Factors affecting the behavioral intention to use e-learning of tourism employees in the Central Coast region of Vietnam
Vol. 130 No. 5A (2021), Original Article
Vol. 130 No. 5A (2021)

Factors affecting the behavioral intention to use e-learning of tourism employees in the Central Coast region of Vietnam

Original Article
https://doi.org/10.26459/hueunijed.v130i5A.6323
Published 2021-06-30
Nguyen Thi Minh Nghia
School of Hospitality and Tourism, Hue University, 22 Lam Hoang St., Hue, Vietnam
Tran Huu Tuan
School of Hospitality and Tourism, Hue University, 22 Lam Hoang St., Hue, Vietnam
Nguyen Tai Phuc
University of Economics, Hue University, 99 Ho Dac Di St., Hue, Vietnam
PDF (Vietnamese)

Keywords

e-learning
employees
behavioral intention
structural model
tourism e-learning
lao động
du lịch
ý định hành vi
mô hình cấu trúc

Abstract

E-learning is a modern approach to education based on technology that can provide tourism employees a flexible way to learn and can decide their own learning pace. It helps respond to the high work intensity and continuous training requirements of this industry. This study aims to explore the factors affecting the behavioral intention to use e-learning programs of tourism employees from the perspective of the technology acceptance model (TAM). The study conducted a survey of 712 tourism employees in the Central Coast region with the PLS-SEM structural modeling method and found that factors including (1) perceived playfulness and (2) attitude toward e-learning affect the behavioral intention to use e-learning. The research results are used to suggest the policy implications for tourism training institutions and tourism enterprises related to the development of e-learning programs for tourism employees in the future.

https://doi.org/10.26459/hueunijed.v130i5A.6323
PDF (Vietnamese)

