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Driven by Davis’s (1989) technology acceptance model (TAM) and Westaby’s (2005) behavioral reasoning theory (BRT), the purpose of this study is to develop and test a conceptual model and examine consumers’ acceptance of artificial intelligence (AI) chatbots for apparel shopping.

Data from 353 eligible US respondents was collected through a self-administered questionnaire distributed on Amazon Mechanical Turk, an online panel. Confirmatory factor analysis and path analysis were used to test all hypothesized relationships using the structural equation model.

The results show that optimism and relative advantage of “reasons for” dimensions have a positive and significant influence on perceived ease of use (PEU), while innovativeness and relative advantage have a positive and significant influence on perceived usefulness (PUF). Discomfort and insecurity have no significant impact on PEU and PUF. However, complexity has a negative and significant impact on PEU but not on PUF. Additionally, PEU has a positive influence on PUF. Both PEU and PUF have a positive and significant influence on consumers’ attitudes toward using AI chatbots, which, in turn, affects the intention to use AI chatbots for apparel shopping. Overall, this study identifies that optimism, innovativeness and relative advantage are enablers and good reasons to adopt AI chatbots. Complexity is a prohibitor, making it the only reason against adopting AI chatbots for apparel shopping.

This study contributes to the literature by integrating TAM and BRT to develop a research model to understand what “reasons for” and “reasons against” factors are enablers or prohibitors that significantly impact consumers’ attitude and intention to use AI chatbots for apparel shopping through PEU and PUF.

Service research offering a view of both the dark and bright sides of smart technology remains scarce. This paper embraces a critical perspective and examines the conflicting outcomes of smart services on the customer experience (CX), with a specific focus on chatbots.

This study uses empirical research methods to examine a single case study where an online retail service provider implemented a chatbot for customer service. Using discourse analysis, we analysed 7,167 conversations between customers and the chatbot over a two-year period.

The analysis identifies seven general themes related to the effects of the chatbot on CX: interaction quality, information gathering, procedure literacy, task achievement, digital trust, shopping stress and shopping journey. We illuminate both positive (i.e. having a pleasant interaction, providing information, knowing procedures, improving tasks, increasing trust, reducing stress and completing the journey) and negative outcomes (i.e. having an unpleasant interaction, increasing confusion, ignoring procedures, worsening tasks, reducing trust, increasing stress and abandoning the journey).

The paper develops a comprehensive framework to offer a clearer view of chatbots as smart services in customer care. It delves into the conflicting effects of chatbots on CX by examining them through relational, cognitive, affective and behavioural dimensions.

The concept of ubiquitous assimilation in e-commerce, denoting the seamless integration of technologies into customer shopping experiences, has played a pivotal role in aiding e-satisfaction and, consequently, fostering patronage intention. Among these, text-based chatbots are significant innovations. In light of this, the paper aims to develop a conceptual framework and comprehend the patronage behaviour of artificial intelligence-enabled chatbot users by using chatbot usability cues and to determine whether the social presence and flow theories impact e-satisfaction, which leads to users’ patronage intention. The current research provides insights into online travel agencies (OTAs), a crucial segment within the travel and tourism sector. Given the significance of building a loyal clientele and cultivating patronage in this industry, these insights are of paramount importance for achieving sustained profitability and growth.

The research framework primarily focused on the factors that precede e-satisfaction and patronage intention among chatbot users, which include social presence, flow, perceived anthropomorphism and need for human interaction. The researchers collected the data by surveying 397 OTA chatbot users by using an online questionnaire. The data of this cross-sectional study were analysed using covariance-based structural equation modelling.

Findings reveal that e-satisfaction is positively linked with patronage intention and the variables of social presence and flow impact e-satisfaction along with chatbot usability cues. There were direct and indirect relations between chatbot usability and e-satisfaction. Moreover, the personal attributes, “need for human interaction” and, “perceived anthropomorphism” were found to moderate relations between chatbot usability cues, social presence and flow.

