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Strategic Decision-Making in the AI Era: An Integrated Approach Classical, Adaptive, Resource-Based, and Processual Views

Yıl 2025, Cilt: 9 Sayı: 17, 67 - 97, 28.02.2025
https://doi.org/10.29064/ijma.1637935

Öz

This study explores how artificial intelligence (AI) can enhance strategic decision-making by integrating with four established strategic schools: Classical, Adaptive, Resource-Based, and Processual. While AI improves data-driven insights, it lacks the strategic foresight, contextual awareness, and ethical judgment inherent in traditional frameworks. Using a structured literature review, this conceptual study evaluates the synergy between AI and strategic schools. Sources were selected from peer-reviewed databases, including Scopus and Web of Science, using keywords such as "AI-driven strategy," "strategic management," and "decision support systems." The findings reveal that AI enhances Classical strategy through predictive analytics and scenario planning, strengthens Adaptive strategy via real-time responsiveness, supports RBV by optimizing resource identification, and complements Processual strategy by facilitating continuous learning. However, AI’s limitations in handling tacit knowledge, ethical considerations, and contextual judgment highlight the need for human oversight. This study proposes a hybrid framework where AI supports, rather than replaces, strategic decision-making. It offers actionable recommendations for business leaders, including AI-powered strategy frameworks, governance policies for ethical AI deployment, and human-AI collaboration to navigate dynamic business environments effectively.

