Strategic response to Industry 4.0: an empirical investigation on the Chinese automotive industry

Danping Lin (Logistics Engineering College, Shanghai Maritime University, Shanghai, China)
C.K.M. Lee (Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong)
Henry Lau (The University of Western Sydney, Sydney, Australia)
Yang Yang (University of Warrick, Coventry, UK)

Industrial Management & Data Systems

ISSN: 0263-5577

Publication date: 9 April 2018

Abstract

Purpose

The purpose of this paper is to examine the strategic response to Industry 4.0 for Chinese automotive industry and to identify the critical factors for its successful implementation.

Design/methodology/approach

A technological, organizational, and environmental framework is used to build the structural models, and statistical tools are used to validate the model. The data analysis helps to determine which factors have impact on the strategic response and whether their relationships are positive or negative. Interpretive structural modeling method is applied to further analyze these derived factors for depicting the relationship.

Findings

The result shows that company size and nature do not increase the use of advanced production technologies, while other factors have positive impacts on improving the technology adoption among the companies surveyed.

Practical implications

A strategic response to Industry 4.0 not only helps in improving organizational competitiveness, but it also has social and economic implications. For this purpose, empirical data are collected to measure the understanding of Industry 4.0 in the Chinese automotive industry.

Originality/value

Despite the fact that the Chinese Government has proposed the “Made in China 2025” approach as a way to promote smart manufacturing, little empirical evidence exists in the literature validating company’s perspective toward Industry 4.0. This paper is to fill the research gap.

Keywords

Citation

Lin, D., Lee, C., Lau, H. and Yang, Y. (2018), "Strategic response to Industry 4.0: an empirical investigation on the Chinese automotive industry", Industrial Management & Data Systems, Vol. 118 No. 3, pp. 589-605. https://doi.org/10.1108/IMDS-09-2017-0403

Download as .RIS

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


1. Introduction

Intensified competition in the market and rapid changes in demand have pushed companies to adapt advanced technologies to improve smart manufacturing. Industry 4.0 is the current trend of automation and data exchange in production technology. As the fourth industrial revolution, Industry 4.0 extends the advanced digital technologies to a broader content, which mainly include cyber-physical systems (CPS), internet of things (IoT), and cloud computing.

The concept of Industry 4.0 stems from Hannover Messe whose original idea is focused on improvements in German industry (MacDougall, 2014). Led by IOT, Industry 4.0 acts as an integral part of the “High-Tech Strategy 2020 for Germany” so as to maintain German leadership in technological innovation competition (Kagermann et al., 2013). Meanwhile, other countries or regions have also proposed similar initiatives, such as the “Industrial Internet” in USA (Annunziata and Evans, 2012) and “Made in China 2025” plan in China (Tong and Lim, 2016). Obviously, advanced technologies have led to a competitive advantage in an individual company’s performance. However, as a new concept to be promoted, researchers have different understandings of advanced technology in smart manufacturing practices. Low et al. (2011) studied the determinants of cloud computing adoption and found that relative advantage, top management support, firm size, competitive pressure, and trading partner pressure characteristics have the significant effects. Similarly, Oliveira et al. (2014) mentioned that relative advantage, complexity, technological readiness, top management support, and firm size have a direct effect on a firm’s adoption of cloud computing. However, the analysis of Low et al. (2011) was conducted in high-tech industries while Oliveira et al. (2014) analyzed the perspectives of the manufacturing and services sectors. For this, companies needed supporting factors for successful implementation of advanced technologies in practice. The response to Industry 4.0 in China is fraught with several challenges. These challenges lead to unbalanced resource distribution which is vital and influential in many other business sectors. Without an adequate understanding of the Industry 4.0 related factors, it is unlikely to accurately generate an appropriate strategy in response to this trend.

The benefits of promoting Industry 4.0 practice is to encourage smart manufacturing from various dimensions and to improve the overall supply chain performance. Despite the widely accepted benefits of Industry 4.0, there is a dearth of literature regarding the implementation of smart manufacturing practices and their impacts on the performance, particularly in developing countries like China.

