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External and internal pressures are undermining the traditional bond between less significant financial institutions (LSFIs) and their local markets. The purpose of this paper is to investigate the impacts of service quality (SQ), motivation, satisfaction, and perceived value on customers’ behavioral intentions (BI) in LSFIs.

A conceptual framework of the relationship, either direct or indirect, between SQ, basic motivational determinants in consumers’ bank selection, satisfaction, and repurchase intentions of the customers is developed. The framework is tested with data from a sample of 600 customers of LSFIs in Italy. The hypotheses were tested with structural equation modeling techniques.

Consumers’ decision-making process is a complex path and a more holistic view of its determinants should be considered to face those weaknesses that negatively affect a balanced management of LSFIs in the future.

The study is focused on a specific context, the Italian LSFIs’ sector, and the results from other countries or other banking sectors should be added before a generalization of the findings can be made.

The findings will be useful to policy makers and banks to improve strategies for enhancing customer satisfaction and purchase intentions in LSFIs. Adopting a simultaneous, holistic, and multivariate approach can also help LSFIs to better understand the main factors which are able to increase consumers’ purchase intentions.

This is the first study to examine customers’ BI in LSFIs even if they represent about 96 percent of the European banking industry.

This paper aims to verify the presence of a management model that confirms or not the one size fits all hypothesis expressed in terms of risk-return. This study will test the existence of stickiness phenomena and discuss the relevance of business model analysis integration with the risk assessment process.

The sample consists of 60 credit institutions operating in Europe for 20 years of observations. This study proposes a classification of banks’ business models (BMs) based on an agglomerative hierarchical clustering algorithm analyzing their performance according to risk and return dimensions. To confirm BM stickiness, the authors verify the tendency and frequency with which a bank migrates to other BMs after exogenous events.

The results show that it is impossible to define a single model that responds to the one size fits all logic, and there is a tendency to adapt the BM to exogenous factors. In this context, there is a propensity for smaller- and medium-sized institutions to change their BM more frequently than larger institutions.

Quantitative metrics seem to be only able to represent partially the intrinsic dynamics of BMs, and to include these metrics, it is necessary to resort to a holistic view of the BM.

This paper provides evidence that BMs’ stickiness indicated in the literature seems to weaken in conjunction with extraordinary events that can undermine institutions’ margins.