Managing Financial Dependence Through AI: A Resource Dependence Theory-Based Study in Textile SMEs
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https://doi.org/10.14419/w8ct1e09
Received date: August 25, 2025
Accepted date: September 2, 2025
Published date: September 6, 2025
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Resource Dependence Theory; Financial Dependence; Artificial Intelligence; Textile; SMEs -
Abstract
This qualitative study explores how finance managers in textile SMEs utilize artificial intelligence (AI) to manage financial resource dependence, framed within the lens of Resource Dependence Theory (RDT), providing preliminary insights in an area where qualitative research remains scarce. Conducted through in-depth interviews with AI-using finance managers from seven textile enterprises based in Istanbul, Türkiye, the research investigates how AI influences access to and reliance on external financial resources such as short-term loans, leasing, and supplier credit. The findings reveal that AI-supported decision-making contributes to strategic financial planning, reduces dependence on short-term credit, and enables proactive resource management. Themes such as data-driven investment timing, AI-based customer risk scoring, and internal cash flow optimization emerged from the analysis. The study also highlights the critical role that managerial competence in leveraging AI plays in transforming traditional financial behavior and dependency structures. By taking into account sector-specific constraints such as foreign exchange input costs and seasonal demand fluctuations, this study extends the theoretical application of RDT in the context of digital finance. While AI may reduce certain types of financial dependency, it may also introduce new forms of technology and data reliance that require deliberate managerial oversight. The research findings suggest that SMEs attempting to reduce their financial exposure with the help of AI are developing a reliance on software companies. This dependency is expected to increase over time, potentially limiting businesses' ability to make critical decisions.
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References
- Hidayat, M. R. Y., Defitri, S. Y., & Hilman, H. (2024). The Impact of Artificial Intelligence (AI) on Financial Management. Management Studies and Business Journal (PRODUCTIVITY), 1(1), 123–129. https://doi.org/10.62207/s298rx18
- Najem, R., Bahnasse, A., Fakhouri Amr, M., & Talea, M. (2025). Advanced AI and big data techniques in E-finance: a comprehensive survey. Dis-cover Artificial Intelligence, 5(1), 102. https://doi.org/10.1007/s44163-025-00365-y
- Vuković, D. B., Dekpo-Adza, S., & Matović, S. (2025). AI integration in financial services: a systematic review of trends and regulatory challenges. Humanities and Social Sciences Communications, 12(1), 1-29. https://doi.org/10.1057/s41599-025-04850-8
- Abwa, W., Shah, B., Louis, B., & Apenteng, B. (2024). Examining the Association Between the Market Factors and Hospital Characteristics and AI Use in Hospitals, Using the Resource Dependency Theory.
- Antony, J., Sony, M., Garza-Reyes, J. A., McDermott, O., Tortorella, G., Jayaraman, R., ... & Maalouf, M. (2023). Industry 4.0 benefits, challenges and critical success factors: a comparative analysis through the lens of resource dependence theory across continents and economies. Journal of Manufacturing Technology Management, 34(7), 1073-1097. https://doi.org/10.1108/JMTM%E2%80%9110%E2%80%912022%E2%80%910371
- Pfeffer, J. and Salancik, G. R., The External Control of Organizations: A Resource Dependence Perspective (1978). University of Illinois at Urba-na-Champaign's Academy for Entrepreneurial Leadership Historical Research Reference in Entrepreneurship, Available at SSRN: https://ssrn.com/abstract=1496213
- Nienhüser, W. (2008). Resource dependence theory—How well does it explain behavior of organizations? Management Revue, 19(1–2), 9–32. https://doi.org/10.5771/0935-9915-2008-1-2-9
- Buchanan, L. (1992). Vertical trade relationships: the role of dependence and symmetry in attaining organizational goals. Journal of Marketing Re-search, 29(1), 65-75. https://doi.org/10.1177/002224379202900106
- Davis, G. F., & Powell, W. W. (1992). Organization-environment relations. Handbook of industrial and organizational psychology, 3, 315-375.