Kaynakça

  • Ahmed, A., Alshurideh, M., & Al Kurdi, B. (2021). Digital transformation and organizational operational decision making: A systematic review. Springer. DOI: 10.1007/978-3-030-58669-0_63
  • Arthur, W. B. (1994). Increasing returns and path dependence in the economy. University of Michigan Press.
  • Ansoff, H. I. (1965). Corporate strategy: An analytic approach to business policy for growth and expansion. McGraw-Hill.
  • Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108
  • Barney, J. B., & Clark, D. (2007). Resource-based theory: Creating and sustaining competitive advantage. Oxford University Press.
  • Barney, J. B., & Mackey, T. B. (2016). Testing resource-based theory. Managerial and Decision Economics, 37(1), 1-17. https://doi.org/10.1016/S1479-8387(05)02001-1
  • Boateng, P. A., Owusu, J., & Yeboah, N. (2024). Strategic decision support systems for enhancing competitive advantage in small and medium enterprises. IEEE Xplore. https://ieeexplore.ieee.org/document/10779545
  • Bourgeois, L. J., & Eisenhardt, K. M. (1988). Strategic decision processes in high-velocity environments. Management Science, 34(7), 816-835. https://doi.org/10.1287/mnsc.34.7.816
  • Brown, S. L., & Eisenhardt, K. M. (1997). The art of continuous change: Linking complexity theory and time-paced evolution in relentlessly shifting organizations. Administrative Science Quarterly, 42(1), 1-34. https://doi.org/10.2307/2393807
  • Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W.W. Norton & Company.
  • Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 95(4), 3-11. https://hbr.org/2017/07/the-business-of-artificial-intelligence
  • Brynjolfsson, E., McAfee, A., & Rock, D. (2019). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. The Economics of Artificial Intelligence: An Agenda, 23(1), 23-42. https://doi.org/10.7208/9780226613475-003
  • Burgelman, R. A. (1991). Intraorganizational ecology of strategy making and organizational adaptation: Theory and field research. Organization Science, 2(3), 239–262. DOI: 10.1287/orsc.2.3.239
  • Chandler, A. D. (1962). Strategy and structure: Chapters in the history of the industrial enterprise. MIT Press.
  • Child, J. (1972). Organizational structure, environment, and performance: The role of strategic choice. Sociology, 6(1), 1-22. https://doi.org/10.1177/003803857200600101
  • Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868-1884. https://doi.org/10.1111/poms.12838
  • Chui, M., Manyika, J., & Miremadi, M. (2018). What AI can and can’t do (yet) for your business. McKinsey Quarterly. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/what-ai-can-and-cant-do-yet-for-your-business
  • Davenport, T., Guha, A., & Grewal, D. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42. https://doi.org/10.1007/s11747-019-00696-0
  • Davenport, T. H., Guha, A., & Grewal, D. (2020). How artificial intelligence will transform management. Academy of Management Perspectives, 34(2), 123-139. https://doi.org/10.5465/amp.2019.0057
  • Davenport, T. H., Guha, A., & Grewal, D. (2020). How artificial intelligence will change strategic management. Journal of Business Research, 125, 100-110. DOI: 10.1016/j.jbusres.2020.03.033
  • Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116. https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
  • Dhamija, P., & Bag, S. (2020). The role of artificial intelligence in operations: A review and bibliometric analysis. The TQM Journal, 32(4), 869-896. https://doi.org/10.1108/TQM-10-2019-0243 Donthu, S., Acharya, B., Hassan, M., & Prasad, S. (2024). HR analytics: Leveraging big data to drive strategic decision-making in human resource management. ResearchGate. https://www.researchgate.net/publication/381904059
  • Doz, Y. L., & Kosonen, M. (2010). Embedding strategic agility: A leadership agenda for accelerating business model renewal. Long Range Planning, 43(2-3), 370-382. https://doi.org/10.1016/j.lrp.2009.07.006
  • Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision-making in the era of big data–evolution, challenges, and research agenda. International Journal of Information Management, 48, 63-71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
  • Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: What are they?. Strategic Management Journal, 21(10-11). https://doi.org/10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-E
  • Ferrara, E. (2023). Fairness and bias in artificial intelligence: A brief survey. Science, 6(1), 3-21. DOI: 10.3390/sci6010003
  • Fountain, J. E. (2022). The moon, the ghetto, and artificial intelligence: Reducing systemic racism in computational algorithms. Government Information Quarterly. https://doi.org/10.1016/j.giq.2021.101600
  • Georgewill, I. A., & Gabriel, P. D. I. (2024). Artificial intelligence and predictive analytics: Revolutionizing strategic business insights in the digital era. ResearchGate. https://www.researchgate.net/publication/389173872
  • Ghemawat, P. (1991). Commitment: The dynamic of strategy. Free Press.
  • Grant, R. M. (1991). The resource-based theory of competitive advantage: Implications for strategy formulation. California Management Review, 33(3), 114-135. https://doi.org/10.2307/41166664
  • Grant, R. M. (2016). Contemporary strategy analysis: Text and cases edition (9th ed.). Wiley.
  • Hannan, M. T., & Freeman, J. (1984). Structural inertia and organizational change. American Sociological Review, 49(2), 149-164. DOI: 10.2307/2095567
  • Hendriksen, C. (2023). Artificial intelligence for supply chain management: Disruptive innovation or innovative disruption? Journal of Supply Chain Management. DOI: 10.1111/jscm.12304
  • Helfat, C. E., & Martin, J. A. (2015). Dynamic managerial capabilities: Review and assessment of managerial impact on strategic change. Journal of Management, 41(5), 1281-1312. https://doi.org/10.1177/0149206314561301
  • Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775-788. https://doi.org/10.1080/09537287.2020.1768450
  • Ivanov, D., & Dolgui, A. (2021). OR models for coping with supply chain disruptions in the wake of COVID-19. European Journal of Operational Research, 291(3), 1-15. https://doi.org/10.1007/s12063-021-00194-z
  • Keding, C. (2021). Understanding the interplay of artificial intelligence and strategic management: Four decades of research in review. Management Review Quarterly, 71(4), 713-745. https://doi.org/10.1007/s11301-020-00181-x
  • Makridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60. https://doi.org/10.1016/j.futures.2017.03.006
  • McGrath, R. G. (2013). The end of competitive advantage: How to keep your strategy moving as fast as your business. Harvard Business Review Press.
  • McKinsey & Company. (2019). Artificial intelligence: The next digital frontier? https://www.mckinsey.com/featured-insights/artificial-intelligence/the-next-digital-frontier
  • Mikalef, P., Gupta, M., Pappas, I. O., & Krogstie, J. (2021). Exploring the interplay between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 58(2), 103393. https://doi.org/10.1016/j.im.2020.103393
  • Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103–120. https://doi.org/10.1016/j.im.2021.103434
  • Mikalef, P., Pappas, I., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: A systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547-578. https://doi.org/10.1007/s10257-017-0362-y
  • Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2021). Artificial intelligence capabilities and their impact on firm performance. Information & Management, 58(3), 103442. DOI: 10.1016/j.im.2020.103442
  • Mintzberg, H. (1994). The rise and fall of strategic planning: Reconceiving roles for planning, plans, planners. Free Press.
  • Mintzberg, H., Ahlstrand, B. W., & Lampel, J. (2005). Strategy safari: A guided tour through the wilds of strategic management. Free Press.
  • Mintzberg, H., Ahlstrand, B. W., & Lampel, J. (2020). Strategy safari: A guided tour through the wilds of strategic management (3rd ed.). Pearson Education.
  • Mintzberg, H., & Waters, J. A. (1985). Of strategies, deliberate and emergent. Strategic Management Journal, 6(3), 257-272. https://doi.org/10.1002/smj.4250060306
  • Neiroukh, S., Emeagwali, O. L., & Aljuhmani, H. Y. (2024). Artificial intelligence capability and organizational performance: Unraveling the mediating mechanisms of decision-making processes. Emerald Insight. https://www.emerald.com/insight/content/doi/10.1108/MD-10-2023-1946/full/html
  • Nikseresht, A., Hajipour, B., & Pishva, N. (2022). Using artificial intelligence to make sustainable development decisions considering VUCA: A systematic literature review. Environmental Science and Pollution Research, 29(41), 61334-61354. https://doi.org/10.1007/s11356-022-19863-y
  • Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press.
  • Nordström, M. (2022). AI under great uncertainty: Implications for public policy. AI & Society. DOI: 10.1007/s00146-021-01263-4
  • Nyakuchena, N., & Tsikada, C. (2024). Enhancing supply chain resilience through artificial intelligence and machine learning: A systematic literature review and framework. IGI Global. https://www.igi-global.com/chapter/enhancing-supply-chain-resilience-through-artificial-intelligence-and-machine-learning/359827
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Strategic Decision-Making in the AI Era: An Integrated Approach Classical, Adaptive, Resource-Based, and Processual Views

