Pub Date : 2024-07-01DOI: 10.1108/jhtt-12-2023-0437
Ataul Karim Patwary, Md Sazzad Hossain, T. Mistry, M. Parvez
Purpose This study aims to analyze workplace ostracism, robot anthropomorphism, employees’ readiness to change and employees’ service adaptive behavior. The moderating role of performance efficacy between employees’ readiness and service adaptive behavior was also assessed. Design/methodology/approach Data were collected from 591 restaurant employees in Malaysia. The data were analyzed using partial least squares-structural equation modeling. Findings Workplace ostracism and robot anthropomorphism positively influence employees’ readiness to change and service-adaptive behavior. Employees’ readiness to change mediates the relationship between ostracism, robot anthropomorphism and service-adaptive behavior. Research limitations/implications This study provides an exclusively applied understanding of robot anthropomorphism and service employee adaptive behavior. In addition to restaurant employees’ readiness to change and collaborate with service robots, a longitudinal study can be conducted to track the advancement of restaurant employees’ technology adaptive behavior over an extended area. Originality/value Service robots have mainly been assessed from consumer perspectives in the hospitality industry. This research used the conservation of resources theory to evaluate the human–computer interaction of service robots and restaurant employees. Organizational and individual factors were considered to assess the impact on employees’ service adaptability.
{"title":"Enhancing service adaptability: a moderated mediation model of workplace ostracism, robot anthropomorphism, employees’ readiness to change, and performance efficacy","authors":"Ataul Karim Patwary, Md Sazzad Hossain, T. Mistry, M. Parvez","doi":"10.1108/jhtt-12-2023-0437","DOIUrl":"https://doi.org/10.1108/jhtt-12-2023-0437","url":null,"abstract":"\u0000Purpose\u0000This study aims to analyze workplace ostracism, robot anthropomorphism, employees’ readiness to change and employees’ service adaptive behavior. The moderating role of performance efficacy between employees’ readiness and service adaptive behavior was also assessed.\u0000\u0000\u0000Design/methodology/approach\u0000Data were collected from 591 restaurant employees in Malaysia. The data were analyzed using partial least squares-structural equation modeling.\u0000\u0000\u0000Findings\u0000Workplace ostracism and robot anthropomorphism positively influence employees’ readiness to change and service-adaptive behavior. Employees’ readiness to change mediates the relationship between ostracism, robot anthropomorphism and service-adaptive behavior.\u0000\u0000\u0000Research limitations/implications\u0000This study provides an exclusively applied understanding of robot anthropomorphism and service employee adaptive behavior. In addition to restaurant employees’ readiness to change and collaborate with service robots, a longitudinal study can be conducted to track the advancement of restaurant employees’ technology adaptive behavior over an extended area.\u0000\u0000\u0000Originality/value\u0000Service robots have mainly been assessed from consumer perspectives in the hospitality industry. This research used the conservation of resources theory to evaluate the human–computer interaction of service robots and restaurant employees. Organizational and individual factors were considered to assess the impact on employees’ service adaptability.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141702195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-18DOI: 10.1108/jhtt-11-2023-0363
Heesup Han, Seongseop (Sam) Kim, Tadesse Bekele Hailu, Amr Al-Ansi, Jiyoung Lee, J. Kim
Purpose This study aims to explore the interplay of cognitive, affective, and normative constituents for their potential acceptance or rejection of artificial intelligence (AI) and ChatGPTs in the hospitality and tourism context. Design/methodology/approach Using an advanced analytical approach (i.e. a fuzzy-set qualitative comparative analysis), the study tested hypotheses based on 474 responses from individuals who have used ChatGPT for hospitality and tourism information. Findings The study found multiple solutions, including cognitive, affective and normative drivers for strong and weak continuance intentions toward AI-based ChatGPT. Informativeness, one of the cognitive drivers, was found to be a necessary condition for achieving the desired outcome. Originality/value This research provides novel insights into the functionality of developing multiple configurations to predict complex travelers behaviors in the context of hospitality and tourism technology consumption.
