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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 study is to explore customer-artificial intelligence (AI) service technology engagement and relationship development drivers, as well as potential negative consequences in the context of social chatbots.

A sequential mixed-method approach combined exploratory qualitative and confirmatory quantitative analyses. A conceptual model developed from Study 1 qualitative content analysis of in-depth interviews with active users of the AI social chatbot Replika was tested in Study 2 by analyzing survey data obtained from current Replika users.

Loneliness, trust and chatbot personification drive consumer engagement with social chatbots, which fosters relationship development and has the potential to cause chatbot psychological dependence. Attachment to a social chatbot intensifies the positive role of engagement in relationship development with the chatbot.

This study was the first to combine qualitative and quantitative approaches to explore drivers, boundary conditions and consequences of relationship and dependence formation with social chatbots. The authors proposed and empirically tested a novel theoretical model that revealed an engagement-based mechanism of relationship and dependence formation with social chatbots.

Chatbots are increasingly used in online retail settings and are becoming a powerful tool for brands to engage customers. However, consumers’ satisfaction with these chatbots is mixed. Thus, this paper aims to investigate how using a social- versus task-oriented anthropomorphic communication style can improve customer satisfaction.

The authors explore the link between the anthropomorphic communication style use and customer perceptions/customer satisfaction in online customer service interactions. Two experiment scenarios were developed to test these links.

Overall, using a social-oriented communication style boosts customer satisfaction. Warmth perception of the chatbot mediates this effect, while chatbot’s anthropomorphised role (servant versus partner) moderates this effect.

This paper enriches the bilateral communication literature by extending the investigation on communication style effects to chatbot service interactions and revealing the psychological process driving the impacts. It also adds to the existing literature on chatbots as a customer service and contributes to the prominent topic examining how consumers react to artificial intelligence that is used to establish and maintain a relationship with them. Additionally, the authors also make contribution to the warmth and competence literature by demonstrating that customers can interpret social cues in chatbot service interactions mainly based on the warmth dimension. Thus, the authors further add to the growing chatbot humanness perception literature and respond to the calls for investigating more anthropomorphic design cues to enhance chatbot humanness. Finally, the authors also provide a way to help reconcile seemingly conflicting prior findings.

This study presents one of the earliest empirical investigations on how brand chatbots' anthropomorphic design and social presence communication strategies may improve consumer evaluation outcomes via the mediators of parasocial interaction and perceived dialogue.

This study employs a 2 (high vs. low social presence communication) by 2 (anthropomorphic vs. non-anthropomorphic bot profile) between-subject experimental design to evaluate how chatbots' high social presence communication and anthropomorphic profile design may enhance perceptions of parasocial interactions and dialogue with the chatbot, which in turn drive user engagement, interaction satisfaction and attitude toward the represented brand.

The influences of chatbots' high social presence communication on consumer engagement outcomes are mediated by perceived parasocial interaction and dialogue. Additionally, chatbots' anthropomorphic profile design can boost the positive effects of social presence communication via the psychological mediators.

This study advances the interactive marketing literature by focusing on an emerging interactive technology, chatbots. Additionally, distinct from prior chatbot studies that focused on the utilitarian use of chatbots for online customer support, this study not only examines which factors of chatbot communication and profile design may drive chatbot effectiveness but also examines the mechanism underlying the messaging and design effects on consumer engagement. The findings highlight the mediating role of interpersonal factors of parasocial interaction and perceived dialogue.

This study investigates the determinants of effective human and artificial intelligence (AI) relationship-building strategies for brands. It explores the antecedents and consequences of consumers' interactant satisfaction with communication and identifies ways to enhance consumer purchase intention via AI chatbot promotion.

Microsoft Xiaoice served as the focal AI chatbot, and 331 valid samples were obtained. A two-stage structural equation modeling-artificial neural network approach was adopted to verify the proposed theoretical model.

Regarding the IQ (intelligence quotient) and EQ (emotional quotient) of AI chatbots, the multi-dimensional social support model helps explain consumers' interactant satisfaction with communication, which facilitates affective attachment and purchase intention. The results also show that chatbots should emphasize emotional and esteem social support more than informational support.

Brands should focus more on AI chatbots' emotional and empathetic responses than functional aspects when designing dialogue content for human–AI interactions. Well-designed AI chatbots can help marketers develop effective brand promotion strategies.

This research enriches the human–AI interaction literature by adopting a multi-dimensional social support theoretical lens that can enhance the interactant satisfaction with communication, affective attachment and purchase intention of AI chatbot users.

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 represent an innovative channel for retailers to meet young customers' needs anywhere and at any time. Being an emergent technology, however, it is important to investigate more thoroughly how users perceive it, and which are the variables that enhance a positive attitude towards this technology. On this premise, this study applies a social relationship perspective to the design of chatbots addressed to younger consumers.

