Adoption of digital technologies of smart manufacturing in SMEs

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Adoption of Digital Technologies of Smart Manufacturing in SMEs Morteza Ghobakhloo , Ng Tan Ching PII: DOI: Reference:

S2452-414X(19)30002-0 https://doi.org/10.1016/j.jii.2019.100107 JII 100107

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Journal of Industrial Information Integration

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3 January 2019 14 June 2019 4 October 2019

Please cite this article as: Morteza Ghobakhloo , Ng Tan Ching , Adoption of Digital Technologies of Smart Manufacturing in SMEs, Journal of Industrial Information Integration (2019), doi: https://doi.org/10.1016/j.jii.2019.100107

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Adoption of Digital Technologies of Smart Manufacturing in SMEs

Morteza Ghobakhloo (corresponding author) Department of Industrial Engineering, Minab Higher Education Center, University of Hormozgan, Bandar Abbas, Iran And Modern Technology Development and Implementation Research Center, University of Hormozgan, Bandar Abbas, Iran Tel: 98 912 973 1509 E-mail: [email protected] And

[email protected]

Ng Tan Ching Department of Mechanical and Material Engineering, Universiti Tunku Abdul Rahman - Kuala Lumpur Campus, Kuala Lumpur, Malaysia E-mail: [email protected]

Corresponding author’ bibliographies;

Morteza Ghobakhloo is an assistant professor at the Department of Industrial Engineering, Minab Higher Education Center, University of Hormozgan. His research interests include lean and sustainable manufacturing, Industry 4.0, smart manufacturing, industrial internet, business value of information technology, and electronic supply chain management. His research have been published in many leading operations management and research journals such as IJPR, IJAMT, IMDS, IJITDM, JMTM, ITD, JSBED, JM2, IRMJ, JOEUC and JOCEC among others. Abstract The Fourth Industrial Revolution-commonly referred to as Industry 4.0-with smart manufacturing currently on its forefront-has arrived. The manufacturing industry is evolving and manufacturers of all sizes, worldwide, need to evolve too. In order not to be left behind from early adopters, Small and Medium-sized Enterprises (SMEs) integrate modern Smart Manufacturing-related Information and Digital Technologies (SMIDT) such as artificial intelligence with their business operations to enable smart manufacturing. The present study is concerned with identifying the determinants of SMIDT adoption within manufacturing SMEs. The study benefits from a cross-sectional survey to capture the opinions of Malaysian and Iranian participating SMEs. Results indicate that a collection of technological, organizational, and environmental factors determine SMEs decision for SMIDT adoption. The study further explores how various combinations of identified determinants have influenced the implementation of 13 individual SMIDT among SMEs. Theoretical contribution and managerial implications of this research are discussed which are believed to offer valuable insights to academicians, executives, and policymakers. Keywords:

Industry 4.0, Smart Manufacturing, Manufacturing Digitalization, Small and Medium-sized Enterprises, Information Technology.

1. Introduction Manufacturing industry nowadays stands on the brink of the next industrial revolution. The rise of new digital industrial transformation, known as Industry 4.0, with smart manufacturing currently on its forefront is changing the way businesses function (Qu et al., 2016). Smart manufacturing is reminiscent of a fully integrated, collaborative production ecosystem that responds, in real-time, to the ever-changing demands and conditions across the value chain (NIST, 2014). Technological revolution necessitated by smart manufacturing is profoundly altering the foundation of value creation and delivery in the manufacturing setting (Lu and Weng, 2018). Interconnectedness and fusion of physical and digital worlds are at the heart of smart manufacturing (Ghobakhloo, 2018). This means the integration of Information and Digital Technologies (IDT) to every facet of manufacturing is a strategic priority for contemporary manufacturers (Lasi et al., 2014). Consistently, it is believed that the adoption of advanced IDT promises many benefits to Small and Medium-sized Enterprises (SMEs). Previous research explains that IDT adoption offers performance improvement for businesses through improved sale, effective customer and supplier relationship, and supporting core organizational capabilities (Abebe, 2014, Singhry et al., 2016). The process of IDT adoption in SMEs is different from their larger counterparts, which is due to their unique characteristics. SMEs are significantly limited regarding financial and human resources (Müller et al., 2018; Tang and Ghobakhloo, 2013). They generally have limited access to the market information (Madrid-Guijarro et al., 2009) and rarely use strategic techniques such as financial analysis, forecasting, and project management (Ghobakhloo et al., 2011). Alternatively, smaller firms tend to be more open to changes enforced by the business environment and make a better balance between the fast decision-making process and quality decisions (Love and Roper, 2015). The way IDT literature has addressed the issue of IDT adoption within SMEs clouds our understating of the manufacturing digitalization process in the Industry 4.0 era. Majority of prior research has defined and operationalized IDT institutionalization as adoption or non-adoption of relatively simple IDT such as Email, Internet, electronic data interchange, and administrate software packages (e.g., Ghobakhloo et al., 2011; Iacovou et al., 1995; Pérez-González et al., 2017). Recent findings show that there has been a significant decrease in the price of generic IDT tools even for SMEs over past decades (Dibrell et al., 2008; Ghobakhloo et al., 2018), and adoption of generic IDT is no longer a competitive advantage for smaller manufacturers, even in developing countries (Tang and Ghobakhloo, 2013). Smart manufacturing involves the use of most advanced IDT and advanced data analytics such as intelligent Enterprise Resource Planning (ERP), Artificial Intelligence (AI), industrial sensors, Augmented/Virtual Reality (AVR), additive manufacturing technologies, industrial automation and intelligent robotics, and cloud data to improve manufacturing operations all throughout the value chain, from shop-floor to supply partners and customers (Ghobakhloo, 2018). The changes arising from the fourth industrial revolution in the production and value creation processes and business models are drastic and pose a real challenge to the contemporary manufacturing firms (Lu and Weng, 2018). In order not to be left behind from the early adopters, SMEs need to develop digitalization strategies and eventually prepare for smart manufacturing transformation. Consistently, it has been recently reported that SMEs, even in developing countries such as Brazil and Iran are striving to adopt complex IDT such as intelligent ERP, computer-aided design and manufacturing, and industrial automation to sustain their competitiveness in the Industry 4.0 era (Ghobakhloo and Azar, 2018; Tortorella and Fettermann, 2018). Compared to generic IDT, smart manufacturing-related IDT are significantly complex and knowledge-intensive and are integrated with the core processes of SMEs (Kamble et al., 2018). Unfortunately, little has been done to identify the key determinants that influence SMEs‟ decision to adopt and implement advanced IDT that enable the transition process toward smart manufacturing. As manufacturing digitalization in the smart manufacturing is in its infancy, and the academia has very recently started to address this research avenue (Zheng et al., 2018), this research gap is indeed expected. Consistently, the present study focuses its attention on the understudied phenomenon of Smart Manufacturing-related IDT (SMIDT1) adoption and implementation among manufacturing SMEs. It should be noted that the institutionalization of technological innovations such as IDT in businesses is considered as a multi-stage process. In fact, the cycles of IDT adoption and IDT implementation are discrete (Ruivo et al., 2014). The primary (initiation) phase is the adoption phase in which the information about IDT is accumulated and evaluated, and the decision about adopting specific IDT is made (Ghobakhloo et al., 2011). The second phase is the implementation phase in which the physical deployment of IDT within the organization occurs (Galy et al., 2014). Therefore, the present study addresses both phases of SMIDT institutionalization, namely SMIDT adoption and SMIDT implementation. Accordingly, the present study aims to fulfill the following objectives: 1

SMIDT represent the advanced information and digital technologies, such as intelligent industrial sensors or distributed systems, through which the concept of smart manufacturing in the Industry 4.0 era is materialized.

