Pub Date : 2024-02-14DOI: 10.1177/10963480241229235
Hyunsu Kim, Kevin Kam Fung So, Seunghun Shin, Jing Li
Given that artificial intelligence (AI) is significantly transforming businesses, it is crucial to examine how AI will change the future of the hospitality and tourism industry. By integrating multiple data sources (i.e., practitioner literature, research literature, and expert opinions), we suggest three trends constituting opportunities and challenges (AI applications in different business sectors, primary AI functions, emerging AI topics), three possible themes of change (adoption and acceptance, operations management, AI in marketing), as well as four directions for future research (AI interaction, AI and organizational decision making, organizational implications, and managerial issues). Our findings present a detailed picture of AI development and applications along with predictions regarding its place in the industry. Finally, we outline a research agenda that addresses key issues for stakeholders in hospitality and tourism: individuals, including customers and employees; organizations and businesses; and public policymakers and governments.
{"title":"Artificial Intelligence in Hospitality and Tourism: Insights From Industry Practices, Research Literature, and Expert Opinions","authors":"Hyunsu Kim, Kevin Kam Fung So, Seunghun Shin, Jing Li","doi":"10.1177/10963480241229235","DOIUrl":"https://doi.org/10.1177/10963480241229235","url":null,"abstract":"Given that artificial intelligence (AI) is significantly transforming businesses, it is crucial to examine how AI will change the future of the hospitality and tourism industry. By integrating multiple data sources (i.e., practitioner literature, research literature, and expert opinions), we suggest three trends constituting opportunities and challenges (AI applications in different business sectors, primary AI functions, emerging AI topics), three possible themes of change (adoption and acceptance, operations management, AI in marketing), as well as four directions for future research (AI interaction, AI and organizational decision making, organizational implications, and managerial issues). Our findings present a detailed picture of AI development and applications along with predictions regarding its place in the industry. Finally, we outline a research agenda that addresses key issues for stakeholders in hospitality and tourism: individuals, including customers and employees; organizations and businesses; and public policymakers and governments.","PeriodicalId":369021,"journal":{"name":"Journal of Hospitality & Tourism Research","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139838580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-14DOI: 10.1177/10963480241229235
Hyunsu Kim, Kevin Kam Fung So, Seunghun Shin, Jing Li
Given that artificial intelligence (AI) is significantly transforming businesses, it is crucial to examine how AI will change the future of the hospitality and tourism industry. By integrating multiple data sources (i.e., practitioner literature, research literature, and expert opinions), we suggest three trends constituting opportunities and challenges (AI applications in different business sectors, primary AI functions, emerging AI topics), three possible themes of change (adoption and acceptance, operations management, AI in marketing), as well as four directions for future research (AI interaction, AI and organizational decision making, organizational implications, and managerial issues). Our findings present a detailed picture of AI development and applications along with predictions regarding its place in the industry. Finally, we outline a research agenda that addresses key issues for stakeholders in hospitality and tourism: individuals, including customers and employees; organizations and businesses; and public policymakers and governments.
{"title":"Artificial Intelligence in Hospitality and Tourism: Insights From Industry Practices, Research Literature, and Expert Opinions","authors":"Hyunsu Kim, Kevin Kam Fung So, Seunghun Shin, Jing Li","doi":"10.1177/10963480241229235","DOIUrl":"https://doi.org/10.1177/10963480241229235","url":null,"abstract":"Given that artificial intelligence (AI) is significantly transforming businesses, it is crucial to examine how AI will change the future of the hospitality and tourism industry. By integrating multiple data sources (i.e., practitioner literature, research literature, and expert opinions), we suggest three trends constituting opportunities and challenges (AI applications in different business sectors, primary AI functions, emerging AI topics), three possible themes of change (adoption and acceptance, operations management, AI in marketing), as well as four directions for future research (AI interaction, AI and organizational decision making, organizational implications, and managerial issues). Our findings present a detailed picture of AI development and applications along with predictions regarding its place in the industry. Finally, we outline a research agenda that addresses key issues for stakeholders in hospitality and tourism: individuals, including customers and employees; organizations and businesses; and public policymakers and governments.","PeriodicalId":369021,"journal":{"name":"Journal of Hospitality & Tourism Research","volume":"24 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139778668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-09DOI: 10.1177/10963480231226083
W. Ko, Tae Ho Song
Reward programs offer rewards, and with further progression toward these rewards, customers become more motivated. This acceleration is called a reward gradient. This study proposes prospect theory as the underlying mechanism of the reward gradient and suggests a nonlinear relationship between motivation and progress level (low vs. middle vs. high). A numerical simulation based on the mathematical model and experiments were conducted to see how the expected values of rewards can change as progress is made, and how this further affects motivation. The results identified a stronger acceleration in the expected value after reaching a middle point of the reward achievement, which is due to a larger deviation in the loss value than the gain value. These findings can help restaurants and tourism companies implement a dynamic perspective in reward programs for enhancing customers’ reward gradient behaviors.
