Next Article in Journal
Noise Pollution Analysis Using Geographic Information System, Agglomerative Hierarchical Clustering and Principal Component Analysis in Urban Sustainability (Case Study: Tehran)
Next Article in Special Issue
Sustainability in Project Management and Project Success with Virtual Teams: A Quantitative Analysis Considering Stakeholder Engagement and Knowledge Management
Previous Article in Journal
The Drying Peace–Athabasca Delta, Canada: Review and Synthesis of Cryo-Hydrologic Controls and Projections to Future Climatic Conditions
Previous Article in Special Issue
Ecological Efficiency Evaluation, Spatial Difference, and Trend Analysis of Logistics Industry and Manufacturing Industry Linkage in the Northeast Old Industrial Base
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 3
10.3390/su15032111
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Striving to Achieve United Nations Sustainable Development Goals of Taiwanese SMEs by Adopting Industry 4.0

by
Hsing-Chun Hung
1 and
Yuh-Wen Chen
2,*
1
Ph.D. Program in Management, Da-Yeh University, Changhua City 515006, Taiwan
2
Department of Information Management, Da-Yeh University, Changhua City 515006, Taiwan
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(3), 2111; https://doi.org/10.3390/su15032111
Submission received: 23 November 2022 / Revised: 15 January 2023 / Accepted: 19 January 2023 / Published: 22 January 2023
(This article belongs to the Special Issue Sustainability Challenges across Industries, Services and Markets)
Browse Figures
Review Reports Versions Notes

Abstract

:
This study aims to investigate the potential transformation of small and medium enterprises (SMEs) in Taiwan, China, to meet the United Nations (UN) sustainable development goals (SDGs) by adopting Industry 4.0. Taiwan is performing excellently at the core of Industry 4.0, information technology competence; however, we are curious if the competence required is available and acquainted by SMEs for achieving SDGs. As the consulting staff of the government, we hypothesized that adopting Industry 4.0 would lead to the success of sustainability. The analytical methodology is the model of technology, organization, and environment (TOE). We conducted the questionnaire survey to test if the adoption of Industry 4.0 will guarantee the success of sustainability. A systemic approach is employed to develop and parametrize the final model between adopting Industry 4.0 and sustainability, using structural equation modeling (SEM). Finally, we found a significant gap for Taiwanese SMEs to achieve sustainability via Industry 4.0 because only three hypotheses are supported: organizational resource availability influences Industry 4.0 adoption, investment costs impact sustainability, and external support pushes the adoption of Industry 4.0. We propose some possible solutions for the government to help SMEs reach the achievement of SDGs.

1. Introduction

Industry 4.0 is the prevailing revolution in the manufacturing industry globally. Large companies enable technologies more rapidly because of the advantages of large-scale economic, human resources, entrepreneur loans, and competence in information technology [1]. Small and medium enterprises (SMEs) face more challenges and difficulties when compared to large ones.
Industry 4.0 is a general idea of operations managed by advanced information and manufacturing technologies such as the internet of things (IoT), cloud services, automation, cyber-physical systems, and data mining to support the right time, the right place, and the correct quantity of decision-making [1]. In 2015, the United Nations announced the sustainable development goals (SDGs), a set of 17 SDGs, including eradicating poverty, mitigating climate change, and promising gender equality to guide global nations’ efforts toward sustainability [2]. Nevertheless, introducing Industry 4.0 to achieve the UN’s sustainable development goals is suitable for industries [3,4,5,6].
SMEs play a major role in most economies, particularly in developing countries. Solving SMEs’ sustainability challenges and difficulties via the technological advantage of Industry 4.0 is valuable [7]. Beier et al. [8] discussed the digitalization brought about in the industrial sector by comparing a highly industrialized (Germany) with a significant emerging industrial economy (China). They investigated how the digitalization of industry will affect the ecological dimension and influence social changes. Furstenau et al. [6] provided a bibliometric performance and network analysis (BPNA) to describe the existing relationship between Industry 4.0 and sustainability, highlighting a need for more technologies to achieve sustainability. Stock et al. [9] qualitatively assessed the potential value of sustainability created by Industry 4.0, from both a macro and micro perspective. They concluded that value creation might positively contribute to SDGs in the long run. Ejsmont et al. [10] examined gaps and opportunities from the literature review of the Web of Science, Scopus, and peer-reviewed papers. They present evidence that most researchers need to provide insight into a realization of initiatives to introduce Sustainable Industry 4.0.
Information and Communication Technology (ICT) defines the core of Industry 4.0 and pushes the digital transformation of business [11,12,13]. Ziemba [14] mentioned that those countries unable to acquire the capabilities for using ICT would be increasingly disadvantaged or excluded from participating in the information society. She encourages adopting ICT in such a way as to gain desirable collective and individual benefits efficiently and effectively in all dimensions, such as economic, social, political, cultural, personal, industrial, or occupational. This point inspires the theoretical, fundamental, and feasibility tests for this study. That is, in the adoption of Industry 4.0 to improve social welfare, sustainability is worthy of exploration.
In addition, the technology acceptance model (TAM) [15] or its extensions, such as the unified theory of acceptance and use of technology (UTAUT) [16], is well-known and popular to analyze the end user in adopting a specified product or service by Industry 4.0 [17]. However, this study has no designed product or service for the end user; thus, TAM or UTAUT is not appropriate for this study. According to the literature survey, there needs to be more research to investigate the link between the technology, organization, and environment (TOE) framework and the adoption of Industry 4.0. There has been little research to rigidly link Industry 4.0 and TOE until the latest research by Raj and Jeyaraj [18] in 2022. They validated the use of the TOE framework [19] to explain the adoption of Industry 4.0. They conducted a meta-analysis of the critical antecedents and consequents of Industry 4.0 and developed a unified framework of the prototypes and consequents for analysis. According to the theoretical basis of [18], the adoption of Industry 4.0 may enhance SDGs, and we try to contribute new insight and test this innovative TOE view in this study.
According to the discussions above, our goal is to achieve SDGs for most SMEs here by adopting Industry 4.0. We use TOE as the analytical methodology. We organize the study as follows: We review the necessary academic background in Section 2. After that, we propose the research framework, hypothesis, and measurement in Section 3. The survey passes the initial test and the second large-scale test, and we summarize the calibration results by SEM in Section 4. Finally, the lessons and management issues will be concluded and recommended in Section 5.

2. Literature Review

We review the necessary knowledge for this study in this section.

2.1. Industry 4.0, Sustainability, and UN SDGs

Industry 4.0 is mainly to enhance the digitization and intelligence of the manufacturing industry [20,21,22,23]. At the same time, we emphasize industrial productivity through ICT. Sharma et al. argued that limited reviews had assessed Industry 4.0 from a sustainability perspective [24], and the crossovers between Industry 4.0 and sustainability paradigms still need to be developed. According to the in-depth review of Kamble et al. [25], Industry 4.0 should contribute and create more sustainable value in the future. We found similar points from Stock et al. [9] and Stock and Seliger [26]. Jabbour et al. [27] proposed seven gaps in the literature review to foster future investigations on big-data-driven sustainable supply chains and offered lessons for business practitioners using big data for sustainable supply chain practices. Kiron and Unruh [28] suggested technology vendors, data suppliers, and city governments should use digital technologies to explore new ways collaboratively to improve sustainable living conditions and reduce costs.
According to the demand for integration between Industry 4.0 and sustainability, seen above, Strandhagen et al. [29] identified sustainability challenges, for example, social performance, working conditions, supplier relationships, and communication in the shipbuilding industry. Their work proposes nine digital solutions to support sustainable operations in shipbuilding as the paper’s primary contribution. They lay the foundation for further empirical research on sustainability and digitalization in shipbuilding and provide enhanced insight into how we can adopt Industry 4.0 technologies in the shipbuilding supply chains. Tan et al. [30] examined how Industry 4.0 impacts circular economy practices and blockchain technology. They collected cross-sectional data from 330 respondents and used partial least squares structural equation modeling (PLS-SEM) to calibrate the research framework. The results show that blockchain technology improves circular economy practices significantly in terms of green manufacturing (GM), recycling and remanufacturing (RR), and green design (GD) in India. Erol [31] argued the conflicts between Industry 4.0 and sustainability; he pointed out that a new industrial revolution should consider the human population’s growth, environmental pollution, the decrease in natural resources, and climate change. Information systems are valued enablers of this vision. Fisher et al. [32] developed the framework of Cloud Manufacturing (CM) for business orienting to share manufacturing capabilities and resources with the recycling waste hierarchy.
Taiwan, China, takes part in the scope of the United Nations as a respected member; therefore, it also encourages SMEs to follow the United Nations sustainable development goals (SDGs) [33,34], which all the member countries follow. SDGs consist of 17 items, which are well-known and summarized as follows: (1) No Poverty (SDG1); (2) Zero Hunger (SDG2); (3) Good Health and Well-Being (SDG3); (4) Quality Education (SDG4); (5) Gender Equality (SDG5); (6) Clean Water and Sanitation (SDG6); (7) Affordable and Clean Energy (SDG7); (8) Decent Work and Economic Growth (SDG8); (9) Industry, Innovation, and Infrastructure (SDG9); (10) Reduced Inequalities (SDG10); (11) Sustainable Cities and Communities (SDG 11); (12) Responsible Consumption and Production (SDG12); (13) Climate Action (SDG13); (14) Life under Water (SDG14); (15) Life on Land (SDG15); (16) Peace, Justice, and Strong Institutions (SDG16); and (17) Partnerships for the Goals (SDG17).

