Sectoral GVC embeddedness and organizational technology adoption. An analysis across sectors in 28 European countries
DOI:
https://doi.org/10.1108/INMR-06-2023-0094Palavras-chave:
Technology-adoption, TOE model, GVC embeddedness, GVC participationResumo
PurposeThis study aims to investigate to what extent the adoption of technology by organizations depends on the characteristics of the sector in which they operate. The characteristics under study are the participation in Global Value Chains (GVCs) at the sectoral level. Using the term GVC embeddedness emphasizes that it is about an organization’s membership in a sector. Conducting this research is intended to contribute to the technology-organization-environment (TOE) model, which dominates research on the technology adoption by organizations. While the technology and the organization components of the model are well understood, this is less so for the environment component that is central in the present study.
Design/methodology/approachThe research was conducted by combining two datasets. The European Company Survey of 2019 provides data about technology adoption by organizations, as it includes information about whether organizations use robots, data analytics to improve processes for the production of goods and services and data analytics to monitor employee performance. At the sectoral level, these data are combined with data from the Trade in Value Added from the Organisation for Economic Co-operation and Development. It was possible to match these data on 15 sectors for 28 countries. Over 20,000 companies were included in the analyses. Logistic multi-level modeling was applied to analyze the nested data with binary outcomes.
FindingsGVC embeddedness is positively associated with technology adoption. GVC participation at the sectoral level explains a large share of variation in the use of robots. While it also explains the use of data analytics to improve production processes and employee monitoring, it does so to a lesser extent. For these two kinds of technologies, forward participation matters more than backward participation, suggesting that pressure from foreign buyers may explain the use of these technologies.
Originality/valueThis study aims to provide the following novel insights to the literature. First, it aims to expand the TOE model by further investigating the environment component of that model. Secondly, it aims to integrate GVC research and research on technology adoption at the organizational level. And, thirdly, it adds novel insights into research concerning the technology-adoption by adding the role of external factors at the sectoral level.
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Referências
Agarwal, A., Kapoor, K., & Walia, S. (2023). Modelling the barriers to blockchain implementation in human resource function. International Journal of Quality & Reliability Management, 41(8), 2075-2094.
Alguacil, M., Turco, A. L., & Martínez-Zarzoso, I. (2022). Robot adoption and export performance: Firm-level evidence from Spain. Economic Modelling, 114, 105912.
Al Hadwer, A., Tavana, M., Gillis, D., & Rezania, D. (2021). A systematic review of organizational factors impacting cloud-based technology adoption using technology-organization-environment framework. Internet of Things, 15, 100407.
Arora, K., & Siddique, A. A. (2024). Dynamic trade and technology linkages: A perspective from global value chain upgradation of Asian countries. International Journal of Emerging Markets, 19(10), 3248-3271.
Baker, J. (2012). The technology–organization–environment framework. Information Systems Theory: Explaining and Predicting Our Digital Society, Vol. 1, 231-245.
Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. M. (2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological Forecasting and Social Change, 170, 120880.
Chittipaka, V., Kumar, S., Sivarajah, U., Bowden, J. L. H., & Baral, M. M. (2022). Blockchain technology for supply chains operating in emerging markets: An empirical examination of technology-organization-environment (TOE) framework. Annals of Operations Research, 1-28.
Choi, D., Chung, C. Y., Seyha, T., & Young, J. (2020). Factors affecting organizations’ resistance to the adoption of blockchain technology in supply networks. Sustainability, 12(1), 8882.
Comin, D. A., Skinner, J. S., & Staiger, D. O. (2022). Overconfidence and Technology Adoption in Health Care (No. w30345). National Bureau of Economic Research.
Dadhich, M., & Hiran, K. K. (2022). Empirical investigation of extended TOE model on corporate environment sustainability and dimensions of operating performance of SMEs: A high order PLS-ANN approach. Journal of Cleaner Production, 363, 132309.
Ehab, M., & Zaki, C. R. (2021). Global value chains and service liberalization: Do they matter for skill-upgrading?. Applied Economics, 53(12), 1342-1360.
Elshaarawy, R., & Ezzat, R. A. (2022). Global value chains, financial constraints, and innovation. Small Business Economics, 1-35.
Eurofound & Cedefop (2020). European Company Survey 2019. Workplace practices unlocking employee potential. Luxembourg.
Fawcett, S. E., Wallin, C., Allred, C., Fawcett, A. M., & Magnan, G. M. (2011). Information technology as an enabler of supply chain collaboration: A dynamic capabilities perspective. Journal of Supply Chain Management, 47(1), 38–59.
Ghobakhloo, M., Iranmanesh, M., Vilkas, M., Grybauskas, A., & Amran, A. (2022). Drivers and barriers of Industry 4.0 technology adoption among manufacturing SMEs: A systematic review and transformation roadmap. Journal of Manufacturing Technology Management, 33(6), 1029-1058.
Gopalan, S., Reddy, K., & Sasidharan, S. (2022). Does digitalization spur global value chain participation? Firm-level evidence from emerging markets. Information Economics and Policy, 59, 100972.
Hanna, H., Haroun, M. H., & Gohar, N. (2020). Developing a framework for block chain adoption using TOE model. Journal of Business and Retail Management Research, 15(1).
