This paper aims to investigate customer intention to adopt online mobile hotel booking. This study integrated perceived cost as moderating effect in the Technology Acceptance Model (TAM). A total of 386 valid responses were collected from individuals that use mobile phone to book a hotel. Structural Equation Modelling (SEM) through AMOS and PROCESS was applied in the data analysis. Results from the analysis confirm that only perceived usefulness and perceived enjoyment significantly affect customer behavioural intention to use mobile hotel booking. Moreover, only perceived enjoyment was found to be moderated by perceived cost toward intention to tested variables. The finding also implied that TAM is still valid to use in the research to examine mobile booking technology acceptance in the hotel industry. The results could be used as a benchmark for future research in the field of technology. This study also provides necessary information for key players in the hospitality industry specifically for those that rely upon the mobile platform as their business channel.
Ab Hamid, M. R., Sami, W., & Sidek, M. H. (2017). Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion. Journal of Physics: Conference Series, 890(1). https://doi.org/10.1088/1742-6596/890/1/012163
Abdullah, D., Jayaraman, K., & Kamal, S. B. M. (2016). A Conceptual Model of Interactive Hotel Website: The Role of Perceived Website Interactivity and Customer Perceived Value Toward Website Revisit Intention. Procedia Economics and Finance, 37(16), 170–175. https://doi.org/10.1016/s2212-5671(16)30109-5
Abdullah, D., Jayaraman, K., Shariff, D. N., Bahari, K. A., & Nor, N. M. (2016). The Effects of Perceived Interactivity, Perceived Ease of Use and Perceived Usefulness on Online Hotel Booking Intention: A Conceptual Framework. International Academic Research Journal of Social Science, 3(1).
Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly: Management Information Systems, 16(2). https://doi.org/10.2307/249577
Ajzen, I. (1992). The theory of planned behaviour. Organizational Behaviour and Human Decision Processes, 50, 179–211. https://doi.org/10.1080/10410236.2018.1493416
Al-Debei, M. M., Akroush, M. N., & Ashouri, M. I. (2015). Consumer attitudes towards online shopping: The effects of trust, perceived benefits, and perceived web quality. Internet Research, 25(5), 707–733. https://doi.org/10.1108/IntR-05-2014-0146
Awang, Z., Hui, L. S., & Zainuddin, N. F. S. (2018). Pendekatan mudah SEM - Structural equation modelling. MPWS Rich Resources Sdn. Bhd.
Bae, S., Mo Kwon, J., & 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
Bakar, A. R. A., & Hashim, F. (2008). The determinants of online hotel reservations among university staffs. Innovation and Knowledge Management in Business Globalization: Theory and Practice - Proceedings of the 10th International Business Information Management Association Conference, 1–2, 682–690.
Chen, J. V., Yen, D. C., & Chen, K. (2009). The acceptance and diffusion of the innovative smart phone use: A case study of a delivery service company in logistics. Information & Management, 46(4), 241–248.
https://doi.org/https://doi.org/10.1016/j.im.2009.03.001
Dabholkar, P. A., & Bagozzi, R. P. (2002). An attitudinal model of technology-based self-service: Moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science, 30(3). https://doi.org/10.1177/0092070302303001
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
https://doi.org/10.1016/j.cell.2017.08.036
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology, 22(14). https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
Department of Statistics Malaysia. (2020). ICT use and access by individuals and households survey report Malaysia 2019. Department of Statistics Malaysia.
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley.
Gao, S., Krogstie, J., & Siau, K. (2014). Adoption of mobile information services: An empirical study. Mobile Information Systems, 10(2). https://doi.org/10.3233/MIS-130176
Gurtner, S., Reinhardt, R., & Soyez, K. (2014). Designing mobile business applications for different age groups. Technological Forecasting and Social Change, 88. https://doi.org/10.1016/j.techfore.2014.06.020
Ha, I., Yoon, Y., & Choi, M. (2007). Determinants of adoption of mobile games under mobile broadband wireless access environment. Information & Management, 44(3), 276–286. https://doi.org/https://doi.org/10.1016/j.im.2007.01.001
Hahn, S., Yoon, J. H., & Kim, J. M. (2014). Extending the technology acceptance model to examine the intention to use tourism applications on smartphone. In Hotel Management Research.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: Global edition. NJ: Pearson Higher Education Upper Saddle River.