2.2. Technology–Organization–Environment (T–O–E)

The technology–organization–environment (TOE) theory is an organization-level theory for business and is very popular for checking the contexts of technology, organization, and the environment for adopting technological innovations [19,35,36]. The TOE framework is available in Figure 1.
The technology context is defined as technological resource availability. The organizational context emphasizes the administrative resource availability; for example, owner/top management support, corporate culture/mission and vision, quality of human resources, and sizes in terms of internal slack resources and specialization [37]. The environmental context concerns factors of, for example, market structure, competitive pressure, business partner readiness, technology support infrastructure, and government regulation and support. Simply speaking, the TOE research framework includes: (a) the technological contexts, which include IT software and hardware issues; (b) organizational contexts that apply to internal and external forces such as market competition; and (c) environmental contexts of an organization such as its scope, size, and management structure using external support for obstacles, please check Figure 1 for details [19]. Industrial characteristics, market structure, technology support, infrastructure, and government regulation determine the environmental context. Formal and informal linking structures, communication processes, size, and slack contribute to the organizational context. Moreover, the technology context considers its availability and characteristics.
We can find some articles addressing the issues of adopting innovations used by the TOE. For example, Bany Mohammad et al. [38] aimed to examine the factors influencing business intelligence and analytics (BIA) usage from 120 employees of Jordan’s Arab bank. The results revealed the critical impact of not only the existence of data and technology infrastructure but, also, the importance and availability of management and human resources in their support and capabilities. Yadav et al. [39] tried to enable sustainability through Industry 4.0, resulting in poorer sustainability adoption in developing nations. New technologies, such as the internet of things, big data analytics, blockchain, and machine learning, can be termed under the Industry 4.0 paradigms to directly or indirectly contribute to sustainability. This study develops a framework to improve sustainability adoption across manufacturing organizations of developing nations using Industry 4.0 technologies. Vrchota et al. [40] investigated the content analysis of literary resources based on the systematic literature review methodology. They studied twenty-nine studies in content analysis. The results show the main focus of the current literature on Industry 4.0, sustainability outcomes, and green processes. The authors present a conceptual Sustainability Green Industry 4.0 (SGI 4.0) framework that helps to structure and evaluate conventional green methods concerning Industry 4.0 and sustainability.

2.3. Achievement for the Industry 4.0 and SDGs

Industry 4.0 provides the digital solution for SMEs dramatically transforming from the old fashion to modern ones. Although Industry 4.0 is still in its introduction period of a life cycle, UN SDGs push businesses towards more sustainable thinking of production and social responsibility [4,41]. This socially responsible and environmentally sustainable pressure requires advanced technologies in Industry 4.0 to promote more sustainable activities of SMEs in practice.
We summarize the contexts to achieve the adoption of Industry 4.0 for SDGs using TOE according to related articles as follows:
(1)
Technological Context
Narula et al. [42] conducted a fuzzy AHP analysis of 132 industry leaders and policymakers from 36 industries to evaluate the significance of Industry 4.0 (I4.0) technologies on the global reporting initiative (GRI) adoption. The findings indicate that I4.0 drives 85% of environmental, 65% of economic, and 50% of societal GRI standards. Nascimento et al. [43] explored how Industry 4.0 can be integrated with circular economy (CE) practices to establish a business model that reuses and recycles waste materials. They validated the value of web technologies, reverse logistics, and additive manufacturing to support CE practices, such as scrap metal or e-waste.
(2)
Organizational Context
Lpui et al. [44] thought there was no formal methodology to define a standard engineer archetype and procedural methods to evaluate such archetypes’ contributions to sustainability. Therefore, they initiated and promoted the organizational culture of defining and measuring the progress and evolution toward a sustainable development path. Kohnová et al. [45] analyzed companies from selected European countries based on seven closely interconnected areas, with the business and technological transformations coming from Industry 4.0. The main questions analyzed focused on sites such as employee education and training, organizational culture, strategy, or organizational processes that will be most affected by radical environmental changes. Research has highlighted the differences between countries due to long-standing cultural differences. The IEA/OECD [46] proposed a comprehensive view of the relationship between digitalization and energy. The report highlights that it is “difficult to present a single figure for the energy savings that digitalization can yield in industry, as potential savings vary according to activity, management systems, culture and the degree of integration along supply value chains.”
(3)
Cost Context
Pajula et al. [47] confirmed the balance and challenges of the financial scope when integrating advanced manufacturing and environmental protection. Fatimah et al. [48] considered that the fourth industrial revolution arrived with many enabling technologies that impact important sociological aspects of the industry. As a result, different social challenges and risks were identified for each technology, starting from vulnerability and implementation costs to social aspects such as education and unemployment caused by those new technologies. Oláh et al. [49] discussed four scenarios: (a) a deployment scenario, (b) an operation scenario, (c) integration and compliance with sustainable development goals, and (d) a long-run scenario. The results indicate a negative relationship between the flow of the production process and the inputs to the final product because of increasing environmental costs.
(4)
External Context
Ziemba [50] revealed transformations in enterprises and government agencies that require implementing and using appropriate ICT. These results suggested that future inquiry surrounding a sustainable information society should account for its stakeholders, competencies, and tasks, enabling them to absorb emerging trends and challenges. External support indeed impacts the success of integrating Industry 4.0 to meet sustainability goals. Chen et al. [51] showed that technological innovation positively impacts energy efficiency, whereas growth in the shadow economy negatively impacts energy efficiency. Their suggested multi-pronged SDG framework for the Middle East and North African (MENA) countries is based on the assumption that the rent-seeking behavior of the government agencies needs to be handled strictly to maintain the environment for ease of doing business. Surana et al. [52] validated the specific case of India to examine how publicly funded incubators could contribute to strengthening science, technology, and innovation-based entrepreneurship.
Our theoretical framework summarizes the gap derived from Section 2.1 and the relationship between TOE and adopting Industry 4.0. We assume that if the gap exists and TOE validates the path to Industry 4.0 [18], then testing the feasibility of SMEs embracing Industry 4.0 for sustainability is valuable. This is the main contribution of this study.

3. Research Methodology and Questionnaire

The primary objective of this research focuses on finding how to achieve sustainability through Industry 4.0 to transform Taiwanese SMEs in Taiwan, China. Our efforts are for SMEs to meet the United Nations SDGs by adopting Industry 4.0. Industry 4.0 is technological diffusion and execution. It generally diffuses from advanced to developing nations; thus, the empirical study of the Taiwanese experience is comparable to other countries. Section 2.1 provides justification for the necessity to integrate Industry 4.0 and sustainability; moreover, Raj and Jeyaraj [18] support using TOE in such a study. We will build the research framework with some hypotheses in the following part.

