درخت حوزه‌های تخصصی

After the Coronavirus diseases (COVID-19) outbreak, the Whole world witnessed the strangest buying behavior within the customers. Suddenly masks, hand sanitizers, and toilet papers became the priority list of the customers. High demand and lesser availability of essentials products like masks, toilet papers, and hand sanitizers create a chaotic situation inside the retail outlet's stores.  The buyer’s behavior and the chaotic scenario inside a retail outlet drew the attention of several consumer psychologists, academicians, and researchers. A panic buying situation is the most common behavior that exists in a time of crisis. This paper discusses the panic buying situation briefly during COVID-19. However, a few types of research and studies were captured in the panic buying behavior. The current study examined the factors that influenced the customers buying decisions during the COVID-19 outbreak and secondly, it examined to what extent the buying decision of a customer creates a panic situation. Furthermore, the study looked at the emotional and cognitive responses of the individuals which play a pivotal role in influencing customer decision making the journey. Qualitative content analysis and thematic methodology were considered for data analysis. The results indicated that fear and uncertainties were the key emotional responses; whereas rumors, fallacious thinking, and survivalist psychological were the key cognitive responses that influenced the customer decision journey.

As electronic commerce has gained widespread popularity, payments made for users' transactions through credit cards also gained an equal amount of reputation. Whenever shopping through the web is made, the chance for the occurrence of fraudulent activities are escalating. In this paper, we have proposed a three-phase scheme to detect fraudulent activities. A profile for the card users based on their behavior is created by employing a machine learning technique in the second phase extraction of a precise communicative pattern for the card users depending upon the accumulated transactions and the user's earlier transactions. A collection of classifiers are then trained based on all behavioral pattern. The trained collection of classifiers are then used to detect the fraudulent online activities that occurred. If an emerging transaction is fraudulent, feedback is taken, which resolves the drift's difficulty in the notion. Experiments performed indicated that the proposed scheme works better than other schemes.

This study aims to investigate the effectiveness of university roles in entrepreneurial intentions among university students. This study is also measuring the functions of the system supports as the control variables towards entrepreneurial intentions. Universities seem to compete in the industry to provide and develop the entrepreneurial skills set to their students. Perhaps they may add more benefit in preparing and equipping their students as entrepreneurs once they completed their studies. The study was conducted in local universities. Three hundred respondents were approached in getting the data compiled. Based on that, only 250 complete and usable data received in returns. An 83% response rate is achieved because the sample was from a list of young entrepreneurs' schemes currently being assessed by the government agencies. Questionnaires used to validate the items from university roles (6 items), entrepreneurial intentions (5 items), and finally support systems (6 items). Likert scales were used to measure all the items. Besides that, there are also seven questions for respondents' profiles. The results indicate that all tested hypothesized were positively significant. Universities should consider finding ways to develop and motivating their students towards entrepreneurial intentions. Further study may continue the model with different sets of control variables for concrete solutions in managing the student's entrepreneurial intentions.