{"title":"Effects of cognitive, affective and normative drivers of artificial intelligence ChatGP T on continuous use intention","authors":"Heesup Han, Seongseop (Sam) Kim, Tadesse Bekele Hailu, Amr Al-Ansi, Jiyoung Lee, J. Kim","doi":"10.1108/jhtt-11-2023-0363","DOIUrl":"https://doi.org/10.1108/jhtt-11-2023-0363","url":null,"abstract":"Purpose\u0000This study aims to explore the interplay of cognitive, affective, and normative constituents for their potential acceptance or rejection of artificial intelligence (AI) and ChatGPTs in the hospitality and tourism context.\u0000\u0000Design/methodology/approach\u0000Using an advanced analytical approach (i.e. a fuzzy-set qualitative comparative analysis), the study tested hypotheses based on 474 responses from individuals who have used ChatGPT for hospitality and tourism information.\u0000\u0000Findings\u0000The study found multiple solutions, including cognitive, affective and normative drivers for strong and weak continuance intentions toward AI-based ChatGPT. Informativeness, one of the cognitive drivers, was found to be a necessary condition for achieving the desired outcome.\u0000\u0000Originality/value\u0000This research provides novel insights into the functionality of developing multiple configurations to predict complex travelers behaviors in the context of hospitality and tourism technology consumption.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141334858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-17DOI: 10.1108/jhtt-01-2024-0006
J. Wang, Xiaoxiao Fu
Purpose This study aims to investigate guests’ experience and perceptions in smart hotels, with a primary focus on the human−robot experience. Design/methodology/approach Utilizing a thematic analysis using the inductive-deductive approach, 546 reviews from Chinese smart hospitality guests, sourced from Ctrip, were examined. Findings This study identified five highest-level categories reflecting guests’ perceptions of smart hotels service with themes and subthemes of utilitarian gratification (smart servicescape and smart service quality), sensual gratification (novelty and coolness), social gratification (social presence and social interaction), experiential gratification (functional and emotional experiential value) and satisfaction. Originality/value This research enriches the current understanding of guests’ experience within smart hotels, focusing on the human−robot interaction. The findings offer insightful implications for the enhancement of smart hotels, specifically in terms of smart facility offerings, service delivery and overall customer experience.
{"title":"Unveiling the human–robot encounter: guests’ perspectives on smart hotel experience","authors":"J. Wang, Xiaoxiao Fu","doi":"10.1108/jhtt-01-2024-0006","DOIUrl":"https://doi.org/10.1108/jhtt-01-2024-0006","url":null,"abstract":"Purpose\u0000This study aims to investigate guests’ experience and perceptions in smart hotels, with a primary focus on the human−robot experience.\u0000\u0000Design/methodology/approach\u0000Utilizing a thematic analysis using the inductive-deductive approach, 546 reviews from Chinese smart hospitality guests, sourced from Ctrip, were examined.\u0000\u0000Findings\u0000This study identified five highest-level categories reflecting guests’ perceptions of smart hotels service with themes and subthemes of utilitarian gratification (smart servicescape and smart service quality), sensual gratification (novelty and coolness), social gratification (social presence and social interaction), experiential gratification (functional and emotional experiential value) and satisfaction.\u0000\u0000Originality/value\u0000This research enriches the current understanding of guests’ experience within smart hotels, focusing on the human−robot interaction. The findings offer insightful implications for the enhancement of smart hotels, specifically in terms of smart facility offerings, service delivery and overall customer experience.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141335428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-14DOI: 10.1108/jhtt-11-2023-0374
H. Zaki, B. Al-Romeedy
Purpose Artificial intelligence-based chatbots are frequently used to handle customer complaints in the hospitality and tourism sectors; however, little is known about their recovery strategies. Further, the widespread usage of chatbots is anticipated to affect customers' favorable responses. Therefore, this study aims to examine how chatbots’ symbolic recovery influences customer forgiveness through customer empathy and explore the moderating effect of time pressure on it. Moreover, it investigates the effect of customer forgiveness on customer reconciliation and customer continuous trust. Design/methodology/approach Structural equation modeling was used to analyze data collected from 994 customers who have experienced chatbot recovery in tourism and hospitality during the past four months. Findings The results show that chatbots’ symbolic recovery stimulates customer forgiveness, which subsequently positively affects customer reconciliation and customer continuous trust. Moreover, customer empathy partially mediates the effect of chatbots’ symbolic recovery on customer forgiveness, and time pressure plays a moderating role in the relationship between chatbots’ symbolic recovery and customer forgiveness. Practical implications The results offer highly persuasive insights that may be used to promote chatbots’ symbolic recovery in tourism organizations. The effectiveness of chatbots’ symbolic recovery in achieving customer forgiveness will motivate tourism organizations to use chatbots efficiently in service recovery. Originality/value This study extends the theoretical scope of chatbot research by investigating the symbolic recovery capabilities of chatbots. Moreover, it expands the application of SOR theory in the context of chatbot service recovery and reveals the underlying mechanism behind the impact of chatbots’ symbolic recovery on customer forgiveness, thus building and testing an integrative model of chatbot service recovery.
