Asia pacific journal of information systems

The Korea Society of Management Information Systems (한국경영정보학회)
권호 표지
DOI QR코드

DOI QR Code

An Empirical Study on the Factors Affecting the Intention to Use M2E Services

  • Sang Hoon Lee (School of Computer and Information Engineering, Daegu University) ;
  • Min-Ju Kim (Department of IT Convergence Engineering, Daegu University) ;
  • Su-Yeon Kim (School of Computer and Information Engineering, Daegu University)
  • Received : 2023.01.31
  • Accepted : 2023.07.18
  • Published : 2023.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, Web 3.0 services such as virtual currency, block chain, and NFT are attracting public attention. Users who depended on platforms are moving away from being dependent on service providers and moving to Web 3.0 services. Representative services of Web 3.0 include P2E (Play to Earn) and M2E (Move to Earn). In the case of P2E, various studies have been conducted as it is widely covered in the press and media, but research on M2E is relatively lacking. This study attempts to identify the intention to use M2E by using the expanded technology acceptance model. External factors were selected based on M2E's own characteristics and personal characteristics, a research model was designed, and the proposed hypotheses were verified through factor analysis and goodness of model fit. As a result of the study, it was confirmed that profitability, innovativeness, and self-expression had a positive effect on perceived characteristics, and that perceived usefulness, perceived enjoyment, and social influence had a positive effect on intention to use. Through the research results, practical implications for efficient service operation that meets the needs of users are presented to M2E platform providers.

Keywords

References

  1. Abbas Borhani, S., Babajani, J., Raeesi Vanani, I., Sheri Anaqiz, S., and Jamaliyanpour, M. (2021). Adopting blockchain technology to improve financial reporting by Using the Technology Acceptance Model (TAM). International Journal of Finance and Managerial Accounting, 6(22), 155-171. Retrieved from https://ijfma.srbiau.ac.ir/article_17481.html
  2. Abdullah, F., and Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036
  3. Agarwal, R., and Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision sciences, 28(3), 557-582. https://doi.org/10.1111/j.1540-5915.1997.tb01322.x
  4. Agudo-Peregrina, A. F., Hernandez-Garcia, A., and Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301-314. https://doi.org/10.1016/j.chb.2013.10.035
  5. Ahmad, I., Ahmad, M. O., Ahmad, M. O., Almazroi, A. A., Khan Khalil, M. I., and Alqarni, M. A. (2021). Using algorithmic trading to analyze short term profitability of Bitcoin. PeerJ Computer Science, 7, 1-19. https://doi.org/10.7717/peerj-cs.337
  6. Ajzen, I., and Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Englewood Cliffs, NJ7 Prentice-Hall
  7. Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27-50. https://doi.org/10.1016/j.aci.2014.09.001
  8. Almaiah, M. A., Alfaisal, R., Salloum, S. A., Al-Otaibi, S., Shishakly, R., Lutfi, A., Alrawad, M., Mulhem, A. A., Awad, A. B., and Al-Maroof, R. S. (2022). Integrating Teachers' TPACK Levels and Students' Learning Motivation, Technology Innovativeness, and Optimism in an IoT Acceptance Model. Electronics (Switzerland), 11(19), 1-16. https://doi.org/10.3390/electronics11193197
  9. Balamoorthy, S., and Chandra, B. (2023). The influence of intrinsic and extrinsic motivational factors on e-WOM behaviour: The role of psychological impact during the time of COVID-19 crisis. Heliyon, 9(2), 1-20.
  10. Campbell, D., and Singh, C. B. (2017). A Study of Customer Innovativeness for the Mobile Wallet Acceptance in Rajasthan. Pacific Business Review Internationa, 10(6), 7-15.
  11. Corporate Finance Institute. (2022). Passive Income - Definition, Reasons for Building, Examples. Retrieved from https://corporatefinanceinstitute.com/resources/accounting/passive-income
  12. Chang, C. C., Yan, C. F., and Tseng, J. S. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28(5), 809-826. https://doi.org/10.14742/ajet.818
  13. Chen, J. (2023). Passive Income: What It Is, 3 Main Categories, and Examples. Retrieved from https://www.investopedia.com/terms/p/passiveincome.asp#citation-11.
