The Nexus Between Strategic Resources, Innovation Ambidexterity and Digital Twin Adoption Intention: A Conceptual Paper
Abstract
This study intends to investigate the relationship among the strategic resources of top management support, financial investment, internal expertise, Information System Infrastructure and government support with innovation ambidexterity towards Digital Twin adoption intention in Malaysian medical devices and pharmaceutical manufacturing companies. This conceptual research integrates the Resource-based View and Diffusion of Innovation theories which will provide a foundation and a more comprehensive understanding of technological adoption intention within these industries by evaluating how firms can leverage their resources for competitive advantage while adapting to the challenges posed by new technologies. This quantitative research specifically focuses on sampling the pharmaceutical and medical manufacturing firms listed in the Federation of Malaysian Manufacturers (FMM) directory, targeting top management comprise of leadership team, functional heads and department heads. The research process will involve administering e-survey questionnaires, conducting a pretest, and analyzing statistical data analysis using the SPSS and Smart-PLS. The results of this research will benefit the Malaysian stakeholders such as manufacturing companies, employers, employees, customers, suppliers, government and society. By understanding the results of this research, this study will help to lead to the next implementation stage of this cutting-edge technology in medical devices and pharmaceutical companies in Malaysia which can improve the operations, reduce downtime, and enhance their overall performance. This research may lead to incremental revenue and greater contribution to the country's economy over time.
References
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Ahmad A, Alkhalil A, Altamimi AB et al (2021) Modernizing legacy software as context—sensitive and portable mobile-enabled application. IT Professional, 23:42–50. https://doi.org/10.1109/MITP.2020.2975997
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Alsheibani, S., Messom, C., Cheung, Y., & Alhosni, M. (2020). Artificial Intelligence Beyond the Hype: Exploring the Organisation Adoption Factors.
Amin, A., Bhuiyan, M. R. I., Hossain,R., Molla, C., Poli, T. A., & Milon, M. N. U. (2024). The adoption of Industry 4.0 technologies by using the technology organizational environment framework: The mediating role to manufacturing performance in a developing country. Business Strategy & Development, 7(2), e363.
Amthiou, H., Arioua, M., & Benbarrad, T. (2023). Digital Twins in Industry 4.0: A Literature Review. In ITM Web of Conferences (Vol. 52, p. 01002). EDP Sciences.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.
Barney, J., Wright, M., & Ketchen Jr, D. J. (2001). The resource-based view of the firm: Ten years after 1991. Journal of Management, 27(6), 625-641.
Bråthen, M., & Doan, E. (2021). Ambidexterity to overcome the challenges of digital transformation A Bibliometric Review (Master's thesis, OsloMet–Oslo Metropolitan University).
Bustinza, O.F., Vendrell-Herrero, F. and Gomes, E. (2020), “Unpacking the effect of strategic ambidexterity on performance: a cross-country comparison of MMNEs developing product service innovation”, International Business Review, Vol. 29 No. 6, 101569, doi: 10.1016/j.ibusrev. 2019.01.004.
Büyüközkan, G., & Göçer, F. (2018). Digital Supply Chain: Literature review and a proposed framework for future research. Computers in Industry, 97, 157-177.
Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM, 54(8), 88-98.
Federation of Malaysian Manufacturers Directory (2020), "FMM directory of Malaysian industries", Available at: www.fmm.org.my/
Freel, M. S. (2005). Patterns of innovation and skills in small firms. Technovation, 25(2), 123-134.
García-Hurtado, D., Devece, C., Zegarra-Saldaña, P. E., & Crisanto-Pantoja, M. (2024). Ambidexterity in entrepreneurial universities and performance measurement systems. A literature review. International Entrepreneurship and Management Journal, 20(1), 345-366.
Ghani, E. K., Ariffin, N., & Sukmadilaga, C. (2022). Factors Influencing Artificial Intelligence Adoption in Publicly Listed Manufacturing Companies: A Technology, Organisation, and Environment Approach. International Journal of Applied Economics, Finance and Accounting, 14(2), 108-117.