Hair, J. J. F., Hult, G., Ringle, C., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). SAGE Publication, Inc. https://doi.org/10.1080/1743727x.2015.1005806
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Press.
Hayes, A. F. (2018). Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. Communication Monographs, 85(1), 4–40. https://doi.org/10.1080/03637751.2017.1352100
Ibrahim, R., Leng, N. S., Yusoff, R. C. M., Samy, G. N., Masrom, S., & Rizman, Z. I. (2017). E-Learning Acceptance Based on Technology Acceptance Mode (TAM). Journal of Fundamental and Applied Sciences ISSN, 9(4S), 871–889. https://doi.org/10.4314/jfas.v9i4S.50
Jun, K., Yoon, B., Lee, S., & Lee, D. S. (2022). Factors influencing customer decisions to use online food delivery service during the covid-19 pandemic. Foods, 11(1), 1–15. https://doi.org/10.3390/foods11010064
K, P. (2014). Continuance Intention to Use Facebook: A Study of Perceived Enjoyment and TAM. Bonfring International Journal of Industrial Engineering and Management Science, 4(1). https://doi.org/10.9756/bijiems.4794
Khalid, N. (2014). The role of perceived usefulness and perceived enjoyment in assessing Students’ intention to use LMS using 3-Tum. Global Summit on Education GSE, 2014(June 2018), 425–432.
Kim, J. (Sunny). (2016). An extended technology acceptance model in behavioral intention toward hotel tablet apps with moderating effects of gender and age. International Journal of Contemporary Hospitality Management, 28(8), 1535–1553. https://doi.org/10.1108/IJCHM-06-2015-0289
Ko, Y.-K., & Kim, K.-H. (2011). Analysis on the Factors that Affect the User’s Intention of Reusing Mobile App-based Tourism Contents. The Journal of the Korea Contents Association, 11(12). https://doi.org/10.5392/jkca.2011.11.12.844
Latip, M. S. A., Tamrin, M., Noh, I., Rahim, F. A., & Latip, S. N. N. A. (2022). Factors affecting e-learning acceptance among students: The moderating effect of self-efficacy. International Journal of Information and Education Technology, 12(2), 116–122. https://doi.org/10.18178/ijiet.2022.12.2.1594
Le, O. T. T., & Cao, Q. M. (2020). Examining the technology acceptance model using cloud-based accounting software of Vietnamese enterprises. Management Science Letters, 10(12), 2781–2788. https://doi.org/10.5267/j.msl.2020.4.032
Linton, H., & Kwortnik, R. J. (2015). The Mobile Revolution Is Here: Are You Ready?
Luarn, P., & Lin, H. H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6). https://doi.org/10.1016/j.chb.2004.03.003
Mo Kwon, J., Bae, J. (Stephanie), & Blum, S. C. (2013). Mobile applications in the hospitality industry. Journal of Hospitality and Tourism Technology, 4(1), 81–92. https://doi.org/10.1108/17579881311302365
Mohamad, M. A., Amron, M. T., & Noh, M. N. H. (2021). Assessing the Acceptance of E-Learning via Technology Acceptance Model (TAM). 2021 6th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2021, 2021. https://doi.org/10.1109/ICRAIE52900.2021.9704019
Suki, M. N., & Suki, M. N. (2017). Flight ticket booking app on mobile devices: Examining the determinants of individual intention to use. Journal of Air Transport Management, 62(July), 146–154. https://doi.org/10.1016/j.jairtraman.2017.04.003
Morosan, C. (2014). Toward an integrated model of adoption of mobile phones for purchasing ancillary services in air travel. International Journal of Contemporary Hospitality Management, 26(2). https://doi.org/10.1108/IJCHM-11-2012-0221
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/https://doi.org/10.1016/j.tourman.2015.06.004
Nelwan, J. Z. C., Yasa, N. N. K., Sukaatmadja, I. P. G., & Ekawati, N. W. (2021). Antecedent behaviour and its implication on the intention to reuse the internet banking and mobile services. International Journal of Data and Network Science, 5(3), 451–464. https://doi.org/10.5267/j.ijdns.2021.4.003
Nguyen, D. (2015). Understanding Perceived Enjoyment and Continuance Intention in Mobile Games. ICFAI Journal of Systems.
