Literature on user technology acceptance in hospitality and tourism services and research gaps addressed by current study
| Study | Independent variables | Mediator | Moderator | Dependent variables | Research focus | Research gaps | Research questions | Theoretical rationale | Methodology | Hospitality and tourism services | Mayor findings | Theoretical contributions |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Leung and Wen (2020) | Placing restaurant takeout orders via different methods (phone, online or chatbot) | None | Restaurant type | Social presence attitudes satisfaction behavior | Apply the contingency theory as the theoretical foundation to explore the fits between restaurant types (i.e. quick-service, full-service) and ordering methods | How chatbots can be added to the current digital ordering system to provide improved customer experiences | RQ1: How similar and different are the perceptions and behaviors of customers when using three different ordering methods | Compare the performance and customer perceptions of chatbots with other restaurant takeaway ordering methods based on social presence theory | Experimental design No real chatbots Structural equation modeling (SEM) | Restaurant | The phone and online ordering methods were both better than the chatbot method both in terms of satisfaction and behavior | The first attempt to examine consumers’ interactions, attitudes and behaviors when using chatbots in food ordering processes |
| McLean et al. (2020) | Perceived usefulness of live chat communication | Attitudes toward the website Trust toward the website | Human warmth of the online travel representative Human safety of the online travel representative Human personalized content of the online travel representative Human attentiveness of the online travel representative | Intention to use (purchase) | The study aims to investigate the influence of perceived usefulness of live chat services and of their unique human attributes on customer attitudes, beliefs and behaviors in the context of online travel shopping | There are no empirical studies examining the variables that influence consumer attitudes, trust and purchase intent with the live chat experience, despite the growth of live chat at the expense of telephone sales and support. There is little research on the role of human elements in live chat communication and their impact on consumer perceptions and behavior | RQ1: How does the perceived usefulness of live chat communication influence customer attitudes toward the website, trust in the website and purchase intention? RQ2: How do human attributes (warmth, safety, personalization and attentiveness) moderate the effect of perceived usefulness of live chat on customer trust in the website, attitude toward the website and purchase intention? | Social presence theory | Quantitative Structural equation modeling (SEM) | Travel agencies | The perceived usefulness of live chat communication positively influences customer attitudes toward the website, trust in the website and purchase intent. The human attributes (warmth, safety, personalization and attentiveness) of the live chat assistant positively moderate this effect, strengthening the relationship between perceived usefulness of live chat and consumer responses | Provides empirical evidence on the impact of human communication via live chat on travel consumers’ attitudes, trust and purchase intent. Extends social presence theory to the context of live chat services in the travel industry |
| Pillai and Sivathanu (2020) | Perceived ease of use Perceived usefulness Perceived trust Perceived intelligence Anthropomorphism Technological anxiety | None | Stickiness to traditional travel agents/planners | Adoption intention Actual usage | To investigate the factors influencing the adoption intention and actual usage of artificial intelligence (AI) powered chatbots in the hotel and tourism industry in India. The study seeks to extend the technology acceptance model (TAM) with context-specific variables | To address the lack of research on the adoption of AI-based chatbots in the hospitality and tourism industry, especially in emerging economies such as India. The study also seeks to better understand how factors such as trust, perceived intelligence and anthropomorphism influence the adoption of chatbots in this specific context | RQ1: What are the antecedents of the adoption of AI-based chatbots by consumers for tourism? | TAM along with human–robot interaction (HRI) variables – ANM, perceived intelligence, technology anxiety and perceived trust | Mixed | Travel agencies | Predictors of intention to adopt chatbots are: perceived ease of use, perceived usefulness, perceived trust, perceived intelligence and anthropomorphism. Technology anxiety does not influence intention to adopt chatbots. Preference for traditional human travel agents negatively moderates the relationship between adoption intention and actual chatbot use in tourism | The study extends the TAM to provide better explanatory power in the context of human-robot interaction by including specific constructs such as perceived trust, perceived intelligence, anthropomorphism and technological anxiety |
| Lalicic and Weismayer (2021) | Values: openness to change, including stimulation and self-direction. Context-specific factors that motivate consumers to use AI-enabled travel service agents, such as personalization, convenience, ubiquity and superior functionality. Barriers and concerns that deter consumers from using AI chatbots, such as usage barriers, technology anxiety, privacy concerns and the need for personal interaction | Perceived value of cocreation: consumers’ assessment of the value they derive from interacting and cocreating value with AI chatbots in the context of travel planning | None | Behavioral intentions: the likelihood that consumers will adopt and use AI-enabled travel service agents in the future | To examine the factors that influence consumers’ attitudes toward AI-enabled travel service agents (perceived value cocreation) and their intentions to adopt these services. To understand consumers’ reasoning for adopting AI-enabled travel service agents by analyzing the relationships between consumers’ context-specific reasons and variables such as values, attitudes toward the service and perceived value cocreation | Lack of research on how new operational resources, such as AI chatbots, influence consumer perceptions of service processes. Need for context-specific variables to understand levels of consumer acceptance and perceived value cocreation in AI-enabled service encounters. The question of what reasons influence consumer perceptions of innovative service adoption remains unanswered | RQ1: How do values influence consumers reasoning process for adopting or not adopting AI-enabled service encounters? RQ2: What reasons affect the perceived value cocreation of AI-enabled service encounters, ultimately leading to usage intentions? | Behavioral reasoning theory (BRT) Service domain logic (S-D) | Fuzzy set comparative qualitative analysis (fsQCA) | Travel planning phase | The results mainly support hypotheses based on behavioral reasoning theory (BRT). It reveals four complex causal combinations of consumers’ reasons and their intention to use AI-enabled services | Insights into a new area of consumer behavior and acceptance of AI-enabled service encounters. Identifies relevant reasoning processes and sheds light on perceived value cocreation and behavioral intentions. Provides a more detailed understanding of consumers’ cognitive decision pathways that influence whether and how they will interact with innovative service encounters |
