The COVID-19 pandemic has remarkably affected the business processes and performance of micro-, small-, and medium-sized enterprises (MSMEs) across the world. MSMEs have had to adopt and implement numerous strategies to sustain their businesses, and their financial and sustainability performance has been impacted by their choice of e-commerce (EC) platforms and digital marketing (DM) strategies. The objective of this research was to explore the effects of EC and DM platforms and strategies on facilitating MSMEs’ financial and sustainability performance amid the devastating COVID-19 pandemic. This study gathered data from 212 MSMEs from three districts of Bangladesh. A partial least squares structural equation modeling (PLS-SEM) approach was undertaken, to test the hypothesized model. The findings revealed that e-commerce had a significant association with MSMEs’ financial performance and sustainability amid the pandemic. It was also observed that digital marketing strategies had a substantial impact on MSMEs’ financial performance. However, the linkage between DM strategies and MSMEs’ sustainability was found to be insignificant. Furthermore, it was found that the financial performance of MSMEs mediated the relationship between e-commerce adoption and their sustainability performance. These findings contribute to the extant technology adoption literature, by exploring the role of e-commerce and digital marketing on firms’ financial outcomes amid a global pandemic. Managers and policymakers of small businesses can learn several things from this study, and understand how crucial digital commerce and digital marketing are to their success and long-term survival.

Purpose: The research paper is an effort to find out the behavior of Pakistani individuals toward usage of Takaful insurance. This paper aims to identify the factors, which influence investors’ intention toward the adoption of Takaful. Islamic finance is growing rapidly in the international market, especially in Islamic countries. Pakistan is an Islamic country, where the majority of the population is Muslim. Therefore, there is a great potential for Takaful exists in the country. Design/methodology/approach: Both explanatory and descriptive research designs used for the research framework. The theory of planned behavior is the base theory and the model incorporates several factors such as relative advantage, compatibility, social influence, awareness and religiosity that may influence the adoption of Takaful. The primary data collected through the distribution of self-administered survey-based questionnaire, containing 23 items scaled at a five-point Likert scale. The non-probability snowball sampling and judgmental sampling techniques are used due to the scarce of Takaful users. The sample consists of 345 individuals (127 Takaful users and 218 non-users) living in the three main cities, namely, Karachi, Lahore and Islamabad. The data are further analyzed and interpreted with IBM SPSS 21. The results are evaluated using descriptive statistics, reliability, confirmatory factor analysis, correlation and binary logistic regression models. Findings: The research findings reveal that factors such as relative advantage, compatibility, social influence, awareness and religiosity have a significant impact on the behavioral intention of Takaful amongst a depicted sample of Pakistani people. Similarly, Takaful has great potential in the Pakistani market, but due to lack of awareness, Takaful share is far behind than conventional insurance. It is further suggested that Takaful operators must devise some policies and plan to spread awareness about Takaful and come up with more innovative products. Practical implications: The Takaful operators must devise plans to aware people about Islamic insurance. The study provides implication to Takaful management; Takaful users; and more importantly, the regularity authorities to operate and successfully conduct Takaful applications. Further, they should advance Takaful operations and produce more innovative products. The study focuses on some factors while there are plenty of others, which should be studied accordingly. For future researchers and students, there is a great potential of other techniques and measures, which can be further used for analysis of Takaful business. Originality/value: This research is a first attempt to trace out the behavior of Pakistani people about Takaful, using the above discussed factors. The behavioral intention is studied using users and non-users combination, which is never done before in the current setting.

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As artificial intelligence (AI) becomes increasingly embedded in financial decision-making, questions of fairness, transparency, and trust have taken center stage, particularly in high-stakes domains like credit allocation. This study investigates the use of AI-based credit scoring for small and medium-sized enterprises (SMEs), combining algorithmic performance analysis with behavioral insights from affected users. Using a convergent mixed-methods design, we train and evaluate a random forest classifier on a US dataset of 5000 anonymized loan applications, achieving an AUC of 0.998 and simulating real-world lending conditions. Fairness diagnostics reveal that approval rates differ significantly by education level, with low-education applicants approved at a rate three times lower than those with advanced degrees, despite similar false positive rates across groups. We frame the decision pipeline as an information-processing system and consider how algorithmic scoring may distort or reduce informational signals relevant to perceived fairness. To contextualize these findings, we conduct twelve semi-structured interviews with SME owners and financial managers in the United States and United Kingdom, coded using the Capability, Opportunity, Motivation, Behavior (COM-B) framework. While the model privileges structural features such as income and digital banking activity, indicators of “capability”, participants place greater emphasis on behavioral cues such as payment reliability and business resilience. A triangulated analysis reveals a stark misalignment between what the algorithm recognizes and what users perceive as fair or valid. Our findings advance the discourse on AI ethics by demonstrating that statistical fairness does not guarantee experiential fairness. We advocate for the integration of behavioral indicators into credit models and call for policy reforms that address the socio-technical gaps in automated finance. While the model demonstrates high predictive performance (AUC ~ 0.998), this result should be interpreted cautiously given the constrained, anonymized dataset lacking demographic attributes such as gender and race. Consequently, intersectional fairness could not be evaluated, and findings primarily reflect lending contexts within advanced economies.

The rapid growth of FinTech services, particularly robo-advisors, has transformed how individuals engage with digital financial platforms. Understanding the behavioral drivers of technology acceptance in this context is critical for enhancing adoption and designing more effective user experiences. This study investigates whether user-level behavioral and transactional data can be leveraged to predict technology acceptance, operationalized through daily app usage. Grounded in the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT), the study uses behavioral proxies such as customer satisfaction, loyalty points, and lifetime value to reflect constructs like perceived usefulness, performance expectancy, and facilitating conditions. Using a real-world dataset of 7000 FinTech users sourced from Kaggle, we applied four machine learning algorithms, Logistic Regression, Support Vector Machine, Random Forest, and XGBoost, to classify users into high and low acceptance categories. Results revealed that ensemble models, particularly XGBoost, outperformed linear classifiers, achieving moderate improvements in precision and recall for the high-acceptance class. However, overall predictive performance remained constrained by class imbalance and overlapping behavioral patterns. These findings suggest that while machine learning can reveal patterns linked to technology acceptance, predictive precision remains limited without richer temporal and psychographic features. The study contributes to the evolving discourse on FinTech adoption by offering a data-driven lens to complement intention-based models and inform adaptive engagement strategies.