  14. Choi, B. H. (2021). Study on smart senior's virtual reality-based realistic content user experience and acceptance intention: Focusing on the extended technology acceptance model. Journal of the Moving Image Technology Associon of Korea, 1(37), 201-229. https://doi.org/10.34269/mitak.2021.1.37.011
  15. Choi, M. S., and Kim, S. J. (2021). The effect of motivation for use of short: Form video SNS platform on the intention of continuous use - Verification of mediating of the flow experience -. Journal of Cultural Product & Design, 64(0), 43-56.
  16. Chong, K. W., Kim, Y. S., and Choi, J. (2021). A study of factors affecting intention to adopt a cloud-based digital signature service. Information (Switzerland), 12(2), 1-15. https://doi.org/10.3390/info12020060
  17. Chou, S. F., Horng, J. S., Liu, C. H., Yu, T. Y., and Kuo, Y. T. (2022). Identifying the critical factors for sustainable marketing in the catering: The influence of big data applications, marketing innovation, and technology acceptance model factors. Journal of Hospitality and Tourism Management, 51(1), 11-21. https://doi.org/10.1016/j.jhtm.2022.02.010
  18. Chung, B. G. (2019). Roles of trust in technology acceptance of augmented reality. Journal of Venture Innovation, 2(2), 1-19.
  19. Cigdem, H., Ozturk, M., and Topcu, A. (2016). Vocational college students' acceptance of web-based summative listening comprehension test in an EFL course. Computers in Human Behavior, 61, 522-531. https://doi.org/10.1016/j.chb.2016.03.070
  20. Davidson, M., and Diamond, T. (2020). On the profitability of selfish mining against multiple difficulty adjustment algorithms. IACR Cryptology EPrint Archive, (2020/094), 1-22.
  21. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  22. De Angelis, P., De Marchis, R., Marino, M., Martire, A. L., and Oliva, I. (2021). Betting on bitcoin: a profitable trading between directional and shielding strategies. Decisions in Economics and Finance, 44(2), 883-903. https://doi.org/10.1007/s10203-021-00324-z
  23. De Jesus, S. B., Austria, D., Marcelo, D. R., Ocampo, C., Tibudan, A. J., and Tus, J. (2022). Play-to-Earn: A qualitative analysis of the experiences and challenges faced by axie infinity online gamers amidst the COVID-19 pandemic. International Journal of Psychology and Counseling, 12(1), 391-424.
  24. Delfabbro, P., Delic, A., and King, D. L. (2022). Understanding the mechanics and consumer risks associated with play-to-earn (P2E) gaming. Journal of Behavioral Addictions, 11(3), 716-726. https://doi.org/10.1556/2006.2022.00066
  25. Dickinger, A., Arami, M., and Meyer, D. (2008). The role of perceived enjoyment and social norm in the adoption of technology with network externalities. European Journal of Information Systems, 17, 4-11. https://doi.org/10.1057/palgrave.ejis.3000726
  26. Ding, Y. (2019). Looking forward: The role of hope in information system continuance. Computers in Human Behavior, 91, 127-137. https://doi.org/10.1016/j.chb.2018.09.002
  27. Doleck, T., Bazelais, P., and Lemay, D. J. (2017). Need for self-expression on instagram: A technology acceptance perspective. 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT) (pp. 1-3). https://doi.org/10.1109/CIACT.2017.7977305
  28. Fox, G., Clohessy, T., van der Werff, L., Rosati, P., and Lynn, T. (2021). Exploring the competing influences of privacy concerns and positive beliefs on citizen acceptance of contact tracing mobile applications. Computers in Human Behavior, 121, 106806. https://doi.org/10.1016/j.chb.2021.106806
