No abstract

The present study compares the influence of text‐based recommendations; traditionally known as online consumer reviews, and the influence of voice‐based recommendations provided by voice‐driven virtual assistants on consumer behaviors. Based on media richness theory, the research model investigates how voice versus text modality influences consumers' perceptions of credibility and usefulness, as well as their behavioral intentions and actual behaviors. In addition, the study analyses if these relationships vary based on the type of product and compares the influence of masculine and feminine voices. Two studies were conducted using between‐subjects experimental designs, partial least squares‐structural equation modeling, and logistic regression. The core finding is that voice‐based recommendations are more effective than online consumer reviews in altering consumer behaviors. In addition, the first study showed that the influence of recommendations on behavioral intentions is mediated by consumers' perceptions of their credibility and usefulness. The second study confirmed, in a realistic setting, that voice‐based recommendations affect consumer choices to a greater extent. Recommendations for search products and provided by males are also found to be more effective. These results contribute to the voice assistant and e‐WOM literature by highlighting the effectiveness of voice‐based recommendations in predicting consumer behaviors, confirming that credibility and usefulness are key factors that determine the influence of recommendations, and showing that recommendations are more effective when they focus on search products.

Purpose Building on both the uncanny valley and construal level theories, the analyses detailed in this paper aims to address customers’ explicit and implicit attitudes toward various service robots, categorized by the degree of their human-like appearance, namely, mechanoids (low human-likeness), humanoids (medium human-likeness) and realistic robots (high human-likeness). Design/methodology/approach The analyses reflect a mixed-method approach, across three studies. A qualitative study uses focus groups to identify consensual attitudes. An experiment measures self-reported, explicit attitudes toward the three categories of robots. Another experiment explores customers’ implicit attitudes (unconscious and unintentional) toward robots, using three implicit association tests. Findings Customers express both positive and negative attitudes toward service robots. The realistic robots lead to both explicit and implicit negative attitudes, suggesting that customers tend to reject these robots in frontline service settings. Robots with lower human-likeness levels generate relatively more positive attitudes and are accepted to nearly the same extent as human employees in hospitality and tourism contexts. Practical implications Because customers reject, both consciously and unconsciously, very human-like robots in service encounters, managers should leverage this key finding, along with the more detailed results, to inform their strategic introduction of robots into hospitality frontline service settings. Originality/value The combined qualitative and quantitative studies specify and clarify customers’ implicit and explicit attitudes toward robots with different levels of human-likeness, in the real-world setting of hospitality and tourism services. Such insights can inform continued research into the effects of these service innovations.

The application of artificial intelligence in services is continuously spreading. In particular, one of the most important recent trends is the development of virtual assistants, more particularly; voice assistants, which provide consumers with various services (e.g. information, music) and with product and service recommendations based on their preferences. There is a need to understand how valuable these recommendations are for consumers. This study contributes to the emerging body of research into consumers’ use of the recommendations that voice assistants make in three key ways: (1) by analysing the roles of the benefits (i.e. convenience, compatibility, personalisation) they derive and costs they expend (i.e. cognitive effort, intrusiveness) in the value creation process related to voice assistants’ recommendations; (2) by evaluating the effect of social presence (the key voice assistant feature) on perceived value of voice assistants’ recommendations, through the benefits and costs associated with voice assistants and (3) by determining how the perceived value of voice assistants’ recommendations affects consumer engagement. An online survey was used to collect data. Partial least squares structural equation modelling (PLS-SEM) was employed to analyse the conceptual model. The core findings of the study are as follows. First, social presence enhances the benefits (especially personalisation) and reduces the costs (except for cognitive effort) associated with voice assistants. Second, personalisation was shown to be the strongest determinant of the perceived value of voice assistants’ recommendations, but their intrusiveness is a potential inhibitor in the way of increasing their value. Third, a positive relationship was observed between the perceived value of voice assistants’ recommendations and consumer engagement with the assistants.