| Leung and Wen (2021) | Ordering method (chatbot, mobile, online) | Emotion | Restaurant type (full service, quick service) | Internal responses (satisfaction, behavioral intention); External behaviors (other items, other amounts) | Examine the role of consumption emotion in the digital food-ordering experience by comparing the performances of the three digital ordering methods in an experimental design | Concern over declining service quality associated with the reduced interaction between customers and restaurant staff. Research is needed to examine and compare consumers’ food-ordering experiences on these platforms, including restaurant websites, mobile apps and smart speakers/chatbots. No prior research has specifically examined consumers emotional responses when placing digital orders at restaurants | RQ1: Differences between internal and external responses resulting from digital ordering experiences using online, mobile and chatbot ordering. RQ2: Identify the role of consumption emotions on the relationship between digital ordering methods and consumers’ responses. RQ3: Explore whether there are differences in the above-mentioned relationships between the two types of restaurants (quick service vs full-service | Feelings-as-information (FaI) theory Expectancy-disconfirmation theory | Experimental design Structural equation modeling (SEM) | Restaurant | The online ordering method worked the best for quick-service restaurants, whereas the mobile ordering method was most suitable for full-service restaurants. Both positive and negative emotions (comfort and annoyance) significantly mediated the relationships between the ordering method and internal responses (satisfaction /BI). Only one negative emotion (anger) significantly mediated the relationship between the ordering method and order amount | First study that attempts to explore and compare consumers’ emotional responses resulting from restaurant digital ordering experiences in the context of the three food-ordering methods. Developed a theoretical framework based on both the FaI theory and the expectancy-disconfirmation theory |
| Li et al. (2021) | Understandability Reliability Responsiveness Assurance Interactivity | Confirmation satisfaction | Technology anxiety (TA) taken positively | Use continuance | This study examines how users view chatbot services in OTAs by investigating the moderating role of technology anxiety | To identify five quality dimensions of chatbot services and investigate their effect on user confirmation, which, in turn, leads to use continuance | RQ1: The relationships between chatbot quality dimensions and postuse confirmation | Depending on how users see the chatbot-enabled services with different levels of TA, either as human-like agents or as another new type of technology-self-services, the way they assess their postuse confirmation against the service quality dimensions should be different | Quantitative Structural equation modeling (SEM) | Travel agencies | Understandability, reliability, assurance and interactivity are antecedents for user’s postacceptance confirmation, as well as by proposing technology anxiety toward chatbots as a moderating factor for these relationships | Technology anxiety positively moderates the relationships between chatbot quality dimensions and postuse confirmation, suggesting some users may treat chatbot services as human-like agents |
| Melián-González et al. (2021) | Performance expectation Effort expectation Social influence Hedonic motivations Habit Perceived innovation Attitude toward self-service technologies Inconveniences Anthropomorphismautomation | Attitude toward self-service technologies (SSTA) mediates the relationship between perceived innovation and intention to use chatbots | None | Chatbots usage intention (CUI): The extent to which individuals plan to use chatbots in the future | Predict the intention to use chatbots for travel and tourism by examining the factors that influence consumers’ willingness to interact with this technology | The study seeks to fill the research gap in the factors that explain why consumers are willing to interact with chatbots in the context of tourism, despite their increasing implementation in the industry | RQ1: What factors influence consumers’ intention to use chatbots for travel and tourism? RQ2: How do performance expectations, ease of use, social influence, hedonic motivations, habitus, perceived innovativeness, attitude toward self-service technologies, inconvenience, anthropomorphism and automation affect intention to use chatbots? | UTAUT2, a model widely used to explain the adoption of new technologies. It incorporates other factors relevant to the context of chatbots, such as attitude toward self-service technologies, inconvenience, anthropomorphism and automation | Quantitative Structural equation modeling (SEM) | Travel and tourism | Intention to use chatbots is positively influenced by: performance expectancy, usage habit, hedonic motivation, SI, Anthropomorphism, belief that chatbots will replace jobs (contrary to expectation). Intention to use chatbots is negatively influenced by: perceived inconvenience of using chatbots. Perceived innovation indirectly influences intention to use through attitudes toward self-service technologies. Effort expectancy has no significant effect on intention to use chatbots | The usefulness of the UTAUT2 model in explaining the adoption of chatbots in the travel and tourism context. Additional chatbot-specific factors, such as inconvenience, anthropomorphism and automation, are used to better understand usage intent. The positive relationship between automation and usage intent suggests that consumers may value the benefits of chatbots more than the potential job losses |
| Cai et al. (2022) | Social presence cues (human avatar, human name, use of client’s name, detailed self-presentation) Emotional message cues (humor, empathy, emotional expressions with emoticons) | Perceived trustworthiness Perceived intelligence Perceived enjoyment | None | Usage intention (UI), intention to use online travel agency (OTA) chatbots | Explore the perceived signs of anthropomorphism in chatbots and their effects on customers’ intention to use them in the context of online travel agencies (OTAs) | Lack of comprehensive research on customer and business concerns regarding the anthropomorphism of chatbots in the context of online travel services. Little research on the mechanisms that convey the impact of various anthropomorphic chatbot signals | RQ1: What are the main anthropomorphic signals of interest to customers and companies when using chatbots in the context of online travel services? RQ2: How do anthropomorphic signals (social presence and emotional messages) affect customers’ intention to use chatbots? RQ3: What are the mechanisms underlying these effects? | The study is based on the theory of uncertainty reduction. Emotion theory as social information – the theory of emotions as social information. The study combines these theories with the technology acceptance model (TAM) | Mixed | Travel agencies | Social presence signals (use of human avatar, mention of customer’s name) and emotional messaging signals (humor, empathy and emoticons) are the main anthropomorphic signals of interest to customers and companies. Perceived trust, perceived intelligence and perceived enjoyment mediate the effect of anthropomorphic cues on intention to use | The study contributes to the literature on chatbot anthropomorphism by providing a holistic understanding of multiple anthropomorphic design cues and their effects on customers’ usage intention. Demonstrates that emotional message cues are more important than social presence cues in influencing chatbot usage intent. Clarifies the mechanisms through which perceived anthropomorphism influences customers’ intention to use OTA chatbots |