  29. Goffman, E. (2002). The Presentation of Self in Everyday Life. NY: Garden City.
  30. Gokcearslan, S., Yildiz Durak, H., and Atman Uslu, N. (2022). Acceptance of educational use of the Internet of Things (IoT) in the context of individual innovativeness and ICT competency of pre-service teachers. Interactive Learning Environments, (July), 1-15. https://doi.org/10.1080/10494820.2022.2091612
  31. Gonzalez Bravo., L., Nistor, N., Castro Ramirez, B., Gutierrez Soto, I., Varas Contreras, M., Nunez Vives, M., and Maldonado Robles, P. (2022). Higher education managers' perspectives on quality management and technology acceptance: A tale of elders, mediators, and working bees in times of Covid-19. Computers in Human Behavior, 131 (February). https://doi.org/10.1016/j.chb.2022.107236
  32. Guan, C., Ding, D., and Guo, J. (2022). Web3.0: A Review And Research Agenda. In 2022 RIVF International Conference on Computing and Communication Technologies (RIVF) (pp. 653-658). https://doi.org/10.1109/RIVF55975.2022.10013794
  33. Hong, W., Liu, R. De, Ding, Y., Jiang, R., Sun, Y., and Jiang, S. (2021). A time-lagged study of two possible routes from personal innovativeness to life satisfaction in adolescents: Learning and social interaction on mobile phones. Personality and Individual Differences, 182(19), 111075. https://doi.org/10.1016/j.paid.2021.111075
  34. Hur, H. J., Lee, H. K., and Choo, H. J. (2017). Understanding usage intention in innovative mobile app service: Comparison between millennial and mature consumers. Computers in human behavior, 73, 353-361. https://doi.org/10.1016/j.chb.2017.03.051
  35. Hwang, J., Kim, H., and Kim, W. (2019). Investigating motivated consumer innovativeness in the context of drone food delivery services. Journal of Hospitality and Tourism Management, 38(September 2018), 102-110. https://doi.org/10.1016/j.jhtm.2019.01.004
  36. Hwang, Y. (2014). User experience and personal innovativeness: An empirical study on the Enterprise Resource Planning systems. Computers in Human Behavior, 34, 227-234. https://doi.org/10.1016/j.chb.2014.02.002
  37. Islam, N., Marinakis, Y., Olson, S., White, R., and Walsh, S. (2022). Is blockchain mining profitable in the long run?, IEEE Transactions on Engineering Management, 70(2), 386-399. https://doi.org/10.1109/TEM.2020.3045774
  38. Jang, M. K. (2022). Why do people play P2E (Play-to-Earn) games?: Focusing on outcome expectation and social influence. Knowledge Management Research, 23(3), 23-44.
  39. Jeong, J. J., and Kim, T. U. (2003). Electronic commerce: An exploratory study for identifying success factors in on-line games: Analysis of game players' behavior. The KIPS Transactions: Part D, 10(6), 1049-1058.
  40. Jo, S. C., and Han, Y. J. (2020). A study on the effect of health belief factors on the acceptance of mobile healthcare: Focusing on mediating effects of perceived usefulness. Regional Industry Review, 43(2), 263-28
  41. Joo, S. (2018). The influence of the tour platform on customer satisfaction and behavioral intention by technology acceptance model. International Journal of Tourism Management and Sciences, 33(4), 57-74. https://doi.org/10.21719/IJTMS.33.4.4
  42. Jung, T., Chung, N., and Leue, M. C. (2015). The determinants of recommendations to use augmented reality technologies: The case of a Korean theme park. Tourism Management, 49, 75-86. https://doi.org/10.1016/j.tourman.2015.02.013
  43. Kim, J. H., Kim, M. S., Hong, R. K., and Ko, J. W. (2019). Continuous use intention of corporate mobile SNS users and its determinants: Application of extended technology acceptance model. Journal of System and Management Sciences, 9(4), 12-28.
  44. Kim, T. G., Lee, J. H., and Law, R. (2008). An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model. Tourism Management, 29(3), 500-513. https://doi.org/10.1016/j.tourman.2007.05.016
  45. Kim, Y. W., Han, S. M., and Kim, K. S. (2018). Determinants of intention to use digital healthcare service of middle and older users. Information Society & Media, 19(3), 1-23.
