An Analysis of Factors Influencing the Intention to Use “Untact” Services by Service Type (original) (raw)

Abstract

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Loading...

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

References (79)

  1. Yun, M.J.; Ko, Y.H. The Effect of Non-Face-to-Face Service Quality on Satisfaction and Intention of Purchase in Food Service Companies. Culin. Sci. Hosp. Res. 2020, 26, 184-194.
  2. Choi, S.H. An Exploratory Study on Definition and categorization of the 'Untact Industry'. In Proceedings of the Korean Institute of Communication Sciences Conference, Gangwon-do, Republic of Korea, 12-14 August 2020; pp. 767-768.
  3. Yoo, Y.S. The Study on the Definition and Category of Digital Contact Service in the future beyond Post-Covid19 era. In Proceedings of the Korea Society of IT Services Conference, Seoul, Republic of Korea, 17 June 2020; pp. 599-602.
  4. Untact Service Trends and Implications Due to COVID-19. Available online: https://www.fkii.org/webzine/FKII\_2005/FKII\_ sub21.php (accessed on 15 August 2022).
  5. Current Status and Tasks of Information and Communication Technology to Revitalize the Untact Economy. Available online: https://www.nars.go.kr/report/view.do?cmsCode=CM0043&brdSeq=32691 (accessed on 15 August 2022).
  6. Jeong, I.Y. A Study on the Effects of Non Face-to-Face Service Channel Usage on Loyalty by Food Service Industry. Ph.D. Thesis, Kyonggi University, Suwon, Republic of Korea, 2020.
  7. Park, Y.M.; Choi, J.S. The Study on the Strategies of Building LPWAN on Local Government. J. Korean Assoc. Reg. Inf. Soc. 2019, 22, 135-157. [CrossRef]
  8. Park, J.W.; Lee, H.R. The Effect of Fast Food Restaurant Customers' Kiosk Use on Acceptance Intention and Continuous Use Intention: Applying UTAUT2 Model and Moderating Effect of Familiarity. J. Tour. Sci. 2020, 44, 207-228.
  9. Park, I.; Sah, Y.J.; Lee, S.; Lee, D. Avatar-Mediated Communication in Video Conferencing: Effect of Self-Affirmation on Debating Participation Focusing on Moderation Effect of Avatar. Int. J. Hum. -Comput. Interact. 2022, 39, 464-475. [CrossRef]
  10. Lee, J.; Lee, J.G.; Lee, D. Influence of Rapport and Social Presence with an Ai Psychotherapy Chatbot on Users' Self-Disclosure. Int. J. Hum. -Comput. Interact. 2022, 1-12. [CrossRef]
  11. Contactless Economy. Available online: https://www2.deloitte.com/content/dam/Deloitte/kr/Documents/consumer- business/2020/kr_consumer_article-20201012.pdf (accessed on 11 September 2022).
  12. Won-young, J.; Min-ji, C.; Hoon-ki, H. Case study on the operation of university non-face-to-face experimental classes following the Corona Virus Infectious Disease-19 pandemic. Study Learn. -Cent. Curric. Educ. 2020, 20, 937-966.
  13. Canning, R. The Use of Video-conferencing for Continuing Personal and Professional Development in Higher Education: A small-group case study. J. Furth. High. Educ. 1999, 23, 117-130. [CrossRef]
  14. Almaiah, M.A.; Hajjej, F.; Shishakly, R.; Lutfi, A.; Amin, A.; Awad, A.B. The Role of Quality Measurements in Enhancing the Usability of Mobile Learning Applications during COVID-19. Electronics 2022, 11, 1951. [CrossRef]
  15. Brown, R.M. Drivers of Student Satisfaction and Student Loyalty in an Australian University Setting. Ph.D. Thesis, University of Western Australia, Crawley, WA, Australia, 2006.
  16. Harasim, L. Educational applications of computer conferencing. Int. J. E-Learn. Distance Educ. /Rev. Int. Du E-Learn. Et La Form. À Distance 1986, 1, 59-70.
  17. Alessi, S.M.; Trollip, S.R. Multimedia for Learning: Methods and Development; Allyn & Bacon: Boston, MA, USA, 2001.
  18. Hannafin, R.D.; Sullivan, H.J. Learner control in full and lean CAI programs. Educ. Technol. Res. Dev. 1995, 43, 19-30. [CrossRef]
  19. Grow, G.O. Teaching learners to be self-directed. Adult Educ. Q. 1991, 41, 125-149. [CrossRef]
  20. Keller, J.M. Motivational design research and development. In Motivational Design for Learning and Performance; Springer: Berlin/Heidelberg, Germany, 2010; pp. 297-323.
