Search Results (208)

Public authorities have increasingly focused on digital financial inclusion (DFI) owing to its potential to enhance overall financial inclusion (FI) and, ultimately, to mitigate households’ financial vulnerability (FV). Although the existing literature generally reports a negative relationship between DFI and FV, most studies focus on economically less developed countries and apply heterogeneous measurement approaches. This study adopts a quantitative methodology to assess DFI as a potential determinant of FV in a developed economy—Spain—using both objective and subjective indicators of FV. DFI is proxied by the diversity of payment and transfer methods conducted via Internet and mobile devices. Empirical findings confirm a negative association between DFI and FV, indicating that higher levels of digital engagement are associated with lower FV. However, results also reveal a potential adverse effect on savings behaviour, possibly linked to the reduced “pain of paying” commonly associated with online transactions. These insights suggest that policies promoting DFI should be complemented by initiatives to enhance financial literacy, strengthen consumer protection laws, and reintroduce the “feel of cashback” within online payment platforms. By providing evidence from a developed country, this paper contributes to the limited literature by also examining subjective measures of FV variables and offline FI. Full article

As cross-border e-commerce expands in emerging economies, consumer participation increasingly depends on perceived transaction risk linked to digital payments, settlement, dispute resolution, and institutional enforceability. This study reconceptualizes online purchase intention (OPI) as a decision embedded in retail international finance. Extending the Theory of Planned Behavior (TPB), it integrates Internet Trustworthiness Behavior (ITB) and Country of Origin (COO) as risk-relevant signals shaping consumer judgment under cross-border uncertainty. Survey data from 390 digitally active consumers in the Dominican Republic were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that ITB strengthens perceived behavioral control, attitudes toward online purchasing, and subjective norms, while also exerting a direct positive effect on OPI. COO emerges as a strong direct predictor of OPI, functioning as a heuristic indicator of country credibility when formal safeguards appear weak. Contrary to standard TPB expectations, perceived behavioral control negatively predicts OPI, suggesting that greater digital competence may heighten awareness of expected losses and limited recourse in high-risk environments. The findings advance international business and finance research by showing how micro-level trust practices and macro-level country signals jointly shape consumer risk management in cross-border digital markets, with implications for inclusive participation and consumer protection. Full article

The prevalence of online transactions and extensive adoption of credit card payments have contributed to the escalation of credit card cyber fraud in modern society. These trends are propelled by technological advancements, which provide fraudulent actors with more opportunities. Fraudsters exploit victims’ financial vulnerabilities by obtaining illegal access to sensitive credit card information through deceptive means, such as phishing, fraudulent phone calls, and fraudulent SMS messages. This study predicts and detects potential instances of cyber fraud in credit card transactions by employing Machine Learning (ML) techniques, including Decision Tree (DT); Random Forest (RF); Logistic Regression (LR); Support Vector Machine (SVM); K-Nearest Neighbors (KNN); XGBoost; CatBoost; and sampling techniques such as Tomek Link, Synthetic Minority oversampling technique (SMOTE), Edited Nearest Neighbor (ENN), Tomek+ENN, and SMOTE+ENN. To determine the performance of the algorithms in terms of accuracy, precision, recall, F1 score, and ROC-AUC for credit card cyber fraud detection, we conducted a comparative analysis of the extant ML techniques. Full article

This study aims to explain what drives consumers to adopt e-tipping in restaurants and determine whether interface-based manipulation reduces perceived autonomy, lowers satisfaction, and weakens brand favorability. Prior research indicates that when autonomy is undermined through manipulative design, satisfaction declines. This study integrates UTAUT2 and Self-Determination Theories to examine the determinants of consumers’ e-tipping intention, satisfaction, and brand favorability in Saudi restaurants. Four UTAUT2 motivators (performance expectancy, facilitating conditions, social influence, hedonic motivation) and service quality were modeled as antecedents, cultural attitude as mediator, and inferred manipulation as moderator. An online survey of restaurant consumers in Saudi Arabia who had previously engaged in e-tipping generated 607 valid responses, providing adequate sample power for PLS-SEM testing. Results show that performance expectancy, social influence, hedonic motivation, and service quality significantly predict intention; cultural attitude mediates several effects; and manipulation weakens the intention–satisfaction relationship, negatively impacting brand favorability. This research offers a theoretical contribution by extending UTAUT2 to the e-tipping context through integrating service quality as an antecedent and positioning satisfaction and brand favorability as core outcomes. It advances theory by demonstrating how technology-driven manipulation within payment interfaces influences cultural attitudes, behavioral intentions, and post-experience evaluations in a high-context, non-tipping society. Managerial and policy implications for responsible e-tipping designs are discussed. Full article

