Harmonizing Education: Exploring Factors Affecting Acceptance of AI-Supported Mobile Apps in Music Education

Journal title CADMO
Author/s Berk Ceviz, Rahmi Baki
Publishing Year 2024 Issue 2024/1
Language English Pages 25 P. 61-85 File size 241 KB
DOI 10.3280/CAD2024-001005
DOI is like a bar code for intellectual property: to have more infomation click here

Below, you can see the article first page

If you want to buy this article in PDF format, you can do it, following the instructions to buy download credits

Article preview

FrancoAngeli is member of Publishers International Linking Association, Inc (PILA), a not-for-profit association which run the CrossRef service enabling links to and from online scholarly content.

Keywords: Artificial Intelligence, Sustainable Education, Mobile Education Applications, Technology Adoption, Instrumental Education, Music Theory Instruction.

  1. Ally, M., Prieto-Blazquez, J. (2014), “What is the future of mobile learning in education?”, RUSC. Universities and Knowledge Society Journal, 11 (1), pp. 142-151.
  2. Alshurideh, M., Al Kurdi, B., Salloum, S. A. (2019, October), Examining the main mobile learning system drivers’ effects: A mix empirical examination of both the Expectation-Confirmation Model (ECM) and the Technology Acceptance Model (TAM). In: A. Hassanien, K. Shaalan, M. Tolba (Eds), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019. AISI 2019. Advances in Intelligent Systems and Computing. Cham: Springer, pp. 406-417.
  3. Bidin, S., Ziden, A. A. (2013), “Adoption and application of mobile learning in the education industry”, Procedia-social and Behavioral Sciences, 90, pp. 720-729.
  4. Bisdikian, C., Gibson, C., Chakraborty, S., Srivastava, M. B., Sensoy, M., Norman, T. J. (2014), “Inference management, trust and obfuscation principles for quality of information in emerging pervasive environments”, Pervasive and Mobile Computing, 11, pp. 168-187.
  5. Chau, P. Y., Lai, V. S. (2003), “An empirical investigation of the determinants of user acceptance of internet banking”, Journal of Organizational Computing and Electronic Commerce, 12 (2), pp. 123-145.
  6. Chan, T. J., Wok, S., Sari, N. N., Muben, M. A. H. A. (2021), “Factors influencing the intention to use mysejahtera application among malaysian citizens during Covid-19”, Journal of Applied Structural Equation Modeling, 5 (2), pp. 1-21.
  7. Chin, A. G., Harris, M. A., Brookshire, R. (2018), “A bidirectional perspective of trust and risk in determining factors that influence mobile app installation”, International Journal of Information Management, 39, pp. 49-59.
  8. Davis, F. D. (1989), “Perceived usefulness, perceived ease of use, and user acceptance of information technology”, MIS Quarterly, pp. 319-340.
  9. En, I. T. X. (2020), Assessing Factors Affecting Purchase Intention of Mobile Application Users, Doctoral dissertation, Swinburne University of Technology Sarawak Campus, Malaysia.
  10. Fornell, C., Larcker, D. F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, 18 (1), pp. 39-50.
  11. Gao, T., Deng, Y. (2012, June), A study on users’ acceptance behavior to mobile e-books application based on UTAUT model. In: 2012 IEEE International Conference on Computer Science and Automation Engineering, Beijing, pp. 376-379.
  12. George, D., Mallery, P. (2019), IBM SPSS statistics 26 step by step: A simple guide and reference. New York: Routledge.
  13. Gill, A. A., Ali, M. H., Aslam, M., Amjad, M. H. (2021), “A Model to analyze the Mobile e-banking Application Quality Factors impact on Consumers’e-Loyalty: Mediating Role of e-Satisfaction”, iRASD Journal of Management, 3 (2), pp. 137-145.
  14. Hair, J., Anderson, R., Tatham, R., Black, W. (1998), Multivariate data analysis. New Jersey: Prentice Hall.
  15. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. (2010), Multivariate data analysis. New Jersey: Prentice Hall, 7th ed.
  16. Harris, M. A., Chin, A. G., Brookshire, R. (2015), “Mobile app installation: the role of precautions and desensitization”, Journal of International Technology and Information Management, 24 (4), pp. 47-62.
  17. Hassandoust, F., Akhlaghpour, S., Johnston, A. C. (2021), “Individuals’ privacy concerns and adoption of contact tracing mobile applications in a pandemic: A situational privacy calculus perspective”, Journal of the American Medical Informatics Association, 28 (3), pp. 463-471.
