Simulation-Based Learning Acceptance Model (SBL-AM): Expanding the Technology Acceptance Model (TAM) into Hospitality Education

References

• Aebersold, M. (2018). Simulation-based learning: No longer a novelty in undergraduate education. OJIN: The Online Journal of Issues in Nursing, 23(2), 1–13. https://doi.org/10.3912/OJIN.Vol23No02PPT39
   Google Scholar
• Agarwal, R., & Venkatesh, V. (2002). Assessing a firm’s web presence: A heuristic evaluation procedure for the measurement of usability. Information Systems Research, 13(2), 168–186. https://doi.org/10.1287/isre.13.2.168.84
   Web of Science ®Google Scholar
• Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.
   Google Scholar
• Al-Azawei, A., Parslow, P., & Lundqvist, K. (2017). Investigating the effect of learning styles in a blended e-learning system: An extension of the technology acceptance model (TAM). Australasian Journal of Educational Technology, 33(2). https://doi.org/10.14742/ajet.2741
   Google Scholar
• Ali, F., Rasoolimanesh, S. M., Sarstedt, M., Ringle, C. M., & Ryu, K. (2018). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of Contemporary Hospitality Management, 30(1), 514–538. https://doi.org/10.1108/IJCHM-10-2016-0568
   Web of Science ®Google Scholar
• Allchin, L. (2006). Caring for the dying: Nursing student perspectives. Journal of Hospice & Palliative Nursing, 8(2), 112–117. https://doi.org/10.1097/00129191-200603000-00015
   Google Scholar
• Austin, E. N., Bastepe-Gray, S. E., Nelson, H. W., Breitenbach, J., Ogle, K. T., Durry, A., Green, S. D., Crabtree, L. A., & Haluska, M. (2014). Pediatric mass-casualty education: Experiential learning through university-sponsored disaster simulation. Journal of Emergency Nursing, 40(5), 428–433. https://doi.org/10.1016/j.jen.2014.05.015
   PubMed Web of Science ®Google Scholar
• Benbasat, I., & Zmud, R. W. (2003). The identity crisis within the is discipline: Defining and communicating the discipline’s core properties. MIS Quarterly, 27(2), 183–194. https://doi.org/10.2307/30036527
   Web of Science ®Google Scholar
• Bujisic, M., Bilgihan, A., & Parsa, H. G. (2016). The role of simulation software in enhancing students’ academic performance: A restaurant management case. International Journal of Technology Enhanced Learning, 8(1), 77–83. https://doi.org/10.1504/IJTEL.2016.075954
   Google Scholar
• Cavusoglu, M. (2019). Online and self-directed learning readiness among hospitality and tourism college students and industry professionals. University of South Florida.
   Google Scholar
• Cenfetelli, R. T., & Bassellier, G. (2009). Interpretation of formative measurement in information systems research. MIS Quarterly, 33(4), 689–708. https://doi.org/10.2307/20650323
   Web of Science ®Google Scholar
• Chen, B., Sivo, S., Seilhamer, R., Sugar, A., & Mao, J. (2013). User acceptance of mobile technology: A campus-wide implementation of blackboard’s mobile learn application. Journal of Educational Computing Research, 49(3), 327–343. https://doi.org/10.2190/EC.49.3.c
   Web of Science ®Google Scholar
• Chernikova, O., Heitzmann, N., Stadler, M., Holzberger, D., Seidel, T., & Fischer, F. (2020). Simulation-based learning in higher education: A meta-analysis. Review of Educational Research, 90(4), 499–541. https://doi.org/10.3102/0034654320933544
   Web of Science ®Google Scholar
• Chin, W. W. (1998). Issues and opinions on structural equation modelling. MIS Quarterly, 22(1), 7–16. https://www.jstor.org/stable/249674
   Web of Science ®Google Scholar
• Chopra, G., Madan, P., Jaisingh, P., & Bhaskar, P. (2019). Effectiveness of e-learning portal from students’ perspective. Interactive Technology and Smart Education, 16(2), 94–116. https://doi.org/10.1108/ITSE-05-2018-0027
   Web of Science ®Google Scholar
• Chung, G. K., Harmon, T. C., & Baker, E. L. (2001). The impact of a simulation-based learning design project on student learning. IEEE Transactions on Education, 44(4), 390–398. https://doi.org/10.1109/13.965789
   Web of Science ®Google Scholar
• Cidral, W. A., Oliveira, T., DiFelice, M., & Aparicio, M. (2018). E-learning success determinants: Brazilian empirical study. Computers & Education, 122, 273–290. https://doi.org/10.1016/j.compedu.2017.12.001
   Web of Science ®Google Scholar
• Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
   Web of Science ®Google Scholar
• Cox, C. (2018). A simulation approach to hotel revenue management training. Journal of Revenue and Pricing Management, 17(5), 356–364. https://doi.org/10.1057/s41272-018-00159-1
   Web of Science ®Google Scholar
• Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475–487. https://doi.org/10.1006/imms.1993.1022
   Google Scholar
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
   Web of Science ®Google Scholar
• De Freitas, S. (2018). Are games effective learning tools? A review of educational games. Journal of Educational Technology & Society, 21(2), 74–84. http://www.jstor.org/stable/26388380
   Web of Science ®Google Scholar
• Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5–22. https://doi.org/10.1177/0047239520934018
   Google Scholar
• Di Vaio, A., Boccia, F., Landriani, L., & Palladino, R. (2020). Artificial intelligence in the agrifood system: Rethinking sustainable business models in the COVID-19 scenario. Sustainability, 12(12), 4851. https://doi.org/10.3390/su12124851
   Web of Science ®Google Scholar
• Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(2), 269–277. https://doi.org/10.1509/jmkr.38.2.269.18845
   Web of Science ®Google Scholar
• Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297–316. https://doi.org/10.25300/MISQ/2015/39.2.02
   Web of Science ®Google Scholar
• Esteban-Millat, I., Martínez-López, F. J., Pujol-Jover, M., Gázquez-Abad, J. C., & Alegret, A. (2018). An extension of the technology acceptance model for online learning environments. Interactive Learning Environments, 26(7), 895–910. https://doi.org/10.1080/10494820.2017.1421560
   Web of Science ®Google Scholar
• Estriegana, R., Medina-Merodio, J. A., & Barchino, R. (2019). Student acceptance of virtual laboratory and practical work: An extension of the technology acceptance model. Computers & Education, 135, 1–14. https://doi.org/10.1016/j.compedu.2019.02.010
   Web of Science ®Google Scholar
• Fornell, C. G., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
   Web of Science ®Google Scholar
• Geden, M., Emerson, A., Carpenter, D., Rowe, J., Azevedo, R., & Lester, J. (2020). Predictive student modeling in game-based learning environments with word embedding representations of reflection. International Journal of Artificial Intelligence in Education, 31(1), 1–23. https://doi.org/10.1007/s40593-020-00220-4
   Google Scholar
• Gibson, S. G., Harris, M. L., & Colaric, S. M. (2008). Technology acceptance in an academic context: Faculty acceptance of online education. Journal of Education for Business, 83(6), 355–359. https://doi.org/10.3200/JOEB.83.6.355-359
   Google Scholar
• Go, H., Kang, M., & Suh, S. C. (2020). Machine learning of robots in tourism and hospitality: Interactive technology acceptance model (iTAM)–cutting edge. Tourism Review, 75(4), 625–636. https://doi.org/10.1108/TR-02-2019-0062
   Web of Science ®Google Scholar
• Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
   Web of Science ®Google Scholar
• Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
   Web of Science ®Google Scholar
• Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage.
   Google Scholar
• Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
   Google Scholar
• Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
   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
• Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing. Emerald Group Publishing Limited.
   Google Scholar
• Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of Research on Technology in Education, 43(4), 343–367. https://doi.org/10.1080/15391523.2011.10782576
   Google Scholar
• Hsiao, H. S., Tsai, F. H., & Hsu, I. Y. (2020). Development and evaluation of a computer detective game for microbial food safety education. Journal of Educational Computing Research, 58(6), 1144–1160. https://doi.org/10.1177/0735633120924924
   Web of Science ®Google Scholar
• Hui, A. N. N., Eason, M. S., Cheung, R. C., Lai, L. C., Lau, D., & Lam, T. (2021). The relationships among simulation-based learning, creativity, and the learning approach in higher education. In Creative and collaborative learning through immersion (pp. 89–104). Springer.
   Google Scholar
• Hung, C. C., Kao, H. F. S., Liu, H. C., Liang, H. F., Chu, T. P., & Lee, B. O. (2021). Effects of simulation-based learning on nursing students’ perceived competence, self-efficacy, and learning satisfaction: A repeat measurement method. Nurse Education Today, 97, 104725. https://doi.org/10.1016/j.nedt.2020.104725
   PubMedGoogle Scholar
• Huni, M. S. P., Shoko, M. J., Shoko, M. T., Dube, M. S. D. G., & Nyon, M. T. (2020). An empirical investigation of the use and acceptance of social media as a marketing tool by small scale farmers in Eastern Zimbabwe: The technology acceptance model perspective. EPRA International Journal of Socio-Economic and Environmental Outlook, 7(4), 1–15. https://doi.org/10.36713/epra0314
   Google Scholar
• Hussein, A., & Saad, M. (2016). Perceived risk and behavioral determinants of using internet banking in Egypt. Journal of Behavioural Economics, Finance, Entrepreneurship, Accounting and Transport, 4(3), 40–48.
   Google Scholar
• IAU. (2020). Covid-19: Higher education challenges and responses. Retrieved November 6, 2020, from https://www.iau-aiu.net/Covid-19-Higher-Education-challenges-and-responses
   Google Scholar
• Jacques, P. H., Deale, C. S., & Garger, J. (2006). Formal and informal applications of the technology acceptance model in the hospitality classroom. Journal of Hospitality & Tourism Education, 18(3), 67–75. https://doi.org/10.1080/10963758.2006.10696865
   Google Scholar
• Keeker, K. (1997). Improving web site usability and appeal. http://jepelet.free.fr/studies/MBA/design/s4/lectures/Improving%20Web%20Site%20Usability%20and%20Appeal.pdf
   Google Scholar
• Kim, B. J., Ke, F., Moon, J., & West, L. (2021). Designing dynamic learning supports for game and simulation-based learning in STEM education. In Game-based learning across the disciplines (pp. 189–212). Springer. https://doi.org/10.1007/978-3-030-75142-5_8
   Google Scholar
• Kwon, J. M., Bae, J. I. S., & Blum, S. C. (2013). Mobile applications in the hospitality industry. Journal of Hospitality and Tourism Technology, 1(4), 81–92. https://doi.org/10.1108/17579881311302365
   Google Scholar
• Lateef, F. (2010). Simulation-based learning: Just like the real thing. Journal of Emergencies, Trauma, and Shock, 3(4), 348. https://doi.org/10.4103/0974-2700.70743
   PubMedGoogle Scholar
• Lemay, D. J., Morin, M. M., Bazelais, P., & Doleck, T. (2018). Modeling students’ perceptions of simulation-based learning using the technology acceptance model. Clinical Simulation in Nursing, 20, 28–37. https://doi.org/10.1016/j.ecns.2018.04.004
   Web of Science ®Google Scholar
• Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & Education, 54(2), 600–610. https://doi.org/10.1016/j.compedu.2009.09.009
   Web of Science ®Google Scholar
• Manis, K. T., & Choi, D. (2019). The virtual reality hardware acceptance model (VR-HAM): Extending and individuating the technology acceptance model (TAM) for virtual reality hardware. Journal of Business Research, 100, 503–513. https://doi.org/10.1016/j.jbusres.2018.10.021
   Web of Science ®Google Scholar
• Matute-Vallejo, J., & Melero-Polo, I. (2019). Understanding online business simulation games: The role of flow experience, perceived enjoyment and personal innovativeness. Australasian Journal of Educational Technology, 35(3). https://doi.org/10.14742/ajet.3862
   Web of Science ®Google Scholar
• McDonald, R. P. (1996). Path analysis with composite variables. Multivariate Behavioral Research, 31(2), 239–270. https://doi.org/10.1207/s15327906mbr3102_5
   PubMed Web of Science ®Google Scholar
• McGrath, G. M., Blaer, M., Williams, M., Wilson-Evered, E., & Whitelaw, P. (2020). A suite of system dynamics T&L simulations and games based upon the Australian qualifications framework (AQF). Journal of Hospitality & Tourism Education, 32(4), 194–205. https://doi.org/10.1080/10963758.2019.1685890