奖励计划提供奖励，随着这些奖励的不断增加，顾客的积极性会越来越高。这种加速被称为奖励梯度。本研究提出了前景理论作为奖励梯度的基本机制，并认为动机与进展水平（低级 vs. 中级 vs. 高级）之间存在非线性关系。在数学模型和实验的基础上进行了数值模拟，以了解奖励的预期值如何随着进步而变化，以及这如何进一步影响学习动机。结果发现，在达到奖励成就的中间点后，预期值会有更强的加速度，这是由于损失值的偏差大于收益值的偏差。这些发现有助于餐饮和旅游公司在奖励计划中实施动态视角，以增强顾客的奖励梯度行为。
{"title":"Nonlinear Reward Gradient Behavior in Customer Reward and Loyalty Programs: Evidence From the Restaurant Industry","authors":"W. Ko, Tae Ho Song","doi":"10.1177/10963480231226083","DOIUrl":"https://doi.org/10.1177/10963480231226083","url":null,"abstract":"Reward programs offer rewards, and with further progression toward these rewards, customers become more motivated. This acceleration is called a reward gradient. This study proposes prospect theory as the underlying mechanism of the reward gradient and suggests a nonlinear relationship between motivation and progress level (low vs. middle vs. high). A numerical simulation based on the mathematical model and experiments were conducted to see how the expected values of rewards can change as progress is made, and how this further affects motivation. The results identified a stronger acceleration in the expected value after reaching a middle point of the reward achievement, which is due to a larger deviation in the loss value than the gain value. These findings can help restaurants and tourism companies implement a dynamic perspective in reward programs for enhancing customers’ reward gradient behaviors.","PeriodicalId":369021,"journal":{"name":"Journal of Hospitality & Tourism Research","volume":" 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139788565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-09DOI: 10.1177/10963480231226083
W. Ko, Tae Ho Song
Reward programs offer rewards, and with further progression toward these rewards, customers become more motivated. This acceleration is called a reward gradient. This study proposes prospect theory as the underlying mechanism of the reward gradient and suggests a nonlinear relationship between motivation and progress level (low vs. middle vs. high). A numerical simulation based on the mathematical model and experiments were conducted to see how the expected values of rewards can change as progress is made, and how this further affects motivation. The results identified a stronger acceleration in the expected value after reaching a middle point of the reward achievement, which is due to a larger deviation in the loss value than the gain value. These findings can help restaurants and tourism companies implement a dynamic perspective in reward programs for enhancing customers’ reward gradient behaviors.
奖励计划提供奖励，随着这些奖励的不断增加，顾客的积极性会越来越高。这种加速被称为奖励梯度。本研究提出了前景理论作为奖励梯度的基本机制，并认为动机与进展水平（低级 vs. 中级 vs. 高级）之间存在非线性关系。在数学模型和实验的基础上进行了数值模拟，以了解奖励的预期值如何随着进步而变化，以及这如何进一步影响学习动机。结果发现，在达到奖励成就的中间点后，预期值会有更强的加速度，这是由于损失值的偏差大于收益值的偏差。这些发现有助于餐饮和旅游公司在奖励计划中实施动态视角，以增强顾客的奖励梯度行为。
{"title":"Nonlinear Reward Gradient Behavior in Customer Reward and Loyalty Programs: Evidence From the Restaurant Industry","authors":"W. Ko, Tae Ho Song","doi":"10.1177/10963480231226083","DOIUrl":"https://doi.org/10.1177/10963480231226083","url":null,"abstract":"Reward programs offer rewards, and with further progression toward these rewards, customers become more motivated. This acceleration is called a reward gradient. This study proposes prospect theory as the underlying mechanism of the reward gradient and suggests a nonlinear relationship between motivation and progress level (low vs. middle vs. high). A numerical simulation based on the mathematical model and experiments were conducted to see how the expected values of rewards can change as progress is made, and how this further affects motivation. The results identified a stronger acceleration in the expected value after reaching a middle point of the reward achievement, which is due to a larger deviation in the loss value than the gain value. These findings can help restaurants and tourism companies implement a dynamic perspective in reward programs for enhancing customers’ reward gradient behaviors.","PeriodicalId":369021,"journal":{"name":"Journal of Hospitality & Tourism Research","volume":"331 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139848469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1177/10963480241229238
Adiyukh Berbekova, A. Assaf, Muzaffer Uysal
Tourism demand modeling remains a critical issue in tourism research and practice. To date, demand studies have predominantly focused on economic variables to explain tourism demand. While this is well established, recent research demonstrates the importance of not limiting demand specification to economic variables only. This study proposes an interdisciplinary approach to tourism demand by incorporating the relative quality of life index into the demand specification. Using data from the United States and its 30 top source markets, the findings demonstrate that, in addition to traditional economic variables, a relative quality of life index that encompasses education, health, and stability is a significant predictor of tourism demand.
