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The tourism and hospitality sectors are experiencing radical innovation boosted by the advancements in Information and Communication Technologies. Increasingly sophisticated chatbots are introducing novel approaches, re-shaping the dynamics among tourists and service providers, and fostering a remarkable behavioral change in the overall sector. Therefore, the objective of this paper is two-folded: (1) to highlight the academic and industrial standing points with respect to the current chatbots designed/deployed in the tourism sector and (2) to develop a proof-of-concept embodying the most prominent opportunities in the tourism sector.

This work elaborates on the outcomes of a Systematic Literature Review (SLR) and a Focus Group (FG) composed of experts from the tourism industry. Moreover, it presents a proof-of-concept relying on the outcomes obtained from both SLR and FG. Eventually, the proof-of-concept has been tested with experts and practitioners of the tourism sector.

Among the findings elicited by this paper, we can mention the quick evolution of chatbot-based solutions, the need for continuous investments, upskilling, system innovation to tackle the eTourism challenges and the shift toward new dimensions (i.e. tourist-to-tourist-to-chatbot and personalized multi-stakeholder systems). In particular, we focus on the need for chatbot-based activity and thematic aggregation for next-generation tourists and service providers.

Both academic- and industrial-centered findings have been structured and discussed to foster the practitioners' future research. Moreover, the proof-of-concept presented in the paper is the first of its kind, which raised considerable interest from both technical and business-planning perspectives.

Artificial intelligence (AI)-based chatbots have brought unprecedented business potential. This study aims to explore consumers' trust and response to a text-based chatbot in e-commerce, involving the moderating effects of task complexity and chatbot identity disclosure.

A survey method with 299 useable responses was conducted in this research. This study adopted the ordinary least squares regression to test the hypotheses.

First, the consumers' perception of both the empathy and friendliness of the chatbot positively impacts their trust in it. Second, task complexity negatively moderates the relationship between friendliness and consumers' trust. Third, disclosure of the text-based chatbot negatively moderates the relationship between empathy and consumers' trust, while it positively moderates the relationship between friendliness and consumers' trust. Fourth, consumers' trust in the chatbot increases their reliance on the chatbot and decreases their resistance to the chatbot in future interactions.

Adopting the stimulus–organism–response (SOR) framework, this study provides important insights on consumers' perception and response to the text-based chatbot. The findings of this research also make suggestions that can increase consumers' positive responses to text-based chatbots.

Extant studies have investigated the effects of automated bots' attributes on consumers' perceptions. However, the boundary conditions of these effects are largely ignored. This research is one of the first attempts to provide a deep understanding of consumers' responses to a chatbot.

AI-based robo-advisory (RA) represents a FinTech application that is already replacing retail investment advisors. In private banking (PB), clients also increasingly expect service provision across different digital channels, but with a higher degree of personalization. Hence, the present study investigates the impact of intelligent RA on the PB investment advisory process to derive both process (re)design knowledge and strategic guidance for artificial intelligence (AI) usage for PB investment advisory.

The present study applies an AI process impact analysis approach by decomposing AI-based RA into three AI application types: conversational agent, customer segmentation and predictive analytics. The analysis results along a reference PB investment advisory process reveal sub-process transformations which are applied for process redesign integrating AI.

The study results imply that AI systems (1) enable seamless client journeys, (2) increase advisor flexibility, (3) support the client–advisor relationship by applying an omnichannel approach and (4) demand advisor skills to be augmented with technical and statistical knowledge.

The research study contributes (1) an AI process impact analysis approach, (2) derives process (re)design knowledge for AI deployment and (3) develops strategic guidance for AI usage in PB investment advisory.

How to embed artificial intelligence (AI) in human resource management (HRM) is one of the core challenges of digital HRM. Despite regulations demanding humans in the loop to ensure human oversight of AI-based decisions, it is still unknown how much decision-makers rely on information provided by AI and how this affects (personnel) selection quality.

This paper presents an experimental study using vignettes of dashboard prototypes to investigate the effect of AI on decision-makers’ overreliance in personnel selection, particularly the impact of decision-makers’ information search behavior on selection quality.

Our study revealed decision-makers’ tendency towards status quo bias when using an AI-based ranking system, meaning that they paid more attention to applicants that were ranked higher than those ranked lower. We identified three information search strategies that have different effects on selection quality: (1) homogeneous search coverage, (2) heterogeneous search coverage, and (3) no information search. The more applicants were searched equally often (i.e. homogeneous) as when certain applicants received more search views than others (i.e. heterogeneous) the higher the search intensity was, resulting in higher selection quality. No information search is characterized by low search intensity and low selection quality. Priming decision-makers towards carrying responsibility for their decisions or explaining potential AI shortcomings had no moderating effect on the relationship between search coverage and selection quality.

Our study highlights the presence of status quo bias in personnel selection given AI-based applicant rankings, emphasizing the danger that decision-makers over-rely on AI-based recommendations.

This study combines the TAM and UGT frameworks to investigate how Vietnamese students' views of ChatGPT and intrinsic needs affect their intentions to use it for education (via variables including perceived ease of use, perceived usefulness, novelty, information seeking and academic content creation). We will employ TAM theory (Davis, 1989) and UGT theory to elucidate university students' motivations for utilising ChatGPT in Vietnam. Simultaneously, we aim to address the limitation stemming from data uniformity. Our research will make a substantial contribution to the understanding of researchers regarding the use of ChatGPT and its varied consequences as it grows and develops.

