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The purpose of this paper is to provide a customer lifetime value (CLV) model to carefully assess and classify banking customers using individual measures and covering customers’ relationships with a portfolio of products of the company.

The proposed model comprises two sub-models: (sub-model 1) modelling and prediction of CLV in a multiproduct context using Hierarchical Bayesian models as input to (sub-model 2) a value-based segmentation specially designed to manage customers and products using the latent class regression. The model is tested using real transaction data of 1,357 customers of a bank.

This research demonstrates which drivers of customer value better predict the contribution margin and product usage for each of the products considered in order to get the CLV measure. Using this measure, the model implements a value-based segmentation, which helps banks to facilitate the process of customer management.

Previous CLV models are mostly conceptual, generalisation is one of their main concerns, are usually focussed on single product categories using aggregated customer data, and they are not design with a special emphasis on their application as support for managerial decisions. In response to these drawbacks, the proposed model will enable decision makers to improve the understanding of the value of each customer and their behaviour towards different financial products.

The purpose of this paper is to examine the impact of customers’ offline transaction behaviour in the form of loyalty and cross-buying on the adoption of self-service technology innovations by non-business customers in the context of online banking.

This study extends the Diffusion of Innovation Theory, as well as the Technology Acceptance Model adapted to describe and model individual customer observed behaviours in the pre-adoption stage of the adoption process. The Log-logistic parametric survival model is applied using panel data for 1,357 randomly selected new customers from a bank.

Significant differences arise among customers’ behaviours related to periodicity of interactions with the bank and quantity of products involved in the interactions, as well as convenience and risk of the interactions. The results corroborate that those customers who are more likely to adopt the online banking faster show an offline behavioural pattern more related to higher periodicity of interactions and convenience, rather than a high number of products involved in their interactions, the use of high-risk products or the maintenance of a higher average monthly liabilities.

While previous research explaining the process of adoption of the online channel has mainly focused on the analysis of customers’ attitudes (i.e. customers’ perceptions) and demographics, in this research an additional explanation is proposed using customers’ offline transaction behaviours. In addition, there is a considerable amount of research about the adoption of new technologies, but there is a scarcity of studies looking specifically at the financial services and banking industry.

The purpose of this paper is to profile grocery retailers in terms of seven value types based on Holbrook’s value typology; to link these value types to three key outcomes (i.e. satisfaction, repurchase intention, and word-of-mouth); and to evaluate the impact of the retail format on performance and importance of the seven value types.

For each retail format, the authors administered a consumer survey, resulting in an aggregate sample of 392 respondents. The authors used partial least squares structural equations modeling to test the relationships between the value types and key outcomes (i.e. importance) and ANOVAs to examine cross-format differences between latent variable scores of the value types (i.e. performance).

The three retail formats included in the study perform differently on Holbrook’s value types (e.g. non-discounters excel in terms of aesthetic value and play, compared to hard and soft discounters). Furthermore, this study reveals that the strategic importance of each value type depends on the key outcome (e.g. whereas efficiency is the main source of satisfaction, play mainly drives the other two outcomes).

The authors randomly assigned respondents to one of the three retail formats irrespective of their personal preference or patronage. To conduct value-based segmentation, respondents should evaluate either their preferred format or all supermarkets.

This study offers positioning advice to retail managers, according to their format and strategic objectives.

Unlike previous research, this paper provides a cross-format comparison of retailers based on a three-dimensional value typology and its key outcomes.

Customer satisfaction continues to be an important topic in the financial services industry. However, there is an increasing awareness that customer satisfaction as such is not enough. Distinguishes between overall satisfied customers and latently dissatisfied customers; the latter being those customers who, although reporting satisfaction in a survey, have other characteristics (i.e. satisfaction with specific service items and/or socio‐demographic characteristics) that resemble dissatisfied customers. The identification of these latently dissatisfied customers may function as an early warning signal. Indeed, their probability to defect is relatively high and can be compared to that of dissatisfied customers. Proposes a data mining technique called “characteristic rules” to identify latently dissatisfied customers of a Belgian bank. Appropriate marketing actions (dissatisfaction management) may help to avoid these customers leaving. Therefore, the objective of this study is to provide scholars and business managers with theoretical, methodological and managerial insights into identifying latently dissatisfied customers.

As many businesses faced economic disruption due to the Covid-19 pandemic and sought financial relief, existing bank relationships became critical to getting a loan. This study examines factors associated with the development of personal relationships of rural small businesses with community bank representatives.

We applied a mixed-method approach. We employed descriptive statistics, principal factor analysis and logistic regression for data analysis. We distributed an online survey to rural small businesses in five states in the United States. Key informant interviews with community bank representatives supplemented the survey results.

A business owner’s trust in a banker was positively associated with the establishment of a business–bank relationship. However, an analysis of individual trust’s components revealed that the nature of trust is complex, and a failure of one or more components may lead to decreased trustworthiness in a banker. Small businesses that preferred personal communication with a bank were more inclined to relationship banking.

Due to the relatively small sample size and cross-sectional data, our results may not be conclusive but should be viewed as preliminary and as suggestions for future research. Bankers should be aware of the importance of trust for small business owners and of the actions that lead to increased trustworthiness.

The study extends the existing knowledge on the business–bank relationship by focusing mainly on social (instead of economic) factors associated with the establishment of the business–bank relationship in times of crisis and high uncertainty.

The purpose of this paper is to examine the potential role of money supply and agricultural informal cross-border trade (ICBT) in Uganda’s food price processes.

The econometric analysis is based on two separate but complementary approaches: vector error correction modeling and Granger causality testing.

The results indicate that long-run domestic food prices adjust to money supply, agricultural output and exchange rate movements. However, the findings do not provide sufficient evidence to support the proposition that agricultural ICBT is an important long-run driver of food price in Uganda. The pair-wise Granger causality test results reveal a unidirectional causality from food prices to agricultural output; unidirectional causality from money supply to food prices; bidirectional causality between food prices and nominal exchange rates; unidirectional causality running from rainfall to food prices; and unidirectional causality running from agricultural ICBT to agricultural output.

Understanding the underlying drivers of food inflation is critically important because food prices are critically important for food security, social stability and general household welfare.

The major innovation in this paper is attempt to model demand side determinants of food prices by focusing on the role of money and ICBT.