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The coronavirus disease 2019 (COVID-19) situation has led to the emergence of virtual teams in all organizations, and the role of leadership has become more pertinent. The current research focuses on understanding the factors for better team performance in virtual teams. Based on the contingency perspective, the behavioral complexity in leadership (BCL) theory is the most appropriate as BCL requires the leader to demonstrate multiple contrasting leadership behaviors according to the situation. Both internal as well external roles were explored, which could facilitate better communication quality and role clarity to increase interpersonal trust and leadership effectiveness in the current crisis.

Data were collected from employees who have worked in virtual teams during the crisis and who have experience of working in a virtual team environment. A total of 200 questionnaires were distributed, and 175 were received. A path model was built applying partial least squares structural equation modeling (PLS-SEM).

Communication quality has come as a partial mediator for the relationship between internal and external leadership roles and trust. Role clarity fully mediated the relationship between external leadership roles and conflict. Internal and external leadership roles showed a significant effect on leadership effectiveness, which were further related to team performance in virtual teams. Additionally, synchronous technology was used more by virtual teams.

The study did not examine cultural differences or cultural adaptation in virtual teams. Instead of the BCL theory, future research may apply attribute-based or relational-based theory to examine leadership roles in virtual team performance.

Using the BCL theory, the current study contributes to an understanding of virtual team performance and the internal as well as external role of leaders. This is relevant in an environment of extreme ambiguity such as COVID-19.

The purpose of this study is to explore the perception of the users concerning the role of artificial intelligence (AI) in enhancing personal learning profile (PLP), personal learning network (PLN) and personal learning environment (PLE) and their effect on the perceived ease of use, perceived effectiveness and perceived usefulness for enhancing the overall attitude and satisfaction of the e-learning.

The data were collected from students and professionals who have ever used the e-learning module, and smart partial least square-structural equational modeling (PLS-SEM) is used to see relations between the different variables.

It was seen that the PLE is affecting both perceived ease of use and perceived usefulness. The research has shown that perceived ease of use showed a mediating effect between PLE and attitude and satisfaction. Further satisfaction mediates between perceived ease of use and intention. PLP has come out to significantly impacting perceived effectiveness. The multigroup analysis also showed that the attitude and satisfaction level affecting intention to use the e-learning module differ across the two groups of learners, i.e. gender and type of learners.

The data are collected from students and professionals who have ever used the e-learning module and wholly based on their perceptions, leading to self-perception bias.

The current research is trying to integrate the user perception of PLP, PLN, PLE into the framework of the technology acceptance model and see how they impact the overall attitude and satisfaction of the learners. AI can be used to improve them and make e-learning more adherent to the users. AI can play an essential role in generating the right environment by matching the profile of the learner.