(1) to identify factors influencing the adoption of SMIDT among manufacturing SMEs; and (2) to identify the key determinants of implementation of individual SMIDT among manufacturing SMEs. 2. Smart manufacturing-related IDT Smart manufacturing relies on leveraging a wide variety of simple to advanced SMIDT to achieve its core characteristics (Liao et al., 2017). Table 1 lists the key SMIDT as identified by the literature. We label these 13 technologies as first-tier SMIDT, meaning manufacturers can implement them on a standalone basis. Although firsttier SMIDT need to interact with each other to fully deliver their functionalities (Hofmann and Rüsch, 2017), however, they can operate independently. Two key second-tier SMIDT, namely Industrial Internet of Things (IIoT) and Cyber-Physical Production Systems (CPPS) are other key enablers of smart manufacturing (Lasi et al., 2014; Wollschlaeger et al., 2017). IIoT relies on Machine-to-Machine communication, industrial sensors, big data analytics, cloud data, and Artificial Intelligence (AI) to improve the efficiency and reliability of industrial operations (Da Xu et al., 2014). Alternatively, CPPS is characterized as a collection of interacting digital, analog, and physical components engineered for function through integrated physics and logic (Griffor et al., 2017). IIoT and CPPS are highly interrelated and codependent (Ghobakhloo, 2018), meaning when CPS is mentioned IIoT is implicitly included and vice versa (Jeschke et al., 2017). Contrary to first-tier SMIDT, CPPS and IIoT are not off-the-shelf technological products. These complex enabling technologies of smart manufacturing rely on implementation and integration of various combinations of first-tier SMIDT. Consistently, the present study is mainly concerned with the adoption of first-tier SMIDT listed in Table 1. Table 1. Key SMIDT identified in the literature. Research Information and digital technologies

x

x

x

x x x

x x

x x x x x

x

x x

x x

x x x

x x

x x

x

x x

x x x

x x

x x

x

x x

x x x x x

x x x

x x x x x x x x x x x x x x x

x x x x x

x x

x x x x x x x x x x x x

x x x x x x x x x x

x x x x x x x x

x x x x x

x x x x x x x x x x x

x x x

Manufacturing simulation

x x x x

x

Manufacturing execution system

x

Machine and process controllers

x x

Industrial actuators and sensors

High-performance computing powered CAD

Enterprise resource planning

Data analytics

Cybersecurity technologies

Cloud data and storage

Additive manufacturing

Autonomous robots

Augmented/virtual reality

Artificial intelligence

Lasi et al. (2014) Posada et al. (2015) Lee et al. (2015) Vogel-Heuser and Hess (2016) Roblek et al. (2016) Wang et al. (2016) Mosterman and Zander (2016) Kang et al. (2016) Gilchrist (2016) Wan et al. (2016) Hofmann and Rüsch (2017) Jeon et al. (2017) Lu (2017) Liao et al. (2017) Thames and Schaefer (2017) Tao and Qi (2017) Moeuf et al. (2018) Jabbour et al. (2018) Qi and Tao (2018) Kusiak (2018) Perales et al. (2018) Ghobakhloo (2018) Zheng et al. (2018) Tao et al. (2018)

x

x x x x x

x x

x x

x x x

x x

x

x

x x x

x x

x x x x x x x x x x

x

x x x

x x x x x x

x

x x

x x x

x x x

3. Hypotheses and model development The literature suggests that the Technology-Organization-Environment (TOE) framework provides an appropriate starting point for studying IDT adoption and usage (Bradford et al., 2014; Lin, 2014). TOE framework is consistent with the Rogers‟ (1983) diffusion of innovation theory since both focus on internal and external characteristics of the organization, as well as technical characteristics in the study of drivers for new technology diffusion. Likewise, the TOE framework provides support for Davis‟ (1989) Technology Acceptance Model (TAM). Similar to TAM, TOE also assumes that user‟s behaviors as internal characteristics of the organization are among determinants of IDT acceptance and usage behavior (Ghobakhloo et al., 2011). TOE framework has been a popular foundational

model in examining issues such as IDT adoption, implementation, and usage (e.g., Bradford et al., 2014; Cao et al., 2014). TOE framework has provided consistent empirical support in a number of IDT adoption domains including electronic fund transfer, electronic data interchange, open systems, and ERP (Jia et al., 2017; Kurnia et al., 2015). Consistently, the present study benefited from TOE while developing the model of SMIDT adoption and related hypothesized relationships. In this study, the technological context refers to technical characteristics of SMIDT including the perceived value of SMIDT, perceived costs of SMIDT, and perceived compatibility of SMIDT. The organizational context addresses three organizational determinants including information processing requirements, IDT knowledge competency, and strategic roadmapping for manufacturing digitalization. The environmental context involves the two determinants of environmental imposition and competitive pressure. Following the IDT literature (e.g., Ghobakhloo and Tang, 2014), the present study considers business size as a control variable for SMIDT adoption. Figure 1 presents the proposed model of SMIDT adoption among SMEs. Each of the hypothesized relationships is briefly explained in the following subsections. Technological context Perceived value of SMIDT

Perceived costs of SMIDT Perceived compatibility of SMIDT Organizational context Information processing requirements

IDT knowledge competency Strategic roadmapping for manufacturing digitalization

If Yes SMIDT adoption

SMIDT implementation

Environmental context Imposition by environment Competitive pressure

Figure 1. The research model of SMIDT adoption and implementation among manufacturing SMEs. 3.1 Technological context 3.1.1 Perceived value of SMIDT Perceived value in the initial adoption stage denotes the degree to which technological innovation is perceived as consistent with the economic values, culture, and needs of potential adopters (Yazici, 2014). Therefore, manufacturing SMEs will consider the future implementation of SMIDT if they perceive them as potentially valuable (Oliveira et al., 2014). Perceived benefit at the implementation stage refers to the experience advantages, the values that have been delivered to the organization by an already implemented technological innovation (Ghobakhloo et al., 2018). Thus, the physical deployment and continued usage of SMIDT in an organization also relies on the business value of these technologies outwaiting their overall implementation and maintenance expenses (Ghobakhloo et al., 2014; Ram et al., 2014). The literature explains that when top management in SMEs, Chief Executive Officer (CEO) in particular, has a higher managerial understanding of the relative advantage of IDT, management would be more intended to allocate managerial, financial, and technological resources necessary to IDT adoption and implementation (Oliveira and Martins, 2010). Therefore, it is rational to propose that in SMEs, if the CEO perceives that the benefits of SMIDT compensate for the risks and costs associated, then the business is more likely to adopt them. H1: Perceived value of SMIDT is significantly related to the SMIDT adoption. 3.1.2 Perceived costs of SMIDT According to the Welsh and White's (1981) framework of resource constraint in smaller businesses, SMEs are operating under severe resource constraints, financial constraints in particular. Financial resource limitation compels top management to be cautious about their investment and capital spending, thus, only SMEs having adequate financial resources would regard the adoption of IDT as a feasible project to undertake (Thong, 1999). Industrial reports indicate that SMIDT implementation costs depend on a wide variety of constant and variable factors such as cost of dismantling previous physical infrastructure, new digital hardware, software applications and modules,