References

  1. Sigala, M. (2002), The evolution of internet pedagogy: Benefits for tourism and hospitality education, Journal of Hospitality, Leisure, Sport and Tourism Education, 1(2), 29–45.
  2. Navigos Group Vietnam JSC (2021), Đào tạo và Phát triển trong doanh nghiệp: Thực trạng và xu hướng trong thời kỳ Chuyển đổi số. Truy cập ngày 11/6/2020 từ https://www.navigosgroup.com/navigos-group-phat-hanh-bao-cao-dao-tao-va-phat-trien-trong-doanh-nghiep-thuc-trang-va-xu-huong-trong-thoi-ky-chuyen-doi/.
  3. Leem, J., & Lim, B. (2007), The current status of e-learning and strategies to enhance educational competitiveness in Korean higher education, International Review of Research in Open and Distance Learning, 8(1), 1–18.
  4. Mehra, V., & Omidian, F. (2012), Development an instrument to measure university students’ attitude towards e-learning, Turkish Online Journal of Distance Education, 13(1), 34–51.
  5. Park, S. Y. (2009), An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning, Journal of Educational Technology & Society, 12(3), 150–162.
  6. Selim, H. M. (2003), An empirical investigation of student acceptance of course websites. Computers & Education, 40(4), 343–360.
  7. Lee, M. J., Huh, C., & Jones, M. F. (2016), Investigating quality dimensions of hospitality higher education: From students’ perspective, Journal of Hospitality & Tourism Education, 28(2), 95–106.
  8. Salloum, S. A., Alhamad, A. Q. M., Al-Emran, M., Monem, A. A., & Shaalan, K. (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model, IEEE Access, 7, 128445–128462.
  9. Cantoni, L., Kalbaska, N., & Inversini, A. (2009), eLearning in tourism and hospitality: a map, Journal of Hospitality, Leisure, Sport and Tourism Education, 8(2), 148–156.
  10. Tarhini, A., Scott, M., Sharma, S., & Abbasi, M. S. (2015), Differences in intention to use educational RSS feeds between Lebanese and British students: A multi group analysis based on the technology acceptance model, Academic Conferences and Publishing International.
  11. Pham, H. H., & Ho, T. T. H. (2020), Toward a ‘new normal’with e-learning in Vietnamese higher education during the post COVID-19 pandemic, Higher Education Research & Development, 39(7), 1327–1331.
  12. Aparicio, M., & Bacao, F. (2013, July), E-learning concept trends, In Proceedings of the 2013 International Conference on Information Systems and Design of Communication (81–86).
  13. Kelly, T., & Bauer, D. (2004), Managing Intellectual capital via e-learning at Cisco. In C. Holsapple (Ed.), Handbook on knowledge management 2: Knowledge directions (511–532), Berlin, Germany: Springer.
  14. Cairncross, S., & Mannion, M. (2001), Interactive multimedia and learning: Realizing the benefits, Innovations in education and teaching international, 38(2), 156–164.
  15. Rodrigues, H., Almeida, F., Figueiredo, V., & Lopes, S. L. (2019), Tracking e-learning through published papers: A systematic review, Computers & Education, 136, 87–98.
  16. Wani, H. A. (2013), The relevance of e-learning in higher education, ATIKAN, 3(2).
  17. Kujala, A. (2017), E-orientation: implementing e-learning in new employee orientation. Thesis
  18. Sangrà, A., Vlachopoulos, D., & Cabrera, N. (2012), Building an inclusive definition of e-learning: An approach to the conceptual framework, International Review of Research in Open and Distributed Learning, 13(2), 145–159.
  19. Al-Adwan, A., & Smedley, J. (2012), Implementing e-learning in the Jordanian Higher Education System: Factors affecting impact, International Journal of Education and Development using ICT, 8(1).
  20. Van Thinh, D. (2016), The Role of E-learning. Management, Enterprise and Benchmarking in the 21st Century, 239.
  21. Cho, W., & Schmelzer, C. D. (2000), Just‐in‐time education: tools for hospitality managers of the future? International Journal of Contemporary Hospitality Management.
  22. Braun, P., & Hollick, M. (2006), Tourism skills delivery: sharing tourism knowledge online. Education+ Training.
  23. Haven, C., & Botterill, D. (2003), Virtual learning environments in hospitality, leisure, tourism and sport: A review, Journal of Hospitality, Leisure, Sport and Tourism Education, 2(1), 75–92.
  24. Calderaro, A. (2015), Internet Governance Capacity Building in Post-Authoritarian Contexts. Telecom Reform and Human Rights in Myanmar, Telecom Reform and Human Rights in Myanmar (May 1, 2015).
  25. Davis, N. L., Gough, M., & Taylor, L. L. (2019), Online teaching: advantages, obstacles and tools for getting it right, Journal of Teaching in Travel & Tourism, 19(3), 256–263.
  26. Goh, E., & Sigala, M. (2020), Integrating Information & Communication Technologies (ICT) into classroom instruction: teaching tips for hospitality educators from a diffusion of innovation approach, Journal of Teaching in Travel & Tourism, 20(2), 156–165.
  27. Fong, L. H. N., Luk, C., & Law, R. (2014), How do hotel and tourism students select internship employers? A segmentation approach, Journal of Hospitality, Leisure, Sport & Tourism Education, 15, 68–79.
  28. Lee, S., Barker, T., & Kumar, V. S. (2016), Effectiveness of a learner-directed model for e-learning, Journal of Educational Technology & Society, 19(3), 221–233.
  29. Radović-Marković, M. (2010), Advantages and disadvantages of e-learning in comparison to traditional forms of learning, Annals of the University of Petroşani, Economics, 10(2), 289–298.
  30. Bilgihan, A., Okumus, F., Nusair, K., & Bujisic, M. (2014), Online experiences: flow theory, measuring online customer experience in e-commerce and managerial implications for the lodging industry, Information Technology & Tourism, 14(1), 49–71.
  31. Mejia, C. (2020), Using VoiceThread as a discussion platform to enhance student engagement in a hospitality management online course, Journal of Hospitality, Leisure, Sport & Tourism Education, 26, 100236.
  32. Ajzen, I., & Fishbein, M. (1975), A Bayesian analysis of attribution processes, Psychological bulletin, 82(2), 261.
  33. Ajzen, I. (1985), From intentions to actions: A theory of planned behavior. In Action control (11–39), Springer, Berlin, Heidelberg.
  34. Ajzen, I. (1991), The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179–211.
