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This study aims to explore customer reactions to using chatbots in the airline industry and to understand the psychological factors influencing their preferences.

Study 1 assesses attitudes toward human versus chatbot service agents in customer service interactions with social presence theory as the theoretical foundation to corroborate prior research, whereas Study 2 applies motivated action theory to analyze the impact of an individual’s goal orientation traits (process and outcome) related to chatbot acceptance.

Results indicate that individuals with outcome-focused personality traits show a preference for human agents when addressing customer service issues, suggesting that psychological factors significantly impact technology acceptance.

This research contributes new insights into the understudied area of psychological predispositions affecting chatbot acceptance in service scenarios within the airline industry.

探讨消费者在航空行业使用聊天机器人的反应, 并分析影响其偏好的心理因素。

研究1以社会临场感理论为基础, 评估消费者对人工客服与聊天机器人的态度, 以验证现有研究。研究2运用动机行动理论, 分析个体目标导向特质（过程导向与结果导向）对聊天机器人接受度的影响。

研究发现, 具有结果导向特质的个体在处理客服问题时更倾向于选择人工客服, 表明心理因素对技术接受度有显著影响。

本研究为航空行业服务场景中聊天机器人接受度的研究提供了新视角, 填补了心理特质对技术采纳影响的研究空白。

The implementation of customer service chatbots in various industries is increasingly accepted globally. Previous research has not extensively explored the relationship between chatbot disclosure, technology anxiety, chatbot quality, customer experience and customer satisfaction derived from using chatbot customer service in e-commerce. Therefore, this paper aims to examine the determinant factors of customer service chatbot continuance intention by extending the expectation confirmation theory (ECT). The researchers integrate chatbot quality, technology anxiety and disclosure into ECT to comprehensively understand the phenomena.

The quantitative study uses the partial least square structural equation model disjoint two-stage approach with a sample of 310 respondents collected using purposive sampling.

The study reveals that perceived usefulness, confirmation and satisfaction positively affect customer service chatbot continuance intentions. Moreover, chatbot disclosure can enhance chatbot quality. However, technology anxiety negatively affects chatbot quality.

This research contributed to adapting customer service chatbots in Indonesian e-commerce, focusing on chatbot quality, technological anxiety and transparency. Furthermore, it underscores the need for clarity, addresses transaction-specific concerns and artificial intelligence-driven customer assistance in the Indonesian market.

This study aims to explore the potential of artificial intelligence with AI-powered humanoid Chatbots (AIPHC) as transformative tools to improve customer service quality in the insurance sector. The usability and efficiency of integrating advanced AI chatbots that can replicate human-like interactions in insurance services will be examined by taking into consideration customers’ technological readiness and chatbots’ anthropomorphism.

This empirical study analysed 688 customer responses collected through purposive sampling using structural equation modelling. With the help of SmartPLS 4.0, the study determines whether anthropomorphism, that is AIPHC system-specific and customer personality-specific dimensions, can influence the acceptance of AIPHC in the insurance sector.

The results show that the chatbot’s anthropomorphism positively influenced customers’ optimism and innovativeness but negatively impacted discomfort and security. Further optimism and innovativeness favourably impact AIPHC adoption. Insecurity had a significant negative impact, while discomfort was insignificant for AIPHC adoption.

The study determines how people will react to AI-powered information systems. The results could help us better understand how the technological readiness of customers can be used in emphasizing the significance of system-specific theories like anthropomorphism in sectors like insurance, where customer interactions and delivery of quality services are important.

The results highlight chatbots’ potential to raise the quality of service, simplify processes and enhance customers’ overall experiences in the insurance sector. This study contributes to the continuing discussion on using AI technologies in customer service by considering the interplay between technology readiness and anthropomorphism. It also provides insightful information for insurance professionals and technology developers.

Anthropomorphic humanoid chatbots can increase the availability, affordability and accessibility of essential services. They have the potential to increase users’ competence, autonomy and—possibly counterintuitively social relatedness.

This empirical research explores the link between anthropomorphism and technology readiness to enhance service quality provided by AI powered chatbots in the insurance sector.

The purpose of the chapter was to examine the impact of customer care chatbots on customer satisfaction levels in the mobile telephony industry in Zimbabwe, with a special focus on the company Econet Wireless. This chapter shows the conceptual framework used. An online questionnaire was administered to a sample of 100 Econet Wireless subscribers who were selected using probability stratified random sampling from Zimbabwe’s 10 provinces. The research data were collected and analysed for correlation, and a multiple regression analysis was carried out to identify the relationship between customer satisfaction and the three customer service improvements brought in by the introduction of customer service chatbots. The study discovered that there is a positive relationship between customer satisfaction levels and each of the three customer service improvements brought in by customer service chatbots, namely customer service convenience, speed of response, and omnichannel strategies. This study thereby proves that the introduction of customer service chatbots in the mobile telephony industry in Zimbabwe can lead to an improvement in customer satisfaction levels. However, addressing service quality only as a determinant of customer satisfaction in isolation is not sufficient to fully improve customer satisfaction levels. Therefore, organisations that seek to improve their customer satisfaction should consider strategies that address all determinants of customer satisfaction, namely price, product quality, service quality, situational factors, and personal factors. This study contributes to the body of knowledge, particularly regarding the use of artificial intelligence (AI) for customer service in developing economies.

While prior research has examined customer acceptance of humanized chatbots, the mechanisms through which they influence customer value creation remain unclear. This study aims to investigate the emerging concept of Perceived Humanization (PH), examining how hedonic motivation, social influence and anthropomorphism influence value creation through the serial mediation of PH and trust. The moderating roles of rapport and social presence are also explored.