   Web of Science ®Google Scholar
• Meakim, C., Boese, T., Decker, S., Franklin, A. E., Gloe, D., Lioce, L., Borum, J. C. (2013). Standards of best practice: Simulation standard I: Terminology. Clinical Simulation in Nursing, 9(6), S3–11. https://doi.org/10.1016/j.ecns.2013.04.001
   Web of Science ®Google Scholar
• Merchant, Z., Goetz, E. T., Cifuentes, L., Keeney-Kennicutt, W., & Davis, T. J. (2014). Effectiveness of virtual reality-based instruction on students’ learning outcomes in K-12 and higher education: A meta-analysis. Computers & Education, 70, 29–40. https://doi.org/10.1016/j.compedu.2013.07.033
   Web of Science ®Google Scholar
• Monecke, A., & Leisch, F. (2012). semPLS: Structural equation modeling using partial least squares. Journal of Statistical Software, 48(3), 1–32. https://doi.org/10.18637/jss.v048.i03
   Web of Science ®Google Scholar
• Moratis, L., Hoff, J., & Reul, B. (2006). A dual challenge facing management education: Simulation-based learning and learning about CSR. Journal of Management Development, 25(3), 213–231. https://doi.org/10.1108/02621710610648150
   Google Scholar
• Navarro, E. O., & van der Hoek, A. (2004). Software process modeling for an educational software engineering simulation game. Software Process Improvement and Practice, 10(3), 311 –325.
   Google Scholar
• Ndubisi, N. (2006). Factors of online learning adoption: A comparative juxtaposition of the theory of planned behaviour and the technology acceptance model. International Journal on E-Learning, 5(4), 571–591. https://www.learntechlib.org/primary/p/6173/
   Google Scholar
• Ngai, E. W., Poon, J. K. L., & Chan, Y. H. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250–267. https://doi.org/10.1016/j.compedu.2004.11.007
   Web of Science ®Google Scholar
• Nubi, O. J., & Vincent, O. R. (2020). Virtual reality: A pedagogical model for simulation-based learning. In 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS) (pp. 1–6). IEEE. https://doi.org/10.1109/ICMCECS47690.2020.240895
   Google Scholar
• O’Shea, M. C., Palermo, C., Rogers, G. D., & Williams, L. T. (2020). Simulation-based learning experiences in dietetics programs: A systematic review. Journal of Nutrition Education and Behavior, 52(4), 429–438. https://doi.org/10.1016/j.jneb.2019.06.015
   PubMed Web of Science ®Google Scholar
• Pan, C. C., Sivo, S. A., & Brophy, J. (2003). Students’ attitude in a web-enhanced hybrid course: A structural equation modeling inquiry. Journal of Educational Media and Library Sciences, 41(2), 181–194.
   Google Scholar
• Pando-Garcia, J., Periañez-Cañadillas, I., & Charterina, J. (2016). Business simulation games with and without supervision: An analysis based on the TAM model. Journal of Business Research, 69(5), 1731–1736. https://doi.org/10.1016/j.jbusres.2015.10.046
   Web of Science ®Google Scholar
• Pratt, M. A., & Hahn, S. (2015). Effects of simulation on student satisfaction with a capstone course. Journal of Hospitality & Tourism Education, 27(1), 39–46. https://doi.org/10.1080/10963758.2015.998911
   Web of Science ®Google Scholar
• Pratt, M. A., & Hahn, S. (2016). Enhancing hospitality student learning through the use of a business simulation. Journal of Hospitality, Leisure, Sport & Tourism Education, 19, 10–18. https://doi.org/10.1016/j.jhlste.2016.05.001
   Web of Science ®Google Scholar
• Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145, 103732.
   Web of Science ®Google Scholar
• Ringle, C. M., Wende, S., & Becker, J. -M. (2015). SmartPLS 3. SmartPLS GmbH. http://www.smartpls.com
   Google Scholar
• Rojas-Salazar, A. J. (2022). Game-based learning of data structures based on analogies: Learning gains and intrinsic motivation in higher education environments.