González-Varona, J. M., Poza, D., Acebes, F., Villafáñez, F., Pajares, J., & López-Paredes, A. (2020). New business models for sustainable spare parts logistics: A case study. Sustainability, 12(8), 3071.
Ghobakhloo, M., & Ching, N. T. (2019). Adoption of digital technologies of smart manufacturing in SMEs. Journal of Industrial Information Integration, 16, 100107.
Green, S. B. (1991). How many subjects does it take to do a regression analysis? Multivariate Behavioral Research, 26(3), 499-510. https://doi.org/10.1207/s15327906mbr2603_7
Grieves, M. (2002, October). Completing the Cycle: Using PLM Information in the Sales and Service Functions [Slides]. In SME Management Forum.
Gui, A., Fernando, Y., Shaharudin, M. S., Mokhtar, M., & Karmawan, I. G. M. (2020). Cloud Computing Adoption Using TOE Framework for Indonesia’s Micro Small Medium Enterprises. JOIV: International Journal on Informatics Visualization, 4(4), 237-242.
Hadjimanolis, A. (2000). A resource-based view of innovativeness in small firms. Technology Analysis & Strategic Management, 12(2), 263-281.
Hairol’azaha, A.S. (2022, November 30). Advancing Malaysia’s Manufacturing Industry with Digitalisation [Press release]. https://www.tmone.com.my/press-release/
Hashem, G., Aboelmaged, M., & Ahmad, I. (2024). Proactiveness, knowledge management capability and innovation ambidexterity: An empirical examination of digital supply chain adoption. Management Decision, 62(1), 129-162.
Horváth, D., & Szabó, R. Z. (2019). Driving forces and barriers of Industry 4.0: Do multinational and small and medium-sized companies have equal opportunities?. Technological Forecasting and Social Change, 146, 119-132.
Helfat, C. E. (2007). Stylized facts, empirical research and theory development in management. Strategic Organization, 5(2), 185-192.
Jiang, Y., Yin, S., Li, K., Luo, H., & Kaynak, O. (2021). Industrial applications of digital twins. Philosophical Transactions of the Royal Society A, 379(2207), 20200360.
Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006). A review of the predictors, linkages, and biases in IT innovation adoption research. Journal of Information Technology, 21(1), 1-23.
Joel, O. S., Oyewole, A. T., Odunaiya, O. G., & Soyombo, O. T. (2024). Navigating the digital transformation journey: Strategies for startup growth and innovation in the digital era. International Journal of Management & Entrepreneurship Research, 6(3), 697-706.
Juckers, A., Knerr, P., Harms, F., & Strube, J. (2024). Digital Twin Enabled Process Development, Optimization and Control in Lyophilization for Enhanced Biopharmaceutical Production. Processes, 12(1), 211.
Katsoulakis, E., Wang, Q., Wu, H., Shahriyari, L., Fletcher, R., Liu, J., ... & Deng, J. (2024). Digital twins for health: A scoping review. Digital Medicine, 7(1), 77.
Khadka, R., Batlajery, B. V., Saeidi, A. M., Jansen, S., & Hage, J. (2014, May). How do professionals perceive legacy systems and software modernization?. In Proceedings of the 36th International Conference on Software Engineering (pp. 36-47).
Khin, S., & Ho, T. C. (2019). Digital technology, digital capability and organizational performance: A mediating role of digital innovation. International Journal of Innovation Science, 11(2), 177-195.
Khin, S., & Kee, D. K M. (2022). Identifying the driving and moderating factors of Malaysian SMEs’ readiness for Industry 4.0. International Journal of Computer Integrated Manufacturing, 35(7), 761-779.
Kortmann, S. (2012). The relationship between organizational structure and organizational ambidexterity: A comparison between manufacturing and service firms. Springer Science & Business Media.
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. Ifac-PapersOnline, 51(11), 1016-1022.
Kumar, A., & Krishnamoorthy, B. (2020). Business analytics adoption in firms: A qualitative study elaborating TOE framework in India. International Journal of Global Business and Competitiveness, 15(2), 80-93.
