Digital innovation is challenging the traditional way of offering financial services to companies; the so-called Fintech phenomenon refers to startups that use the latest technologies to offer innovative financial services. Within the framework of the Theory of Planned Behavior (TPB) and the Theory of Reasoned Action (TRA), the primary purpose of this paper is to develop a causal-predictive analysis of the relationship between Subjective Norms, Attitudes, and Perceived Behavioral Control with the Intention to Use and Behavioral Use of the Fintech services by companies. Partial Least Squares Structural Equation Modeling methodology was used with data collected from a survey of 300 companies. Our findings support the TRA and TPB models and confirm their robustness in predicting companies’ intention and use of Fintech services. Financial technology innovators must understand the processes involved in users’ adoption to design sound strategies that increase the viability of their services. Studying the antecedents of behavioral intention to adopt Fintech services can greatly help understand the pace of adoption, allowing these players to attract and retain customers better. This study contributes to the literature by formulating and validating TPB to predict Fintech adoption, and its findings provide useful information for banks and Fintech companies and lead to an improvement in organizational performance management in formulating marketing strategies.

The purpose of this study is to perform a literature review of risk management in megaprojects and systematize the risks studied in the literature. A systematic search of the main databases has been performed. The contributions include: (1) a bibliometric analysis of articles; (2) the systematization and classification into nine categories of the risks found in a content analysis of the articles; and (3) the identification of possible areas of interest for research and practice. Risks are comprehensively categorized to assist practitioners during the identification phase, while potential areas for future lines of research are presented for academics.

Purpose: The purpose of this study is to assess and contrast the impact of various factors, including both bank-specific and macroeconomic factors, on the financial performance of Islamic and conventional banks (I&CB) in countries with a dual banking system. Design/methodology/approach: A general least square model is applied to a large data set of 103 I&CB operating in the Middle East and North Africa (MENA) region, comprising unbalanced annual panel data spanning the period from 2015 to 2020. The financial performance index (FPI) derived from capital adequacy, asset quality, management efficiency, earnings, and liquidity (CAMEL) ratios is used as the dependent variable. Findings: Key factors, such as overhead expenses, gross domestic product (GDP) and retained earnings, exert a substantial influence on the financial performance of both I&CB. Moreover, the findings suggest that certain parameters, including deposits, inflation and cellular banking usage, significantly impact on the financial performance of conventional banks, while bank size specifically affects the financial performance of Islamic banks. Research limitations/implications: While this study provides valuable insights, it is essential to acknowledge its limitations. The research focuses on a specific region (MENA) and may not be universally applicable to other geographical areas or banking systems. The study’s findings are based on historical data and might not fully reflect current or future market conditions. Additionally, the choice of variables and methodology may introduce bias or limitations, as with any empirical study. The theoretical implications of the research paper lie in the distinct ethical principles that constitute the foundation of Islamic finance. The ethical opposition to Riba is poised to have extensive implications, influencing market stability, commercial and economic impact and contributing to responsible banking practices within the Islamic banking sector. The study suggests that adherence to these sacred principles not only aligns with ethical considerations but also fosters social responsibility within Islamic banking institutions. This holds significance for broader societal and economic impacts, as responsible banking practices contribute to sustainable and equitable economic development. Practical implications: The study underscores the significance of efficient overhead cost management for conventional banks, particularly in the context of a rapidly evolving digital banking environment. The call for adaptation and innovation in operational strategies aligns with the broader principles of efficiency and effectiveness emphasized in Islamic finance. Social implications: In essence, the theoretical and practical implications of the study surpass the narrow focus on financial performance, resonating with the broader societal and economic landscape within the Islamic banking sector. The integration of ethical principles not only reinforces the unique identity of Islamic finance but also positions it as a model for responsible and sustainable banking practices in the MENA region and beyond. Originality/value: CAMEL ratios are used to build an FPI to evaluate bank performance, providing a more precise and comprehensive assessment compared to traditional return ratios like return on assets or return on equity. Second, the authors conduct a thorough analysis covering factors across bank-specific, financial and macroeconomic dimensions. Thus, the study stands out by not only examining bank-specific factors but also by considering external factors such as GDP, interest rates and the development of the financial sector. The focus on the MENA region allows us to offer generalizable findings, highlighting distinctions between I&CB and considering a period with boom years (2015–2019) and a recession year (2020).

Purpose: This study aims to identify which determinants are responsible for impacting the user experience of three peer-to-peer (P2P) payment services in the Spanish market. Design/methodology/approach: A sample of all online reviews (n = 16,048) published in Google Play of three paytech apps—Bizum, Twyp, and Verse—was analyzed using text mining and sentiment analysis. Findings: A holistic interpretation of the seed terms included in each aspect allowed to label them based on the preferences expressed by paytech app users in their reviews. Six latent aspects were identified: ease of use, usefulness, perceived value, performance expectancy, perceived quality, and user experience. In addition, the results of the analysis suggest a positivity bias in the online reviews of fintech P2P app users. Our results also show that online reviews of apps associated with banks or financial institutions, such as Bizum (to a greater extent) or Twyp, show more negative emotions, whereas independent apps (Verse) show more positive emotions. Moreover, the most critical users are those of unidentified gender, while women remain in a more neutral position, and men tend to express their opinions more positively regarding P2P payment apps. Practical implications: Paytech providers should analyze the problems faced by users immediately after an encounter. By applying text mining analysis, service providers can gain efficiency in understanding user sentiments and emotions without tedious and time-consuming reviews. Originality/value: This is a pioneering study on peer-to-peer (P2P) mobile payment systems from the user’s perspective because it investigates the emotions and sentiments that users convey through bank reviews.

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction