organizations (more than 10 years) have better innovation. This implies that there are significant differences of innovation in terms of organization age in embracing Industry 4.0.
Second, considering innovation as the dependent variable, ANOVA analysis was conducted with organization type. The output of the ANOVA analysis in Table 12 shows that there is also a statistically insignificant difference between the group means. As the significance value is p = 0.291 (which is not below 0.05), there is a statistically insignificant difference among the groups reported. This indicates that there are no significant differences of innovation in terms of organization type in embracing Industry 4.0.
Research Question 5:
 What are the differences of innovation in embracing Industry 4.0 according to the age and type of the organization?
It is interesting to note that innovation in organization in embracing Industry 4.0 is associated with organization age but not organization type. First, this finding reflects that older companies (10 years and above) have better innovation than younger companies (3 to 5 years) in embracing Industry 4.0. This implies that innovation in terms of Industry 4.0 cannot be largely expected from small or startup companies. Second, this finding suggests that manufacturing and service type organizations both can be innovative in terms of Industry 4.0. It is assumed that manufacturing type companies have more opportunities and muscles to execute innovative interventions and projects as compared to service-based organizations [56,57]. However, this research paper confirms that there is no significant difference of innovation in terms of organization type in embracing Industry 4.0.

5. Conclusions

In the landscape of today’s technological developments and organizational sustainability, embracing Industry 4.0 is a need, more than a want [54,55,56,57,58]. This research paper draws empirical insights from 238 technology companies in Malaysia to understand and support the prevalent approaches of organizations to Industry 4.0. In summary, this paper unfolds 10 key empirical insights that can help organizations improve their curve on the adoption of Industry 4.0.
These 10 theoretical contributions are: (i) older organizations (over 10 years) in terms of Industry 4.0 readiness have a better approach in embracing Industry 4.0; (ii) large size service organizations in terms of Industry 4.0 readiness are more advanced in embracing Industry 4.0; (iii) older organizations (over 10 years) in terms of Industry 4.0 technologies have a better approach to embracing Industry 4.0; (iv) large size manufacturing organizations in contrast to the large size service organizations are more advanced in terms of Industry 4.0 technologies in embracing Industry 4.0; (v) older organizations (over 10 years) have a better approach in terms of leadership to embracing Industry 4.0; (vi) there is no significant difference of leadership in terms of organization type in embracing Industry 4.0; (vii) older organizations (over 10 years) have a better approach in terms of strategy to embracing Industry 4.0; (viii) large size service organizations are more advanced in terms of strategy in embracing Industry 4.0; (ix) older organizations (over 10 years) have a better approach in terms of innovation to embracing Industry 4.0; and (x) there is no significant difference of innovation in terms of organization type in embracing Industry 4.0.
Thereon, these key insights clubbed together answer the five research objectives of this paper: the differences of Industry 4.0 readiness, Industry 4.0 technologies, leadership, strategy, and innovation in embracing Industry 4.0. In terms of practical contributions, this study can be used by the government for policymaking, industry players for benchmarking, and managers for decision-making and project management of Industry 4.0. This study was focused on technology companies alone, but future studies can survey non-technology companies from different industry sectors. That study will be also insightful as it will gauge the ability of non-technology companies as opposed to technology companies towards embracing Industry 4.0, and prove if there are any similar patterns or observations. Future studies can also focus on the factor of organizational age and organizational type, from other countries and cultures, to further validate the existing findings. In addition to organizational age and organizational type, personnel qualifications can also be considered in future studies to draw new insights on Industry 4.0.

Author Contributions

Conceptualization, M.H.-H. and M.A.S.; methodology, M.A.S. and M.H.-H.; software, M.H.-H., M.A.S., N.L.A., M.H.A. and M.S.J.; validation, M.H.-H., M.A.S., N.L.A., M.H.A. and M.S.J.; formal analysis, M.A.S. and M.H.-H.; investigation, M.A.S. and M.H.-H.; resources, M.H.-H., M.A.S., N.L.A., M.H.A. and M.S.J.; data curation, M.H.-H., M.A.S., N.L.A., M.H.A. and M.S.J.; writing—original draft preparation, M.H.-H. and M.A.S.; visualization, M.A.S. and M.H.-H.; supervision, M.H.-H., M.A.S., N.L.A., M.H.A. and M.S.J.; funding acquisition, M.H.-H., M.A.S., N.L.A., M.H.A. and M.S.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Malaysian Technology Development Corporation (MTDC), grant number EP-2019-014, awarded to Universiti Kebangsaan Malaysia.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions by MTDC.

