References

• Ahorsu, D., Lin, C., Imani, V., Saffari, M., Griffiths, M., & Pakpour, A. (2022). The Fear of COVID-19 Scale: Development and Initial Validation. International Journal of Mental Health and Addiction, 20(3), 1537–1545. https://doi.org/10.1007/s11469-020-00270-8
   PubMed Web of Science ®Google Scholar
• Ahuja, K., Chandra, V., Lord, V., & Peens, C. (2021). Ordering in: The rapid evolution of food delivery. McKinsey & Company. Retrieved from https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/ordering-in-the-rapid-evolution-of-food-delivery
   Google Scholar
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-59789190020-T
   Web of Science ®Google Scholar
• Apaolaza, V., Paredes, M. R., Hartmann, P., García-Merino, J. D., & Marcos, A. (2022). The effect of threat and fear of COVID-19 on booking intentions of full board hotels: The roles of perceived coping efficacy and present-hedonism orientation. International Journal of Hospitality Management, 105, 103255. https://doi.org/10.1016/j.ijhm.2022.103255
   PubMed Web of Science ®Google Scholar
• Au, W. C. W., & Tsang, N. K. F. (2022). Gig workers’ self-protective behaviour against legal risks: An application of protection motivation theory. International Journal of Contemporary Hospitality Management, 35(4), 1376–1397. https://doi.org/10.1108/IJCHM-12-2021-1537
   Web of Science ®Google Scholar
• Baek, Y. M. (2014). Solving the privacy paradox: A counter-argument experimental approach. Computers in Human Behavior, 38, 33–42. https://doi.org/10.1016/j.chb.2014.05.006
   Web of Science ®Google Scholar
• Baum, T., Mooney, S. K. K., Robinson, R. N. S., & Solnet, D. (2020). COVID-19’s impact on the hospitality workforce – New crisis or amplification of the norm. International Journal of Contemporary Hospitality Management, 32(9), 2813–2829. https://doi.org/10.1108/IJCHM-04-2020-0314
   Web of Science ®Google Scholar
• Byrd, K., Her, E., Fan, A., Almanza, B., Liu, Y., & Leitch, S. (2021). Restaurants and COVID-19: What are consumers’ risk perceptions about restaurant food and its packaging during the pandemic? International Journal of Hospitality Management, 94, 102821. https://doi.org/10.1016/j.ijhm.2020.102821
   PubMed Web of Science ®Google Scholar
• Cai, R., & Leung, X. Y. (2020). Mindset matters in purchasing online food deliveries during the pandemic: The application of construal level and regulatory focus theories. International Journal of Hospitality Management, 91, 102677. https://doi.org/10.1016/j.ijhm.2020.102677
   Web of Science ®Google Scholar
• Chai, L. T., & Yat, D. N. C. (2019). Online food delivery services: Making food delivery the new normal. Journal of Marketing Advances and Practices, 1(1), 62–77.
   Google Scholar
• Chandrasekhar, N., Gupta, S., & Nanda, N. (2019). Food delivery services and customer preference: A comparative analysis. Journal of Foodservice Business Research, 22(4), 375–386. https://doi.org/10.1080/15378020.2019.1626208
   Google Scholar
• Chang, R. C., Kivela, J., & Mak, A. H. (2011). Attributes that influence the evaluation of travel dining experience: When East meets West. Tourism Management, 32(2), 307–316. https://doi.org/10.1016/j.tourman.2010.02.009
   Web of Science ®Google Scholar
• Chen, F., Dai, S., Zhu, Y., & Xu, H. (2020). Will concerns for ski tourism promote pro-environmental behavior? An implication of protection motivation theory. International Journal of Tourism Research, 22(3), 303–313. https://doi.org/10.1002/jtr.2336
   Web of Science ®Google Scholar
• Cheng, C., Chang, Y., & Chen, C. (2021). Construction of a service quality scale for the online food delivery industry. International Journal of Hospitality Management, 95, 102938. https://doi.org/10.1016/j.ijhm.2021.102938
   Web of Science ®Google Scholar
• Chen, L., & Yang, X. (2019). Using EPPM to evaluate the effectiveness of fear appeal messages across different media outlets to increase the intention of breast self-examination among Chinese women. Health Communication, 34(11), 1369–1376. https://doi.org/10.1080/10410236.2018.1493416
   PubMed Web of Science ®Google Scholar