The products of the automotive industry link our daily life on the one hand, and on the other hand, link a wide range of upstream and downstream industries. The automotive industry has played a critical role in ensuring green and sustainable environment. Though many researchers have addressed the economic and environmental issues of the automotive industry (Luglietti et al., 2014; Yang et al., 2015), there is a gap between the necessity of promoting Industry 4.0 in theory and the practical response in implementation. The Chinese automotive industry faces comprehensive challenges as the whole supply chain needs to reach globally. It is recognized that the adoption of Industry 4.0 in China is still at an early stage, but has been gaining attention recently. It is clear that there is not much work on the investigation of strategic issues from the Chinese scenario and there is a large research gap in this field.

Based on the above consideration, this paper analyzes the relationships among the Industry 4.0 determinants and the decision of increasing use of advanced production technologies (APT). In this analysis, the determinants are organized in the framework of technological, organizational, and environmental perspectives where six indicators are explored and samples of automotive manufacturers in China are considered for this investigation.

The reminder of the paper is structured as follows. Section 2 reviews related studies on Industry 4.0 and technology-organization-environment (TOE) framework. Section 3 constructs the research framework and hypotheses. In Section 4, the research methodology is presented. Section 5 analyzes the results. The discussion is given in Section 6 and conclusions and limitations are presented in Section 7.

2. Literature review

2.1 Industry 4.0 review

Industry 4.0 refers to the idea of industrial revolution that allows the manufacturing to be customized by integrating production processes and the information technologies and techniques. Though different definitions of Industry 4.0 are provided, they shared the common assumptions such as the use of internet, production flexibility, and virtualization of the process. Thereby, many researchers and practitioners experienced the impacts of Industry 4.0 on their life and literature on Industry 4.0 can be classified into three streams. The first stream focused on analyzing the requirements of Industry 4.0 on production such as architecture configuration (Theorin et al., 2017), system reconstruction (Poonpakdee et al., 2017), and information provision (Unger et al., 2017).

The second stream links the innovation of technology to Industry 4.0 to investigate the social acceptance of the technology. For example, Masoni et al. (2017) overcame some technological limitations of augmented reality (AR) and applied it on the remote maintenance service, and therefore AR can be considered as an effective industry tool again. Li et al. (2017) reviewed the current wireless networks by discussing their features and related techniques, and then provided an architecture based on the quality of service and quality of data. Through such comparison, it is obvious to figure out the achievement and explain why the wireless network is important for Industry 4.0.

The third stream of studies has examined these impacts from the perspective of communication. Within this stream, the focus of Industry 4.0 is laid on the new requirement for human beings such as risk management (Tupa et al., 2017) and education, and qualification (Benešová and Tupa, 2017; Motyl et al., 2017).

From the above review on Industry 4.0, we found that great enthusiasm is created among companies and researchers where such research has paved the path toward a better connected smart factory ecosystem. However, there is no work that analyzed the strategic response to Industry 4.0 from the empirical approach especially in the automotive industry within which advanced technologies is widely applied. In this paper, we bridge this gap by linking a TOE framework to strategic response to Industry 4.0 in the automotive industry through examining the factors that impact the advanced technology adoption.

2.2 TOE framework review

As an organizational-level theory, TOE framework describes how the firm context influences the adoption and implementation of innovation from three different dimensions (Tornatzky and Fleischer, 1990). Technological dimension focuses on the technological characteristics that influence firm adoption, organizational dimension contributes the adoption to the organizational attributes, and environmental dimension reflects the surrounding factors (Henderson et al., 2012). Though this framework does not provide a concrete set of factors for the analyzed problem, it classified the factors to respective construct where the technology adoption happens.

Researchers have applied TOE framework to investigate the adoption of various technologies such as EDI, Enterprise 2.0, mobile reservation systems, ERP systems, e-SCM system, e-commerce, ICT, and so on. The summary of typical studies has been listed in Table I which provides empirical evidence about the applicability of TOE framework. For instance, Saldanha and Krishnan (2012) suggested that open architectures, firm size, and industry knowledge intensity play pivotal roles in Web 2.0 technology adoption. The work of Lin (2014) revealed that firms with certain perceived benefits (P.B.), perceived costs, top management support, absorptive capacity, and competitive pressure are more likely to adopt e-SCM. Similarly, Reyes et al. (2016) identified and explored the determinants of radio frequency identification (RFID) adoption based on the TOE framework and their research showed that RFID adoption stage had a significant positive impact on each perceived benefit. Jia et al. (2017) extended the generalizability of prior studies on Enterprise 2.0 from initial adoption to continuance usage which is examined from the perspective of confirmation, perceived usefulness, firm size, firm scope, subjective norms, and competitive pressure. Generally, the benefit of TOE framework is its strong theoretical background and easy empirical implementation which provide useful guidance for the researchers.

