Improving Recommender Systems using Hybrid Techniques of Collaborative Filtering and Content-Based Filtering

Deciphering Digital Social Dynamics: A Comparative Study of Logistic Regression and Random Forest in Predicting E-Commerce Customer Behavior

Business activities using social media are currently growing and its development has intrigued practitioners and academics. The purpose of doing this present research is to build a conceptual model, which connects the concepts of customer perceived value and customer experiences with the concept of customer relationship quality to produce sustainable customer intention in social media commerce. Considering the results of two exogenous constructions, namely customer perceived value and customer experience, it can be concluded that both have an impact on relationship quality and customer sustainable intention. In this study, there are six hypotheses, consisting of five accepted hypotheses and one rejected hypothesis. After doing a deeper analysis, it is revealed that good relationship quality determines the significance of sustainable customer intention. In addition, this conducted research examines and experimentally confirms the role of customer perceived value and the customer experience in determining relationship quality and sustainability of customers founded in social media commerce. The novelty shown in this study lies in the integration of two exogenous variables, namely customer perceived value and customer experience, with the variables of relationship quality and customer sustainability intention. Finally, the findings provide theoretical, managerial, and social value and impact for academics, practitioners, and society.

The world is currently battling the coronavirus disease 2019 (COVID-19) that has been declared as a global pandemic since mid-March 2020. The pandemic has inevitably impacted social and economic activities of the nation, including those in the education sector. As the governments have mandated universities to close their campuses, learning activities has been forced to move online. In this difficult situation, many university administrators, lecturers, as well as students may find it difficult to adapt to the so-called “new normal” ways of learning. Hence, as this is an unprecedented situation, this study explores the challenges faced by universities as academic institutions, lecturers, and students. This study starts by conducting a literature review concerning the challenges in e-learning in context of a pandemic. Data collected are then analyzed qualitatively using inductive thematic analysis technique, resulting in a taxonomy of e-learning challenges during a pandemic. Our analysis shows that students are challenged by connectivity, e-learning system support, and technological and self-regulation issues. On the other hand, lecturers are being challenged by competency, operational, self-regulation, and isolation issues. Meanwhile, institutions identify financial support and change management as challenging issues. This study highlights the need for further investigation to address the aforementioned challenges in e-learning.

The primary focus of this research is to examine the pivotal role of Artificial Intelligence (AI) in driving business transformation, with a specific emphasis on its impact within the realm of human resource management (HR). The study seeks to assess the substantial influence brought about by the incorporation of AI in HR. Online data collection involved 110 respondents with professional backgrounds in HR. In pursuit of enhancing entrepreneurial success, the research adopts the Smart Partial Least Square (Smart PLS) approach to seamlessly integrate artificial intelligence into HR management. The analysis using Smart PLS delves into the examination of AI's effects on the recruitment process, employee development, and performance management. The findings of the research reveal that the utilization of AI in HR significantly expedites processes, enhances decision accuracy, and positively contributes to the attainment of entrepreneurial objectives. The practical implications of these outcomes are thoroughly discussed, and potential avenues for future research are outlined. This not only provides valuable insights for stakeholders but also offers guidance on optimizing the application of AI in the context of human resource management.