Lutfi, A., Al-Khasawneh, A. L., Almaiah, M. A., Alshira’h, A. F., Alshirah, M. H., Alsyouf, A., ... & Ali, R. A. (2022). Antecedents of big data analytic adoption and impacts on performance: Contingent effect. Sustainability, 14(23), 15516.
Maroufkhani, P., Tseng, M. L., Iranmanesh, M., Ismail, W. K. W., & Khalid, H. (2020). Big data analytics adoption: Determinants and performances among small to medium-sized enterprises. International Journal of Information Management, 54, 102190.
Meet, R. K., Kala, D., & Al-Adwan, A. S. (2022). Exploring factors affecting the adoption of MOOC in Generation Z using extended UTAUT2 model. Education and Information Technologies, 27(7), 10261-10283.
Möhring, M., Keller, B., Radowski, C. F., Blessmann, S., Breimhorst, V., & Müthing, K. (2022, June). Empirical insights into the challenges of implementing digital twins. In Human Centred Intelligent Systems: Proceedings of KES-HCIS 2022 Conference (pp. 229-239). Singapore: Springer Nature Singapore.
Parrott, A., & Warshaw, L. (2017). Industry 4.0 and the digital twin. Deloitte Insights.
Preda, G. (2014). Organizational ambidexterity and competitive advantage: Toward a research model. Management & Marketing-Craiova, (1), 67-74.
Qasem, Y. A., Asadi, S., Abdullah, R., Yah, Y., Atan, R., Al-Sharafi, M. A., & Yassin, A. A. (2020). A multi-analytical approach to predict the determinants of cloud computing adoption in higher education institutions. Applied Sciences, 10(14), 4905.
Raj, A., Dwivedi, G., Sharma, A., de Sousa Jabbour, A. B. L., & Rajak, S. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. International Journal of Production Economics, 224, 107546
Renko, M., Carsrud, A., & Brännback, M. (2009). The effect of a market orientation, entrepreneurial orientation, and technological capability on innovativeness: A study of young biotechnology ventures in the United States and in Scandinavia. Journal of Small Business Management, 47(3), 331-369.
Ridzuan (2023, January 17). The Rise of Artificial Intelligence in Malaysia: Exploring Trends and Opportunities. Avalatble at: https://deeplearningmy.com/the-rise-of-artificial-intelligence-in-malaysia-exploring-trends-and-opportunities/
Rogers, E. M. (2003). Diffusion of Innovations. (5th Ed.). New York, US. The Free Press.
Rojo, A., Llorens-Montes, J., & Perez-Arostegui, M. N. (2016). The impact of ambidexterity on supply chain flexibility fit. Supply Chain Management: An International Journal, 21(4), 433-452.
Rosen, R., Von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015). About the importance of autonomy and digital twins for the future of manufacturing. Ifac-papersonline, 48(3), 567-572.
Saleh, R. H., Durugbo, C. M., & Almahamid, S. M. (2023). What makes innovation ambidexterity manageable: A systematic review, multi-level model and future challenges. Review of Managerial Science, 17(8), 3013-3056.
Santoro, G., Bresciani, S., & Papa, A. (2020). Collaborative modes with cultural and creative industries and innovation performance: the moderating role of heterogeneous sources of knowledge and absorptive capacity. Technovation, 92, 102040.
Shi, P., & Yan, B. (2016). Factors affecting RFID adoption in the agricultural product distribution industry: empirical evidence from China. SpringerPlus, 5(1), 1-11.
Sintharapantorn, V., Saengnoree, A., Teerawatananond, T., & Simcox, J. A. (2023). An Inferential Statistical Analysis of the Key Factors Influencing the Adoption of New Technology by MEEC (MICE) Organizers in Thailand. International Journal of Professional Business Review, 8(7), e02681-e02681.