目的 基于人工智能的聊天机器人经常被用于处理酒店和旅游业的客户投诉；然而，人们对其恢复策略却知之甚少。此外，聊天机器人的广泛使用预计会影响客户的良好反应。因此，本研究旨在研究聊天机器人的象征性恢复如何通过客户移情影响客户原谅，并探讨时间压力对其的调节作用。研究结果结果表明，聊天机器人的象征性恢复激发了客户的原谅，从而对客户和解和客户持续信任产生了积极影响。此外，客户移情部分调节了聊天机器人的象征性恢复对客户原谅的影响，而时间压力在聊天机器人的象征性恢复与客户原谅之间的关系中起到了调节作用。聊天机器人的符号恢复在实现客户原谅方面的有效性将促使旅游组织在服务恢复中有效使用聊天机器人。原创性/价值本研究通过调查聊天机器人的符号恢复能力，扩展了聊天机器人研究的理论范围。此外，本研究还拓展了 SOR 理论在聊天机器人服务恢复中的应用，揭示了聊天机器人符号恢复对客户原谅的内在影响机制，从而构建并检验了聊天机器人服务恢复的综合模型。
{"title":"Chatbot symbolic recovery and customer forgiveness: a moderated mediation model","authors":"H. Zaki, B. Al-Romeedy","doi":"10.1108/jhtt-11-2023-0374","DOIUrl":"https://doi.org/10.1108/jhtt-11-2023-0374","url":null,"abstract":"Purpose\u0000Artificial intelligence-based chatbots are frequently used to handle customer complaints in the hospitality and tourism sectors; however, little is known about their recovery strategies. Further, the widespread usage of chatbots is anticipated to affect customers' favorable responses. Therefore, this study aims to examine how chatbots’ symbolic recovery influences customer forgiveness through customer empathy and explore the moderating effect of time pressure on it. Moreover, it investigates the effect of customer forgiveness on customer reconciliation and customer continuous trust.\u0000\u0000Design/methodology/approach\u0000Structural equation modeling was used to analyze data collected from 994 customers who have experienced chatbot recovery in tourism and hospitality during the past four months.\u0000\u0000Findings\u0000The results show that chatbots’ symbolic recovery stimulates customer forgiveness, which subsequently positively affects customer reconciliation and customer continuous trust. Moreover, customer empathy partially mediates the effect of chatbots’ symbolic recovery on customer forgiveness, and time pressure plays a moderating role in the relationship between chatbots’ symbolic recovery and customer forgiveness.\u0000\u0000Practical implications\u0000The results offer highly persuasive insights that may be used to promote chatbots’ symbolic recovery in tourism organizations. The effectiveness of chatbots’ symbolic recovery in achieving customer forgiveness will motivate tourism organizations to use chatbots efficiently in service recovery.\u0000\u0000Originality/value\u0000This study extends the theoretical scope of chatbot research by investigating the symbolic recovery capabilities of chatbots. Moreover, it expands the application of SOR theory in the context of chatbot service recovery and reveals the underlying mechanism behind the impact of chatbots’ symbolic recovery on customer forgiveness, thus building and testing an integrative model of chatbot service recovery.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141339254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-27DOI: 10.1108/jhtt-04-2023-0098
Yang Liu, Maomao Chi, Qiong Sun
Purpose This study aims to detect consumer sarcasm through inconsistencies in sentiment features between text and images of hotel reviews. Design/methodology/approach This paper proposes a model for sarcasm detection based on multimodal deep learning using reviews of three hotel brands collected from two travel platforms, which can identify emotional inconsistencies within a modality and across modalities. Text-image interaction information is explored using graph neural networks (GNN) to detect essential clues in sarcasm sentiment. Findings The research results show that the multimodal deep learning model outperforms other baseline models, which can help to understand hotel service evaluation and provide hotel managers with decision-making opinions. Originality/value This research can help hoteliers in two ways: detecting service quality and formulating strategies. By selecting reference hotel brands, hoteliers can better assess their level of service quality (optimal resource allocation ensues); therefore, sarcasm detection research is not only beneficial for hotel managers seeking to improve service quality. The multimodal deep learning method introduced in the present study can be replicated in other industries to help travel platforms optimize their products and services.