This study was conducted at a private university in Vietnam with an estimated population of 15,000 students. One of Vietnam’s top private information technology institutions requires its students to use a variety of information and communication technologies (ICTs) on a regular basis to facilitate and enjoy their academic pursuits (Ngo, 2024; Nguyen). Students who are familiar with ChatGPT and have access to it for educational purposes are the ones that were chosen. This research is a quantitative study that utilises primary data through a survey method. Participants answered a questionnaire online through the Google Form platform sharing via social media platforms from October to December 2023. The questionnaire was divided into two sections: the first contained screening questions and demographic information and the second had five-point Likert-scale questions that measured the study’s components. Two screening questions are used to separate out the intended responders. (i.e. “I have heard the name ChatGPT” and “I know about ChatGPT”) were set to find whether the participants had any knowledge of ChatGPT. If participants were unaware of ChatGPT, their responses were not included in the study. A total of 283 responses were received. The participant’s demographic information is shown in Table 1. It is believed that a sample size of more than 200 provides adequate statistical power for data analysis in structural equation modelling. It is evident that the 283-sample size in this study is adequate to evaluate the research hypothesis and the fitting model. 42.9% of the 283 research samples were made up of men, while 57.1% were women. Business administration accounted for 40.1% of survey respondents, followed by information technology (25.2%) and English language (14.5%). The average ChatGPT usage time of respondents was 56 min in a single use. The study sample’s average age is 20–72 years old.

The present study contributes to the existing AI chatbot literature in the educational industry in several ways. First, this study addresses a gap in the literature by investigating the factors that influence students’ ITU ChatGPT for educational purposes in Vietnam. Using the extended model, we investigated factors influencing students’ intentions to use ChatGPT. It integrates three motive factors of the UGT (ACT, IS and N) with the core factors of TAM (PeoU and PU). The integrated framework’s findings indicate that in a Vietnamese educational setting, ChatGPT is a novel technology that should be considered in conjunction with PU and PEoU.

First, only Vietnamese students make up the research sample. To increase the relevance of the findings, it is advised that future research look at the study model in various geographic regions. Second, the present investigation’s constraints arise from the lack of clarity about the Chat GPT version utilised by the respondents, specifically whether it was the free or premium edition. Furthermore, the limited duration of the survey presents an obstacle to gathering thorough data. Due to their reliance on the particular features and functionalities of the Chat GPT version used, these restrictions may have an effect on the researcher’s objectivity. Furthermore, the limited survey time may curtail the coverage of the collected answers, particularly considering that our survey predominantly focuses on business administration students, thus limiting the diversity and richness of the gathered data. We recommend that future studies should conduct comparative research between different versions of GPT Chat, including free and paid variants that can provide valuable insights into potential differences in performance and results. Such analysis can provide a deeper understanding of the strengths and limitations of different GPT Chat versions. Third, because the study focused on behavioural intention, actual usage and post-usage behaviour may not be covered by the findings. To gain a deeper understanding of users' actual behaviour, we suggest doing research on their usage and post-usage behaviours.

The findings will assist service providers and legislators in determining critical variables and influencing students' incentives to use ChatGPT in educational settings that use constructivist teaching methods. As a result, the information will assist service providers in creating AI chatbots that are more user-friendly, visually appealing, efficient, safe and convenient for education. Governments, in conjunction with service providers, have the potential to significantly accelerate the adoption of AI-based chatbots by highlighting their ethical and sustainable use. The findings demonstrate that students' ITU towards ChatGPT is substantially impacted by PU and PEoU. It is recommended that service providers emphasize the advantages and ease of use of AI chatbots in order to draw new clients. Additionally, in order to promote ChatGPT or related technologies, marketers should concentrate on raising the technology’s perceived novelty value. This is because people are open to new technologies as long as they believe they are interesting and innovative.

ChatGPT is an advanced AI-powered chatbot that has the potential to advance and revolutionize the learning and teaching process. This study attempted to look at the elements that lead students to want to use ChatGPT from an academic standpoint by combining the UGT and TAM. For practitioners, academics and educators, the findings provide a solid knowledge of and encouragement for the sustainable use of such AI tools. Despite having important practical consequences, the study contains a number of limitations that indicate possible research gaps that should be filled by further investigation.

Service research offering a view of both the dark and bright sides of smart technology remains scarce. This paper embraces a critical perspective and examines the conflicting outcomes of smart services on the customer experience (CX), with a specific focus on chatbots.

This study uses empirical research methods to examine a single case study where an online retail service provider implemented a chatbot for customer service. Using discourse analysis, we analysed 7,167 conversations between customers and the chatbot over a two-year period.

The analysis identifies seven general themes related to the effects of the chatbot on CX: interaction quality, information gathering, procedure literacy, task achievement, digital trust, shopping stress and shopping journey. We illuminate both positive (i.e. having a pleasant interaction, providing information, knowing procedures, improving tasks, increasing trust, reducing stress and completing the journey) and negative outcomes (i.e. having an unpleasant interaction, increasing confusion, ignoring procedures, worsening tasks, reducing trust, increasing stress and abandoning the journey).

The paper develops a comprehensive framework to offer a clearer view of chatbots as smart services in customer care. It delves into the conflicting effects of chatbots on CX by examining them through relational, cognitive, affective and behavioural dimensions.