| Pereira et al. (2022) | Information quality Service quality Perceived usefulness Perceived enjoyment Ease of use | Satisfaction Brand attachment | Need for employee interaction (NFI-SE): The extent to which a person prefers to interact with a human employee rather than an automated system | Continuous intention to use | Analyze the relationship between the dimensions of the technology acceptance model (TAM) and the information systems success model (ISS) with the intention of continuous use of chatbots in the context of tourism. The study also seeks to understand the role of satisfaction and brand attachment as mediators, and the need for employee interaction as a moderator | The study seeks to address the lack of research on the factors that contribute to consumers’ intention to continue using chatbots, especially in the tourism sector. It also seeks to better understand the role of brand attachment in this context and how the need for human interaction may moderate the relationships between the variables | RQ1: How do information quality, service quality, perceived usefulness, perceived enjoyment and perceived ease of use influence user satisfaction with chatbots in the context of tourism? RQ2: How do user satisfaction and brand attachment influence the intention for continued use of chatbots? RQ3: How does the need for employee interaction moderate the relationships between ease of use, perceived usefulness, perceived enjoyment and user satisfaction, as well as the relationship between satisfaction and brand attachment? | Technology acceptance model (TAM) Information systems success model (ISS) | Quantitative Structural equation modeling (SEM) | Travel planning | Information quality, perceived usefulness, perceived ease of use and perceived enjoyment positively influence user satisfaction with chatbots. User satisfaction positively influences brand attachment and continued usage intention. Brand attachment positively influences continuous usage intention. The need for employee interaction moderates the relationship between satisfaction and brand attachment, so that this relationship is stronger for users with a greater need for human interaction | The study extends the literature on the intention to continuously use chatbots by integrating the TAM and ISS models and incorporating brand attachment as a mediator. It provides empirical evidence that the need for employee interaction may moderate the relationship between satisfaction and brand attachment in the context of chatbots. It contributes to the understanding of the factors that drive the continued use of chatbots in the tourism sector, which may be useful for companies and developers seeking to improve customer experience and foster loyalty |
| Rafiq et al. (2022) | Perceived usability Interactivity Perceived intelligence Anthropomorphism | Cognitive attitude Affective attitude | None | Adoption intention | Identify the key factors influencing consumer attitudes toward the adoption of AI chatbots in tourism. Determine how consumer attitude influences their response (adoption intention) toward AI chatbots in tourism | The study aims to fill the gap in research on the adoption of AI chatbots in tourism, especially in developing markets. It addresses the lack of studies using the stimulus-organism-response (S-O-R) theoretical framework to analyze the influence of AI chatbot characteristics on adoption intention | RQ1: What are the key attributes that determine attitudes toward the adoption of AI-based chatbots in tourism? RQ2: To what extent do consumer attitudes influence their responses to tourism chatbots? | Stimulus-organism-response (S-O-R) theoretical framework | Quantitative Structural equation modeling (SEM) | Travel agencies | The S-O-R theoretical framework is suitable for assessing intentions to adopt chatbots in tourism. Perceived usability, interactivity, perceived intelligence and anthropomorphism positively influence consumers’ attitudes toward the adoption of AI chatbots. Cognitive and affective attitudes positively influence consumers’ intention to adopt AI chatbots | The study extends the literature on the adoption of AI chatbots by applying the S-O-R theoretical framework in the context of tourism. It provides empirical evidence that both rational (cognitive attitude) and emotional (affective attitude) factors are important in predicting AI chatbot adoption intention. The study validates the relevance of the S-O-R model for understanding the adoption of emerging technologies such as AI chatbots in the tourism industry |
| Yoon and Yu (2022) | Findable Usable Desirable Valuable Accessible Attitude | Attitude | None | Utilization intention | Measures the characteristics of consumer chatbot experiences and analyzes their impact on future acceptance intentions through their attitudes toward the RMC chatbot service | There is a lack of research on the attitudes and intentions regarding chatbot using services in the actual dine-out process | RQ1: What are the antecedents of possible dine-out consumers’ restaurant-menu curation (RMC) chatbot services acceptance? | Morville’sUX honeycomb model (2007) may affect future use intentions or attitudes toward new technology such as chatbots | Experimental design (Chatbot prototype) Structural equation modeling (SEM) | Restaurant | All experience characteristics, except usable facets, had a significant positive impact on attitudes toward the chatbot. Three experience characteristics, usable, usefulness and valuable, revealed a significant positive effect on intention of use. Attitudes also significantly affected intention of use | Evaluate the introduction of curation chatbot services in the restaurant sector, by developing and testing the dining-out curation service protocol to help customers’ smart choices in the information technology environment |
| Dhiman and Jamwal (2023) | Task characteristics Technology characteristics | Task-technology fit Confirmation Perceived usefulness | None | Satisfaction Intention of continued use | Investigating the factors triggering customers to continue to use chatbots in a travel planning context | The study seeks to address the lack of research on why people continue to use chatbots despite existing concerns such as misinterpretation of language, privacy concerns and chatbot performance. It also seeks to address the lack of research explaining how customers perceive chatbot services in relation to their various task requirements | RQ1: How do task and technology characteristics influence perceived task-technology fit in chatbot users? RQ2: How does task-technology fit affect confirmation of expectations, perceived usefulness, satisfaction and intention for continued use of chatbots? RQ3: What are the main predictors of satisfaction and intention for continued use of chatbots in the context of travel planning? | Task-technology fit model (TTF). Expectation confirmation model (ECM) | Quantitative Structural equation modeling (SEM) | Travel agencies | User expectations are confirmed when they believe that the chatbots’ technology features meet their task-related characteristics. Task-technology fit and expectation confirmation have a positive impact on the perceived usefulness of chatbots. Perceived usefulness and confirmation positively influence customers’ satisfaction with chatbots. Customer satisfaction is the predominant predictor of their intention to continue using chatbots | The study contributes to the literature on AI technology by identifying potential determinants of the continued use of chatbots. It provides an alternative explanation of the factors that drive the continuous usage intention of AI-based chatbots. It offers an integrated model (TTF and ECM) for understanding the continuous usage intention of chatbots, which can serve as a guide for future research in similar contexts |