Yıl 2025, Cilt: 9 Sayı: 17, 67 - 97, 28.02.2025
https://doi.org/10.29064/ijma.1637935

Öz

This study explores how artificial intelligence (AI) can enhance strategic decision-making by integrating with four established strategic schools: Classical, Adaptive, Resource-Based, and Processual. While AI improves data-driven insights, it lacks the strategic foresight, contextual awareness, and ethical judgment inherent in traditional frameworks. Using a structured literature review, this conceptual study evaluates the synergy between AI and strategic schools. Sources were selected from peer-reviewed databases, including Scopus and Web of Science, using keywords such as "AI-driven strategy," "strategic management," and "decision support systems." The findings reveal that AI enhances Classical strategy through predictive analytics and scenario planning, strengthens Adaptive strategy via real-time responsiveness, supports RBV by optimizing resource identification, and complements Processual strategy by facilitating continuous learning. However, AI’s limitations in handling tacit knowledge, ethical considerations, and contextual judgment highlight the need for human oversight. This study proposes a hybrid framework where AI supports, rather than replaces, strategic decision-making. It offers actionable recommendations for business leaders, including AI-powered strategy frameworks, governance policies for ethical AI deployment, and human-AI collaboration to navigate dynamic business environments effectively.

Kaynakça

  • Ahmed, A., Alshurideh, M., & Al Kurdi, B. (2021). Digital transformation and organizational operational decision making: A systematic review. Springer. DOI: 10.1007/978-3-030-58669-0_63
  • Arthur, W. B. (1994). Increasing returns and path dependence in the economy. University of Michigan Press.
  • Ansoff, H. I. (1965). Corporate strategy: An analytic approach to business policy for growth and expansion. McGraw-Hill.
  • Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108
  • Barney, J. B., & Clark, D. (2007). Resource-based theory: Creating and sustaining competitive advantage. Oxford University Press.
  • Barney, J. B., & Mackey, T. B. (2016). Testing resource-based theory. Managerial and Decision Economics, 37(1), 1-17. https://doi.org/10.1016/S1479-8387(05)02001-1
  • Boateng, P. A., Owusu, J., & Yeboah, N. (2024). Strategic decision support systems for enhancing competitive advantage in small and medium enterprises. IEEE Xplore. https://ieeexplore.ieee.org/document/10779545
  • Bourgeois, L. J., & Eisenhardt, K. M. (1988). Strategic decision processes in high-velocity environments. Management Science, 34(7), 816-835. https://doi.org/10.1287/mnsc.34.7.816
  • Brown, S. L., & Eisenhardt, K. M. (1997). The art of continuous change: Linking complexity theory and time-paced evolution in relentlessly shifting organizations. Administrative Science Quarterly, 42(1), 1-34. https://doi.org/10.2307/2393807
  • Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W.W. Norton & Company.
  • Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 95(4), 3-11. https://hbr.org/2017/07/the-business-of-artificial-intelligence
  • Brynjolfsson, E., McAfee, A., & Rock, D. (2019). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. The Economics of Artificial Intelligence: An Agenda, 23(1), 23-42. https://doi.org/10.7208/9780226613475-003
  • Burgelman, R. A. (1991). Intraorganizational ecology of strategy making and organizational adaptation: Theory and field research. Organization Science, 2(3), 239–262. DOI: 10.1287/orsc.2.3.239
  • Chandler, A. D. (1962). Strategy and structure: Chapters in the history of the industrial enterprise. MIT Press.
  • Child, J. (1972). Organizational structure, environment, and performance: The role of strategic choice. Sociology, 6(1), 1-22. https://doi.org/10.1177/003803857200600101
  • Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868-1884. https://doi.org/10.1111/poms.12838
  • Chui, M., Manyika, J., & Miremadi, M. (2018). What AI can and can’t do (yet) for your business. McKinsey Quarterly. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/what-ai-can-and-cant-do-yet-for-your-business
  • Davenport, T., Guha, A., & Grewal, D. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42. https://doi.org/10.1007/s11747-019-00696-0
  • Davenport, T. H., Guha, A., & Grewal, D. (2020). How artificial intelligence will transform management. Academy of Management Perspectives, 34(2), 123-139. https://doi.org/10.5465/amp.2019.0057
  • Davenport, T. H., Guha, A., & Grewal, D. (2020). How artificial intelligence will change strategic management. Journal of Business Research, 125, 100-110. DOI: 10.1016/j.jbusres.2020.03.033
  • Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116. https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
  • Dhamija, P., & Bag, S. (2020). The role of artificial intelligence in operations: A review and bibliometric analysis. The TQM Journal, 32(4), 869-896. https://doi.org/10.1108/TQM-10-2019-0243 Donthu, S., Acharya, B., Hassan, M., & Prasad, S. (2024). HR analytics: Leveraging big data to drive strategic decision-making in human resource management. ResearchGate. https://www.researchgate.net/publication/381904059
  • Doz, Y. L., & Kosonen, M. (2010). Embedding strategic agility: A leadership agenda for accelerating business model renewal. Long Range Planning, 43(2-3), 370-382. https://doi.org/10.1016/j.lrp.2009.07.006
  • Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision-making in the era of big data–evolution, challenges, and research agenda. International Journal of Information Management, 48, 63-71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
  • Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: What are they?. Strategic Management Journal, 21(10-11). https://doi.org/10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-E
  • Ferrara, E. (2023). Fairness and bias in artificial intelligence: A brief survey. Science, 6(1), 3-21. DOI: 10.3390/sci6010003
  • Fountain, J. E. (2022). The moon, the ghetto, and artificial intelligence: Reducing systemic racism in computational algorithms. Government Information Quarterly. https://doi.org/10.1016/j.giq.2021.101600
  • Georgewill, I. A., & Gabriel, P. D. I. (2024). Artificial intelligence and predictive analytics: Revolutionizing strategic business insights in the digital era. ResearchGate. https://www.researchgate.net/publication/389173872
  • Ghemawat, P. (1991). Commitment: The dynamic of strategy. Free Press.
  • Grant, R. M. (1991). The resource-based theory of competitive advantage: Implications for strategy formulation. California Management Review, 33(3), 114-135. https://doi.org/10.2307/41166664
  • Grant, R. M. (2016). Contemporary strategy analysis: Text and cases edition (9th ed.). Wiley.
  • Hannan, M. T., & Freeman, J. (1984). Structural inertia and organizational change. American Sociological Review, 49(2), 149-164. DOI: 10.2307/2095567
  • Hendriksen, C. (2023). Artificial intelligence for supply chain management: Disruptive innovation or innovative disruption? Journal of Supply Chain Management. DOI: 10.1111/jscm.12304
  • Helfat, C. E., & Martin, J. A. (2015). Dynamic managerial capabilities: Review and assessment of managerial impact on strategic change. Journal of Management, 41(5), 1281-1312. https://doi.org/10.1177/0149206314561301
  • Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775-788. https://doi.org/10.1080/09537287.2020.1768450
  • Ivanov, D., & Dolgui, A. (2021). OR models for coping with supply chain disruptions in the wake of COVID-19. European Journal of Operational Research, 291(3), 1-15. https://doi.org/10.1007/s12063-021-00194-z
  • Keding, C. (2021). Understanding the interplay of artificial intelligence and strategic management: Four decades of research in review. Management Review Quarterly, 71(4), 713-745. https://doi.org/10.1007/s11301-020-00181-x
  • Makridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60. https://doi.org/10.1016/j.futures.2017.03.006
  • McGrath, R. G. (2013). The end of competitive advantage: How to keep your strategy moving as fast as your business. Harvard Business Review Press.
  • McKinsey & Company. (2019). Artificial intelligence: The next digital frontier? https://www.mckinsey.com/featured-insights/artificial-intelligence/the-next-digital-frontier
  • Mikalef, P., Gupta, M., Pappas, I. O., & Krogstie, J. (2021). Exploring the interplay between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 58(2), 103393. https://doi.org/10.1016/j.im.2020.103393
  • Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103–120. https://doi.org/10.1016/j.im.2021.103434
  • Mikalef, P., Pappas, I., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: A systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547-578. https://doi.org/10.1007/s10257-017-0362-y
  • Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2021). Artificial intelligence capabilities and their impact on firm performance. Information & Management, 58(3), 103442. DOI: 10.1016/j.im.2020.103442
  • Mintzberg, H. (1994). The rise and fall of strategic planning: Reconceiving roles for planning, plans, planners. Free Press.
  • Mintzberg, H., Ahlstrand, B. W., & Lampel, J. (2005). Strategy safari: A guided tour through the wilds of strategic management. Free Press.