{"title":"Sarcasm detection in hotel reviews: a multimodal deep learning approach","authors":"Yang Liu, Maomao Chi, Qiong Sun","doi":"10.1108/jhtt-04-2023-0098","DOIUrl":"https://doi.org/10.1108/jhtt-04-2023-0098","url":null,"abstract":"Purpose\u0000This study aims to detect consumer sarcasm through inconsistencies in sentiment features between text and images of hotel reviews.\u0000\u0000Design/methodology/approach\u0000This paper proposes a model for sarcasm detection based on multimodal deep learning using reviews of three hotel brands collected from two travel platforms, which can identify emotional inconsistencies within a modality and across modalities. Text-image interaction information is explored using graph neural networks (GNN) to detect essential clues in sarcasm sentiment.\u0000\u0000Findings\u0000The research results show that the multimodal deep learning model outperforms other baseline models, which can help to understand hotel service evaluation and provide hotel managers with decision-making opinions.\u0000\u0000Originality/value\u0000This research can help hoteliers in two ways: detecting service quality and formulating strategies. By selecting reference hotel brands, hoteliers can better assess their level of service quality (optimal resource allocation ensues); therefore, sarcasm detection research is not only beneficial for hotel managers seeking to improve service quality. The multimodal deep learning method introduced in the present study can be replicated in other industries to help travel platforms optimize their products and services.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141098470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-16DOI: 10.1108/jhtt-05-2023-0128
Ting Zhang, Bin Li, Nan Hua, Pei Zhang
Purpose The purpose of this study is to investigate the effects of employee live streamers on consumers' purchase behaviors and brand image, as well as to understand the mediating roles of friendship and self-congruity. Design/methodology/approach A framework was proposed to explain the influence of employee live streamers' qualities on consumers' behaviors and brand image through the mediators of friendship and self-congruity. Primary data was collected from 225 valid survey responses in China, and the PLS-SEM analysis was employed to test the statistical significance of the hypothesized relationships. Findings The study found that four qualities of employee live streamers – trustworthiness, attractiveness, responsiveness and expertise – had significant effects on consumers' purchase behaviors and brand image through the mediators of self-congruity and friendship. Originality/value This research provides valuable insights into the varying roles of employee live streamers in consumers' decision-making and brand image formation. It offers a theoretical basis for scholars to understand the factors of PSI (parasocial interaction) between consumers and an employee streamer, contributing to the growing body of literature on live streaming and consumer behavior.