| Jha et al. (2023) | Motivated consumer innovativeness (MCI): it consists of four dimensions: fMCI (functional) hMCI (hedonic) sMCI (social) cMCI (cognitive) | Attitude Trust | None | Intention to use | Examine how the four dimensions of the ICM influence consumers’ attitude and trust toward chatbots. Analyze how attitude and trust affect consumers’ intention to use chatbots in the travel and tourism sector | The lack of studies examining ICM in the context of chatbots: previous research has mainly focused on models such as the technology acceptance model (TAM) and the performance expectancy model (PEM), but has not explored in depth how consumer motivation influences chatbot adoption. Little research on the impact of ICM on chatbot usage intention in the travel and tourism sector: this study seeks to fill this gap by investigating how different dimensions of ICM affect attitude, trust and ultimately, chatbot usage intention in this specific sector | RQ1: How do functional, hedonic, social and cognitive dimensions of ICM influence consumers’ attitudes toward chatbots? RQ2: How do the functional, hedonic, social and cognitive dimensions of ICM affect consumers’ trust in chatbots? RQ3: What is the impact of attitude and trust on consumers’ intention to use chatbots? | Motivated consumer innovation theory (MCI). Theory of reasoned action (TRA) | Mixed | Travel and tourism | All dimensions of the ICM positively influence attitude: consumers who are motivated by functional, hedonic, social and cognitive factors have a more positive attitude toward the use of chatbots. Only the functional and cognitive dimensions of the ICM positively influence trust: consumers who seek utility and intellectual stimulation from chatbots are more likely to trust them. Attitude and trust positively influence intention to use: consumers with a positive attitude and trust in chatbots are more likely to use them in the future | Expands the literature on chatbot adoption: applies ICM theory to the context of chatbots and provides empirical evidence of its impact on attitude, trust and intention to use. Introduces a dual mediation model: examines the mediating role of both attitude and trust in the relationship between ICM and intention to use, providing a more complete understanding of the drivers of chatbot adoption |
| Jin and Youn (2023) | Anthropomorphism (human-likeness, animacy and intelligence) | Social presence Imagery processing | None | Psychological ownership of the products/services promoted by AI-chatbots. AI-chatbot continuance intention | Examine the associations among AI-powered chatbots’ anthropomorphism, social presence, imagery processing, psychological ownership, and continuance intention in the context of Human-AI-Interaction | No prior research has examined feelings of social presence and imagery processing as variables correlated with continuance intention toward AI-chatbots in the fashion/tourism marketing context. There is a dearth of empirical research on users’ psychological ownership in the emerging context of human-AI-interaction | RQ1: How do the dimensions of anthropomorphic chatbots (human-likeness, animacy and intelligence) affect consumers’ feelings of social presence and imagery processing? RQ2: How do social presence and imagery processing influence psychological ownership and continuance intention of AI-chatbots? | The study draws on the literature on AI-chatbots, consumer psychology and theories of social presence. It is based on the premise that anthropomorphic chatbots can induce feelings of social presence and imagery processing, which, in turn, can lead to psychological ownership and continuance intention | Quantitative Structural equation modeling (SEM) | Fashion and tourism industries | Perceived human-likeness of AI-powered chatbots is a positive predictor of social presence and imagery processing. Imagery processing is a positive predictor of psychological ownership. Social presence and imagery processing are positive predictors of AI-chatbot continuance intention | Adds original theoretical propositions about the association between anthropomorphic AI, social presence and imagery processing to the human-AI-interaction literature. Provides empirical evidence for the positive impact of anthropomorphism, social presence and imagery processing on chatbot continuance intention |
| Lei et al. (2023) | Perceived ease of use Perceived usefulness Media richness Social presence Task attraction Social attraction | The user’s trust in the chatbot | None | Reuse intention | To investigate the determinants of customers’ intention to reuse chatbots and instant messaging (IM) in the context of the tourism and hospitality industry, integrating three theoretical perspectives: the technology acceptance model (TAM), computer-mediated communication (CMC) theories and interpersonal communication theories | Previous research on conversational agents has mainly focused on physical bots rather than chatbots. There is a lack of empirical evidence on what factors determine customer adoption of chatbots, and understanding of chatbots has been limited to research dominated by a single theoretical perspective, primarily TAM, which ignores the social and relationship-building aspects of communication technologies | RQ1: How do perceived ease of use, perceived usefulness, media richness, social presence, task attractiveness and social attractiveness influence trust and intention to reuse chatbots? RQ2: Are there differences in the effects of these factors between chatbot users and instant messaging (IM) users with human customer service representatives? | TAM CMC theories Theories of interpersonal communication | Quantitative Multigroup structural equation modeling | Tourism and hospitality services | Interpersonal attraction theory-related variables (task attraction, social attraction) have the largest effects on customer trust and reuse intention for conversational agents. For chatbot users, TAM-related variables (perceived ease of use and perceived usefulness) are significant predictors of trust and reuse intention. For IM users, social presence and task attractiveness are important determinants of reuse intention | The study provides empirical evidence on the relevance of different theoretical perspectives (TAM, CMC and interpersonal attraction) in explaining the reuse intention of chatbots and IM in tourism and hospitality. It highlights the interpersonal attraction factors in the formation of trust and reuse intention of chatbots. Provides insights into the differences in factors influencing the reuse of chatbots compared to IM with human customer service representatives |
