Skip to main content

Advertisement

SpringerLink
Log in

Modelling Determinants for the Integration of Web 2.0 Technologies into Hospitality Education: A Taiwanese Case

  • Regular Article
  • Published:
The Asia-Pacific Education Researcher Aims and scope Submit manuscript

Abstract

This study explores the applicability of various Web 2.0 technologies in hospitality education in Taiwan. The affordances of Web 2.0 technologies have dramatically changed the landscape of higher education, and hospitality education is no exception. The research presented here involved the distribution of a large-scale survey to all hospitality major students at Taiwanese universities; 839 valid responses were received and subjected to statistical analyses. The results indicated that blogs are the most popular Web 2.0 technology exploited in hospitality education, followed by Facebook. Facebook was shown to cause the greatest cognitive load for learners, although the effect size was small. Next, structural equation modelling was employed to examine nine research hypotheses, in the effort to investigate causal relationships among five proposed constructs: perceived ease of use, perceived usefulness, cognitive load, learning effectiveness and learning satisfaction. Five out of nine hypotheses were supported, showcasing, in particular, the significant effect that perceived ease of use had on perceived usefulness and on learning effectiveness. In addition, perceived usefulness positively affected cognitive load and learning effectiveness, and learning effectiveness positively affected learning satisfaction; these effects were also significant.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Amoako-Gyampah, K. (2007). Perceived usefulness, user involvement and behavioral intention: An empirical study of ERP implementation. Computers in Human Behavior, 23(3), 1232–1248. doi:10.1016/j.chb.2004.12.002.

    Article  Google Scholar 

  • Anastasi, J. S. (2007). Full semester and abbreviated summer courses: An evaluation of student performance. Teaching of Psychology, 34(1), 19–22.

    Google Scholar 

  • Antonenko, P., Paas, F., Grabner, R., & Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22(4), 425–438. doi:10.1007/s10648-010-9130-y.

    Article  Google Scholar 

  • Arquero, J. L., & Romero-Frías, E. (2013). Using social network sites in Higher Education: An experience in business studies. Innovations in Education and Teaching International, 1–12. doi:10.1080/14703297.2012.760772.

  • Beckmann, J. F. (2010). Taming a beast of burden—On some issues with the conceptualisation and operationalisation of cognitive load. Learning and Instruction, 20(3), 250–264. doi:10.1016/j.learninstruc.2009.02.024.

    Article  Google Scholar 

  • Bernard, R., Abrami, P., Lou, Y., Borokhovski, E., Wade, A., & Wozney, L. (2004). How does distance education compare with classroom instruction? A meta-analysis of empirical literature. Review of Educational Research, 74(3), 379–439.

    Article  Google Scholar 

  • Bower, M., Hedberg, J. G., & Kuswara, A. (2010). A framework for Web 2.0 learning design. Educational Media International, 47(3), 177–198.

    Article  Google Scholar 

  • Bradford, G. R. (2011). A relationship study of student satisfaction with learning online and cognitive load: Initial results. The Internet and Higher Education, 14(4), 217–226. doi:10.1016/j.iheduc.2011.05.001.

    Article  Google Scholar 

  • Byrne, B. M. (2001). Structural equation modeling with AMOS. Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Cardoso, A. P., Ferreira, M., Abrantes, J. L., Seabra, C., & Costa, C. (2011). Personal and pedagogical interaction factors as determinants of academic achievement. Procedia—Social and Behavioral Sciences, 29, 1596–1605. doi:10.1016/j.sbspro.2011.11.402.

    Article  Google Scholar 

  • Carr, S. (2000). As distance education comes of age, the challenge is keeping the students. Chronicle of Higher Education, 46(23), A39–A41.

    Google Scholar 

  • Chalmers, P. A. (2003). The role of cognitive theory in human–computer interface. Computers in Human Behavior, 19(5), 593–607. doi:10.1016/S0747-5632(02)00086-9.

    Article  Google Scholar 

  • Chesney, T. (2006). An acceptance model for useful and fun information systems. An Interdisciplinary Journal on Humans in ICT Environments, 2(2), 225–235.

    Article  Google Scholar 

  • Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers and Education, 63, 160–175. doi:10.1016/j.compedu.2012.12.003.

    Article  Google Scholar 

  • Chin, W. W., Johnson, N., & Schwarz, A. (2008). A fast Form approach to measuring technology acceptance and other constructs. MIS Quarterly, 32(4), 687–703.