  21. Karaman, S.; Aydemir, M.; Kucuk, S.; Yildirim, G. Virtual classroom participants' views for effective synchronous education process. Turk. Online J. Distance Educ. 2013, 14, 290-301.
  22. Bennett, A.A.; Campion, E.D.; Keeler, K.R.; Keener, S.K. Videoconference fatigue? Exploring changes in fatigue after videoconfer- ence meetings during COVID-19. J. Appl. Psychol. 2021, 106, 330. [CrossRef] [PubMed]
  23. Bailenson, J.N. Nonverbal overload: A theoretical argument for the causes of Zoom fatigue. Technol. Mind Behav. 2021, 2. [CrossRef]
  24. Professional Convention Management Association (PCMA); UMB Studios; Virtual Edge Institute. Business Motivations and Social Behaviors for In-Person and Online Events. 2011. Available online: https://visiofair.wordpress.com/2013/04/23/23/ (accessed on 11 September 2022).
  25. Hugel, M. Virtual Events Vs. In-Person Events: Why You Should Host Your Event Online. 2020. Available online: https: //info.workcast.com/blog/virtual-events-vs-in-person-events (accessed on 11 September 2022).
  26. Morrow, S. The Art of the Show, Dallas, Texas; IAEM Education Foundation: Falls Church, VA, USA, 2002.
  27. Cohen, A. Cyber-Show must go on. In Brandweek; Adweek L.P.: Minneapolis, MN, USA, 2001; pp. 29-40.
  28. Leong, C.K.; Chennupati, K.R. An Overview of Online Exhibitions. DESIDOC J. Libr. Inf. Technol. 2008, 28, 7-21.
  29. Vervoort, D.; Dearani, J.A.; Starnes, V.A.; Thourani, V.H.; Nguyen, T.C. Brave New World: Virtual conferencing and surgical education in the Coronavirus Disease 2019 era. J. Thorac. Cardiovasc. Surg. 2021, 161, 748. [CrossRef]
  30. Diethart, M.; Zimmermann, A.; Mulà, I. Guidelines for virtual conferencing-inspired by the COPERNICUS Alliance Online Conference 2019. 2020. Available online: https://www.copernicus-alliance.org/news-archive/279-guidelines-for-virtual- conferencing (accessed on 11 September 2022).
  31. McCabe, S.; Stokoe, E.H. Place and identity in tourists' accounts. Ann. Tour. Res. 2004, 31, 601-622. [CrossRef] 32. 10 Smart Tips for Keeping Attendees Engaged before, during and after a Virtual Event. Available online: https: //www.bizbash.com/production-strategy/programming-entertainment/article/21132306/virtual-event-engagement- strategies-how-to-keep-attendees-interested-before-during-and-after (accessed on 11 September 2022).
  32. 10 Tips for Navigating Virtual Event Time Zone. Available online: https://www.worldspanplc.com/blog/2020/08/10/10-tips- navigating-virtual-event-time-zones/ (accessed on 11 September 2022).
  33. Poushneh, A. Augmented reality in retail: A trade-off between user's control of access to personal information and augmentation quality. J. Retail. Consum. Serv. 2018, 41, 169-176. [CrossRef]
  34. Javornik, A. 'It's an illusion, but it looks real!'Consumer affective, cognitive and behavioural responses to augmented reality applications. J. Mark. Manag. 2016, 32, 987-1011. [CrossRef]
  35. Rauschnabel, P.A.; Felix, R.; Hinsch, C. Augmented reality marketing: How mobile AR-apps can improve brands through inspiration. J. Retail. Consum. Serv. 2019, 49, 43-53. [CrossRef]
  36. Smink, A.R.; Van Reijmersdal, E.A.; Van Noort, G.; Neijens, P.C. Shopping in augmented reality: The effects of spatial presence, personalization and intrusiveness on app and brand responses. J. Bus. Res. 2020, 118, 474-485. [CrossRef]
  37. Nikhashemi, S.; Knight, H.H.; Nusair, K.; Liat, C.B. Augmented reality in smart retailing: A (n)(A) Symmetric Approach to continuous intention to use retail brands' mobile AR apps. J. Retail. Consum. Serv. 2021, 60, 102464. [CrossRef]
  38. Dacko, S.G. Enabling smart retail settings via mobile augmented reality shopping apps. Technol. Forecast. Soc. Change 2017, 124, 243-256. [CrossRef]
  39. McLean, G.; Wilson, A. Shopping in the digital world: Examining customer engagement through augmented reality mobile applications. Comput. Hum. Behav. 2019, 101, 210-224. [CrossRef]
  40. Zimmer, F.; Scheibe, K.; Stock, W.G. A model for information behavior research on social live streaming services (SLSSs). In International Conference on Social Computing and Social Media; Springer: Berlin/Heidelberg, Germany, 2018; pp. 429-448.