| Maar et al. (2023) | Customer generation (GenX, GenZ); Chatbot communication style (warmth chatbot; chatbot competences); Service context (medical or restaurants) | Warm chatbot and chatbot competence) customers’ chatbot-related attitudes | Service context and customer generation | Customers’ chatbot-related attitudes Chatbot-related usage intention | Analyze customers’ chatbot-related attitudes and usage intentions in service retailing (restaurant and medical services) | The increasingly relevant segments of hyperconnected digital natives seem to differ from the older digital immigrant customer generations (Prensky, 2001) in terms of chatbot acceptance. Few studies on chatbot communication style that focus on specific age groups or settings | RQ1: How service retailers can overcome the limited chatbot acceptance of their customers by shedding greater light on how a chatbot’s communication style, the customer’s generation and the service context affect the attitude toward and ultimately the intention to use chatbots | Reasoned action perspective with the key dimensions (i.e. warmth and competence) of the stereotype content model (SCM) | Experimental design | Restaurant | 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 for GenZ. These effects do not significantly differ between the considered contexts | To provide first insights into the extent to which service retailers need to consider differences between these segments when designing chatbots. Examine specific user groups accounting for generational differences. Warmth and competence as mediators to better understand the underlying processes that shape a customer’s chatbot-related attitude. The use of chatbots for routine tasks in medical and restaurant settings. Medical and restaurant |
| Meng et al. (2023) | A double-sided message strategy. Double-sided messages | Perceived authenticity | Types of customer demand: inquiries (questions about information) versus complaints (expressions of dissatisfaction) | Intention of use with chatbot | Examine how a two-sided message strategy (as opposed to one-sided positive messages) can increase customers’ willingness to communicate with AI chatbots after disclosing their nonhuman identity. Investigate the mediating role of perceived authenticity in this relationship. Explore how customer demand types (inquiries versus complaints) moderate the effect of messaging strategy on willingness to communicate | Lack of research on how to increase the acceptance of AI chatbots after identity disclosure. Need to understand the underlying mechanisms: more research is needed to understand the psychological processes (such as perceived authenticity) that explain why certain strategies work. Limited exploration of moderating effects: it is unclear whether the effectiveness of messaging strategies varies across different types of customer interactions | RQ1: Do bilateral (versus unilateral positive) messages increase customers’ willingness to communicate with AI chatbots after revealing their nonhuman identity? RQ2: Does perceived authenticity mediate the relationship between message strategy and willingness to communicate? RQ3: Does customer demand type (query versus complaint) moderate the effect of message strategy on willingness to communicate? | Inoculation theory | Experimental design | Hotel reservations and travel agencies | Study indicates that the adoption of a two-sided message strategy can be effective in inoculating customers against negative perceptions. Results indicate that the two-sided message strategy may be more effective than the one-sided positive message strategy for inquiries, but not for complaints | Demonstrates how and what actions can be taken to mitigate the potential negative effects of nonhuman identity disclosure of AI chatbots. Extends the application of inoculation theory to tourism, particularly in the context of the interaction between AI chatbots and customers. Explains the relationship between a two-sided messaging strategy and customers’ willingness to communicate with AI chatbots |
| Zhang et al. (2023) | Time risk Performance expectancy Effort expectancy Social influence Hedonic motivation Habit Anthropomorphism Personalization | None | Gender | Continuance intention to use | Predicting continuance intention to use AI-based chatbots for tourism | Lack of research on the determinants that explain why customers continuously use chatbots for tourism | RQ1: Identify the potential predictors for the use of chatbots for tourism and examine the moderating role of gender differences in the integrated model | Unified theory of adoption and use of technology 2 (UTAUT2). The theory of perceived risk (TPR), anthropomorphism and personalization | Quantitative Structural equation modeling (SEM) | Travel agencies | Positive effects of performance expectancy, social influence, habit, anthropomorphism and personalization. Time risk and privacy risk have negative influences. Although the moderating test did find two differences due to gender, many other relationships showed no differences between male and female customers | First to construct an integrated model in the context of chatbot use through integration of UTAUT2 and TPR. Contrary to Melian-González et al. the influence of hedonic motivation in this research was insignificant associated with customers’ continuance intention to use chatbots for tourism. This study is one of the earliest attempts to explore the moderating role of gender differences in continuance intention of chatbots for tourism |
| Zhu et al. (2023) | Interaction (control, responsiveness, personalization) Quality of information | Perceived usefulness Perceived usefulness of the AI chatbot | Familiarity with the product | Customer confidence Purchase intention Customer trust | Investigate how customers’ perceptions of AI chatbots in online travel agencies (OTAs) influence their cognitive and emotional states, ultimately impacting their trust and purchase intentions, with a focus on the moderating role of product familiarity | The study addresses the knowledge gap on how the characteristics of AI chatbots influence consumer responses. Despite the growing use of chatbots in tourism and hospitality, there was a lack of research on how the interaction and quality of information provided by chatbots affecting customer trust and purchase intent. In addition, the study extends previous research by considering the moderating role of product familiarity in these relationships | RQ1: What characteristics of AI chatbots in OTA environments influence the attitude and cognitive behavior of potential tourists? RQ2: How do potential tourists with different levels of product familiarity respond to AI chatbots on perceptions of usefulness, trust and purchase intent in OTAs? | This study integrates the stimulus-organism-response (SOR) framework with cognitive consistency theory to explain how AI chatbot features, perceived usefulness and product familiarity influence customer trust and purchase intent | Quantitative | Travel agencies | AI chatbot interactivity (control, responsiveness and personalization) and information quality significantly influence customer trust and purchase intent. Perceived usefulness mediates the relationship between interactivity, information quality and customer trust/purchase intent. Product familiarity positively moderates the relationship between perceived usefulness and customer trust. However, it does not moderate the relationship between perceived usefulness and purchase intention | Validates the application of the SOR framework to the context of AI chatbots in OTAs. Expands understanding of the mechanisms of human-computer interaction influence and information quality on customer responses to AI chatbots. Highlights the importance of considering product familiarity when designing and implementing AI chatbots services in OTAs |