    Google Scholar 

  • Chu, H.-C., Hwang, G.-J., & Tsai, C.-C. (2010). A knowledge engineering approach to developing mindtools for context-aware ubiquitous learning. Computers & Education, 54(1), 289–297. doi:10.1016/j.compedu.2009.08.023.

    Article  Google Scholar 

  • Chung, K. H. (2008). What effect do mixed sensory mode instructional formats have on both novice and experienced learners of Chinese characters? Learning and Instruction, 18(1), 96–108. doi:10.1016/j.learninstruc.2007.01.001.

    Article  Google Scholar 

  • Clark, R. E. (1994). Media will never influence learning. ETRandD-Educational Technology Research and Development, 42(2), 21–29.

    Article  Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Creswell, J. W. (2008). Research design: Qualitative, quantitative, and mixed methods approaches. Los Angeles, CA: Sage.

    Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

    Article  Google Scholar 

  • DeLeeuw, K. E., & Mayer, R. E. (2008). A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load. Journal of Educational Psychology, 100(1), 223–234. doi:10.1037/0022-0663.100.1.223.

    Article  Google Scholar 

  • Develotte, C., Guichon, N., & Vincent, C. (2010). The use of the webcam for teaching a foreign language in a desktop videoconferencing environment. ReCALL, 22(3), 293–312. doi:10.1017/S09583440100000170.

    Article  Google Scholar 

  • Drennan, J., Dennedy, J., & Pisarski, A. (2005). Factors affecting student attitudes toward flexible online learning in management education. The Journal of Educational Research, 98(6), 331–338.

  • Farrell, A. M. (2010). Insufficient discriminant validity: A comment on Bove, Pervan, Beatty, and Shiu (2009). Journal of Business Research, 63, 324–327. doi:10.1016/j.jbusres.2009.05.003.

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation model with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  • Fredericksen, E., Pickett, A., & Shea, P. (2006). Student satisfaction and perceived learning with on-line courses: Principles and examples from the SUNY learning network. Journal of Asynchronous Learning Networks, 4(2), 2–31.

    Google Scholar 

  • Hayes, A. F. (2013). Introduction to mediation, moderation and conditional process analysis. A regression-based approach. New York, NY: Guilford Press.

    Google Scholar 

  • Hu, P. J.-H., & Hui, W. (2012). Examining the role of learning engagement in technology-mediated learning and its effects on learning effectiveness and satisfaction. Decision Support Systems, 53(4), 782–792. doi:10.1016/j.dss.2012.05.014.

    Article  Google Scholar 

  • Huang, Y.-C., Backman, S. J., & Backman, K. F. (2010a). Student attitude toward virtual learning in Second Life: A Flow Theory approach. Journal of Teaching in Travel and Tourism, 10(4), 312–334. doi:10.1080/15313220.2010.525425.

    Article  Google Scholar 

  • Huang, T.-C., Huang, Y.-M., & Yu, F.-Y. (2011). Cooperative Weblog Learning in higher education: Its facilitating effects on social interaction, time lag, and cognitive load. Educational Technology and Society, 14(1), 95–106.

    Google Scholar 

  • Huang, A. F. M., Yang, S. J. H., & Hwang, G.-J. (2010b). Situational language teaching in ubiquitous learning environment. Knowledge Management and E-Learning: An International Journal, 2(3), 312–327.

    Google Scholar 

  • Johnson, R. D., Hornik, S., & Salas, E. (2008). An empirical examination of factors contributing to the creation of successful e-learning environments. International Journal of Human-Computer Studies, 66(5), 356–369. doi:10.1016/j.ijhcs.2007.11.003.

    Article  Google Scholar 

  • Jung, I., Choi, S., Lim, C., & Leem, J. (2002). Effects of different types of interaction on learning achievement, satisfaction and participation in web-based instruction. Innovations in Education and Teaching International, 39(2), 153–162.

    Article  Google Scholar 

  • Kilic, E., & Yildirim, Z. (2010). Evaluating working memory capacity and cognitive load in learning from goal based scenario centered 3D multimedia. Procedia—Social and Behavioral Sciences, 2(2), 4480–4486. doi:10.1016/j.sbspro.2010.03.715.

    Article  Google Scholar 

  • Kolfschoten, G., Lukosch, S., Verbraeck, A., Valentin, E., & de Vreede, G.-J. (2010). Cognitive learning efficiency through the use of design patterns in teaching. Computers & Education, 54(3), 652–660. doi:10.1016/j.compedu.2009.09.028.

    Article  Google Scholar 

  • Lambert, J., Kalyuga, S., & Capan, L. A. (2009). Student Perceptions and Cognitive Load: What can they tell us about e-learning Web 2.0 course design? E-Learning, 6(2), 150–163. doi:10.2304/elea.2009.6.2.150.

