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Evaluating Industry 4.0 Technology Application in SMEs: Using a Hybrid MCDM Approach

Author

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  • Shih-Chia Chang

    (College of Management, National Taipei University of Business, Taipei 10051, Taiwan)

  • Hsu-Hwa Chang

    (Department of Business Administration, National Taipei University of Business, Taipei 10051, Taiwan)

  • Ming-Tsang Lu

    (College of Management, National Taipei University of Business, Taipei 10051, Taiwan)

Abstract

Evaluating Industry 4.0 technology application in small and medium-sized enterprises (SMEs) is an issue that requires a multi-criteria strategy comprising quantitative and qualitative elements. The purpose of this study is to integrate performance estimation of Industry 4.0 technology application using the technology–organization–environment (TOE) framework. Relating TOE to Industry 4.0 technology application evaluation is more multifaceted than other methods and it requires comprehensive analysis. In this study, we applied a multiple-criteria decision-making (MCDM) approach to develop a model which integrates MCDM to perform an assessment that prioritizes the influence weights of Industry 4.0 technology application to SMEs’ factors. Firstly, we carried out a review of the literature and the TOE framework was selected to generate nine elements, along with three aspects used to measure Industry 4.0 technology application in SMEs. Secondly, the approach of the decision-making trial and evaluation laboratory (DEMATEL) was set up using an influence network relations digraph (INRD). The DEMATEL-based analytic network process (DANP) was used to indicate the influence weights linking the above aspects and elements. Lastly, the modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique applied influence weights to assess the aspects/elements in the gaps identified and to investigate how to reduce the gaps so as to estimate the application of Industry 4.0 technology by SMEs. The results show that the technology aspect is the most influential factor.