  41. Hamilton, W.A.; Garretson, O.; Kerne, A. Streaming on twitch: Fostering participatory communities of play within live mixed media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Toronto, ON, Canada, 26 April-1 May 2014; pp. 1315-1324.
  42. Turley, L.W.; Fugate, D.L. The multidimensional nature of service facilities. J. Serv. Mark. 1992, 6, 37-45. [CrossRef]
  43. Fullerton, G. The service quality-loyalty relationship in retail services: Does commitment matter? J. Retail. Consum. Serv. 2005, 12, 99-111. [CrossRef]
  44. Alsyouf, A.; Lutfi, A.; Al-Bsheish, M.; Jarrar, M.; Al-Mugheed, K.; Almaiah, M.A.; Alhazmi, F.N.; Masa'deh, R.; Anshasi, R.J.; Ashour, A. Exposure Detection Applications Acceptance: The Case of COVID-19. Int. J. Environ. Res. Public Health 2022, 19, 7307.
  45. Alrawad, M.; Lutfi, A.; Alyatama, S.; Elshaer, I.A.; Almaiah, M.A. Perception of Occupational and Environmental Risks and Hazards among Mineworkers: A Psychometric Paradigm Approach. Int. J. Environ. Res. Public Health 2022, 19, 3371. [CrossRef]
  46. Moon, A.; Lee, H.K.; Jung, S.M.; Oh, D.; Lee, J.; Kim, K.; Kim, Y.; Lee, D.; Lee, J.; Lee, C.; et al. A Study for Evidence-Based Policy Concerning Users of Intelligent Information Society; Korea Information Society Development Institute: Gwacheon, Republic of Korea, 2021; pp. 18-21.
  47. Ryan, R.M.; Deci, E.L. Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemp. Educ. Psychol. 2000, 25, 54-67. [CrossRef]
  48. Al-Azawei, A.; Parslow, P.; Lundqvist, K. Investigating the effect of learning styles in a blended e-learning system: An extension of the technology acceptance model (TAM). Australas. J. Educ. Technol. 2017, 33, 1-23. [CrossRef]
  49. Al-Mushasha, N.F.A. Determinants of e-learning acceptance in higher education environment based on extended technology acceptance model. In Proceedings of the 2013 Fourth International Conference on e-Learning "Best Practices in Management, Design and Development of e-Courses: Standards of Excellence and Creativity", Manama, Bahrain, 7-9 May 2013; pp. 261-266.
  50. Chow, M.; Herold, D.K.; Choo, T.-M.; Chan, K. Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education. Comput. Educ. 2012, 59, 1136-1144. [CrossRef]
  51. Lee, Y.H.; Hsiao, C.; Purnomo, S.H. An empirical examination of individual and system characteristics on enhancing e-learning acceptance. Australas. J. Educ. Technol. 2014, 30, 562-579. [CrossRef]
  52. Nagy, J.T. Evaluation of online video usage and learning satisfaction: An extension of the technology acceptance model. Int. Rev. Res. Open Distrib. Learn. 2018, 19, 160-185. [CrossRef]
  53. Agudo-Peregrina, Á.F.; Hernández-García, Á.; Pascual-Miguel, F.J. Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Comput. Hum. Behav. 2014, 34, 301-314.
  54. Farahat, T. Applying the technology acceptance model to online learning in the Egyptian universities. Procedia-Soc. Behav. Sci. 2012, 64, 95-104. [CrossRef]
  55. Moghadam, A.H.; Bairamzadeh, S. Extending the technology acceptance model for e-learning: A case study of Iran. In Proceedings of the 2009 Sixth International Conference on Information Technology: New Generations, Las Vegas, NV, USA, 27-29 April 2009; pp. 1659-1660.
  56. Chen, Y.C.; Lin, Y.C.; Yeh, R.C.; Lou, S.J. Examining factors affecting college students' intention to use web-based instruction systems: Towards an integrated model. Turk. Online J. Educ. Technol. 2013, 12, 111-121.
  57. Lin, Y.C.; Chen, Y.C.; Yeh, R.C. Understanding college students' continuing intentions to use multimedia e-learning systems. World Trans. Eng. Technol. Educ. 2010, 8, 488-493.
  58. Zare, H.; Yazdanparast, S. The causal Model of effective factors on intention to use of information technology among payamnoor and traditional universities students. Life Sci. J. 2013, 10, 46-50.
