Journal of Information Technology Applications and Management

Korea Data Strategy Society (한국데이터전략학회)
권호 표지
DOI QR코드

DOI QR Code

A SEM-ANN Two-step Approach for Predicting Determinants of Cloud Service Use Intention

SEM-Artificial Neural Network 2단계 접근법에 의한 클라우드 스토리지 서비스 이용의도 영향요인에 관한 연구

  • Received : 2023.11.27
  • Accepted : 2023.12.29
  • Published : 2023.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

This study aims to identify the influencing factors of intention to use cloud services using the SEM-ANN two-step approach. In previous studies of SEM-ANN, SEM presented R2 and ANN presented MSE(mean squared error), so analysis performance could not be compared. In this study, R2 and MSE were calculated and presented by SEM and ANN, respectively. Then, analysis performance was compared and feature importances were compared by sensitivity analysis. As a result, the ANN default model improved R2 by 2.87 compared to the PLS model, showing a small Cohen's effect size. The ANN optimization model improved R2 by 7.86 compared to the PLS model, showing a medium Cohen effect size. In normalized feature importances, the order of importances was the same for PLS and ANN. The contribution of this study, which links structural equation modeling to artificial intelligence, is that it verified the effect of improving the explanatory power of the research model while maintaining the order of importance of independent variables.

