Sowing the seeds of love: Cultivating perceptions of culture of companionate love through listening and its effects on organizational outcomes

The effect of feedback and a self-set goal on the relationship between a goal primed in the subconscious and performance were examined in three laboratory experiments and one field experiment (n = 241, 465, 201, 74 respectively), using normative (bogus) and absolute feedback manipulations, and different performance tasks that were coded for both performance quality (i.e. creativity) and quantity. The hypothesis that providing feedback, a moderator in goal-setting theory, amplifies the causal effect of a primed goal on performance was supported. Specifically, in experiment 1, participants were randomly assigned to a 2 (prime of effective vs. ineffective performance) × 3 (positive, negative, no feedback) factorial design. The primed goal for effective performance led to higher performance than the negative primed goal. In addition, feedback, regardless of its sign, increased both task and creative performance when a primed goal for effective performance was presented but did not do so when the goal primed ineffective performance. This effect was replicated in two subsequent laboratory experiments which employed three primed goal conditions (effective/neutral/ineffective). In experiments 2 and 3, a consciously set goal, with no prompting by an experimenter, mediated the relationship between a primed goal and performance when feedback was provided. Experiment 4 provided a conceptual replication in a work setting, involving employees in a customer service department of a large communication company. Finally, a meta-analysis of these four experiments indicated an average effect size of d = 0.36, 95 percent CI [0.23, 0.49] with no evidence of heterogeneity across the four experiments. These findings suggest that not only are subconscious goals a foundation for the difficulty level of consciously set goals but in addition, subconscious goals and conscious goals work together in affecting performance.

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.