A Possible Dark Side of Listening? Teachers Listening to Pupils Can Increase Burnout
Abstract
A growing body of the literature on interpersonal listening has revealed numerous positive outcomes in the workplace. For example, employees wholisten well are perceived as leaders, perform better at work, gain trust, and succeed in negotiations, among other benefits. However, there is a gap in the literature regarding the potential negative consequences of listening in the workplace, especially when it is effortful and challenging. This study explored the potential relationship between teachers listening to their pupils and burnout. Conducted in 2024, this field study involved 106 middle and high school teachers from Israel. We used multiple regression analysis to control for well-known predictors of job burnout: motivation, job satisfaction, and competence. The results indicated that teachers’ perception of their listening quality significantly and positively predicted job burnout, even whenaccounting for these variables as well as seniority and school-type; 0.24 ≤ βs ≤ 0.36. This study highlights the potential negative consequences of workplace listening and contributes to the less explored aspect of listening in the literature with important implications for work-related outcomes.
Learning to listen: Downstream effects of listening training on employees' relatedness, burnout, and turnover intentions
Guy Itzchakov, Netta Weinstein, Arik Cheshin
Listening
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.
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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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