security measures, licensing, external experts and consultation, in-house training of employees, new system integration and debugging, and maintenance (Ghobakhloo and Tang, 2014). Investment in emerging SMIDT such as IIoT or intelligent ERP has been reported to be significantly risky and challenging in many instances (Gilchrist, 2016; Saade and Nijher, 2016). It is clear that complex SMIDT require SMEs to invest significantly more to cover the initial cost of hardware, software, and licensing, as well as indirect implementation expenses (Tang and Ghobakhloo, 2013). Thus, it is believed that when the CEOs or owner-managers in SMEs perceive the adoption of SMIDT not worthwhile financially, they will be less willing to invest in manufacturing digitalization. H2: Perceived costs of SMIDT is significantly related to SMIDT adoption. 3.1.3 Perceived compatibility of SMIDT IDT compatibility can be defined as the extent to which IDT are consistent with the existing technology infrastructure, culture, values, and preferred work practices of SMEs (Beatty et al., 2001). Several studies on the digitalization process within SMEs found that IDT adoption is significantly affected by IDT compatibility (Ramdani et al., 2013; Ruivo et al., 2014). Similarly, scholars reported that even within SMEs controlling required financial resources for IDT adoption, compatibility is still a significant discriminator between adopters and non-adopters of IDT (Sutanonpaiboon and Pearson, 2006). Prior literature suggests that SMEs will not consider the adoption of IDT if they perceive these technologies are not suited to their product, way of doing business, business partners‟ business strategy, and organizational culture (Ghobakhloo et al., 2015; Hong and Zhu, 2006). Smart manufacturing is not the same as merely investing in modern IDT and infrastructure. Almost all companies can invest in new SMIDT, nevertheless, a small portion has been able to effectively benefit from them to reshape their manufacturing business models (Legner et al., 2017). It widely accepted that SMIDT integrate modern digital technologies with core business technology, and significantly affect general business administration among SMEs (Schröder, 2016). Thus, the entire business processes of SMEs would be potentially affected by the adoption of SMIDT, which means, CEOs of SMEs would be less intended to adopt challenging SMIDT that are incompatible, necessitate significant changes in organizations, and are less likely to be used as intended. H3: Perceived compatibility of SMIDT is significantly related to the SMIDT adoption. 3.2 Organizational context 3.2.1 Information processing requirement Experts nowadays believe that SMEs‟ decision to implement SMIDT can be regarded as a response to their information processing requirement (Schröder, 2016), which is defined as the gap between the information required by an organization and information available to it (Melville and Ramirez, 2008). Based on this argument, new information requirements raised from internal and environmental uncertainty in Industry 4.0 era could be a significant driver for a firm to implement and use SMIDT (Ghobakhloo, 2018). These uncertainties are attributable to the production methods, supply chains, industry clock speed or the larger competitive scenery within the smart manufacturing context (Schumacher et al., 2016; Rogers and Bamford, 2002). Since the development of information processing capability results in the well-executed business strategy and performance superiority (Ramrattan and Patel, 2010), SMEs nowadays commit to SMIDT adoption to better develop this valuable organization capability (Kagermann, 2015). Overall, SMIDT play a significant role as a major enabler of information processing capability to process an increased volume of information in the smart manufacturing context (Gilchrist, 2016). Therefore, SMEs characterized by a higher information-processing requirement are expected to be more inclined toward SMIDT adoption. H4: Information processing requirement is significantly related to the SMIDT adoption decision. 3.2.2 IDT knowledge competency Smart manufacturing is associated with industrial automation, removal of information silos, the emergence of networks of connected and intelligent machines and materials, and the fusion of real and the virtual worlds (Hecklau et al., 2016). Therefore, employees in the smart manufacturing context must possess new sets of skills in the domains of IDT, cybernetics, and data analytics, meaning skilled employees should also go through skills evolution (Kagermann, 2015). For example, the networking of production system components in the smart manufacturing context relies on the interconnectivity and interoperability across intelligent components of a distributed system as well as data streams (Kane et al., 2017). This means SMEs should also possess IDT knowledge competency in the areas of intelligent vertical and horizontal networking, data analytics, man-machine interaction, and software engineering (Ghobakhloo, 2018). Due to the general lack of IDT expertise in SMEs and difficulty in recruiting IDT professionals, SMIDT operators in SMEs with adequate IDT knowledge can make a more effective contribution to the SMIDT institutionalization by their involvement in initial adoption as well as the implementation phases (Nguyen et al., 2015). H5: IDT knowledge competency is significantly related to SMIDT adoption. 3.2.3 Strategic roadmapping for manufacturing digitalization Leading organizations nowadays adopt technology roadmapping extensively as a powerful strategic planning technique for supporting research, development, and implementation of future technologies that could sustain their competitive position (Lee et al., 2013). The digital transformation mandated by smart manufacturing not only

