Trust, Satisfaction and Online Repurchase Intention: The Moderating Role of Perceived Effectiveness of E-commerce Institutional Mechanisms

The effects of e-commerce institutional mechanisms on trust and online purchase have traditionally been understood in the initial online purchase context. This study extends this literature by exploring the role of e-commerce institutional mechanisms in the online repurchase context. In doing so, it responds to the emerging call for understanding the institutional context under which customer trust operates in an e-commerce environment. Specifically, this study introduces a key moderator, perceived effectiveness of e-commerce institutional mechanisms (PEEIM), to the relationships between trust, satisfaction, and repurchase intention. Drawing on the theory of organizational trust, and based on a survey of 362 returning online customers, we find that PEEIM negatively moderates the relationship between trust in an online vendor and online customer repurchase intention, as it decreases the importance of trust to promoting repurchase behavior. We also find that PEEIM positively moderates the relationship between customer satisfaction and trust as it enhances the customer’s reliance on past transaction experience with the vendor to reevaluate trust in the vendor. Consistent with the predictions made in the literature, PEEIM does not directly affect trust or repurchase intention. Academic and practical implications and future research directions are discussed.

What Went Wrong With the Mate-Tricks After-School Program, Which Increased Antisocial Behavior and Child/Parent Conflict?

This paper draws from an independent RCT evaluation on a behavior based afterschool intervention for called Mate-Tricks for 9-10 year old children and their families (N=592). This paper explores practical and theoretical issues that may have contributed to a range of iatrogenic effects found by the evaluation. To do this the paper focuses on key practical implementation factors such as: program exposure; engagement; and program quality. The paper also relates these results to popular theories of social development, including social interdependence theory. Finally, the paper discusses what the results suggest about the impact of cooperative/competitive goal structures in child and parent interventions of this type.

Malware detection: program run length against detection rate

N-gram analysis is an approach that investigates the structure of a program using bytes, characters or text strings. This research uses dynamic analysis to investigate malware detection using a classification approach based on N-gram analysis. A key issue with dynamic analysis is the length of time a program has to be run to ensure a correct classification. The motivation for this research is to find the optimum subset of operational codes (opcodes) that make the best indicators of malware and to determine how long a program has to be monitored to ensure an accurate support vector machine (SVM) classification of benign and malicious software. The experiments within this study represent programs as opcode density histograms gained through dynamic analysis for different program run periods. A SVM is used as the program classifier to determine the ability of different program run lengths to correctly determine the presence of malicious software. The findings show that malware can be detected with different program run lengths using a small number of opcodes