{"title":"When employees become streamers: the mediating power of friendship and self-congruity","authors":"Ting Zhang, Bin Li, Nan Hua, Pei Zhang","doi":"10.1108/jhtt-05-2023-0128","DOIUrl":"https://doi.org/10.1108/jhtt-05-2023-0128","url":null,"abstract":"Purpose\u0000The purpose of this study is to investigate the effects of employee live streamers on consumers' purchase behaviors and brand image, as well as to understand the mediating roles of friendship and self-congruity.\u0000\u0000Design/methodology/approach\u0000A framework was proposed to explain the influence of employee live streamers' qualities on consumers' behaviors and brand image through the mediators of friendship and self-congruity. Primary data was collected from 225 valid survey responses in China, and the PLS-SEM analysis was employed to test the statistical significance of the hypothesized relationships.\u0000\u0000Findings\u0000The study found that four qualities of employee live streamers – trustworthiness, attractiveness, responsiveness and expertise – had significant effects on consumers' purchase behaviors and brand image through the mediators of self-congruity and friendship.\u0000\u0000Originality/value\u0000This research provides valuable insights into the varying roles of employee live streamers in consumers' decision-making and brand image formation. It offers a theoretical basis for scholars to understand the factors of PSI (parasocial interaction) between consumers and an employee streamer, contributing to the growing body of literature on live streaming and consumer behavior.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140970631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-08DOI: 10.1108/jhtt-03-2023-0068
Jing Ma
Purpose The diffusion of technologies from other sectors, and innovations in kitchen equipment, fueled structural changes within the foodservice industry. However, this change comes at a price of disrupting the critical step of assessing the demand forecast accuracy. This study aims to explore a surprisingly unique and elevated complexity when assessing the critically important demand forecast accuracy. Design/methodology/approach The paper develops a mathematical model to describe and explore the nature of the problem in structural biased demand forecast accuracy assessment. It then uses numerical simulation to construct a market example to gain better insights on the bias characteristics. Finally, the forecast accuracy measurement’s inherent bias is contrasted with that of other typical hospitality forecasting setups. Findings This paper outlines the theoretical underpinnings of how demand forecasts in the central kitchen setup are dynamic and thus produce a structural bias. More specifically, this paper discovers how, in this context of orders from a central location, the forecasts set the capacity constraints, and, consequently, generate a considerably more biased forecast accuracy measure. Relying on such forecast accuracy measures can lead to serious negative business outcomes. Originality/value To the best of the author’s knowledge, this study is the first to show that in the unique new technology enabled environment of central kitchen operation, where daily dish demand forecasts set the daily constrained capacity levels, the accuracy measure is severely biased, and consequently accuracy is likely to deteriorate, which in turn, could lead to suboptimal decisions. The major theoretical contribution of this study is a novel analytical model which explains and describes the bias in the accuracy measurement.
{"title":"Estimating restaurants’ unconstrained demand: a systematic approach to reducing structural bias in forecast accuracy measures","authors":"Jing Ma","doi":"10.1108/jhtt-03-2023-0068","DOIUrl":"https://doi.org/10.1108/jhtt-03-2023-0068","url":null,"abstract":"\u0000Purpose\u0000The diffusion of technologies from other sectors, and innovations in kitchen equipment, fueled structural changes within the foodservice industry. However, this change comes at a price of disrupting the critical step of assessing the demand forecast accuracy. This study aims to explore a surprisingly unique and elevated complexity when assessing the critically important demand forecast accuracy.\u0000\u0000\u0000Design/methodology/approach\u0000The paper develops a mathematical model to describe and explore the nature of the problem in structural biased demand forecast accuracy assessment. It then uses numerical simulation to construct a market example to gain better insights on the bias characteristics. Finally, the forecast accuracy measurement’s inherent bias is contrasted with that of other typical hospitality forecasting setups.\u0000\u0000\u0000Findings\u0000This paper outlines the theoretical underpinnings of how demand forecasts in the central kitchen setup are dynamic and thus produce a structural bias. More specifically, this paper discovers how, in this context of orders from a central location, the forecasts set the capacity constraints, and, consequently, generate a considerably more biased forecast accuracy measure. Relying on such forecast accuracy measures can lead to serious negative business outcomes.\u0000\u0000\u0000Originality/value\u0000To the best of the author’s knowledge, this study is the first to show that in the unique new technology enabled environment of central kitchen operation, where daily dish demand forecasts set the daily constrained capacity levels, the accuracy measure is severely biased, and consequently accuracy is likely to deteriorate, which in turn, could lead to suboptimal decisions. The major theoretical contribution of this study is a novel analytical model which explains and describes the bias in the accuracy measurement.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141001857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-07DOI: 10.1108/jhtt-09-2023-0255
Xinzhe Li, Qinglong Li, Dasom Jeong, Jaekyeong Kim
Purpose Most previous studies predicting review helpfulness ignored the significance of deep features embedded in review text and instead relied on hand-crafted features. Hand-crafted and deep features have the advantages of high interpretability and predictive accuracy. This study aims to propose a novel review helpfulness prediction model that uses deep learning (DL) techniques to consider the complementarity between hand-crafted and deep features. Design/methodology/approach First, an advanced convolutional neural network was applied to extract deep features from unstructured review text. Second, this study used previous studies to extract hand-crafted features that impact the helpfulness of reviews and enhance their interpretability. Third, this study incorporated deep and hand-crafted features into a review helpfulness prediction model and evaluated its performance using the Yelp.com data set. To measure the performance of the proposed model, this study used 2,417,796 restaurant reviews. Findings Extensive experiments confirmed that the proposed methodology performs better than traditional machine learning methods. Moreover, this study confirms through an empirical analysis that combining hand-crafted and deep features demonstrates better prediction performance. Originality/value To the best of the authors’ knowledge, this is one of the first studies to apply DL techniques and use structured and unstructured data to predict review helpfulness in the restaurant context. In addition, an advanced feature-fusion method was adopted to better use the extracted feature information and identify the complementarity between features.