3.1. Research Hypotheses and Framework

This study’s methodology uses the TOE theory [19] and model. The review of Section 2 established the research framework between adopting Industry 4.0 and sustainability. Therefore, this study employed the TOE framework, including technological, organizational, and environmental aspects, to test our hypotheses. From the TOE framework, the technological dimension of this study determines whether the IT resources of the firm will hamper or promote Industry 4.0 adoption and sustainability. Factors specific to the organizational aspect are management leadership and teamwork. Governance and costs are used to establish how the environmental dimension can affect Industry 4.0 progress toward sustainability.

3.1.1. Technological Context

According to Section 2.1 and the intelligent manufacturing dimension [53], the related technologies of Industry 4.0 could be classified into some main applications [54]: (a) vertical or horizontal integration, (b) cyber-physical systems/digital twins, (c) automation and robotics, (d) traceability, (e) mass customization, and (f) energy monitoring and saving. We refer to similar ideas from Venkatesh et al. [55] and Kumar [56]:
H1. 
There is a significant relationship between technological resource availability and Industry 4.0 adoption.
H2. 
There is a significant relationship between technological resource availability and sustainability.

3.1.2. Organizational Context

From the organizational perspective, this study views management leadership and teamwork as the success factors for accomplishing Industry 4.0, assisting organizations in attaining sustainability goals. Leadership is vital in digitally transforming organizations to introduce Industry 4.0, where the adoption process requires adequate investment and preparedness. Digitalization involves new technology adoption, requiring proper managerial leadership. The study of Jamwal et al. [57] initially conducted an exhaustive literature review on papers extracted from the WoS (Web of Science) and the Scopus database on Industry 4.0 and sustainable manufacturing. They define the enablers for sustainability in Industry 4.0 with expert discussions by keeping the context for SMEs of emerging economies in mind. The professionals and experts from industry and academia are helping with the Fuzzy-AHP and DEMATEL results. They revealed that supply chain, environmental, informational, and technological factors are the leading cause enablers, whereas organizational, social, and economic factors are the effect enablers. Braccini et al. [58] implicated the leadership and vision of the organization as drivers for a booming Industry 4.0 transition. Accordingly, this implies that the adoption of Industry 4.0 leads to fulfilling the requirements of the environment, economy, and society exclusively when organizations design it for this purpose. The results raise the importance of studying the antecedents of Industry 4.0 adoption to shed light on how they influence the actual adoption and the impacts they have on sustainability.
H3. 
There is a significant relationship between organizational resource availability and Industry 4.0 adoption.
H4. 
There is a significant relationship between organizational resource availability and sustainability.

3.1.3. Environmental Context

External support and costs establish how the environmental dimension influences Industry 4.0 adoption and sustainability. The essential 17 items from UN SDGs (in Section 2.1) present the sustainability dimension of this study.
Zimba [59] (2021) explained how the quality, information, culture, and management of ICT adoption by local governments contribute to sustainability. The perceived monetary cost comes from the literature on IT resulting from the incomplete implementation of sustainable and innovative technical processes that may impact the performance of SMEs [60], which recommends the cost as an additional construct that might influence the users to accept any new system. The cost is similar to the price value in the model of the unified theory of acceptance and use of technology (UTAUT) [61], which can be defined as “the individuals’ cognitive trade-off between the perceived benefits of the applications and the monetary cost for using them.” Cost can be seen as monetary sacrifices for applying a service or/and a sign of service quality. SMEs in developing countries such as India cannot ensure sustainable manufacturing operations due to the unreasonably high costs of sustainable practices, lack of skills and training, lack of standardized metrics, and adoption of emerging technologies [62]. Based on the prior discussion, users are expected to be interested in the perceived benefits of IT system services if the financial cost is acceptable. Hence, we propose the following hypothesis:
H5. 
There is a significant relationship between cost and Industry 4.0 adoption.
H6. 
There is a significant relationship between cost and sustainability.
H7. 
There is a significant relationship between external support and Industry 4.0 adoption.
H8. 
There is a significant relationship between external support and sustainability.
Finally, according to the review and discussion of Section 2.1, we arbitrarily assume the relationship between the adoption of Industry 4.0 and sustainability:
H9. 
There is a significant relationship between the adoption of Industry 4.0 and sustainability.
According to the assumptions above, the initial research framework is proposed in Figure 2.

3.2. Questionnaire Design

We used the TOE theory to assume the technological, organizational, and environmental contexts dependent on the research framework in Figure 2, and we collected the primary data from SEMs. We review the necessary articles to design the draft questionnaire, as in Table 1, our initial questionnaire for the first survey.

3.3. Survey Reliability and Validity

We conducted two surveys to improve the reliability and validity. The initial questionnaire in Table 1 is for the first survey of 40 respondents. After that, we modified the initial questionnaire for the second survey by deleting or adding some items for the final respondents, the details are available in Table 2 on improving reliability and validity. The last respondents were selected from the traditional industrial park of Taiwan, Fangyuan Industrial Park (https://www.moeaidb.gov.tw/iphw/fangyuan/, accessed on 1 January 2022) when we proceeded with the consulting service for SMEs. This park focuses on traditional manufacturing far from the metropolitan areas and near the sea. Most of them are the chairpersons, chief executive officers, or general managers. We used Google Forms to launch the second survey and collected 68 responses. The online survey is conducted by Google Forms, which is available in the part of Supplementary Materials of this paper.
Given the internal consistency reliability, we use Cronbach’s for the test [69]. Cronbach’s value ranges between 0 and 1, with higher values indicating that the survey or questionnaire is more reliable. In terms of validity, this study used the Kaiser–Meyer–Olkin measure of sampling adequacy (KMO) proposed by Kaiser [70] and Bartlett’s sphericity test offered by Bartlett [71] to determine the suitability of the questionnaire for analysis. According to Kaiser’s KMO value, when the value is more considerable, more common factors exist of each item, and the more suitable for analysis. KMO > 0.9 is excellent, KMO > 0.7 is suitable, KMO > 0.6 is ordinary, and KMO < 0.5 is unusable. First, this study tested each context in technology, organization, environment, and the perspective for adopting industry 4.0 and sustainability. This modification is shown in Table 2 by the comparison of KMO and Cronbach’s values. After the initial test of 40 respondents, we modified the questionnaire to increase the KMO and Cronbach’s values by adding/deleting some items in the initial questionnaire of Table 1. These improvements are summarized in Table 2, and the added items are available in Table 3.

4. Model Calibration and Discussion

In this section, we will set up the model, and calibrate the model for final discussions.

4.1. Model Calibration

We deleted the items whose responses were not normally distributed to improve the overall KMO and Cronbach’s α values before model calibration. Nevertheless, the final model structure is presented in Table 4. The overall KMO value is 0.7688, and Cronbach’s α value is 0.9521, which validates the model is suitable for further calibration.
In this study, we optimize the model of Figure 1 by structural equation modeling (SEM) in Python with semopy. The semopy package is open-source and free (https://semopy.com/, accessed on 31 December 2022). We checked our calibration results one by one, based on the impact coefficients (labels) and statistical significance (p-values), shown in Figure 2, which illustrate the explanatory power of the measurement variables (items). The calibration path coefficients are available in Figure 3, and the hypothesis (path), influence coefficients, z-values, and p-values are organized in Table 5.
According to the study by Curran-Everett and Benos [69], we can distinguish the p-value into three levels, 0.05, 0.001, and 0.1. When the p-value ranges within [0, 0.001], we regard the results as statistically and enormously significant. When the p-value goes within [0, 0.1], we may view the results as statistically significant but not so strong. If the p-value is greater than 0.1, then the results are not statistically significant. If we follow the rules of the p-value as 0.1, as outlined above, only hypotheses H3, H6, and H8 are supported, while the other hypotheses failed. We will discuss the calibrated phenomena in Section 4.2.