challenges SMEs‟ capacity to innovate, but also requires new strategies and organizational models for managing organization-wide changes in physical infrastructure, manufacturing operations and technologies, human resources, and execution of practices (Gilchrist, 2016; Tao and Qi, 2017). Therefore, the development of an accurate technological and strategic roadmap is indispensable for securing success throughout the SMIDT adoption process (Vogel-Heuser and Hess, 2016). Strategic roadmapping enables SMEs to see how SMIDT should be implemented to support the achievement of the design principles of smart manufacturing such as workforce competency, decentralization, and horizontal and vertical integration (Gilchrist, 2016). Overall, the strategic roadmap would facilitate the implementation of SMIDT by allowing manufacturing SMEs to better time, visualize, and understand each move and decisions that they need to make to facilitate the digital transformation (Ghobakhloo, 2018). H6: Strategic roadmapping for manufacturing digitalization is significantly related to SMIDT adoption. 3.3 Environmental context 3.3.1 Imposition by the environment Imposition from trading partners, customers, and the society, which is termed „imposition by environment‟ in this research, is expected to be one of the most important determinants of SMIDT adoption among SMEs (Kurnia et al., 2015). As the weaker partners in inter-organizational relationships, SMEs are extremely susceptible to impositions by their larger partners (Ghobakhloo et al., 2015), as well as an imposition from customers for receiving better services (Caldeira and Ward, 2003; Riemenschneider et al., 2003). Accordingly, the imposition by environment represents the pressure exercised on SMEs by trading partners, customers, and society to adopt SMIDT for process integration, better communication, and more efficient data interchange (e.g., Pilbeam et al., 2012; Riemenschneider et al., 2003). Trading partners may pursue three different strategies to induce a small business to adopt SMIDT including recommendation, promises (providing SMEs with specific support and/or reward), and threats (e.g., in terms of discontinuation of the partnership) (Schröder, 2016). For Iranian SMEs that are the first tier suppliers of international automotive industries, as an example, communications, transactions, and data interchange should merely be conducted through an integrated could ERP, which forces them to heavily invest in IDT. Since the scope of smart manufacturing extends beyond organizational boundaries, and involves intelligent supply chain and connected (smart) business partners (Gilchrist, 2016), when a particular value chain moves toward smart manufacturing, all value chain members including SMEs are forced to consider SMIDT adoption and enhance their digitalization competency (Ghobakhloo et al., 2015). H7: Imposition by environment is significantly related to SMIDT adoption. 3.3.2 Competitive pressure In addition to the pressure from trading partners and customers for digitalization, SMIDT adoption decision among SMEs can be attributed to the SMEs‟ desire for competitiveness and survival in the Industry 4.0 era (Oliveira and Martins, 2010). Competitive pressure in the present study refers to the extent to which SMEs perceive themselves threatened by their counterparts within their industry or substitute sectors. This threat is mainly defined as losing customers and market share (Ghobakhloo et al., 2011). Referring to the turbulence of recent competitive business environment in which dynamic capabilities are crucial for the survival of businesses (Barrales-Molina et al., 2010), IDT plays a significant role as the enabler of dynamic organizational capabilities (Benitez-Amado et al., 2018). Thus, it seems rational to believe that the competitive pressure affects the adoption of SMIDT when SMEs perceive that manufacturing digitalization may strengthen their competitive position and assist them to achieve superior firm performance (Müller et al., 2018). SMIDT may indeed enable SMEs to change the rules of competition, alter the industry structure, and leverage new strategies to stand ahead of their competitors, altering the competitive landscape consequently (Lerch et al., 2015; Moeuf et al., 2018). SMEs active in industries having a high rate of innovation and intense competitive challenges are more probable to perceive SMIDT as a stronger driver for strategic change and enabler of corporate survivability (Müller et al., 2018). This postulation is consistent with the recent literature arguing that current generic IDT alone cannot be a source of competitive advantage for businesses (Ghobakhloo and Tang, 2014), even among SMEs (Tang and Ghobakhloo, 2013). In reality, SMEs that adopt SMIDT ahead of competitors to make their IDT resources firm-specific and imperfectly mobile across competitors are more likely to achieve sustained competitiveness as compared to the late adopters. Consistently, it is proposed that SMEs active in a more competitive environment would be more likely to adopt SMIDT ahead of competitors. H8: Competitive pressure is significantly related to SMIDT adoption. 3.4 Control variable The present study will additionally assess the effect of business size, as a control variable, on SMIDT adoption. The literature explains that, even among SMEs, business size can be directly related to the availability of the financial resource for new technology adoption (Ghobakhloo and Tang, 2015). 4. Research methodology 4.1 Operationalization of variables In this study, the constructs of measurement instrument (questionnaire) and their corresponding measures for the suggested model of SMIDT adoption have been developed on the foundation of validated items from prior research. For new measures and significantly modified ones, the study drew on guidelines and exemplars in the literature

(e.g., Sethi and King, 1991). Table 2 lists the number of items for each construct, as well as the prior research that the items were developed based on. Table A1 lists the properties of items used in the questionnaire. Two wellestablished IDT scholars highly expert in the area of smart manufacturing were asked to assess the initial questionnaire and recommend the necessary improvements. The questionnaire and all the scales were translated to Persian by a native professional English translator. Two native IDT experts further helped us with the process of „back-translation‟ of items into English and further ensured the validity of the questionnaire. For Malaysian manufacturers, the English version of the questionnaire was distributed. In this study, all the questions measuring dependent variables, which were derived from the literature, are interval-scaled. The eight independent variables were asked by a set of 28 questions, applying a five-point Likert ranging from 1-strongly disagree to 5-strongly agree. Following Ghobakhloo et al. (2011), SMIDT adoption was assessed through a single question measuring how SMEs decide to adopt 13 different SMIDT. This question employed a five-point Likert scale, which ranged from 1more than four years; 2-next three-four years; 3-next two-three years; 4-within one year; to the 5-current user. The questionnaire also included some general questions about the organizational characteristics of participating firms. Table 2. Measurement items of the study. Type of Number Variable References variable of items SMIDT adoption DV* 1 Kang et al. (2016), Thames and Schaefer (2017); Tuptuk and Hailes, (2018) Perceived value of SMIDT IV 5 Thames and Schaefer (2017), Ustundag and Cevikcan (2017) Perceived costs of SMIDT IV 3 Kang et al. (2016), Schröder (2016) Perceived compatibility of SMIDT IV 4 Kagermann (2015), Schlechtendahl et al. (2015) Information processing requirements IV 3 Melville and Ramirez (2008), Srinivasan and Swink (2015) IDT knowledge competency IV 4 Ghobakhloo et al. (2011), Thoben et al. (2017), Thong (1999) Strategic roadmapping for IV 4 Chofreh et al. (2017), Santos et al. (2017) manufacturing digitalization Imposition by environment IV 3 Ghobakhloo et al. (2011) Competitive pressure IV 3 Awa and Ojiabo (2016), Ke et al. (2017) * DV: dependent variable; IV: independent variable 4.2 Sampling and data collection The sampling frame of this research included all manufacturing SMEs located in the main industrial areas of Iran and manufacturing SMEs located in Peninsular Malaysia. For Iranian manufacturing SMEs data collection was conducted through cooperation with the „Administration of Industry, Mine, and Trade‟, and the „Small Industries and Industrial Parks Organization‟ in different provinces. The database of the Malaysian manufacturing SMEs was obtained from directories of Federation of Malaysian Manufacturers and cooperation with SME Corporation Malaysia. In this study, a small business in both Iran and Malaysia refers to manufacturers with full-time employees not exceeding 50. Moreover, manufacturers with full-time employees not exceeding 150 are considered mediumsized enterprises. In SMEs, top management directly affects IDT adoption process, and in most cases, the owner, chief information officer, and CEO are the same person (Ghobakhloo et al., 2011). Hence, CEOs (owners and/or executive managers) of the manufacturing SMEs were targeted as the key respondents within this research. This decision is support by the fact that CEOs in SMEs oversee entire business operations, and are regarded as the decision-maker for SMIDT adoption. Consistently, the questionnaire was first piloted among 60 SMEs (30 Iranian SMEs and 30 Malaysia SMEs) with the purpose of testing and assuring face validity of the questionnaire. As a result, some minor revisions (in terms of wording, question sequencing, and item layout) were applied to the questionnaire before the final data collection. Through the cooperation with different organizations in both countries, respondents were first contacted by different ways such as telephone, email, or participation in some briefings. Besides using an electronic survey, questionnaires were also distributed directly through seminar series and workshop sessions organized by cooperating organizations, as well as through “drop off and collect” mode. Finally, 183 usable responses from Iranian manufacturing SMEs and 177 usable responses from Malaysian manufacturing SMEs were received, which resulted in a final sample of 360 usable responses with the response rate of 32.82 %. 4.3 Demographics Demographic information of participating manufacturers is presented in Table 3. This table shows that the majority of Iranian SMEs (18.03%) who participated in this study are producers of automobile parts. Table 3, however, suggests that the largest concentration of Malaysian manufacturing SMEs is in the food and beverage sector (16.38%). Table 4 explains the SMIDT adoption rate among participating SMEs. Overall, „industrial actuators and

sensors‟ and „manufacturing simulation‟ are, respectively, the most and least frequently implemented SMIDT among participating SMEs. Table 3. Demographic findings of sample Iranian SMEs (183) Malaysian SMEs (177) Measure Items Frequency Percent Frequency Percent Gender Male 132 72.13 106 59.89 Female 51 27.87 71 40.11 Key informant´s age

20-30 30-40 40-50 50 and above

16 49 62 56

8.74 26.78 33.88 30.60

10 49 64 54

5.65 27.68 36.16 30.51

Education

Diploma Associate's degree Bachelor Master Ph.D.