{"title":"A novel deep learning method to use feature complementarity for review helpfulness prediction","authors":"Xinzhe Li, Qinglong Li, Dasom Jeong, Jaekyeong Kim","doi":"10.1108/jhtt-09-2023-0255","DOIUrl":"https://doi.org/10.1108/jhtt-09-2023-0255","url":null,"abstract":"Purpose\u0000Most previous studies predicting review helpfulness ignored the significance of deep features embedded in review text and instead relied on hand-crafted features. Hand-crafted and deep features have the advantages of high interpretability and predictive accuracy. This study aims to propose a novel review helpfulness prediction model that uses deep learning (DL) techniques to consider the complementarity between hand-crafted and deep features.\u0000\u0000Design/methodology/approach\u0000First, an advanced convolutional neural network was applied to extract deep features from unstructured review text. Second, this study used previous studies to extract hand-crafted features that impact the helpfulness of reviews and enhance their interpretability. Third, this study incorporated deep and hand-crafted features into a review helpfulness prediction model and evaluated its performance using the Yelp.com data set. To measure the performance of the proposed model, this study used 2,417,796 restaurant reviews.\u0000\u0000Findings\u0000Extensive experiments confirmed that the proposed methodology performs better than traditional machine learning methods. Moreover, this study confirms through an empirical analysis that combining hand-crafted and deep features demonstrates better prediction performance.\u0000\u0000Originality/value\u0000To the best of the authors’ knowledge, this is one of the first studies to apply DL techniques and use structured and unstructured data to predict review helpfulness in the restaurant context. In addition, an advanced feature-fusion method was adopted to better use the extracted feature information and identify the complementarity between features.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141002886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-06DOI: 10.1108/jhtt-12-2023-0416
Yue (Darcy) Lu, Yifeng Liang, Yao-Chin Wang
Purpose This study aims to conceptualize the characteristics of artificial intelligence (AI) dogs while exploring their applications in tourism and hospitality settings. Design/methodology/approach The total of 30 in-depth interviews were conducted, and data were analyzed through thematic analysis. Findings This study proposed differences between AI dogs and real dogs and human-like robots, core characteristics of AI dogs’ functions, a matrix of appearance and expectation regarding intelligence for AI dogs and human-like robots, the relationship between ethical barriers and task complexity, adoptions of AI dogs in different user segments and practical applications in hospitality and tourism settings, such as restaurants, city tour guides, extended-stay resorts and event organizations. Research limitations/implications This research advances the field of tourism and hospitality studies by introducing the new concept of AI dogs and their practical applications. This present study adds new insights into the opportunities and contexts of human–robot interaction in the field of tourism and hospitality. Originality/value To the best of the authors’ knowledge, this research is one of the first studies of AI dogs in tourism and hospitality.