  18. Venkatesh, V., Morris, M. G., Davis, G. B., Davis, F. D. (2003), “User acceptance of information technology: Toward a unified view”, MIS Quarterly, 27 (3), pp. 425-478.
  19. Hoehle, H., Aljafari, R., Venkatesh, V. (2016), “Leveraging Microsoft’ s mobile usability guidelines: Conceptualizing and developing scales for mobile application usability”, International Journal of Human-Computer Studies, 89, pp. 35-53.
  20. Hsiao, C. H., Chang, J. J., Tang, K. Y. (2016), “Exploring the influential factors in continuance usage of mobile social Apps: Satisfaction, habit, and customer value perspectives”, Telematics and Informatics, 33 (2), pp. 342-355.
  21. Huang, D. H., Chueh, H. E. (2022), “Usage intention model of mobile apps in membership application”, Journal of Business Research, 139, pp. 1255-1260.
  22. Huang, J., Saleh, S., Liu, Y. (2021), “A review on artificial intelligence in education”, Academic Journal of Interdisciplinary Studies, 10, pp. 206-217.
  23. Jeng, Y. L., Wu, T. T., Huang, Y. M., Tan, Q., Yang, S. J. (2010), “The add-on impact of mobile applications in learning strategies: A review study”, Journal of Educational Technology & Society, 13 (3), pp. 3-11.
  24. Jou, Y. T., Mariñas, K. A., Saflor, C. S., Young, M. N. (2022), “Investigating accessibility of Social Security System (SSS) mobile application: A Structural Equation Modeling Approach”, Sustainability, 14 (13), 7939.
  25. Koç, T., Turan, A. H., Okursoy, A. (2016), “Acceptance and usage of a mobile information system in higher education: An empirical study with structural equation modelling”, The International Journal of Management Education, 14 (3), pp. 286-300.
  26. Lallmahomed, M. Z., Lallmahomed, N., Lallmahomed, G. M. (2017), “Factors influencing the adoption of e-Government services in Mauritius”, Telematics and Informatics, 34 (4), pp. 57-72.
  27. Li, Y. M., Yeh, Y. S. (2010), “Increasing trust in mobile commerce through design aesthetics”, Computers in Human Behavior, 26 (4), pp. 673-684.
  28. Liu, Y., Li, H. (2011), “Exploring the impact of use context on mobile hedonic services adoption: An empirical study on mobile gaming in China”, Computers in Human Behavior, 27 (2), pp. 890-898.
  29. Marchewka, J. T., Kostiwa, K. (2007), “An application of the UTAUT model for understanding student perceptions using course management software”, Communications of the IIMA, 7 (2), 10.
  30. Martínez-Torres, M. D. R., Toral Marín, S. L., García, F. B., Vázquez, S. G., Oliva, M. A., Torres, T. (2008), “A technological acceptance of e-learning tools used in practical and laboratory teaching, according to the European higher education area”, Behaviour & Information Technology, 27 (6), pp. 495-505.
  31. Maune, A., Themalil, M. T. (2022), “Mobile Applications Adoption and Use in Strategic Competitive Intelligence: A Structural Equation Modelling Approach”, Journal of Intelligence Studies in Business, 12 (1), pp. 65-82.
  32. Maziriri, E., Mapuranga, M., Mushwana, J., Madinga, N. (2020), “Antecedents that influence the intention to use the Uber mobile application: Customer perspectives in South Africa”, International Journal of Interactive Mobile Technologies, 14 (8), pp. 76-95.
  33. Medeiros, M., Ozturk, A., Hancer, M., Weinland, J., Okumus, B. (2022), “Understanding travel tracking mobile application usage: An integration of self determination theory and UTAUT2”, Tourism Management Perspectives, 42 (1), pp. 1-11.
  34. Munoz-Leiva, F., Climent-Climent, S., Liébana-Cabanillas, F. (2017), “Determinants of intention to use the mobile banking apps: An extension of the classic TAM model”, Spanish Journal of Marketing-ESIC, 21 (1), pp. 25-38.
  35. Naranjo-Zolotov, M., Oliveira, T., Casteleyn, S. (2018), “Citizens’ intention to use and recommend e-participation: Drawing upon UTAUT and citizen empowerment”, Information Technology & People, 32 (2), pp. 364-386.
  36. Naruetharadhol, P., Ketkaew, C., Hongkanchanapong, N., Thaniswannasri, P., Uengkusolmongkol, T., Prasomthong, S., Gebsombut, N. (2021), “Factors affecting sustainable intention to use mobile banking services”, Sage Open, 11 (3), 21582440211029925.
  37. Ong, A. K. S., Prasetyo, Y. T., Kusonwattana, P., Mariñas, K. A., Yuduang, N., Chuenyindee, T., ... Nadlifatin, R. (2022), “Determining factors affecting the perceived usability of air pollution detection mobile application ‘AirVisual’ in Thailand: A structural equation model forest classifier approach”, Heliyon, 8 (12), pp. 1-13.