{"title":"Interdisciplinary Approach to Tourism Demand Modeling: Quality of Life Indicators","authors":"Adiyukh Berbekova, A. Assaf, Muzaffer Uysal","doi":"10.1177/10963480241229238","DOIUrl":"https://doi.org/10.1177/10963480241229238","url":null,"abstract":"Tourism demand modeling remains a critical issue in tourism research and practice. To date, demand studies have predominantly focused on economic variables to explain tourism demand. While this is well established, recent research demonstrates the importance of not limiting demand specification to economic variables only. This study proposes an interdisciplinary approach to tourism demand by incorporating the relative quality of life index into the demand specification. Using data from the United States and its 30 top source markets, the findings demonstrate that, in addition to traditional economic variables, a relative quality of life index that encompasses education, health, and stability is a significant predictor of tourism demand.","PeriodicalId":369021,"journal":{"name":"Journal of Hospitality & Tourism Research","volume":"139 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139793730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1177/10963480231223150
Bobbie Rathjens, Aili Wu, Lu Zhang, Wei Wei
By applying the two-step flow theory, this research examines if the level of brand equity impacts consumers’ attitudes toward social media influencers and consumers’ visit intention, and if influencers’ recommendation type plays a moderating role in the process. This research employs a scenario-based experimental design. Study 1 examines the main effect of brand equity on consumers’ attitudes toward the influencer and consumers’ visit intention. Results demonstrated that high brand equity enhances consumers’ positive attitudes toward the influencer and visit intention. Study 2 examines the moderating effect of recommendation type on consumers’ attitudes and visit intention. Findings revealed that while explicit recommendations of high-equity brands yield higher visit intention and attitudes towards the influencer, explicit recommendations of low-equity brands backfire, resulting in lower attitudes toward the influencer. Additionally, trust emerges as a significant mediator when recommendations are explicit rather than implicit. When the recommendation is implicit, there is no (indirect) effect.
{"title":"When Social Media Influencer Endorsement Backfires: Unpacking Fallout From Explicit Endorsements Across Brand Equity Levels","authors":"Bobbie Rathjens, Aili Wu, Lu Zhang, Wei Wei","doi":"10.1177/10963480231223150","DOIUrl":"https://doi.org/10.1177/10963480231223150","url":null,"abstract":"By applying the two-step flow theory, this research examines if the level of brand equity impacts consumers’ attitudes toward social media influencers and consumers’ visit intention, and if influencers’ recommendation type plays a moderating role in the process. This research employs a scenario-based experimental design. Study 1 examines the main effect of brand equity on consumers’ attitudes toward the influencer and consumers’ visit intention. Results demonstrated that high brand equity enhances consumers’ positive attitudes toward the influencer and visit intention. Study 2 examines the moderating effect of recommendation type on consumers’ attitudes and visit intention. Findings revealed that while explicit recommendations of high-equity brands yield higher visit intention and attitudes towards the influencer, explicit recommendations of low-equity brands backfire, resulting in lower attitudes toward the influencer. Additionally, trust emerges as a significant mediator when recommendations are explicit rather than implicit. When the recommendation is implicit, there is no (indirect) effect.","PeriodicalId":369021,"journal":{"name":"Journal of Hospitality & Tourism Research","volume":"111 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139852648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1177/10963480231223150
Bobbie Rathjens, Aili Wu, Lu Zhang, Wei Wei
By applying the two-step flow theory, this research examines if the level of brand equity impacts consumers’ attitudes toward social media influencers and consumers’ visit intention, and if influencers’ recommendation type plays a moderating role in the process. This research employs a scenario-based experimental design. Study 1 examines the main effect of brand equity on consumers’ attitudes toward the influencer and consumers’ visit intention. Results demonstrated that high brand equity enhances consumers’ positive attitudes toward the influencer and visit intention. Study 2 examines the moderating effect of recommendation type on consumers’ attitudes and visit intention. Findings revealed that while explicit recommendations of high-equity brands yield higher visit intention and attitudes towards the influencer, explicit recommendations of low-equity brands backfire, resulting in lower attitudes toward the influencer. Additionally, trust emerges as a significant mediator when recommendations are explicit rather than implicit. When the recommendation is implicit, there is no (indirect) effect.