Local Owner-Operated Retail Outlets (LOOROs) in India faced unprecedented disruption during the COVID-19 pandemic, with digital transformation emerging as both a challenge and an opportunity. The growing dominance of larger online and offline competitors, who swiftly adopted digital payments, posed a threat to traditional business models of these small neighborhood retailers. This study employs the Stimulus–Organism–Response (S-O-R) framework to examine the antecedents shaping LOORO owners’ attitudes toward digital payment practices and how these attitudes influence their intention and actual adoption. A survey of 175 LOOROs in Navi Mumbai was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that resource availability and customer care significantly influenced adoption, whereas competitor and customer pressure had little effect. Overall, LOORO owners demonstrated a positive outlook toward integrating digital payment systems, indicating their adaptive capacity to navigate the digital shift accelerated by the COVID-19 pandemic. Full article

Mobile payment platforms not only streamline users’ financial transactions but also encourage their participation in investment activities and additional services. To deliver personalized financial services, it is essential to collect users’ personal information. This study aims to investigate the factors influencing users’ willingness to engage in self-disclosure within mobile payment platforms, thereby assisting practitioners in efficiently allocating resources and maximizing returns on investments dedicated to promoting user self-disclosure. Consequently, this study focuses on examining how institutional mechanisms influence users’ self-disclosure behavior within these platforms. The authors developed a comprehensive framework that elucidates the influence of institutional mechanisms on users’ self-disclosure, mediated by trust and privacy concerns. To empirically validate our research model, we administered an online survey targeting Alipay users in China. Subsequently, we analyzed 559 valid survey responses utilizing partial least squares structural equation modeling (PLS-SEM). The results indicate that trust and privacy concerns jointly influence users’ self-disclosure behavior when utilizing mobile payment platforms. Moreover, key institutional mechanisms can effectively foster trust and alleviate privacy concerns, ultimately facilitating users’ willingness to self-disclose. Our research shifts scholarly focus from conventional adoption to users’ self-disclosure in the mobile payment field and enhances the existing self-disclosure research by identifying the impact of institutional mechanisms on users’ self-disclosure behavior. Full article

Exemplified by EcoStruxure from Schneider and CASICloud, production-capacity-sharing platforms typically operate as two-sided platforms. These platforms contribute to carbon emission reductions while generating revenue through uniform membership fees, fixed service charges, or commission fees applied to one or both sides of the platform. As B2B platforms, however, they must determine whether to offer online payment options to participants on both sides of the market. We employ the theory of two-sided markets and the method of comparative analysis, developing a two-sided market model to investigate: (1) the platform’s optimal payment method selection strategies and (2) how same-side network externalities and user online search costs affect platform performance. We examine two payment modes: M mode (offline payments only) and F mode (combined offline/online payments). The F mode comprises two sub-modes—FF (two-sided users choose to pay offline) and FN (two-sided users choose to pay online)—determined by users’ payment preferences on each side. We conduct pairwise comparisons of the platform’s membership fee, fixed service fee, and online service level between: (i) FF and M modes and (ii) FN and M modes. Our results indicate that the optimal payment method selection varies across market conditions, with each mode demonstrating superior performance under specific market characteristics. The FF mode consistently yields higher profits compared to the M mode. When suppliers’ expected revenues fall below a certain threshold, the FN mode outperforms the M mode in terms of profit generation. Conversely, the M mode becomes preferable above this threshold. Furthermore, the effects of same-side network externalities and search costs vary significantly across different payment modes. Under the M mode and the FF₁ mode, the effects of the same-side network externality on the platform’s membership fee are associated with two-sided users’ online search costs, which are more monotonous. Under the FN₁ and FN₂ modes, both the same-side network externality and two-sided users’ online search costs impact the platform’s optimal strategies monotonously, but they are not always the same in these two modes. Full article