• Choi, J., Lee, A., & Ok, C. (2013). The effects of consumers’ perceived risk and benefits on attitude and behavioral intention: A study of street food. Journal of Travel & Tourism Marketing, 30(3), 222–237. https://doi.org/10.1080/10548408.2013.774916
   Web of Science ®Google Scholar
• Chuo, H. Y. (2014). Restaurant diners’ self-protective behavior in response to an epidemic crisis. International Journal of Hospitality Management, 38, 74–83. https://doi.org/10.1016/j.ijhm.2014.01.004
   PubMed Web of Science ®Google Scholar
• Davlembayeva, D., Papagiannidis, S., & Alamanos, E. (2020). Sharing economy: Studying the social and psychological factors and the outcomes of social exchange. Technological Forecasting & Social Change, 158, 120143. https://doi.org/10.1016/j.techfore.2020.120143
   Web of Science ®Google Scholar
• Dedeoğlu, B. B., & Boğan, E. (2021). The motivations of visiting upscale restaurants during the COVID-19 pandemic: The role of risk perception and trust in government. International Journal of Hospitality Management, 95, 102905. https://doi.org/10.1016/j.ijhm.2021.102905
   PubMed Web of Science ®Google Scholar
• Do Valle, P. O., & Assaker, G. (2016). Using partial least squares structural equation modelling in tourism research: A review of past research and recommendations for future applications. Journal of Travel Research, 55(6), 695–708. https://doi.org/10.1177/0047287515569779
   Web of Science ®Google Scholar
• Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2014). Online purchasing tickets for low cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model. Tourism Management, 43, 70–88. https://doi.org/10.1016/j.tourman.2014.01.017
   Web of Science ®Google Scholar
• Fakih, K., Assaker, G., Assaf, A. G., & Hallak, R. (2016). Does restaurant menu information affect customer attitudes and behavioral intentions? A cross-segment empirical analysis using PLS-SEM. International Journal of Hospitality Management, 57, 71–83. https://doi.org/10.1016/j.ijhm.2016.06.002
   Web of Science ®Google Scholar
• Fisher, M. C., Hawkins, N. J., Sanglard, D., & Gurr, S. J. (2018). Worldwide emergence of resistance to antifungal drugs challenges human health and food security. Science, 360(6390), 739–742. https://doi.org/10.1126/science.aap7999
   PubMed Web of Science ®Google Scholar
• Floyd, D. L., Prentice‐dunn, S., & Rogers, R. W. (2000). A meta‐analysis of research on protection motivation theory. Journal of Applied Social Psychology, 30(2), 407–429. https://doi.org/10.1111/j.1559-1816.2000.tb02323.x
   Web of Science ®Google Scholar
• Foodpanda Wordpress. (2020). Foodpanda introduces contactless delivery button during COVID-19. Foodpanda. Retrieved from https://www.foodpanda.com/2020/04/foodpanda-introduces-contactless-delivery-button-during-covid-19/
   Google Scholar
• Fornell C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
   Web of Science ®Google Scholar
• Furunes, T., & Mkono, M. (2019). Service-delivery success and failure under the sharing economy. International Journal of Contemporary Hospitality Management, 31(8), 3352–3370. https://doi.org/10.1108/IJCHM-06-2018-0532
   Web of Science ®Google Scholar
• Gavilan, D., Balderas-Cejudo, A., Fernández-Lores, S., & Martinez-Navarro G. (2021). Innovation in online food delivery: Learnings from COVID-19. International Journal of Gastronomy and Food Science, 24, 100330. https://doi.org/10.1016/j.ijgfs.2021.100330
   PubMed Web of Science ®Google Scholar
• Gibbons, F. X., Gerrard, M., Ouellette, J. A., & Burzette, R. (1998). Cognitive antecedents to adolescent health risk: Discriminating between behavioral intention and behavioral willingness. Psychology & Health, 13(2), 319–339. https://doi.org/10.1080/08870449808406754
   Web of Science ®Google Scholar
• Grondys, K. (2019). Implementation of the sharing economy in the B2B sector. Sustainability, 11(14), 3976. https://doi.org/10.3390/su11143976
   Web of Science ®Google Scholar
• Hair, J. F., Hult, T. M., Ringle, C. M., & Sarsterdt, M. (2021). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Thousand Oaks: Sage.
