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This study investigates whether and how the service quality of artificial intelligence (AI) chatbots affects customer loyalty to an organization.

Based on the sequential chain model of service quality loyalty, this study first classifies AI chatbot service quality into nine attributes and then develops a research model to explore the internal mechanism of how AI chatbot service quality affects customer loyalty. The analysis of survey data from 459 respondents provided insights into the interrelationships among AI chatbot service quality attributes, perceived value, cognitive and affective trust, satisfaction and customer loyalty.

The results show that AI chatbot service quality positively affects customer loyalty through perceived value, cognitive trust, affective trust and satisfaction.

This study captures the attributes of the service quality of AI chatbots and reveals the significant influence of service quality on customer loyalty. This study develops research on service quality in the information system (IS) field and extends the sequential chain model of quality loyalty to the context of AI services. The findings not only help an organization find a way to improve customers' perceived value, trust, satisfaction and loyalty but also provide guidance in the development, adoption, and post-adoption stages of AI chatbots.

This paper aims to examine how consumers’ perceptions of artificial intelligence (AI) chatbots influence individuals’ cognitive and emotional states and their subsequent behavioural intentions vis-à-vis online travel agencies (OTAs).

The survey sample comprised 566 customers who had experienced the use of travel AI chatbots in China using a combination of online and offline questionnaires. Partial least squares structural equation modelling was used to test the hypotheses.

The results revealed that interaction and information quality, as AI chatbot stimuli, significantly increase potential tourists’ trust and purchase intention. Perceived usefulness plays a mediating role in the relationship among interactivity, information quality, customer trust and purchase intention. Furthermore, the findings indicated that customers with high product familiarity exhibited greater trust in products demonstrating a high level of perceived usefulness.

By integrating cognitive consistency theory, this study theoretically validates the applicability of the stimulus–organism–response framework on AI chatbots and provides academics with useful insights regarding the influence mechanisms of human–computer interaction and information quality on customer response within OTA settings.

研究旨在检验消费者对人工智能聊天机器人的感知如何影响潜在旅游者个体的认知、情绪状态以及购买意愿。

研究结合线上、线上问卷调查方式共调研了566名体验过在线旅行社中聊天机器人的消费者, 进一步利用偏最小二乘结构方程模型对理论假设进行实证检验。

研究发现人机互动性和信息质量作为聊天机器人的外在刺激, 显著影响潜在游客的信任和购买意愿; 感知有用性在互动性、信息质量、顾客信任和购买意愿之间起中介作用。此外, 产品熟悉度高的顾客对具有高感知有用性的产品会产生更大的信任。

研究结合认知一致性理论, 从理论上拓展了SOR框架在旅游聊天机器人的适用性, 解释了消费者响应人机交互的内在机制。

Chatbots represent an undeniable player between online retailers and customers as they boost operational efficiency and bring cost savings to businesses while offering convenience for customers in terms of timing and immediacy. However, as chatbots represent a new-born online touchpoint in retailing, especially when it comes to online pre-purchase and purchase experience, this study examines whether and how effort expectation, facilitating condition, performance expectancy, social influence, trust, perceived risk and flow affect consumers' intention to use chatbots for online shopping. The purpose of this paper is to address this issue.

A total of 226 respondents participated in an online survey. Participants were asked to try a new online service and interact with a chatbot designed using Chatfuel, a platform within the Facebook Messenger setting. Structural equation modelling was used to test the proposed research model regarding the intention to use chatbots.

This study discusses the importance of offering useful and trustworthy conversational agents for online shopping and argues and explains the insignificant paths amongst other studied factors and intention to use chatbots concluding with the need to explore more drivers for such contemporary technologies. Moreover, the findings indicate that trust turns out to be an important predictor of behavioural intention towards chatbots, in addition to its role in mitigating perceived risk and enhancing flow experience.

Given the lack of empirical evidence related to chatbots applied for business purposes, this paper fills a gap in this research field and provides a deeper understanding of what leverages consumers' intention to use chatbots for online shopping.

This research work aims to arise from developing new communication channels for customer service in micro, small and medium enterprises (MSMEs), such as chatbots. In particular, the results of the usability testing of three chatbots implemented in MSMEs are presented.

The methodology employed includes participants, chatbot development platform, research methodology, software development methodology and usability test to contextualize the study's results.

Based on the results obtained from the System Usability Scale (SUS) and considering the accuracy of the chatbot's responses, it is concluded that the level of satisfaction in using chatbots is high; therefore, if the chatbot is well integrated with the communication systems/channels of the MSMEs, the client receives an excellent, fast and efficient service.

The paper analyzes chatbots for customer service and presents the usability testing results of three chatbots implemented in MSMEs.

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.

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.

This paper aims to detail how a university library developed an AI chatbot to meet a growing need for virtual reference services. This chatbot was developed using Google's free Dialogflow bot platform and embedded in the library's website. With the onset of COVID-19 and a greater reliance on virtual services, chatbots have become of increasing interest to libraries as a tool to provide enhanced services during non-staffed hours and to perform basic information triage when virtual chat transactions reach an overwhelming number of available staff.

Using in-depth research into current practices and readily available tools, a small non-technical team at a university library designed and piloted an AI chatbot that employs natural language processing and AI training. This article describes the chatbot development and implementation process. Results of chatbot interactions after one academic year of usage are also reviewed.

This study reveals that a university library chatbot may be developed and deployed with minimal coding knowledge using existing tools. Chatbot content can be populated through current library information sources and trained to address typical information inquiries. However, additional development and testing is needed to increase user engagement.

This study indicates that libraries can develop and deploy chatbots to meet user information inquiries without onerous technical training or IT resources. It describes best practices for chatbots and the steps necessary to deploy a chatbot on a library website.

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 attempts to provide significant information on the increased growth of literature of chatbots and virtual assistants. Technological changes are dynamic and keep changing at regular intervals. Therefore, it becomes highly crucial to review the performance of chatbots and virtual assistants. This paper aims to review the literature by eminent researchers in the form of authors, keywords and major contributing organizations.

Systematic literature review and bibliometric analysis is used to analyze the growth of literature on chatbots. A sum of articles has been extracted from Scopus database with selected keywords and with certain filters. VOSviewer software is used for analysis of data. A total of 130 documents are extracted from Scopus database from 2017 to 2021 (31st August).

This study provides a significant contribution to an existing literature and provides the understanding of research in the area of chatbots and virtual assistants. The authors with maximum number of citations are Yan, Zaho, Bengio, Weizenbaum, Song, Zhou and Maedche with jointly 180 citations. Research is been contributed by different countries where the United States is the country with highest number of documents published. The United States contributes 17% of the total production in the area of chatbots and virtual assistants. The analysis shows that the area is gaining momentum as contribution in this area is been increasing in last few years.

The study shows that several branches of chatbots are also in mainstream like natural language processing, e-learning, behavioural research, conversational agents, virtual assistants, human–computer interaction, natural language and so on. It provides a wider scope to authors and researchers to gain useful insights. The bibliometric study will provide a broader spectrum in this area.

The developments in technology and also the effect of COVID-19 pandemic is boosting the adoption of technology in different sectors. Deployment of technology will uplift the economy and social infrastructure. Society can avail different services from their comfort area and also in real time. This will help in reducing wastage of resources like people visiting offices for routine jobs which can be easily availed from their workplace. Society may access better services without much human interaction.

This paper adds significant contribution to the existing literature by analysing the published papers from Scopus database. The study contains new and significant information as this study covers all industries where chatbots and virtual assistants are being applied whereas in previous literature only specific industry has been taken into consideration.