{"title":"AI dogs vs. real dogs and human-like robots: clarification, conceptualization, and applications in tourism and hospitality settings","authors":"Yue (Darcy) Lu, Yifeng Liang, Yao-Chin Wang","doi":"10.1108/jhtt-12-2023-0416","DOIUrl":"https://doi.org/10.1108/jhtt-12-2023-0416","url":null,"abstract":"Purpose\u0000This study aims to conceptualize the characteristics of artificial intelligence (AI) dogs while exploring their applications in tourism and hospitality settings.\u0000\u0000Design/methodology/approach\u0000The total of 30 in-depth interviews were conducted, and data were analyzed through thematic analysis.\u0000\u0000Findings\u0000This study proposed differences between AI dogs and real dogs and human-like robots, core characteristics of AI dogs’ functions, a matrix of appearance and expectation regarding intelligence for AI dogs and human-like robots, the relationship between ethical barriers and task complexity, adoptions of AI dogs in different user segments and practical applications in hospitality and tourism settings, such as restaurants, city tour guides, extended-stay resorts and event organizations.\u0000\u0000Research limitations/implications\u0000This research advances the field of tourism and hospitality studies by introducing the new concept of AI dogs and their practical applications. This present study adds new insights into the opportunities and contexts of human–robot interaction in the field of tourism and hospitality.\u0000\u0000Originality/value\u0000To the best of the authors’ knowledge, this research is one of the first studies of AI dogs in tourism and hospitality.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141009319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-22DOI: 10.1108/jhtt-10-2023-0309
Pınar Şenel, Hacer Turhan, Erkan Sezgin
Purpose Three-dimentional (3D) food printers are innovative technologies that contribute to healthy, personalized and stainable nutrition. However, many consumers are still vigilant about 3D printed food in the age of technology. The purpose of this study is to develop a scale and propose a model for consumption preferences associated with 3D-printed food (3DPF). Design/methodology/approach The developed questionnaire was handed to 192 Z and Y generation participants (Data1) for the exploratory factor analysis stage initially. Then, the questionnaire was handed to another group of 165 participants (Data 2) for verification by confirmatory factor analysis. Finally, the dimensions “healthy and personalized nutrition,” “sustainable nutrition” and “socio-cultural nutrition” were analyzed by structural equation modeling. Findings The results indicated that there was a high relationship between “healthy and personalized nutrition” and “sustainable nutrition” as well as between “sustainable nutrition” and “socio-cultural nutrition” when 3DPF was considered. Originality/value The study would contribute to the new survey area related to 3DPF by presenting a scale and proposing a model. Also, the study reveals which nutritional factors affect the Z and Y generation’s consumption of 3DPF. In this context, the study aims to make marketing contributions to the food production, restaurant and hotel sectors.
{"title":"The relations among the dimensions of 3D-printed food: a case of Z and Y generations’ preferences","authors":"Pınar Şenel, Hacer Turhan, Erkan Sezgin","doi":"10.1108/jhtt-10-2023-0309","DOIUrl":"https://doi.org/10.1108/jhtt-10-2023-0309","url":null,"abstract":"Purpose\u0000Three-dimentional (3D) food printers are innovative technologies that contribute to healthy, personalized and stainable nutrition. However, many consumers are still vigilant about 3D printed food in the age of technology. The purpose of this study is to develop a scale and propose a model for consumption preferences associated with 3D-printed food (3DPF).\u0000\u0000Design/methodology/approach\u0000The developed questionnaire was handed to 192 Z and Y generation participants (Data1) for the exploratory factor analysis stage initially. Then, the questionnaire was handed to another group of 165 participants (Data 2) for verification by confirmatory factor analysis. Finally, the dimensions “healthy and personalized nutrition,” “sustainable nutrition” and “socio-cultural nutrition” were analyzed by structural equation modeling.\u0000\u0000Findings\u0000The results indicated that there was a high relationship between “healthy and personalized nutrition” and “sustainable nutrition” as well as between “sustainable nutrition” and “socio-cultural nutrition” when 3DPF was considered.\u0000\u0000Originality/value\u0000The study would contribute to the new survey area related to 3DPF by presenting a scale and proposing a model. Also, the study reveals which nutritional factors affect the Z and Y generation’s consumption of 3DPF. In this context, the study aims to make marketing contributions to the food production, restaurant and hotel sectors.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140675523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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