4.2. Discussion

Based on Figure 2 and Table 5, we examined each of the study hypotheses as follows:
(1)
H1: There is a significant relationship between technological resource availability and Industry 4.0 adoption. The p-value is 0.2084 > 0.1; therefore, we reject the hypothesis. We infer most of the respondents come from the traditional industrial park. The labor-intense jobs could affect this belief. This observation is similar to the findings of Kumar [79]. SMEs may need more awareness about this technological availability within their organizations and for other stakeholders. Usually, SMEs make their decisions based on short-term gains such that they ignore the circumstance have changed;
(2)
H2: There is a significant relationship between technological resource availability and sustainability. The p-value is 0.6570 > 0.1; therefore, we reject the hypothesis. Again, most of the respondents come from the traditional industrial park with a graduation degree below a bachelor’s. They are very experienced with handcraft but the urgent need for more low-level labor has become their challenge for keeping productivity. In the short run, we infer the sustainability vision needs to be more specific for these respondents to catch. These phenomena may relate to the literature of Crabtree and Hes [80], who suggested sustainability is not so much a technological problem as an institutional one. This idea leads us back to thinking about the improvement of administration in an institution/enterprise;
(3)
H3: There is a significant relationship between organizational resource availability and Industry 4.0 adoption. Here, the p-value is less than 0.1; therefore, we accept the hypothesis. Although these respondents in the traditional industry face the challenges of digital transformation, most are young and inherit the family business as a precious heritage. Nevertheless, they confirm upgrading the staffing is crucial to adopting Industry 4.0. This finding supports the research of Khanzode et al. [81]; their view is not only on the practitioners implementing Industry 4.0 but also rooting for administrative reforms within the organization. They have suggested better administrative control over firms, which might lead to better prospects for Industry 4.0;
(4)
H4: There is a significant relationship between organizational resource availability and sustainability. The p-value is 0.9174 > 0.1; therefore, we reject the hypothesis. Most of these respondents devoted themselves to traditional manufacturers: the Original Equipment/Design Manufacturer (OEM/ODM) type. They emphasize the technologies for on-time capacity rather than sustainability. The study of Rosati and Faria [82], using a logit model to calibrate 408 organizations’ responses worldwide, indicated that early adoption of SDG reporting is related to a larger size, a higher level of intangible assets, a higher commitment to sustainability frameworks, and external assurances. However, most Taiwanese SMEs are the opposite of those Rosati and Faria summarized in large companies;
(5)
H5: There is a significant relationship between cost and Industry 4.0 adoption. The p-value is 0.8429 > 0.1; therefore, we reject the hypothesis. Most respondents do not hold the view that cost will affect their adoption of Industry 4.0. We infer this observation as resulting from the full governmental support to promote new technologies here. For example, SEMs can easily apply for funding for academia–industry collaboration, such as Conventional Industry Technology Development (CITD), or Small Business Innovation Research (SBIR), to reduce the cost of innovations from Industry 4.0;
(6)
H6: There is a significant relationship between cost and sustainability. The p-value is 0.0660 < 0.1; therefore, we accept the hypothesis. Because of the traditional manufacturers in Taiwan, for example, the electro-plating industry is usually associated with high pollution. Therefore, these respondents think more investments are necessary to reduce such pollution (sustainability). This finding is also similar to that of Das et al. [83], who mentioned the high cost of sustainability based on the “reduce,” “reuse,” and “recycle” (3R) principles;
(7)
H7: There is a significant relationship between external support and Industry 4.0 adoption. The p-value is 0.6721 > 0.1; therefore, we reject the hypothesis. For many years, the Taiwanese government has emphasized the importance of automation, the internet of things (IOTs), artificial intelligence (AI), and cloud services, and entrepreneurs are familiar with the channels for assistance. This result is an excellent diffusion of higher college or university education here in Taiwan, and the acquiring cost of adopting Industry 4.0 is acceptable now for SMEs; please also refer to (5) above;
(8)
H8: There is a significant relationship between external support and sustainability. The p-value is 0.0289 < 0.1; therefore, we accept the hypothesis. The influence coefficient is interestingly negative for this path. We should not view this as more external support leading to less sustainability. We refer to this issue as controversial as most respondents think they did not receive enough external support for sustainability. However, the government thought it did its best to serve SMEs. This observation matches the study of Songling et al. [84]: government bodies and policymakers should provide more financial and nonfinancial support to SMEs, as this can upsurge economic growth and sustainability;
(9)
H9: There is a significant relationship between the adoption of Industry 4.0 and sustainability. The p-value is 0.1906 > 0.1; therefore, we reject the hypothesis. According to the discussions above from (1) to (8), because the respondents lack insight into adopting Industry 4.0 for sustainability quickly, the atmosphere for pushing Industry 4.0 is not mature here for SMEs; this hypothesis failed with no doubts.
According to the supported hypotheses, H3, H6, and H8 in this study, we suggest the following strategies for better links between the adoption of Industrial 4.0 and sustainability:
(1)
Most Taiwanese respondents are production-oriented (hardware-oriented) thinking; thus, if using the applications from Industry 4.0 to support sustainability is the government’s ultimate goal, the government should embed the production technologies with a sustainability vision. For example, the carbon issue is critical and dramatically impacts SMEs in exporting commodities. We can use the tracing technology from IOTs to convince the value of Industry 4.0 for sustainability. In that case, such a practical example could attract these respondents for a clear sustainability map. In addition, a practical and straightforward solution for SMEs will be more popularly promoting Industry 4.0 toward sustainability;
(2)
In the short term, the respondents recognized that the investment will increase as the standard of sustainability rises. The government could continuously consolidate/push green policies and regulations. For example, constantly enhancing pollutant restrictions and promoting green/recycled energy for SMEs are possible pressures to integrate Industry 4.0 and sustainability;
(3)
We should fully support the academia–industry cooperation toward Industry 4.0 with sustainability to reduce the SME complaints of H8. Traditional SEMs are more conservative than IT companies or large businesses. Government should simplify the wordy application documents for solving more advanced issues such as environmental, social, and governance (ESG), net zero-emission, or carbon neutrality by cooperative/joint ventures among scholars, SMEs, and officers from the government. SMEs may benefit much from this journey;
(4)
In tradition, the Taiwanese government always plays the role of an elder in the family, and it will do its best to take care of the SMEs and satisfy their needs. However, such a babysitter culture could spoil SMEs and destroy the possibility of building an entrepreneurial ecosystem toward sustainability. As government resources are more scarce than before, the demand for SMEs’ entrepreneurial ecosystem becomes critical.

5. Conclusions

This study proposes an analytical method to figure out how Taiwanese SMEs in Taiwan, China, adopt Industry 4.0 based on the TOE framework Raj and Jeyaraj [18] suggested, and our research successfully finds some interesting phenomena. The theoretical/managerial contributions, limitations, and future research are summarized as follows:
(1)
Theoretical/Managerial Contributions
While the idea of sustainability is vivid today, there needs to be more research to study the main links between the adoption of Industry 4.0 and sustainability, especially launching the related investigation based on the TOE framework. Based on the rigid basis of [18], we reasonably inferred that the adoption of Industry 4.0 enhances the SDGs, then contributed new insight and research framework in Figure 2 to test the related hypotheses in this study. We used SEM to identify the antecedents and consequents of Figure 2. Our results show that most of the links between antecedents and consequences are not supported by the SMEs here. This study extends the TOE framework within the context of adopting Industry 4.0 for sustainability. The managerial implications demonstrate that adopting Industry 4.0 for sustainability is still in the early emerging stages for Taiwanese SMEs. This is supported by the insufficient organizational resource availability, the high investment costs of Industry 4.0 adoption, and inadequate external support. To resolve the organizational challenges, we suggest a higher education (e.g., digital transformation of business) for the leader over the firm that might lead to better administrative control and performance for understanding the value of Industry 4.0 for sustainability. The government should encourage close cooperation between industry and academia to reduce SMEs’ risks and investment costs when adopting Industry 4.0 for sustainability: resolving the difficulties from (3), (6), and (8) in Section 4.2, simultaneously;
(2)
Limitations
The number of SMEs in Taiwan, China, accounts for 98.93% of all enterprises. Moreover, the number of SME employees accounts for 80.94% of the total employment in Taiwan. However, we only collected 68 responses from a single Fangyuan Industrial Park. The Fangyuan Industrial Park is geographically distant from the metropolitan area. Therefore, we should interpret the study’s results with an awareness of the limited samples. The calibrated results may vary when different Industrial Parks, even nations, are investigated;
(3)
Future Research
Although the atmosphere for this study idea is not mature, we still provide a basic research framework for further testing in the near future. When following the governmental planned roadmap to sustainability, we need to provide persuadable examples in practices for SMEs. Nevertheless, the study results here need to be more satisfying with further study. We suggest a complete ecosystem and entrepreneurship of business could be an innovative solution [85,86,87]. We will keep an eye on this topic to see if respondents change their attitudes when the idea matures as time goes by and the entrepreneurial ecosystem is ready. In addition, international and comparative research of this study is also welcomed.

