Tourist Decision-making Process Research Articles

Immersive Decision-Making : A Quantitative Analysis of Metaverse Applications in Tourism Marketing

This article investigates the transformative impact of metaverse-based virtualization on tourism marketing and consumer experiences. Through quantitative analysis of data collected via standardized questionnaires, we examine how virtual reality tours, 3D representations, and simulated experiences in the metaverse influence tourist decision-making processes and pre-travel engagement. Our findings reveal that metaverse applications significantly enhance tourists' ability to make informed decisions, reduce travel anxiety, and engage with personalized marketing content. The article demonstrates a positive correlation between virtual destination previews and increased consumer confidence, with 78% of respondents reporting higher satisfaction in their travel choices. Moreover, the study identifies a 35% reduction in pre-travel uncertainty among first-time visitors to unfamiliar destinations who utilized metaverse-based virtual tours. These results underscore the potential of metaverse technology to revolutionize tourism marketing strategies, offering implications for industry practitioners and paving the way for future research in digital tourism experiences.

Food tourism to revitalize the Basque Country

The research examines the impacts of sensory experiences, taste, and destination food imagery on tourists' attitude. This study changes risks into positive attributes like safety and security, which significantly shape tourist decision-making processes. It underscores the significance of emotional well-being and safety concerns impacting tourists' visiting intentions. Employing Partial Least Squares Structural Equation Modelling, the study focuses on exploring the relationships among food tourism motivators, destination food image, safety perceptions, and the pandemic's impact on tourists' behavioural intentions within the Basque region. With a sample of 601 participants, this research emphasizes the importance of integrating risk mitigation actions and managing emotional states to design strategies for the sustained growth of the tourism industry.

Refocusing on the start and end of tourists’ decision-making: Measuring thresholds and information loop limits

In this era of abundant travel-related information, tourist decision-making processes have become increasingly complex and nonrational. This study focuses on the initiation and conclusion instances of decision-making between the pre-decision, decision-in-progress, and post-decision phases to capture the diversity of decisional behaviors. Building upon the Cyclic Model of Tourist Decision-Making, two new constructs, the Decision-Making Threshold (DMT) and Information Loop Limit (ILL), were validated with measurement tools developed, with which we empirically recognized the individual differences in the propensity to start and end decisions. Moreover, the dimensions of each construct were confirmed by incorporating the DMT for internal and external stimuli and the ILL based on cognitive capacity and willingness. The validation of the original theoretical concepts provides a basis for future research, including the examination of correlations among DMT, ILL, and other consumption-related behaviors. Four distinct decision-making segments are identified and practical implications are discussed.

Using Virtual Reality in Tourism Destination Decision: A Young Saudi Tourists’ Perspective with Habit is a Mediator

ABSTRACT This study investigates the impact of Virtual Reality (VR) on Tourist Destination Decision (TDD), surveying 425 participants in Saudi Arabia. We explored the influence of self-efficacy, hedonic motivation, and social influence within the VR context in tourism. Our findings reveal the significant, yet non-dominant role of hedonic motivations and the unexpected prominence of habitual behaviors in TDD. Notably, the study challenges traditional assumptions by highlighting VR’s transformative impact on tourist decisions, diverging from previous research that underplayed technology’s role. This research contributes new insights into the interplay of technology and human behavior in tourism, offering valuable implications for industry practices. It emphasizes the importance of integrating VR into tourism strategies in today’s digital age, signaling a shift in the landscape of tourist decision-making processes. This study investigates the influence of Virtual Reality (VR) on Tourist Destination Decision (TDD) against the backdrop of the digital age, surveying 425 participants from major cities in Saudi Arabia. It uniquely contributes to the existing literature by examining the interplay of factors like self-efficacy, hedonic motivation, social influence, and habitual behaviors within the context of VR and tourism. Contrary to traditional assumptions, our findings reveal that while hedonic motivations are significant, they do not dominate VR-influenced TDD. Instead, habitual behaviors and social influence emerge as more influential. These challenges prevailing perspectives in tourism research and underscore the importance of VR in reshaping tourist decision-making processes. The study’s novelty lies in integrating contemporary technological trends with established tourism theories, thereby offering fresh insights into how technology, particularly VR, interacts with human behaviors in tourism. It also highlights the need for industry adaptation to these technological advancements. In essence, this research provides a foundational understanding of the evolving tourism landscape, marked by the synergy of VR technology and human predispositions, and sets the stage for further exploration into how these factors interplay with various demographic, cultural, and economic contexts.

