Application of Attride-Stirling Thematic Analysis in Examining the Indicators of a FinTech Model for Improving Asset Quality in the Banking Industry, Considering the Role of Financial Strategy and Financial Inclusion Based on Competition–Stability Theory

Zahra  Kamrani; Azita Jahanshad; Masoumeh  Alavi

Authors

Zahra Kamrani Department of Accounting, CT.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0009-0000-0107-8406
Azita Jahanshad Department of Accounting, CT.C., Islamic Azad University, Tehran, Iran https://orcid.org/0000-0002-6897-937X
Masoumeh Alavi Department of Accounting, CT.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0000-0001-9556-7277

Keywords:

FinTech, asset quality, digital financial inclusion, financial strategy, competition–stability theory, banking industry

Abstract

The present study aimed to examine and identify the indicators of a financial technology (FinTech) model for improving asset quality in the banking industry, with emphasis on the role of financial strategy and financial inclusion based on competition–stability theory. For this purpose, a qualitative approach based on Attride-Stirling’s thematic network analysis was employed using ATLAS.ti software. The statistical population of the study consisted of 18 experts and specialists in banking and financial technology, whose perspectives were collected through in-depth interviews and a review of the relevant literature. The thematic analysis process was conducted in six structured stages, resulting in the extraction and classification of concepts. Ultimately, 108 open codes (basic themes), 27 first-level organizing themes, and 9 second-level organizing themes were extracted, all of which were integrated and explained under one global theme. The identified organizing themes included “FinTech technology maturity,” “asset quality management,” “bank financial strategy,” “digital financial inclusion,” “competition in the banking ecosystem,” “bank financial stability,” “FinTech governance and regulation,” “customer experience and trust,” and “intelligent bank performance.” Assessment of the validity and reliability of the qualitative analysis using inter-coder agreement indices, including Holsti’s coefficient (0.887), Scott’s Pi (0.755), Cohen’s kappa (0.725), and Krippendorff’s alpha (0.817), indicated highly satisfactory levels of credibility, dependability, transferability, and confirmability of the research findings. The results indicate that the application of advanced FinTech capabilities, such as artificial intelligence, intelligent credit assessment, and big data analytics, together with the adoption of optimal financial strategies and the expansion of financial inclusion, can moderate the consequences of competition within the framework of competition–stability theory, thereby reducing non-performing loans, improving the structure of resource allocation, and ultimately enhancing resilience and asset quality within the national banking network.

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