20 35 70 36 22

10.93 19.14 38.25 19.68 12.02

24 30 77 34 12

13.56 16.95 43.50 19.21 6.78

Job title

CEO (owner) CEO (shareholder) CEO (executive)

70 51 62

38.25 27.87 33.88

49 47 81

27.68 26.55 45.76

Business sector

Automotive parts Agriculture products Chemical Construction products Electronic parts Food & beverage Metal Oil & related products Rubber Textiles & apparel Wood/tissue/paper products

33 13 27 15 9 24 15 10 7 10 20

18.03 7.10 14.75 8.20 4.92 13.11 8.20 5.46 3.83 5.46 10.93

24 14 12 10 13 29 22 4 9 23 17

13.56 7.91 6.78 5.65 7.34 16.38 12.43 2.26 5.08 12.99 9.60

Firm size (number of employees)

50 or less

83

45.36%

84

47.46%

50-100 54 29.51% 53 100-150 46 25.14% 40 Table 4. Level of SMIDT usage among surveyed SMEs. Types of IT tools Level of adoption (%) Iranian SMEs Malaysian SMEs Artificial intelligence 10.93% 14.69% Augmented/virtual reality 17.49% 19.21% Autonomous robots 25.14% 16.38% Additive manufacturing 14.21% 15.82% Cloud data and storage 46.99% 52.54% Cybersecurity technologies (e.g., CABA and VDN) 37.70% 41.81% Data analytics 37.70% 36.72% Enterprise resource planning 55.74% 47.46% High-performance computing-powered CAD 32.24% 18.64% Industrial actuators and sensors 55.74% 53.67% Machine and process controllers (PLC, SCADA, DCS, …) 59.02% 49.15% Manufacturing simulation 10.38% 11.30% Manufacturing execution system 58.47% 38.98%

29.94% 22.60%

Total 12.78% 18.33% 20.83% 15.00% 49.72% 39.72% 37.22% 51.67% 25.56% 54.72% 54.17% 10.83% 48.89%

To compare early responses against late responses, the study defined the first 25 percent of the questionnaires received as early responses and the last 25 percent of questionnaires received were as the late responses. The t-test

results revealed no significant difference in sample characteristics among the two groups. More importantly, a series of multiple independent-sample t-tests showed that there is no specific difference in any of key measurement items or variables among Iranian and Malaysian SMEs who participated in this study. This means data obtained from Iranian and Malaysian SMEs could merge into a single database. To address the issue of method bias, and following Burton-Jones (2009) and Podsakoff et al., (2003), the study primarily limited the sources of method bias by minimizing respondent knowledge bias, and reduced rating bias in instrument and procedure. Using a pilot test, randomizing questions in the questionnaire, using appropriate coding schemes, keeping the responses anonymous, and keeping measurement items comprehendible are examples of steps used to minimize method bias. The present study relies on one respondent (rater) which is the proper strategy since (1) it is the only way to truly test the suggested theory, because the theory defines the constructs in terms of CEO‟s perception, and (2) common method bias can be minimized through the judicious selection of instruments, procedures, and data analysis techniques (Burton-Jones, 2009; Spector, 2006). Yet, given the perceptual assessment of variables in the present study, this strategy could lead to the Common Method Variance (CMV) bias. Therefore, the study performed Harman‟s single-factor test and assessed the possibility of CMV in the single-respondent data of the sample (Podsakoff et al., 2003). Harman's single-factor test is among the most frequently used methods of CMV assessment in a single-method research design (Malhotra et al., 2006). Based on this test, the threat of CMV is high if a single factor is obtained or if one factor accounts for a majority of covariance in the independent and dependent variables (Devaraj et al., 2002). The Exploratory Factor Analysis (EFA) extracted 8 factors (independent variables) with eigenvalues of 1.00 or higher (Table A1), which accounted for 78.324% of the total variance. The first extracted factor accounted for 18.136% of the variance. Since EFA did not indicate a single-factor structure explaining a majority of the covariance, nor, a single factor emerging from un-rotated factor solutions, CMV concern is insignificant within the sample of the present study (Podsakoff et al., 2003). 5. Results We used IBM SPSS V. 22 for performing different statistical analysis. To assess the validity of the measurement instrument and scales, internal consistency reliability was first examined. Results indicated high internal consistency reliability because all the variables had a Cronbach α value of more than 0.70 (Table 5), which exceeded the threshold recommended by the literature (Ho, 2006). To confirm construct validity, EFA was performed on the measurement items using principal component analysis and varimax rotation method with Kaiser Normalization (Table A1). Kaiser‟s overall measure of sampling adequacy was 0.803, which indicated the appropriateness of data for factor analysis (Ghobakhloo et al., 2011). Moreover, the results explained 78.324 percent in cumulative variance for extracted categories, showing an acceptable and satisfactory level of construct validity. The pattern of extortion (8 independent variables in Table A1) provided support for we defined the independent variables and their underlying measurement items. Table 5. Cronbach α values of independent variables Cronbach Variables Abbreviation Mean Standard deviation α Perceived value of SMIDT PVSMD 4.238 0.110 0.791 Perceived costs of SMIDT PCSMD 4.601 0.996 0.803 Perceived compatibility of SMIDT COSMD 4.298 1.065 0.784 Information processing requirements IPR 4.503 0.854 0.855 IDT knowledge competency IDTKC 4.008 1.104 0.801 Strategic roadmapping for manufacturing SRMD 4.320 1.207 0.786 digitalization Imposition by environment IMPE 3.984 1.216 0.837 Competitive pressure COMP 4.373 1.052 0.838 The statistical analysis procedure continued with the application of the Pearson correlation for testing the relationships between different variables. The Pearson correlation matrix presented in Table 6 did not include any exceptionally high correlation (the highest correlation among principal constructs is r = 0.658), whereas evidence of common method bias is usually reflected by a correlation value of 0.9 or even higher (Ghobakhloo et al., 2018). Table 6. Correlation Matrix of variables of the study. Variables

1. PVSMD 2. PCSMD 3. COSMD 4. IPR 5. IDTKC 6. SRMD

1 1.00 -.048 .166*

2

3

1.00 .046

1.00

.100 .277** .055

-.106 -.218* -.353**

.170* .033 .007

4

5

6

1.00 .326** .209*

1.00 .284*

1.00

7

8

9

10

7. IMPE 8. COMP 9. Business size 10. SMIDT adoption

.431** .045 .416** .483**

.012 -.062 .043 -.440**

-.081 .136 -.013 .292**

.338** .049 .640** .573**

-.106 .027 .505** .204*

.088 .150* .171* .658**

1.00 .212* .459** .573**

1.00 .238* .256**

1.00 .104

1.00

* p<0.05; ** p<0.01.