Suggested Citation

  • Shih-Chia Chang & Hsu-Hwa Chang & Ming-Tsang Lu, 2021. "Evaluating Industry 4.0 Technology Application in SMEs: Using a Hybrid MCDM Approach," Mathematics, MDPI, vol. 9(4), pages 1-21, February.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:4:p:414-:d:502516
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    1. Fernanda Strozzi & Claudia Colicchia & Alessandro Creazza & Carlo Noè, 2017. "Literature review on the ‘Smart Factory’ concept using bibliometric tools," International Journal of Production Research, Taylor & Francis Journals, vol. 55(22), pages 6572-6591, November.
    2. Schniederjans, Dara G. & Curado, Carla & Khalajhedayati, Mehrnaz, 2020. "Supply chain digitisation trends: An integration of knowledge management," International Journal of Production Economics, Elsevier, vol. 220(C).
    3. Raj, Alok & Dwivedi, Gourav & Sharma, Ankit & Lopes de Sousa Jabbour, Ana Beatriz & Rajak, Sonu, 2020. "Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective," International Journal of Production Economics, Elsevier, vol. 224(C).
    4. Li, Ling, 2018. "China's manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0”," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 66-74.
    5. Reynolds, Elisabeth B. & Uygun, Yilmaz, 2018. "Strengthening advanced manufacturing innovation ecosystems: The case of Massachusetts," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 178-191.
    6. Yeh, Ching-Chiang & Chen, Yi-Fan, 2018. "Critical success factors for adoption of 3D printing," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 209-216.
    7. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2007. "Extended VIKOR method in comparison with outranking methods," European Journal of Operational Research, Elsevier, vol. 178(2), pages 514-529, April.
    8. Kummitha, Rama Krishna Reddy & Crutzen, Nathalie, 2019. "Smart cities and the citizen-driven internet of things: A qualitative inquiry into an emerging smart city," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 44-53.
    9. Julien Gardan, 2016. "Additive manufacturing technologies: state of the art and trends," International Journal of Production Research, Taylor & Francis Journals, vol. 54(10), pages 3118-3132, May.
    10. Osterrieder, Philipp & Budde, Lukas & Friedli, Thomas, 2020. "The smart factory as a key construct of industry 4.0: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 221(C).
    11. Dalenogare, Lucas Santos & Benitez, Guilherme Brittes & Ayala, Néstor Fabián & Frank, Alejandro Germán, 2018. "The expected contribution of Industry 4.0 technologies for industrial performance," International Journal of Production Economics, Elsevier, vol. 204(C), pages 383-394.
    12. de Sousa Jabbour, Ana Beatriz Lopes & Jabbour, Charbel Jose Chiappetta & Foropon, Cyril & Godinho Filho, Moacir, 2018. "When titans meet – Can industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 18-25.
    13. Fukuda, Kayano, 2020. "Science, technology and innovation ecosystem transformation toward society 5.0," International Journal of Production Economics, Elsevier, vol. 220(C).
    14. Yong Yin & Kathryn E. Stecke & Dongni Li, 2018. "The evolution of production systems from Industry 2.0 through Industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 848-861, January.
    15. Lu, Ming-Tsang & Hsu, Chao-Che & Liou, James J.H. & Lo, Huai-Wei, 2018. "A hybrid MCDM and sustainability-balanced scorecard model to establish sustainable performance evaluation for international airports," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 9-19.
    16. Tortorella, Guilherme Luz & Cawley Vergara, Alejandro Mac & Garza-Reyes, Jose Arturo & Sawhney, Rapinder, 2020. "Organizational learning paths based upon industry 4.0 adoption: An empirical study with Brazilian manufacturers," International Journal of Production Economics, Elsevier, vol. 219(C), pages 284-294.
    17. James J. H. Liou & Ming-Tsang Lu & Shu-Kung Hu & Chia-Hua Cheng & Yen-Ching Chuang, 2017. "A Hybrid MCDM Model for Improving the Electronic Health Record to Better Serve Client Needs," Sustainability, MDPI, vol. 9(10), pages 1-13, October.
    18. Ming-Tsang Lu & Gwo-Hshiung Tzeng & Hilary Cheng & Chih-Cheng Hsu, 2015. "Exploring mobile banking services for user behavior in intention adoption: using new hybrid MADM model," Service Business, Springer;Pan-Pacific Business Association, vol. 9(3), pages 541-565, September.
    19. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2004. "Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS," European Journal of Operational Research, Elsevier, vol. 156(2), pages 445-455, July.
    20. Mellor, Stephen & Hao, Liang & Zhang, David, 2014. "Additive manufacturing: A framework for implementation," International Journal of Production Economics, Elsevier, vol. 149(C), pages 194-201.
    21. Mehrdokht Pournader & Yangyan Shi & Stefan Seuring & S.C. Lenny Koh, 2020. "Blockchain applications in supply chains, transport and logistics: a systematic review of the literature," International Journal of Production Research, Taylor & Francis Journals, vol. 58(7), pages 2063-2081, April.
    22. Urbinati, Andrea & Bogers, Marcel & Chiesa, Vittorio & Frattini, Federico, 2019. "Creating and capturing value from Big Data: A multiple-case study analysis of provider companies," Technovation, Elsevier, vol. 84, pages 21-36.
    23. Lu, Yang & Papagiannidis, Savvas & Alamanos, Eleftherios, 2018. "Internet of Things: A systematic review of the business literature from the user and organisational perspectives," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 285-297.
    24. Scott J. Wallsten, 2000. "The Effects of Government-Industry R&D Programs on Private R&D: The Case of the Small Business Innovation Research Program," RAND Journal of Economics, The RAND Corporation, vol. 31(1), pages 82-100, Spring.
    25. Doh, Soogwan & Kim, Byungkyu, 2014. "Government support for SME innovations in the regional industries: The case of government financial support program in South Korea," Research Policy, Elsevier, vol. 43(9), pages 1557-1569.
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    Cited by:

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    2. Alok Raj & Anand Jeyaraj, 2023. "Antecedents and consequents of industry 4.0 adoption using technology, organization and environment (TOE) framework: A meta-analysis," Annals of Operations Research, Springer, vol. 322(1), pages 101-124, March.
    3. Shih-Hsien Tseng & Hsiu-Chuan Chen & Tien Son Nguyen, 2022. "Key Success Factors of Sustainable Organization for Traditional Manufacturing Industries: A Case Study in Taiwan," Mathematics, MDPI, vol. 10(22), pages 1-17, November.

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