The study adopts a between-participants factorial design to investigate the effects of visual cues (avatar presence vs avatar absence) and interaction styles (social-oriented vs task-oriented) on social presence and how this, in turn, enhances millennials' perceived enjoyment, trust and, ultimately, attitude towards the chatbot. A survey experiment was employed to conduct the study on data collected from 193 Italian millennials.

The results show that applying a social-oriented interaction style increases users' perception of social presence, while an insignificant effect was found for avatar presence. The partial least square structural equation modeling (PLS-SEM) analysis further confirms the hypothesised model.

The adoption of new digital technologies such as chatbots is likely to have a far reaching effect on retailers, consumers, employees and society. For this reason, a broad understanding of the phenomenon is needed. To the best of our knowledge, this is the first study to provide results from an experimental design in which both interaction style (social- vs task-oriented) and avatar (presence vs absence) of a chatbot are manipulated to directly explore social presence and its effect on trust, perceived enjoyment and millennials' attitude towards a chatbot applied for retailing purposes.

This article draws on a reasoned action perspective and the two fundamental dimensions (i.e. warmth and competence) of the Stereotype Content Model (SCM) to analyze customers' chatbot-related attitudes and usage intentions in service retailing. The authors investigate how chatbot, customer, and contextual characteristics, along with perceptions of chatbot warmth and competence, shape customers' chatbot-related attitudes. Furthermore, the authors analyze whether the customer generation or the service context moderates the relationship between chatbot-related attitudes and usage intentions.

The results are based on two studies (N = 807). Study 1 relies on a 2 (chatbot communication style: high vs low social orientation) × 2 (customer generation: generation X [GenX] vs generation Z [GenZ]) × 2 (service context: restaurant vs medical) between-subjects design. Study 2 relies on a similar number of respondents from GenX and GenZ who answered questions on scheduling a service with either the dentist or the favorite restaurant of the respondents.

GenZ shows more positive attitudes toward chatbots than GenX, due to higher perceptions of warmth and competence. While GenZ has similar attitudes toward chatbots with a communication style that is high or low in social orientation, GenX perceives chatbots with a high social orientation as warmer and has more favorable attitudes toward chatbots. Furthermore, the positive effect of chatbot-related attitudes on usage intentions is stronger for GenX than GenZ. These effects do not significantly differ between the considered contexts.

This research formulates future directions to stimulate debate on factors that service retailers should consider when employing chatbots.

Chatbot users’ communication experience with disembodied conversational agents was compared with instant messaging (IM) users’ communication experience with human conversational agents. The purpose of this paper is to identify what affects users’ intention to reuse and whether they perceive any difference between the two.

A conceptual model was developed based on computer-mediated communication (CMC) and interpersonal communication theories. Data were collected online from four different continents (North America, Europe, Asia and Australia). Partial least squares structural equation modeling was applied to examine the research model.

The findings mainly reveal that media richness and social presence positively influence trust and reuse intention through task attraction and social attraction; IM users reported significantly higher scores in terms of communication experience, perceived attractiveness of the conversational agent, and trust than chatbot users; users’ trust in the conversational agents is mainly determined by perceived task attraction.

Customers’ evaluation of the communication environment is positively related to their perceived competence of the conversational agent which ultimately affect their intention to reuse chatbot/IM. The findings reveal determinants of chatbot/IM adoption which have rarely been mentioned by previous work.

Practitioners should note that consumers in general still prefer to interact with human conversational agents. Practitioners should contemplate how to combine chatbot and human resources effectively to deliver the best customer service.

This study goes beyond the Computer as Social Actor paradigm and Technology Acceptance Model to understand chatbot and IM adoption. It is among one of the first studies that compare chatbot and IM use experience in the tourism and hospitality literature.

• Connecting with customers at the organizational frontline is not only a matter of transaction but is also a matter of emotional connection

• Customers interact with retailers to seek social contact in order to recover their affective and cognitive balance

• Chatbots are well suited to resolve simple problems; they keep social interactions simple, and they provide cognitive clarity and personalized answers without engaging customers in socioaffective interactions

• Chatbots must develop the ability to read customers' emotions in order to identify the exact point at which the conversation must be managed by a human agent

Connecting with customers at the organizational frontline is not only a matter of transaction but is also a matter of emotional connection

Customers interact with retailers to seek social contact in order to recover their affective and cognitive balance

Chatbots are well suited to resolve simple problems; they keep social interactions simple, and they provide cognitive clarity and personalized answers without engaging customers in socioaffective interactions

Chatbots must develop the ability to read customers' emotions in order to identify the exact point at which the conversation must be managed by a human agent