Soto-Acosta, P., Popa, S., & Martinez-Conesa, I. (2018). Information technology, knowledge management and Environmental dynamism as drivers of innovation ambidexterity: A study in SMEs. Journal of Knowledge Management, 22(4), 824-849.
Stahlberg, E. A., Abdel-Rahman, M., Aguilar, B., Asadpoure, A., Beckman, R. A., Borkon, L. L., ... & Zervantonakis, I. (2022). Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation. Frontiers in Digital Health, 4, 1007784.
Tao, F., Cheng, J.,Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94, 3563-3576.
Thong, J. Y. (1999). An integrated model of information systems adoption in small businesses. Journal of Management Information Systems, 15(4), 187-214.
Tortorella, G. L., Giglio, R., & Van Dun, D. H. (2019). Industry 4.0 adoption as a moderator of the impact of lean production practices on operational performance improvement. International Journal of Operations & Production Management, 39(6/7/8), 860-886.
Veliu, L., & Manxhari, M. (2017). The impact of managerial competencies on business performance: SME’s in Kosovo. Journal of Management, 30(1), 59-65
Venkatesh, K. P., Brito, G., & Kamel Boulos, M. N. (2024). Health digital twins in life science and health care innovation. Annual Review of Pharmacology and Toxicology, 64, 159-170.
Wahid, R. A., & Zulkifli, N. A. (2021). Factors affecting the adoption of digital transformation among SME’s in Malaysia.
Waqar, A., Othman, I., Almujibah, H., Khan, M. B., Alotaibi, S., & Elhassan, A. A. (2023). Factors influencing adoption of digital twin advanced technologies for smart city development: Evidence from Malaysia. Buildings, 13(3), 775.
Wernerfelt, of organizational learning and knowledge management. B. (1984). A resource‐based view of the firm. Strategic Management Journal, 5(2), 171–180. https://doi.org/10.1002/smj.4250050207
Wong., W, J. & Yap, K. H. A. (2024). Factors Influencing the Adoption of Artificial Intelligence in Accounting Among Micro, Small Medium Enterprises (MSMES). Quantum Journal of Social Sciences and Humanities, 5(1), 16-28.
Zian, L. Q., Zulkarnain, N. Z., & Kumar, Y. J. (2024). Challenges in big data adoption for Malaysian organizations: A review. Indonesian Journal of Electrical Engineering and Computer Science, 33(1), 507-517.
Adam, Z. (2021). Industry Briefing: IoT in the Supply Chain & Control Systems Sector.
Ahmad A, Alkhalil A, Altamimi AB et al (2021) Modernizing legacy software as context—sensitive and portable mobile-enabled application. IT Professional, 23:42–50. https://doi.org/10.1109/MITP.2020.2975997
Alsheibani, S., Cheung, Y., & Messom, C. H. (2019, July). Towards An Artificial Intelligence Maturity Model: From Science Fiction To Business Facts. In PACIS (p. 46).
Alsheibani, S., Messom, C., Cheung, Y., & Alhosni, M. (2020). Artificial Intelligence Beyond the Hype: Exploring the Organisation Adoption Factors.
Amin, A., Bhuiyan, M. R. I., Hossain,R., Molla, C., Poli, T. A., & Milon, M. N. U. (2024). The adoption of Industry 4.0 technologies by using the technology organizational environment framework: The mediating role to manufacturing performance in a developing country. Business Strategy & Development, 7(2), e363.
Amthiou, H., Arioua, M., & Benbarrad, T. (2023). Digital Twins in Industry 4.0: A Literature Review. In ITM Web of Conferences (Vol. 52, p. 01002). EDP Sciences.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.
Barney, J., Wright, M., & Ketchen Jr, D. J. (2001). The resource-based view of the firm: Ten years after 1991. Journal of Management, 27(6), 625-641.
Bråthen, M., & Doan, E. (2021). Ambidexterity to overcome the challenges of digital transformation A Bibliometric Review (Master's thesis, OsloMet–Oslo Metropolitan University).
