مدیریت استراتژیک هوشمند

مدیریت استراتژیک هوشمند

مدیریت هوشمند خدمات مالی: چارچوبی استراتژیک برای پذیرش هوش مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان
1 گروه کارآفرینی، پردیس بین المللی کیش، دانشگاه تهران
2 گروه کارآفرینی فناورانه، دانشکده کارآفرینی، دانشگاه تهران، تهران، ایران
3 دانشکده کارآفرینی دانشگاه تهران
چکیده
تحول دیجیتال و گسترش هوش مصنوعی (AI) چشم‌انداز صنعت مالی را به‌طور بنیادین دگرگون کرده است. با وجود مزایای بالقوه این فناوری در مدیریت ریسک، اعتبارسنجی و کشف تقلب، بسیاری از پروژه‌های هوش مصنوعی در سازمان‌های مالی به دلیل عوامل سازمانی و محیطی با شکست مواجه می‌شوند. هدف پژوهش حاضر، شناسایی و تبیین عوامل مؤثر بر پذیرش سازمانی هوش مصنوعی در خدمات مالی و ارائه چارچوبی مفهومی چندسطحی است. این پژوهش با رویکرد کیفی و در چارچوب نظریه‌پردازی داده‌بنیاد انجام شد. داده‌ها از طریق ۱۵ مصاحبه نیمه‌ساختاریافته با مدیران ارشد بانکی، فعالان فین‌تک و استادان دانشگاه گردآوری و با روش کدگذاری باز، محوری و انتخابی تحلیل گردید. یافته‌ها نشان داد پذیرش هوش مصنوعی در سه سطح فردی (نگرش، مهارت‌ها و اعتماد کارکنان)، سازمانی (فرهنگ نوآوری، حمایت مدیریت ارشد، منابع داده و زیرساخت‌ها) و محیطی (قوانین، فشار رقابتی و انتظارات مشتریان) شکل می‌گیرد. بر اساس این ابعاد، چارچوبی پارادایمی برای تبیین فرآیند پذیرش سازمانی AI در خدمات مالی ارائه شد. نتایج پژوهش می‌تواند به مدیران و سیاست‌گذاران در طراحی استراتژی‌های تحول دیجیتال و کاهش ریسک‌های فناورانه یاری رساند.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Intelligent Financial Services Management: A Strategic Framework for Artificial Intelligence Adoption

نویسندگان English

Maryam Sahebi 1
Ayatollah Momayez 2
Jahangir Yadollahi Farsi 3
1 Entrepreneurship Department, Kish International Campus, University of Tehran
2 Department of technology Entrepreneurship, Faculty of Entrepreneurship, University of Tehran, Tehran, Iran
3 Faculty of Entrepreneurship, University of Tehran
چکیده English

Digital transformation and the rapid diffusion of Artificial Intelligence (AI) are fundamentally reshaping the financial services industry. Despite the potential benefits of AI in risk management, credit scoring, and fraud detection, many AI initiatives in financial organizations fail due to organizational and environmental challenges. The purpose of this study is to identify and explain the key factors influencing the organizational adoption of AI in financial services and to propose a multi-level conceptual framework. Adopting a qualitative approach within the grounded theory methodology, data were collected through 15 semi-structured interviews with senior bank managers, fintech practitioners, and academic experts. The data were analyzed using open, axial, and selective coding. The findings indicate that AI adoption is shaped by factors at three levels: individual (attitudes, skills, and trust of employees), organizational (innovation culture, top management support, data resources, and infrastructure), and environmental (regulations, competitive pressures, and customer expectations). Based on these dimensions, a paradigmatic model was developed to explain the organizational adoption of AI in financial services. The results provide valuable insights for managers and policymakers in designing digital transformation strategies and mitigating technological risks.

