Search results

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.

This study aims to understand a customer-purchase mechanism in the artificial intelligence (AI)-powered chatbot context based on the elaboration likelihood model (ELM) and technology acceptance model (TAM). The first objective is to examine how to boost chatbot adoption. The second objective is to investigate the role of information characteristics, technology-related characteristics and attitude toward AI in purchase intention.

Data was collected from a sample of 492 users in Vietnam, who are potential customers of chatbots for purchase. Structural equation modeling was applied for data analysis.

Results illustrate that chatbot adoption is significantly influenced by information credibility, technology-related factors (i.e. interactivity, relative advantage and perceived intelligence), attitude toward AI and perceived usefulness. Moreover, information quality and persuasiveness motivate information credibility. Information credibility and attitude toward AI are the essential motivations for perceived usefulness. Finally, chatbot adoption and information credibility determine purchase intention.

The results are insightful for practitioners to envisage the importance of chatbot use for customer purchase in the AI scenario. Additionally, this research offers a framework to practitioners for shaping customer engagement in chatbots.

The value of this work lies in the incorporation of technology-related characteristics into the two well-established theories, the ELM and TAM, to identify the importance of AI and its applications (i.e. chatbots) for purchase and to understand the formation of perceived usefulness and chatbot use through information credibility and attitude toward AI.

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.

To influence consumer pre-purchase decision-making processes, such as brand selection and perceived brand experience, brands are interested in adopting hyperconnected technological stimuli, such as artificial intelligence, augmented reality (AR), virtual reality, social media and tech devices. However, the understanding of different hyperconnected touchpoints remained shallow and results mixed in previous literature, despite the fact that these touchpoints span different technological interfaces/devices and may influence consumer brand selection. This paper aims to solidify the conceptual underpinnings of the role of online hyperconnected stimuli, which may influence consumer psychological reactions in terms of brand selection and experience.

This paper is conceptual and presents a discussion based on extant literature from various international publishers.

The authors revealed different technological stimuli in the online hyperconnected environment that may influence consumer online hyperconnected brand selection (OHBS), perceived online hyperconnected brand experience (OHBE), perceived well-being and behavioral intention.

The conceptual understanding of OHBS and perceived OHBE was mixed and inconsistent in previous studies. This paper brings together extant literature to establish the conceptual understanding of antecedents and outcomes of OHBS, i.e. perceived OHBE, perceived well-being and behavioral intention, and presents a cohesive conceptual framework.

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.

The purpose of this paper is to investigate students' adoption intention (ADI) and actual usage (ATU) of artificial intelligence (AI)-based teacher bots (T-bots) for learning using technology adoption model (TAM) and context-specific variables.

A mixed-method design is used wherein the quantitative and qualitative approaches were used to explore the adoption of T-bots for learning. Overall, 45 principals/directors/deans/professors were interviewed and NVivo 8.0 was used for interview data analysis. Overall, 1,380 students of higher education institutes were surveyed, and the collected data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique.

The T-bot's ADI’s antecedents found were perceived ease of use, perceived usefulness, personalization, interactivity, perceived trust, anthropomorphism and perceived intelligence. The ADI influences the ATU of T-bots, and its relationship is negatively moderated by stickiness to learn from human teachers in the classroom. It comprehends the insights of senior authorities of the higher education institutions in India toward the adoption of T-bots.

The research provides distinctive insights for principals, directors and professors in higher education institutes to understand the factors affecting the students' behavioral intention and use of T-bots. The developers and designers of T-bots need to ensure that T-bots are more interactive, provide personalized information to students and ensure the anthropomorphic characteristics of T-bots. The education policymakers can also comprehend the factors of T-bot adoption for developing the policies related to T-bots and their implications in education.

T-bot is a new disruptive technology in the education sector, and this is the first step in exploring the adoption factors. The TAM model is extended with context-specific factors related to T-bot technology to offer a comprehensive explanatory power to the proposed model. The research outcome provides the unique antecedents of the adoption of T-bots.

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.

The study’s objective is to measure the importance consumers attach to AI-based attributes, namely, chatbots, face recognition, virtual fitting room, smart parking and cashier-free station in retail stores. The study also examines the specific purpose of using these attributes for shopping.

A conjoint experiment was conducted using fractional factorial design. Consumers were given 14 profiles (AI attributes and its levels) to rank according to their visiting preferences.

The results revealed that the retail chatbot was considered the most important attribute, followed by face recognition, virtual fitting room, smart parking system and cashier-free station. Moreover, consumers prefer to use chatbots for in-store shopping assistance over alerts and updates, customer support and feedback. Similarly, consumers wish a face recognition facility for greetings while entering the store over other services. In addition, cluster analyses revealed that customer groups significantly differ in their preferences for AI-based attributes.

The study guides retail managers to invest in AI technologies to provide consumers with a technology-oriented shopping experience.

Our results provide an insight into the receptivity of AI technologies that consumers would like to experience in their favorite retail stores. The present study contributes to the literature by investigating consumer preferences for various AI technologies and their specific uses for shopping.

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.

Given the rapid development in artificial intelligence (AI), the hotel industry is deploying AI-based systems. In line with this important development, this study aims to examine the impact of trust in the hotel and AI-related performance ambiguity on consumers’ engagement with AI-based systems. This study ultimately examined the impact of engagement on consumers’ intentions to stay in hotels offering such systems, and intentions to tip.

This study developed a conceptual model based on the social cognition theory. The study used an online survey methodology and collected data from a nationwide sample of 400 hotel consumers from the USA. The data analysis was conducted with structural equation modeling.

Consumers’ engagement is strongly influenced by their trust in the hotel but not by performance ambiguity associated with AI. In turn, engagement strongly influenced consumers’ intentions to stay in hotels that have such systems and their intentions to tip.

As AI systems capable of making decisions for consumers are becoming increasingly present in hotels, little is known about the way consumers engage with such systems and whether their engagement leads to economic impact. This is the first study that validated a model that explains intentions to stay and tip for services facilitated by autonomous AI-based systems that can make decisions for consumers.

鉴于人工智能领域的快速发展, 酒店业正在部署基于人工智能的系统。为此, 本研究探讨了客人对酒店的信任和与AI相关的性能模糊性对消费者与基于AI的系统互动的影响。最终, 本研究考察了参与度对客人在提供此类系统的酒店住宿意愿和小费意愿的影响。

本研究基于社会认知理论开发了一个概念模型。研究采用在线调查方法, 从美国全国范围的400名酒店消费者中收集数据, 并采用结构方程建模进行数据分析。

消费者的参与度受酒店的信任强烈影响, 但不受与AI相关的性能模糊性的影响。反过来, 参与度强烈影响了消费者在提供此类系统的酒店住宿和给小费的意愿。

随着能够代表消费者做出决策的人工智能（AI）系统在酒店中日益普及, 人们对消费者如何与这类系统互动以及他们的互动是否会产生经济影响知之甚少。这是第一项验证了一个可以解释在自主的基于AI系统的服务下住宿和给小费意愿的模型的研究。