    Article  Google Scholar 

  • Lau, A., & Tsui, E. (2009). Knowledge management perspective on e-learning effectiveness. Knowledge-Based Systems, 22(4), 324–325. doi:10.1016/j.knosys.2009.02.014.

    Article  Google Scholar 

  • Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers and Education, 54(2), 506–516. doi:10.1016/j.compedu.2009.09.002.

    Article  Google Scholar 

  • Lee, Y.-H., Hsieh, Y.-C., & Hsu, C.-N. (2011). Adding innovation diffusion theory to the technology acceptance model: Supporting employees’ intentions to use e-learning systems. Educational Technology and Society, 14(4), 124–137.

    Google Scholar 

  • Liaw, S.–S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864–873.

  • Liaw, S.-S., & Huang, H.-M. (2012). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14–24. doi:10.1016/j.compedu.2012.07.015.

    Article  Google Scholar 

  • Liburd, J., Hjalager, A.-M., & Christensen, I.-M. F. (2011). Valuing tourism education 2.0. Journal of Teaching in Travel and Tourism, 11(1), 107–130. doi:10.1080/15313220.2011.548745.

    Article  Google Scholar 

  • Manuel, J., & Sanchez-Franco, (2009). WebCT—The quasimoderating effect of perceived affective quality on an extending technology acceptance model. Computers & Education, 54, 37–46.

    Google Scholar 

  • Martens, R., Bastiaens, T., & Kirschner, P. A. (2007). Newlearning design in distance education: The impact on student perception and motivation. Distance Education, 28(1), 81–93.

    Article  Google Scholar 

  • Merriënboer, J. G., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147–177. doi:10.1007/s10648-005-3951-0.

    Article  Google Scholar 

  • Olaniran, B. A. (2009). Culture, learning styles, and Web 2.0. Interactive Learning Environment, 17(4), 261–271. doi:10.1080/1494820903195124.

    Article  Google Scholar 

  • Paas, F., Renkel, A., & Sweller, J. (2004). Cognitive load theory: Instructional implications of the interaction between information structures and cognitive architecture. Instructional Science, 32, 1–8. doi:10.1023/B:TRUC.0000021806.17516.d0.

    Article  Google Scholar 

  • Padilla-Meléndez, A., Garrido-Moreno, A., Aguila-Obra, A., & Del Aguila-Obra, A. R. (2008). Factors affecting e-collaboration technology use among management students. Computers and Education, 5(1), 609–623.

    Article  Google Scholar 

  • Park, Y., Son, H., & Kim, C. (2012). Investigating the determinants of construction professionals’ acceptance of web-based training: An extension of the technology acceptance model. Automation in Construction, 22, 377–386. doi:10.1016/j.autcon.2011.09.016.

    Article  Google Scholar 

  • Penfold, P. (2008). Learning through the world of second life—A hospitality and tourism experience. Journal of Teaching in Travel and Tourism, 8(2), 139–160.

    Google Scholar 

  • Penfold, P., Ma, H., & Kong, W. F. (2007). Developing a virtual learning environment for teaching hotel management students. Paper presented at the IADIS International Conference e-Learning.

  • Piccoli, G., Ahmad, R., & Ives, B. (2001). Web-based virtual learning environments: A research framework and preliminary assessment of effectiveness in basic IT skills training. MIS Quarterly, 25(4), 401–426.

    Article  Google Scholar 

  • Plass, J. L., Homer, B. D., & Hayward, E. O. (2009). Design factors for educationally effective animations and simulations (Vol. 21, pp. 31–61). New York, NY: Springer.

    Google Scholar 

  • Rovai, A. P., & Barnum, K. T. (2003). On-line course effectiveness: An analysis of student interactions and perceptions of learning. Journal of Distance Learning, 18(1), 57-73.

  • Saadé, G. R., & Otrakji, A. C. (2007). First impressions last a lifetime: Effect of interface type on disorientation and cognitive load. Computers in Human Behavior, 23(1), 525–535. doi:10.1016/j.chb.2004.10.035.

    Article  Google Scholar 

  • Schoonenboom, J. (2014). Using an adapted, task-level technology acceptance model to explain why instructors in higher education intend to use some learning management system tools more than others. Computers & Education, 71, 247–256.

    Article  Google Scholar 

  • Schrader, C., & Bastiaens, T. J. (2012). The influence of virtual presence: Effects on experienced cognitive load and learning outcomes in educational computer games. Computers in Human Behavior, 28(2), 648–658. doi:10.1016/j.chb.2011.11.011.