Multiple regressions analysis was further used to study the effects of all independent variables on the dependent variable simultaneously. The results and assessment of hypotheses are summarized in Table 7, which provide support for hypotheses H1, H2, H3, H4, H5, H6, and H8. The result of the Variance Inflation Factor (VIF) analysis (Table 7) demonstrated that the VIF values for all the variables ranged from 1.092 to 1.261. These results showed that VIF values of the eight independent variables do not exceed the cut-off value of 3.3 for identifying suspect items (Petter et al., 2007), which indicates that no multicollinearity problem exists within the variables. Similarly, Durbin-Watson value of 2.207, which is within the recommended range of 1.5 and 2.5, indicates there is no autocorrelation problem within data used in this study (Ghobakhloo et al., 2011). The results showed that strategic roadmapping for manufacturing digitalization is the most significant determinant of SMIDT adoption. Information processing requirements, imposition by environment, perceived costs of SMIDT, perceived value of SMIDT, perceived compatibility of SMID, and IDT knowledge competency are, respectively, other significant determinants that positively influence SMIDT adoption. Table 7 shows that perceived costs is the only determinant negatively influencing SMIDT adoption among SMEs. The coefficient of determination (R2) value shows that 78.4 percent of the variance associated with SMIDT adoption decision is explained by the independent variables included in the present research. Table 7. Multiple regression analysis for determinants of SMIDT initiation. Standardized coefficients t-value Sig. Tolerance VIF Standard Variable B Beta error PVSMD 0.513 0.197 0.203 3.016 0.006 0.876 1.142 PCSMD -0.493 0.179 -0.216 -3.444 0.002 0.793 1.261 COSMD 0.297 0.213 0.149 2.104 0.029 0.902 1.109 IPR 0.993 0.161 0.288 4.913 0.000 0.835 1.198 IDTKC 0.193 0.103 0.174 1.639 0.043 0.916 1.092 SRMD 2.206 0.236 0.345 5.065 0.000 0.839 1.192 IMPE 0.813 0.207 0.250 4.628 0.000 0.797 1.255 COMP 0.127 0.159 0.076 0.935 0.318 0.905 1.105 Business size 0.072 0.276 0.039 0.508 0.667 0.873 1.145 Notes: F = 64.130, Sig. F Change = 0.000, R2 = 0.784, Durbin-Watson = 2.207 To test the effects of identified determinants on adoption (implementation) and non-adoption of individual SMIDT investigated in this research, Logistic Regression (LR) was used. Table A2 in appendix summarizes the output from different LR runs across the sample. The factors that did not appear as significant for a specific SMIDT in Table A2 can be interpreted as those factors not playing an important role in the SMEs‟ decision on adoption of that particular SMIDT tool. For example, Table A2 suggests that information processing requirements and IDT knowledge competency are the only discriminators between adopters and non-adopters of cybersecurity in this study, which implies that the existence of these two factors has persuaded/forced Iranian and Malaysian SMEs to implement this type of SMIDT. Table 8 lists and ranks factors (determinants) that significantly influence the implementation of each individual SMIDT (identified in Table A2). In his table, factors influencing the adoption of each SMIDT have been sorted based on their significance. For example, Table 8 suggests that perceived value is the most important reason for the implementation of AVR. Perceived compatibility and perceived costs, respectively, are the second and third important determinants of AVR implementation. Table 8. Determinants of implementation of individual SMIDT. Artificial intelligence Augmented/virtual reality Autonomous robots Additive manufacturing Cloud data and storage Cybersecurity technologies (e.g., CABA and VDN) Data analytics Enterprise resource planning

PVSMD 2 1 1

PCSMD 1 3 

COSMD 3 2 3

IPR

IDTKC 4

2 3

2

1 1 2

1

SRMD 

2 5

IMPE  1 2

COMP  4 

2 1

3 4

3

High-performance computing-powered 3 1 4 CAD Industrial actuators and sensors 1 2 3 4 3 2  Machine and process controllers (PLC,    1 SCADA, DCS, …)  Manufacturing execution system 2 3 1 Indicates that the particular factor has the most effect on the implementation of a specific SMIDT tool.

2  





1 

6. Discussion The results suggest that perceived value of SMIDT is a significant determinant of SMIDT adoption. It was also observed that implementers of AI, AVR, autonomous robots, and high-performance computing-powered CAD believe these applications provide them with organizational improvement and productivity. This finding implies that to achieve IDT-enabled organizational improvements, SMEs should migrate from the adoption of simple IDT tools to advanced and innovative SMIDT. Results, however, showed that perceived costs has a significant negative influence on SMIDT adoption. It was observed that higher perceived cost has resulted in non-adoption of complex SMIDT including AVR, AI, additive manufacturing, ERP, industrial sensors, and machine and process controllers. This finding is in line with the majority of prior IDT literature introducing the implementation costs as a major barrier to IDT adoption by SMEs (e.g., Gupta et al., 2017; Love et al., 2005). In addition, results of regression tests demonstrated that perceived compatibility significantly influences SMIDT adoption among SMEs, which provide support for prior IDT literature emphasizing the importance of perceived compatibility in the context of SMEs (Ghobakhloo et al., 2011; Grandon and Pearson, 2004). It was further observed that perceived compatibility is one of the main determinants for adoption or non-adoption of several SMIDT such as manufacturing exclusion system, ERP, autonomous robots, and machine and process controllers. Since the implementation of modern SMIDT requires significant organizational and structural alteration (Schröder, 2016) and changes in existing work practices (Ghobakhloo, 2018), the importance of SMIDT compatibility within SMEs is indeed expected. Moving toward the organizational determinants of SMIDT adoption, SMEs with greater information processing requirements were found more likely to adopt SMIDT. In particular, the results suggest that SMEs implement data management-related SMIDT such as cloud data and storage, cybersecurity technologies, data analytics, ERP, industrial actuators, and machine controllers to enhance their information processing capabilities in response to their information processing requirements. This finding supports prior literature arguing that the need for the development of information processing capabilities is associated with higher IDT innovation diffusion in industries (Akhtar et al., 2018; Melville and Ramirez, 2008). Findings also suggest that IDT knowledge competency is another organizational determinant of SMIDT adoption among SMEs, which provides support for prior studies in corresponding research streams (Nguyen et al., 2015; Ruivo et al., 2014). Overall, higher IDT knowledge competency can facilitate and speed up the adoption of SMIDT, offer more effective users' contribution in SMIDT deployment process, bring about more realistic and pragmatic expectations of implemented SMIDT, and lower the potential anxiety and risk entangled with new SMIDT (Ghobakhloo, 2018). Not surprisingly, strategic roadmapping for manufacturing digitalization was found to significantly facilitate SMIDT adoption, which is consistent with findings from other studies in the technological innovation literature (e.g., Ghobakhloo, 2018; Lu and Weng, 2018). This organizational characteristic was observed to be one of the most significant discriminators between adopters and non-adopters of complex and expensive SMIDT such as manufacturing execution system, ERP, autonomous robots, and machine and process controllers. Strategic roadmapping for manufacturing digitalization facilitates SMIDT implementation as it offers a thorough review of organizational goals and a comprehensive objective analysis, which leads to the selection of best-suited SMIDT that support the smart manufacturing vision. The results show that SMIDT is significantly influenced by the imposition from the environment, which implies that SMEs perceiving more pressure from their environment for manufacturing digitalization are more likely to decide to adopt SMIDT. This imposition, exerted by government, customers, suppliers, and larger counterparts, is also the discriminator between adopter and non-adopter of additive manufacturing, cloud data and storage, manufacturing execution system, and ERP, the SMIDT that are crucial to the materialization of „digital supply network‟ concept (Deloitte, 2018). Contrary to hypothesis H8, the study found that greater competitive pressure experienced by SMEs has no impact on SMEs‟ decision for SMIDT adoption. However, the study found that participating SMEs experiencing more competitive pressure have invested in modern expensive SMIDT including additive manufacturing, data analytics, and high-performance computing-powered CAD. This contradictory finding can be attributed to the fact that competitive pressure resulting from the turbulence of the digitalized business environment is a strong rivalry that forces businesses to be innovative (Hanelt et al., 2017). Since SMIDT has become technologically feasible and socially acceptable due to the contemporary digital revolution, employing these technologies by firms has become a strategic necessity (Legner et al., 2017; Kane et al., 2017; Ustundag and Cevikcan, 2017). Since generic IDT alone can no longer be a source of competitive advantage (Tang and Ghobakhloo, 2013), manufacturing SMEs need to invest in modern SMIDT, additive manufacturing, data analytics,

