Leveraging Psychological Characteristics to Influence Mobile Hotel Bookings During a Global Health Crisis

References

• Agrebi, S., & Jallais, J. (2015). Explain the intention to use smartphones for mobile shopping. Journal of Retailing and Consumer Services, 22, 16–23. https://doi.org/10.1016/j.jretconser.2014.09.003
   Web of Science ®Google Scholar
• Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261. https://doi.org/10.1037/h0076477
   Web of Science ®Google Scholar
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
   Web of Science ®Google Scholar
• Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Williams, M. D. (2016). Consumer adoption of mobile banking in Jordan: Examining the role of usefulness, ease of use, perceived risk, and self-efficacy. Journal of Enterprise Information Management, 29(1), 118–139. https://doi.org/10.1108/JEIM-04-2015-0035
   Web of Science ®Google Scholar
• Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002
   Web of Science ®Google Scholar
• Aliperti, G., & Cruz, A. M. (2020). Promoting built-for-disaster-purpose mobile applications: An interdisciplinary literature review to increase their penetration rate among tourists. Journal of Hospitality and Tourism Management, 44, 193–210. https://doi.org/10.1016/j.jhtm.2020.06.006
   Web of Science ®Google Scholar
• Bae, S., Kwon, J. M., & Bosley, A. (2020). Factors influencing consumers’ rejection to smartphone transactions in the lodging industry. International Hospitality Review, 34(1), 29–40. https://doi.org/10.1108/IHR-09-2019-0020
   Google Scholar
• Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191
   PubMed Web of Science ®Google Scholar
• Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147. https://doi.org/10.1037/0003-066X.37.2.122
   Web of Science ®Google Scholar
• Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall.
   Google Scholar
• Bentler, P. M., & Bonett, D. G. (1980). Significance Tests and Goodness-of-Fit in the Analysis of Covariance Structures. Psychological Bulletin, 88(3), 588–600. https://doi.org/10.1037/0033-2909.88.3.588
   Web of Science ®Google Scholar
• Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Using Mechanical Turk as a subject recruitment tool for experimental research. Political Analysis, 20 (3) 351–368. https://doi.org/10.1093/pan/mpr057
   Web of Science ®Google Scholar
• Buhrmester, M., Kwang, T., & Gosling, S. D. (2016). Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality data? In A. E. Kazdin (Ed.), Methodological issues and strategies in clinical research (pp. 133–139). American Psychological Association.
   Google Scholar
• Buhrmester, M. D., Talaifar, S., & Gosling, S. D. (2018). An evaluation of Amazon’s Mechanical Turk, its rapid rise, and its effective use. Perspectives on Psychological Science, 13(2), 149–154. https://doi.org/10.1177/1745691617706516
   Web of Science ®Google Scholar
• Chandler, J., Mueller, P., & Paolacci, G. (2014). Nonnaïveté among Amazon Mechanical Turk workers: Consequences and solutions for behavioral researchers. Behavior Research Methods, 46(1), 112–130. https://doi.org/10.3758/s13428-013-0365-7
   PubMed Web of Science ®Google Scholar
• Cheah, J. H., Sarstedt, M., Ringle, C. M., Ramayah, T., & Ting, H. (2018). Convergent validity assessment of formatively measured constructs in PLS-SEM: On using single-item versus multi-item measures in redundancy analyses. International Journal of Contemporary Hospitality Management, 30(11), 3192–3210. https://doi.org/10.1108/IJCHM-10-2017-0649
   Web of Science ®Google Scholar
• Chryssochoidis, G., Strada, A., & Krystallis, A. (2009). Public trust in institutions and information sources regarding risk management and communication: Towards integrating extant knowledge. Journal of Risk Research, 12(2), 137–185. https://doi.org/10.1080/13669870802637000
   Web of Science ®Google Scholar
• Chunxiang, L. (2014). Study on mobile commerce customer based on value adoption. Journal of Applied Sciences, 14(9), 901–909. https://doi.org/10.3923/jas.2014.901.909
   Google Scholar
• Compeau, D., Higgins, C. A., & Huff, S. (1999). Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study. MIS Quarterly, 23(2), 145–158. https://doi.org/10.2307/249749
   Web of Science ®Google Scholar
• Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling Theory: A Review and Assessment. Journal of Management, 37(1), 39–67. https://doi.org/10.1177/0149206310388419
   Web of Science ®Google Scholar
• Criteo. (2018). 7 Key Trends for the Travel Industry in 2018. Criteo. https://www.criteo.com/blog/travel-market-research/.