   Google Scholar
• Sanchez-Franco, M. J. (2010). WebCT–The quasimoderating effect of perceived affective quality on an extending technology acceptance model. Computers & Education, 54(1), 37–46. https://doi.org/10.1016/j.compedu.2009.07.005
   Web of Science ®Google Scholar
• Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial least squares structural equation modeling. Handbook of Market Research, 26, 1–40. https://doi.org/10.1007/978-3-319-57413-4_15
   Google Scholar
• Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
   Web of Science ®Google Scholar
• Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2020). Effects of COVID-19 in E-learning on higher education institution students: The group comparison between male and female. Quality & Quantity, 55(3), 1–22.
   Google Scholar
• Sivo, S. A., Ku, C. H., & Acharya, P. (2018). Understanding how university student perceptions of resources affect technology acceptance in online learning courses. Australasian Journal of Educational Technology, 34(4). https://doi.org/10.14742/ajet.2806
   Google Scholar
• Sivo, S. A., Pan, C. C., & Brophy, J. (2004). Temporal cross-lagged effects between subjective norms and students’ attitudes regarding the use of technology. Journal of Educational Media and Library Sciences, 42(1), 63–74.
   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
• Steuer, J. (1992). Defining virtual reality: Dimensions determining telepresence. Journal of Communication, 42(4), 73–93.
   Web of Science ®Google Scholar
• Surry, D. W., Ensminger, D. C., & Haab, M. (2005). A model for integrating instructional technology into higher education. British Journal of Educational Technology, 36(2), 327–329. https://doi.org/10.1111/j.1467-8535.2005.00461.x
   Web of Science ®Google Scholar
• Sykes, D., & Roy, J. (2017). A review of internship opportunities in online learning: Building a new conceptual framework for a self-regulated internship in hospitality. The Journal of Distance Education/Revue de L’ducation Distance, 32(1). https://www.learntechlib.org/p/183658/
   Google Scholar
• Tatnall, A. (2013). Technological innovation and the adoption of ICT in Thai universities: A TAM study re-analysed using ANT. International Journal of Actor-Network Theory and Technological Innovation (IJANTTI), 5(4), 27–46. https://doi.org/10.4018/ijantti.2013100103
   Google Scholar
• Teo, T., & Zhou, M. (2014). Explaining the intention to use technology among university students: A structural equation modeling approach. Journal of Computing in Higher Education, 26(2), 124–142. https://doi.org/10.1007/s12528-014-9080-3
   Web of Science ®Google Scholar
• Thomas, L. J., Parsons, M., & Whitcombe, D. (2019). Assessment in smart learning environments: Psychological factors affecting perceived learning. Computers in Human Behavior, 95, 197–207. https://doi.org/10.1016/j.chb.2018.11.037
   Web of Science ®Google Scholar
• Venkatesan, L., & Joshi, P. (2015). Textbook of nursing education-E-Book. Elsevier Health Sciences.
   Google Scholar
• Venkatesh, V., & Ramesh, V. (2006). Web and wireless site usability: Understanding differences and modeling use. MIS Quarterly, 30(1), 181–206. https://doi.org/10.2307/25148723
   Web of Science ®Google Scholar
• Vinzi, V. E., Chin, W. W., Henseler, J., & Wang, H. (2010). Handbook of partial least squares (Vol. 201). Springer. https://doi.org/10.1007/978-3-540-32827-8_1
   Google Scholar
• Wong, E., Leung, R., & Law, R. (2020). Significance of the dimensions and attributes of hotel mobile website from the perceptions of users. International Journal of Hospitality & Tourism Administration, 21(1), 15–37. https://doi.org/10.1080/15256480.2018.1429338
   Web of Science ®Google Scholar
• Yeou, M. (2016). An investigation of students’ acceptance of Moodle in a blended learning setting using technology acceptance model. Journal of Educational Technology Systems, 44(3), 300–318. https://doi.org/10.1177/0047239515618464
   Google Scholar
• Yu, H., Sirsat, S. A., & Madera, J. M. (2018). Enhancing hospitality students’ motivation to learn food safety knowledge using tablet personal computer (TPC)-based simulation game class activity. Journal of Hospitality, Leisure, Sport & Tourism Education, 23, 82–94. https://doi.org/10.1016/j.jhlste.2018.08.002
   Web of Science ®Google Scholar
• Zakrzewski, C., Feinstein, A. H., & Sammons, G. (2005). A new approach to CAI: Online applications for procedural based activities. Journal of Hospitality & Tourism Education, 17(1), 48–55. https://doi.org/10.1080/10963758.2005.10696815
   Google Scholar