Acknowledgments

The authors are grateful to the editors and reviewers for their invaluable contribution.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Table 1. Country-wise notions used for Industry 4.0.
Table 1. Country-wise notions used for Industry 4.0.
CountryIndustry 4.0 Notion
GermanyIndustrie 4.0
United StatesAdvanced Manufacturing Partnership (AMP)
FranceLa Nouvelle France Industrielle
United KingdomFuture of Manufacturing for 2050
European UnionFactories of the Future (FOF)
South KoreaInnovation in Manufacturing 3.0
ChinaMade in China 2025
JapanSuper Smart Society
SingaporeSmart Readiness Index
MalaysiaIndustry4WRD
Table 2. Selected items from the questionnaire.
Table 2. Selected items from the questionnaire.
Item No.Items/Questions
Industry 4.0 Readiness
A5  competencies to work on Industry 4.0
A6  motivation to work on Industry 4.0
Industry 4.0 Technologies
B1  uses Big Data and Analytics
B2  uses Autonomous Robots
Leadership
C3  leadership supports Industry 4.0
C4  leadership is comfortable with Industry 4.0 technologies
Strategy
D2  strategic planning for Industry 4.0
D5  investments in Industry 4.0 technologies
Innovation
E2  promotes innovation
E3  grasp of new business ideas for Industry 4.0
Table 3. Industry 4.0 readiness and organization age.
Table 3. Industry 4.0 readiness and organization age.
Sum of SquaresdfMean SquareFSig.
Between Groups15.84927.9258.6230.000
Within Groups215.9702350.919
Total231.819237
NMeanStd. Deviation
Three to Five Years203.150.875
Six to Ten Years402.901.081
More than 10 Years1783.560.938
Total2383.420.989
Table 4. Industry 4.0 readiness and organization Type.
Table 4. Industry 4.0 readiness and organization Type.
Sum of SquaresdfMean SquareFSig.
Between Groups4.71750.9432.4870.032
Within Groups88.0132320.379
Total92.729237
NMeanStd. Deviation
Manufacturing Large453.800.693
Manufacturing Medium223.730.546
Manufacturing Small223.610.670
Services and Others Large734.020.564
Services and Others Medium493.910.567
Services and Others Small273.680.705
Total2383.850.626
Table 5. Industry 4.0 technologies and organization age.
Table 5. Industry 4.0 technologies and organization age.
Sum of SquaresdfMean SquareFSig.
Between Groups3.32221.6612.6780.071
Within Groups145.7552350.620
Total149.077237
NMeanStd. Deviation
Three to Five Years203.170.929
Six to Ten Years403.010.779
More than 10 Years1783.320.773
Total2383.260.793
Table 6. Industry 4.0 technologies and organization type.
Table 6. Industry 4.0 technologies and organization type.
Sum of SquaresdfMean SquareFSig.
Between Groups12.28952.4584.1690.001
Within Groups136.7892320.590
Total149.077237
NMeanStd. Deviation
Manufacturing Large453.450.679
Manufacturing Medium223.410.559
Manufacturing Small222.690.958
Services and Others Large733.370.767
Services and Others Medium493.270.860
Services and Others Small272.980.702
Total2383.260.793
Table 7. Leadership and organization age.
Table 7. Leadership and organization age.
Sum of SquaresdfMean SquareFSig.
Between Groups3.81021.9054.1410.017
Within Groups108.0952350.460
Total111.905237
NMeanStd. Deviation
Three to Five Years203.630.596
Six to Ten Years404.050.722
More than 10 Years1784.090.676
Total2384.050.687
Table 8. Leadership and organization type.
Table 8. Leadership and organization type.
Sum of SquaresdfMean SquareFSig.
Between Groups3.56950.7141.5290.182
Within Groups108.3362320.467
Total111.905237
NMeanStd. Deviation
Manufacturing Large454.030.733
Manufacturing Medium224.020.638
Manufacturing Small223.800.689
Services and Others Large734.120.651
Services and Others Medium494.180.716
Services and Others Small273.860.652
Total2384.050.687
Table 9. Strategy and organization age.
Table 9. Strategy and organization age.
Sum of SquaresdfMean SquareFSig.
Between Groups2.90621.4532.6330.074
Within Groups129.7262350.552
Total132.633237
NMeanStd. Deviation
Three to Five Years203.370.610
Six to Ten Years403.660.628
More than 10 Years1783.760.779
Total2383.710.748
Table 10. Strategy and organization type.
Table 10. Strategy and organization type.
Sum of SquaresdfMean SquareFSig.
Between Groups6.20251.2402.2760.048
Within Groups126.4312320.545
Total132.633237
NMeanStd. Deviation
Manufacturing Large453.730.833
Manufacturing Medium223.630.543
Manufacturing Small223.560.772
Services and Others Large733.920.677
Services and Others Medium493.660.817
Services and Others Small273.420.682
Total2383.710.748
Table 11. Innovation and organization age.
Table 11. Innovation and organization age.
Sum of SquaresdfMean SquareFSig.
Between Groups1.97520.9872.3550.097
Within Groups98.5302350.419
Total100.505237
NMeanStd. Deviation
Three to Five Years203.580.684
Six to Ten Years403.830.610
More than 10 Years1783.910.652
Total2383.870.651
Table 12. Innovation and organization type.
Table 12. Innovation and organization type.
Sum of SquaresdfMean SquareFSig.
Between Groups2.61550.5231.2400.291
Within Groups97.8892320.422
Total100.505237
NMeanStd. Deviation
Manufacturing Large453.870.655
Manufacturing Medium223.890.559
Manufacturing Small223.810.708
Services and Others Large733.950.604
Services and Others Medium493.910.696
Services and Others Small273.600.689
Total2383.870.651
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