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This study aims to establish that the relationship between the risky indebtedness behavior (RIB) of consumers and their attitude toward adopting buy-now-pay-later (BNPL) is not immediate but is mediated through impulse buying. Moreover, it explores how perceived risk moderates the association between the attitude to adopt BNPL and its adoption intention.

This study used the existing theoretical and empirical evidence to propose a model and validated it using the data collected from 339 young shoppers in India. Analysis of data is conducted using partial least squares structural equation modeling.

The study results show that consumers’ RIB is not directly related to their attitude toward BNPL. However, impulse buying fully mediates this relationship, influencing the attitude toward BNPL. Impulse buying and attitude serially mediate the relationship between RIB and BNPL adoption intention. Further, in the context of BNPL, perceived risk strengthens the attitude-intention gap.

This study advises policymakers and BNPL providers to carefully assess users’ creditworthiness to prevent those already in debt from entering into a detrimental loop.

This study provides novel perspectives on consumer’s RIB and BNPL within the Indian context. The study additionally identifies the mediating influence of impulse buying and the moderating effect of perceived risk on BNPL adoption intention.

This research analyzes borrowers' credit utilization through prepayment behavior in peer-to-peer (P2P) lending. The authors investigate factors influencing the decision to prepay and assess the role of P2P lending as an alternative source of consumer credits.

The authors use individual loan-level data from the LendingClub, one of the largest P2P platforms in the USA. The authors use a Logit model and a sample selection model estimated by the two-stage Heckman method. The empirical analysis considers borrower-specific and loan-specific characteristics as well as macroeconomic factors.

The authors present a number of significant findings that can enhance understanding consumers' financing decisions. The authors offer evidence that borrowers are able to take advantage of cheaper loans offered by P2P lending to better manage credit card balance and consolidate debt. The authors find that borrowers tend to prepay P2P loans quickly when the aggregate cost of borrowing is low, suggesting that P2P lending offers an efficient alternative to obtain credit. This is particularly true for creditworthy borrowers that are able to take advantage of competing sources of finance. The authors' results provide evidence that P2P lending can improve consumers' optimal credit utilization.

P2P lending has grown exponentially and has become a significant credit supplier to consumers and small businesses. While the existing literature mostly focuses on default risks, prepayment has received much less attention. This research fills in the gap and investigates borrowers' prepayment behavior in P2P loans and the role of P2P lending as an alternative source of consumer credits.

This paper aims to analyze decisions about payment rates on credit card statements by using background factors and perceptions that indirectly influence beliefs, according to the theory of planned behavior.

Since legal and institutional frameworks and household financial surveys are heterogeneous among countries, household data on the Chilean economy is used as the starting point in this matter.

The probability that an individual chooses to pay amounts less than the total billing of their credit cards rises with essential variables related to perceived behavioral control. Being the head of the household, being younger, perceiving a high or excessive financial burden of debt and facing unfavorable and unexpected situations that divert the budget, among others, are relevant to repayment decisions.

The novelty of this article is that its psychological approach differs from the traditional focus of economic rationality regarding credit cards. The results are relevant for policymakers and financial regulators due to implications for household behavioral finance and means of payment.

Analizamos la decisión de la tasa de pago de los estados de cuenta de tarjetas de crédito a través del uso de factores de fondo y percepciones que indirectamente inciden en las creencias de acuerdo a la teoría del comportamiento planeado.

Debido a que los marcos legales e institucionales, así como también las encuestas financieras de hogares son heterogéneas entre países, se utilizan datos de los hogares de la economía chilena como un punto de partida en esta materia.

La probabilidad de que un individuo elija pagar un monto menor que el total de facturación de sus tarjetas de crédito es afectada por variables proxy asociadas al control conductual percibido. La condición de ser jefe de hogar, ser más joven, la percepción de una alta o excesiva carga financiera de la deuda, y enfrentar situaciones desfavorables e inesperadas que desvían del presupuesto, entre otras, son relevantes para las decisiones de pago.