{"title":"When Social Media Influencer Endorsement Backfires: Unpacking Fallout From Explicit Endorsements Across Brand Equity Levels","authors":"Bobbie Rathjens, Aili Wu, Lu Zhang, Wei Wei","doi":"10.1177/10963480231223150","DOIUrl":"https://doi.org/10.1177/10963480231223150","url":null,"abstract":"By applying the two-step flow theory, this research examines if the level of brand equity impacts consumers’ attitudes toward social media influencers and consumers’ visit intention, and if influencers’ recommendation type plays a moderating role in the process. This research employs a scenario-based experimental design. Study 1 examines the main effect of brand equity on consumers’ attitudes toward the influencer and consumers’ visit intention. Results demonstrated that high brand equity enhances consumers’ positive attitudes toward the influencer and visit intention. Study 2 examines the moderating effect of recommendation type on consumers’ attitudes and visit intention. Findings revealed that while explicit recommendations of high-equity brands yield higher visit intention and attitudes towards the influencer, explicit recommendations of low-equity brands backfire, resulting in lower attitudes toward the influencer. Additionally, trust emerges as a significant mediator when recommendations are explicit rather than implicit. When the recommendation is implicit, there is no (indirect) effect.","PeriodicalId":369021,"journal":{"name":"Journal of Hospitality & Tourism Research","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139792923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1177/10963480231226086
Yeqiang (Kevin) Lin, Ondrej Mitas, Ye (Sandy) Shen, Marcel Bastiaansen, W. Strijbosch
Understanding the complex and dynamic nature of experiences requires the use of proper measurement tools. As interest grows in the objective measurement of experiences within tourism and hospitality, there is an urgent need to consolidate and synthesize these studies. Thus, this study investigated prevalent objective measurement techniques via a systematic review. We analyzed physiological measures such as electroencephalography (EEG), heart rate variability (HRV), skin conductance (SC), and facial electromyography (fEMG) along with behavioral measures, including eye tracking and location tracking. This review identified 100 empirical studies that employed objective measurement to examine tourism and hospitality experiences over the last decade, highlighting trends, research contexts and designs, and the synergies between different methods. Our discussion on methodological issues and best practices will help researchers and practitioners identify the best tools to capture people’s experiences and promote more standardized practices and comparable findings on studying experiences in tourism and hospitality settings.
{"title":"Objective Measurement of Experiences in Tourism and Hospitality: A Systematic Review of Methodological Approaches and Best Practices","authors":"Yeqiang (Kevin) Lin, Ondrej Mitas, Ye (Sandy) Shen, Marcel Bastiaansen, W. Strijbosch","doi":"10.1177/10963480231226086","DOIUrl":"https://doi.org/10.1177/10963480231226086","url":null,"abstract":"Understanding the complex and dynamic nature of experiences requires the use of proper measurement tools. As interest grows in the objective measurement of experiences within tourism and hospitality, there is an urgent need to consolidate and synthesize these studies. Thus, this study investigated prevalent objective measurement techniques via a systematic review. We analyzed physiological measures such as electroencephalography (EEG), heart rate variability (HRV), skin conductance (SC), and facial electromyography (fEMG) along with behavioral measures, including eye tracking and location tracking. This review identified 100 empirical studies that employed objective measurement to examine tourism and hospitality experiences over the last decade, highlighting trends, research contexts and designs, and the synergies between different methods. Our discussion on methodological issues and best practices will help researchers and practitioners identify the best tools to capture people’s experiences and promote more standardized practices and comparable findings on studying experiences in tourism and hospitality settings.","PeriodicalId":369021,"journal":{"name":"Journal of Hospitality & Tourism Research","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139854236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1177/10963480231226085
Tingting Zhang, Lu Lu, Oscar Hengxuan Chi, Can Lu, C. Cobanoglu
This study explores the key features of the smart service interactional experience (SSIE) and develops and validates a scale based on the perspectives of family travelers. Through multiple stages of qualitative and quantitative investigations using U.S. and U.K. samples, this study identified eight distinct features of SSIE: efficiency, ease of use, learning, socialization, newness, safety, flow, and seamlessness. The study findings also validate the aforementioned scale through two robustness tests and reveal significant relationships between the aforementioned SSIE attributes and the service encounter evaluations of family travelers. This study theoretically contributes to the development of smart hotel conceptualizations and the nascent research on family travelers. Furthermore, the study findings imply many valuable, practical suggestions for hotel practitioners who plan to invest in smart hotel operations and target family travelers.
