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This study aims to investigate the customers’ behavioral intention and actual usage (AUE) of artificial intelligence (AI)-powered chatbots for hospitality and tourism in India by extending the technology adoption model (TAM) with context-specific variables.

To understand the customers’ behavioral intention and AUE of AI-powered chatbots for tourism, the mixed-method design was used whereby qualitative and quantitative techniques were combined. A total of 36 senior managers and executives from the travel agencies were interviewed and the analysis of interview data was done using NVivo 8.0 software. A total of 1,480 customers were surveyed and the partial least squares structural equation modeling technique was used for data analysis.

As per the results, the predictors of chatbot adoption intention (AIN) are perceived ease of use, perceived usefulness, perceived trust (PTR), perceived intelligence (PNT) and anthropomorphism (ANM). Technological anxiety (TXN) does not influence the chatbot AIN. Stickiness to traditional human travel agents negatively moderates the relation of AIN and AUE of chatbots in tourism and provides deeper insights into manager’s commitment to providing travel planning services using AI-based chatbots.

This research presents unique practical insights to the practitioners, managers and executives in the tourism industry, system designers and developers of AI-based chatbot technologies to understand the antecedents of chatbot adoption by travelers. TXN is a vital concern for the customers; so, designers and developers should ensure that chatbots are easily accessible, have a user-friendly interface, be more human-like and communicate in various native languages with the customers.

This study contributes theoretically by extending the TAM to provide better explanatory power with human–robot interaction context-specific constructs – PTR, PNT, ANM and TXN – to examine the customers’ chatbot AIN. This is the first step in the direction to empirically test and validate a theoretical model for chatbots’ adoption and usage, which is a disruptive technology in the hospitality and tourism sector in an emerging economy such as India.

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.

While customer experience (CE) is recognized as a critical determinant of business success, both academics and managers are yet to find a means to gain a comprehensive understanding of CE cost-effectively. The authors argue that the application of relevant AI technology could help address this challenge. Employing interactively prompted narrative storytelling, and the authors investigate the effectiveness of sentiment analysis (SA) on extracting valuable CE insights from primary qualitative data generated via chatbot interviews.

Drawing on a granular and semantically clear framework for studying CE feelings, an artificial intelligence (AI) augmented chatbot was designed. The chatbot interviewed a crowdsourced sample of consumers about their recalled service experience feelings. By combining free-text and closed-ended questions, the authors were able to compare extracted sentiment polarities against established measurement scales and empirically validate our novel approach.

The authors demonstrate that SA can effectively extract CE feelings from primary chatbot data. This findings also suggest that further enhancement in accuracy can be achieved via improvements in the interplay between the chatbot interviewer and SA extraction algorithms.

The proposed customer-centric approach can help service companies to study and better understand CE feelings in a cost-effective and scalable manner. The AI-augmented chatbots can also help companies to foster immersive and engaging relationships with customers. This study focuses on feelings, warranting further research on AI's value in studying other CE elements.

The unique inquisitive role of AI-infused chatbots in conducting interviews and analyzing data in realtime, offers considerable potential for studying CE and other subjective constructs.

For decades, artificial intelligence (AI) has been utilized within the field of mental healthcare. This paper aims to examine AI chatbots, specifically as offered through mobile applications for mental healthcare (MHapps), with attention to the social implications of these technologies. For example, AI chatbots in MHapps are programmed with therapeutic techniques to assist people with anxiety and depression, but the promise of this technology is tempered by concerns about the apps' efficacy, privacy, safety and security.

Utilizing a social informatics perspective, a literature review covering MHapps, with a focus on AI chatbots was conducted from the period of January–April 2019. A borrowed theory approach pairing information science and social work was applied to analyze the literature.

Rising needs for mental healthcare, combined with expanding technological developments, indicate continued growth of MHapps and chatbots. While an AI chatbot may provide a person with a place to access tools and a forum to discuss issues, as well as a way to track moods and increase mental health literacy, AI is not a replacement for a therapist or other mental health clinician. Ultimately, if AI chatbots and other MHapps are to have a positive impact, they must be regulated, and society must avoid techno-fundamentalism in relation to AI for mental health.

This study adds to a small but growing body of information science research into the role of AI in the support of mental health.

The purpose of this study is to review the extant literature on chatbots and stakeholder interactions to identify major trends and shed light on knowledge gaps.

A systematic literature review was conducted combining qualitative and quantitative approaches. A code book based on early systematic literature reviews was developed and used to extract information from 62 discrete peer-reviewed English articles. An inductive approach was used to analyse definitions of chatbots, topics, metrics, perspectives and implications.

Chatbots have been studied by many different disciplines, but not much from organizational, stakeholder and corporate communication perspectives. Existing studies focus on the technical developments of chatbots and chatbot language and conversations skills. Research has remained anchored at the micro-level understanding of the phenomenon, that is, the nature of chatbots, but has not yet taken into consideration the meso (organizational) or macro (societal) levels.