and high-performance computing-powered CAD in particular, to achieve manufacturing agility, increase their market responsiveness, and eventually sustain their competitiveness in the Industry 4.0 era (Schröder, 2016). 7. Conclusion, implications, and future research The majority of the enabling technologies of smart manufacturing have been available over the past few decades, yet they have been coming to maturity within the past few years. The way manufacturing SMEs could successfully adopt the required combinations of SMIDT will determine whether a manufacturer dies, survives, or thrives in the Industry 4.0 era. Drawing on the TOE framework and IDT innovation literature, this study developed and tested an integrated model of SMIDT institutionalization. The study is among the first to analyze the technological, organizational, and environmental determinants of SMIDT adoption and implementation within SMEs. The study first introduced how the eight influencing factors identified can alter the SMEs‟ decision for SMIDT adoption. At a more micro level of analysis, the study explained what discriminatory role each of the influencing factor has played with regard to the implementation of 12 individual SMIDT in this study. 7.1 Implications for executives and policymakers We believe our findings have several managerial implications. Results indicate that top management in SMEs, CEOs in particular, could support their strategic position in a highly competitive and information intensive environment with a high level of external pressure through higher investments in SMIDT. This study highlights the importance of leveraging more complex IDT in different SMEs' competitive actions. It is well agreed that generic IDT have become affordable to all SMEs, even in developing countries. Thus, besides investing in „simple‟ IDT with the aim of only improving firm's daily activities, SMEs can integrate SMIDT with their competitive actions and existing business resources and processes, thus rendering IDT-based competitive advantages planned and sustained. Adopting and using SMIDT ahead of competitors may increase the possibility that SMEs receive differential benefits. SMIDT that are unique and imperfectly mobile across firms can provide SMEs with higher efficiency ahead of competitors, the benefits that would not be available to late adopters. Industrial reports indicate that many of manufacturing SMEs have yet to become aware of opportunities and advantages offered by SMIDT. In response to this challenge, national and international associations must develop and implement smart manufacturing supportive policies, similar to what the European Commission has initiated via WATIFY awareness-raising campaign to stimulate the modernization of European industry. Results obtained in this research suggest that although some manufacturing SMEs intentionally adopt SMIDT to benefit from advertised benefits of smart manufacturing, many SMEs are primarily pushed by trading partners, competitors, and even the government to implemented SMIDT. Although SMIDT can act as a strategic tool to assist SMEs with hyper-competition in the globalized market, yet, not all SMEs have the adequate IDT maturity to embrace smart manufacturing, and not all manufacturers with working IDT-enabled production or service systems are mature enough to handle the horizontal integration across the value chain. SMEs should, therefore, note that SMIDT adoption might not be necessarily instrumental and justified for their businesses, and entering into a highly competitive business environment might force them to prematurely implement SMIDT. This means the future of SMEs may be jeopardized by unsuccessful investments in SMIDT, and in extreme conditions, these setbacks may even result in business failure. Thus, SMEs are advised to cautiously decide on adopting SMIDT, in particular, complex ones that require major organization-wide changes. Results also indicate that SMIDT adoption is still costly for the majority of SMEs, an expected finding given smaller manufacturers are generally characterized by financial resource limitation. We particularly found that despite Iranian and Malaysian governments‟ efforts for promoting IDT institutionalization within SMEs, SMIDT adoption is still hindered by the perceived costs of these technologies. This inconsistency can be attributed to the gap between the support that SMEs need for manufacturing digitalization and what currently is being provided by the governments. For instance, the Iranian government has provided a vast series of SMIDT-related incentives, workshops, and training for Iranian SMEs, however, majority of Iranian SMEs cannot afford the initial investment needed by AVR, AI, and industrial robots. Accordingly, we recommend governments to change their current encouraging and facilitating support policies to the welfare-based support model and hand out assistance packages such as low-price SMIDT, gratis training, financial aids, incentives, and cybersecurity guides directly to the SMEs. Our findings show that IDT compatibility and strategic roadmapping for manufacturing digitalization are crucial to SMEs‟ decision on SMIDT adoption. Industrial reports indicate that vendors have pushed digital technologies through radical research and development during the past decade, and introduced them to the market without proper consideration of whether or not these technologies are compatible with or satisfy SMEs. Nevertheless, digital technology push has reduced the price of SMIDT significantly, in the way digital tools are accessible to and affordable for the majority of SMEs worldwide. The digital market analysis indicates that IDT vendors have always developed their products and services mainly in response to needs identified among large firms, and SMEs should always readjust themselves with the existing products that are mainly designed for larger firms. The question is how SMEs should avoid radical leapfrogging in technology, and at the same time, implement necessary SMIDT and not left behind in the digital transformation race. The answer would be through a comprehensive strategic roadmap that judiciously recognizes and plans every single step SMEs need to take, as well as the timeline, and the costs and

benefits associated with the adoption of each SMIDT. This strategic roadmap in SMEs should address issues such as operations technology maturity, digitalization maturity, overall organizational readiness, digitalization knowledge competency, seamless integration capability, and cybersecurity maturity when it comes to planning for SMIDT adoption and implementation. The strategic roadmap for digitalization should also include various strategic tools such as maturity models and readiness assessment. Overall, SMEs should note that developing a comprehensive, yet flexible, digitalization roadmap that can act as a blueprint of actions for aligning SMIDT with the organizational short-term and long-term goals would negotiate the risk of manufacturing digitalization and warrant digital initiatives deliver expected advantages. In addition, SMIDT vendors and service providers are advised to cooperate with SMEs to jointly improve the compatibility of SMIDT with regard to the specific characteristics of these businesses active in different industries. Finally, yet importantly, the study revealed that, due to general lack of IDT knowledge within SMEs and difficulty in recruiting IDT professionals, overall IDT knowledge competency across the organization can significantly facilitate a more effective SMIDT implementation. Industrial reports indicate that IDT knowledge competency decreases the degree of uncertainty intertwined with new SMIDT within SMEs, and escalates the contribution that employees voluntarily make to different stages of SMIDT adoption. 7.2 Future research The purpose of our study has been theory building, and the study benefited from the standard procedure in the development of the research model, survey administration, data analysis, and data interpretation. Although we identified the mechanism through which SMEs decide to adopt various SMIDT, however, the particularities of our data, research design, and cross-sectional nature of this research did not allow us to develop a maturity model to assess the SMEs overall readiness for smart manufacturing. Therefore, our finding is limited in a sense it holds a holistic perspective to assess the state of manufacturing digitalization within the entire SME sector, whereas performing longitudinal case studies, and using case-wise strategic planning techniques (such as SWOT analysis) or decision support techniques (such as Interpretive Structural Modeling) can offer more enriched information about the manufacturing digitalization phenomenon. Since the application of strategic planning, decision support techniques, and semi-qualitative case studies are indeed the logical step in extending our work in the process of investigation of manufacturing digitalization, future studies are advised to conduct in-depth case studies as time series data and analytical modeling techniques are required to assess SMEs‟ maturity and smart manufacturing readiness as well as their impact on SMIDT adoption. More importantly, the research model of this study was explored among Iranian and Malaysian manufacturing SMEs. Although globalization, digital supply networks, and the emergence of international supply chains have standardized the environmental challenges faced by SMEs in both developed and developing countries, yet, testing and further extending the research model proposed within nonAsian business context would be an interesting avenue for future research.