   Google Scholar
• DeFranco, A., & Morosan, C. (2017). Coping with the risk of internet connectivity in hotels: Perspectives from American consumers traveling internationally. Tourism Management, 61, 380–393. https://doi.org/10.1016/j.tourman.2017.02.022
   Web of Science ®Google Scholar
• Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61–80. https://doi.org/10.1287/isre.1060.0080
   Web of Science ®Google Scholar
• Fong, L. H. N., Lam, L. W., & Law, R. (2017). How locus of control shapes intention to reuse mobile apps for making hotel reservations: Evidence from Chinese consumers. Tourism Management, 61, 331–342. https://doi.org/10.1016/j.tourman.2017.03.002
   Web of Science ®Google Scholar
• Franke, N., Keinz, P., & Steger, C. J. (2009). Testing the value of customization: When do customers really prefer products tailored to their preferences? Journal of Marketing, 73(5), 103–121. https://doi.org/10.1509/jmkg.73.5.103
   Web of Science ®Google Scholar
• Freudenburg, W. R. (1993). Risk and recreancy: Weber, the division of labor, and the rationality of risk perceptions. Social Forces, 71(4), 909–932. https://doi.org/10.2307/2580124
   Web of Science ®Google Scholar
• Gupta, A., Dogra, N., & George, B. (2018). What determines tourist adoption of smartphone apps? An analysis based on the UTAUT-2 framework. Journal of Hospitality and Tourism Technology, 9(1), 50–64. https://doi.org/10.1108/JHTT-02-2017-0013
   Web of Science ®Google Scholar
• Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer of partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage.
   Google Scholar
• Hotelier, L. (2021). Surprising statistics around mobile booking for small hotels. Little Hotelier Blog. https://www.littlehotelier.com/blog/running-your-property/surprising-statistics-around-mobile-bookings-small-hotels/
   Google Scholar
• Hsu, M. H., Chuang, L. W., & Hsu, C. S. (2014). Understanding online shopping intention: The roles of four types of trust and their antecedents. Internet Research, 24(3), 332–352. https://doi.org/10.1108/IntR-01-2013-0007
   Web of Science ®Google Scholar
• Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424. https://doi.org/10.1037/1082-989X.3.4.424
   Web of Science ®Google Scholar
• Imhoff, C., Loftis, L., & Geiger, J. (2001). Building the Customer-Centric Enterprise, Data Warehousing Techniques for Supporting Customer Relationship Management. John Wiley and Sons.
   Google Scholar
• Jungermann, H., Pfister, H. R., & Fischer, K. (1996). Credibility, information preferences, and information interests. Risk Analysis, 16(2), 251–261. https://doi.org/10.1111/j.1539-6924.1995.tb00783.x
   Web of Science ®Google Scholar
• Kang, J. W., & Namkung, Y. (2019). The role of personalization on continuance intention in food service mobile apps. International Journal of Contemporary Hospitality Management, 31(2), 734–752. https://doi.org/10.1108/IJCHM-12-2017-0783
   Web of Science ®Google Scholar
• Kansakar, P., Munir, A., & Shabani, N. (2019). Technology in the hospitality industry: Prospects and challenges. IEEE Consumer Electronics Magazine, 8(3), 60–65. https://doi.org/10.1109/MCE.2019.2892245
   Web of Science ®Google Scholar
• Khasawneh, M. H. A., Hujran, O., & Abdrabbo, T. (2018). A quantitative examination of the factors that influence users’ perceptions of trust towards using mobile banking services. International Journal of Internet Marketing and Advertising, 12(2), 181–207. https://doi.org/10.1504/IJIMA.2018.090957
   Google Scholar
• Kim, K. K., Shin, H. K., & Kim, B. (2011). The role of psychological traits and social factors in using new mobile communication services. Electronic Commerce Research and Applications, 10(4), 408–417. https://doi.org/10.1016/j.elerap.2010.11.004
   Web of Science ®Google Scholar
• Kim, S. H., Bae, J. H., & Jeon, H. M. (2019). Continuous intention on accommodation apps: Integrated value-based adoption and expectation–confirmation model analysis. Sustainability, 11(6), 1578–1595. https://doi.org/10.3390/su11061578
   Web of Science ®Google Scholar
• Klussmann, M. (2020). Will the Coronavirus Crisis Force Hoteliers to Implement New Technologies Faster? Hotel online. https://www.hotel-online.com/press_releases/release/will-the-coronavirus-crisis-force-hoteliers-to-implement-new-technologies-faster/
   Google Scholar
• Kobsa, A., & Teltzrow, M. (2004). Contextualized communication of privacy practices and personalization benefits: Impacts on users’ data sharing and purchase behavior. In International Workshop on Privacy Enhancing Technologies (pp. 329–343). Springer.
