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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.

Customer service conversations are becoming increasingly digital and automated, leaving service encounters impersonal. The purpose of this paper is to identify how customer service agents and conversational artificial intelligence (AI) applications can provide a personal touch and improve the customer experience in customer service. The authors offer a conceptual framework delineating how text-based customer service communication should be designed to increase relational personalization.

This paper presents a systematic literature review on conversation styles of conversational AI and integrates the extant research to inform the development of the proposed conceptual framework. Using social information processing theory as a theoretical lens, the authors extend the concept of relational personalization for text-based customer service communication.

The conceptual framework identifies conversation styles, whose degree of expression needs to be personalized to provide a personal touch and improve the customer experience in service. The personalization of these conversation styles depends on available psychological and individual customer knowledge, contextual factors such as the interaction and service type, as well as the freedom of communication the conversational AI or customer service agent has.

The article is the first to conduct a systematic literature review on conversation styles of conversational AI in customer service and to conceptualize critical elements of text-based customer service communication required to provide a personal touch with conversational AI. Furthermore, the authors provide managerial implications to advance customer service conversations with three types of conversational AI applications used in collaboration with customer service agents, namely conversational analytics, conversational coaching and chatbots.

The purpose of this paper is to examine the effectiveness of social media guidelines (SMGs), as well as their impact on control mutuality, a sub-dimension of the organization–employee relationship (OER). A total of two studies compare guidelines with a focus that is either predominantly incentive or restrictive. In addition, they investigate the moderating effect of guideline writing style and enforcement.

In total, two online experiments were conducted among Belgian employees. Participants read a social media policy manipulated in terms of focus (restrictive vs incentive) and style (conversational vs corporate; Study 1) or enforcement (signature requested vs not requested; Study 2).

Incentive guidelines increase employee branding behavior more than their restrictive counterparts, while also safeguarding employees' perceived control mutuality. However, solid SMGs will not compensate for an organization's bad reputation among employees. The guidelines' style and manner of enforcement did not seem to matter.

Communication executives can use our findings to draft SMGs in a way that increases opportunities (e.g. ambassadorship) and reduces risks (e.g. criticism) associated with employee social media use.

Prior research on SMGs is predominantly descriptive and focused on the organizational perspective. This research paper contributes to both theory and practice by studying the causal impact of these guidelines on employees.

University students often struggle with academic writing because of the challenges involved in negotiating the hidden rules and implicit discursive practices in academic writing. An academic literacies approach has emphasized writing as social practice and recognized that the literacy practices of the university are often epistemological. Blogs provide an opportunity for students to immerse themselves in situated, socially interactive writing in academic contexts. This study sought to explore blog writing from an academic literacies perspective. Data were collected from of two cohorts of students (Winter 2010 and 2011 terms) participating in a small university fourth year seminar class. The data consisted of blog postings from the two cohorts, interviews with the instructor, and course evaluations. The blog posts and comments were analysed using an intertextual analytical framework. Findings indicate that students do develop academic literacies through blog writing because of particular features of blogs: the immediate audience, the flexibility of purpose of blogs and the informal style of language.

Conversational agents are natural language interaction interfaces designed to simulate conversation with a real person. This paper seeks to investigate current development and applications of these systems worldwide, while focusing on their availability in Canadian libraries. It aims to argue that it is both timely and conceivable for Canadian libraries to consider adopting conversational agents to enhance – not replace – face‐to‐face human interaction. Potential users include library web site tour guides, automated virtual reference and readers' advisory librarians, and virtual story‐tellers. To provide background and justification for this argument, the paper seeks to review agents from classic implementations to state‐of‐the‐art prototypes: how they interact with users, produce language, and control conversational behaviors.

The web sites of the 20 largest Canadian libraries were surveyed to assess the extent to which specific language‐related technologies are offered in Canada, including conversational agents. An exemplified taxonomy of four pragmatic purposes that conversational agents currently serve outside libraries – educational, informational, assistive, and socially interactive – is proposed and translated into library settings.

As of early 2010, artificially intelligent conversational systems have been found to be virtually non‐existent in Canadian libraries, while other innovative technologies proliferate (e.g. social media tools). These findings motivate the need for a broader awareness and discussion within the LIS community of these systems' applicability and potential for library purposes.

This paper is intended for reflective information professionals who seek a greater understanding of the issues related to adopting conversational agents in libraries, as this topic is scarcely covered in the LIS literature. The pros and cons are discussed, and insights offered into perceptions of intelligence (artificial or not) as well as the fundamentally social nature of human‐computer interaction.