   Google Scholar
• Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory & Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
   Google Scholar
• Harris, K. J., Ali, F., & Ryu, K. (2018). Foodborne illness outbreaks in restaurants and patrons’ propensity to return. International Journal of Contemporary Hospitality Management, 30(3), 1273–1292. https://doi.org/10.1108/IJCHM-12-2016-0672
   Web of Science ®Google Scholar
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
   Web of Science ®Google Scholar
• Herrero, A., San Martín, H., & Collado, J. (2018). Market orientation and SNS adoption for marketing purposes in hospitality microenterprises. Journal of Hospitality & Tourism Management, 34, 30–40. https://doi.org/10.1016/j.jhtm.2017.11.005
   Web of Science ®Google Scholar
• Heylighen, F. (2017). Towards an intelligent network for matching offer and demand: From the sharing economy to the global brain. Technological Forecasting & Social Change, 114, 74–85. https://doi.org/10.1016/j.techfore.2016.02.004
   Web of Science ®Google Scholar
• Huang, E. Y., Lin, S. W., & Fan, Y. C. (2015). MS-QUAL: Mobile service quality measurement. Electronic Commerce Research and Applications, 14(2), 126–142. https://doi.org/10.1016/j.elerap.2015.01.003
   Web of Science ®Google Scholar
• Hwang, J., Kim, J. J., & Lee, K. W. (2021). Investigating consumer innovativeness in the context of drone food delivery services: Its impact on attitude and behavioral intentions. Technological Forecasting & Social Change, 163, 120433. https://doi.org/10.1016/j.techfore.2020.120433
   Web of Science ®Google Scholar
• Hwang, J., Lee, J. S., & Kim, H. (2019). Perceived innovativeness of drone food delivery services and its impacts on attitude and behavioral intentions: The moderating role of gender and age. International Journal of Hospitality Management, 81, 94–103. https://doi.org/10.1016/j.ijhm.2019.03.002
   Web of Science ®Google Scholar
• Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption: Testing the UTAUT model. Information & Management, 48(1), 1–8. https://doi.org/10.1016/j.im.2010.09.001
   Web of Science ®Google Scholar
• Jeon, H. M., Sung, H. J., & Kim, H. Y. (2020). Customers’ acceptance intention of self-service technology of restaurant industry: Expanding UTAUT with perceived risk and innovativeness. Service Business, 14(4), 533–551. https://doi.org/10.1007/s11628-020-00425-6
   Web of Science ®Google Scholar
• Kanur, P., Dhir, A., Talwar, S., & Ghuman, K. (2021). The value proposition of food delivery apps from the perspective of theory of consumption value. International Journal of Contemporary Hospitality Management, 33(4), 1129–1159. https://doi.org/10.1108/IJCHM-05-2020-0477
   Web of Science ®Google Scholar
• Kaur, P., Dhir, A., Bodhi, R., Singh, T., & Almotairi, M. (2020). Why do people use and recommend m-wallets? Journal of Retailing & Consumer Services, 56, 102091. https://doi.org/10.1016/j.jretconser.2020.102091
   Web of Science ®Google Scholar
• Khalilzadeh, J., Ozturk, A. B., & Bilgihan, A. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior, 70, 460–474. https://doi.org/10.1016/j.chb.2017.01.001
   Web of Science ®Google Scholar
• Kim, J. J., & Hwang, J. (2020). Merging the norm activation model and the theory of planned behavior in the context of drone food delivery services: Does the level of product knowledge really matter? Journal of Hospitality & Tourism Management, 42, 1–11. https://doi.org/10.1016/j.jhtm.2019.11.002
   Web of Science ®Google Scholar
• Kim, J. J., Kim, I., & Hwang, J. (2021). A change of perceived innovativeness for contactless food delivery services using drones after the outbreak of COVID-19. International Journal of Hospitality Management, 93, 102758. https://doi.org/10.1016/j.ijhm.2020.102758
   PubMed Web of Science ®Google Scholar
• Kline, R. B. (2011). Principles and practice of structural equation modelling. Guilford Press.
