Discovering meaningful engagement through interaction between customers and service robots

Hyun, Youyung; Hlee, Sunyoung; Park, Jaehyun; Chang, Younghoon

doi:10.1080/02642069.2022.2088738

Cited by 24 publications

(7 citation statements)

References 76 publications

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“…For example, Saxena (2022), Xiao and Kumar (2021), and Grundner and Neuhofer (2021) suggest that AI may reduce, or (co)destroy, perceived value, including in cases of service failure or unmet expectations (e.g., when the algorithm is still learning). Likewise, while authors, including Hlee et al (2022) and Hyun et al (2022), show that elevated AI friendliness, coolness, or competence boost CE, at low levels, these may hamper the development of these variables.…”

Section: Resultsmentioning

confidence: 99%

“…Thinking and feeling, or generative and predictive, AI technologies, in particular, are able to improve their performance over time (Dwivedi et al, 2023), yielding pertinent implications for human/AI collaboration. For example, while Hyun et al (2022) propose that AI-based CE impacts the viability of service that is jointly provided by employees and service robots, AI-related learning also transpires by virtue of the technology's collaboration with other human or nonhuman agents (Pradeep et al, 2019). Overall, despite growing interest in the human/AI collaboration consequences of AI-based CE (Peng et al, 2022), our review reveals a relative paucity of studies-and, thus, a need for further exploration-in this area.…”

Section: Conceptual Modelmentioning

confidence: 99%

“…Another pertinent AI classification is (a) generative AI , which is used to create novel, original, or creative (e.g., textual/image‐based) content (e.g., ChatGPT/Copy.ai; Dwivedi et al, 2023), and (b) predictive AI , which uncovers patterns in historical data to predict or forecast specific future outcomes, behaviors, or events (e.g., predictive SMS; Hollebeek et al, 2021). By helping consumers execute specific tasks more efficiently or effectively (e.g., through predictive text), AI technologies can boost their engagement, whether with the technology or with the brand/firm (Hyun et al, 2022; Kull et al, 2021). For example, personalized AI‐generated solutions can help raise consumers' (e.g., monetary/referral‐based) resource investments in their brand interactions (Barnes & de Ruyter, 2022), boosting their engagement (Bertrandias et al, 2021).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

See 2 more Smart Citations

Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research

Hollebeek,

Menidjel,

Sarstedt

et al. 2024

Psychology and Marketing

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While consumer engagement (CE) in the context of artificially intelligent (AI‐based) technologies (e.g., chatbots, smart products, voice assistants, or autonomous cars) is gaining traction, the themes characterizing this emerging, interdisciplinary corpus of work remain indeterminate, exposing an important literature‐based gap. Addressing this gap, we conduct a systematic review of 89 studies using the Preferred Reporting Items for Systematic reviews and Meta‐Analyses (PRISMA) approach to synthesize the AI‐based CE literature. Our review yields three major themes of AI‐based CE, including (i) Increasingly accurate service provision through AI‐based CE; (ii) Capacity of AI‐based CE to (co)create consumer‐perceived value, and (iii) AI‐based CE's reduced consumer effort in their task execution. We also develop a conceptual model that proposes the AI‐based CE antecedents of personal, technological, interactional, social, and situational factors, and the AI‐based CE consequences of consumer‐based, firm‐based, and human‐AI collaboration outcomes. We conclude by offering pertinent implications for theory development (e.g., by offering future research questions derived from the proposed themes of AI‐based CE) and practice (e.g., by reducing consumer‐perceived costs of their brand/firm interactions).