  59. Park, Y.; Son, H.; Kim, C. Investigating the determinants of construction professionals' acceptance of web-based training: An extension of the technology acceptance model. Autom. Constr. 2012, 22, 377-386. [CrossRef]
  60. Purnomo, S.H.; Lee, Y.-H. E-learning adoption in the banking workplace in Indonesia: An empirical study. Inf. Dev. 2013, 29, 138-153. [CrossRef]
  61. Liu, X. Empirical testing of a theoretical extension of the technology acceptance model: An exploratory study of educational wikis. Commun. Educ. 2010, 59, 52-69. [CrossRef]
  62. Saadé, R.G.; Kira, D. The emotional state of technology acceptance. Issues Inf. Sci. Inf. Technol. 2006, 3, 529-539.
  63. Rini, G.P.; Khasanah, I. Intention to use online meeting applications during Covid-19 pandemic: A Technology Acceptance Model perspective. J. Manaj. Dan Pemasar. JASA 2021, 14, 77-94. [CrossRef]
  64. Alturki, U.; Aldraiweesh, A. Adoption of Google Meet by Postgraduate Students: The Role of Task Technology Fit and the TAM Model. Sustainability 2022, 14, 15765. [CrossRef]
  65. Djojo, B.W.; Hafizh, W.; Gui, A.; Shaharudin, M.S.; Karmawan, I.G.M. Analysist Acceptance of Video Conference at Zoom Application using Technology Acceptance Model. In Proceedings of the 2021 8th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), Semarang, Indonesia, 23-24 September 2021; pp. 18-23.
  66. Purwanto, E.; Tannady, H. The factors affecting intention to use Google Meet amid online meeting platforms competition in Indonesia. Technol. Rep. Kansai Univ. 2020, 62, 2829-2838.
  67. Bailey, D.R.; Almusharraf, N.; Almusharraf, A. Video conferencing in the e-learning context: Explaining learning outcome with the technology acceptance model. Educ. Inf. Technol. 2022, 27, 7679-7698. [CrossRef]
  68. Lee, J.S.; Yang, S.H.; Song, B.W. Study on the factors affecting the intention to use real-time video conferencing using extended technology acceptance model. J. Korea Contents Assoc. 2021, 21, 292-310.
  69. Park, N.; Rhoads, M.; Hou, J.; Lee, K.M. Understanding the acceptance of teleconferencing systems among employees: An extension of the technology acceptance model. Comput. Hum. Behav. 2014, 39, 118-127. [CrossRef]
  70. Choi, B.H. A Study on Acceptance of Online Concerts Based on Mobile Augmented Reality: Focusing on the Extended Technology Acceptance Model. J. Digit. Converg. 2021, 19, 315-325.
  71. Resta, G.; Dicuonzo, F.; Karacan, E.; Pastore, D. The impact of virtual tours on museum exhibitions after the onset of covid-19 restrictions: Visitor engagement and long-term perspectives. SCIRES-IT-SCIentific RESearch Inf. Technol. 2021, 11, 151-166.
  72. Chang, C.J.; Hsu, B.C.Y.; Chen, M.Y. Viewing Sports Online during the COVID-19 Pandemic: The Antecedent Effects of Social Presence on the Technology Acceptance Model. Sustainability 2022, 14, 341. [CrossRef]
  73. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319-340.
  74. Cho, H.; Lee, D.; Lee, J.G. User acceptance on content optimization algorithms: Predicting filter bubbles in conversational AI services. Univers. Access Inf. Soc. 2022, 1-14. [CrossRef]
  75. Lee, J.; Lee, D.; Park, Y.; Lee, S.; Ha, T. Autonomous vehicles can be shared, but a feeling of ownership is important: Examination of the influential factors for intention to use autonomous vehicles. Transp. Res. Part C Emerg. Technol. 2019, 107, 411-422.
  76. Kim, N.; Park, Y.; Lee, D. Differences in consumer intention to use on-demand automobile-related services in accordance with the degree of face-to-face interactions. Technol. Forecast. Soc. Change 2019, 139, 277-286. [CrossRef]
  77. Lee, J.; Ryu, M.H.; Lee, D. A study on the reciprocal relationship between user perception and retailer perception on platform- based mobile payment service. J. Retail. Consum. Serv. 2019, 48, 7-15. [CrossRef]
  78. Lee, C.; Lee, K.; Lee, D. Mobile healthcare applications and gamification for sustained health maintenance. Sustainability 2017, 9, 772. [CrossRef]
  79. Disclaimer/Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.