Keywords

References

  1. Abbasi, G. A., Tiew, L. Y., Tang, J., Goh, Y. N., and Thurasamy, R., "The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis", Plos one, Vol. 16, No. 3, 2021, p. e0247582.
  2. According to a New IDC Forecast. [Online], "The Growth in Connected IoT Devices Is Expected to Generate", 79.4ZB of Data in 2025, 2020, Available: https://www.idc.com/getdoc.jsp?containerId=prUS45213219. Accessed on: Mar. 1.
  3. Agarwal, R. and Prasad, J., "A conceptual and operational definition of personal innovativeness in the domain of information technology", Information Systems Research, Vol. 9, No. 2, 1998, pp. 204-215. https://doi.org/10.1287/isre.9.2.204
  4. Ahmed, W., Hizam, S. M., Sentosa, I., Ali, J., and Ali, T., "Structural equation modeling for acceptance of cloud computing", In 2019 International conference on advances in the emerging computing technologies (AECT), IEEE, February 2020, pp. 1-6.
  5. Alam, M. Z., Hu, W., Hoque, M. R., and Kaium, M. A., "Adoption intention and usage behavior of mHealth services in Bangladesh and China: A cross-country analysis", International Journal of Pharmaceutical and Healthcare Marketing, Vol. 14, No. 1, 2020, pp. 37-60. https://doi.org/10.1108/IJPHM-03-2019-0023
  6. Alhumaid, K., "Developing an educational framework for using mobile learning during the era of COVID-19", International Journal of Data and Network Science, Vol. 5, No. 3, 2021, p. 215.
  7. Al-Sharafi, M. A., Al-Emran, M., Arpaci, I., Marques, G., Namoun, A., and Iahad, N. A., "Examining the impact of psychological, social, and quality factors on the continuous intention to use virtual meeting platforms during and beyond COVID-19 pandemic: A hybrid SEM-ANN approach", International Journal of Human-Computer Interaction, Vol. 39, No. 13, 2023, pp. 2673-2685.
  8. Alharbi, A. and Sohaib, O., "Technology readiness and cryptocurrency adoption: PLS-SEM and deep learning neural network analysis", IEEE Access, Vol. 9, 2021, pp. 21388-21394. https://doi.org/10.1109/ACCESS.2021.3055785
  9. Ali, U., Mehmood, A., Majeed, M. F., Muhammad, S., Khan, M. K., Song, H., and Malik, K. M., "Innovative citizen's services through public cloud in Pakistan: user's privacy concerns and impacts on adoption", Mobile Networks and Applications, Vol. 24, 2019, pp. 47-68. https://doi.org/10.1007/s11036-018-1132-x
  10. Almazroi, A. A., Kabbar, E., Naser, M., and Shen, H., "Gender effect on cloud computing services adoption by university students: Case study of Saudi Arabia", International Journal of Innovation, Vol. 7, No. 1, 2019, pp. 155-177. https://doi.org/10.5585/iji.v7i1.351
  11. Arpaci, I., "Understanding and predicting students' intention to use mobile cloud storage services", Computers in Human Behavior, Vol. 58, 2016, pp. 150-157. https://doi.org/10.1016/j.chb.2015.12.067
  12. Asadi, S., Nilashi, M., Husin, A. R. C., and Yadegaridehkordi, E., "Customers' perspectives on adoption of cloud computing in the banking sector", Information Technology and Management, Vol. 18, 2017, pp. 305-330. https://doi.org/10.1007/s10799-016-0270-8
  13. Asadi, Z., Abdekhoda, M., and Nadrian, H., "Cloud computing services adoption among higher education faculties: development of a standardized questionnaire", Education and Information Technologies, Vol. 25, 2020, pp. 175-191. https://doi.org/10.1007/s10639-019-09932-0
  14. Bandura, A., "Self-efficacy: toward a unifying theory of behavioral change", Psychological Review, Vol. 84, No. 2, 1977, p. 191.
  15. Bhatia, T. and Verma, A. K., "Data security in mobile cloud computing paradigm: A survey, taxonomy and open research issues", The Journal of Supercomputing, Vol. 73, 2017, pp. 2558-2631. https://doi.org/10.1007/s11227-016-1945-y
  16. Burda, D. and Teuteberg, F., "The role of trust and risk perceptions in cloud archiving-Results from an empirical study", The Journal of High Technology Management Research, Vol. 25, No. 2, 2014, pp. 172-187. https://doi.org/10.1016/j.hitech.2014.07.008
  17. Binsawad, M. H., "Corporate social responsibility in higher education: A PLSSEM neural network approach", IEEE Access, Vol. 8, 2020, pp. 29125-29131. https://doi.org/10.1109/ACCESS.2020.2972225
  18. Cohen, J. E., "Statistical Power Analysis for the Behavioral Sciences", Hillsdale, NJ: Lawrence Erlbaum Associates, Inc., 1988.