Supplementary Materials

The blank sheet of our SDG questionnaire is available as a Google Form at: https://forms.gle/Sdd6gECqmCFwcGev5. Moreover, the blank pdf sheet can be downloaded at: https://drive.google.com/file/d/1clNmOcxM-aUmnAw7kCS5xkqMptEberad/view?usp=share_link.

Author Contributions

H.-C.H. is in charge of conceptualization and literature review; Y.-W.C. focused on methodology and proposed managerial implications. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Informed Consent Statement

Not applicable.

Data Availability Statement

SEM source: https://semopy.com/ (accessed on 31 December 2022).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Çetinkaya, A.Ş.; Niavand, A.; Rashid, M. Organizational change and competitive advantage: Business size matters. Bus. Manag. Stud. Int. J. 2019, 7, 40–67. [Google Scholar] [CrossRef] [Green Version]
  2. Easterly, W. The trouble with the sustainable development goals. Curr. Hist. 2015, 114, 322. [Google Scholar] [CrossRef]
  3. Sun, X.; Yu, H.; Solvang, W.D.; Wang, Y.; Wang, K. The application of Industry 4.0 technologies in sustainable logistics: A systematic literature review (2012–2020) to explore future research opportunities. Environ. Sci. Pollut. Res. 2021, 29, 9560–9591. [Google Scholar] [CrossRef] [PubMed]
  4. Ghobakhloo, M. Industry 4.0, digitization, and opportunities for sustainability. J. Clean. Prod. 2020, 252, 119869. [Google Scholar] [CrossRef]
  5. Javaid, M.; Haleem, A.; Singh, R.P.; Suman, R.; Gonzalez, E.S. Understanding the adoption of Industry 4.0 technologies in improving environmental sustainability. Sustain. Oper. Comput. 2022, 3, 203–217. [Google Scholar] [CrossRef]
  6. Furstenau, L.B.; Sott, M.K.; Kipper, L.M.; Machado, E.L.; Lopez-Robles, J.R.; Dohan, M.S.; Cobo, M.J.; Zahid, A.; Abbasi, Q.H.; Ali Imran, M. Link between sustainability and industry 4.0: Trends, challenges and new perspectives. IEEE Access 2020, 8, 140079–140096. [Google Scholar] [CrossRef]
  7. Prasanna, R.P.I.R.; Jayasundara, J.M.S.B.; Naradda Gamage, S.K.; Ekanayake, E.M.S.; Rajapakshe, P.S.K.; Abeyrathne, G.A.K.N.J. Sustainability of SMEs in the competition: A systemic review on technological challenges and SME performance. J. Open Innov. Technol. Mark. Complex. 2019, 5, 100. [Google Scholar] [CrossRef] [Green Version]
  8. Beier, G.; Niehoff, S.; Ziems, T.; Xue, B. Sustainability aspects of a digitalized industry–A comparative study from China and Germany. Int. J. Precis. Eng. Manuf. Green Technol. 2017, 4, 227–234. [Google Scholar] [CrossRef]
  9. Stock, T.; Obenaus, M.; Kunz, S.; Kohl, H. Industry 4.0 as enabler for a sustainable development: A qualitative assessment of its ecological and social potential. Process Saf. Environ. Prot. 2018, 118, 254–267. [Google Scholar] [CrossRef]
  10. Ejsmont, K.; Gladysz, B.; Kluczek, A. Impact of industry 4.0 on sustainability—Bibliometric literature review. Sustainability 2020, 12, 5650. [Google Scholar] [CrossRef]
  11. Santos, B.P.; Charrua-Santos, F.; Lima, T.M. Industry 4.0: An overview. In Proceedings of the World Congress on Engineering, London, UK, 4–6 July 2018; Volume 2, pp. 4–6. [Google Scholar]
  12. Oluyisola, O.E.; Bhalla, S.; Sgarbossa, F.; Strandhagen, J.O. Designing and developing smart production planning and control systems in the industry 4.0 era: A methodology and case study. J. Intell. Manuf. 2022, 33, 311–332. [Google Scholar] [CrossRef]
  13. Kagermann, H. Change through digitization—Value creation in the age of Industry 4.0. In Management of Permanent Change; Springer Gabler: Wiesbaden, Germany, 2015; pp. 23–45. [Google Scholar]
  14. Ziemba, E. (Ed.) Towards a Sustainable Information Society: People, Business and Public Administration Perspectives; Cambridge Scholars Publishing: Newcastle upon Tyne, UK, 2016. [Google Scholar]
  15. Davis, F.D.; Bagozzi, R.P.; Warshaw, P.R. User acceptance of computer technology: A comparison of two theoretical models. Manag. Sci. 1989, 35, 982–1003. [Google Scholar] [CrossRef] [Green Version]
  16. Arfi, W.B.; Nasr, I.B.; Khvatova, T.; Zaied, Y.B. Understanding acceptance of eHealthcare by IoT natives and IoT immigrants: An integrated model of UTAUT, perceived risk, and financial cost. Technol. Forecast. Soc. Chang. 2021, 163, 120437. [Google Scholar] [CrossRef]
  17. Masood, T.; Egger, J. Augmented reality in support of Industry 4.0—Implementation challenges and success factors. Robot. Comput. Integr. Manuf. 2019, 58, 181–195. [Google Scholar] [CrossRef]
  18. Raj, A.; Jeyaraj, A. Antecedents and consequents of industry 4.0 adoption using technology, organization and environment (TOE) framework: A meta-analysis. Ann. Oper. Res. 2022, 1–24. [Google Scholar] [CrossRef]
  19. Tornatzky, L.G.; Fleischer, M. The Processes of Technological Innovation; Lexington Books: Lexington, MA, USA, 1990. [Google Scholar]
  20. Hirsch-Kreinsen, H.; Ittermann, P.; Niehaus, J. (Eds.) Digitalisierung Industrieller Arbeit: Die Vision Industrie 4.0 und ihre Sozialen Herausforderungen; Nomos Verlag: Baden-Baden, Germany, 2018. [Google Scholar]
  21. Dalenogare, L.S.; Benitez, G.B.; Ayala, N.F.; Frank, A.G. The expected contribution of Industry 4.0 technologies for industrial performance. Int. J. Prod. Econ. 2018, 204, 383–394. [Google Scholar] [CrossRef]
  22. Thoben, K.D.; Wiesner, S.; Wuest, T. Industrie 4.0 and smart manufacturing-a review of research issues and application examples. Int. J. Autom. Technol. 2017, 11, 4–16. [Google Scholar] [CrossRef] [Green Version]
  23. Frank, A.G.; Dalenogare, L.S.; Ayala, N.F. Industry 4.0 technologies: Implementation patterns in manufacturing companies. Int. J. Prod. Econ. 2019, 210, 15–26. [Google Scholar] [CrossRef]
  24. Sharma, R.; Jabbour, C.J.C.; de Sousa Jabbour, A.B.L. Sustainable manufacturing and industry 4.0: What we know and what we don’t. J. Enterp. Inf. Manag. 2021, 34, 230–266. [Google Scholar] [CrossRef]
  25. Kamble, S.S.; Gunasekaran, A.; Gawankar, S.A. Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Saf. Environ. Prot. 2018, 117, 408–425. [Google Scholar] [CrossRef]
  26. Stock, T.; Seliger, G. Opportunities of sustainable manufacturing in industry 4.0. Procedia CIRP 2016, 40, 536–541. [Google Scholar] [CrossRef] [Green Version]
  27. Jabbour, C.J.C.; Fiorini, P.D.C.; Ndubisi, N.O.; Queiroz, M.M.; Piato, É.L. Digitally-enabled sustainable supply chains in the 21st century: A review and a research agenda. Sci. Total Environ. 2020, 725, 138177. [Google Scholar] [CrossRef]
  28. Kiron, D.; Unruh, G. The Convergence of Digitalization and Sustainability. MIT Sloan Management Review. 2018. Available online: https://sloanreview.mit.edu/article/the-convergence-of-digitalization-and-sustainability/ (accessed on 31 December 2022).
  29. Strandhagen, J.W.; Buer, S.V.; Semini, M.; Alfnes, E.; Strandhagen, J.O. Sustainability challenges and how Industry 4.0 technologies can address them: A case study of a shipbuilding supply chain. Prod. Plan. Control 2022, 33, 995–1010. [Google Scholar] [CrossRef]