   Google Scholar
• Lai, W.-T. (2015). Exploring Use Intention of a Smart Bike-Sharing System-Extending Technology Acceptance Model with Trust BT - LISS 2014: Proceedings of 4th International Conference on Logistics, Informatics and Service Science (Z. Zhang, Z. M. Shen, J. Zhang, & R. Zhang (eds.); pp. 1597–1603). USA: Springer Berlin Heidelberg.
   Google Scholar
• Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191–204. https://doi.org/10.1016/S0378-7206(01)00143-4
   Web of Science ®Google Scholar
• Lema, J. D. (2009). Preparing hospitality organizations for self-service technology. Journal of Human Resources in Hospitality & Tourism, 8(2), 153–169. https://doi.org/10.1080/15332840802269791
   Google Scholar
• Leung, D., & Ma, J. (2020). Antecedents and consequences of consumers’ trust in hybrid travel websites. Journal of Travel & Tourism Marketing, 37(6), 756–772. https://doi.org/10.1080/10548408.2020.1812468
   Web of Science ®Google Scholar
• Li, H., Sarathy, R., & Xu, H. (2011). The role of affect and cognition on online consumers’ decision to disclose personal information to unfamiliar online vendors. Decision Support Systems, 51(3), 434–445. https://doi.org/10.1016/j.dss.2011.01.017
   Web of Science ®Google Scholar
• Lock, S. (2022) COVID-19ʹs effect on hotel KPIs in the U.S. as of December 25, 2021 Statista. S. Lock. https://www.statista.com/statistics/1109880/coronavirus-hotel-sector-kpis/
   Google Scholar
• López-Navarro, M. Á., Llorens-Monzonís, J., & Tortosa-Edo, V. (2013). The effect of social trust on citizens’ health risk perception in the context of a petrochemical industrial complex. International Journal of Environmental Research and Public Health, 10(1), 399–416. https://doi.org/10.3390/ijerph10010399
   PubMed Web of Science ®Google Scholar
• Luszczynska, A., Sarkar, Y., & Knoll, N. (2007). Received social support, self-efficacy, and finding benefits in disease as predictors of physical functioning and adherence to antiretroviral therapy. Patient Education and Counseling, 66(1), 37–42. https://doi.org/10.1016/j.pec.2006.10.002
   PubMed Web of Science ®Google Scholar
• MacSween, S., & Canziani, B. (2021). Travel booking intentions and information searching during COVID-19. International Hospitality Review, 35(2), 208–224. https://doi.org/10.1108/IHR-08-2020-0046
   Google Scholar
• Malik, G., & Rao, A. S. (2019). Extended expectation-confirmation model to predict continued usage of ODR/ride hailing apps: Role of perceived value and self-efficacy. Information Technology & Tourism, 21(4), 461–482. https://doi.org/10.1007/s40558-019-00152-3
   Web of Science ®Google Scholar
• Mao, Z., Jones, M., Li, M., Wei, W., & Lyu, J. (2020). Sleeping in a stranger’s home: A trust formation model for Airbnb. Journal of Hospitality and Tourism Management, 42, 67–76. https://doi.org/10.1016/j.jhtm.2019.11.012
   Web of Science ®Google Scholar
• Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from986 to 2013. Universal Access in the Information Society, 14(1), 81–95. https://doi.org/10.1007/s10209-014-0348-1
   Web of Science ®Google Scholar
• Morosan, C., & DeFranco, A. (2016). Modeling guests’ intentions to use mobile apps in hotels. International Journal of Contemporary Hospitality Management, 28(9), 1968–1991. https://doi.org/10.1108/IJCHM-07-2015-0349
   Web of Science ®Google Scholar
• Murphy, H. C., Chen, M. M., & Cossutta, M. (2016). An investigation of multiple devices and information sources used in the hotel booking process. Tourism Management, 52, 44–51. https://doi.org/10.1016/j.tourman.2015.06.004
   Web of Science ®Google Scholar
• Nyheim, P., Xu, S., Zhang, L., & Mattila, A. S. (2015). Predictors of avoidance towards personalization of restaurant smartphone advertising: A study from the Millennials’ perspective. Journal of Hospitality and Tourism Technology, 6(2), 145–159. https://doi.org/10.1108/JHTT-07-2014-0026
   Web of Science ®Google Scholar