    Article  Google Scholar 

  • Schreiber, J. B. (2008). Core reporting practices in structural equation modeling. Research in Social and Administrative Pharmacy, 4(2), 83–97. doi:10.1016/j.sapharm.2007.04.003.

    Article  Google Scholar 

  • Schumacker, R. E., & Lomax, R. G. (2004). A beginner’s guide to structural equation model (2nd ed.). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Schwamborn, A., Thillmann, H., Opfermann, M., & Leutner, D. (2011). Cognitive load and instructionally supported learning with provided and learner-generated visualizations. Computers in Human Behavior, 27(1), 89–93. doi:10.1016/j.chb.2010.05.028.

    Article  Google Scholar 

  • Stein, D. (2004). Student satisfaction depends on course structure. Online Classroom, 2(1),4–5.

  • Stonebraker, P. W., & Hazeltine, J. E. (2004). Virtual learning effectiveness: An examination of the process. The Learning Organization, 11(3), 209–225. doi:10.1108/09696470410532987.

    Article  Google Scholar 

  • Summers, J. J., Waigandt, A., & Whittaker, T. A. (2005). A comparison of student achievement and satisfaction in an online versus a traditional face-to-face statistics class. Innovative Higher Education, 29(3). doi:10.1007/s10755-005-1938-x.

  • Sun, P.-C., Tsai, R. J., Finger, G., Chen, Y.-Y., & Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers and Education, 50(4), 1183–1202. doi:10.1016/j.compedu.2006.11.007.

    Article  Google Scholar 

  • Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123–138. doi:10.1007/s10648-010-9128-5.

    Article  Google Scholar 

  • Sweller, J. (2011). Cognitive load theory and e-learning. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Artificial intelligence in education (Vol. 6738, pp. 5–6). Berlin, Heidelberg: Springer.

    Chapter  Google Scholar 

  • Sweller, J., Merrienboer, J. G. V., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296.

    Article  Google Scholar 

  • Tarhini, A., Hone, K., & Liu, X. (2013). Factors affecting students’ acceptance of e-Learning environments in developing countries: A Structural Equation Modeling approach. International Journal of Information and Education Technology, 3(1), 54–59. doi:10.7763/IJIET.2013.V3.233.

    Article  Google Scholar 

  • Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440.

    Article  Google Scholar 

  • van Merriënboer, J. G., & Ayres, P. (2005). Research on cognitive load theory and its design implications for e-learning. ETR and D, 53(3), 5–13.

    Article  Google Scholar 

  • Vasile, C., Marhan, A.-M., Singer, F. M., & Stoicescu, D. (2011). Academic self-efficacy and cognitive load in students. Procedia—Social and Behavioral Sciences, 12, 478–482. doi:10.1016/j.sbspro.2011.02.059.

    Article  Google Scholar 

  • Verhoeven, L., Schnotz, W., & Paas, F. (2009). Cognitive load in interactive knowledge construction. Learning and Instruction, 19(5), 369–375. doi:10.1016/j.learninstruc.2009.02.002.

    Article  Google Scholar 

  • Wu, I.-L., & Chen, J.-L. (2005). An extension of trust and TAM model with TPB in the initial adoption of on-line tax: An empirical study. International Journal of Human-Computer Studies, 62(6), 784–808. doi:10.1016/j.ijhcs.2005.03.003.

    Article  Google Scholar 

  • Young, M. R., Klemz, B. R., & Murphy, J. W. (2003). Enhancing learning outcomes: The effects of instructional technology, learning styles, instructional methods, and student behavior. Journal of Marketing Education, 25(2), 130–142. doi:10.1177/0273475303254004.

    Article  Google Scholar 

  • Zumbach, J., & Mohraz, M. (2008). Cognitive load in hypermedia reading comprehension: Influence of text type and linearity. Computers in Human Behavior, 24(3), 875–887. doi:10.1016/j.chb.2007.02.015.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Kaohsiung University of Hospitality and Tourism, No. 1, Songhe Road, Hsiao Kang District, Kaohsiung City, Taiwan

    Liwei Hsu

Authors
  1. Liwei Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liwei Hsu.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 18 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hsu, L. Modelling Determinants for the Integration of Web 2.0 Technologies into Hospitality Education: A Taiwanese Case. Asia-Pacific Edu Res 24, 625–633 (2015). https://doi.org/10.1007/s40299-014-0208-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40299-014-0208-z

Keywords

Search

Navigation