  19. Caudill, E. M. and Murphy, P. E., "Consumer online privacy: Legal and ethical issues", Journal of Public Policy & Marketing, Vol. 19, No. 1, 2000, pp. 7-19. https://doi.org/10.1509/jppm.19.1.7.16951
  20. Chen, S. L., Chen, J. H., and Chang, S. C., "Understanding the antecedents of individuals' intention of using cloud services", Journal of Economics and Management, Vol. 13, No. 2, 2017, pp. 139-166.
  21. Chen, Y., Hu, Y., Zhou, S., and Yang, S., "Investigating the determinants of performance of artificial intelligence adoption in the hospitality industry during COVID-19", International Journal of Contemporary Hospitality Management, Vol. 35, No. 8, 2023, pp. 2868-2889. https://doi.org/10.1108/IJCHM-04-2022-0433
  22. Chong, A. Y. L. and Bai, R., "Predicting open IOS adoption in SMEs: An integrated SEM-neural network approach", Expert Systems with Applications, Vol. 41, No. 1, 2014, pp. 221-229. https://doi.org/10.1016/j.eswa.2013.07.023
  23. Compeau, D. R. and Higgins, C. A., "Application of social cognitive theory to training for computer skills", Information Systems Research, Vol. 6, No. 2, 1995, pp. 118-143. https://doi.org/10.1287/isre.6.2.118
  24. Davis, F. D., "Perceived usefulness, perceived ease of use, and user acceptance of information technology", MIS Quarterly, 1989, pp. 319-340.
  25. De Ruyter, K., Wetzels, M., and Kleijnen, M., "Customer adoption of e-service: an experimental study", International Journal of Service Industry Management, Vol. 12, No. 2, 2001, pp. 184-207. https://doi.org/10.1108/09564230110387542
  26. Duc, M. L. and Viet, Q. N. K., "Analysis Affect Factors of Smart Meter: A PLSSEM Neural Network", International Research Journal on Advanced Science Hub, Vol. 4, No. 12, 2022, pp. 288-301. https://doi.org/10.47392/irjash.2022.071
  27. Duc, M. L., Bilik, P., and Martinek, R., "Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis", IEEE Access, 2023.
  28. Elareshi, M., Habes, M., Youssef, E., Salloum, S. A., Alfaisal, R., and Ziani, A., "SEM-ANN-based approach to understanding students' academic-performance adoption of YouTube for learning during Covid", Heliyon, Vol. 8, No. 4, 2022.
  29. Featherman, M. S. and Pavlou, P. A., "Predicting e-services adoption: A perceived risk facets perspective", International Journal of Human-Computer Studies, Vol. 59, No. 4, 2003, pp. 451-474. https://doi.org/10.1016/S1071-5819(03)00111-3
  30. Featherman, M. S., Miyazaki, A. D., and Sprott, D. E., "Reducing online privacy risk to facilitate e-service adoption: The influence of perceived ease of use and corporate credibility", Journal of Services Marketing, Vol. 24, No. 3, 2010, pp. 219-229. https://doi.org/10.1108/08876041011040622
  31. Featherman, M. S. and Pavlou, P. A., "Predicting e-services adoption: A perceived risk facets perspective", International Journal of Human-Computer Studies, Vol. 59, No. 4, 2003, pp. 451-474. https://doi.org/10.1016/S1071-5819(03)00111-3
  32. Grewal, R., Cote, J. A., and Baumgartner, H. "Multicollinearity and measurement error in structural equation models: Implications for theory testing", Marketing Science, Vol. 23, No. 4, 2004, pp. 519-529. https://doi.org/10.1287/mksc.1040.0070
  33. Gartner, "Gartner Forecasts Worldwide Public Cloud Revenue to Grow 17% in 2020", Accessed February 2020. [Online]. Available: https://www.gartner.com/en/newsroo m/press-releases/2019-11-13-gartner-f orecasts-worldwide-public-cloud-reven ue-to-grow-17-percent.
  34. Gharaibeh, A., Salahuddin, M. A., Hussini, S. J., Khreishah, A., Khalil, I., Guizani, M., and Al-Fuqaha, A. "Smart cities: A survey on data management, security, and enabling technologies", IEEE Communications Surveys & Tutorials, Vol. 19, No. 4, 2017, pp. 2456-2501. https://doi.org/10.1109/COMST.2017.2736886
  35. Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M., "A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)" (2nd ed.), Los Angeles, CA: Sage Publications, 2016.
  36. Hammouri, Q. and Abu-Shanab, E. A., "Major factors influencing the adoption of cloud computing in Jordan", International Journal of Technology and Human Interaction (IJTHI), Vol. 16, No. 4, 2020, pp. 55-69. https://doi.org/10.4018/IJTHI.2020100104
  37. Hayat, N., Salameh, A. A., Mamun, A. A., Helmi Ali, M., and Makhbul, Z. K. M., "Tax compliance behavior among Malaysian taxpayers: A dual-stage PLSSEM and ANN analysis", SAGE Open, Vol. 12, No. 3, 2022, p. 21582440221127190.
