Managing Financial Dependence Through AI: A Resource Dependence Theory-Based Study in 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, re-duces 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. © 2025 Elsevier B.V., All rights reserved.