La novedad de este artículo es que su enfoque difiere del enfoque tradicional de la racionalidad económica en relación a las tarjetas de crédito. Los resultados son relevantes para los hacedores de política y reguladores financieros debido a sus implicancias para las finanzas conductuales de los hogares y sus medios de pago.

This study aims to develop and validate a cross-national framework to identify the motivation underpinning consumers' (i.e. the general public's) loyalty toward credit card usage. The following research questions guided the study: (1) What factors motivate consumers to stay loyal to their credit card? (2) Does the investment model (regarding satisfaction and investment size) mediate the relationship between factors motivating consumers to stay loyal to their credit card?

This study employs the investment model theory (Rusbult, 1980) as a theoretical framework and uses structural equation modeling to develop and validate a cross-national framework, addressing factors that motivate consumers to stay loyal to credit card brands. In addition, the authors test the mediating effect of the investment model on the relationship. Survey data were collected from the United States and France.

The findings revealed four factors (incentives, customer service, investment size and satisfaction) that impact consumer credit card loyalty behavior in the two mature credit card markets. The authors find empirical support for two of four hypotheses. That is, investment size mediates the relationship between incentives and consumer loyalty, and satisfaction mediates the relationship between customer service and consumer loyalty. Moreover, unlike the French sample, the American sample produced a significant finding for investment size to mediate the relationship between customer service and consumer loyalty.

This paper validates and extends the investment model theory in the marketing of credit cards within a cross-national setting. Most studies on credit card consumption focus on the college student segment, and there is less understanding of the motivation to stay loyal to using a credit card from the general public who are not necessarily college students. Given the scarce stream of empirical studies dealing with cross-national consumer motivation, choice criteria of credit cards, and loyalty toward credit cards, this research comes at an opportune moment as credit card firms differentiate their card brands in the global marketplace. Further, a dataset originating from two mature Western economies has been put forward for the benefit of practitioners and researchers.

This study aims to explain how delinquency shocks in one type of debt contaminate the others. That is, the authors aim to shed light on the time pattern of delinquencies in different debt types.

This study analyzes the interdependencies between mortgage, credit card and auto loans delinquency rates in the USA from 2003 to 2019, using a panel VAR-X, the panel Granger causality tests and the Geweke linear dependence measures. The authors also compute the impulse response functions of a shock to one kind of debt on the others and decompose the variance of the forecast errors.

The authors find a statistically significant bidirectional Granger causality between the delinquencies. The Geweke measures of linear dependence and the Dumitrescu and Hurlin Granger non-causality tests support that mortgage predominantly causes credit card and auto loan delinquencies. Auto loans also cause credit card delinquencies. The impulse response functions confirm this pattern. This scenario aligns with a sequence where debtors consider rational first to default on credit cards, second on auto loans and only on mortgages in the last instance. Indeed, credit card delinquencies Granger-cause delinquencies in other debts when it occurs.

To the best of the authors’ knowledge, this is the first study to focus on the temporal pattern of delinquency rates for all the US states, using panel data. Furthermore, the results call for policymakers to design regulations to break the transmission channel from debt delinquencies.

This study aims to develop a novel approach for predicting default risk in bancassurance, which plays a crucial role in the relationship between interest rates in banks and premium rates in insurance companies. The proposed method aims to improve default risk predictions and assist with client segmentation in the banking system.

This research introduces the group method of data handling (GMDH) technique and a diversified classifier ensemble based on GMDH (dce-GMDH) for predicting default risk. The data set comprises information from 30,000 credit card clients of a large bank in Taiwan, with the output variable being a dummy variable distinguishing between default risk (0) and non-default risk (1), whereas the input variables comprise 23 distinct features characterizing each customer.

The results of this study show promising outcomes, highlighting the usefulness of the proposed technique for bancassurance and client segmentation. Remarkably, the dce-GMDH model consistently outperforms the conventional GMDH model, demonstrating its superiority in predicting default risk based on various error criteria.