Bustinza, O.F., Vendrell-Herrero, F. and Gomes, E. (2020), “Unpacking the effect of strategic ambidexterity on performance: a cross-country comparison of MMNEs developing product service innovation”, International Business Review, Vol. 29 No. 6, 101569, doi: 10.1016/j.ibusrev. 2019.01.004.
Büyüközkan, G., & Göçer, F. (2018). Digital Supply Chain: Literature review and a proposed framework for future research. Computers in Industry, 97, 157-177.
Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM, 54(8), 88-98.
Federation of Malaysian Manufacturers Directory (2020), "FMM directory of Malaysian industries", Available at: www.fmm.org.my/
Freel, M. S. (2005). Patterns of innovation and skills in small firms. Technovation, 25(2), 123-134.
García-Hurtado, D., Devece, C., Zegarra-Saldaña, P. E., & Crisanto-Pantoja, M. (2024). Ambidexterity in entrepreneurial universities and performance measurement systems. A literature review. International Entrepreneurship and Management Journal, 20(1), 345-366.
Ghani, E. K., Ariffin, N., & Sukmadilaga, C. (2022). Factors Influencing Artificial Intelligence Adoption in Publicly Listed Manufacturing Companies: A Technology, Organisation, and Environment Approach. International Journal of Applied Economics, Finance and Accounting, 14(2), 108-117.
González-Varona, J. M., Poza, D., Acebes, F., Villafáñez, F., Pajares, J., & López-Paredes, A. (2020). New business models for sustainable spare parts logistics: A case study. Sustainability, 12(8), 3071.
Ghobakhloo, M., & Ching, N. T. (2019). Adoption of digital technologies of smart manufacturing in SMEs. Journal of Industrial Information Integration, 16, 100107.
Green, S. B. (1991). How many subjects does it take to do a regression analysis? Multivariate Behavioral Research, 26(3), 499-510. https://doi.org/10.1207/s15327906mbr2603_7
Grieves, M. (2002, October). Completing the Cycle: Using PLM Information in the Sales and Service Functions [Slides]. In SME Management Forum.
Gui, A., Fernando, Y., Shaharudin, M. S., Mokhtar, M., & Karmawan, I. G. M. (2020). Cloud Computing Adoption Using TOE Framework for Indonesia’s Micro Small Medium Enterprises. JOIV: International Journal on Informatics Visualization, 4(4), 237-242.
Hadjimanolis, A. (2000). A resource-based view of innovativeness in small firms. Technology Analysis & Strategic Management, 12(2), 263-281.
Hairol’azaha, A.S. (2022, November 30). Advancing Malaysia’s Manufacturing Industry with Digitalisation [Press release]. https://www.tmone.com.my/press-release/
Hashem, G., Aboelmaged, M., & Ahmad, I. (2024). Proactiveness, knowledge management capability and innovation ambidexterity: An empirical examination of digital supply chain adoption. Management Decision, 62(1), 129-162.
Horváth, D., & Szabó, R. Z. (2019). Driving forces and barriers of Industry 4.0: Do multinational and small and medium-sized companies have equal opportunities?. Technological Forecasting and Social Change, 146, 119-132.
Helfat, C. E. (2007). Stylized facts, empirical research and theory development in management. Strategic Organization, 5(2), 185-192.
Jiang, Y., Yin, S., Li, K., Luo, H., & Kaynak, O. (2021). Industrial applications of digital twins. Philosophical Transactions of the Royal Society A, 379(2207), 20200360.
Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006). A review of the predictors, linkages, and biases in IT innovation adoption research. Journal of Information Technology, 21(1), 1-23.
Joel, O. S., Oyewole, A. T., Odunaiya, O. G., & Soyombo, O. T. (2024). Navigating the digital transformation journey: Strategies for startup growth and innovation in the digital era. International Journal of Management & Entrepreneurship Research, 6(3), 697-706.
Juckers, A., Knerr, P., Harms, F., & Strube, J. (2024). Digital Twin Enabled Process Development, Optimization and Control in Lyophilization for Enhanced Biopharmaceutical Production. Processes, 12(1), 211.
