The Power of Listening in Helping People Change

Drawing on results from 32 published and 20 unpublished laboratory and field experiments, we conducted an enumerative review of the primed goal effects on outcomes of organizational relevance including performance and the need for achievement. The enumerative review suggests that goal-setting theory is as applicable for subconscious goals as it is for consciously set goals. A meta-analysis of 23 studies revealed that priming an achievement goal, relative to a no-prime control condition, significantly improves task/job performance (d = 0.44, k = 34) and the need for achievement (d = 0.69, k = 6). Three moderators of the primed goal effects on the observed outcomes were identified: (1) context-specific vs. a general prime, (2) prime modality (i.e., visual vs. linguistic), and (3) experimental setting (i.e., field vs. laboratory). Significantly stronger primed goal effects were obtained for context-specific primes, visual stimuli, and field experiments. Theoretical and managerial implications of and future directions for goal priming are discussed.

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.