  30. Tang, Y.M.; Chau, K.Y.; Fatima, A.; Waqas, M. Industry 4.0 technology and circular economy practices: Business management strategies for environmental sustainability. Environ. Sci. Pollut. Res. 2022, 29, 49752–49769. [Google Scholar] [CrossRef]
  31. Erol, S. Where is the Green in Industry 4.0? or How Information Systems can play a role in creating Intelligent and Sustainable Production Systems of the Future. Work. Pap. Inf. Syst. Inf. Bus. Oper. 2016, 2, 1–4. [Google Scholar]
  32. Fisher, O.; Watson, N.; Porcu, L.; Bacon, D.; Rigley, M.; Gomes, R.L. Cloud manufacturing as a sustainable process manufacturing route. J. Manuf. Syst. 2018, 47, 53–68. [Google Scholar] [CrossRef]
  33. Axon, S.; James, D. The UN Sustainable Development Goals: How can sustainable chemistry contribute? A view from the chemical industry. Curr. Opin. Green Sustain. Chem. 2018, 13, 140–145. [Google Scholar] [CrossRef]
  34. Biggeri, M.; Clark, D.A.; Ferrannini, A.; Mauro, V. Tracking the SDGs in an ‘integrated’ manner: A proposal for a new index to capture synergies and trade-offs between and within goals. World Dev. 2019, 122, 628–647. [Google Scholar] [CrossRef]
  35. Puklavec, B.; Oliveira, T.; Popovič, A. Understanding the determinants of business intelligence system adoption stages: An empirical study of SMEs. Ind. Manag. Data Syst. 2018, 118, 236–261. [Google Scholar] [CrossRef]
  36. Skafi, M.; Yunis, M.M.; Zekri, A. Factors influencing SMEs’ adoption of cloud computing services in Lebanon: An empirical analysis using TOE and contextual theory. IEEE Access 2020, 8, 79169–79181. [Google Scholar] [CrossRef]
  37. Chatzoglou, P.; Chatzoudes, D. Factors affecting e-business adoption in SMEs: An empirical research. J. Enterp. Inf. Manag. 2016, 29, 327–358. [Google Scholar] [CrossRef]
  38. Bany Mohammad, A.; Al-Okaily, M.; Al-Majali, M.; Masa’deh, R.E. Business Intelligence and Analytics (BIA) Usage in the Banking Industry Sector: An Application of the TOE Framework. J. Open Innov. Technol. Mark. Complex. 2022, 8, 189. [Google Scholar] [CrossRef]
  39. Yadav, G.; Kumar, A.; Luthra, S.; Garza-Reyes, J.A.; Kumar, V.; Batista, L. A framework to achieve sustainability in manufacturing organisations of developing economies using industry 4.0 technologies’ enablers. Comput. Ind. 2020, 122, 103280. [Google Scholar] [CrossRef]
  40. Vrchota, J.; Pech, M.; Rolínek, L.; Bednář, J. Sustainability outcomes of green processes in relation to industry 4.0 in manufacturing: Systematic review. Sustainability 2020, 12, 5968. [Google Scholar] [CrossRef]
  41. Mohan, S.V.; Katakojwala, R. The circular chemistry conceptual framework: A way forward to sustainability in industry 4.0. Curr. Opin. Green Sustain. Chem. 2021, 28, 100434. [Google Scholar] [CrossRef]
  42. Narula, S.; Puppala, H.; Kumar, A.; Frederico, G.F.; Dwivedy, M.; Prakash, S.; Talwar, V. Applicability of industry 4.0 technologies in the adoption of global reporting initiative standards for achieving sustainability. J. Clean. Prod. 2021, 305, 127141. [Google Scholar] [CrossRef]
  43. Nascimento, D.L.M.; Alencastro, V.; Quelhas, O.L.G.; Caiado, R.G.G.; Garza-Reyes, J.A.; Rocha-Lona, L.; Tortorella, G. Exploring Industry 4.0 technologies to enable circular economy practices in a manufacturing context: A business model proposal. J. Manuf. Technol. Manag. 2018, 30, 607–627. [Google Scholar] [CrossRef]
  44. Lupi, F.; Mabkhot, M.M.; Finžgar, M.; Minetola, P.; Stadnicka, D.; Maffei, A.; Litwin, P.; Boffa, E.; Ferreira, P.; Podržaj, P.; et al. Toward a sustainable educational engineer archetype through Industry 4.0. Comput. Ind. 2022, 134, 103543. [Google Scholar] [CrossRef]
  45. Kohnová, L.; Papula, J.; Salajová, N. Internal factors supporting business and technological transformation in the context of Industry 4.0. Business Theory and Practice 2019, 20, 137–145. [Google Scholar] [CrossRef]
  46. IEA/OECD. Digitalization & Energy; IEA/OECD: Paris, France, 2017. [Google Scholar]
  47. Pajula, T.; Behm, K.; Vatanen, S.; Saarivuori, E. Managing the Life Cycle to Reduce Environmental Impacts. In Dynamics of Long-Life Assets; Springer: Cham, Switzerland, 2017; pp. 93–113. [Google Scholar]
  48. Fatimah, Y.A.; Govindan, K.; Murniningsih, R.; Setiawan, A. Industry 4.0 based sustainable circular economy approach for smart waste management system to achieve sustainable development goals: A case study of Indonesia. J. Clean. Prod. 2020, 269, 122263. [Google Scholar] [CrossRef]
  49. Oláh, J.; Aburumman, N.; Popp, J.; Khan, M.A.; Haddad, H.; Kitukutha, N. Impact of Industry 4.0 on environmental sustainability. Sustainability 2020, 12, 4674. [Google Scholar] [CrossRef]
  50. Ziemba, E. The holistic and systems approach to the sustainable information society. J. Comput. Inf. Syst. 2013, 54, 106–116. [Google Scholar] [CrossRef]
  51. Chen, M.; Sinha, A.; Hu, K.; Shah, M.I. Impact of technological innovation on energy efficiency in industry 4.0 era: Moderation of shadow economy in sustainable development. Technol. Forecast. Soc. Chang. 2021, 164, 120521. [Google Scholar] [CrossRef]
  52. Surana, K.; Singh, A.; Sagar, A.D. Strengthening science, technology, and innovation-based incubators to help achieve Sustainable Development Goals: Lessons from India. Technol. Forecast. Soc. Chang. 2020, 157, 120057. [Google Scholar] [CrossRef]
  53. Osterrieder, P.; Budde, L.; Friedli, T. The smart factory as a key construct of industry 4.0: A systematic literature review. Int. J. Prod. Econ. 2020, 221, 107476. [Google Scholar] [CrossRef]
  54. Qu, Y.J.; Ming, X.G.; Liu, Z.W.; Zhang, X.Y.; Hou, Z.T. Smart manufacturing systems: State of the art and future trends. Int. J. Adv. Manuf. Technol. 2019, 103, 3751–3768. [Google Scholar] [CrossRef]
  55. Venkatesh, V.; Thong, J.Y.; Xu, X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Q. 2012, 36, 157–178. [Google Scholar] [CrossRef] [Green Version]
  56. Kumar, R. Espousal of Industry 4.0 in Indian Manufacturing Organizations: Analysis of Enablers. In Handbook of Research on Engineering Innovations and Technology Management in Organizations; Gaur, L., Solanki, A., Jain, V., Khazanchi, D., Eds.; IGI Global: Hershey, PA, USA, 2020; pp. 245–252. [Google Scholar]
  57. Jamwal, A.; Agrawal, R.; Sharma, M.; Kumar, V.; Kumar, S. Developing A sustainability framework for Industry 4.0. Procedia CIRP 2021, 98, 430–435. [Google Scholar] [CrossRef]
  58. Braccini, A.M.; Margherita, E.G. Exploring organizational sustainability of industry 4.0 under the triple bottom line: The case of a manufacturing company. Sustainability 2018, 11, 36. [Google Scholar] [CrossRef] [Green Version]
  59. Ziemba, E. The contribution of ICT adoption by local governments to sustainability—Empirical evidence from Poland. Inf. Syst. Manag. 2021, 38, 116–134. [Google Scholar] [CrossRef]
  60. Shashi, P.C.; Roberto, C.; Rajwinder, S. The impact of leanness and innovativeness on environmental and financial performance: Insights from Indian SMEs. Int. J. Prod. Econ. 2019, 212, 111–124. [Google Scholar] [CrossRef]
  61. Ain, N.; Kaur, K.; Waheed, M. The influence of learning value on learning management system use: An extension of UTAUT2. Inf. Dev. 2016, 32, 1306–1321. [Google Scholar] [CrossRef]
