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.
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.
Keep reading
The Unintentional Nonconformist: Habits Promote Resistance to Social influence
Asaf Mazar, Guy Itzchakov, Alicea Lieberman, and Wendy Wood
Attitudes
This research tests a novel source of resistance to social influence—the automatic repetition of habit. In three experiments,
participants with strong habits failed to align their behavior with others. Specifically, participants with strong habits to drink
water in a dining hall or snack while working did not mimic others’ drinking or eating, whereas those with weak habits
conformed. Similarly, participants with strong habits did not shift expectations that they would act in line with descriptive
norms, whereas those with weak habits reported more normative behavioral expectations. This habit resistance was not
due to a failure to perceive influence: Both strong and weak habit participants’ recalled others’ behavior accurately, and it
was readily accessible. Furthermore, strong habit participants shifted their normative beliefs but not behavior in line with
descriptive norms. Thus, habits create behavioral resistance despite people’s recognition and acceptance of social influence.
Keep reading