The Effect of a Dilemma on the Relationship Between Ability to Identify the Criterion (ATIC) and Scores on a Validated Situational Interview

Environmental cues (e.g. achievement-related words and pictures) can prime/activate, in the absence of awareness, a mental representation of importance stored in memory. Chen et al.'s 2021 Applied Psychology: An International Review70, 216–253. (doi:10.1111/apps.12239) meta-analysis revealed a moderate, significant overall effect for the goal priming-organizational behaviour relationship, with three moderators identified: context-specific versus a general prime, prime modality (i.e. visual versus linguistic) and experimental setting (field versus laboratory). An independent researcher found that their finding was negligibly affected by a publication bias. Shanks & Vadillo (2021), Royal Society Open Science8, 210544. (doi:10.1098/rsos.210544) (field: k = 13, N = 683, d = 0.64), questioned Chen et al.'s conclusion regarding the effect size found in field studies (field: k = 8, N = 357, d = 0.68). In this paper, we discussed Shanks & Vadillo's selection of additional field experiments that led to their conclusion of a publication bias. We updated Chen et al.'s meta-analysis to include relevant studies conducted since that study's publication. The present meta-analysis reproduced the original findings in Chen et al. (field: k = 11, N = 534, d = 0.67). The updated findings are consistent with: (i) laboratory findings, (ii) the findings obtained in field experiments on consciously set goals and (iii) goal setting theory (Latham & Locke, 2018 In Handbook of industrial, work & organizational Psychology, vol. 1 (eds D Ones, N Anderson, C Viswesvaran, H Sinangil), pp. 103–124).

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.