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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.

The purpose of this study was to comprehend the adoption of artificial intelligence (AI) technology-driven natural large language model (LLM)-based chatbots by customers.

A qualitative research study method was conducted. This was to explore managerial perspectives towards consumer centric technology adoption of AI plus LLM-based chatbots. This was specifically for AI-driven natural LLM-based chatbots services. The author conducted conducted in-depth personal interviews with 32 experts of digital content AI + LLM chatbot services. Thematic content analysis was undertaken to analyse the data.

The advent of natural language processing tools driven by AI technology chatbots has altered human-firm interaction. The research findings indicated that the push-pull-mooring (PPM) factors captured the phenomenon in the most comprehensive way. A total of 15 key factors influencing the adoption of AI technology-driven natural LLM-based chatbots by customers during firm customer interaction were identified in this study by the author. The thematic content analysis unraveled insights regarding transformed consumer adoptions towards AI-driven LLM-based chatbots by means of the PPM framework factors.

The empirical research investigation contributed to the literature on the PPM theoretical framework. This was specifically in the context of adoption of AI technology-driven natural LLM-based chatbots by customers during firm customer interaction.

The research study insights would help managers to restructure and reconfigure their organizational processes. This would neccessiated a shift in firm-customer interactions as demanded because of the availability of AI technology-driven natural LLM-based chatbots by customers.

This research study was based upon the PPM theoretical framework. This study provided a unique analysis of the altered firm customer interaction needs and requirements. This was one of the first studies that applied the framework of PPM theory regarding the adoption of AI technology-driven natural LLM-based chatbots by customers.

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 serves two purposes: (1) to evaluate the effects of organizational ambidexterity by examining how the balanced and the combined sales–service configurations of chatbots differ in their abilities to enhance customer experience and patronage and (2) to apply information boundary theory to assess the contingent role that chatbot sales–service ambidexterity can play in adapting to customers' personalization–privacy paradox.

An online survey of artificial intelligence chatbots users was conducted, and a mixed-methods research design involving response surface analysis and polynomial regression was adopted to address the research aim.

The results of polynomial regressions on survey data from 507 online customers indicated that as the benefits of personalization decreased and the risk to privacy increased, the inherently negative (positive) effects of imbalanced (combined) chatbots' sales–service ambidexterity had an increasing (decreasing) influence on customer experience. Furthermore, customer experience fully mediated the association of chatbots' sales–service ambidexterity with customer patronage.

First, this study enriches the literature on frontline ambidexterity and extends it to the setting of human–machine interaction. Second, the study contributes to the literature on the personalization–privacy paradox by demonstrating the importance of frontline ambidexterity for adapting to customer concerns. Third, the study examines the conduit between artificial intelligence (AI) chatbots' ambidexterity and sales performance, thereby helping to reconcile the previously inconsistent evidence regarding this relationship.

This study investigated the contribution of artificial intelligence (AI) to the efficiency of customer service. This study contributes to services technological innovation in process management, a field not yet settled in the literature.

AI is a multidisciplinary field of research that has stood out for the technological dynamism provided to organizational products and processes. The study was carried out at an Analytical Intelligence Unit (AIU) of a Brazilian commercial bank that applies AI integrated with IBM's Watson system. The study used data content analysis, structured and supported by Atlas.ti software.

The notorious AI cognitive maturity evolution allowed 181 million interactions and 7.6 million attendances in 2020, improving services efficiency, with gains in agility, availability, accessibility, resoluteness, predictability and transshipment capacity. The chatbot service reduced the queues of call centers and relationship centers, allowing the human attendant to perform more complex attendances.

The main limitations of this study relate to the research cutout and its borders, such as the choice of participants and their areas of activity, and the choice of the unit of analysis.

The results indicated that attendance through the virtual assistant increased by more than a 1,000% from 2019 to 2020, demonstrating the bank was technologically ready to face the Covid-19 pandemic effects.

In line with the evolutionary theory of innovation, the authors concluded that technological scaling in AI allows exponential gains in customer service efficiency and business process management. They also conclude that the strategy for creating AIUs is successful, once it allows centralizing, structuring and coordinating AI projects in R&D cooperation, cognitive computing and analytics.

This study aims to investigate the impact of AI chatbots on customer trust in AI chatbots for hotel services.

The probability sampling method was employed to develop a research sample. The research uses correlation analysis and structural equation modeling to analyze the data of 413 valid observations collected in the structured questionnaire survey in Vietnam.

The paper reports that empathy response, anonymity and customization significantly impact interaction. Empathy response is found to be the strongest influence on interaction. Meanwhile, empathy response and anonymity were revealed to indirectly affect customer trust. This paper also contributes several implications for hotel providers in emerging economies.

To the best of the authors’ knowledge, this is the first study to shed light on the role of AI chatbots in explaining customers’ behavior. The results provide an enhanced understanding of how the AI chatbot system influences customers’ decision-making. It has been used to plan the chatbot application and highlight which implementation issues need the most attention in the hospitality industry.

This study investigates how artificial intelligence (AI) chatbots persuade customers to accept their recommendations in the online shopping context.

Drawing on the elaboration likelihood model, this study establishes a research model to reveal the antecedents and internal mechanisms of customers' adoption of AI chatbot recommendations. The authors tested the model with survey data from 530 AI chatbot users.

The results show that in the AI chatbot recommendation adoption process, central and peripheral cues significantly affected a customer's intention to adopt an AI chatbot's recommendation, and a customer's cognitive and emotional trust in the AI chatbot mediated the relationships. Moreover, a customer's mind perception of the AI chatbot, including perceived agency and perceived experience, moderated the central and peripheral paths, respectively.

This study has theoretical and practical implications for AI chatbot designers and provides management insights for practitioners to enhance a customer's intention to adopt an AI chatbot's recommendation.

1. The study investigates customers' adoption of AI chatbots' recommendation.

2. The authors develop research model based on ELM theory to reveal central and peripheral cues and paths.

3. The central and peripheral cues are generalized according to cooperative principle theory.

4. Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice.

5. Central and peripheral cues affect customers' adoption to recommendation through trust in AI.

6. Customers' mind perception positively moderates the central and peripheral paths.

The study investigates customers' adoption of AI chatbots' recommendation.

The authors develop research model based on ELM theory to reveal central and peripheral cues and paths.

The central and peripheral cues are generalized according to cooperative principle theory.

Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice.

Central and peripheral cues affect customers' adoption to recommendation through trust in AI.

Customers' mind perception positively moderates the central and peripheral paths.

The purpose of this study is to explore customer-artificial intelligence (AI) service technology engagement and relationship development drivers, as well as potential negative consequences in the context of social chatbots.

A sequential mixed-method approach combined exploratory qualitative and confirmatory quantitative analyses. A conceptual model developed from Study 1 qualitative content analysis of in-depth interviews with active users of the AI social chatbot Replika was tested in Study 2 by analyzing survey data obtained from current Replika users.

Loneliness, trust and chatbot personification drive consumer engagement with social chatbots, which fosters relationship development and has the potential to cause chatbot psychological dependence. Attachment to a social chatbot intensifies the positive role of engagement in relationship development with the chatbot.

This study was the first to combine qualitative and quantitative approaches to explore drivers, boundary conditions and consequences of relationship and dependence formation with social chatbots. The authors proposed and empirically tested a novel theoretical model that revealed an engagement-based mechanism of relationship and dependence formation with social chatbots.

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.

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.