The study aims to examine the role that gendered talk plays in the workplace in both task and non‐task related interactions.

The research undertaken is a hybrid of qualitative and quantitative research using a single case study. The case study, comprising mixed gender and mixed status employees of an American multinational corporation, demonstrates similarities and differences between women's and men's speech patterns in this workplace setting. Through the recording and subsequent transcription of meetings that took place among the participants, a data archive was created, enabling analysis of the conversations to take place.

The research findings imply that organisations may need to move away from cultures that favour particular talk related norms to ones that facilitate the integration and assimilation of different types of talk, recognising that women and men use language differently.

The speaking dimension of communication is very rich and can be understood at many different levels. Thus, by virtue of the nature of this undertaking along with the richness and the time and energy constraints within which it operated, it was impossible to broaden the scope of the inquiry any further. It is necessary to continue this research involving various other combinations of participants on a gender and a status dimension.

This research uncovers the impact of gendered talk on decision making and leadership in the organisation.

This paper offers valuable insights for practitioners in relation to the challenge faced by organisations in their need to achieve a more balanced representation of women and men in decision‐making positions.

Conversational agents (chatbots, avatars and robots) are increasingly substituting human employees in service encounters. Their presence offers many potential benefits, but customers are reluctant to engage with them. A possible explanation is that conversational agents do not make optimal use of communicative behaviors that enhance relational outcomes. The purpose of this paper is to identify which human-like communicative behaviors used by conversational agents have positive effects on relational outcomes and which additional behaviors could be investigated in future research.

This paper presents a systematic review of 61 articles that investigated the effects of communicative behaviors used by conversational agents on relational outcomes. A taxonomy is created of all behaviors investigated in these studies, and a research agenda is constructed on the basis of an analysis of their effects and a comparison with the literature on human-to-human service encounters.

The communicative behaviors can be classified along two dimensions: modality (verbal, nonverbal, appearance) and footing (similarity, responsiveness). Regarding the research agenda, it is noteworthy that some categories of behaviors show mixed results and some behaviors that are effective in human-to-human interactions have not yet been investigated in conversational agents.

By identifying potentially effective communicative behaviors in conversational agents, this study assists managers in optimizing encounters between conversational agents and customers.

This is the first study that develops a taxonomy of communicative behaviors in conversational agents and uses it to identify avenues for future research.

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.

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 paper aims to study the role of self-concept in consumer relationships with anthropomorphised conversational artificially intelligent (AI) agents. First, the authors investigate how the self-congruence between consumer self-concept and AI and the integration of the conversational AI agent into consumer self-concept might influence such relationships. Second, the authors examine whether these links with self-concept have implications for mental well-being.

This study conducted in-depth interviews with 20 consumers who regularly use popular conversational AI agents for functional or emotional tasks. Based on a thematic analysis and an ideal-type analysis, this study derived a taxonomy of consumer–AI relationships, with self-congruence and self–AI integration as the two axes.

The findings unveil four different relationships that consumers forge with their conversational AI agents, which differ in self-congruence and self–AI integration. Both dimensions are prominent in replacement and committed relationships, where consumers rely on conversational AI agents for companionship and emotional tasks such as personal growth or as a means for overcoming past traumas. These two relationships carry well-being risks in terms of changing expectations that consumers seek to fulfil in human-to-human relationships. Conversely, in the functional relationship, the conversational AI agents are viewed as an important part of one’s professional performance; however, consumers maintain a low sense of self-congruence and distinguish themselves from the agent, also because of the fear of losing their sense of uniqueness and autonomy. Consumers in aspiring relationships rely on their agents for companionship to remedy social exclusion and loneliness, but feel this is prevented because of the agents’ technical limitations.

Although this study provides insights into the dynamics of consumer relationships with conversational AI agents, it comes with limitations. The sample of this study included users of conversational AI agents such as Siri, Google Assistant and Replika. However, future studies should also investigate other agents, such as ChatGPT. Moreover, the self-related processes studied here could be compared across public and private contexts. There is also a need to examine such complex relationships with longitudinal studies. Moreover, future research should explore how consumers’ self-concept could be negatively affected if the support provided by AI is withdrawn. Finally, this study reveals that in some cases, consumers are changing their expectations related to human-to-human relationships based on their interactions with conversational AI agents.

This study enables practitioners to identify specific anthropomorphic cues that can support the development of different types of consumer–AI relationships and to consider their consequences across a range of well-being aspects.

This research equips marketing scholars with a novel understanding of the role of self-concept in the relationships that consumers forge with popular conversational AI agents and the associated well-being implications.