- Hillman, A. J., Withers, M. C., & Collins, B. J. (2009). Resource dependence theory: A review. Journal of management, 35(6), 1404-1427. https://doi.org/10.1177/0149206309343469
- Davis, G. F., & Adam Cobb, J. (2010). Chapter 2 Resource dependence theory: Past and future. Stanford's organization theory renaissance, 1970–2000, 21-42. https://doi.org/10.1108/S0733%E2%80%91558X(2010)0000028006
- Finkelstein, S. (1997). Interindustry merger patterns and resource dependence: A replication and extension of Pfeffer (1972). Strategic Manage-ment Journal, 18(10), 787-810. https://doi.org/10.1002/(SICI)1097-0266(199711)18:10%3c787::AID-SMJ913%3e3.0.CO;2-R
- Drees, J. M., & Heugens, P. P. (2013). Synthesizing and extending resource dependence theory: A meta-analysis. Journal of Management, 39(6), 1666–1698. https://doi.org/10.1177/0149206312471391
- 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
- Abousaber, I., & Abdalla, H. F. (2023). Review of using technologies of artificial intelligence in companies. International Journal of Communica-tion Networks and Information Security, 15(1), 101-108.
- Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. Interna-tional journal of information management, 53, 102104.
- Ughulu, P. D. (2022). The role of Artificial intelligence (AI) in Starting, automating and scaling businesses for Entrepreneurs. ScienceOpen Pre-prints. https://doi.org/10.51505/ijebmr.2025.9119
- Mohammed, I. A., Sofia, R., Radhakrishnan, G. V., Jha, S., & Al Said, N. (2024). The Role of Artificial Intelligence in Enhancing Business Effi-ciency and Supply Chain Management. DOI: 10.52783/jisem. v10i10s.1413
- Kotter, J. P. (1979). Managing external dependence. Academy of management Review, 4(1), 87-92. https://doi.org/10.5465/amr.1979.4289188
- Ahangar, M. N., Farhat, Z. A., & Sivanathan, A. (2025). AI trustworthiness in manufacturing: challenges, toolkits, and the path to Industry 5.0. Sensors, 25(14), 4357. https://doi.org/10.3390/s25144357
- Fahrezi, M. (2024). A Systematic Literature Review: The Use of Artificial Intelligence and Machine Learning in Financial Risk Management and Predictive Analytics. International Journal of Research and Applied Technology (INJURATECH), 4(2), 60-72.
- Akande, J. O. (2024). Usıng artifıcial intelligence (aı) and deep learning technıques in financial risk management. International Journal of Social and Educational Innovation (IJSEIro), 11(22), 233-246. https://doi.org/10.5281/zenodo.14721978
- Ahmed, S., Alshater, M. M., El Ammari, A., & Hammami, H. (2022). Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61, 101646. https://doi.org/10.1016/j.ribaf.2022.101646
- Stake, R. E. (1995), The Art of Case Study Research, Sage Publications, USA.
- Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches (3rd ed.). SAGE Publications.
- Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE Publications.
- Kachlami, H., & Yazdanfar, D. (2016). Determinants of SME growth: The influence of financing pattern. An empirical study based on Swedish data. Management research review, 39(9), 966-986. https://doi.org/10.1108/MRR-04-2015-0093
- Eniola, A. A., & Entebang, H. (2015). Small and medium business management-financial sources and difficulties. International Letters of Social and Humanistic Sciences, 58, 49-57. doi: 10.18052/www.scipress.com/ILSHS.58.49
- Ključnikov, A. (2023). The role of executive characteristics in their evaluation of financial conditions of European SMEs. DOI: 10.15240/tul/001/2023-2-005
- Chen, X. (2019). Antecedents of Technological Diversification: A Resource Dependence Logic. Journal of Open Innovation: Technology, Market, and Complexity, 5(4), 80. https://doi.org/10.3390/joitmc5040080
- 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. https://doi.org/10.1016/j.techsoc.2024.102733
- Hessels, J., & Terjesen, S. (2010). Resource dependency and institutional theory perspectives on direct and indirect export choices. Small business economics, 34(2), 203-220. DOI 10.1007/s11187-008-9156-4
- Balzano, M., Marzi, G., & Turzo, T. (2025). SMEs and institutional theory: major inroads and opportunities ahead. Management Decision, 63(13), 1-27. https://www.emerald.com/insight/0025-1747.htm
- Mubeen, S. K., Vafaei-Zadeh, A., Amran, A., & Ruiqi, C. (2025). Adoption of Internet of Things (IoT) among manufacturing SMEs in developing countries: a TOE framework perspective. International Journal of Communication Networks and Distributed Systems, 31(4), 428-456. https://doi.org/10.1504/IJCNDS.2025.147310
- Yin, R. K. (2003), Applications of Case Study Research (2nd ed.), Sage, Thousand Oaks, CA.
