The searching artificial intelligence: Consumers show less aversion to algorithm‐recommended search product

Consumers frequently use mobile phones in a store to search for external information as an alternative to consulting with frontline employees. Mobile phone usage is especially prevalent among young consumers. Drawing on qualitative study results and existing literature, we conceptualize the effects of different in-store information sources on choice overload, responsibility, and confidence among young consumers, as well as the moderating role of product category knowledge. A field experiment suggests that when knowledge is low, consulting with frontline employees (vs. mobile phone) leads to lower choice overload and, consequently, increases choice confidence. When knowledge is high, these beneficial effects are attenuated. At the same time, young consumers perceive greater choice responsibility when their phone is the information source; however, this does not influence choice confidence. This work contributes to extant literature by extending the knowledge of customer experience at the point of sale, the role of technology usage in in-store retailing, and the role of frontline employees as an information source. It also provides managerial implications for retailers by highlighting the importance of providing an opportunity for an in-person frontline employee interaction especially when customers have low product category knowledge.

Section: Theory and Hypothesesmentioning

confidence: 99%

Asking your phone or a frontline employee? The influence of in‐store information source on choice overload, responsibility, and confidence among young consumers

Schaefers

Kessenbrock

Cziehso³

et al. 2023

“…Compared with experience products, search product information is usually standard and objective (e.g., features, performance, etc. ), which is difficult to change (Huang et al, 2009); information sources have a weak impact on consumer decisions; and AI chatbots are on an equal footing with humans in terms of information provision (Xie et al, 2022). AI chatbots are considered to have higher agent effectiveness for search products (Gray et al, 2007).…”

Section: Literature Review and Research Hypothesesmentioning

confidence: 99%

Chatbot or human? The impact of online customer service on consumers' purchase intentions

Chen

Liu

et al. 2023

Artificial intelligence (AI) chatbots and human employees have emerged as the dominant forms of online customer service. However, existing research rarely connects the service differences between them in terms of product type, ignoring the interactivity between the two. This study reveals the effect of matching customer service type (AI chatbot vs. human) to product type (search vs. experience) on consumers' purchase intentions through four experiments, revealing the psychological mechanism and boundary condition for the existence of this effect. It shows that (1) the match between customer service type and product type positively affects consumers' purchase intentions; (2) this matching effect is mediated by processing fluency and perceived service quality; and (3) the matching effect works only when consumers' demand certainty is low. These findings enrich the theoretical study of online customer service, and provide marketing insights for companies to improve the adoption of AI chatbots and human employees.

“…2.1 | (Mis) match between AI recommenders and utilitarian (hedonic) products AI recommenders refer to an intelligent system that utilizes AI algorithms to provide personalized recommendations or suggestions (Häubl & Murray, 2003), which provides both overall fit with consumer desires (i.e., overall recommendations) and evaluation of product attributes. Prior literature has suggested that a good match between a lay belief about AI recommenders and products' attributes may result in a positive evaluation and response from consumers (Mariani et al, 2022;Whang & Im, 2021;Xie et al, 2022). For example, Longoni and Cian (2022) further proposed a new concept of the "word-of-machine" effect, the phenomenon by which AI-based recommendation tends to favor utilitarian products and human-based recommendation tends to favor hedonic products.…”

Section: Theoretical Background and Predictionsmentioning

confidence: 99%

To be precise (imprecise) in utilitarian (hedonic) contexts: Examining the influence of numerical precision on consumer reactions to artificial intelligence‐based recommendations

Zhu,

et al. 2023

The precision of artificial intelligence (AI)‐generated information has been suggested in the past as a method of nudging consumers' evaluations and intentions, but little is known about whether such effects are also context‐sensitive. Based on four studies, we find a matched condition under which consumers are more likely to make a positive response when precise (imprecise) numbers presented by AI recommenders are used in a utilitarian (hedonic) consumption context (Study 1). Additionally, we show that consumer conceptual fluency also mediates this matching effect on consumer purchase decision‐making (Studies 2). We further show the matching effect is moderated by the recommender type (Study 3) and consumer lay beliefs about the AI and human recommenders (Study 4). This study shows that when consumers' lay belief changes from “AI performs objective tasks well” and “Human performs subjective tasks well” to “AI performs subjective tasks well” and “Human performs objective tasks well,” it can change the difference in the matching relationship between human and AI recommenders.