In the modern ever-changing organizational environment, where hybrid workplace arrangement is becoming increasingly common, and artificial intelligence (AI) technology has been used widely, employees tend to face a situation characterized by ambiguity of work and it is difficult to perceive an understanding of role, expectations, and employment. The paper explores the interrelationship between task ambiguity, risk-taking by managers, AI-induced job insecurity, and employee outcomes in a hybrid working environment. This is based on Social Information Processing Theory where we advance a theoretical model that explores how workforce members actively learn and process information in their social context to get through ambiguity and foster resilience. The evidence of the proposed relationships is substantiated by three studies. Study 1 focuses on the way task ambiguity influences active lurking and also job engagement. Study 2 explores with the moderating factor on the relationship among the variables of task ambiguity, active lurking, and job engagement on managerial risk-taking. Study 3 examines how AI-induced job insecurity can moderate the link between task ambiguity and active lurking and job engagement. The results emphasize the need to ensure clear task specification, active lurking, management risk-taking and proactive efforts to reduce the issue of AI-induced job insecurity as factors that enhance employee engagement. The implications of the study are given and recommendations to conduct further research are outlined.

The growing pace of the introduction of generative artificial intelligence (GenAI) into organizational processes is changing the way workers cooperate with technology. Based on Social Exchange Theory, we propose that the perception of employees regarding the value of GenAI-based agents, their vulnerability and privacy, and their self-concern would determine the collaboration with GenAI agents, which subsequently would predict the knowledge sharing, job thriving, and well-being. The findings show that perceived GenAI-based value has a strong positive impact on human-GenAI-based agent collaboration, but data vulnerability and privacy concerns have no significance. Interestingly, self-concern has a positive effect, which implies that identity-based fears can be used to drive active use of GenAI-based agents. Knowledge sharing, job thriving, and well-being are highly predicted by human-GenAI-based agent collaboration, and organizational exploratory innovation moderates these correlations. These results extrapolate the Social Exchange Theory to human-AI situations, dispel the assumptions of the consistently negative impact of risk perception, and emphasize the relevance of organizational climate to the implementation of the advantages of AI cooperation. The paper provides both theoretical and practical understanding of the way in which employees interact with GenAI-based agents to ensure that organizational learning and psychological outcomes of employees are achieved.