Listen to this: Why consumer behavior researchers should care about listening
Abstract
Consumers’ decisions are intricately interwoven with their conversations. Whether it
is an animated discussion with a trusted friend extolling the virtues of a newly
acquired car (i.e., Word-of-Mouth), an engaging dialogue with a salesperson, or a clarifying call to a help center seeking guidance on a just-purchased smartwatch, every
exchange hinges on a pivotal factor: the quality of listening. Listening quality shapes
perceptions, affects social influence, drives behavioral intentions, and, ultimately,
determines purchase and post-purchase outcomes. Yet, despite its importance to
these consumer behavior outcomes, listening has received scant attention in consumer psychology. In this paper, we review the effects of listening on consumer
behavior-relevant outcomes and unpack the components of quality listening to
reveal their independent mechanisms. We also point to new frontiers in listening
research beyond the in-person, dyadic interactions that have been the primary focus
of listening research to date. By doing this, we elucidate how listening and consumer
behavior are connected and encourage more research on listening in consumer
psychology.
Exploring the connecting potential of AI: Integrating human interpersonal listening and parasocial support into human-computer interactions
Netta Weinstein, Guy Itzchakov, Michael R. Maniaci
Attitudes
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.
Keep reading
Avoiding harm, benefits of interpersonal listening, and social equilibrium adjustment: An applied psychology approach to side effects of organizational interventions
Guy Itzchakov, Justin B. Keeler, Walter J. Sowden, Walter Slipetz, and Kent S. Faught
Listening
Creating positive change in the direction intended is the goal of organizational interventions. Watts et al. (2021) raise this issue of “side effects,” which include changes that are unintended and often in the opposite direction of the organizational intervention. With our expertise in applied psychology, military psychiatry/neuroscience, organizational behavior, and corporate safety, we argue for three additional factors for consideration: avoiding harm, the benefits of high-quality interpersonal listening, and a discussion of side effects as a natural part of the change process. We offer these as a means of extending the conversation begun by Watts et al.
Keep reading