کلیدواژه‌ها English

Artificial Intelligence
Organizational Adoption
Financial Services
Grounded Theory
and ADO Model
طالبی، ک.، داوری، ع.، و دهقان نجم‌آبادی، ع. (۱۴۰۰). طراحی مدل توانمندسازی کسب‌وکارهای کوچک و متوسط دانش‌بنیان با رویکرد دیمتل. فصلنامه انجمن علوم مدیریت ایران، 16(61)، 23-42.
Creswell, J. W. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage.
Denzin, N. K., & Lincoln, Y. S. (2017). The Sage Handbook of Qualitative Research (5th ed.). Sage.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., et al. (2021). ADO model for AI adoption: Theoretical foundations and research agenda. International Journal of Information Management, 57, 102–106.
Financial Stability Board. (2024, November 14). The financial stability implications of artificial intelligence. https://www.fsb.org/2024/11/the-financial-stability-implications-of-artificial-intelligence/
Glaser, B., & Strauss, A. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.
Gomber, P., Koch, J. A., & Siering, M. (2018). Digital finance and fintech: Current research and future research directions. Journal of Business Economics, 88(5), 537–580.
Guest, G., Namey, E., & Chen, M. (2020). A simple method to assess and report thematic saturation in qualitative research. PLoS ONE, 15(5), e0232076.
Hoehle, H., Huff, S., & Goode, S. (2012). The role of continuous trust in information systems: A literature review and research agenda. Journal of Information Technology, 27(1), 1–25.
Jamali, B., Yadollahi Farsi, J., & Mobini, A. (2018). The study on the theories' gap of technological entrepreneurship opportunities emergence. Journal of International Business Research, 11(2), 79–88.
Kim, Y., et al. (2024). Determinants of generative AI system adoption and usage. arXiv preprint. https://doi.org/10.48550/arXiv.2404
Kvale, S., & Brinkmann, S. (2015). InterViews: Learning the Craft of Qualitative Research Interviewing (3rd ed.). Sage.
Mariani, M., & Borghi, M. (2023). Industry 4.0 and Artificial Intelligence adoption: Managing technological change in tourism and hospitality. Technological Forecasting and Social Change, 186, 122–135.
Mobini, A. D., MohammadKazemi, R., & Baghestani, B. (2017). IT-enabled value co-creation process for product design. In Internet of Things Business Models, Users, and Networks Conference. IEEE.
Momayez A, Ghasemi SA, Ghasemi Sf. Factors Affecting Women's Entrepreneurship in Business. Roshd-e-Fanavari 2013; 9(35): 15-22
Momayez, A., Rasouli, N., Alimohammadirokni, M. and Rasoolimanesh, S.M. (2023), “Green entrepreneurship orientation, green innovation and hotel performance: the moderating role of managerial environmental concern”, Journal of Hospitality Marketing and Management, Vol. 32 No. 8, pp. 981-1004, doi: 10.1080/19368623.2023.2225495.
Nafisi, F., & MohammadKazemi, R. (2024). Providing an open innovation model for high-tech startups in the unit of industries related to information technology. International Journal of Nonlinear Analysis and Applications, 15(4), 159–172.
Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. Electronic Journal of Information Systems Evaluation, 14(1), 110–121.
Organisation for Economic Co-operation and Development. (2024, September 2). Regulatory approaches to artificial intelligence in finance. https://www.oecd.org/finance/regulatory-approaches-to-ai-in-finance.htm
Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Research, 14(3), 224–235.
PwC. (2017). Sizing the prize: What’s the real value of AI for your business and how can you capitalise? PricewaterhouseCoopers.
Rai, A., Constantinides, P., & Sarker, S. (2019). Next-generation digital platforms: Toward human–AI hybrids. MIS Quarterly, 43(1), iii–ix.
RAND Corporation. (2024). Artificial Intelligence in Financial Services: Opportunities and Risks. RAND Report.
Rasouli, N., Rasoolimanesh, S. M., Rahmani, A. K., Momayez, A., & Torabi, M. A. (2022). Effects of customer forgiveness on brand betrayal and brand hate in restaurant service failures: does apology letter matter? Journal of Hospitality Marketing & Management, 31(6), 662–687.
Rasouli, N., Rasoolimanesh, S. M., Alimohammadirokni, M., & Momayez, A. (2025). The effect of perceived brand betrayal on brand hate, avoidance-like and attack-like strategies: A comparative study of customers with/without past negative experiences. International Journal of Hospitality Management, 126, 104056
Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.
Shahidi, M. H., R. T., Shabankareh, N., and Momayez, A(2013). Investigating the impact of performance management on human resource performance across head offices of Agricultural Bank branches in Tehran. International Journal of Academic Research in Business and Social Sciences, 3(8), 177–187. https://doi.org/10.6007/IJARBSS/v3-i8/135
SME Finance Forum. (2024). State of AI in financial services annual report – 2024. International Finance Corporation (World Bank Group). https://www.smefinanceforum.org/post/state-of-ai-in-financial-services-annual-report-2024
Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). Sage.
Tornatzky, L. G., & Fleischer, M. (1990). The Processes of Technological Innovation. Lexington Books.
U.S. Department of the Treasury. (2024, December 6). Artificial intelligence in financial services. https://home.treasury.gov/news/press-releases/jy2235
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443–488.
Zhu, K., Kraemer, K. L., & Xu, S. (2006). The process of innovation assimilation by firms in different countries: A technology diffusion perspective on e-business. Management Science, 52(10), 1557–1576.