  38. Hsieh, P. J., "An empirical investigation of patients' acceptance and resistance toward the health cloud: The dual factor perspective", Computers in Human Behavior, Vol. 63, 2016, pp. 959-969. https://doi.org/10.1016/j.chb.2016.06.029
  39. Hsieh, P. J. and Lin, W. S., "Explaining resistance to system usage in the PharmaCloud: A view of the dual-factor model", Information & Management, Vol. 55, No. 1, 2018, pp. 51-63. https://doi.org/10.1016/j.im.2017.03.008
  40. Iimedia Research, 2022. Retrieved from: https://www.iimedia.cn/c400/84607.html.
  41. Koehler, P., Anandasivam, A., and Ma, D., "Cloud services from a consumer perspective", AIS, 2010.
  42. Kranthi, A. K. and Ahmed, K. A., "Determinants of smartwatch adoption among IT professionals: An extended UTAUT2 model for smartwatch enterprise", International Journal of Enterprise Network Management, Vol. 9, No. 3-4, 2018, pp. 294-316. https://doi.org/10.1504/IJENM.2018.094669
  43. Kwon, S. D., Chun, D. Y., and Kim, Y. Y., "Reexamination of Effect of Perceived Risk on Purchasing Intention", Journal of Information Technology Applications and Management, Vol. 19, No. 2, 2012, pp. 233-247.
  44. Lau, A. J., Tan, G. W. H., Loh, X. M., Leong, L. Y., Lee, V. H., and Ooi, K. B., "On the way: Hailing a taxi with a smartphone? A hybrid SEM-neural network approach", Machine Learning with Applications, Vol. 4, 2021, p. 100034.
  45. Leong, L. Y., Hew, T. S., Ooi, K. B., Lee, V. H., and Hew, J. J., "A hybrid SEM-neural network analysis of social media addiction", Expert Systems with Applications, Vol. 133, 2019, pp. 296-316. https://doi.org/10.1016/j.eswa.2019.05.024
  46. Leong, L. Y., Hew, T. S., Tan, G. W. H., and Ooi, K. B., "Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach", Expert Systems with Applications, Vol. 40, No. 14, 2013, pp. 5604-5620. https://doi.org/10.1016/j.eswa.2013.04.018
  47. Leong, L. Y., Hew, T. S., Ooi, K. B., and Dwivedi, Y. K., "Predicting trust in online advertising with an SEM-artificial neural network approach", Expert Systems with Applications, Vol. 162, 2020, p. 113849.
  48. Lopes, N. and Ribeiro, B., "An efficient gradient-based learning algorithm applied to neural networks with selective actuation neurons", Neural, Parallel & Scientific Computations, Vol. 11, No. 3, 2003, pp. 253-272.
  49. Lo, P. S., Dwivedi, Y. K., Tan, G. W. H., Ooi, K. B., Aw, E. C. X., and Metri, B., "Why do consumers buy impulsively during live streaming? A deep learningbased dual-stage SEM-ANN analysis", Journal of Business Research, Vol. 147, 2022, pp. 325-337. https://doi.org/10.1016/j.jbusres.2022.04.013
  50. Mariani, M. M., Styven, M. E., and Teulon, F., "Explaining the intention to use digital personal data stores: An empirical study", Technological Forecasting and Social Change, Vol. 166, 2021, p. 120657.
  51. Mollah, M. B., Azad, M. A. K., and Vasilakos, A., "Security and privacy challenges in mobile cloud computing: Survey and way ahead", Journal of Network and Computer Applications, Vol. 84, 2017, pp. 38-54. https://doi.org/10.1016/j.jnca.2017.02.001
  52. Nicolaou, A. I. and McKnight, D. H., "Perceived information quality in data exchanges: Effects on risk, trust, and intention to use", Information Systems Research, Vol. 17, No. 4, 2006, pp. 332- 351. https://doi.org/10.1287/isre.1060.0103
  53. Nguyen, P. H., Tsai, J. F., Lin, M. H., and Hu, Y. C., "A hybrid model with spherical fuzzy-AHP, PLS-SEM, and ANN to predict vaccination intention against COVID-19", Mathematics, Vol. 9, No. 23, 2021, p. 3075.
  54. Phillips, P., Zigan, K., Silva, M. M. S., and Schegg, R., "The interactive effects of online reviews on the determinants of Swiss hotel performance: A neural network analysis", Tourism Management, Vol. 50, 2015, pp. 130-141. https://doi.org/10.1016/j.tourman.2015.01.028
  55. Rimal, R. N., "Closing the knowledge-behavior gap in health promotion: The mediating role of self-efficacy", Health Communication, Vol. 12, No. 3, 2000, pp. 219-237. https://doi.org/10.1207/S15327027HC1203_01
  56. Ratten, V., "Cloud computing: A social cognitive perspective of ethics, entrepreneurship, technology marketing, computer self-efficacy, and outcome expectancy on behavioral intentions", Australasian Marketing Journal, Vol. 21, No. 3, 2013, pp. 137-146. https://doi.org/10.1016/j.ausmj.2013.02.008