| Majid et al. (2024) | Performance expectation Expectation of effort Customization Credibility Privacy control Habit Timeliness Accessibility Efficiency (cost, effort and time) Government support Income Educational level Location | Use of chatbots (adoption and continued use) | Past behavior Environmental identity Environmental values Environmental awareness | Spillover of pro-environmental behavior in the use of environmentally friendly transport | Conceptualize a chatbot designed to facilitate pro-environmental behavior (PEB) transfer among domestic tourists in the Gili Islands, Indonesia. The study focuses on how nudges delivered through the chatbot can encourage tourists to continue green transportation practices after their trip | Limited research on the use of chatbots for prosocial nudging, particularly in the context of tourism and transfer EBP | RQ1: How can a chatbot be designed to facilitate proenvironmental transfer behavior among tourists? RQ2: What factors enable and hinder the effectiveness of chatbot-based nudges to promote transfer EBP? | Nudging theory with the EBP theory of transfer. Theories of technology acceptance to identify factors influencing the adoption of chatbots | Exploratory design Structural equation modeling (SEM) | Sustainable tourism | The study conceptualizes a chatbot designed to interact with tourists via WhatsApp, providing personalized messages and information about their sustainable travel behavior on islands. It identifies several factors that may influence chatbot adoption (e.g. performance expectation, effort expectation, personalization, credibility, privacy control, habit and temporality) and transfer EBP (e.g. accessibility, efficiency, government support, income, education level and location). The findings suggest that the chatbot is best implemented by the Indonesian central government in collaboration with regional governments and tourism stakeholders | Provides a framework for developing and implementing chatbots for prosocial nudging in tourism, particularly related to transfer EBP. Contributes to the limited literature on the use of chatbots to facilitate behavior change with a proenvironmental focus. Offers insights on the potential of AI and chatbots to transform tourism experiences and shape lasting responsible behaviors |
| Study | Independent variables | Mediator | Moderator | Dependent variables | Research focus | Research gaps | Research questions | Theoretical rationale | Methodology | Hospitality and tourism services | Mayor findings | Theoretical contributions |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Placing restaurant takeout orders via different methods (phone, online or chatbot) | None | Restaurant type | Social presence attitudes satisfaction behavior | Apply the contingency theory as the theoretical foundation to explore the fits between restaurant types (i.e. quick-service, full-service) and ordering methods | How chatbots can be added to the current digital ordering system to provide improved customer experiences | Compare the performance and customer perceptions of chatbots with other restaurant takeaway ordering methods based on social presence theory | Experimental design | Restaurant | The phone and online ordering methods were both better than the chatbot method both in terms of satisfaction and behavior | The first attempt to examine consumers’ interactions, attitudes and behaviors when using chatbots in food ordering processes | ||
| Perceived usefulness of live chat communication | Attitudes toward the website | Human warmth of the online travel representative | Intention to use (purchase) | The study aims to investigate the influence of perceived usefulness of live chat services and of their unique human attributes on customer attitudes, beliefs and behaviors in the context of online travel shopping | There are no empirical studies examining the variables that influence consumer attitudes, trust and purchase intent with the live chat experience, despite the growth of live chat at the expense of telephone sales and support. | Social presence theory | Quantitative | Travel agencies | The perceived usefulness of live chat communication positively influences customer attitudes toward the website, trust in the website and purchase intent. | Provides empirical evidence on the impact of human communication via live chat on travel consumers’ attitudes, trust and purchase intent. | ||
| Perceived ease of use | None | Stickiness to traditional travel agents/planners | Adoption intention | To investigate the factors influencing the adoption intention and actual usage of artificial intelligence (AI) powered chatbots in the hotel and tourism industry in India. The study seeks to extend the technology acceptance model (TAM) with context-specific variables | To address the lack of research on the adoption of AI-based chatbots in the hospitality and tourism industry, especially in emerging economies such as India. | TAM along with human–robot interaction (HRI) variables – ANM, perceived intelligence, technology anxiety and perceived trust | Mixed | Travel agencies | Predictors of intention to adopt chatbots are: perceived ease of use, perceived usefulness, perceived trust, perceived intelligence and anthropomorphism. | The study extends the TAM to provide better explanatory power in the context of human-robot interaction by including specific constructs such as perceived trust, perceived intelligence, anthropomorphism and technological anxiety | ||
| Values: openness to change, including stimulation and self-direction. | Perceived value of cocreation: consumers’ assessment of the value they derive from interacting and cocreating value with AI chatbots in the context of travel planning | None | Behavioral intentions: the likelihood that consumers will adopt and use AI-enabled travel service agents in the future | To examine the factors that influence consumers’ attitudes toward AI-enabled travel service agents (perceived value cocreation) and their intentions to adopt these services. | Lack of research on how new operational resources, such as AI chatbots, influence consumer perceptions of service processes. | RQ1: How do values influence consumers reasoning process for adopting or not adopting AI-enabled service encounters? | Behavioral reasoning theory (BRT) | Fuzzy set comparative qualitative analysis (fsQCA) | Travel planning phase | The results mainly support hypotheses based on behavioral reasoning theory (BRT). | Insights into a new area of consumer behavior and acceptance of AI-enabled service encounters. | |
| Ordering method (chatbot, mobile, online) | Emotion | Restaurant type (full service, quick service) | Internal responses (satisfaction, behavioral intention); External behaviors (other items, other amounts) | Examine the role of consumption emotion in the digital food-ordering experience by comparing the performances of the three digital ordering methods in an experimental design | Concern over declining service quality associated with the reduced interaction between customers and restaurant staff. | Feelings-as-information (FaI) theory | Experimental design | Restaurant | The online ordering method worked the best for quick-service restaurants, whereas the mobile ordering method was most suitable for full-service restaurants. Both positive and negative emotions (comfort and annoyance) significantly mediated the relationships between the ordering method and internal responses (satisfaction /BI). Only one negative emotion (anger) significantly mediated the relationship between the ordering method and order amount | First study that attempts to explore and compare consumers’ emotional responses resulting from restaurant digital ordering experiences in the context of the three food-ordering methods. Developed a theoretical framework based on both the FaI theory and the expectancy-disconfirmation theory | ||
