The evolution of bank branch performance assessment processes and methods: Insights from a systematic review

This study synthesizes ۱۳۳ peer-reviewed articles (۱۹۹۲-۲۰۲۴) to map the methodological and thematic evolution of bank branch performance evaluation. Dominated by Data Envelopment Analysis (DEA) (۳۵% of studies), the field has progressed from foundational efficiency models to advanced frameworks integrating network structures, dynamic intertemporal analysis, and hybrid techniques (e.g., fuzzy-DEA, game theory-DEA). Stochastic Frontier Analysis (SFA) complements DEA by isolating managerial inefficiencies from statistical noise, while fuzzy logic and grey systems address data uncertainty in service quality and customer satisfaction metrics. Emerging machine learning applications (e.g., ANN, random forests) signal a paradigm shift toward predictive analytics, though adoption remains nascent (۹% of post-۲۰۲۰ studies). Key findings reveal DEA's enduring centrality, the underutilization of sustainability metrics, and a critical gap in real-time, IoT-driven efficiency models. Thematic clusters highlight the interplay between operational rigor (DEA/SFA), contextual adaptability (fuzzy/spatial models), and technological innovation (AI/ML), offering a roadmap for researchers to reconcile methodological diversity with banking's evolving digital and ESG imperatives.

کلیدواژه ها:

Bank Branches Efficiency ، Performance Evaluation ، Bibliometric Analysis ، Systematic Literature Review ، Content Analysis