- Buyun, B. (2024). Does Exchange Rate Volatility Affect the Bank Lending Channel? Journal of Economic Policy Researches / İktisat Politikası Araştırmaları Dergisi, 11(1), 51–61. DOI :10.26650/JEPR1343548
- Condronegoro, A. & Hasibuan, H. T. (2023). Leverage, Firm Size, Likuiditas, Financial Distress, dan Aktivitas Hedging Dengan Instrumen Derivatif. E-Jurnal Akuntansi, 33 (8), 2102-2116. https://doi.org/10.24843/EJA.2023.V33.I08.P10
- Khan, B.F., & Siddiqui, D.A. (2023). The Effect of Financial Leverage, Supply Chain Finance and Liquidity on Firm Performance in Pakistan: A Comparative Analysis of Cement, Textile, Pharmaceutical and Sugar Sectors. Social Science Research Network (SSRN)Electronic Journal. http://dx.doi.org/10.2139/ssrn.4432266
- Ihuoma, N., Okeke, N.I., Bakare, O.A., & Achumie, G.O. (2024). Artificial Intelligence in SME financial decision-making: Tools for enhancing efficiency and profitability. Open Access Research Journal of Multidisciplinary Studies, 08(01), 150–163. https://doi.org/10.53022/oarjms.2024.8.1.0056
- Abdul-Azeez, O., Ogadimma, A., & Idemudia, C. (2024). Promoting financial inclusion for SMEs: Leveraging AI and data analytics in the banking sector. International Journal of Multidisciplinary Research Updates, 08(01), 001–014. https://doi.org/10.53430/ijmru.2024.8.1.0037
- Omokhoa, H.E., Odionu, C.S., Azubuike, C., & Sule, A.K. (2024). AI-Powered Fintech innovations for credit scoring, debt recovery, and financial access in Microfinance and SMEs. Gulf Journal of Advance Business Research, 2 (6), 411-422. https://doi.org/10.51594/gjabr.v2i6.55
- Zamil, M.H. (2025). AI-Driven Business Analytics for Financial Forecasting: A Systematic Review of Decision Support Models in SMEs. Review of Applied Science and Technology, 4(02), 86-117. https://doi.org/10.63125/gjrpv442
- Alirezaie, M., Hoffman, W., Zabihi, P., Rahnama, H. & Pentland, A. (2024). Decentralized Data and Artificial Intelligence Orchestration for Transparent and Efficient Small and Medium-Sized Enterprises Trade Financing. Journal of Risk and Financial Management, 17, 38. https://doi.org/10.3390/jrfm17010038
- Tawil, A.H., Mohamed, M.A., Schmoor, X., Vlachos, K., & Haidar, D. (2023). Trends and Challenges Towards an Effective Data-Driven Decision Making in UK SMEs: Case Studies and Lessons Learnt from the Analysis of 85 SMEs. ArXiv, abs/2305.15454. DOI:10.48550/arXiv.2305.15454
- Rawashdeh, A., Bakhit, M., Abaalkhail, L. (2023). Determinants of artificial intelligence adoption in SMEs: The mediating role of accounting au-tomation. International Journal of Data and Network Science, 7, 25–34. DOI: 10.5267/j.ijdns.2022.12.010
- Schönberger, M. (2023). Artificial intelligence for small and medium-sized enterprises: Identifying key applications and challenges. Journal of Business Management, 21, 89-112. DOI: 10.32025/JBM23004
- Kok Wah, J.N. (2025). Revolutionizing SME Financing: AI and Fintech for Transparency, Efficiency and Inclusion. Journal of Southwest Jiaotong University, 60 (2), https://doi.org/10.35741/issn.0258-2724.60.2.6
- Mammadov, H., Ruiz-Gándara, A., González-Abril, L., & Romero, I. 2024. Adoption of Artificial Intelligence in Small and Medium-Sized Enter-prises in Spain: The Role of Competences and Skills. Amfiteatru Economic, 26(67), 848-866. DOI: https://doi.org/10.24818/EA/2024/67/848
- Schwaeke, J., Peters, A., Kanbach, D.K., Kraus, S., & Jones, P. (2024). The new normal: The status quo of AI adoption in SMEs. Journal of Small Business Management, 63, 1297-1331. DOI:10.1080/00472778.2024.2379999