The impact of chatbot’s usability cues/attributes on e-satisfaction, along with perceived attributes – social presence and flow in the realm of OTAs contributes to the human–chatbot interaction literature. Moreover, the interacting effects of perceived anthropomorphism and the need for human interaction are unique in the current contextual relations.

Chatbots are increasingly engaged in service marketing. Some academics and managers think using anthropomorphism chatbots will improve positive attitudes and behaviors in the customer journey, but at a high degree of anthropomorphism, consumers may experience negative emotions such as fear and disgust due to the feeling that the robots resemble humans too much, which is known as the uncanny valley effect. Therefore, the authors aim to explore whether chatbot anthropomorphism will promote or limit the development of the customer journey and explore the moderating factors and the antecedent factors affecting consumers' perceptions of chatbot anthropomorphism.

The authors collected 72,782 unique data points from 42 articles and 82 samples using a meta-analysis. Based on the stimuli-organism-response (SOR) model, the impact of anthropomorphic chatbots on the consumer journey was discussed.

The authors’ findings show that chatbot anthropomorphism positively impacts the customer journey but not their negative attitudes. Further moderator analysis reveals that the impact depends on service result, chatbot gender and sample source. The chatbot anthropomorphism is significantly influenced by social presence cues, emotional message cues and mixed cues.

This research contributes to the chatbot anthropomorphism literature and offers guidance for managers on whether and how to enhance chatbot anthropomorphism to facilitate the customer journey and improve service sustainability.

Driven by artificial intelligence technology, chatbots have begun to play an important customer service role in the online retail environment. This study aims to explore how conversational styles improve the interaction experience between consumers and chatbots in different social crowding environments, and the moderating role of product categories is considered.

Three studies are conducted to understand the influences of conversational styles, social crowding and product categories on consumer acceptance, assessed using situational experiments and questions.

In a low social crowding environment, consumers prefer chatbots with a social-oriented (vs. task-oriented) conversational style, while in a high social crowding environment, consumers prefer a task-oriented (vs. social-oriented) conversational style, and warmth and competence mediate these effects. The moderating effect of product categories is supported.

This study expands the application of the stereotype content model to improve the interaction experience level between consumers and chatbots in online retail. The findings can provide managerial suggestions for retailers to select a chatbot's conversational style and promote a more continuous interaction between consumers and chatbots.

The purpose of this paper is to demonstrate the advancements in artificial intelligence (AI) and conversational agents, emphasizing their potential benefits while also highlighting the need for vigilant monitoring to prevent unethical applications.

As AI becomes more prevalent in academia and research, it is crucial to explore ways to ensure ethical usage of the technology and to identify potentially unethical usage. This manuscript uses a popular AI chatbot to write the introduction and parts of the body of a manuscript discussing conversational agents, the ethical usage of chatbots and ethical concerns for academic researchers.

The authors reveal which sections were written entirely by the AI using a conversational agent. This serves as a cautionary tale highlighting the importance of ethical considerations for researchers and students when using AI and how educators must be prepared for the increasing prevalence of AI in the academy and industry. Measures to mitigate potential unethical use of this evolving technology are also discussed in the manuscript.

As conversational agents and chatbots increase in society, it is crucial to understand how they will impact the community and how we can live with technology instead of fighting against it.

This study aims to determine whether or not fashion retail brands can maintain their essence by providing personalized care through conventional face-to-face interactions or the use of e-services.

An exploratory investigation is being conducted to attain this goal. According to the findings of this research, Chatbots have an impact on consumer loyalty. The quality of a Chatbot’s system, service and information are all critical to providing a positive consumer experience.

The study concluded that Chatbot e-services might potentially enable dynamic and fascinating interactions between firms and their consumers. To personalize a Chatbot, firms might change the tone of the language used. Customers are more likely to use a Chatbot if it resembles a real person, which increases their pleasure and confidence in the product.

