Journal Screenshot

International Journal of Academic Research in Economics and Management Sciences

Open Access Journal

ISSN: 2226-3624

Reimagining Strategic Foresight through the Lens of Digital Technology Adoption

Hasliza Abdul Halim, Haniruzila Md Hanifah, Ali Waqas, Noor Hazlina Ahmad

http://dx.doi.org/10.6007/IJAREMS/v15-i1/27890

Open access

Purpose: This study aims to conceptualize the role of emerging digital technologies—Artificial Intelligence (AI), Internet of Things (IoT), Big Data Analytics (BDA), Blockchain, and Social Media Applications in developing strategic foresight among small and medium-sized enterprises (SMEs). In an increasingly volatile and technology-driven business environment, the study highlights how the adoption of these technologies enhances firms’ ability to anticipate, interpret, and act upon future changes. Methodology: The paper adopts a conceptual and theoretical approach grounded in Dynamic Capability Theory (DCT). It synthesizes current literature, integrates findings from previous empirical works, and develops a conceptual framework linking digital technology adoption to strategic foresight dimensions, namely environmental scanning, strategic choice capability, and integration capability. The proposed framework will be validated through a mixed-method design involving qualitative interviews with SME managers and quantitative analysis using Smart PLS and NVivo for triangulation. Findings: The conceptual model proposes that advanced digital technologies collectively enhance SMEs’ strategic foresight. Specifically, AI and BDA provide predictive insights; IoT enhances real-time situational awareness; blockchain ensures data transparency and trust; and social media platforms strengthen environmental scanning and stakeholder intelligence. Together, these technologies enable SMEs to move from reactive to proactive strategies, fostering innovation, adaptability, and resilience. Limitations: As a conceptual study, empirical validation is yet to be conducted. Future research should test the proposed relationships across different sectors and regions. Moreover, contextual factors such as digital maturity, regulatory environment, and organizational culture may moderate the effects of technology adoption on foresight. Practical Implications: The study offers actionable insights for SME policymakers, managers, and technology developers. It suggests that structured foresight workshops, scenario planning, and trend monitoring can be enhanced through digital technologies. Policymakers can use the framework to guide SME digital transformation programs that emphasize long-term strategic agility instead of short-term survival. Originality/Value: This study extends Dynamic Capability Theory by positioning digital technology adoption as a critical enabler of strategic foresight in SMEs. It bridges a theoretical gap by explaining how multiple emerging technologies interact to build future-oriented decision-making capabilities, thus advancing the discourse on digital transformation and foresight integration in resource-constrained firms.