FinTech-based lending has rapidly expanded in emerging economies, offering convenience and inclusion but also raising concerns about over-indebtedness. In Indonesia, the surge of digital loans has been accompanied by growing signs of risky borrowing behavior, including late payments, high debt-to-income ratios, and poor credit discipline. This study investigates the determinants of individuals’ propensity to indebtedness in FinTech-based loans, focusing on the influence of financial behavior biases, emotions, culture, and materialism, as well as the moderating effects of financial literacy, job security, and religiosity. Data were collected from 400 Indonesian civil servants and private/self-employed workers through an online questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results show that all proposed determinants significantly increase indebtedness, with financial behavior biases having the strongest impact. Financial literacy and job security amplify these effects, while religiosity weakens the influence of emotions and materialism. These findings contribute to behavioral finance theory and underscore the importance of promoting financial literacy, strengthening job stability, and integrating responsible lending policies to mitigate debt risks in emerging economies. Full article

This entry explains how China’s e-CNY, the retail form of its Central Bank Digital Currency, translates three design choices into improved access, affordability, and reliability: (1) enabling wallet-to-wallet payments on the CBDC ledger with settlement upon payment (SUP); (2) ensuring seamless integration at checkout with existing QR-code systems and popular payment apps; and (3) providing users with practical control through credentials stored on their devices and managed by licensed operators. With payment finality clarified in law and a two-tier structure in place, offline payments can shift to a hybrid architecture. It blends account- and token-based functionality across online and offline settings, incorporates tiered identity verification, and supports low-cost solutions. In essence, e-CNY demonstrates that strategic decisions regarding settlement, interoperability, and user control can expand financial inclusion while maintaining robust regulatory safeguards. Full article

Materialism is recognized as an important determinant of consumer behavior. However, studies exploring how materialism influences impulsive buying through the Diderot effect in digital contexts remain scarce. This study analyzes data from 416 adult consumers in Türkiye using Structural Equation Modeling (SEM). Four hypotheses were tested: (i) materialism increases the Diderot effect, (ii) the Diderot effect strengthens impulsive buying, (iii) materialism directly influences impulsive buying, and (iv) materialism indirectly affects impulsive buying through the Diderot effect. The findings support all four hypotheses. Materialism significantly increases both the Diderot effect and impulsive buying in online environments. The Diderot effect partially mediates this relationship and acts as an independent predictor of impulsive tendencies. The study also points out that digital stimuli such as recommendation systems, fast payment options, and social proof may reinforce this mechanism, highlighting the importance of understanding consumer behavior in digital settings. Overall, the results underline that materialistic desires, when combined with the completion motive of the Diderot effect, strongly shape impulsive tendencies. Beyond advancing theory, the study emphasizes that marketing strategies should go beyond promoting higher consumption and instead foster conscious and sustainable shopping practices. This approach not only supports consumer well-being, but also contributes to building ethical and sustainable markets. Full article

The aim of this study was to analyze international differences in online consumer behavior. The analysis was carried out on a sample of 763 participants from the countries of Spain, France, Poland and Russia. Online consumer behavior was examined from the perspective of seven dimensions: shipping-related concerns and preferences, price sensitivity and perceived cost advantage, quality perception, security concerns, time-related benefits, availability and quality of information, and shopping service satisfaction. Data were verified using Average inter-item correlation, the Shapiro–Wilk test and Levene Statistic. Subsequently, Welch’s ANOVA and one-way ANOVA and the Games–Howell and Tukey HSD post hoc tests were applied. Statistically significant differences were fully identified in all examined dimensions. The largest differences were recorded in price sensitivity, shipping-related concerns and security concerns. The effect measurements, in addition to ANOVA and post hoc tests, confirm the significance of these differences. National context, shaped by culture, institutional trust and digital infrastructure, continues to influence online consumer behavior. The strategies that the businesses should adopt should focus on approaches that are tailor-made for a specific market. This means that adapting pricing models, strengthening trust (e.g., through secure payments and strengthening safe return policies), and adapting delivery options to local preferences can lead to improved customer satisfaction in cross-border e-commerce. Full article