This study presents a unique approach to predicting default risk in bancassurance by using the GMDH and dce-GMDH neural network models. The proposed method offers a valuable contribution to the field by showcasing improved accuracy and enhanced applicability within the banking sector, offering valuable insights and potential avenues for further exploration.

The sustainable development goals (SDGs) devised by the United Nations (UN) call on countries – whether rich or poor – to solve global issues, improve lives and save the planet for future generations. However, the UN predicts that between $5 and $7tn will need to be spent annually between now and 2030 to accomplish these goals, posing a major financial hurdle. Islamic social finance, if used ethically, seeks to realise SDGs through fairness, justice and equity. Thus, this study aims to determine how Islamic social finance instruments such as Zakat, Waqf, Sadaqat and Qard-hasan contribute to realising SDGs.

This study used a preferred reporting items for systematic reviews and meta-analyses-based systematic literature review. Scopus and Google Scholar were chosen for the qualitative and meta-analysis of studies. The topic was reviewed in 178 academic papers from 2000 to 2022. The required articles were analysed after careful review.

Islamic social financing mechanisms have the capacity to solve many social issues and create better welfare conditions by ensuring economic, social and environmental sustainability in line with the SDGs. Indonesia and Malaysia lead Islamic social finance research, the survey found. The review revealed that Islamic social funding can achieve 11 out of 17 SDGs. Islamic commercial finance can be used for the remaining goals. The paper highlights Islamic social funding research limitations and opportunities.

The review study shows that Islamic social finance can fill the SDG funding gap, especially considering the post-pandemic financial crisis that has increased global income inequality and social disparities.

To the best of the authors’ knowledge, this article is the first of its kind to review the potential of Islamic social financing instruments to help achieve the SDGs.

A convenient payment system is increasingly recognized as an asset of tourism destinations. By using data on payments with cards issued in foreign countries, together with other monthly tourism flow variables, the authors assess the importance of card payments to identify seasonality in inbound tourism in Portugal.

The authors compute seasonality measures using Portuguese data on card payments from 2003 to 2019, together with data on nights spent and the Balance of Payments travel credit. The authors also assess seasonal behaviour in the timespan of the different tourism strategic plans in place during this period.

Card payments grew at a faster pace than the other inbound tourism variables and show a seasonal pattern similar to the other variables. Seasonality decreases when variables measured in quantities are considered (nights spent and number of card transactions). However, when the authors use value variables (Balance of Payments travel credit and value of card transactions), seasonality in 2019 is higher than in 2003.

The widespread use of digital payments makes card payment information an even better proxy of tourism activity and since it is available in a short time-span it has informational potential for tourism stakeholders and for researchers in this field.

The authors study the seasonal behaviour of foreign card payments along with other international tourism flow variables. The authors’ results highlight the informational potential of card payment data and the importance of electronic payment infrastructure for tourist activity.

The new ways to complete financial transactions have been developed by setting up mobile payment (m-payment) platforms and such platforms to access banking in the financial mainstream can transact as never before. But, does m-payment have veiled consequences? To seek an answer, the research was undertaken to explore the dark sides of m-payment for consumers by extending the theory of innovation resistance (IR) and by measuring non-adoption intention (NAI).

Three hundred individuals using popular online m-payment apps such as Paytm, PhonePe, Amazon Pay and Google Pay were surveyed for the primary data. IBM AMOS based structural equation modelling (SEM) was used to analyse the data.

Each m-payment transaction leaves a digital record, making some vulnerable consumers concerned about privacy threats. Lack of global standards prevents consumers from participating in the m-payment system properly until common interfaces are established based on up-to-date standards. Self-compassion (SC) characteristics such as anxiety, efficacy, fatigue, wait-and-see tendencies and the excessive choice of technology effect contribute to the non-adoption of m-payment.

This study proposes a threat model and empirically explores the dark sides of m-payment. In addition, it also unveils the moderator's role of SC in building the structural relationship between IR and NAI.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.