  46. King, D. L., Delfabbro, P. H., Gainsbury, S. M., Dreier, M., Greer, N., and Billieux, J. (2019). Unfair play? Video games as exploitative monetized services: An examination of game patents from a consumer protection perspective. Computers in Human Behavior, 101(July), 131-143. https://doi.org/10.1016/j.chb.2019.07.017
  47. Koivisto, K., Makkonen, M., Frank, L., and Riekkinen, J. (2016). Extending the technology acceptance model with personal innovativeness and technology readiness: A comparison of three models. 29th Bled EConference: Digital Economy, BLED 2016, 113-128.
  48. Koo, B. (2018). A study on the role of art museums and experience of museum visitors based on social platform and IT technology. Korean Journal of Arts Education, 16(4), 139-154.
  49. Kwang, M. S., Cho, K. M., and Lee, K. Y. (2014). Verification of an adaptive model of technology acceptance model and theory of planned behavior for online purchasing intentions of sports products: The moderating effects of lifestyle. The Korean Journal of Physical Education, 53(3), 423-441.
  50. Kwon, S. W., and Kim, G. (2014). Effects of game character customizing and game story on MMORPG game attitude and skin conductance response. Journal of Consumer Studies, 25(3), 21-44.
  51. Lee, I. S., and Lee, S. Y. (2017). The relationships among needs for self-expression, SNS's social function and continued use intention of SNS users. Korea Logistics Review, 27(3), 147-161. https://doi.org/10.17825/klr.2017.27.3.147
  52. Lee, S. H., and Kim, S. Y. (2022). An empirical study on factors affecting NFT purchase intention. Journal f the Korea Industrial Information Systems Research, 27(4), 93-104.
  53. Lee, S. I., Yoo W. J., Park, H. S., and Kim, S. H. (2016). An empirical study on acceptance intention towards healthcare wearable device. The Journal of Information Systems, 25(2), 27-50. https://doi.org/10.5859/KAIS.2016.25.2.27
  54. Lee, W. J., Hong, S. T., and Min, T. (2019). Bitcoin distribution in the age of digital transformation: Dual-path approach. Journal of Distribution Science, 16(12), 47-56. https://doi.org/10.15722/jds.16.12.201812.47
  55. Lewinski, J. S. (1999). Developer's Guide to Computer Game Design. Wordware Publishing Inc.
  56. Liu, K., and Tao, D. (2022). The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services. Computers in Human Behavior, 127(August 2021), 107026. https://doi.org/10.1016/j.chb.2021.107026
  57. Mohammadi, H. (2015). A study of mobile banking loyalty in Iran. Computers in Human Behavior, 44, 35-47. https://doi.org/10.1016/j.chb.2014.11.015
  58. Muniz, A. M., and O'guinn, T. C. (2001). Brand community. Journal of Consumer Research, 27(4), 412-432. https://doi.org/10.1086/319618
  59. Munoz-Leiva, F., Climent-Climent, S., and Liebana-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), 25-38. https://doi.org/10.1016/j.sjme.2016.12.001
  60. Mutambara, D., and Bayaga, A. (2021). Determinants of mobile learning acceptance for STEM education in rural areas. Computers and Education, 160 (September 2020), 104010. https://doi.org/10.1016/j.compedu.2020.104010
  61. Newsom, J. T., McFarland, B. H., Kaplan, M. S., Huguet, N., and Zani, B. (2005). The health consciousness myth: implications of the near independence of major health behaviors in the North American population. Social Science & Medicine, 60(2), 433-437. https://doi.org/10.1016/j.socscimed.2004.05.015
  62. Ngafeeson, M. N., and Sun, J. (2015). The effects of technology innovativeness and system exposure on student acceptance of e-textbooks. Journal of Information Technology Education, 14(1), 55-71. https://doi.org/10.28945/2101
  63. O'Brien, L., and Murnane, J. (2009). An investigation into how avatar appearance can affect interactions in a virtual world. International Journal of Social and Humanistic Computing, 1(2), 192-202. https://doi.org/10.1504/IJSHC.2009.031007
  64. Park, K. S., Chun, B. Y., Kam, S., Yeh, M. H., Kang, Y. S., Kim, K. Y., Son, J. H., Lee, Y. S., and Park, K. S. (1999). Structural relationships among health concern, health practice and health status of the disabled. Korean Journal of Preventive Medicine, 32(3), 276-288.