| Understandability | Confirmation satisfaction | Technology anxiety (TA) taken positively | Use continuance | This study examines how users view chatbot services in OTAs by investigating the moderating role of technology anxiety | To identify five quality dimensions of chatbot services and investigate their effect on user confirmation, which, in turn, leads to use continuance | Depending on how users see the chatbot-enabled services with different levels of TA, either as human-like agents or as another new type of technology-self-services, the way they assess their postuse confirmation against the service quality dimensions should be different | Quantitative | Travel agencies | Understandability, reliability, assurance and interactivity are antecedents for user’s postacceptance confirmation, as well as by proposing technology anxiety toward chatbots as a moderating factor for these relationships | Technology anxiety positively moderates the relationships between chatbot quality dimensions and postuse confirmation, suggesting some users may treat chatbot services as human-like agents | ||
| Performance expectation | Attitude toward self-service technologies (SSTA) mediates the relationship between perceived innovation and intention to use chatbots | None | Chatbots usage intention (CUI): The extent to which individuals plan to use chatbots in the future | Predict the intention to use chatbots for travel and tourism by examining the factors that influence consumers’ willingness to interact with this technology | The study seeks to fill the research gap in the factors that explain why consumers are willing to interact with chatbots in the context of tourism, despite their increasing implementation in the industry | UTAUT2, a model widely used to explain the adoption of new technologies. It incorporates other factors relevant to the context of chatbots, such as attitude toward self-service technologies, inconvenience, anthropomorphism and automation | Quantitative | Travel and tourism | Intention to use chatbots is positively influenced by: performance expectancy, usage habit, hedonic motivation, SI, Anthropomorphism, belief that chatbots will replace jobs (contrary to expectation). | The usefulness of the UTAUT2 model in explaining the adoption of chatbots in the travel and tourism context. | ||
| Social presence cues (human avatar, human name, use of client’s name, detailed self-presentation) | Perceived trustworthiness | None | Usage intention (UI), intention to use online travel agency (OTA) chatbots | Explore the perceived signs of anthropomorphism in chatbots and their effects on customers’ intention to use them in the context of online travel agencies (OTAs) | Lack of comprehensive research on customer and business concerns regarding the anthropomorphism of chatbots in the context of online travel services. | The study is based on the theory of uncertainty reduction. | Mixed | Travel agencies | Social presence signals (use of human avatar, mention of customer’s name) and emotional messaging signals (humor, empathy and emoticons) are the main anthropomorphic signals of interest to customers and companies. | The study contributes to the literature on chatbot anthropomorphism by providing a holistic understanding of multiple anthropomorphic design cues and their effects on customers’ usage intention. | ||
| Information quality | Satisfaction | Need for employee interaction (NFI-SE): The extent to which a person prefers to interact with a human employee rather than an automated system | Continuous intention to use | Analyze the relationship between the dimensions of the technology acceptance model (TAM) and the information systems success model (ISS) with the intention of continuous use of chatbots in the context of tourism. The study also seeks to understand the role of satisfaction and brand attachment as mediators, and the need for employee interaction as a moderator | The study seeks to address the lack of research on the factors that contribute to consumers’ intention to continue using chatbots, especially in the tourism sector. | Technology acceptance model (TAM) | Quantitative | Travel planning | Information quality, perceived usefulness, perceived ease of use and perceived enjoyment positively influence user satisfaction with chatbots. | The study extends the literature on the intention to continuously use chatbots by integrating the TAM and ISS models and incorporating brand attachment as a mediator. | ||
| Perceived usability | Cognitive attitude | None | Adoption intention | Identify the key factors influencing consumer attitudes toward the adoption of AI chatbots in tourism. | The study aims to fill the gap in research on the adoption of AI chatbots in tourism, especially in developing markets. | Stimulus-organism-response (S-O-R) theoretical framework | Quantitative | Travel agencies | The S-O-R theoretical framework is suitable for assessing intentions to adopt chatbots in tourism. | The study extends the literature on the adoption of AI chatbots by applying the S-O-R theoretical framework in the context of tourism. | ||
| Findable | Attitude | None | Utilization intention | Measures the characteristics of consumer chatbot experiences and analyzes their impact on future acceptance intentions through their attitudes toward the RMC chatbot service | There is a lack of research on the attitudes and intentions regarding chatbot using services in the actual dine-out process | Morville’sUX honeycomb model (2007) may affect future use intentions or attitudes toward new technology such as chatbots | Experimental design (Chatbot prototype) | Restaurant | All experience characteristics, except usable facets, had a significant positive impact on attitudes toward the chatbot. Three experience characteristics, usable, usefulness and valuable, revealed a significant positive effect on intention of use. Attitudes also significantly affected intention of use | Evaluate the introduction of curation chatbot services in the restaurant sector, by developing and testing the dining-out curation service protocol to help customers’ smart choices in the information technology environment | ||
| Task characteristics | Task-technology fit | None | Satisfaction | Investigating the factors triggering customers to continue to use chatbots in a travel planning context | The study seeks to address the lack of research on why people continue to use chatbots despite existing concerns such as misinterpretation of language, privacy concerns and chatbot performance. | Task-technology fit model (TTF). | Quantitative | Travel agencies | User expectations are confirmed when they believe that the chatbots’ technology features meet their task-related characteristics. | The study contributes to the literature on AI technology by identifying potential determinants of the continued use of chatbots. | ||