  62. Tamilmani, K.; Rana, N.P.; Wamba, S.F.; Dwivedi, R. The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation. Int. J. Inf. Manag. 2021, 57, 102269. [Google Scholar] [CrossRef]
  63. Hansen, E.B.; Bøgh, S. Artificial intelligence and internet of things in small and medium-sized enterprises: A survey. J. Manuf. Syst. 2021, 58, 362–372. [Google Scholar] [CrossRef]
  64. Pérez-Lara, M.; Saucedo-Martínez, J.A.; Marmolejo-Saucedo, J.A.; Salais-Fierro, T.E.; Vasant, P. Vertical and horizontal integration systems in Industry 4.0. Wirel. Netw. 2020, 26, 4767–4775. [Google Scholar] [CrossRef]
  65. Shashi, S.; Cerchione, R.; Centobelli, P.; Shabani, A. Sustainability orientation, supply chain integration, and SMEs performance: A causal analysis. Benchmarking Int. J. 2018, 25, 3679–3701. [Google Scholar] [CrossRef]
  66. Aggarwal, A.; Gupta, S.; Ojha, M.K. Evaluation of key challenges to industry 4.0 in Indian context: A DEMATEL approach. In Advances in Industrial and Production Engineering; Springer: Singapore, 2019; pp. 387–396. [Google Scholar]
  67. Bonilla, S.H.; Silva, H.R.; Terra da Silva, M.; Franco Gonçalves, R.; Sacomano, J.B. Industry 4.0 and sustainability implications: A scenario-based analysis of the impacts and challenges. Sustainability 2018, 10, 3740. [Google Scholar] [CrossRef] [Green Version]
  68. United Nations Development Programme. Available online: https://www.undp.org/ (accessed on 29 September 2022).
  69. Curran-Everett, D.; Benos, D.J. Guidelines for reporting statistics in journals published by the American Physiological Society. Physiol. Genom. 2004, 18, 249–251. [Google Scholar] [CrossRef]
  70. Kaiser, H.F. An index of factorial simplicity. Psychometrika 1974, 39, 31–36. [Google Scholar] [CrossRef]
  71. Bartlett, M.S. The effect of standardization on a χ2 approximation in factor analysis. Biometrika 1951, 38, 337–344. [Google Scholar] [CrossRef]
  72. Lu, B.; Gungor, V.C. Online and remote motor energy monitoring and fault diagnostics using wireless sensor networks. IEEE Trans. Ind. Electron. 2009, 56, 4651–4659. [Google Scholar]
  73. Masood, T.; Sonntag, P. Industry 4.0: Adoption challenges and benefits for SMEs. Comput. Ind. 2020, 121, 103261. [Google Scholar] [CrossRef]
  74. Kwiotkowska, A.; Gębczyńska, M. Job Satisfaction and Work Characteristics Combinations in Industry 4.0 Environment—Insight from the Polish SMEs in the Post–Pandemic Era. Sustainability 2022, 14, 12978. [Google Scholar] [CrossRef]
  75. Narayanamurthy, G.; Tortorella, G. Impact of COVID-19 outbreak on employee performance–moderating role of industry 4.0 base technologies. Int. J. Prod. Econ. 2021, 234, 108075. [Google Scholar] [CrossRef]
  76. Shaturaev, J. Economies and Management as A Result of The Fourth Industrial Revolution: An Education Perspective. Indones. J. Educ. Res. Technol. 2022, 3, 51–58. [Google Scholar]
  77. Mourtzis, D.; Angelopoulos, J.; Panopoulos, N. A Literature Review of the Challenges and Opportunities of the Transition from Industry 4.0 to Society 5.0. Energies 2022, 15, 6276. [Google Scholar] [CrossRef]
  78. Liu, Z.; Sampaio, P.; Pishchulov, G.; Mehandjiev, N.; Cisneros-Cabrera, S.; Schirrmann, A.; Jiru, F.; Bnouhanna, N. The architectural design and implementation of a digital platform for Industry 4.0 SME collaboration. Comput. Ind. 2022, 138, 103623. [Google Scholar] [CrossRef]
  79. Kumar, R.; Singh, R.K.; Dwivedi, Y.K. Application of industry 4.0 technologies in SMEs for ethical and sustainable operations: Analysis of challenges. J. Clean. Prod. 2020, 275, 124063. [Google Scholar] [CrossRef]
  80. Crabtree, L.; Hes, D. Sustainability uptake in housing in metropolitan Australia: An institutional problem, not a technological one. Hous. Stud. 2009, 24, 203–224. [Google Scholar] [CrossRef]
  81. Khanzode, A.G.; Sarma, P.R.S.; Mangla, S.K.; Yuan, H. Modeling the Industry 4.0 adoption for sustainable production in Micro, Small & Medium Enterprises. J. Clean. Prod. 2021, 279, 123489. [Google Scholar]
  82. Rosati, F.; Faria, L.G.D. Business contribution to the Sustainable Development Agenda: Organizational factors related to early adoption of SDG reporting. Corp. Soc. Responsib. Environ. Manag. 2019, 26, 588–597. [Google Scholar] [CrossRef]
  83. Das, S.; Lee, S.H.; Kumar, P.; Kim, K.H.; Lee, S.S.; Bhattacharya, S.S. Solid waste management: Scope and the challenge of sustainability. J. Clean. Prod. 2019, 228, 658–678. [Google Scholar] [CrossRef]
  84. Songling, Y.; Ishtiaq, M.; Anwar, M.; Ahmed, H. The role of government support in sustainable competitive position and firm performance. Sustainability 2018, 10, 3495. [Google Scholar] [CrossRef] [Green Version]
  85. Neumeyer, X.; Ashton, W.S.; Dentchev, N. Addressing resource and waste management challenges imposed by COVID-19: An entrepreneurship perspective. Resour. Conserv. Recycl. 2020, 162, 105058. [Google Scholar] [CrossRef] [PubMed]
  86. Neumeyer, X.; Santos, S.C. Sustainable business models, venture typologies, and entrepreneurial ecosystems: A social network perspective. J. Clean. Prod. 2018, 172, 4565–4579. [Google Scholar] [CrossRef]
  87. Bocken, N.; Boons, F.; Baldassarre, B. Sustainable business model experimentation by understanding ecologies of business models. J. Clean. Prod. 2019, 208, 1498–1512. [Google Scholar] [CrossRef]
Sustainability 15 02111 g001 550
Figure 1. TOE framework.
Figure 1. TOE framework.
Sustainability 15 02111 g001
Sustainability 15 02111 g002 550
Figure 2. Research framework.
Figure 2. Research framework.
Sustainability 15 02111 g002
Sustainability 15 02111 g003 550
Figure 3. Calibration results using SEM.
Figure 3. Calibration results using SEM.
Sustainability 15 02111 g003
Table
Table 1. The initial version of questionnaire.
Table 1. The initial version of questionnaire.
SectionItemSource
T1: I had learned about the technologies of Sensors, actuators and Programmable Logic Controllers (PLC), and had used them to enhance productivity.T1–T7: [53,54]
T2: I had learned about Supervisory Control and Data Acquisition, and had used them to enhance productivity.
Technological ContextT3: I had learned the technologies of Enterprise Resource Planning (ERP), and had used them in the business.
T4: I had learned the technologies of automation, and had used them well in production.
T5: I had learned artificial intelligence, and had used them well in production planning.
T6: I had learned that using the remote identification and traceability of raw materials or final products.
T7: I had learned that using Additive manufacturing in fast prototyping, and had used it in production.
O1: Our organization’s management leader has a vision for the future toward Industry 4.0.O1–O5: [63]
O6–O7: [64]
O2: Our organization’s management leader has the ability to quickly build and coordinate competent networked teams toward Industry 4.0.
O3: Our organization’s management leader has the technical skills, IT-skills and skills about digital technologies and digital culture.