   Google Scholar
• Ko, Y. H., Son, J. H., & Kim, G. J. (2022). An exploratory study of changes in consumer dining out behavior before and during COVID-19. Journal of Foodservice Business Research, 1–19. https://doi.org/10.1080/15378020.2022.2036569
   Google Scholar
• Kumar, S., & Shah, A. (2021). Revisiting food delivery apps during COVID-19 pandemic? Investigating the role of emotions. Journal of Retailing & Consumer Services, 62, 102595. https://doi.org/10.1016/j.jretconser.2021.102595
   Web of Science ®Google Scholar
• LaTour, M. S., & Rotfeld, H. J. (1997). There are threats and (maybe) fear-caused arousal: Theory and confusions of appeals to fear and fear arousal itself. Journal of Advertising, 26(3), 45–59. https://doi.org/10.1080/00913367.1997.10673528
   Web of Science ®Google Scholar
• Leung, X. Y., & Cai, R. (2021). How pandemic severity moderates digital food ordering risks during COVID-19: An application of prospect theory and risk perception framework. Journal of Hospitality & Tourism Management, 47, 497–505. https://doi.org/10.1016/j.jhtm.2021.05.002
   Web of Science ®Google Scholar
• Leung, K., & Low, Z. (2020). Work from home, call for meals: Food delivery platforms provide lifeline to Hong Kong’s empty restaurants. South China Morning Post. Retrieved from https://www.scmp.com/news/hong-kong/hong-kong-economy/article/3079508/work-home-call-meals-food-delivery-platforms
   Google Scholar
• Lin, P. M., Au, W. C., Leung, V. T., & Peng, K. L. (2020). Exploring the meaning of work within the sharing economy: A case of food-delivery workers. International Journal of Hospitality Management, 91, 102686. https://doi.org/10.1016/j.ijhm.2020.102686
   Web of Science ®Google Scholar
• Lin, P. M. C., Ok, C. M., & Au, W. C. (2022). Peer-to-peer dining: A motivation study. Journal of Hospitality & Tourism Research, 46(8), 1596–1621. https://doi.org/10.1177/1096348021990709
   Web of Science ®Google Scholar
• Lin, P. M. C., Peng, K. L., Wang, S., Au, W. C., & Feng, S. (2022). Impact of online food delivery services on China’s hotel catering businesses. Journal of China Tourism Research, 18(4), 871–891. https://doi.org/10.1080/19388160.2021.1934213
   Web of Science ®Google Scholar
• Mehrolia, S., Alagarsamy, S., & Solaikutty, V. M. (2021). Customers response to online food delivery services during COVID‐19 outbreak using binary logistic regression. International Journal of Consumer Studies, 45(3), 396–408. https://doi.org/10.1111/ijcs.12630
   PubMed Web of Science ®Google Scholar
• Michaelson, D., & Stacks, D. W. (2011). Standardization in public relations measurement and evaluation. The Public Relations Journal, 5(2), 1–22.
   Google Scholar
• Min, Q., Ji, S., & Qu, G. (2008). Mobile commerce user acceptance study in China: A revised UTAUT model. Tsinghua Science and Technology, 13(3), 257–264. https://doi.org/10.1016/S1007-02140870042-7
   Google Scholar
• Min, S., So, K. K. F., & Jeong, M. (2019). Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model. Journal of Travel & Tourism Marketing, 36(7), 770–783. https://doi.org/10.1080/10548408.2018.1507866
   Web of Science ®Google Scholar
• Mody, M. A., Hanks, L., & Cheng, M. (2021). Sharing economy research in hospitality and tourism: A critical review using bibliometric analysis, content analysis and a quantitative systematic literature review. International Journal of Contemporary Hospitality Management, 33(5), 1711–1745. https://doi.org/10.1108/IJCHM-12-2020-1457
   Web of Science ®Google Scholar
• Nguyen, T. H. D., & Vu, D. C. (2020a). Food delivery service during social distancing: Proactively preventing or potentially spreading Coronavirus Disease–2019? Disaster Medicine and Public Health Preparedness, 14(3), e9–10. https://doi.org/10.1017/dmp.2020.135
   PubMed Web of Science ®Google Scholar
• Nguyen, T. H. D., & Vu, D. C. (2020b). The largest epicenter of the coronavirus outbreak in Vietnam. Infection Control & Hospital Epidemiology, 41(8), 984–985. https://doi.org/10.1017/ice.2020.128
   PubMed Web of Science ®Google Scholar
• Nunnally, J. C., & Bernstein, I. R. (1994). Psychometric theory. McGraw-Hill.