  35. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003), User acceptance of information technology: Toward a unified view, MIS quarterly, 425–478.
  36. Davis, F. D. (1985), A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of Technology).
  37. King, W. R., & He, J. (2006), A meta-analysis of the technology acceptance model. Information & management, 43(6), 740–755.
  38. Davis, F. D. (1989), Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS quarterly, 319–340.
  39. Masrom, M. (2007), Technology acceptance model and e-learning. Technology, 21(24), 81.
  40. Lee, B. C., Yoon, J. O., & Lee, I. (2009), Learners’ acceptance of e-learning in South Korea: Theories and results. Computers & education, 53(4), 1320–1329.
  41. Qteishat, M., Alshibly, H., & Al-Ma’aitah, M. (2013), Factors influencing the adoption of E-learning in Jordan: An extended TAM model, European Journal of Business and Management, 5(18), 84–100.
  42. Mohammadi, H. (2015), Investigating users’ perspectives on e-learning: An integration of TAM and IS success model, Computers in human behavior, 45, 359–374.
  43. 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).
  44. Baby, A., & Kannammal, A. (2020), Network Path Analysis for developing an enhanced TAM model: A user-centric e-learning perspective, Computers in Human Behavior, 107, 106081.
  45. Jimenez, I. A. C., García, L. C. C., Violante, M. G., Marcolin, F., & Vezzetti, E. (2021), Commonly Used External TAM Variables in e-Learning, Agriculture and Virtual Reality Applications. Future Internet, 13(1), 7.
  46. Mailizar, M., Almanthari, A., & Maulina, S. (2021), Examining Teachers’ Behavioral Intention to Use E-learning in Teaching of Mathematics: An Extended TAM Model, Contemporary Educational Technology, 13(2), ep298.
  47. Ong, C. S., Lai, J. Y., & Wang, Y. S. (2004), Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Information & management, 41(6), 795–804.
  48. Chen, H. R., & Tseng, H. F. (2012), Factors that influence acceptance of web-based e-learning systems for the in-service education of junior high school teachers in Taiwan. Evaluation and program planning, 35(3), 398–406.
  49. 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 & education, 50(4), 1183–1202.
  50. Cho, V., Cheng, T. E., & Lai, W. J. (2009), The role of perceived user-interface design in continued usage intention of self-paced e-learning tools, Computers & Education, 53(2), 216–227.
  51. Lee, M. C. (2010), Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model, Computers & Education, 54(2), 506–516.
  52. Liu, G. Z., & Hwang, G. J. (2010), A key step to understanding paradigm shifts in e‐learning: towards context‐aware ubiquitous learning. British Journal of Educational Technology, 41(2), E1–E9.
  53. Van Raaij, E. M., & Schepers, J. J. (2008), The acceptance and use of a virtual learning environment in China, Computers & education, 50(3), 838–852.
  54. Chen, I. J., Yang, K. F., Tang, F. I., Huang, C. H., & Yu, S. (2008), Applying the technology acceptance model to explore public health nurses’ intentions towards web-based learning: A cross-sectional questionnaire survey, International journal of nursing studies, 45(6), 869–878.
  55. Roca, J. C., & Gagné, M. (2008), Understanding e-learning continuance intention in the workplace: A self-determination theory perspective, Computers in human behavior, 24(4), 1585–1604.
  56. Yeung, P., & Jordan, E. (2007). The continued usage of business e-learning courses in Hong Kong corporations, Education and Information Technologies, 12(3), 175–188.
  57. Martocchio, J. J., & Webster, J. (1992), Effects of feedback and cognitive playfulness on performance in microcomputer software training, Personnel Psychology, 45(3), 553–578.
  58. Venkatesh, V., & Brown, S. A. (2001), A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges, MIS quarterly, 71–102.
  59. Moon, J. W., & Kim, Y. G. (2001), Extending the TAM for a World-Wide-Web context. Information & management, 38(4), 217–230.
  60. Lin, C. S., Wu, S., & Tsai, R. J. (2005), Integrating perceived playfulness into expectation-confirmation model for web portal context, Information & management, 42(5), 683–693.
  61. Schiffman, L. G., & Kanuk, L. L. (2009), Consumer behavior, Harlow, England: Prentice Hall.
  62. Hsu, L. (2016), An empirical examination of EFL learners' perceptual learning styles and acceptance of ASR-based computer-assisted pronunciation training, Computer Assisted Language Learning, 29(5), 881–900.
  63. TSang, P., Fong, J., & Tse, S. (2004), Using e-learning platform in open and flexible learning, In New horizon in Web-based learning (214–224).
  64. Lin, K. M. (2011), e-Learning continuance intention: Moderating effects of user e-learning experience, Computers & Education, 56(2), 515–526.
  65. Chính phủ Việt Nam (2011), Chiến lược phát triển du lịch Việt Nam đến năm 2020, tầm nhìn đến năm 2030. Truy cập ngày 1/6/2021 từ http://www.chinhphu.vn/portal/page/portal/English/strategies/strategiesdetails?categoryId=30&articleId=10051267.
  66. Hair Jr, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014), Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research, European business review.
  67. 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.
  68. Hulland, J. (1999), Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic management journal, 20(2), 195–204.
  69. Fornell, C., & Larcker, D. F. (1981), Evaluating structural equation models with unobservable variables and measurement error, Journal of marketing research, 18(1), 39–50.
  70. Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012), An assessment of the use of partial least squares structural equation modeling in marketing research, Journal of the academy of marketing science, 40(3), 414–433.
  71. Hair Jr, 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.
  72. Liu, A., Hodgson, G., & Lord, W. (2010), Innovation in construction education: the role of culture in e-learning, Architectural Engineering and Design Management, 6(2), 91–102.
  73. Hashim, J. (2008), Factors influencing the acceptance of web‐based training in Malaysia: applying the technology acceptance model, International Journal of Training and Development, 12(4), 253–264.
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