Fraudulent online payment operations represent a persistent challenge in digital commerce, particularly in sectors like air travel, where credit and debit card payments dominate. This study presents a novel fraud detection framework tailored to airline ticket purchases, combining a synthetic dataset generator with a modular, customizable feature engineering process. These are two machine learning models—support vector machines (SVMs) and the light gradient boosting machine (LightGBM)—for real-time fraud detection. A synthetic dataset was generated, including a rich set of engineered features reflecting realistic user, transaction, and flight-related attributes. While both models were evaluated using classification-evaluation metrics, LightGBM outperformed SVMs in terms of overall performance with an accuracy of 94.2% and a recall of 71.3% for fraudulent cases. The main contribution of this study is the design of a reusable, customizable feature engineering framework for fraud detection in the airline sector, along with the development of a lightweight, adaptable fraud detection system for merchants, especially small and medium-sized enterprises. These findings support the use of advanced machine learning methods to enhance security in digital airline transactions. Full article

While IPV is often studied as a predictor of housing insecurity, few U.S. studies explore how different forms of housing instability may contribute to intimate partner violence (IPV) risk. Using a mixed-methods approach and a cross-sectional design, this study examined the association between four housing instability domains and IPV among a sample of tenants that had either experienced eviction or were at high risk for eviction. Tenants in Harris and Travis counties (Texas, USA) completed an online survey (n = 1085; March–July 2024). Housing instability was assessed across four domains: homelessness, lease violations, utility hardship, and poor housing quality. IPV was measured using the Hurt, Insult, Threaten, Scream Screener. Covariate-adjusted logistic regression models suggest indicators within the four housing instability domains were associated with IPV risk. Within the homelessness domain, experiences with lifetime homelessness (AOR = 1.92, 95%CI 1.61–2.28), in the past 12 months living in unconventional spaces (AOR = 2.10, 95%CI 1.92–2.29), and moving in with others (AOR = 1.20, 95%CI 1.06–1.36) were associated with IPV. Within the lease violations domain, missed rent payments (AOR = 1.69, 95%CI 1.68–1.71) and non-payment lease violations (AOR = 2.50, 95%CI 2.29–2.73) in the past 12 months were associated with IPV. Utility shutoffs (AOR = 1.62, 95%CI 1.37–1.91) and unsafe housing (AOR = 1.65, 95%CI 1.31–2.09) in the past 12 months were associated with IPV. Homelessness, housing-related economic hardships and substandard living conditions predict an elevated risk of IPV. Full article

Engineering contract management is a critical component of project management systems, serving as a key mechanism for ensuring successful project implementation. This study systematically analyzes 349 s-instance judicial cases related to construction engineering contract disputes in the Yangtze River Delta Economic Zone from 2017 to 2021, based on data obtained from the China Judgments Online database. The research identifies contractual risk characteristics across dimensions such as regional distribution, dispute terminology, legal citation patterns, and appellate role transitions. The key findings include the following: (1) Primary risks involve payment disputes, quality assurance failures, contractual validity issues, and schedule compliance challenges. (2) Litigation patterns reveal complex interdependencies between contracting parties and stakeholders, posing significant risk management challenges. (3) High second-instance modification rates stem from procedural irregularities, new evidence, improper legal application, and factual errors in initial trials. The study proposes stratified risk mitigation strategies, including governmental regulatory improvements and enterprise-level management optimizations. These findings offer valuable insights into advancing risk governance in construction contract administration, particularly through an enhanced understanding of dispute complexity and systemic vulnerabilities. Full article

Over the past decade, the Chongqing land ticket model has played a pivotal role in the market-oriented reform of rural land factors and serves as a representative practice of the TDR program in China. This paper constructs a systematic evaluation framework from two perspectives—policy effectiveness and citizen feedback—to comprehensively understand the policy effect of this model. The study employs methods of policy texts bibliometrics and content analysis based on big data. The results indicate that the effectiveness of land ticket policies exhibit significant fluctuations, with peaks aligning with milestones in the model’s development. Policy measures are well-aligned with the goals set forth. However, policymakers in Chongqing have historically focused more on institutional construction within the land ticket model, only recently shifting attention to the protection of farmers’ rights and interests. This imbalance may have led to potential risks regarding the loss of farmers’ property rights. The analysis of citizen feedback from the online space further took into account the impact of policy content on its audience (farmers), revealing that shortening the compensation payment time rather than increasing the compensation amount is the most common and critical demand among farmers. This underscores the urgent need for a policy-related response from the government to meet farmer’s demands for “procedural justice”. Our conclusions address a gap in the existing literature by integrating policy text analysis with public opinion, thereby offering referential insights into understanding the evolutionary process, policy features, and implementation effects of TDR program in China. Full article