   Google Scholar
• Polizzi, C., Lynn, S. J., & Perry, A. (2020). Stress and coping in the time of covid-19: Pathways to resilience and recovery. Clinical Neuropsychiatry, 17(2), 59–62. https://doi.org/10.36131/CN20200204
   PubMed Web of Science ®Google Scholar
• Prasetyo, Y., Tanto, H., Mariyanto, M., Hanjaya, C., Young, M., Persada, S., Miraja, B., & Redi, A. (2021). Factors Affecting Customer Satisfaction and Loyalty in Online Food Delivery Service during the COVID-19 Pandemic: Its Relation with Open Innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 76. https://doi.org/10.3390/joitmc7010076
   Google Scholar
• Prayag, G., & Ozanne, L. K. (2018). A systematic review of peer-to-peer (P2P) accommodation sharing research from 2010 to 2016: Progress and prospects from the multi-level perspective. Journal of Hospitality Marketing & Management, 27(6), 649–678. https://doi.org/10.1080/19368623.2018.1429977
   Web of Science ®Google Scholar
• Rather, R. A. (2021). Monitoring the impacts of tourism-based social media, risk perception and fear on tourist’s attitude and revisiting behavior in the wake of COVID-19 pandemic. Current Issues in Tourism, 24(23), 3275–3283. https://doi.org/10.1080/13683500.2021.1884666
   Web of Science ®Google Scholar
• Ray, A., & Bala, P. K. (2021). User generated content for exploring factors affecting intention to use travel and food delivery services. International Journal of Hospitality Management, 92, 102730. https://doi.org/10.1016/j.ijhm.2020.102730
   Web of Science ®Google Scholar
• Ray, A., Dhir, A., Bala, P. K., & Kaur, P. (2019). Why do people use food delivery apps (FDA)? A uses and gratification theory perspective. Journal of Retailing & Consumer Services, 51, 221–230. https://doi.org/10.1016/j.jretconser.2019.05.025
   Web of Science ®Google Scholar
• Reicher, S., & Scott, C. (2020). On order and disorder during the COVID-19 pandemic. British Journal of Social Psychology, 59(3), 694–702. https://doi.org/10.1111/bjso.12398
   PubMed Web of Science ®Google Scholar
• Reuters. (2020). Leave it on the doorstep: Deliveroo starts contactless delivery. Reuters. Retrieved from https://www.reuters.com/article/us-health-coronavirus-britain-deliveroo-idUSKBN20Z1UV
   Google Scholar
• Rogers, E. M. (1995). Diffusion of Innovations: Modifications of a model for telecommunications. In M. W. Stoetzer & A. Mahler (Eds.), Die diffusion von innovationen in der telekommunikation (pp. 25–38). Springer.
   Google Scholar
• Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change1. The Journal of Psychology, 91(1), 93–114. https://doi.org/10.1080/00223980.1975.9915803
   PubMed Web of Science ®Google Scholar
• San Martín, H., & Herrero, Á. (2012). Influence of the user’s psychological factors on the online purchase intention in rural tourism: Integrating innovativeness to the UTAUT framework. Tourism Management, 33(2), 341–350. https://doi.org/10.1016/j.tourman.2011.04.003
   Web of Science ®Google Scholar
• Song, H., Ruan, W. J., & Jeon, Y. J. J. (2021). An integrated approach to the purchase decision making process of food-delivery apps: Focusing on the TAM and AIDA models. International Journal of Hospitality Management, 95, 102943. https://doi.org/10.1016/j.ijhm.2021.102943
   Web of Science ®Google Scholar
• Southey, F. (2020). Online food delivery ‘one of the only winners’ in coronavirus outbreak. Food Navigator. Retrieved from https://www.foodnavigator.com/Article/2020/03/19/Online-food-delivery-one-of-the-only-winners-in-coronavirus-outbreak
   Google Scholar
• Sundarraj, R. P., & Manochehri, N. (2011). Application of an extended TAM model for online banking adoption: A study at a Gulf-region university. Information Resources Management Journal, 24(1), 1–13. https://doi.org/10.4018/irmj.2011010101
   Google Scholar
• Tanner, J. F., Jr., Hunt, J. B., & Eppright, D. R. (1991). The protection motivation model: A normative model of fear appeals. Journal of Marketing, 55(3), 36–45. https://doi.org/10.1177/002224299105500304
   Web of Science ®Google Scholar
• Taylor, J. W. (1974). The Role of Risk in Consumer Behavior. Journal of Marketing, 38(2), 54. https://doi.org/10.2307/1250198
   Web of Science ®Google Scholar
• Todoli-Signes, A. (2017). The ‘gig economy’: Employee, self-employed or the need for a special employment regulation? European Review of Labor and Research, 23(2), 193–205. https://doi.org/10.1177/1024258917701381
   Google Scholar
• Tran, T. T. H., Robinson, K., & Paparoidamis, N. G. (2022). Sharing with perfect strangers: The effects of self-disclosure on consumers’ trust, risk perception, and behavioral intention in the sharing economy. Journal of Business Research, 144, 1–16. https://doi.org/10.1016/j.jbusres.2022.01.081
   Web of Science ®Google Scholar
• Ullman, J. B. (2001). Structural equation modeling. In B. G. Tabachnick & L. S. Fidell (Eds.), Using multivariate statistics (4th ed., pp. 653–771). Allyn and Bacon.