Autonomous travel decision-making: An early glimpse into ChatGPT and generative AI

Generative AI technologies, including large language models (LLMs), have the potential to bring significant advancements to the tourism and hospitality industry through an array of ingenious features. ChatGPT, as a type of generative AI, is a state-of-the-art LLM that is bundled with extensive capabilities. This commentary provides an early snapshot of ChatGPT's ability to enhance the tourist decision-making process in the pre-trip, en-route, and post-trip stages. Particularly, ChatGPT greatly differentiates itself from the traditional travel decision-making process by putting the tourist in the driver's seat to actively probe for highly relevant information through a question-and-answer mode. In this research note, we present different scenarios with cases demonstrating how ChatGPT can improve tourists' experience in these three travel stages, with trip planning efficiency, customized recommendations, 24/7 personal assistant, AI-mediated communication fluency, autonomous guided tours, enriched sharing experience, and AI-induced prolonged satisfaction. In summary, these value-added autonomous services provide tourists with travel solutions that are highly cost-effective, offering customized information that allows them to travel with ease.

Towards travel recommendation interpretability: Disentangling tourist decision-making process via knowledge graph

Understanding tourists’ decision-making processes, in which many factors ranging from functional attributes to geographical configurations are highly intertwined, has long been a crux for tourism management. Existing studies are typically based on manual surveys that extract the intricate psychological or behavioural mechanisms, but the huge expense of the required samplings limits the generalization and comprehensiveness of the findings. This study proposes a novel explainable recommendation method—Knowledge-Graph-aware Disentangled Auto-Encoder (KGDAE)—to automatically unravel the tourists’ decision processes from massive historical behaviour data. Based on the constructed tourism-KG that integrates multidimensional factors into 23 types of entities corresponding to 37 semantic and geographic relationships, KGDAE realizes a macro-micro supervised disentangled learning for the interaction of multiple determinants. Macroscopically, the hierarchical attention mechanisms are designed to distinguish the dominance of either functional or geographical factors, and capture the effect of the residential environment; microscopically, the preference-propagation-based technique is introduced to infer the fine-grained characteristics and relations of tourist interests on the tourism-KG. Extensive experiments show that KGDAE can effectively restore tourists’ decision processes according to two empirical studies while boosting the recommendation performance compared to multiple state-of-the-art methods with an increase of 1∼19%. Furthermore, the advantaged interpretability also guarantees the robustness of sparse recommendation scenario to achieve the lowest degradation at 7.8%.

Sigurnost i odabir destinacije u post-pandemijskom periodu - studija Opatijske Rivijere

Safety is one of the fundamental elements in the tourist's decision-making process when choosing a holiday destination. Over the years numerous destinations were faced with challenges how to function and survive on the tourist market. To plan future developmental activities, it is mandatory to observe how tourists perceive safety of a destination, and what are the motivational factors that are connected with safety, that influence their decision-making process the most, and in post pandemic period in particular. Empirical research was conducted with the use of a structured questionnaire to examine what are the safety motivation factors that influence tourists to choose Opatija Riviera as a holiday destination in the post pandemic period. Descriptive statistics was used for data analysis. The results indicate that tourists pay significant interest for communication with service providers, general information about the destination, what is the current criteria for entering the country, organization of health insurance and health protection during their stay, and possibility of doing a simple test for COVID. Moderate relevance was given to Opatija Riviera belonging to the "green region" and keeping the social distance during stay. Determined findings represent a good basis for formation of developmental activities which will improve the level of Opatija Riviera's safety and its competitiveness on the tourist market in the future.

Tourist scams: cues and processes in decision-making

ABSTRACT Tourist scams have long been a challenge for tourist security and destination management. Understanding the tourist decision-making process can improve scam prevention. Through a quasi-experiment, 609 participants made, and documented reasons for, decisions after viewing animated scam videos. Thematic analysis was conducted to explore the external (operational) cues and internal (respondent-related) references, and revealed two categories of cues, namely scam-covering and scam-revealing cues. Three internal references—emotions, knowledge and needs—exerted a crucial impact. Multiple heuristics strategies were identified, and a model was made to illustrate the decision-making process. Theoretic and pragmatic implications are discussed for communicating scam detection.

