Learning to listen: Downstream effects of listening training on employees' relatedness, burnout, and turnover intentions

Changing attitudes does not necessarily involve the same psychological processes as changing behavior, yet social psychology is only just beginning to identify the different mechanisms involved. We contribute to this understanding by showing that the moderators of attitude change are not necessarily the moderators of behavior change. The results of three studies (Ns = 98, 104, 137) employing an ego depletion manipulation indicate that although people are more likely to agree with a persuasive message when executive control is reduced they are not more likely to change their behavior. Rather, under conditions of ego depletion, attitudes became less correlated with behaviors after persuasion. Moreover, in Study 3, we provide an explanation for this phenom- enon: People are more likely to agree with a persuasive message when depleted but are also more likely to fall back on habits that may conflict with their new evaluations. A mini meta-analysis of the data indicated that ego- depletion had a medium effect size on the difference between attitude change and behavior change, N = 339, d = −0.51, 95% CI [−0.72, −0.29]. Jointly, these studies suggest an integrative, resource-based explanation to attitude-behavior discrepancies subsequent to persuasion.

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.