Organizational ContextO4: Our organization’s management leader acts as a digital talent scout, who finds ways to attract new high digital capabilities and to invest in open and flexible talent recruitment and management systems.
O5: Our organization’s management leader is knowledge-oriented. That means the leader himself/herself is a curious, questioning and profound thinker.
O6: My organizational team must contain those with technical expertise to be able to perform effectively.
O7: My organizational team has the spirit and desire to succeed that brings out the best in employees toward enhanced performance.
EC1: My business lacks the ability to integrate data sources during production, and the outsourcing cost is expensive.EC1–EC3: [50,65]
Environmental Context: CostEC2: My business scale (market) is not large; therefore, the financial resources for digital transformation is also limited.
EC3: The acquiring cost toward Industry 4.0 and sustainability knowledge is too high for my business.
ES1: My business would like to learn more about how the government supports the SEMs toward Industry 4.0 and Sustainability.ES1–ES3: [66]
Environmental Context: External SupportES2: Because of the lack of green purchase/regulation for Industry 4.0 and sustainability, my business is hesitant to step forward.
ES3: My business is totally aware of the UN SDGs, and believe these goals should be achieved in the long run.
I1: My business enables our organization to reduce production costs when adopting Industry 4.0.I1–I5: [67]
Adoption of Industry 4.0I2: My business enables our organization to increase resource utilization efficiency when adopting Industry 4.0.
I3: My business enables our organization to enhance production flexibility when adopting Industry 4.0.
I4: The embedded Industry 4.0 fits our organization well.
I5: The embedded Industry 4.0 is clear and understandable to our employees.
S1: I am aware of SDG1 No Poverty, and will support it.S1–S17: [68]
S2: I am aware of SDG2 Zero Hunger, and will support it.
S3: I am aware of SDG3 Good Health and Well-being, and will support it.
S4: I am aware of SDG4 Quality Education, and will support it.
S5: I am aware of SDG5 Gender Equality, and will support it.
S6: I am aware of SDG6 Clean Water and Sanitation, and will support it.
S7 I am aware of SDG7 Affordable and Clean Energy, and will support it.:
S8: I am aware of SDG8 Decent Work and Economic Growth, and will support it.
UN SDGsS9: I am aware of SDG9 Industry, Innovation and Infrastructure, and will support it.
S10: I am aware of SDG10 Reduced Inequalities, and will support it.
S11: I am aware of SDG11 Sustainable Cities and Communities, and will support it.
S12: I am aware of SDG12 Responsible Consumption and Production, and will support it.
S13: I am aware of SDG13 Climate Action, and will support it.
S14: I am aware of SDG14 Life below Water, and will support it.
S15: I am aware of SDG15 Life on land, and will support it.
S16: I am aware of SDG16 Peace, Justice and Strong Institutions, and will support it.
S17: I am aware of SDG17 Partnerships for the Goals, and will support it.
Table
Table 2. Modification for the final questionnaire from Table 1.
Table 2. Modification for the final questionnaire from Table 1.
ItemsDeleted ItemsAdded ItemsKMO (before)KMO (after)α (before)α (after)
Technology T_1, T_2, T_30.77560.84660.85530.8997
OrganizationO2O_1, O_2, O_30.87210.88830.94930.9458
Environment EC_1, EC_2, ES_1, ES_20.73930.85740.73060.8932
Adoption of Industry 4.0 I_1, I_2, I_3, I_4, I_50.82670.90630.92900.9568
SustainabilityS8 0.88620.90450.97440.9734
α: Cronbach’s α value.
Table
Table 3. Added items for the final questionnaire.
Table 3. Added items for the final questionnaire.
SectionItemSource
T_1: I had learned about monitoring energy technologies and used them well in production to improve energy efficiency.T_1: [72]
T_2, T_3: [73]
Technological ContextT_2: I had learned that the technologies of cyber-physical system, and had used them in simulating the production processes.
T_3: I had learned about Robots, and had used them well in production to increase efficiency.
O_1: My organizational manager had well planned an appropriate reward system for the team members and encourage effective participation in the team.O_1 [74]
O_2 [75]
O_3 [76]
Organizational ContextO_2: My organizational team trust provides an atmosphere for the team members to discuss their mistakes for enhancing synergy.
O_3: Each of My organizational Team members can perform effectively and understand his/her complete job description.
EC_1: My business lacks ICT infrastructure, and this investment is expensive.
Environmental ContextEC_2: My business is hard to recruit skilled IT employees, and their labor costs are high.
ES_1: My business lacks the channel to apply for innovation funds toward Industry 4.0 and sustainability.		[77]
ES_2: My business is hesitant to step forward with the market uncertainty for Industry 4.0 and sustainability.
I_1: My business allows employees to be flexible and collaborative when adopting Industry 4.0.[78]
I_2: My business enables employees to interpret data competently when adopting Industry 4.0.
Adoption of Industry 4.0I_3: My business enables cyber-physical interaction in production lines when adopting Industry 4.0.
I_4: My business makes the production information visible throughout the supply chain when adopting Industry 4.0.
I_5: My business helps to build cloud-based customer service data management when adopting Industry 4.0.
Table
Table 4. Model structure.
Table 4. Model structure.
ContextItem
TechnologyT1, T5, T6, T7, T_2
OrganizationO1, O3, O4, O5, O6, O7, O_1, O_2, O_3
EnvironmentEC_2, EC1, EC2, EC3, ES_1, ES_2, ES3
Adoption of Industry 4.0I_1, I_2, I_3, I_4, I_5, I1, I2, I3, I4, I5
SustainabilityS1, S2, S3, S4, S5, S6, S7, S9, S10, S11, S12, S13, S14, S15, S16, S17
Table
Table 5. Hypothesis, influence coefficients, z-values, and p-values.
Table 5. Hypothesis, influence coefficients, z-values, and p-values.
HypothesisPathInfluence Coeff.z-valuep-valueStd.
H1T-> I0.24821.30860.20840.1973
H2T-> S0.13240.44390.6570.2983
H3O-> I0.5633.938700.1429
H4O-> S0.02310.10360.91740.223
H5C-> I0.03080.19810.84290.1558
H6C-> S0.50531.83810.0660.2749
H7E-> I−0.0532−0.42320.67210.1257
H8E-> S−0.4947−2.18350.02890.2265
H9I-> S0.29761.30860.19060.2274
Std. means the standard deviation.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Hung, H.-C.; Chen, Y.-W. Striving to Achieve United Nations Sustainable Development Goals of Taiwanese SMEs by Adopting Industry 4.0. Sustainability 2023, 15, 2111. https://doi.org/10.3390/su15032111

AMA Style

Hung H-C, Chen Y-W. Striving to Achieve United Nations Sustainable Development Goals of Taiwanese SMEs by Adopting Industry 4.0. Sustainability. 2023; 15(3):2111. https://doi.org/10.3390/su15032111

Chicago/Turabian Style

Hung, Hsing-Chun, and Yuh-Wen Chen. 2023. "Striving to Achieve United Nations Sustainable Development Goals of Taiwanese SMEs by Adopting Industry 4.0" Sustainability 15, no. 3: 2111. https://doi.org/10.3390/su15032111

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop