Understanding the relationships between bank-customer relations, financial advisory services and saving behavior

Abstract: While the saving environment has become more complex in recent years, so has the demand for individual activity. Important impetuses include financial deregulation, globalization, technological change, and reformed pension systems. Financial institutions can provide financial advisory services to help their customers to obtain positive net benefits by avoiding mistakes and using economies of scale, and they can also attract and maintain their customers by creating strong relationships. Earlier studies show that the incentive structure often leads to advice that is not to the benefit of the customers. In addition, not all customers seek and receive advisory services. The objective of this study is to increase our understanding of the relationship between the bank advisor – customer relation and the bank customer’s saving behavior.The scope of the study is to analyze relevant theories and develop a model that includes financial advisors as a mediator of saving behavior, and to understand the relational attributes that can affect saving behavior. Also, the characteristics of customers with a relational versus a transactional exchange form with the bank are explored. Given the problems of establishing causality, the scope is also to understand the impact of the relationship and, in particular, face-to-face advisory meetings on saving behavior. The analysis is mainly carried out with the help of customer data – both objective bank register data and subjective survey data – while the advisor characteristics are to a lesser extent part of the data material.Five studies are carried out using various methodologies, i.e., theoretical review and model development, probit and multinomial logistic regressions, difference-in-difference regression, and structural equation modelling. In addition, a case study is made analyzing dyads of customers and advisors in order to explore theoretical assumptions. Economics and relationship marketing are used to explain saving behavior with transactional, interimistic relational, and enduring relational exchange forms (Paper 1). Several major findings emerge in the quantitative analysis: First, the attributes are longer and stronger, the more relational the exchange form is (Paper 2). A second finding is that relational attributes also surface in transactional exchange, a finding that requires further research to be understood in more detail (Paper 2). Third, among relational attributes, duration and context have the largest total effects on saving behavior, while trust is a mediating variable (Paper 5). Fourth, not only demographic and socioeconomic factors can predict whether customers use the relational exchange form; psychological factors, such as saving motives and risk attitudes, are also predictors. Results are clearly different for women and men (Paper 3). Finally, financial advisory meetings are found to increase saving volumes and saving products held in stock. The largest effects are found for young customers with low wealth and low profitability to the bank, i.e., customers who initially have low activity levels and thus create a large potential (Paper 4).Limitations include endogeneity problems in general, and selection bias in particular, making it difficult to establish causality, and internal and external validity. Future research should focus on data management, especially building time series with enhanced methods to adjust for selection bias. In addition, studies to better understand the transactional exchange form are needed, as well as studies that deepen the definition of relational exchange, not least when alternative channels to face-to-face meetings include mobile banking and internet banking, and the digitalization of  the social know-how of financial advisors.Managerial implications include understanding the relational attributes that affect saving behavior, such as context, duration, and trust. Also useful to know are the factors that can help to predict the probability of a customer’s having a transactional or relational exchange form, i.e., including demographics, socioeconomics, psychology, and gender, to see how channels and customers can be better matched. Policy implications include using the model in this study to match relational attributes to the degree of financial literacy, since the risk of misselling is particularly large for relational-oriented customers with low financial literacy.