  • Mintzberg, H., Ahlstrand, B. W., & Lampel, J. (2020). Strategy safari: A guided tour through the wilds of strategic management (3rd ed.). Pearson Education.
  • Mintzberg, H., & Waters, J. A. (1985). Of strategies, deliberate and emergent. Strategic Management Journal, 6(3), 257-272. https://doi.org/10.1002/smj.4250060306
  • Neiroukh, S., Emeagwali, O. L., & Aljuhmani, H. Y. (2024). Artificial intelligence capability and organizational performance: Unraveling the mediating mechanisms of decision-making processes. Emerald Insight. https://www.emerald.com/insight/content/doi/10.1108/MD-10-2023-1946/full/html
  • Nikseresht, A., Hajipour, B., & Pishva, N. (2022). Using artificial intelligence to make sustainable development decisions considering VUCA: A systematic literature review. Environmental Science and Pollution Research, 29(41), 61334-61354. https://doi.org/10.1007/s11356-022-19863-y
  • Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press.
  • Nordström, M. (2022). AI under great uncertainty: Implications for public policy. AI & Society. DOI: 10.1007/s00146-021-01263-4
  • Nyakuchena, N., & Tsikada, C. (2024). Enhancing supply chain resilience through artificial intelligence and machine learning: A systematic literature review and framework. IGI Global. https://www.igi-global.com/chapter/enhancing-supply-chain-resilience-through-artificial-intelligence-and-machine-learning/359827
  • Oliveira, F., Kakabadse, N., & Khan, N. (2022). Board engagement with digital technologies: A resource dependence framework. Journal of Business Research, 144, 151-162. https://doi.org/10.1016/j.jbusres.2021.10.010
  • Peteraf, M. A. (1993). The cornerstones of competitive advantage: A resource-based view. Strategic Management Journal, 14(3), 179-191. https://doi.org/10.1002/smj.4250140303
  • Pettigrew, A. (1985). The awakening giant: Continuity and change in Imperial Chemical Industries. Wiley-Blackwell.
  • Porter, M. E. (1980). Competitive strategy: Techniques for analyzing industries and competitors. Free Press.
  • Rodriguez-Garcia, P., Lopez-Lopez, D., & Juan, A. A. (2023). Strategic decision-making in smart home ecosystems: A review on the use of artificial intelligence and Internet of things. Internet of Things, 21, 100623. https://doi.org/10.1016/j.iot.2023.100623
  • Sull, D., Homkes, R., & Sull, C. (2015). Why strategy execution unravels—and what to do about it. Harvard Business Review, 93(3), 57-66.
  • Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15-42. https://doi.org/10.1177/0008125619867910
  • Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40-49. https://doi.org/10.1016/j.lrp.2017.06.007
  • Teece, D. J. (2018). Dynamic capabilities as (workable) management systems theory. Journal of Management & Organization, 24(4), 476-486. https://doi.org/10.1017/jmo.2017.75
  • Teece, D. J. (2020). Fundamental issues in strategy: Time to reassess? Strategic Management Review, 1(1), 103-144. https://doi.org/10.1561/111.00000001
  • Teece, D. J., Peteraf, M. A., & Leih, S. (2016). Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California Management Review, 58(4), 13-35. https://doi.org/10.1525/cmr.2016.58.4.13
  • Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533. https://doi.org/10.1002/smj.4250180702
  • Tegmark, M. (2017). Life 3.0: Being human in the age of artificial intelligence. Alfred A. Knopf.
  • Trunk, A., Birkel, H., & Hartmann, E. (2020). On the current state of combining human and artificial intelligence for strategic organizational decision making. Business Research. DOI: 10.1007/s40685-020-00133-x
  • Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2017). How big data analytics can enable sustainable business models for digital enterprises: A framework and empirical insights. Technological Forecasting and Social Change, 142, 183-198.
  • Wenzel, M., Stanske, S., & Lieberman, M. B. (2021). Strategic responses to crisis: A framework for environmental adaptation. Business Horizons, 64(2), 221-229. https://doi.org/10.1016/j.bushor.2020.11.002
  • Wenzel, M., Stanske, S., & Lieberman, M. (2021). Strategic responses to crisis: The role of improvisation, foresight, and agility in managing COVID-19 disruptions. Journal of Management Studies, 58(1), 173-188.
  • Wernerfelt, B. (1984). A resource‐based view of the firm. Strategic Management Journal, 5(2), 171-180. https://doi.org/10.1002/smj.4250050207
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  • Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96(4), 114-123. Accessed via https://hometownhealthonline.com/wp-content/uploads/2019/02/ai2-R1804J-PDF-ENG.pdf
Toplam 73 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Strateji, Uluslararası İşletme
Bölüm Teorik Makale
Yazarlar