| Motivated consumer innovativeness (MCI): it consists of four dimensions: | Attitude | None | Intention to use | Examine how the four dimensions of the ICM influence consumers’ attitude and trust toward chatbots. | The lack of studies examining ICM in the context of chatbots: previous research has mainly focused on models such as the technology acceptance model (TAM) and the performance expectancy model (PEM), but has not explored in depth how consumer motivation influences chatbot adoption. | Motivated consumer innovation theory (MCI). | Mixed | Travel and tourism | All dimensions of the ICM positively influence attitude: consumers who are motivated by functional, hedonic, social and cognitive factors have a more positive attitude toward the use of chatbots. | Expands the literature on chatbot adoption: applies ICM theory to the context of chatbots and provides empirical evidence of its impact on attitude, trust and intention to use. | ||
| Anthropomorphism (human-likeness, animacy and intelligence) | Social presence | None | Psychological ownership of the products/services promoted by AI-chatbots. | Examine the associations among AI-powered chatbots’ anthropomorphism, social presence, imagery processing, psychological ownership, and continuance intention in the context of Human-AI-Interaction | No prior research has examined feelings of social presence and imagery processing as variables correlated with continuance intention toward AI-chatbots in the fashion/tourism marketing context. | The study draws on the literature on AI-chatbots, consumer psychology and theories of social presence. | Quantitative | Fashion and tourism industries | Perceived human-likeness of AI-powered chatbots is a positive predictor of social presence and imagery processing. | Adds original theoretical propositions about the association between anthropomorphic AI, social presence and imagery processing to the human-AI-interaction literature. Provides empirical evidence for the positive impact of anthropomorphism, social presence and imagery processing on chatbot continuance intention | ||
| Perceived ease of use | The user’s trust in the chatbot | None | Reuse intention | To investigate the determinants of customers’ intention to reuse chatbots and instant messaging (IM) in the context of the tourism and hospitality industry, integrating three theoretical perspectives: the technology acceptance model (TAM), computer-mediated communication (CMC) theories and interpersonal communication theories | Previous research on conversational agents has mainly focused on physical bots rather than chatbots. | TAM | Quantitative | Tourism and hospitality services | Interpersonal attraction theory-related variables (task attraction, social attraction) have the largest effects on customer trust and reuse intention for conversational agents. | The study provides empirical evidence on the relevance of different theoretical perspectives (TAM, CMC and interpersonal attraction) in explaining the reuse intention of chatbots and IM in tourism and hospitality. | ||
| Customer generation (GenX, GenZ); Chatbot communication style (warmth chatbot; chatbot competences); Service context (medical or restaurants) | Warm chatbot and chatbot competence) customers’ chatbot-related attitudes | Service context and customer generation | Customers’ chatbot-related attitudes | Analyze customers’ chatbot-related attitudes and usage intentions in service retailing (restaurant and medical services) | The increasingly relevant segments of hyperconnected digital natives seem to differ from the older digital immigrant customer generations ( | Reasoned action perspective with the key dimensions (i.e. warmth and competence) of the stereotype content model (SCM) | Experimental design | Restaurant | 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 for GenZ. These effects do not significantly differ between the considered contexts | To provide first insights into the extent to which service retailers need to consider differences between these segments when designing chatbots. | ||
| A double-sided message strategy. | Perceived authenticity | Types of customer demand: inquiries (questions about information) versus complaints (expressions of dissatisfaction) | Intention of use with chatbot | Examine how a two-sided message strategy (as opposed to one-sided positive messages) can increase customers’ willingness to communicate with AI chatbots after disclosing their nonhuman identity. | Lack of research on how to increase the acceptance of AI chatbots after identity disclosure. | Inoculation theory | Experimental design | Hotel reservations and travel agencies | Study indicates that the adoption of a two-sided message strategy can be effective in inoculating customers against negative perceptions. | Demonstrates how and what actions can be taken to mitigate the potential negative effects of nonhuman identity disclosure of AI chatbots. | ||
| Time risk | None | Gender | Continuance intention to use | Predicting continuance intention to use AI-based chatbots for tourism | Lack of research on the determinants that explain why customers continuously use chatbots for tourism | Unified theory of adoption and use of technology 2 (UTAUT2). | Quantitative | Travel agencies | Positive effects of performance expectancy, social influence, habit, anthropomorphism and personalization. | First to construct an integrated model in the context of chatbot use through integration of UTAUT2 and TPR. Contrary to Melian-González | ||
| Interaction (control, responsiveness, personalization) | Perceived usefulness | Familiarity with the product | Customer confidence | Investigate how customers’ perceptions of AI chatbots in online travel agencies (OTAs) influence their cognitive and emotional states, ultimately impacting their trust and purchase intentions, with a focus on the moderating role of product familiarity | The study addresses the knowledge gap on how the characteristics of AI chatbots influence consumer responses. Despite the growing use of chatbots in tourism and hospitality, there was a lack of research on how the interaction and quality of information provided by chatbots affecting customer trust and purchase intent. In addition, the study extends previous research by considering the moderating role of product familiarity in these relationships | This study integrates the stimulus-organism-response (SOR) framework with cognitive consistency theory to explain how AI chatbot features, perceived usefulness and product familiarity influence customer trust and purchase intent | Quantitative | Travel agencies | AI chatbot interactivity (control, responsiveness and personalization) and information quality significantly influence customer trust and purchase intent. | Validates the application of the SOR framework to the context of AI chatbots in OTAs. | ||
| Performance expectation | Use of chatbots (adoption and continued use) | Past behavior | Spillover of pro-environmental behavior in the use of environmentally friendly transport | Conceptualize a chatbot designed to facilitate pro-environmental behavior (PEB) transfer among domestic tourists in the Gili Islands, Indonesia. The study focuses on how nudges delivered through the chatbot can encourage tourists to continue green transportation practices after their trip | Limited research on the use of chatbots for prosocial nudging, particularly in the context of tourism and transfer EBP | Nudging theory with the EBP theory of transfer. | Exploratory design | Sustainable tourism | The study conceptualizes a chatbot designed to interact with tourists via WhatsApp, providing personalized messages and information about their sustainable travel behavior on islands. | Provides a framework for developing and implementing chatbots for prosocial nudging in tourism, particularly related to transfer EBP. |
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