Advancing Primed Goal Research in Organizational Behavior

Conversations with people who hold opposite partisan attitudes can elicit defensiveness, reinforce extreme attitudes, and undermine relationships with those with opposing views. However, this might not be the case when speakers experience high-quality (attentive, 2 understanding, and non-judgmental) listening from their conversation partners. We hypothesized that high-quality listening will increase speakers’ positive views toward, and their willingness to further interact with, others who hold politically opposed attitudes, and that these effects will be mediated by greater state openness. We conducted three experiments using different modalities to manipulate listening. In Study 1 (N = 379), participants recalled a conversation with an opposing political party member, with listening quality described as high-quality, low-quality, or control. Study 2 (N = 269) used imagined interactions, with participants reading vignettes describing either high-quality listening or a control condition. In Study 3 (preregistered; N = 741), participants watched a video of a listener modeling high-quality or moderate-quality listening and imagined themselves engaging in a similar interaction. Across studies, we found that high-quality listening consistently increased speakers’ state openness to politically opposed others, but did not change political attitudes. We found inconsistent evidence for speakers’ increased willingness to engage in future interactions (meta-analytic effect: 𝑑 = 0.20, p = .015). However, the indirect effect of listening on positive attitudes and willingness for future interactions through increased openness was consistently significant.

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.