3. Research model and hypotheses

3.1 Technological dimension

The implementation of Industry 4.0 is featured by the transformation from traditional production to the use of advanced industrial applications, as in the earlier industrial revolutions. The unique characteristic of the fourth industrial revolution is that the integration and convergence of existing advanced technologies create unprecedented innovational breakthroughs (Kagermann, 2015; Schwab, 2017). The integration ranged from physical items to embedded systems, from CPS to IoT, from the diffusion of big data to cloud computing, from intelligent devices to the internet of data and services. With the rapid development of IoT, which further developed as the internet of service and internet of everything, the seamless connection of physical objects in the embedded system helps to realize the latest status in the cyber world. The concept of the CPS under the Industry 4.0 framework requires in-depth investigation. This section explores the concept and roles of APT including the factors of IT maturity (IT.M.), internal incentive, P.B., company size and nature (C.S.N.), external pressure (E.P.), and government policies (G.P.) that form the hypotheses of the framework.

IT.M. described the degree of the information technology adoption and implementation of advanced IT to assist the industrial production. Especially, we focus on the advancement of IT in automotive smart manufacturing, such as CPS, which offer an intensive package of integrated systems for customization. Weyer et al. (2016) explored the full potential of simulation technology in the smart factory that conducted the automotive production in scalable and modular structure. Poudel and Munir (2017) proposed a new architecture for automotive ECUs using a steer-by-wire application, which is capable of incorporating security and dependability primitives.

Furthermore, IT.M. enables superior analytic abilities for decision making via demand forecasting, just-in-time manufacturing, and flexible planning, for example, and thereby optimizing supply chain performance. The typical IT used for Industry 4.0 includes big data analytics and cloud computing. Lee et al. (2014) argued that the inclusion of big data and related technologies can enhance the performance of various applications in smart factories, e.g., integrated platforms, predictive analytics, and visualization. Ge and Jackson (2014) drove down the automotive cost by linking product design and manufacturing phase to the aggregated real-time customers’ application pattern. Wu et al. (2015) pointed out that the concept of cloud manufacturing is not just the simple direct utilization of cloud-computing technologies in existing manufacturing environment (Xu, 2012), but also has more requirements such as affordable computing, ubiquitous access, crowdsourcing, etc., when compared to other more traditional distributed manufacturing systems. Thus, the maturity of IT in Industry 4.0 is expected to encourage more positive understanding and adoption of APT. This leads to the following hypothesis:

H1.

IT.M. positively affects the increased use of APT as part of the strategic response to Industry 4.0.

The technological incentive (T.I.) is the technical things that motivate an individual or an organization to take certain action. It provides value for money and contributes to organizational success (Armstrong, 2010). Belfo (2013) summarized an incentive framework for business and information technology alignment by considering five aspects: compensation, benefits, work-life, performance/recognition, and development opportunities. The continuous time model of Kasahara (2015) described different phenomena of new technology adoption under different information acquisition, which implied the degree of information technology maturity. In this work, an incentive scheme was addressed that helps to achieve efficient collective adoption. Eschenbrenner (2016) investigated the influences and variations in performance objectives along with incentive structure and he examined the adoption of a new technology and subsequent performance outcomes. Hence, this study hypothesizes the following:

H2.

T.I. positively affects the increased use of APT as part of the strategic response to Industry 4.0.