  38. Oppong-Tawiah, D., Webster, J., Staples, S., Cameron, A. F., de Guinea, A. O., Hung, T. Y. (2020), “Developing a gamified mobile application to encourage sustainable energy use in the office”, Journal of Business Research, 106, pp. 388-405.
  39. Ouyang, F., Jiao, P. (2021), “Artificial intelligence in education: The three paradigms”, Computers and Education: Artificial Intelligence, 2, 100020.
  40. Pedró, F., Subosa, M., Rivas, A., Valverde, P. (2019), Artificial Intelligence in Education: Challenges and Opportunities for Sustainable Development. Paris: UNESCO.
  41. Putra, D. M. (2022), “The Effect of using the Mobile Application of JKN Health Social Security Implementing Agency on JKN-KIS Participant Satisfaction in the City of Padang using the Unified Theory of Acceptance and use of Technology Model”, Enrichment: Journal of Management, 12 (4), pp. 2825-2837.
  42. Putra, P. O. H., Kirana, R. A. W. W. C., Budi, I. (2022), “Usability factors that drive continued intention to use and loyalty of mobile travel application”, Heliyon, 8 (9), pp. 1-16.
  43. Puriwat, W., Tripopsakul, S. (2017), “Mobile banking adoption in Thailand: an integration of technology acceptance model and mobile service quality”, European Research Studies Journal, 10 (4A), pp. 200-210.
  44. Rashotte, L. (2007), Social influence. In: G. Ritzer (Ed), The Blackwell Encyclopedia of Social Psychology. Hoboken: Wiley.
  45. Rezaei, A., Mai, N., Pesaranghader, A. (2013, September), Effectiveness of using English vocabulary mobile applications on ESL’s Learning performance. In: 2013 International Conference on Informatics and Creative Multimedia, Kuala Lumpur, pp. 114-118.
  46. Roll, I., Wylie, R. (2016), “Evolution and revolution in artificial intelligence in education”, International Journal of Artificial Intelligence in Education, 26, pp. 582-599.
  47. Roy, S. (2017), “App adoption and switching behavior: applying the extended tam in smartphone app usage”, JISTEM-Journal of Information Systems and Technology Management, 14, pp. 239-261.
  48. Suryaningsih, I. W., Sunarjo, W. A., Satrio, D. (2023), “The Effect of Perceived Ease to Use and Subjective Norm on Intention to Use With Perceived Usefulness and Attitude Towards Use as Intervening Variables”, INCOSHA, 1, pp. 133-140.
  49. Tak, P., Gupta, M. (2021), “Examining travel mobile app attributes and its impact on consumer engagement: An application of SOR framework”, Journal of Internet Commerce, 20 (3), pp. 293-318.
  50. Talukder, S., Chiong, R., Dhakal, S., Sorwar, G., Bao, Y. (2019), “A two-stage structural equation modeling-neural network approach for understanding and predicting the determinants of m-government service adoption”, Journal of Systems and Information Technology, 21 (4), pp. 419-438.
  51. Venkatesh, V., Thong, J. Y., Xu, X. (2012), “Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology”, MIS Quarterly, 36 (1), pp. 157-178.
  52. Wang, Y. (2021), “An improved machine learning and artificial intelligence algorithm for classroom management of English distance education”, Journal of Intelligent & Fuzzy Systems, 40 (2), pp. 3477-3488.
  53. Wang, H. Y., Liao, C., Yang, L. H. (2013), “What affects mobile application use? The roles of consumption values”, International Journal of Marketing Studies, 5 (2), 11.
  54. Williamson, B. (2018), “The hidden architecture of higher education: building a big data infrastructure for the ‘smarter university’”, International Journal of Educational Technology in Higher Education, 15 (1), pp. 1-26.
  55. Wu, J. H., Wang, S. C., Lin, L. M. (2007), “Mobile computing acceptance factors in the healthcare industry: A structural equation model”, International Journal of Medical Informatics, 76 (1), pp. 66-77.
  56. Xiong, L., Wang, H., Wang, C. (2022), “Predicting mobile government service continuance: A two-stage structural equation modeling-artificial neural network approach”, Government Information Quarterly, 3 9(1), 101654.
  57. Yang, S., Bai, H. (2020), “The integration design of artificial intelligence and normal students’ education”, Journal of Physics: Conference Series, 1453 (1), p. 012090.

Berk Ceviz, Rahmi Baki, Harmonizing Education: Exploring Factors Affecting Acceptance of AI-Supported Mobile Apps in Music Education in "CADMO" 1/2024, pp 61-85, DOI: 10.3280/CAD2024-001005