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Appendices Table A1. Varimax rotation factor pattern for the items of determinants of SMIDT adoption. Component Statement Item 1 2 3 4 5 Improvement of staff creativity and innovativeness PVSMD1 .055 .036 .038 .013 .879 Improved manufacturing agility PVSMD2 -.035 .050 .055 .000 .851 Improved response to internal and external changes PVSMD3 .010 .087 -.018 .024 .766 Improved information sharing PVSMD4 .208 .048 .075 -.021 .712 Improvement of manufacturing productivity PVSMD5 -.044 .048 .090 -.006 .667 Costs of SMIDT training PCSMD1 .032 .008 .082 -.007 .892 Costs of new SMIDT integration and debugging PCSMD2 .005 .077 -.044 .047 .844 Costs of required SMIDT infrastructure PCSMD3 -.004 -.085 .102 .013 .812 Compatibility with preferred work practices COSMD1 .095 .056 .090 -.011 .872 Compatibility with existing IDT infrastructure COSMD2 .093 .027 .055 -.032 .831 Compatibility with legal issues COSMD3 .079 .044 .074 -.018 .762 Compatibility with organizational culture and values COSMD4 -.052 .076 .103 .028 .712 Access to the reliable and precise information IPR1 -.032 .003 .180 .087 .848 Access to the up-to-date information IPR2 .079 .026 .100 .067 .811 Rapid access to the required information at the time of need IPR3 .010 -.024 .076 .086 .724 Data analytics and management skills IDTKC1 .092 .183 .100 .204 .831 Software engineering and application development skills IDTKC2 .108 .203 -.010 .182 .782 Network engineering skills IDTKC3 -.007 .046 .005 .034 .735 Cybersecurity skills IDTKC4 -.077 .059 .029 .012 .693 IDT maturity and governance strategy SRMD1 .059 -.029 .079 .193 .882 Smart supply chain management strategy SRMD2 .078 .012 .063 .088 .813 Human resource strategy for smart manufacturing SRMD3 .066 .036 .117 .210 .734 Fulfilling the national and/or international requirements IMPE1 .073 .022 .036 .048 .001 Fulfilling the requirements of customer/supplier standards IMPE2 .179 .030 .053 .078 .081 Requests for better information transmission and communication IMPE3 .065 .026 .027 .091 .084 Threat of companies offering similar or substitute products COMP1 .087 -.003 .004 -.079 .053 Threat of losing market share to digitalized counterparts COMP2 .070 .040 .120 .150 .040 Survival being threatened by counterparts within the industry COMP3 .167 -.008 .006 .053 -.041

6 .167 .090 .047 .119 .033 .218 .060 -.038 -.048 -.044 .008 -.081 .110 -.059 .024 .073 .042 -.020 -.047 .120 .012 .100 .093 -.013 -.058 -.002 .019 .118

7 .086 -.046 -.150 -.085 -.044 .345 .294 .108 -.006 -.019 .008 -.001 -.018 -.012 -.020 .184 .081 -.005 -.001 .002 -.022 .046 .877 .846 .819 -.020 .044 .025

8 -.018 .019 .083 .016 .054 .226 -.004 .069 .018 .042 .044 .045 .006 -.010 .021 .005 -.032 .036 -.004 .063 .092 -.012 .068 .087 .131 .883 .860 .715

Table A2. Predictors of SMIDT implementation among participating SMEs. IT Products

Adoption/non-adoption determinants PVSMD Wald Sig.

Artificial intelligence

7.208

.007

PCSMD Wald Sig. 11.345

.000

Omnibus tests of model coefficients

COSMD Wald Sig.

IPR Wald Sig.

IDTKC Wald Sig.

SRMD Wald Sig.

IMPE Wald Sig.

COMP Wald Sig.

6.631

2.077

4.234

1.405

2.239

.458

.011

.121

.036

.236

.122

.486

CSRS*

NGRS*

Chisquare

Sig.

.088

.131

17.855

.002

Augmented/virtual 10.477 .000 4.146 .039 5.013 reality Autonomous robots 1.216 .259 13.202 .000 3.446 Additive .393 .531 2.222 6.515 .004 manufacturing Cloud data and 1.615 .204 2.733 .088 .784 storage Cybersecurity .818 .425 1.018 .296 .234 technologies (e.g., CABA and VDN) Data analytics 1.825 .177 .676 .472 2.187 Enterprise resource .086 .847 12.246 .000 13.018 planning High-performance .585 .448 6.114 .013 7.792 computing-powered CAD Industrial actuators 3.123 .077 14.035 .000 1.334 and sensors Machine and 1.134 .278 5.758 .041 8.844 process controllers (PLC, SCADA, DCS, …) Manufacturing 3.499 .061 2.255 .177 6.503 execution system *CSRS: Cox and Snell R Square, NGRS: Nagelkerke R Square. Note: No significant LR tests for manufacturing simulation.

.011

2.360

.125

0.385

.523

.156

.693

.802

.311

1.156

.198

.141

.203

30.220

.000

.047 .084

1.027 1.909

.273 .145

.485 1.270

.433 .196

6.547 5.162

.008 .023

.057 17.778

.812 .000

.426 4.107

.514 .043

.134 .162

.175 .248

31.055 36.220

.000 .000

.441

11.424

.000

.755

.463

1.382

.182

10.336

.000

.335

.563

.124

.184

25.931

.000

.655

12.550

.000

4.630

.032

.776

.321

1.129

.213

1.908

.097

.084

.134

18.168

.000

.155 .000

8.773 5.200

.003 .045

9.130 .721

.002 .366

.593 6.023

.441 .014

1.141 6.520

.286 .034

5.804 .228

.038 .633

.061 .156

.087 .345

29.105 42.412

.011 .000

.004

1.924

.138

5.346

.019

3.987

.081

1.197

.274

7.424

.006

.141

.196

29.176

.000

.245

13.446

.000

.542

.438

8.073

.004

.201

.520

.945

.331

.139

.218

33.025

.000

.003

11.072

.000

.271

.610

12.979

.000

2.058

.151

.389

.587

.162

.231

35.753

.000

.010

1.384

.227

.781

.377

5.212

.029

10.939

.001

.716

.397

.093

.126

16.442

.004