In addition, the primary motivation for companies to adopt new technology is the perceived benefit. The perceived benefit refers to the perceived usefulness (Davis et al., 1989), i.e., enrichment and enhancement of their resources like value-added products, innovation capabilities, and core competencies (Brush, 2001). According to Venkatesh and Davis (2000), prospective adopters assess the consequences of their adoption behavior based on the ongoing desirability of usefulness derived from a technology. Many scholars provided empirical support for the positive association between the P.B. and the attitude toward the technology adoption. Sethuraman and Parasuraman (2005) found that the operating economies of scale of Big Middle retailers were capable to receive higher perceived benefit by adopting new technology compared to that of niche players. Chao and Lin (2009) used the technology acceptance model to test the effect of P.B. to users on the emergence of container security services. Therefore, the incentives for more resources would positively influence companies’ responses to industrial transformations. Consistent with this, it is hypothesized that P.B. will increase the likelihood of APT adoption:

H3.

P.B. positively affects the increased use of APT as part of the strategic response to Industry 4.0.

3.2 Organizational dimension

In 1978, China’s Government implemented foreign direct investment as an integral part of its “trading market access for technology” strategy (He and Qing, 2012). Since then, a large number of foreign automotive corporations have established their Chinese branches by joint ventures. Renowned auto companies include General Motors, Toyota, and Volkswagen. After China joined the World Trade Organization in 2001, the Chinese automobile market became more competitive as some domestic automakers, e.g., Geely, developed rapidly to win a market share. However, joint venture auto companies still enjoy advantages in knowledge transfer processes, product design, and quality management (Tang, 2012). China’s automotive companies are eager to promote core competencies based on innovation and flexibility.

Furthermore, the size of the company is associated with the resources available and the capability of understanding and implementing innovation. Several studies has confirmed that the size of the company is one of the good indicators that influence the adoption of new technology because the larger companies have more incentives, resources, skills, experiences, and even more positive risk attitude to adopt new technology (Henderson et al., 2012; Saldanha and Krishnan, 2012; Aboelmaged, 2014; Asare et al., 2016). For example, in the work of Fu et al. (2006), for the securities industry, the “capital factor” in the criteria layer, which represents the scale of the enterprise, was recognized as the most important factor for increasing the likelihood of successful adoption of the electronic marketplace by allocating more resources. Joo (2011) found that the absorptive capacity influenced the ability of firms to evaluate, accept, and apply innovation to achieve higher objectives. Absorptive capacity depends on company size, company resources, knowledge base, and cooperative willingness to introduce technology (Jones et al., 2005; Buchanan et al., 2013; Sila, 2013). This discussion suggests that more and more companies have started to recognize the benefits of smart manufacturing and the positive results to be gained from the implementation of smart manufacturing. Meanwhile, the nature of the company refers to the way how the company is organized. So, it leads to the following hypotheses:

H4.

C.S.N. positively affects the increased use of APT as part of the strategic response to Industry 4.0.

3.3 Environmental dimension

Literatures consistently recognized E.P. and G.P. as the environmental factors that impact the adoption of new technology (Chan et al., 2012; Henderson et al., 2012; Aboelmaged, 2014; Azmi et al., 2016). In a highly competitive market like automotive production, companies are forced to adapt advanced technology either to maintain the edge (Huang et al., 2008) or to respond to a customer request (Kamaruddin and Udin, 2009). When the environment changes, companies’ ability to transform from their previous status to a more appropriate management strategy greatly affects their performances (Chang et al., 2002). As one advanced representative of modern manufacturing, the automotive industry has become the leading edge of implementing Industry 4.0 in nations with a huge manufacturing sector, e.g., Germany and China.

Meanwhile, China’s automotive industry apparently needs to overcome a lot of challenges, including sudden changes in global energy structure, fierce global competition, emergence of innovation business modes, transformation of consumption ideas in the automotive market, extrusive competition from foreign automotive brands, reduction of market shares, low vehicle export volume, lack of innovation of key components, and weak brand protection of domestic auto brands. All such difficulties have their great impacts in encouraging the upgrading and transformation of China’s automotive industry. E.P. has been widely found to have a positive impact on the technology adoption, thus we propose that:

H5.

E.P. positively affects the increased use of APT as part of the strategic response to Industry 4.0.