This study focused only on academic peer-reviewed papers in English and excluded conference proceeding, books, book chapters and editorials that may have offered other important and relevant reflections. The limited number of studies in communication-related disciplines shows that corporate communication scholars could contribute more to the discussion of chatbot–stakeholder interactions.

This is the first research in the field of corporate communication that examines organizational chatbot–stakeholder interactions. Results of this review offer important information on chatbots' organizational capabilities and affordances, which, arguably, must be taken into consideration when stakeholder engagement strategies are set.

Brands are increasingly considering the use of chatbots to supplement, or even replace, humans in service interactions. Like humans, chatbots can follow certain service scripts in their encounters, which can subsequently determine the customer experience. Service scripts are verbal prescriptions that seek to standardize customer service interactions. However, while the role of service scripts is well documented, despite the increasing use of chatbots as a service mechanism, less is known about the effect, on consumers, of different service scripts presented during chatbot service encounters.

An experimental scenario was developed to test the research hypotheses. Respondents were randomly allocated to scenarios representing a 2 (service interaction: human, chatbot) × 2 (service script: education, entertainment) design. A total of 262 US consumers constituted the final sample for the study.

The findings indicate that when employing an education script, a significant positive effect occurs for human service agents (compared to chatbots) in terms of both satisfaction and purchase intention. These effects are fully mediated by emotion and rapport, showing that the bonds developed through the close proximity to a human service agent elicit emotion and develop rapport, which in turn influence service outcomes. However, this result is present only when an educational script is used.

This paper contributes to the emerging service marketing literature on the use of digital services, in particular chatbots, in service interactions. We show that differences occur in key outcomes dependent on the type of service script employed (education or entertainment). For managers, this study indicates that chatbot interactions can be tailored (in script delivered) in order to maximize emotion and rapport and subsequently consumer purchase intention and satisfaction.

Purpose: The information-intensive nature of the tourism and hospitality industry requires regular communication between firms and customers. Yet, customer service personnel’s high contact levels with customers often lead to customer dissatisfaction arising from embarrassment in emotion-inducing service encounters. Accordingly, such companies have been seeking a cost-effective means of maintaining consistency in customer contact. Thus, it seems that the future of chatbots is here.

Design/methodology/approach: This chapter examines chatbots in two ways: the technical composition and its adoption by tourism firms. The technical perspective is represented by a diagram which espouses the functioning of chatbots from inputs query to output reply. On its adoption by tourism firms, two main organizational theories were proposed.

Findings: While chatbots are diffusing rapidly in other areas, their use in the tourism and hospitality industry remains low. We have examined the role of chatbots in various areas of the tourism and hospitality industry and highlighted the barriers to their successful adoption. By applying a conceptual and theoretical approach, our study used a hybrid of institutional theory and organizational learning theory and diagrammatically espouses how the integration of these theories can aid subsequent studies to understand the environmental and organization-specific factors influencing chatbots adoption.

Research limitations/implications: This study is conceptual, consequently, we recommend future studies to empirical test and validate our proposed conceptual framework.

Originality/value: This study is one of the earliest studies that advances firm-level adoption of chatbots by integrating two key organizational theories.

• 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

This paper aims to explore integrating chatbot applications into libraries to improve reference services.

This paper explores the benefits of using chatbots as virtual reference librarians. Emma the Mentor Public Library’s Catbot is used as a case study.

Chatbots cannot replicate the complexity of human interaction (both knowledge and emotional), but these can provide a cost-effective way to answer the majority of routine reference questions and direct users to the appropriate service.

Readers will increase their awareness of how chatbots can streamline the work of the reference department by answering the majority of routine reference questions and freeing library staff to focus on more demanding research and tasks uniquely suited to humans.

Recent technological and digital developments have opened new avenues for customer data utilization in insurance services. One form of this data transformation is automated chatbots that provide convenient access to data leveraged through a discussion-like interface. The purpose of this paper is to uncover how insurance chatbots support customers’ value creation.

Three complementary theoretical perspectives – artificial intelligence, service logic, and reverse use of customer data – are briefly discussed and integrated into a conceptual framework. The suggested framework is further shown through illustrative case examples that characterize different ways of supporting customers’ value creation.

Chatbots represent a new type of interaction through which companies can influence customers’ value creation by providing them with additional resources. Based on the proposed conceptual framework and the illustrative case examples, four metaphors are identified that characterize how insurance chatbots can support customers’ value creation.

The study is conceptual in nature, and the case examples are used for illustrative purposes. No representative data from those users who will eventually determine whether chatbots are of value was used.

Using the suggested framework, which is aligned with provider service logic, insurance companies can consider what kind of a role they wish to play in customers’ value-creating processes.

Automated chatbots provide convenient access to data leveraged through a discussion-like interface. This study is among the earliest to address their value-creating potential in insurance.