  65. Raffaghelli, J. E., Rodriguez, M. E., Guerrero-Roldan, A. E., and Baneres, D. (2022). Applying the UTAUT model to explain the students' acceptance of an early warning system in Higher Education. Computers and Education, 182(February). https://doi.org/10.1016/j.compedu.2022.104468
  66. Rogers, E. M., and Shoemaker, F. F. (1971). Communication of Innovations; A Cross-Cultural Approach. New York: The Free Press.
  67. Rouibah, K., and Abbas, H. A. (2010). Effect of personal innovativeness, attachment motivation and social norms on the acceptance of camera mobile phones. International Journal of Handheld Computing Research, 1(4), 41-62. https://doi.org/10.4018/jhcr.2010100103
  68. Scovell, M. D. (2022). Explaining hydrogen energy technology acceptance: A critical review. International Journal of Hydrogen Energy, in Press, 1-19. https://doi.org/10.1016/j.ijhydene.2022.01.099
  69. Shanmugavel, N., and Micheal, M. (2022). Exploring the marketing related stimuli and personal innovativeness on the purchase intention of electric vehicles through Technology Acceptance Model. Cleaner Logistics and Supply Chain, 3, 100029.
  70. Sharabati, A. A., Al-Haddad, S., Al-Khasawneh, M., Nababteh, N., Mohammad, M., Ghoush, Q. A. (2022). The Impact of TikTok User Satisfaction on Continuous Intention to Use the Application. Journal f Open Innovation: Technology, Market, and Complexity, 8(3), 1-20, https://doi.org/10.3390/joitmc8030125.
  71. Shin, J., Moon, S., Cho, B. ho, Hwang, S., and Choi, B. (2022). Extended technology acceptance model to explain the mechanism of modular construction adoption. Journal of Cleaner Production, 342 (February), 130963. https://doi.org/10.1016/j.jclepro.2022.130963
  72. Sung, H. J., and Ko, J. Y. (2012). The effect of SNS information quality characteristics on the satisfaction and the intention of continuous use: Based on ETAM(Extended Technology Acceptance Model). Journal of Tourism and Leisure Research, 24(2), 197-216.
  73. Venkatesh, V., and Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
  74. Wang, R., Zhao, X., Wang, W., and Jiang, L. (2021). What factors affect the public acceptance of new energy vehicles in underdeveloped regions? A case study of Gansu Province, China. Journal of Cleaner Production, 318(967), 128432.
  75. Xu, Q., Hwang, B. G. (BG), and Lu, Y. (2021). Households' acceptance analysis of a marketized behavioral intervention - Household energy-saving option. Journal of Cleaner Production, 318(July), 128493. https://doi.org/10.1016/j.jclepro.2021.128493
  76. Yang, Y., and Wang, X. (2019). Modeling the intention to use machine translation for student translators: An extension of Technology Acceptance Model. Computers and Education, 133(January), 116-126. https://doi.org/10.1016/j.compedu.2019.01.015
  77. Yoon, C., (2018). Extending the TAM for Green IT: A normative perspective. Computers in Human Behavior, 83, 129-139. https://doi.org/10.1016/j.chb.2018.01.032
  78. Youm D. S. (2014). User satisfaction factors influencing uses the reward application of smart phone: Focus on motives, perceived attributes, audience innovation. Journal of Korea Design Forum, (43), 131-140. https://doi.org/10.21326/KSDT.2014..43.012
  79. Yu, J., Zhang, M., Chen, X., and Fang, Z. (2022). SoK: Play-to-Earn Projects. Cornell University, Retrieved from http://arxiv.org/abs/2211.01000