The beauty premium of tour guides in the customer decision-making process: An AI-based big data analysis

This study investigates how the facial cues of tour guides in their profile pictures, and especially aesthetically pleasing facial features, play a role in the different stages of a tourist's decision-making process. Facilitated by an artificial intelligence (AI) facial recognition system, a comprehensive model is developed which incorporates the facial and service cues of 3786 tour guides. The results show that beauty scores and smiles have a positive effect on purchase decisions, while post-service ratings are only subject to service cues. The contingency effects of the beauty premium are also examined. The results indicate greater beauty premium effects for females, and for tour guides who are responsible for chauffeured, package or walking tours. This research is a pioneering study in AI-based facial analyses in the context of tourism, and offers insights into the impression management of online profiles in the customer decision-making stages.

Celebrity Endorsement Influence on Destination Image

Abstract Destination image influences the tourist decision-making process, and their intention to visit, revisit and recommend. Therefore, a positive destination image is considered one of the key destination success factors. Tourists will form the image according to their own or others’ experiences and information related to the destination, one option to provide destination information is one of the most effective promotional tools called celebrity endorsement. With the assumption that a positive celebrity image will be transferred to the destination, this study will explore the effect of celebrity endorsement on destination image. The study aims to test the celebrity endorsement effect on destination image through the source attractiveness model, source credibility model, and the Match-up model. Data were collected using the online survey technique, the sample size was 204 respondents. Research hypotheses were tested by regression analysis. Results indicated that celebrity attractiveness, trustworthiness, expertise, and match-up impact the destination image. The theoretical contribution is reflected in new information regarding the role of celebrity endorsement in tourism, which is limited in foreign and domestic scientific literature. As a practical contribution, the study provides information that may be useful to tourism service providers in creating a promotional campaign.

CAFOB: Context-aware fuzzy-ontology-based tourism recommendation system

The ever-increasing volume of information available on tourist attractions in cyberspace has made the tourist decision-making process a crucial task. Therefore, tourism recommendation systems can significantly benefit tourists in terms of comfort and satisfaction. In this paper, a context-aware fuzzy-ontology-based tourism recommendation system is proposed. In the proposed system, we have two new propositions that can be individually used in other tourism recommendation systems: a fuzzy-weighted ontology and a new sentiment/emotion score scheme. In CAFOB, the ontology-based scores of a user’s reviews are then multiplicatively modulated by sentiment/emotion scores to generate the total scores of the reviews’ ontology words, representing the user preferences. Then, the nearby-open-3+-bubbled touristic attractions’ characteristics are extracted based on their past visitors’ reviews. Finally, a recommendation list is produced using a maximum hybrid semantic similarity between the user preferences and attractions’ characteristics. The employment of contextual information, including weather, location, and time makes CAFOB context-aware and improves the accuracy and quality of the recommendations. The F-measure, NDCG, and MRR results show the outperformance of CAFOB against the state-of-the-art tourism recommendation systems for all relevant Top N recommendation options and different geographical spans of interest.

Proposing a Cyclic Model of Tourist Decision Making: A Review and Integration of Behavioral and Choice-Set Models

Tourist decision studies focus on modeling decision-making behaviors, conceptualizing phases in decision making, and influential factors. Incorporating behavioral and choice-set model strategies, the current study proposes a generalizable cyclic model of tourist decision-making processes with a structure of repeatable behavioral stages integrated with relevant consideration sets. A “decision-making threshold” and “information loop limit” are introduced to control how and when the decision-making process starts or ends. The proposed model makes it possible to represent different decision-making styles by capturing the dynamic repetition of behavioral stages and the revision of consideration sets. The integration allows a novel approach for analyzing the formation of final decisions resulting from decision makers’ limited subjective evaluations and for studying decision rules as the combinations of “evaluation rule” and “information loop limit.” Practical implications and measures are provided for tourism practitioners to better understand and influence potential consumers. Future research questions are also suggested.

