Learning to listen: Downstream effects of listening training on employees' relatedness, burnout, and turnover intentions
Abstract
The present work focuses on listening training as an example of a relational human resource practice that can improve human resource outcomes: Relatedness to colleagues, burnout, and turnover intentions. In two quasi-field experiments, employees were assigned to either a group listening training or a control condition. Both immediately after training and 3 weeks later, receiving listening training was shown to be linked to higher feelings of relatedness with colleagues, lower burnout, and lower turnover intentions. These findings suggest that listening training can be harnessed as a powerful human resource management tool to cultivate stronger relationships at work. The implications of Relational Coordination Theory, High-Quality Connections Theory, and Self-Determination Theory are discussed.
The Listening Circle: A Simple Tool to Enhance Listening and Reduce Extremism Among Employees
Guy Itzchakov, Avraham N. Kluger
Listening
An employee’s listening ability has implications for the effectiveness of the work team, the organization, and for the employee’s own success. Estimates of the frequency of listening suggest that workers spend about 30% of their communication time listening. However, the ability to listen might be even more important to managers, as empirical evidence suggest that they spent more than 60% of their time listening. Hence, the success of both the employee and the manager in communication, and thus in the organization, rests in part on possessing good listening abilities.
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Exploring the connecting potential of AI: Integrating human interpersonal listening and parasocial support into human-computer interactions
Netta Weinstein, Guy Itzchakov, Michael R. Maniaci
Attitudes
Conversational artificial intelligence (AI) can be harnessed to provide supportive parasocial interactions that rival or even exceed social support from human interactions. High-quality listening in human conversations fosters social connection that heals interpersonal wounds and lessens loneliness. While AI can furnish advice, listening involves the speakers’ perceptions of positive intention, a quality that AI can only simulate. Can such deep-seated support be provided by AI? This research examined two previously siloed areas of knowledge: the healing capabilities of human interpersonal listening, and the potential for AI to produce parasocial experiences of connection. Three experiments (N = 668) addressed this question through manipulating conversational AI listening to test effects on perceived listening, psychological needs, and state loneliness. We show that when prompted, AI could provide high-quality listening, characterized by careful attention and a positive environment for self-expression. More so, AI’s high-quality listening was perceived as better than participants’ average human interaction (Studies 1–3). Receiving high-quality listening predicted greater relatedness (Study 3) and autonomy (Studies 2 and 3) need satisfaction after participants discussed rejection (Study 2–3), loneliness (Study 3), and isolating attitudes (Study 3). Despite this, we did not observe downstream lessening of loneliness typically observed in human interactions, even for those who were high in trait loneliness (Study 3). These findings clearly contrast with research on human interactions and hint at the potential power, but also the limits, of AI in replicating supportive human interactions.
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