Adoption factors of Financial Technology (Fintech) services have been the subject of investigation in a growing body of extant literature. Macro-level as well as user-specific factors that contribute to the adoption of customer-facing fintech services have been studied. Emerging market studies mostly considered targeted demographic and socio-economic segments, limiting their ability to reflect a wide spectrum of relevant factors. We conducted a nationwide representative survey of 1282 individuals in Bangladesh. A total of 16 administrative districts from all 8 administrative divisions were included. Addressing sample imbalance with Synthetic Minority Oversampling Technique (SMOTE), we deployed Recursive Feature Elimination (RFE) to reduce number of customer features down to the most important. Using Library of Large Linear Classification (LIBLINEAR) for multivariate Logistic Regression, we identified significant features that predict customer-facing fintech adoption among individual respondents. We found that customers were less likely to adopt fintech services if they had higher reported levels of concern with security, information secrecy, limited government control, and high levels of reported service intuitiveness obstacles. Our evidence suggests these concern factors constitute the prominent factor behind fintech adoption, as opposed to demographic variables, for example. Our findings hold insights for fintech services providers and policy makers.

Test case prioritization is a technique for selecting those test cases, which are expected to outperform for determining faulty modules earlier. Different phases of software development lifecycle represent the total software from different point of views, where priority module may vary from phase to phase. However, information from different phases of software development lifecycle is rarely introduced and no one integrates that information to prioritize test cases. This paper presents an effective test case prioritization framework, which takes software requirements specification, design diagrams, source codes and test cases as input and provides a prioritized order of test cases using their collaborative information as output. Requirement IDs are split into words or terms excluding stop words to calculate requirements relativity. Design diagrams are extracted as readable XML format to calculate the degree of interconnectivity among the activities. Source codes are parsed as call graphs where vertices and edges represent classes, and calls between two classes respectively. Requirements relativity, design interconnectivity and class dependencies are multiplied by their assigned weight to calculate final weight and select test cases by mapping the customers' requirements and test cases using that weight. The proposed framework is validated with an academic project and the results show that use of collaborative information during prioritization process can be beneficial.