Katsoulakis, E., Wang, Q., Wu, H., Shahriyari, L., Fletcher, R., Liu, J., ... & Deng, J. (2024). Digital twins for health: A scoping review. Digital Medicine, 7(1), 77.
Khadka, R., Batlajery, B. V., Saeidi, A. M., Jansen, S., & Hage, J. (2014, May). How do professionals perceive legacy systems and software modernization?. In Proceedings of the 36th International Conference on Software Engineering (pp. 36-47).
Khin, S., & Ho, T. C. (2019). Digital technology, digital capability and organizational performance: A mediating role of digital innovation. International Journal of Innovation Science, 11(2), 177-195.
Khin, S., & Kee, D. K M. (2022). Identifying the driving and moderating factors of Malaysian SMEs’ readiness for Industry 4.0. International Journal of Computer Integrated Manufacturing, 35(7), 761-779.
Kortmann, S. (2012). The relationship between organizational structure and organizational ambidexterity: A comparison between manufacturing and service firms. Springer Science & Business Media.
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. Ifac-PapersOnline, 51(11), 1016-1022.
Kumar, A., & Krishnamoorthy, B. (2020). Business analytics adoption in firms: A qualitative study elaborating TOE framework in India. International Journal of Global Business and Competitiveness, 15(2), 80-93.
Lutfi, A., Al-Khasawneh, A. L., Almaiah, M. A., Alshira’h, A. F., Alshirah, M. H., Alsyouf, A., ... & Ali, R. A. (2022). Antecedents of big data analytic adoption and impacts on performance: Contingent effect. Sustainability, 14(23), 15516.
Maroufkhani, P., Tseng, M. L., Iranmanesh, M., Ismail, W. K. W., & Khalid, H. (2020). Big data analytics adoption: Determinants and performances among small to medium-sized enterprises. International Journal of Information Management, 54, 102190.
Meet, R. K., Kala, D., & Al-Adwan, A. S. (2022). Exploring factors affecting the adoption of MOOC in Generation Z using extended UTAUT2 model. Education and Information Technologies, 27(7), 10261-10283.
Möhring, M., Keller, B., Radowski, C. F., Blessmann, S., Breimhorst, V., & Müthing, K. (2022, June). Empirical insights into the challenges of implementing digital twins. In Human Centred Intelligent Systems: Proceedings of KES-HCIS 2022 Conference (pp. 229-239). Singapore: Springer Nature Singapore.
Parrott, A., & Warshaw, L. (2017). Industry 4.0 and the digital twin. Deloitte Insights.
Preda, G. (2014). Organizational ambidexterity and competitive advantage: Toward a research model. Management & Marketing-Craiova, (1), 67-74.
Qasem, Y. A., Asadi, S., Abdullah, R., Yah, Y., Atan, R., Al-Sharafi, M. A., & Yassin, A. A. (2020). A multi-analytical approach to predict the determinants of cloud computing adoption in higher education institutions. Applied Sciences, 10(14), 4905.
Raj, A., Dwivedi, G., Sharma, A., de Sousa Jabbour, A. B. L., & Rajak, S. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. International Journal of Production Economics, 224, 107546
Renko, M., Carsrud, A., & Brännback, M. (2009). The effect of a market orientation, entrepreneurial orientation, and technological capability on innovativeness: A study of young biotechnology ventures in the United States and in Scandinavia. Journal of Small Business Management, 47(3), 331-369.
Ridzuan (2023, January 17). The Rise of Artificial Intelligence in Malaysia: Exploring Trends and Opportunities. Avalatble at: https://deeplearningmy.com/the-rise-of-artificial-intelligence-in-malaysia-exploring-trends-and-opportunities/
Rogers, E. M. (2003). Diffusion of Innovations. (5th Ed.). New York, US. The Free Press.
Rojo, A., Llorens-Montes, J., & Perez-Arostegui, M. N. (2016). The impact of ambidexterity on supply chain flexibility fit. Supply Chain Management: An International Journal, 21(4), 433-452.
