Can high quality listening predict lower speakers' prejudiced attitudes?
Abstract
Theorizing from humanistic and motivational literature suggests attitude change may occur because high-quality listening facilitates the insight needed to explore and integrate potentially threatening information relevant to the self. By extension, self-insight may enable attitude change as a result of conversations about prejudice. We tested whether high-quality listening would predict attitudes related to speakers' prejudices and whether self-insight would mediate this effect. Study 1 (preregistered) examined scripted conversations characterized by high, regular, and poor listening quality. In Study 2, we manipulated high versus regular listening quality in the laboratory as speakers talked about their prejudiced attitudes. Finally, Study 3 (preregistered) used a more robust measure of prejudiced attitudes to testing whether perceived social acceptance could be an alternative explanation to Study 2 findings. Across these studies, the exploratory (pilot study and Study 2) and confirmatory (Studies 1 & 3) findings were in line with expectations that high, versus regular and poor, quality listening facilitated lower prejudiced attitudes because it increased self-insight. A meta-analysis of the studies (N = 952) showed that the average effect sizes for high-quality listening (vs. comparison conditions) on self-insight, openness to change and prejudiced attitudes were, ds = 1.19, 0.46, 0.32 95%CIs [0.73, 1.51], [0.29, 0.63] [0.12, 0.53], respectively. These results suggest that when having conversations about prejudice, high-quality listening modestly shapes prejudice following conversations about it, and underscores the importance of self-insight and openness to change in this process.
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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A Possible Dark Side of Listening? Teachers Listening to Pupils Can Increase Burnout
Eli Vinokur, Guy Itzchakov and Avinoam Yomtovian
Listening
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.
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