Harun Büber 0000-0002-3447-6272

Emrullah Seven 0000-0002-2820-9434

Erken Görünüm Tarihi 26 Şubat 2025
Yayımlanma Tarihi 28 Şubat 2025
Gönderilme Tarihi 11 Şubat 2025
Kabul Tarihi 26 Şubat 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 9 Sayı: 17

Kaynak Göster

APA Büber, H., & Seven, E. (2025). Strategic Decision-Making in the AI Era: An Integrated Approach Classical, Adaptive, Resource-Based, and Processual Views. International Journal of Management and Administration, 9(17), 67-97. https://doi.org/10.29064/ijma.1637935

Dergide aşağıdaki alanların kapsamına giren nitelikli çalışmalar yayımlanabilir;

İşletme, İktisat, Çalışma Ekonomisi ve Endüstri İlişkileri, Maliye, Kamu Yönetimi, Uluslararası İlişkiler ve Siyaset Bilimi, Ekonometri, Yönetim Bilişim Sistemleri, Eğitim Yönetimi, Sağlık Yönetimi, Turizm Yönetimi, Havacılık Yönetimi, Denizcilik İşletmeleri Yönetimi, Mühendislik ve Teknoloji Yönetimi, Enerji Yönetimi, Lojistik Yönetimi, Çevre Yönetimi, Medya ve İletişim Yönetimi, Afet Yönetimi, Multidisipliner Yönetim ve Ekonomi Çalışmaları.

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