G.P. refer to the official support from the government so as to promote the new technology. Such legal support can be represented as the nationalized legislation, tax refund compliance, industrial standards, hardware infrastructure, or even media publication (Lin and Ho, 2009; Chan and Chong, 2012). Recently, the Chinese Government has been embracing the ideas of Industry 4.0 with great expectation (Li, 2017). China has included “Made in China 2025” in its 13th five-year plan from 2016 to 2020. The “Made in China 2025” strategy aims at promoting innovation-driven development in China’s manufacturing industries and seeks breakthroughs in key areas including robotics, cloud computing, new energy, software, and so on (Jin, 2015). Overall, the results in the literature have shown that there are relationships between G.P. and the strategic response to Industry 4.0. From this perspective, we framed the following hypotheses:

H6.

G.P positively affects the increased use of APT as part of the strategic response to Industry 4.0.

Based on the preceding elaboration, a proposed research model is presented in Figure 1. The model illustrates the research hypotheses through six factors from TOE determinants to show the strategic response to Industry 4.0.

4. Research methodology

Items for survey measurement have been adopted from the literature, as discussed in Section 2, with the questionnaire developed using a five-point Likert scale. The reason for using a Likert scale is that strategic response to Industry 4.0 can be considered as involving cognitive understanding (Byrch et al., 2007), and Likert scale has been widely confirmed as an appropriate tool for questionnaire surveys (Vinodkumar and Bhasi, 2010). Researchers that used questionnaires to investigate the possible impacts of Industry 4.0 include Schmidt et al. (2015), Veza et al. (2015), and Sackey and Bester (2016).

4.1 Sampling and data collection

The target population of this study is senior managers and technical executives in the automotive companies who are in charge of production, information management, and maintenance. These companies are obtained from the website of Ministry of Commerce of the PRC. As a result, a preliminary list of 37 automotive companies that have branches in Sichuan and Hunan Provinces is prepared, then their potential contacts for this research was obtained through the address book. A survey instrument was developed to investigate the hypotheses. The questionnaires were designed through discussions with academicians and experts from automotive companies.

After a few items were deleted, 31 related questions were finalized using the five-point Likert scale. All the questions and items were presented both in English and Chinese to decrease any misunderstanding, and questionnaires were built on sojump.com, a professional questionnaire website. The e-mail is sent to inform the participants about the purpose of this study and the data collected are mainly for research only. After that, the online questionnaire website was sent to guide targeted participants to fill out the survey. In the end, a total number of 187 returns were received, of which 165 questionnaires were valid with a response rate of 88.2 percent. The participants are working in the automotive companies such as FAW-Volkswagen (Chengdu plant), Geely-Volvo auto (Chengdu branch), BYD Co. Ltd (Changsha industrial park), and Bosch (Chengdu plant). Table II shows the profile of respondents where it is found that the majority of respondents are in their early middle age (55 percent), have at least bachelor degree education (86 percent) and have working experience of more than five years (875). Most of the respondents (67 percent) are working in the production, R&D, administration, finance, and auditing departments. These findings indicate that respondents have knowledge of the advanced techniques and they are capable to answer the questions about Industrial 4.0. In addition, it is found that female accounts for 55 percent of total responses who are more willing to finish all the questions.

It is also found that there are huge diversities regarding the participated employees’ knowledge about Industry 4.0 and related technologies: 16 percent know the concepts of Industry 4.0 very well; 45 percent have a basic understanding of the concepts; 34 percent have heard the ideas with sparse understanding; and 5 percent of the respondents have never heard about Industry 4.0. Hence, most of the employees from the automotive industry are aware of Industry 4.0. Although many of them do not have deep insight into related concepts, they are likely to embrace Industry 4.0 actively.

4.2 Validity and reliability

To test the assumptions of the factor analysis, the multiple regression analysis (Norušis, 2006; Field, 2013) was applied using IBM SPSS 22.0 software. A reliability test was performed on six variables. The aim of reliability test was to measure the dependability of the questionnaire results for further analysis, especially the internal consistency of the research (Nunnally et al., 1975). Cronbach’s α coefficient test was chosen to assess the reliability of the data. As shown in Table III, Cronbach’s αs for all variables were above 0.8, which ensures that the consistency level of all investigated items is reliable (Nunnally and Bernstein, 1994).