Rosen, R., Von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015). About the importance of autonomy and digital twins for the future of manufacturing. Ifac-papersonline, 48(3), 567-572.
Saleh, R. H., Durugbo, C. M., & Almahamid, S. M. (2023). What makes innovation ambidexterity manageable: A systematic review, multi-level model and future challenges. Review of Managerial Science, 17(8), 3013-3056.
Santoro, G., Bresciani, S., & Papa, A. (2020). Collaborative modes with cultural and creative industries and innovation performance: the moderating role of heterogeneous sources of knowledge and absorptive capacity. Technovation, 92, 102040.
Shi, P., & Yan, B. (2016). Factors affecting RFID adoption in the agricultural product distribution industry: empirical evidence from China. SpringerPlus, 5(1), 1-11.
Sintharapantorn, V., Saengnoree, A., Teerawatananond, T., & Simcox, J. A. (2023). An Inferential Statistical Analysis of the Key Factors Influencing the Adoption of New Technology by MEEC (MICE) Organizers in Thailand. International Journal of Professional Business Review, 8(7), e02681-e02681.
Soto-Acosta, P., Popa, S., & Martinez-Conesa, I. (2018). Information technology, knowledge management and Environmental dynamism as drivers of innovation ambidexterity: A study in SMEs. Journal of Knowledge Management, 22(4), 824-849.
Stahlberg, E. A., Abdel-Rahman, M., Aguilar, B., Asadpoure, A., Beckman, R. A., Borkon, L. L., ... & Zervantonakis, I. (2022). Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation. Frontiers in Digital Health, 4, 1007784.
Tao, F., Cheng, J.,Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94, 3563-3576.
Thong, J. Y. (1999). An integrated model of information systems adoption in small businesses. Journal of Management Information Systems, 15(4), 187-214.
Tortorella, G. L., Giglio, R., & Van Dun, D. H. (2019). Industry 4.0 adoption as a moderator of the impact of lean production practices on operational performance improvement. International Journal of Operations & Production Management, 39(6/7/8), 860-886.
Veliu, L., & Manxhari, M. (2017). The impact of managerial competencies on business performance: SME’s in Kosovo. Journal of Management, 30(1), 59-65
Venkatesh, K. P., Brito, G., & Kamel Boulos, M. N. (2024). Health digital twins in life science and health care innovation. Annual Review of Pharmacology and Toxicology, 64, 159-170.
Wahid, R. A., & Zulkifli, N. A. (2021). Factors affecting the adoption of digital transformation among SME’s in Malaysia.
Waqar, A., Othman, I., Almujibah, H., Khan, M. B., Alotaibi, S., & Elhassan, A. A. (2023). Factors influencing adoption of digital twin advanced technologies for smart city development: Evidence from Malaysia. Buildings, 13(3), 775.
Wernerfelt, of organizational learning and knowledge management. B. (1984). A resource‐based view of the firm. Strategic Management Journal, 5(2), 171–180. https://doi.org/10.1002/smj.4250050207
Wong., W, J. & Yap, K. H. A. (2024). Factors Influencing the Adoption of Artificial Intelligence in Accounting Among Micro, Small Medium Enterprises (MSMES). Quantum Journal of Social Sciences and Humanities, 5(1), 16-28.
Zian, L. Q., Zulkarnain, N. Z., & Kumar, Y. J. (2024). Challenges in big data adoption for Malaysian organizations: A review. Indonesian Journal of Electrical Engineering and Computer Science, 33(1), 507-517.
Published
2024-12-01
How to Cite
CHIN PON, Eng; LING TAN, Cheng.
The Nexus Between Strategic Resources, Innovation Ambidexterity and Digital Twin Adoption Intention: A Conceptual Paper.
International Journal of Business and Technology Management, [S.l.], v. 6, n. 4, p. 409-424, dec. 2024.
ISSN 2682-7646.
Available at: <https://myjms.mohe.gov.my/index.php/ijbtm/article/view/28494>. Date accessed: 15 jan. 2025.
Section
English Section