Based on data from an online survey involving 257 respondents, this study employs Partial Least Squares Structural Equation Modeling utilizing SmartPLS3 software.

Hedonic motivation leads to value creation via two routes: PH and affective trust; and PH and cognitive trust. Social influence and anthropomorphism also positively impact value creation through similar pathways. Rapport moderates the impact of social influence on PH, while social presence moderates the relationship between PH and both affective and cognitive trust. A cross-cultural analysis of China, India and New Zealand highlights varying cultural dimensions influencing PH and its effects on value creation.

For practitioners in the tourism industry, the findings highlight the strategic importance of enhancing PH in chatbot interactions. By understanding and optimizing these elements, businesses can significantly improve their customer value-creation process.

This study contributes to the service marketing literature by generating a comprehensive framework for the comprehension and application of PH. Its cross-cultural perspective provides rich insights, offering valuable information for service marketers aiming to thrive in the dynamic and competitive tourism industry.

The purpose of this study is twofold: first, to identify the categories of artificial intelligence (AI) chatbot service failures in frontline, and second, to examine the effect of the intensity of AI emotion exhibited on the effectiveness of the chatbots’ autonomous service recovery process.

We adopt a mixed-methods research approach, starting with a qualitative research, the purpose of which is to identify specific categories of AI chatbot service failures. In the second stage, we conduct experiments to investigate the impact of AI chatbot service failures on consumers’ psychological perceptions, with a focus on the moderating influence of chatbot’s emotional expression. This sequential approach enabled us to incorporate both qualitative and quantitative aspects for a comprehensive research perspective.

The results suggest that, from the analysis of interview data, AI chatbot service failures mainly include four categories: failure to understand, failure to personalize, lack of competence, and lack of assurance. The results also reveal that AI chatbot service failures positively affect dehumanization and increase customers’ perceptions of service failure severity. However, AI chatbots can autonomously remedy service failures through moderate AI emotion. An interesting golden zone of AI’s emotional expression in chatbot service failures was discovered, indicating that extremely weak or strong intensity of AI’s emotional expression can be counterproductive.

This study contributes to the burgeoning AI literature by identifying four types of AI service failure, developing dehumanization theory in the context of smart services, and demonstrating the nonlinear effects of AI emotion. The findings also offer valuable insights for organizations that rely on AI chatbots in terms of designing chatbots that effectively address and remediate service failures.

This study aims to examine the interaction effects of chatbots’ language style and customers’ decision-making journey stage on customer’s service encounter satisfaction and the mediating role of customer perception of emotional support and informational support using the construal level theory and social support theory as conceptual frameworks.

This study used a scenario-based experiment with a 2 (chatbot’s language style: abstract language vs concrete language) × 2 (decision-making journey stage: informational stage vs transactional stage) between-subjects design.

Findings show that during the informational stage, chatbots that use abstract language style exert a strong influence on service encounter satisfaction through emotional support. During the transactional stage, chatbots that use concrete language style exert a strong impact on service encounter satisfaction through informational support.

Findings provide some suggestions for improving customer–chatbot interaction quality during online service encounters.

This study offers a novel perspective on customer interaction experience with chatbots by investigating the chatbot’s language styles at different decision-making journey stages.

This study explores how chatbot gender and symbolic service recovery may improve the satisfaction of angry customers in the context of service failures. It provides a strategy for companies to deploy chatbots effectively in customer anger.

This research relies upon a systematic literature review to propose three hypotheses, and we recruit 826 participants to examine the effect of chatbot gender on angry customers through one lab study and one field study.

This research shows that female chatbots are more likely to increase the satisfaction of angry customers than male chatbots in service failure scenarios. In addition, symbolic recovery (apology vs. appreciation) moderates the effect of chatbot gender on angry customers. Specifically, male (vs. female) chatbots are more effective in increasing the satisfaction of angry customers when using the apology method, whereas female (vs. male) chatbots are more effective when using the appreciation method.

The rapid advancements in artificial intelligence technology have significantly enhanced the effectiveness of chatbots as virtual agents in the field of interactive marketing. Previous research has concluded that chatbots can reduce negative customer feedback following a service failure. However, these studies have primarily focused on the level of chatbot anthropomorphism and the design of conversational texts, rather than the gender of chatbots. Therefore, this study aims to bridge that gap by examining the effect of chatbot gender on customer feedback, specifically focusing on angry customers following service failures.

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.

This study aims to investigates customer satisfaction from the use of bank chatbots and the effect of perceived trust in chatbots and banks' reputation on customer satisfaction.

A survey is conducted in Turkey involving 240 customers who experienced banking transactions using a chatbot. Partial least squares structural equation modeling (PLS-SEM) is used to investigate the relationships between the variables. The data were analyzed using SPSS 21 and SmartPLS programs.

Perceived performance, perceived trust and corporate reputation significantly affect customer satisfaction with chatbot use. Customer expectations and confirmation of customer expectations have no direct impact on customer satisfaction, but customer expectations positively affect perceived performance. Customer expectations exert an indirect influence on customer satisfaction through perceived performance. Perceived performance has a positive impact on the confirmation of customer expectations, but customer expectations do not significantly impact the confirmation of customer expectations.

This study relies on a limited number of participants. Moreover, its sample is not representative of the target population due to the convenience sampling technique. Even if the results may not be generalized to the entire population of Turkey, they reflect the reality of emerging markets with relatively high technology sensitivity and a young population.

The results provide new insights regarding banking service delivery channels, which may be of interest to professionals, academics, banks' top management, product development teams, design teams and customer satisfaction units.

This study is believed to help the community make their lives easier by providing them with knowledge and awareness about chatbots.

This study extends expectations confirmation theory's predictions to chatbot use in banking.