5. Results

Though it is well known that the size of the company would impact the access to the advanced technology, further analysis is conducted to identify whether the same phenomena applied when the nature of the company changed. F-test was applied to conduct a significance test for H4. The least squares use the mean differences to test the variance so as to determine the variables that cause significant impact. Results of the significance test are shown in Table IV, in which F-value is 0.727 while the significance value is 0.575. Such results indicate that there is insufficient evidence in showing significant differences between various natures of companies with regards to the application of advanced manufacturing technologies. Thus, H4 is rejected.

The other five hypotheses were evaluated through multiple regression analysis and before that, a Pearson correlation test was conducted to check the feasibility to apply multiple regression methods. The results of the Pearson correlation in Table V validate the mutual correlation of variables as significant at the 0.01 level. Hence, multiple regression analysis can be conducted and the regression results are shown in Table VI and Figure 2. Each variable has a positive regression coefficient at a significance level less than 0.01, indicating that all the variables, i.e., IT.M., technology incentive, perceived benefit, E.P., and government policy, have positive impacts on increasing the use of APT. In addition, the factor named “perceived benefits” has the most significant impact. This indicated that benefits are the driver for the automotive industry. Thus, H1, H2, H3, H5, and H6 are confirmed. Internal incentives, E.P., P.B., IT maturating, and G.P. all play a positive promoting role in adapting Industry 4.0 technologies.

6. Discussion

The contribution of this study is to present the existing body of knowledge on Industry 4.0 by examining factors that affect the adoption of advanced technology in automotive companies. Going through the results, some interesting implications can also be delineated based on the TOE framework. From technical dimension, the results confirmed the importance of technique development on the technology adoption. First, information technology represents the cornerstone of the advanced technologies of Industry 4.0. The maturity of information technology covers the infrastructure, readiness, competence, complexity, compatibility, and standard. This is in line with Yeh et al. (2015) with regard to the role of IT.M. in the technology adoption. Second, the results highlight the role of IT.M. in the automotive industry. As one of the typical industries that obsessed with applying advanced technology, the automotive industry recognizes the value of technology and disseminates the technology throughout the industry, which conversely encourages the breakthrough of technology and theory in automobile industry. Third, the results confirmed the critical role of T.I. of the adoption of advanced technology for Industry 4.0. This implies the necessity of exploring intrinsic force of technology that drives the adoption. Fourth, this study confirmed the most significant role of perceived benefit in driving technology adoption. This result is consistent with other findings (e.g. Racherla and Hu, 2008; Lin, 2014; Leung et al., 2015; Osakwe et al., 2016) that realize the role of perceived benefit in the adoption of Industry 4.0’s advanced technologies.

On the organizational level, despite company size can be the determination factor for the technology adoption (Saldanha and Krishnan, 2012; Aboelmaged, 2014; Wang et al., 2016; Jia et al., 2017). The findings indicate that C.S.N. does not impose the increase use of advanced production technology for industry 4.0. This is consistent with Smith et al. (2008) who found a weak relationship between firm size and willingness of technology adoption. One possible explanation is the industrial and product characteristics (e.g. high value, the high requirement for safety and reliability, global sourcing, large batch production, and big scale). That is the reason why automotive industry intrinsically calls for new technology and welcome Industry 4.0.

Referring to the environmental dimension, the relationship between E.P. and G.P. have been warranted. These findings support the view of Schlaepfer et al. (2015) who pointed out the necessity of paying attention to environmental items, especially in pursuing government support. In addition, such results are also in line with Fabiani et al. (2005), and Bayomoriones and Leralópez (2007). This suggests that government can create a friendly circumstance to attract the companies to adopt the technology. When the government uses media or policy to allocate more social resource for innovation, companies are more likely to respond to such calls and try the alternative technology.

7. Conclusions and limitations

This research investigation enhances the literature of Industry 4.0. In comparison to previous research, the contribution of this study first figured out the factors that had the positive impact on the intention of using APT for Industry 4.0. Second, TOE framework revealed that analyzing factors from different dimensions can extensively represent the strategic response to Industry 4.0 in the automotive industry. The TOE framework posits the relationship between the increased use of advanced production technology and six factors including IT.M., T.I., P.B., C.S.N., E.P., and G.P. The model is validated based on a survey that is distributed to automotive companies in China. Through results analysis, this paper found that automotive companies tend to demonstrate the highest willingness to adopt advanced production technology when they have noticed the great perceived benefit from Industry 4.0. Contrary to what the hypotheses states, the relationship between the technology adoption and C.S.N. was not significant, which requires further exploration from the other organizational perspective that incorporated with product characteristics. In addition, the impacts of factors “IT maturity” and “technological incentive” are more significant than that of “external pressure” and “government policy.” This implies that the direct and internal factors play an important role in influencing the technology adoption. From a managerial perspective, this study offers a guidance for company managers to check their current technology adoption status in the Industry 4.0 era.