Ooi, K.-B., & Tan, G. W.-H. (2016). Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Systems with Applications, 59, 33–46. https://doi.org/https://doi.org/10.1016/j.eswa.2016.04.015
Ozbek, V., Gunalan, M., Koc, F., Sahin, N. K., & Kas, E. (2015). The Effects of Perceived Risk and Cost on Technology Acceptance: A Study on Tourists’ Use of Online Booking. Celal Bayar Universitesi Sosyal Bilimler Dergisi, 13(2). https://doi.org/10.18026/cbusos.49782
Ozturk, A. B., Bilgihan, A., Nusair, K., & Okumus, F. (2015). Mobile Hotel Booking Technology in the Hotel Industry. In The 3rd International Academics Conference on Social Sciences (pp. 295–301).
Rodzi, F. N. A., Nasir, E. A. M., Azmi, A. L. M., Abdullah, D., Azmi, A., & Kamal, S. B. M. (2016). The Role of Compatibility, Information Quality and e-Service Quality in Predicting Mobile Hotel Booking Adoption: A Conceptual Framework. International Academic Research Journal of Business and Technology, 2(2), 123–128.
Saad, O., & Rana, E. M. (2019). Cloud Computing Adoption for Software Engineering Learning Environment: Set of Guidelines derived through Primary Research. September.
Saade, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information and Management, 42(2), 317–327.
https://doi.org/10.1016/j.im.2003.12.013
Saito, T., Takahashi, A., Koide, N., & Ichifuji, Y. (2019). Application of online booking data to hotel revenue management. International Journal of Information Management, 46, 37–53. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2018.11.003
Saw, S. L., Goh, Y. N., & Isa, S. M. (2015). Exploring consumers’ intention toward online hotel reservations: Insights from Malaysia. Problems and Perspectives in Management, 13(2), 249–257.
Sekaran, U., & Bougie, R. (2016). Research Methods For Business: A Skill Building Approach, 7th Edition. John Wiley & Sons Ltd.
Shaw, N. (2016). Adoption of Smartphone Apps by Hotel Guests: The Roles of Trust and Word of Mouth BT - HCI in Business, Government, and Organizations: Information Systems (F. F.-H. Nah & C.-H. Tan, Eds.; pp. 457–468). Springer International Publishing.
Su, P., Wang, L., & Yan, J. (2018). How users’ Internet experience affects the adoption of mobile payment: a mediation model. Technology Analysis and Strategic Management, 30(2), 186–197. https://doi.org/10.1080/09537325.2017.1297788
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2).
https://doi.org/10.1287/isre.6.2.144
Thong, J. Y. L., Hong, S. J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human Computer Studies, 64(9). https://doi.org/10.1016/j.ijhcs.2006.05.001
Tian, Z., Shi, Z., & Cheng, Q. (2021). Examining the antecedents and consequences of mobile travel app engagement. PLOS ONE, 16(3), e0248460.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3). https://doi.org/10.2307/30036540
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), 157–178. https://doi.org/10.2307/41410412
Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: The case for an augmented technology acceptance model. Information and Management, 41(6). https://doi.org/10.1016/j.im.2003.08.011
Wang, Y.-S., Lin, H.-H., & Luarn, P. (2006). Predicting consumer intention to use mobile service. Information Systems Journal, 16(2), 157–179.
https://doi.org/https://doi.org/10.1111/j.1365-2575.2006.00213.x
Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information and Management, 42(5), 719–729. https://doi.org/10.1016/j.im.2004.07.001
Xue, P. (2019). Hotel Online Booking Decisions Based On Price Complexity, Alternative Attractiveness and Confusion [University of Guelph].
https://doi.org/10.1016/j.jhtm.2020.08.013
Yang, Y., Zhong, Z., & Zhang, M. (2013). Predicting Tourists Decisions to Adopt Mobile Travel Booking. International Journal of U- and e- Service, Science and Technology, 6(6), 9–20. https://doi.org/10.14257/ijunesst.2013.6.6.02
