Explore to Learn

Abstract

Explorative information systems behaviors are the ways in which individuals actively revise their usage and discover creative means of applying the system, extending the potentials of the system, and enhancing task performance. This study has developed a specific concept of explorative system behavior-explorative information technology (IT) learning-to describe users' post-training learning behavior-novel use. Adapting a herding lens, we study the individual and team-level triggers of explorative learning cognitions. The study tests the proposed cross-level model and collects data from 225 individuals who are divided into 51 teams. The results indicate that individuals tend to innovatively use a system through developed exploration cognitions in team settings as a response to examined herd behavior triggers. The study uncovered that members of cohesive teams and learners with high uncertainty perceptions are more likely to imitate the observed explorative learning behaviors of their peers and reproduce such behaviors in the long term.

Navigating virtual teams in generative AI-led learning: The moderation of team perceived virtuality

“…By exploring the association between HAT characteristics and knowledge update, we extend our understanding of the belief updating theory, which illustrates the process of belief alteration through a sequential updating mechanism and recommends stage-based (i.e., pre-training and post-training) learning frameworks (Gupta & Bostrom, 2013). This further expands the applicability of the belief updating theory at the team level (Darban, 2022b).…”

“…HLM8.1 is practical for assessing cross-level associations and nested data, generating estimation results utilizing a restricted maximum likelihood algorithm. It has preferred for realworld applications, such as group projects (Darban, 2022b).…”

“…Future research could expand this scope by including other team or course-level variables to capture the diversity in virtual team learning environments. Lastly, the literature suggests that team members' attributes (Darban, 2022b) and the nature of their collaborative tasks, for instance, the diversity of teamwork (Lee et al, 2015), may influence learning outcomes in virtual teams. Future research should identify specific conditions that may determine the success, failure, or possible harmful effects of incorporating an AI teammate.…”

“…The third contribution stems from a multilevel approach. While current research on human-agent teams predominantly features single-level mechanisms, intra-team dynamics can also affect an individual's learning journey through different group-level factors (Darban, 2022b). Therefore, drawing from recent studies that underscore the significance of virtuality perceptions in project-based teams (Costa & Handke, 2023;Handke et al, 2022), we synthesize individual and team-level characteristics to create a multilevel model, examining the interplay between AI attributes and learner's perceptions.…”