Therefore, a useful framework for strengthening Industry 4.0 practice in the automotive industry can be built according to the fully supported hypotheses. First, it is necessary to foster the industry awareness on the technology maturity by organizing training and forum. Hence, the employees would be aware of cutting-edge knowledge and skills and keep pace with the current industrial trend. Second, linking the potential technology benefits with employee welfare motivate the employee to adapt the advanced technology. This can be done via integrating hardware, software, and networks, which fits employees into the overall smart manufacturing structure. Although the impact of environment determinants are not that significant compared with technological factors, the third approach can deepen the roles of government supervision in facilitating industrial standard and constructing a technology-friendly environment. Example of government supporting issues may involve tax reduction, regulation, public training, roadshow, cross-country cooperation, and even pilot scheme.

Considering the limitations of this study, not all the staff of automobile industry has basic knowledge and understanding about the concept of Industry 4.0. Therefore, it is difficult to have a large sample size to repose to the survey. The further research will include the following content. The future qualitative study will cover major auto companies located in various cities in China and comparative studies can be conducted to investigate different responses to Industry 4.0 technologies from China’s automakers compared with other countries. Another approach such as structural equation modeling and fuzzy set qualitative comparative analysis can be used for analyze the survey data to figure out the combination of TOE factors on the adoption of APT. In addition, the future conceptual model can include both enhancing and impeding factors on the potentials of Industry 4.0 in the Chinese auto sector. Interviews with experts and managers who have deep insights into Industry 4.0 will also benefit.

Figures

Proposed research model

Figure 1

Proposed research model

Impact of TOE determinants on the increased use of advanced production technologies

Figure 2

Impact of TOE determinants on the increased use of advanced production technologies

Extant literature on advanced technologies adoption based on TOE framework

Reference Study object Examined variables
Kuan and Chau (2001) EDI Perceived direct benefits, perceived indirect benefits, perceived financial cost, perceived technical competence, perceived industry pressure, perceived government pressure
Scupola (2003) Internet commerce Government intervention, public administration and external pressure from customers, suppliers and competitors
Pan and Jang (2008) ERP IT infrastructure, technology readiness, size, perceived barriers, production and operations improvement, enhancement of products and services, competitive pressure, regulatory policy
Racherla and Hu (2008) eCRM Perceived direct benefits, perceived indirect benefits, compatibility, existing technical skills of personnel, financial resources allocated, top management support, customer knowledge management, firm size, perceived threat from competitors, pressure from business partners and the industry, customer expectations
Lee et al. (2009) Knowledge management system Organizational IT competence, KMS characteristics (compatibility, relative advantage, complexity), top management commitment, hierarchical organizational structure, Guanxi and Renqing cultural value (with external vendors, among internal employees
Saldanha and Krishnan (2012) Web 2.0 technologies Importance to open standards, organization size, industry knowledge intensity and industry competitive intensity
Henderson et al. (2012) XBRL Relative advantage, compatibility, complexity, expertise, learning from external sources, mimetic pressure, coercive pressure, normative pressure
Lin (2014) e-SCM Perceived benefits, perceived costs, firm size, top management support, absorptive capacity, trading partner influence, competitive pressure
Aboelmaged (2014) e-maintenance technology readiness Technological infrastructure, technological competence, perceived E-M benefits, perceived E-M challenges, maintenance priority, firm size, competitive pressure significantly impacts the adoption of e-maintenance readiness
Leung et al. (2015) ICT Expected/achieved direct benefits, expected/achieved indirect benefits, cross-technology compatibility, expected risk, financial readiness, technological readiness, top management support, perceived pressure from industry, perceived pressure from partners, perceived pressure from customers
Ramanathan and Krishnan (2015) Open source software Reliability, license concern, legal concern, software cost, management support, IT outsourcing, OSS support availability, software vendor relationship
Song et al. (2015) e-commerce Technology, organization, environment, confirmation, and satisfaction
Yeh et al. (2015) e-business IT IT maturity, IT infrastructure, IT human resources, support from top management, partnership quality, and competitive pressure
Hwang et al. (2016) Green supply chain Relative advantage, compatibility, complexity, organizational resources, organizational innovativeness, internal stakeholder, government regulation, customer, competitor, social community
Maduku et al. (2016) Mobile marketing Relative advantage, complexity, cost, top management, financial resource, employee capability, competitive pressure, customer pressure, vendor support
Osakwe et al. (2016) Corporate website Expected benefit, information intensity, organizational readiness, educational attainment of decision maker, gender of decision maker, government support
Reyes et al. (2016) RFID Internal drivers, external drivers, top management leadership, mid-level management leadership, cost issues, lack of understanding, technical issues, privacy issues
Wang et al. (2016) Mobile reservation system Relative advantage, compatibility, complexity, firm size, technology competence, top management support, critical mass, competitive pressure, information intensity
Hassan et al. (2017) e-procurement Relative advantage, compatibility, complexity, top management support, employee knowledge, eternal pressure
Jia et al. (2017) Enterprise 2.0 Perceived usefulness, firm scope, subjective norms, competitive pressure