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Section: Resultsmentioning

confidence: 99%

Section: Conceptual Modelmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research

Hollebeek,

Menidjel,

Sarstedt

et al. 2024

Psychology and Marketing

View full text Add to dashboard Cite

show abstract

“…Hence, security and privacy concerns may negatively impact users' intention to use AI-based systems (Cai et al, 2022), willingness to disclose personal information and preferences, such as engaging with the technology (Lee and Cranage, 2011;Shin et al, 2022). Perceived competence of AI-based systems was validated to be effective on utilitarian value expectations, loyalty (Belanche et al, 2021), intentions to use (Liu et al, 2022), continuance usage intention (Hu et al, 2021) and engagement (Hyun et al, 2022). Accordingly, scholars demonstrated that performance ambiguity negatively influences customers' trust in the technology (Johnson et al, 2008), and behavioral responses, such as technology adoption, purchasing and satisfaction (Yoganathan et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Tips and trips: a structural model of guests’ intentions to stay and tip for AI-based services in hotels

Morosan,

Dursun-Cengizci

2023

JHTT

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Purpose Given the rapid development in artificial intelligence (AI), the hotel industry is deploying AI-based systems. In line with this important development, this study aims to examine the impact of trust in the hotel and AI-related performance ambiguity on consumers’ engagement with AI-based systems. This study ultimately examined the impact of engagement on consumers’ intentions to stay in hotels offering such systems, and intentions to tip. Design/methodology/approach This study developed a conceptual model based on the social cognition theory. The study used an online survey methodology and collected data from a nationwide sample of 400 hotel consumers from the USA. The data analysis was conducted with structural equation modeling. Findings Consumers’ engagement is strongly influenced by their trust in the hotel but not by performance ambiguity associated with AI. In turn, engagement strongly influenced consumers’ intentions to stay in hotels that have such systems and their intentions to tip. Originality/value As AI systems capable of making decisions for consumers are becoming increasingly present in hotels, little is known about the way consumers engage with such systems and whether their engagement leads to economic impact. This is the first study that validated a model that explains intentions to stay and tip for services facilitated by autonomous AI-based systems that can make decisions for consumers.

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“…Those virtual agents are algorithm‐based bots that provide guidance and feedback to consumers as they proceed through the customization process (Chattaraman et al, 2012; Söderlund & Oikarinen, 2021). The existing research has widely examined the role of virtual agents in various marketing contexts, such as online retailing (Chattaraman et al, 2012) and restaurants (Hyun et al, 2022). However, prior research paid scant attention to the use of virtual agents in the e‐customization context in general and the role of the feedback they provide during the e‐customization process.…”

Section: Introductionmentioning

confidence: 99%

Virtual agents that flatter you: Moderating effects of self‐esteem and customization target in e‐customization services

Li,

Si,

Chan

2023

Psychology and Marketing

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As e‐customization services have grown in popularity, companies widely use virtual agents in such services to improve consumers' online shopping experience. However, extant research has not thoroughly clarified the best way to leverage the use of virtual agents and involve consumers in the e‐customization process. Drawing on self‐enhancement and self‐verification theories, this research investigates the effects of an instrumental social influence tactic employed by virtual agents—specifically, flattering feedback—and the pivotal roles of consumers' self‐esteem and customization target when evaluating and using e‐customization services. Through four experiments that simulate e‐customization experiences using different products, we find that the effect of virtual agents' flattering feedback on customization outcomes (i.e., word‐of‐mouth, product interest, and actual purchase) is contingent on consumers' self‐esteem. Among consumers with high self‐esteem, flattering (vs. generic) feedback from virtual agents in the e‐customization process elicits more positive customization outcomes. In contrast, the opposite is true among those with low self‐esteem. Moreover, consumers' process involvement mediates the interaction effects, and the interaction effects are attenuated when consumers customizing the product for others (vs. oneself). Our work contributes to online customization research by unveiling the mechanism and boundary conditions of the potentially double‐edged effect of virtual agents' flattery.

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Discovering meaningful engagement through interaction between customers and service robots

Cited by 24 publications

References 76 publications

Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research

Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research

Tips and trips: a structural model of guests’ intentions to stay and tip for AI-based services in hotels

Virtual agents that flatter you: Moderating effects of self‐esteem and customization target in e‐customization services

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