- Peretz-Andersson, E., Tabares, S., Mikalef, P., & Parida, V. (2024). Artificial intelligence implementation in manufacturing SMEs: A resource or-chestration approach. International Journal of Information Management, 77, 102781. https://doi.org/10.1016/j.ijinfomgt.2024.102781
- Oldemeyer, L., Jede, A., & Teuteberg, F. (2025). Investigation of artificial intelligence in SMEs: a systematic review of the state of the art and the main implementation challenges. Management Review Quarterly, 75(2), 1185-1227. https://doi.org/10.1007/s11301-024-00405-4
- Jacobides, M.G., Brusoni, S., & Candelon, F. (2021) The Evolutionary Dynamics of the Artificial Intelligence Ecosystem. Strategy Science, 6(4):412-435. https://doi.org/https://doi.org/10.3929/ethz-b-000512872
- Hoffreumon, C., Forman, C., & Van Zeebroeck, N. (2024). Make or buy your artificial intelligence? Complementarities in technology sourcing. Journal of Economics & Management Strategy, 33(2), 452-479.
- Sharma, G.D., Yadav, A., & Chopra, R. (2020). Artificial intelligence and effective governance: A review, critique and research agenda. https://doi.org/10.1016/j.sftr.2019.100004
- Safari, K., McKenna, L., & Davis, J. (2023). Promoting generalisation in qualitative nursing research using the multiple case narrative approach: A methodological overview. Journal of Research in Nursing, 28(5), 367–381. https://doi.org/10.1177/17449871231194177
- Broderick, K., Vaidyanathan, A., Ponticiello, M. et al. (2024). Generalizing from qualitative data: a case example using critical realist thematic anal-ysis and mechanism mapping to evaluate a community health worker-led screening program in India. Implementation Science, 19 (1), 1-13. https://doi.org/10.1186/s13012-024-01407-2
- Fernandez-Vidal, J., Gonzalez, R., Gasco, J. & Llopis, J. (2022). Digitalization and corporate transformation: The case of European oil & gas firms, Technological Forecasting and Social Change, vol. 174, 121293, https://doi.org/10.1016/j.techfore.2021.121293
- Zhu, S. (2025). Enhancing Competitive Advantage through AI and Digital Technology: A Case Study of Jiangling Motors Group. Asia Pacific Economic and Management Review, 2 (1). https://doi.org/10.62177/apemr.v2i1.150
- Yang, X, Cao, D, Chen, J, Xiao, Z & Daowd, A 2020, 'AI and IoT based collaborative business ecosystem: A case in Chinese fish farming indus-try', International Journal of Technology Management, 82 (2) ,151-171. https://dx.doi.org/10.1504/IJTM.2020.107856
- Reddy, P.K., Gotur, R., & Bhat, V. (2025). Generative AI Adoption in Enterprise: A Comprehensive Case Study Analysis of Implementation Strategies and Outcomes Across Diverse Sectors. 2025 6th International Conference on Recent Advances in Information Technology (RAIT), 1-6. https://doi.org/10.1109/RAIT65068.2025.11089431
- Weng, Y., Wu, J., Kelly, T., & Johnson, W. (2024). Comprehensive Overview of Artificial Intelligence Applications in Modern Industries. ArXiv, abs/2409.13059. https://doi.org/10.48550/arXiv.2409.13059
- Apu, K.U. (2025). AI-Driven Data Analytics and Automation: A Systematic Literature Review of Industry Applications. Strategic Data Manage-ment and Innovation. https://doi.org/10.71292/sdmi.v2i01.9
- López-García, J., & Manrique Rojas, E. (2024). Barriers to AI adoption and their influence on technological advancement in the manufacturing and finance and insurance industries. IEEE Conference Proceedings. DOI: 10.1109/COLCOM62950.2024.10720309
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How to Cite
Karademir, A. Çillioğlu . (2025). Managing Financial Dependence Through AI: A Resource Dependence Theory-Based Study in Textile SMEs. International Journal of Accounting and Economics Studies, 12(5), 209-218. https://doi.org/10.14419/w8ct1e09