   Google Scholar
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
   Web of Science ®Google Scholar
• Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412
   Web of Science ®Google Scholar
• Walle, A. H. (1996). Tourism and the Internet: Opportunities for direct marketing. Journal of Travel Research, 35(1), 72–77. https://doi.org/10.1177/004728759603500111
   Google Scholar
• Wang, J., Liu Lastres, B., Ritchie, B. W., & Mills, D. J. (2019). Travellers’ self-protections against health risks: An application of the full Protection Motivation Theory. Annals of Tourism Research, 78, 102743. https://doi.org/10.1016/j.annals.2019.102743
   Web of Science ®Google Scholar
• Wang, Y. S., Wu, S. C., Lin, H. H., Wang, Y. M., & He, T. R. (2012). Determinants of user adoption of web ‘automatic teller machines’: An integrated model of ‘transaction cost theory’ and ‘innovation diffusion theory’. The Service Industries Journal, 32(9), 1505–1525. https://doi.org/10.1080/02642069.2010.531271
   Web of Science ®Google Scholar
• Webb, T. L. & Sheeran, P. (2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychological Bulletin, 132(2), 249–268. https://doi.org/10.1037/0033-2909.132.2.249
   PubMed Web of Science ®Google Scholar
• Yang, H., Guo, X., Wu, T., & Ju, X. (2015). Exploring the effects of patient-generated and system-generated information on patients’ online search, evaluation and decision. Electronic Commerce Research and Applications, 14(3), 192–203. https://doi.org/10.1016/j.elerap.2015.04.001
   Web of Science ®Google Scholar
• Yang, F. X., Li, X., Lau, V. M. C., & Zhu, V. Z. (2021). To survive or to thrive? China’s luxury hotel restaurants entering O2O food delivery platforms amid the COVID-19 crisis. International Journal of Hospitality Management, 94, 102855. https://doi.org/10.1016/j.ijhm.2020.102855
   PubMed Web of Science ®Google Scholar
• Yeh, H. (2020). Factors in the ecosystem of mobile payment affecting its use: From the customers’ perspective in Taiwan. Journal of Theoretical & Applied Electronic Commerce Research, 15(1), 13–29. https://doi.org/10.4067/S0718-18762020000100103
   Web of Science ®Google Scholar
• Yi, J., Yuan, G., & Yoo, C. (2020). The effect of the perceived risk on the adoption of the sharing economy in the tourism industry: The case of Airbnb. Information Processing & Management, 57(1), 102108. https://doi.org/10.1016/j.ipm.2019.102108
   Web of Science ®Google Scholar
• Yost, E., & Cheng, Y. (2021). Customers’ risk perception and dine-out motivation during a pandemic: Insight for the restaurant industry. International Journal of Hospitality Management, 95, 102889. https://doi.org/10.1016/j.ijhm.2021.102889
   PubMed Web of Science ®Google Scholar
• Yuan, T., Honglei, Z., Xiao, X., Ge, W., & Xianting, C. (2021). Measuring perceived risk in sharing economy: A classical test theory and item response theory approach. International Journal of Hospitality Management, 96, 102980. https://doi.org/10.1016/j.ijhm.2021.102980
   Web of Science ®Google Scholar
• Zhao, Y., & Bacao, F. (2020). What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period? International Journal of Hospitality Management, 91, 102683. https://doi.org/10.1016/j.ijhm.2020.102683
   PubMed Web of Science ®Google Scholar
• Zhao, X., & Knobel, P. (2021). Face mask wearing during the COVID-19 pandemic: Comparing perceptions in China and three European countries. Translational Behavioral Medicine, 11(6), 1199–1204. https://doi.org/10.1093/tbm/ibab043
   PubMed Web of Science ®Google Scholar
• Zheng, D., Luo, Q., & Ritchie, B. W. (2021). Afraid to travel after COVID-19? Self-protection, coping and resilience against pandemic ‘travel fear’. Tourism Management, 83, 104261. https://doi.org/10.1016/j.tourman.2020.104261
   Web of Science ®Google Scholar
• Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760–767. https://doi.org/10.1016/j.chb.2010.01.013
   Web of Science ®Google Scholar