• Oyedele, A., & Simpson, P. M. (2007). An empirical investigation of consumer control factors on intention to use selected self‐service technologies. International Journal of Service Industry Management, 18(3), 287–306. https://doi.org/10.1108/09564230710751497
   Google Scholar
• Palos-Sanchez, P. R., Hernandez-Mogollon, J. M., & Campon-Cerro, A. M. (2017). The behavioral response to location based services: An examination of the influence of social and environmental benefits, and privacy. Sustainability, 9(11), 1988–2009. https://doi.org/10.3390/su9111988
   Web of Science ®Google Scholar
• Park, S., & Huang, Y. (2017). Motivators and inhibitors in booking a hotel via smartphones. International Journal of Contemporary Hospitality Management, 29(1), 161–178. https://doi.org/10.1108/IJCHM-03-2015-0103
   Web of Science ®Google Scholar
• Piccoli, G., Lui, T. W., & Grün, B. (2017). The impact of IT-enabled customer service systems on service personalization, customer service perceptions, and hotel performance. Tourism Management, 59, 349–362. https://doi.org/10.1016/j.tourman.2016.08.015
   Web of Science ®Google Scholar
• Pillai, S. G., Haldorai, K., Seo, W. S., & Kim, W. G. (2021). COVID-19 and hospitality 5.0: Redefining hospitality operations. International Journal of Hospitality Management, 94, 102869. https://doi.org/10.1016/j.ijhm.2021.102869
   PubMed Web of Science ®Google Scholar
• Poortinga, W., & Pidgeon, N. F. (2003). Exploring the dimensionality of trust in risk regulation. Risk Analysis: An International Journal, 23(5), 961–972. https://doi.org/10.1111/1539-6924.00373
   PubMed Web of Science ®Google Scholar
• Rakshit, S., Islam, N., Mondal, S., & Paul, T. (2021). Mobile apps for SME business sustainability during COVID-19 and onwards. Journal of Business Research, 135, 28–39. https://doi.org/10.1016/j.jbusres.2021.06.005
   PubMed Web of Science ®Google Scholar
• Richer, F. (2021). This is how the covid 29 crisis has affected international tourism. Statista. https://www.weforum.org/agenda/2021/12/covid-crisis-drags-on-for-international-tourism/
   Google Scholar
• Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after all: A cross-discipline view of trust. Academy of Management Review, 23(3), 393–404. https://doi.org/10.5465/amr.1998.926617
   Web of Science ®Google Scholar
• Sarkar, S., Chauhan, S., & Khare, A. (2020). A meta-analysis of antecedents and consequences of trust in mobile commerce. International Journal of Information Management, 50, 286–301. https://doi.org/10.1016/j.ijinfomgt.2019.08.008
   Web of Science ®Google Scholar
• Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007
   Web of Science ®Google Scholar
• Sheng, H., Nah, F. F. H., & Siau, K. (2008). An experimental study on ubiquitous commerce adoption: Impact of personalization and privacy concerns. Journal of the Association for Information Systems, 9(6), 125–150. https://doi.org/10.17705/1jais.00161
   Web of Science ®Google Scholar
• Shin, H., & Kang, J. (2020). Reducing perceived health risk to attract hotel customers in the COVID-19 pandemic era: Focused on technology innovation for social distancing and cleanliness. International Journal of Hospitality Management, 91, 102664. https://doi.org/10.1016/j.ijhm.2020.102664
   PubMed Web of Science ®Google Scholar
• Siegrist, M., Gutscher, H., & Earle, T. C. (2005). Perception of risk: The influence of general trust, and general confidence. Journal of Risk Research, 8(2), 145–156. https://doi.org/10.1080/1366987032000105315
   Web of Science ®Google Scholar
• Simintiras, A. C., Dwivedi, Y. K., & Rana, N. P. (2014). Can marketing strategies enhance the adoption of electronic government initiatives? International Journal of Electronic Government Research (IJEGR), 10(2), 1–7. https://doi.org/10.4018/ijegr.2014040101
   Google Scholar
• Spence, M. (1974). Job market signaling. In Uncertainty in economics (pp. 281–306). Academic Press.