Are customer reviews just reviews? Hotel forecasting using sentiment analysis

Despite the important role of the sentiment of customer reviews in the tourist decision-making process, it has received limited attention from the field of tourism demand forecasting. This study aims to examine the sentiment information of customer reviews and explore its potential in enhancing hotel demand forecast. Empirically, four Macau luxury hotels are selected and their customer reviews are crawled from two popular online platforms. A deep learning method, the Long Short-Term Memory model, is used to extract sentiment information from consumer reviews. Three sentiment indices, namely, bullish index, average index and variance index, are constructed and examined. The effectiveness of these sentiment indices is further evaluated by the autoregressive integrated moving average with exogenous variables model. Empirical results indicate that the inclusion of the sentiment indices helps with improving forecast accuracy. The findings of this study further emphasise the importance of textual content to hotel practitioners in terms of strategy formulation, revenue management and competitiveness enhancement.

Obtaining Data Values from Tourist Preferences

Satisfied customers are the main sustainability factor for the viability of any activity, and tourism has increasing relevance to the global economy and the economic development of many regions. In order to create better matches between tourist demands and preferences and the local supply, an understanding of tourists as decision makers is necessary. The aim of this work is to introduce a mathematical model that explains the decision-making process of tourists, other consumers, and tourism business managers. We used a mathematical model, taking into consideration the preferences of individuals and their strengths during the exploration and use of tourism resources. The integration of preferences into an IT tool provided extra support to the decisions of tourists and allowed better choices to be made in the execution of travel plans. In addition, the model can be used by resource managers. Understanding how tourists make decisions in each different situation can improve the allocation of available resources to satisfy their expectations. The proposed model is also adaptable to situations where it is necessary to decide among different options with a high degree of complexity.

Evaluation of Tourist Decision-Making Process by Eye-Tracking Method – Focused on Methodology Gap and Cross-National Comparison

The dynamic development of information and communication technologies and online marketing in the hospitality and tourism industry changes customer behavior significantly.. This article presents the research results focused on measuring the visual attention of potential clients while choosing the hotel online. Eye-tracking-based data collection followed by appropriate statistical analyses was selected to study customers' behaviour and, consequently, judge the effectiveness and impact of used marketing tools. Three scenarios selected for this study covered different clients' segments: 1/overnight business guests (on the road), 2/weekend package guests (couples), and 3/team-building groups (small companies) and their specific needs. The behaviour of the respondents was monitored in two phases: browsing and deliberation phases. Our study reveals the specifics in Middle and East European consumers' behaviour in both phases, the differences in their priorities, their personal filters used, specifics of their decision-making process, etc., compared with Noone & Robson studies from 2014 and 2016 on US customers. From the methodological point of view, the study reveals the importance of so-called scenarios since it clearly shows that preferences during the browsing phase (e.g. photos of hotels, rooms, bathrooms, its environment, surrounding landscape, etc.) depend on the simulated life situation. The differences in both groups of clients' behaviour and especially in their decision-making process are discussed in the conclusions.

Does Risk Awareness of COVID-19 Affect Visits to National Parks? Analyzing the Tourist Decision-Making Process Using the Theory of Planned Behavior.

This study aimed to determine whether risk awareness of coronavirus disease (COVID-19) affects visits to national parks. We analyzed the tourist decision-making process during the current pandemic using the theory of planned behavior as a framework, adding variables relevant to the pandemic, such as risk perception and risk reduction behavior, to the model. Based on a literature review, we developed a research model describing the impact relationship between risk perception, the theory of planned behavior, and risk reduction behavior and tested nine hypotheses. Results of a survey of 555 visitors to two national parks supported eight of the nine hypotheses. Although the results are limited, they reaffirm the usefulness of the theory of planned behavior in explaining tourism behavior. This work is significant in that we would be able to extend the scope of subsequent research beyond a discussion of the direct effects on optimistic perceptions (bias) and risk reduction behavior as well as visit intention, by explaining the probability even in unprecedented crises such as COVID-19. Humans may be negotiating the constraints (COVID-19) or embodied tourism need through the personal bias. Furthermore, we discuss the theoretical implications of the results for tourism behavior research.

