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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.

Artificial intelligence (AI) customer service chatbots are a new application service, and little is known about this type of service. This study applies service quality, trust and satisfaction to predict users' continuance intention to use a food-ordering chatbot.

The proposed model and hypotheses are tested using online questionnaire responses to collect users' perceptions of such services. One hundred and eleven responses of actual users were received.

Empirical results show that anthropomorphism and service quality, such as problem-solving, are the antecedents of trust and satisfaction, while satisfaction has the most significant direct effect on the users' intention.

The results provide further useful insights for service providers and chatbot developers to improve services.

The purpose of this study is to assess the possibility of introducing a restaurant-menu curation (RMC) chatbot service to help consumers quickly and effectively decide on their restaurant or menu choices. To this end, it measures the characteristics of consumer chatbot experiences and analyzes their impact on future acceptance intentions through their attitudes toward the RMC chatbot service.

This study consists of three parts: developing a RMC chatbot prototype, testing the chatbot prototype and a customer survey based on experience. A convenience sample method was used to collect data from 368 adults who tried the RMC chatbot service before answering a self-administered questionnaire. Partial least squares structural equation modeling (PLS-SEM) was used to test the proposed structural model.

The results showed that all experience characteristics, except usable facets, had a significant positive impact on attitudes toward the chatbot. Three experience characteristics, “usable,” “usefulness” and “valuable,” revealed a significant positive effect on utilization intention. Attitudes toward chatbot services also significantly affected utilization intention.

The results of this study can offer practical and academic implications for establishing curation services in the restaurant industry that can increase customer acceptance and utilization intentions. Follow-up studies are required to explore and verify the various personal and psychological factors related to the intention to accept RMC chatbot services.

This study is meaningful because it makes it possible to evaluate the introduction of curation chatbot services in the restaurant sector, by developing and testing the dining-out curation service protocol to help customers’ smart choices in the information technology environment.

本研究的目的是评估引入餐厅菜单管理 (RMC) 聊天机器人服务以帮助消费者快速有效地决定他们的餐厅或菜单选择的可能性。为此, 本研究衡量了消费者聊天机器人体验的特征, 并通过他们对餐厅菜单管理聊天机器人服务的态度来分析它们对未来接受意图的影响。

研究由三部分组成; 开发餐厅菜单管理聊天机器人原型, 测试聊天机器人原型, 并根据经验进行客户调查。使用便利样本方法收集 368 名成年人的数据, 这些成年人在回答问卷之前尝试了 RMC 聊天机器人服务。 PLS-SEM 被用于测试提出的结构模型。

结果表明, 除可用方面外, 所有体验特征都对聊天机器人的态度产生了显着的积极影响。 “可用性”、“有用”和“有价值”三个体验特征对使用意愿有显着的正向影响。对聊天机器人服务的态度也显着影响了使用意愿。

本研究的结果可以为在餐饮业建立策展服务提供实践和学术意义, 从而提高客户的接受度和使用意图。需要进行后续研究, 以探索和验证与接受 RMC 聊天机器人服务的意图相关的各种个人和心理因素。

这项研究的重大意义在于它可以通过开发和测试外出就餐策展服务协议来评估在餐厅行业引入策展聊天机器人服务, 以帮助客户在信息技术 (IT) 方面做出明智的选择环境。

Artificial intelligence chatbots are shifting the nature of online services by revolutionizing the interactions of service providers with consumers. Thus, this study aims to explore the antecedents (e.g. compatibility, perceived ease of use, performance expectancy and social influence) and consequences (e.g. chatbot usage intention and customer engagement) of chatbot initial trust.

A sample of 184 responses was collected in Lebanon using a questionnaire and analyzed using structural equation modeling (SEM) by AMOS 24.

The results revealed that except for performance expectancy, all the other three factors (compatibility, perceived ease of use and social influence) significantly boost customers’ initial trust toward chatbots. Further, initial trust in chatbots enhances the intention to use chatbots and encourages customer engagement.

The study provides insights into some variables influencing initial chatbot trust. Future studies could extend the model by adding other variables (e.g. customer experience and attitude), in addition to exploring the dark side of artificial intelligence chatbots.

This study suggests key insights for marketing managers on how to build chatbot initial trust, which, in turn, will lead to an increase in customers’ interactions with the brand.

The current study marks substantial contributions to the artificial intelligence marketing literature by proposing and testing a novel conceptual model that examines for the first time the factors that impact chatbot initial trust and the key outcomes of the latter.

The purpose of this article is to investigate the factors that explain the reasons why customers may be willing to use chatbots in Zimbabwe as an e-banking customer service gateway, an area that remains under researched.

The research study applied a cross-sectional survey of 430 customers from five selected commercial banks conducted in Harare, the capital city of Zimbabwe. Hypotheses were tested using structural equation modelling.

The research study showed that a counterintuitive intention to use chatbots is directly affected by chatbots' expected performance, the habit of using them and other factors.