  57. Rehman, I. H. U., Ahmad, A., Akhter, F., and Aljarallah, A., "A dual-stage SEM-ANN analysis to explore consumer adoption of smart wearable healthcare devices", Journal of Global Information Management (JGIM), Vol. 29, No. 6, 2021, pp. 1-30. https://doi.org/10.4018/JGIM.294123
  58. Scott, J. E. and Walczak, S., "Cognitive engagement with a multimedia ERP training tool: Assessing computer selfefficacy and technology acceptance", Information & Management, Vol. 46, No. 4, 2009, pp. 221-232. https://doi.org/10.1016/j.im.2008.10.003
  59. Senyo, P. K., Addae, E., and Boateng, R., "Cloud computing research: A review of research themes, frameworks, methods, and future research directions", International Journal of Information Management, Vol. 38, No. 1, 2018, pp. 128-139. https://doi.org/10.1016/j.ijinfomgt.2017.07.007
  60. Sharma, M., Joshi, S., Luthra, S., and Kumar, A., "Impact of digital assistant attributes on millennials' purchasing intentions: A multi-group analysis using PLS-SEM, artificial neural network, and fsQCA", Information Systems Frontiers, 2022, pp. 1-24.
  61. Sheehan, K. B. and Hoy, M. G., "Dimensions of privacy concern among online consumers", Journal of Public Policy & Marketing, Vol. 19, No. 1, 2000, pp. 62-73. https://doi.org/10.1509/jppm.19.1.62.16949
  62. Sultana, J., "Determining the factors that affect the uses of Mobile Cloud Learning (MCL) platform Blackboard-a modification of the UTAUT model", Education and Information Technologies, Vol. 25, No. 1, 2020, pp. 223-238. https://doi.org/10.1007/s10639-019-09969-1
  63. Sohaib, O., Hussain, W., Asif, M., Ahmad, M., and Mazzara, M., "A PLS-SEM neural network approach for understanding cryptocurrency adoption", IEEE Access, Vol. 8, 2019, pp. 13138-13150. https://doi.org/10.1109/ACCESS.2019.2960083
  64. Teing, Y. Y., Dehghantanha, A., Choo, K. K. R., and Yang, L. T., "Forensic investigation of P2P cloud storage services and backbone for IoT networks: BitTorrent Sync as a case study", Computers & Electrical Engineering, Vol. 58, 2017, pp. 350-363. https://doi.org/10.1016/j.compeleceng.2016.08.020
  65. Trcek, D., Trobec, R., Pavesic, N., and Tasic, J. F., "Information systems security and human behavior", Behaviour & Information Technology, Vol. 26, No. 2, 2007, pp. 113-118. https://doi.org/10.1080/01449290500330299
  66. Venkatesh, V. and Brown, S. A., "A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges", MIS Quarterly, 2001, pp. 71-102.
  67. Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D., "User acceptance of information technology: Toward a unified view", MIS Quarterly, 2003, pp. 425-478.
  68. Venkatesh, V., Thong, J. Y., and Xu, X., "Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology", MIS Quarterly, 2012, pp. 157-178.
  69. Wu, K., Vassileva, J., and Zhao, Y., "Understanding users' intention to switch personal cloud storage services: Evidence from the Chinese market", Computers in Human Behavior, Vol. 68, 2017, pp. 300- 314. https://doi.org/10.1016/j.chb.2016.11.039
  70. Wu, K., Vassileva, J., Noorian, Z., and Zhao, Y., "How do you feel when you see a list of prices? The interplay among price dispersion, perceived risk, and initial trust in the Chinese C2C market", Journal of Retailing and Consumer Services, Vol. 25, 2015, pp. 36-46. https://doi.org/10.1016/j.jretconser.2015.03.007
  71. Xia, Z., Shi, T., Xiong, N. N., Sun, X., and Jeon, B., "A privacy-preserving handwritten signature verification method using combinational features and secure kNN", IEEE Access, Vol. 6, 2018, pp. 46695-46705. https://doi.org/10.1109/ACCESS.2018.2866411
  72. Xia, Z., Xiong, N. N., Vasilakos, A. V., and Sun, X., "EPCBIR: An efficient and privacy-preserving content-based image retrieval scheme in cloud computing", Information Sciences, Vol. 387, 2017, pp. 195-204. https://doi.org/10.1016/j.ins.2016.12.030
  73. Yang, H. L. and Lin, S. L., "User continuance intention to use cloud storage service", Computers in Human Behavior, Vol. 52, 2015, pp.219-232. https://doi.org/10.1016/j.chb.2015.05.057
  74. Sternad Zabukovsek, S., Kalinic, Z., Bobek, S., and Tominc, P., "SEM-ANN based research of factors' impact on extended use of ERP systems", Central European Journal of Operations Research, Vol. 27, 2019, pp. 703-735. https://doi.org/10.1007/s10100-018-0592-1
  75. Zhang, J., Chen, B., Zhao, Y., Cheng, X., and Hu, F., "Data security and privacy-preserving in edge computing paradigm: Survey and open issues", IEEE Access, Vol. 6, 2018, pp. 18209-18237. https://doi.org/10.1109/ACCESS.2018.2820162