   Google Scholar
• Spence, M. (2002). Signaling in retrospect and the informational structure of markets. American Economic Review, 92(3), 434–459. https://doi.org/10.1257/00028280260136200
   Web of Science ®Google Scholar
• Talwar, S., Dhir, A., Kaur, P., & Mäntymäki, M. (2020). Barriers toward purchasing from online travel agencies. International Journal of Hospitality Management, 89, 102593. https://doi.org/10.1016/j.ijhm.2020.102593
   Web of Science ®Google Scholar
• Taylor, E. (2016). Mobile payment technologies in retail: A review of potential benefits and risks. International Journal of Retail & Distribution Management, 44(2), 159–177. https://doi.org/10.1108/IJRDM-05-2015-0065
   Web of Science ®Google Scholar
• Treiblmaier, H., & Pollach, I. (2007). Users’ perceptions of benefits and costs of personalization. ICIS 2007 Proceedings, 141.
   Google Scholar
• U.S. Census Bureau (2019). Quick facts United States Demographics. https://www.census.gov/quickfacts/fact/table/US/PST045219
   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
• Wang, C., Harris, J., & Patterson, P. (2013). The roles of habit, self-efficacy, and satisfaction in driving continued use of self-service technologies: A longitudinal study. Journal of Service Research, 16(3), 400–414. https://doi.org/10.1177/1094670512473200
   Web of Science ®Google Scholar
• Wang, E. S. T., & Lin, R. L. (2017). Perceived quality factors of location-based apps on trust, perceived privacy risk, and continuous usage intention. Behaviour & Information Technology, 36(1), 2–10 https://doi.org/10.1080/0144929X.2016.1143033
   Web of Science ®Google Scholar
• Wattal, S., Telang, R., & Mukhopadhyay, T. (2009). Information personalization in a two-dimensional product differentiation model. Journal of Management Information Systems, 26(2), 69–95. https://doi.org/10.2753/MIS0742-1222260204
   Web of Science ®Google Scholar
• Xu, H., Luo, X. R., Carroll, J. M., & Rosson, M. B. (2011). The personalization privacy paradox: An exploratory study of decision-making process for location-aware marketing. Decision Support Systems, 51(1), 42–52. https://doi.org/10.1016/j.dss.2010.11.017
   Web of Science ®Google Scholar
• Yang, K. (2012). Consumer technology traits in determining mobile shopping adoption: An application of the extended theory of planned behavior. Journal of Retailing and Consumer Services, 19(5), 484–491. https://doi.org/10.1016/j.jretconser.2012.06.003
   Google Scholar
• Zarmpou, T., Saprikis, V., Markos, A., & Vlachopoulou, M. (2012). Modeling users’ acceptance of mobile services. Electronic Commerce Research, 12(2), 225–248. https://doi.org/10.1007/s10660-012-9092-x
   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
• Zhou, T. (2011). An empirical examination of initial trust in mobile banking. Internet Research, 21(5), 527–540. https://doi.org/10.1108/10662241111176353
   Web of Science ®Google Scholar