Amuna, Y. M. A., Al Shobaki, M. J., & Naser, S. S. A. (2017). Strategic environmental scanning: An approach for crises management. International Journal of Information Technology and Electrical Engineering, 6(3), 28-34.
Arroyabe, M. F., Arranz, C. F., De Arroyabe, I. F., & de Arroyabe, J. C. F. (2024). Analyzing AI adoption in European SMEs: A study of digital capabilities, innovation, and external environment. Technology in Society, 79, 102733.
Asmai, S. A., Almansoori, B. S. A., & Kamalrudin, M. (2022). Mediation Model of Strategic Foresight Influencing Dynamic Capability. International Journal of Sustainable Construction Engineering and Technology, 13(2), 120-133.
Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 102225.
Bytniewski, A., Matouk, K., Rot, A., Hernes, M., & Kozina, A. (2020). Towards industry 4.0: Functional and technological basis for ERP 4.0 systems. Towards Industry 4.0—Current Challenges in Information Systems, 3-19.
Danneels, E. (2008). Organizational antecedents of second?order competences. Strategic management journal, 29(5), 519-543.
Demneh, M. T., Zackery, A., & Nouraei, A. (2023). Using corporate foresight to enhance strategic management practices. European Journal of Futures Research, 11(1), 5.
Flaih, L. H., & Chalab, I. D. (2022). Strategic Foresight And Its Impact On Strategic Agility: An Analytical Study Of The Opinions Of A Sample Of University Leaders In Private Universities In The Middle Euphrates Region. Journal of Positive School Psychology, 3154-3167.
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049-1064.
Haarhaus, T., & Liening, A. (2020). Building dynamic capabilities to cope with environmental uncertainty: The role of strategic foresight. Technological forecasting and social change, 155, 120033.
Jelonek, D., Mesjasz-Lech, A., St?pniak, C., Turek, T., & Ziora, L. (2020). The artificial intelligence application in the management of contemporary organization: Theoretical assumptions, current practices and research review. Advances in Information and Communication: Proceedings of the 2019 Future of Information and Communication Conference (FICC), Volume 1,
Kohler, K. (2021). Strategic foresight: Knowledge, tools, and methods for the future. CSS Risk and Resilience Reports.
Kozanoglu, D., Chakraborty, S., Murad, M., & Uddin, A. (2022). Public Procurement, Big Data Analytics Capabilities, and Healthcare Supply Chain Sustainability.
Luu, T. D. (2024). Should SMEs diversify their global destinations? The role of market insights and digital transformation. Marketing Intelligence & Planning, 42(3), 438-458.
Miethke, M., Pieroni, M., Weber, T., Brönstrup, M., Hammann, P., Halby, L., . . . Bode, H. B. (2021). Towards the sustainable discovery and development of new antibiotics. Nature Reviews Chemistry, 5(10), 726-749.
Mikalef, P., Lemmer, K., Schaefer, C., Ylinen, M., Fjørtoft, S. O., Torvatn, H. Y., . . . Niehaves, B. (2022). Enabling AI capabilities in government agencies: A study of determinants for European municipalities. Government information quarterly, 39(4), 101596.
Murphy, J., Vallières, F., Bentall, R. P., Shevlin, M., McBride, O., Hartman, T. K., . . . Gibson-Miller, J. (2021). Psychological characteristics associated with COVID-19 vaccine hesitancy and resistance in Ireland and the United Kingdom. Nature communications, 12(1), 29.
Nikou, S., De Reuver, M., & Mahboob Kanafi, M. (2022). Workplace literacy skills—how information and digital literacy affect adoption of digital technology. Journal of Documentation, 78(7), 371-391.
Paliokait?, A. (2013). LONG TERM NATIONAL CHALLENGES FACING LITHUANIA’S ECONOMY AND SOCIETY. In: Background Discussion Paper to Support Development of Smart Specialisation ….
Paramesha, M., Rane, N., & Rane, J. (2024). Big data analytics, artificial intelligence, machine learning, internet of things, and blockchain for enhanced business intelligence. Artificial Intelligence, Machine Learning, Internet of Things, and Blockchain for Enhanced Business Intelligence (June 6, 2024).
Prentice, C., & Nguyen, M. (2020). Engaging and retaining customers with AI and employee service. Journal of Retailing and Consumer Services, 56, 102186.
Qahtan, F. A., & Al Himyari, B. A. (2022). The interactive role of organizational flexibility among strategic foresight and organizational excellence: an analytical research on the southern cement company. The Journal of Modern Project Management, 10(2), 228-239.
Rane, N., Choudhary, S., & Rane, J. (2024). Artificial intelligence (AI), internet of things (IoT), and blockchain-powered chatbots for improved customer satisfaction, experience, and loyalty. Internet of Things (IoT), and blockchain-powered chatbots for improved customer satisfaction, experience, and loyalty (May 29, 2024).
Rhisiart, M., Miller, R., & Brooks, S. (2015). Learning to use the future: developing foresight capabilities through scenario processes. Technological Forecasting and Social Change, 101, 124-133.
Sallam, K., Mohamed, M., & Mohamed, A. W. (2023). Internet of Things (IoT) in supply chain management: challenges, opportunities, and best practices. SMIJ, 2(2), 32.
Sjödin, D., Parida, V., & Kohtamäki, M. (2023). Artificial intelligence enabling circular business model innovation in digital servitization: Conceptualizing dynamic capabilities, AI capacities, business models and effects. Technological forecasting and social change, 197, 122903.
Teece, D. J. (2007). Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic management journal, 28(13), 1319-1350.
Vecchiato, R. (2015). Creating value through foresight: First mover advantages and strategic agility. Technological forecasting and social change, 101, 25-36.
Wyrwicka, M. K., & Erdeli, O. (2018). Strategic foresight as the methodology of preparing innovation activities.
Yasmin, M., Tatoglu, E., Kilic, H. S., Zaim, S., & Delen, D. (2020). Big data analytics capabilities and firm performance: An integrated MCDM approach. Journal of business research, 114, 1-15.
Zhang, Z., Shang, Y., Cheng, L., & Hu, A. (2022). Big data capability and sustainable competitive advantage: The mediating role of ambidextrous innovation strategy. Sustainability, 14(14), 8249.

Halim, H. A., Hanifah, H. M., Waqas, A., & Ahmad, N. H. (2026). Reimagining Strategic Foresight through the Lens of Digital Technology Adoption. International Journal of Academic Research in Economics and Management Sciences, 15(1), 492-504.