{"title":"Smart Service Interactional Experience for Family Travelers: Scale Development and Validation","authors":"Tingting Zhang, Lu Lu, Oscar Hengxuan Chi, Can Lu, C. Cobanoglu","doi":"10.1177/10963480231226085","DOIUrl":"https://doi.org/10.1177/10963480231226085","url":null,"abstract":"This study explores the key features of the smart service interactional experience (SSIE) and develops and validates a scale based on the perspectives of family travelers. Through multiple stages of qualitative and quantitative investigations using U.S. and U.K. samples, this study identified eight distinct features of SSIE: efficiency, ease of use, learning, socialization, newness, safety, flow, and seamlessness. The study findings also validate the aforementioned scale through two robustness tests and reveal significant relationships between the aforementioned SSIE attributes and the service encounter evaluations of family travelers. This study theoretically contributes to the development of smart hotel conceptualizations and the nascent research on family travelers. Furthermore, the study findings imply many valuable, practical suggestions for hotel practitioners who plan to invest in smart hotel operations and target family travelers.","PeriodicalId":369021,"journal":{"name":"Journal of Hospitality & Tourism Research","volume":"225 S717","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139793593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1177/10963480231226086
Yeqiang (Kevin) Lin, Ondrej Mitas, Ye (Sandy) Shen, Marcel Bastiaansen, W. Strijbosch
Understanding the complex and dynamic nature of experiences requires the use of proper measurement tools. As interest grows in the objective measurement of experiences within tourism and hospitality, there is an urgent need to consolidate and synthesize these studies. Thus, this study investigated prevalent objective measurement techniques via a systematic review. We analyzed physiological measures such as electroencephalography (EEG), heart rate variability (HRV), skin conductance (SC), and facial electromyography (fEMG) along with behavioral measures, including eye tracking and location tracking. This review identified 100 empirical studies that employed objective measurement to examine tourism and hospitality experiences over the last decade, highlighting trends, research contexts and designs, and the synergies between different methods. Our discussion on methodological issues and best practices will help researchers and practitioners identify the best tools to capture people’s experiences and promote more standardized practices and comparable findings on studying experiences in tourism and hospitality settings.
{"title":"Objective Measurement of Experiences in Tourism and Hospitality: A Systematic Review of Methodological Approaches and Best Practices","authors":"Yeqiang (Kevin) Lin, Ondrej Mitas, Ye (Sandy) Shen, Marcel Bastiaansen, W. Strijbosch","doi":"10.1177/10963480231226086","DOIUrl":"https://doi.org/10.1177/10963480231226086","url":null,"abstract":"Understanding the complex and dynamic nature of experiences requires the use of proper measurement tools. As interest grows in the objective measurement of experiences within tourism and hospitality, there is an urgent need to consolidate and synthesize these studies. Thus, this study investigated prevalent objective measurement techniques via a systematic review. We analyzed physiological measures such as electroencephalography (EEG), heart rate variability (HRV), skin conductance (SC), and facial electromyography (fEMG) along with behavioral measures, including eye tracking and location tracking. This review identified 100 empirical studies that employed objective measurement to examine tourism and hospitality experiences over the last decade, highlighting trends, research contexts and designs, and the synergies between different methods. Our discussion on methodological issues and best practices will help researchers and practitioners identify the best tools to capture people’s experiences and promote more standardized practices and comparable findings on studying experiences in tourism and hospitality settings.","PeriodicalId":369021,"journal":{"name":"Journal of Hospitality & Tourism Research","volume":"79 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139794331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：