AN INFLUENCE OF SOCIAL DISTANCE ON TOURIST DECISION-MAKING PROCESS: A CASE OF TOURISTS VISITING THE MAASAI MARA NATIONAL GAME RESERVE

Purpose of the study: The study aims to assess the influence of social distance on the tourist decision-making process on tourists visiting the Maasai Mara National Game Reserve using the construal level theory.
 Methodology: Exploratory research design was used using regression equation modeling. The Borgardus social distance scale was adapted to structure the questionnaire. A sample of 157 tourists was selected. The data were analyzed using descriptive and inferential statistics which were used to determine the relationship between the variables. The hypothesis was tested at a 5% level of confidence.
 Main Findings: The study established that the majority of the respondents understood and indicated that social distancing influenced their choice of a destination. Social distance (R=0.580, p=0.00<0.05) had a strong positive and significant influence on the contractual level of tourist choice of a destination. It was further established that construal level accounted for up to 33.6 percent of the variance in choice of destination (R2=0.336).
 Applications of this study: The study recommends that the conventional consumer behaviour model can be enhanced in decision making by incorporating the construal level of destination decision making for tourists. It will be beneficial to the tourists visiting Maasai Mara National Game Reserve, the marketers, hotels and destination owners, and the government at large.
 Novelty/Originality of this study: Decision-making is central to the satisfaction of a customer that seeks value for his/her money. Though research in consumer decision-making has been conducted before its application in tourist’s destination decision-making process remains new. This study sought to bring to light this link and fill this gap in the literature.

Technological evolution and tourist decision-making: a perspective article

PurposeThis paper aims to demonstrate the importance of technological advancements in shaping tourist decision-making. It reviews relevant theories and underlines the need for new theoretical frameworks that would illustrate the role of cognitive technologies, i.e. artificial intelligence (AI) applications, in tourist decision-making.Design/methodology/approachA critical analysis and synthesis of interdisciplinary literature was undertaken, aiming to discuss the theoretical frameworks and approaches used and to offer a future perspective based on major technological advances.FindingsThe technological advancements play a structural role in tourist decision-making processes, with digital technologies and cyber-physical systems becoming extensions of a tourist’s self.Research limitations/implicationsThe study offers the basis for a new theoretical framework in the area of tourism decision-making, considering the future of cyber-physical-human systems.Originality/valueThis study exemplifies the main elements of tourism decision-making theory and describes the need for a paradigm shift, as new forms of AI will complement human intelligence.

The SOMOAR operationalization: a holistic concept to travel decision modelling

PurposeMost state-of-the-art approaches for the analysis of the process of travel decision-making follow Woodworth’s neo-behaviouristic S–R (stimulus–response) or S–O–R (stimulus–organism–response) model. However, within this model, scholars primarily focus on the S–R relationship, investigating specific decisions by describing or explaining an outcome as the result of an input of several stimuli. There is a lack of investigation into the “O” dimension of the S–O–R model. This paper aims to contribute towards closing of this gap by conceptually and holistically expanding existing models with new perspectives and components.Design/methodology/approachThe authors base the conceptual process on a subjective/interpretative research paradigm, by combining outcomes from different theories and concepts into a new, more holistic approach; and challenging this approach by seeking counterarguments as well as supportive arguments at three conferences and workshops.FindingsThe paper expands the body of literature by positing a generic conceptual operationalization model focusing on the organism (“O”) domain of decision-making. To achieve this, and further to operationalize the S–O–R model, the paper proposes to integrate an M–O–A (motivation–opportunity–ability) approach.Originality/valueThe analysis of the body literature reveals that there is still a lack of analytical and especially workable models/approaches for the analysis of the process of tourist decision-making. The paper contributes to that discussion by offering an alternative and generic operationalization of the tourist decision-making process by inducing a theoretical framework from the deductions gleaned from a number of existing theories.

Twitter usage in Tourism: Literature Review

Abstract Background: Twitter is the most popular microblog platform. Individuals, companies, organizations, and even governments use Twitter on a daily bases and get vast benefits from it. Twitter also has been valuable for the tourism sector, especially in developing business strategies, planning and studying tourist decision-making processes. Objectives: Goal of the paper is to identify the trends, patterns and the research gaps of the research focusing on the Twitter usage in tourism. Methods/Approach: A bibliometric analysis was conducted in order to identify significant authors, journals, and institutions who engaged in the research-oriented towards Twitter utilization in tourism. In addition, text-mining analysis has been conducted in order to extract and identify the topics of the papers investigating the utilization of Twitter for tourism research. Results: Research of Twitter utilization in tourism has increased substantially in the last decade, with most of the research conducted in the United States and Japan. Extracted topics are focused on distinctive themes, such as network analysis, word of mouth, and destination management. Conclusions: New topics have emerged, such as the utilization of Twitter in crisis communication and terrorist attacks, as well as the integration of Twitter and other social media such as Flickr.