To better appreciate the current research concept, there is a need to replicate the same study in other contexts to enhance generalisability.

Chatbots are a trending new technology and are starting to be increasingly adopted by banks and they have to consider that customers need to get used to them.

This study contributes to bridging the knowledge gap as it investigates the factors that explain why bank customers may be willing to use chatbots in five selected commercial Zimbabwean banks. This is a pioneering study in the context of a developing economy such as Zimbabwe.

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.

Despite the proliferation of service chatbots in the tourism industry, the question on its continuance intentions among customers has largely remain unanswered. Building on an integrated framework using the task–technology fit theory (TTF) and the expectation–confirmation model (ECM), the present study aims to settle this debate by investigating the factors triggering customers to continue to use chatbots in a travel planning context.

The research followed a quantitative approach in which a survey of 322 chatbot users was undertaken. The model was empirically validated using the structural equation modelling approach using AMOS.

The results reveal that users’ expectations are confirmed when they believe that the technological characteristics of chatbots satisfy their task-related characteristics. Simply, the results reveal a significant and direct effect of TTF on customers’ confirmation and perceived usefulness towards chatbots. Moreover, perceived usefulness and confirmation were found to positively impact customers’ satisfaction towards chatbots, in which the former exerts a relatively stronger impact. Not surprisingly, customers’ satisfaction with the artificial intelligence(AI)-based chatbots emerged as a predominant predictor of their continuance use.

The findings have various practical ramifications for developers who must train chatbot algorithms on massive data to increase their accuracy and to answer more exhaustive inquiries, thereby generating a task–technology fit. It is recommended that service providers give consumers hassle-free service and precise answers to their inquiries to guarantee their satisfaction.

The present work attempted to empirically construct and evaluate the combination of the TTF model and the ECM, which is unique in the AI-based chatbots available in a tourism context. This research presents an alternate method for understanding the continuance intentions concerning AI-based service chatbots.

Despite the fact that chatbots have been largely adopted for the last few years, a comprehensive literature review research focusing on the intention of individuals to adopt chatbots is rather scarce. In this respect, the present paper attempts a literature review investigation of empirical studies focused on the specific issue in nine scientific databases during 2017-2021. Specifically, it aims to classify extant empirical studies which focus on the context of individuals' adoption intention toward chatbots.

The research is based on PRISMA methodology, which revealed a total of 39 empirical studies examining users' intention to adopt and utilize chatbots.

After a thorough investigation, distinct categorization criteria emerged, such as research field, applied theoretical models, research types, methods and statistical measures, factors affecting intention to adopt and further use chatbots, the countries/continents where these surveys took place as well as relevant research citations and year of publication. In addition, the paper highlights research gaps in the examined issue and proposes future research directions in such a promising information technology solution.

As far as the authors are concerned, there has not been any other comprehensive literature review research to focus on examining previous empirical studies of users' intentions to adopt and use chatbots on the aforementioned period. According to the authors' knowledge, the present paper is the first attempt in the field which demonstrates broad literature review data of relevant empirical studies.

By extending the service robot acceptance model (sRAM), this study aims to explore and enhance the acceptance of chatbots. The study considered functional, relational, social, user and gratification elements in determining the acceptance of chatbots.

By using the purposive sampling technique, data of 321 service customers, gathered from millennials through a questionnaire and subsequent PLS-SEM modeling, was applied for hypotheses testing.

Findings revealed that the functional elements, perceived usefulness and perceived ease of use affect acceptance of chatbots. However, in social elements, only perceived social interactivity affects the acceptance of chatbots. Moreover, both user and gratification elements (hedonic motivation and symbolic motivation) significantly influence the acceptance of chatbots. Lastly, trust is the only contributing factor for the acceptance of chatbots in the relational elements.

The study extends the literature related to chatbots and offers several guidelines to the service industry to effectively employ chatbots.

This is one of the first studies that used newly developed sRAM in determining chatbot acceptance. Moreover, the study extended the sRAM by adding user and gratification elements and privacy concerns as originally sRAM model was limited to functional, relational and social elements.

AI-based chatbots are revamping employee communication in organizations. This paper examines the adoption of AI-based employee experience chatbots by employees.

The proposed model is developed using behavioral reasoning theory and empirically validated by surveying 1,130 employees and data was analyzed with PLS-SEM.

This research presents the “reasons for” and “reasons against” for the acceptance of AI-based employee experience chatbots. The “reasons for” are – personalization, interactivity, perceived intelligence and perceived anthropomorphism and the “reasons against” are perceived risk, language barrier and technological anxiety. It is found that “reasons for” have a positive association with attitude and adoption intention and “reasons against” have a negative association. Employees' values for openness to change are positively associated with “reasons for” and do not affect attitude and “reasons against”.

This is the first study exploring employees' attitude and adoption intention toward AI-based EEX chatbots using behavioral reasoning theory.