More precisely, the emphasis of the inquiry was on Chatbot, a relatively new digital tool that offers user-friendly, personalized and one-of-a-kind support to customers. Using information supplied by consumers, the authors examine a five-dimensional model that gauges how customers feel about Chatbots in terms of their ability to communicate with users, offer amusement, be trendy, personalize interactions and solve problems.

This study aims to understand a customer-purchase mechanism in the artificial intelligence (AI)-powered chatbot context based on the elaboration likelihood model (ELM) and technology acceptance model (TAM). The first objective is to examine how to boost chatbot adoption. The second objective is to investigate the role of information characteristics, technology-related characteristics and attitude toward AI in purchase intention.

Data was collected from a sample of 492 users in Vietnam, who are potential customers of chatbots for purchase. Structural equation modeling was applied for data analysis.

Results illustrate that chatbot adoption is significantly influenced by information credibility, technology-related factors (i.e. interactivity, relative advantage and perceived intelligence), attitude toward AI and perceived usefulness. Moreover, information quality and persuasiveness motivate information credibility. Information credibility and attitude toward AI are the essential motivations for perceived usefulness. Finally, chatbot adoption and information credibility determine purchase intention.

The results are insightful for practitioners to envisage the importance of chatbot use for customer purchase in the AI scenario. Additionally, this research offers a framework to practitioners for shaping customer engagement in chatbots.

The value of this work lies in the incorporation of technology-related characteristics into the two well-established theories, the ELM and TAM, to identify the importance of AI and its applications (i.e. chatbots) for purchase and to understand the formation of perceived usefulness and chatbot use through information credibility and attitude toward AI.

This paper aims to examine the relationships between anthropomorphic cues (i.e. degrees of the humanized profile picture and naming) in artificial intelligence (AI) chatbots and business types (utilitarian-centered business vs hedonic-centered business) on consumers’ attitudes toward the AI chatbot and intentions to use the AI chatbot app and to accept the AI chatbot’s recommendation.

An online experiment with a 2 (humanized profile pictures: low [semihumanoid] vs high [full-humanoid]) × 2 (naming: Mary vs virtual assistant) × 2 (business types: utilitarian-centered business [bank] vs hedonic-centered business [café]) between-subjects design (N = 520 Mturk samples) was used.

The results of this study show significant main effects of anthropomorphic cues (i.e. degrees of profile picture and naming) in AI chatbots and three-way interactions among humanized profile pictures, naming and business types (utilitarian-centered business vs hedonic-centered business) on consumers’ attitudes toward the AI chatbot, intentions to use the AI chatbot app and intentions to accept the AI chatbot’s recommendation. This indicates that the high level of anthropomorphism generates more positive attitudes toward the AI chatbot and intentions to use the AI chatbot app and to accept the AI chatbot’s recommendation in the hedonic-centered business condition. Moreover, the mediated role of parasocial interaction occurs in this relationship.

This study is the original endeavor to examine the moderating role of business types influencing the effect of anthropomorphism on consumers’ responses, while existing literature overweighted the value of anthropomorphism in AI chatbots without considering the variation of businesses.

This study aims to investigate the impact of AI chatbots on customer trust in AI chatbots for hotel services.

The probability sampling method was employed to develop a research sample. The research uses correlation analysis and structural equation modeling to analyze the data of 413 valid observations collected in the structured questionnaire survey in Vietnam.

The paper reports that empathy response, anonymity and customization significantly impact interaction. Empathy response is found to be the strongest influence on interaction. Meanwhile, empathy response and anonymity were revealed to indirectly affect customer trust. This paper also contributes several implications for hotel providers in emerging economies.

To the best of the authors’ knowledge, this is the first study to shed light on the role of AI chatbots in explaining customers’ behavior. The results provide an enhanced understanding of how the AI chatbot system influences customers’ decision-making. It has been used to plan the chatbot application and highlight which implementation issues need the most attention in the hospitality industry.