Profile of respondents

Demographics n % Demographics n %
Gender Firm size
Male 74 45 SME 13 35
Female 91 55 Large 24 65
Department Working experience
Production 24 15 <5 years 21 13
R&D 32 19 5-15 years 109 66
Sales 15 9 >15 years 34 21
Marketing/PR 19 11
Administration 29 18 Age
HR 11 7 <25 29 18
Finance and auditing 25 15 26-35 91 55
Others 10 6 36-45 31 19
>45 14 8
Educational level Knowledge of Industry 4.0
Below undergraduate 21 18 Very well 26 16
Undergraduate 109 55 Basic understanding 75 45
Postgraduate 34 1 Heard with rare understanding 56 34
Others 1 8 Never heard about 8 5

Cronbach’s α in reliability test

Variables IT maturity Technological incentives Perceived benefits Company size and nature External pressure Government polices
Cronbach’s α 0.858 0.872 0.876 0.824 0.877 0.862

Results of ANOVA

Company type n Mean SD F-value Significance value
State-owned joint venture 48 4.1319 0.50407 0.727 0.575
Private joint venture 31 4.2043 0.69750
Foreign companies 25 3.9600 0.67577
Domestic companies 35 4.0952 0.55761
Others 26 4.2051 0.66718
Total 165

Pearson correlation coefficients

IT.M. T.I. P.B. E.P. G.P. I.U.
IT.M. 1
T.I. 0.357* 1
P.B. 0.495* 0.721* 1
E.P. 0.277* 0.503* 0.407* 1
G.P. 0.588* 0.369* 0.403* 0.322* 1
I.U. 0.471* 0.591* 0.740* 0.396* 0.469* 1

Note: *Significant at 0.01 level

Regression analysis

F-value Adjust R2 B β t
IT.M. 46.344* 0.217 0.488 0.471 6.808*
T.I. 87.498* 0.345 0.547 0.591 9.354*
P.B. 197.861* 0.546 0.845 0.740 14.066*
E.P. 30.286* 0.152 0.331 0.396 5.503*
G.P. 43.555* 0.206 0.368 0.459 6.600*

Note: *Significant at 0.01 level

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Further reading

Armstrong, M. (2015), Armstrong’s Handbook of Reward Management Practice: Improving Performance through Reward, 5th ed., Kogan Page, London and Philadelphia, PA.

Acknowledgements

The authors’ gratitude is extended to the research committee and the Department of Industrial and Systems Engineering of the Hong Kong Polytechnic University for support in this project (H-ZDAR and G-YBWR). In addition, this work is sponsored by National Natural Science Foundation of China (71701126) and the Shanghai Pujiang Program (No. 15PJ1402800).

Corresponding author

C.K.M. Lee can be contacted at: ckm.lee@polyu.edu.hk