Horani, O. M., Al-Adwan, A. S., Yaseen, H., Hmoud, H., Al-Rahmi, W. M., & Alkhalifah, A. (2023). The critical determinants impacting artificial intelligence adoption at the organizational level. Information Development, 02666669231166889.
Jackson, D., & Allen, C. (2024). Enablers, barriers and strategies for adopting new technology in accounting. International Journal of Accounting Information Systems, 52, 100666.
Kano, L., Tsang, E. W., & Yeung, H. W. C. (2020). Global value chains: A review of the multi-disciplinary literature. Journal of International Business Studies, 51, 577-622.
Kinkel, S., Baumgartner, M., & Cherubini, E. (2022). Prerequisites for the adoption of AI technologies in manufacturing–Evidence from a worldwide sample of manufacturing companies. Technovation, 110, 102375.
Koch, M., Manuylov, I., & Smolka, M. (2021). Robots and firms. The Economic Journal, 131(638), 2553-2584.
Koster, F. (2022). Organizations in the knowledge economy. An investigation of knowledge-intensive work practices across 28 European countries. Journal of Advances in Management Research, 20(1), 140-159.
Kumar, A., Singh, R. K., & Swain, S. (2022). Adoption of technology applications in organized retail outlets in India: A TOE model. Global Business Review, 09721509211072382.
Lakhani, T., Kuruvilla, S., & Avgar, A. (2013). From the firm to the network: Global value chains and employment relations theory. British Journal of Industrial Relations, 51(3), 440-472.
MacCarthy, B. L., Blome, C., Olhager, J., Srai, J. S., & Zhao, X. (2016). Supply chain evolution. Theory, concepts and science. International Journal of Operations & Production Management, 36, 1696-1718.
Malik, S., Chadhar, M., Vatanasakdakul, S., & Chetty, M. (2021). Factors affecting the organizational adoption of blockchain technology: Extending the technology–organization–environment (TOE) framework in the Australian context. Sustainability, 13(16), 9404.
Muralidharan, E., & Pathak, S. (2020). Contextualizing technology adoption and self-expression for technology entrepreneurial innovation. International Journal of Innovation and Technology Management, 17(04), 2050026.
Ndubuisi, G., & Owusu, S. (2021). How important is GVC participation to export upgrading?. The World Economy, 44(10), 2887-2908.
Nicoletti, G., von Rueden, C., & Andrews, D. (2020). Digital technology diffusion: A matter of capabilities, incentives or both?. European Economic Review, 128, 103513.
OECD (2021). Guide to OECD’s Trade in Value Added (TiVA) Indicators, 2021 edition. OECD.
Oliveira, T., Martins, R., Sarker, S., Thomas, M., & Popovič, A. (2019). Understanding SaaS adoption: The moderating impact of the environment context. International Journal of Information Management, 49, 1-12.
Orji, I. J., Ojadi, F., & Okwara, U. K. (2022). The nexus between e-commerce adoption in a health pandemic and firm performance: The role of pandemic response strategies. Journal of Business Research, 145, 616-635.
Pandza, K., & Thorpe, R. (2009). Creative search and strategic sense-making: Missing dimensions in the concept of dynamic capabilities. British Journal of Management, 20(S1), S118–S131.
Rigo, D. (2021). Global value chains and technology transfer: New evidence from developing countries. Review of World Economics, 157(2), 271-294.
Roppelt, J. S., Kanbach, D. K., & Kraus, S. (2023). Artificial intelligence in healthcare institutions: A systematic literature review on influencing factors. Technology in Society, 102443.
Ruiz-Ortega, M. J., Rodrigo-Alarcón, J., & Parra-Requena, G. (2024). New directions to create dynamic capabilities: The role of pioneering orientation and interorganizational relationships. European Management Journal, 42(3), 371-384.
Simões, A. C., Soares, A. L., & Barros, A. C. (2020). Factors influencing the intention of managers to adopt collaborative robots (cobots) in manufacturing organizations. Journal of Engineering and Technology Management, 57, 101574.
Thi Mai Hoa, T., Ha, L. T., Thi Thanh Huyen, N., Thi Thu Ha, N., & Ngoc, T. A. (2023). Global value chains and environment performance: Insights from global database. Energy & Environment, 0958305X221146952.
Tian, K., Dietzenbacher, E., & Jong‐A‐Pin, R. (2022). Global value chain participation and its impact on industrial upgrading. The World Economy, 45(5), 1362-1385.
Tong, S., Jia, N., Luo, X., & Fang, Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management Journal, 42(9), 1600-1631.
Van Zijl, A. L., & Koster, F. (2024). GVC embeddedness and innovation performance–an analysis across 28 European countries. European Journal of Management Studies. Ahead of print.
Venkatesh, V., & Bala, H. (2012). Adoption and impacts of interorganizational business process standards: Role of partnering synergy. Information Systems Research, 23(4), 1131-1157.
Wang, M., & Xiao, S. (2024). Why do MNE foreign subsidiaries differ in supply chain management capability, and how does it matter?. International Journal of Development Issues, 23(2), 260-281.
Zhong, Y., & Moon, H. C. (2023). Investigating the impact of Industry 4.0 technology through a TOE-based innovation model. Systems, 11(6), 277.
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