The Journal of Society for e-Business Studies (한국전자거래학회지)

Society for e-Business Studies (한국전자거래학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on the Factors Influencing a Company's Selection of Machine Learning: From the Perspective of Expanded Algorithm Selection Problem

기업의 머신러닝 선정에 영향을 미치는 요인 연구: 확장된 알고리즘 선택 문제의 관점으로

  • Received : 2022.02.16
  • Accepted : 2022.05.03
  • Published : 2022.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

As the social acceptance of artificial intelligence increases, the number of cases of applying machine learning methods to companies is also increasing. Technical factors such as accuracy and interpretability have been the main criteria for selecting machine learning methods. However, the success of implementing machine learning also affects management factors such as IT departments, operation departments, leadership, and organizational culture. Unfortunately, there are few integrated studies that understand the success factors of machine learning selection in which technical and management factors are considered together. Therefore, the purpose of this paper is to propose and empirically analyze a technology-management integrated model that combines task-tech fit, IS Success Model theory, and John Rice's algorithm selection process model to understand machine learning selection within the company. As a result of a survey of 240 companies that implemented machine learning, it was found that the higher the algorithm quality and data quality, the higher the algorithm-problem fit was perceived. It was also verified that algorithm-problem fit had a significant impact on the organization's innovation and productivity. In addition, it was confirmed that outsourcing and management support had a positive impact on the quality of the machine learning system and organizational cultural factors such as data-driven management and motivation. Data-driven management and motivation were highly perceived in companies' performance.

인공지능의 사회적수용도가 증가하면서 머신러닝 기법을 기업에 적용하는 사례가 증가하고 있다. 머신러닝 기법의 선정에는 주로 정확성이나 해석 가능성 등 기술적 요인이 주로 기준이 되어왔다. 그러나 머신러닝 채택의 성공은 개발부서, 사용부서, 리더십과 조직문화 등 경영관리 요인도 영향을 주기도 한다. 아쉽게도 기술적 요인과 경영관리적 요인이 함께 고려된 머신러닝 선정의 성공 요인을 이해하는 통합 연구가 거의 존재하지 않는다. 이에 본 논문의 목적은 기업 내 머신러닝 선정을 이해하기 위해 John Rice의 algorithm selection process model과 task-technology fit, 그리고 IS Success Model 이론을 결합한 기술-경영관리 통합 모형을제안하고 실증적 분석을 하는 것이다. 머신러닝을 도입한 국내 기업 240곳을 대상으로 설문 분석을 실시한 결과 알고리즘 품질과 데이터 품질이 높을수록 문제-알고리즘 적합성에 높게 영향을 주는 것으로 나타났으며, 문제-알고리즘 적합성은 조직의 생산성과 혁신성에도 유의한 영향을 미치는 것으로 검증되었다. 또한 외주화와 경영진 지원이 머신러닝 시스템 품질에 긍정적인 영향을 미치고, 데이터 중심 경영 및 동기화와 같은 조직문화 요인은 활용성과에 높은 영향을 미치는 것으로 확인되었다.

Keywords

Acknowledgement

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2020S1A3A2A02093277).

References

  1. Agrawal, A., McHale, J., and Oettl, A., "Finding needles in haystacks: Artificial intelligence and recombinant growth," University of Chicago Press, pp. 149-17, 2020.
  2. Aljarboa, S. and Miah, S. J., "An integration of UTAUT and task-technology fit frameworks for assessing the acceptance of clinical decision support systems in the context of a developing country," In of Sixth International Congress on Information and Communication Technology, pp. 127-137, 2022.
  3. Alsheibani, S. and Cheung, Y., "Artificial intelligence adoption: AI-readiness at firm-level," PACIS 2018 Proceedings, p. 37, 2018.
  4. Alyoussef, I. Y., "Massive Open Online Course (MOOCs) acceptance: The role of Task-Technology Fit (TTF) for higher education sustainability," Sustainability, Vol. 13, No. 13, p. 7374, 2021. https://doi.org/10.3390/su13137374
  5. Anthony R. N., "Planning and control systems: A framework for analysis," Boston: Harvard Business School, Division of Research, 1965.
  6. Aral, S., Brynjolfsson, E., and Wu, D, "Which came first, it or productivity? The virtuous cycle of investment and use in enterprise systems," SSRN Electronic Journal, pp. 1-22, 2006.
  7. Attaran, M. and Deb, P., "Machine learning: The new 'big thing' for competitive advantage," Int. J. Knowledge Engineering and Data Mining, Vol. 5, No. 4, pp. 277-305, 2018. https://doi.org/10.1504/ijkedm.2018.095523
  8. Aubert, B. A., Rivard, S., and Patry, M., "A transaction cost approach to outsourcing behavior: Some empirical evidence," Information and Management, Vol. 30, No. 2, pp. 51-64, 1996. https://doi.org/10.1016/0378-7206(95)00045-3
  9. Bagozzi, R. P. and Yi, Y., "On the evaluation of structural equation models," Journal of the academy of marketing science, Vol. 16, No. 1, pp. 74-94, 1988. https://doi.org/10.1007/BF02723327
  10. Bradley, S. and Nolan, R., "Sense & Respond," Boston: Harvard Business School Press, 1998.
  11. Brock V. and Khan, H. U., "Big data analytics: Does organizational factor matters impact technology acceptance?," Journal of Big Data, Vol. 4, No. 1, pp. 1-28, 2017. https://doi.org/10.1186/s40537-016-0062-3
  12. Brynjolfsson, E., Hitt, L. M., and Kim, H. H., "Strength in numbers: How does data-driven decision making affect firm performance?," Available at SSRN 1819486, 2011.
  13. Chen, Y., Wang, H., Li, W., Sakaridis, C., Dai, D., and Van Gool, L., "Scale-aware domain adaptive faster R-Cnn," International Journal of Computer Vision, Vol. 129, No. 7, pp. 2223-2243, 2021. https://doi.org/10.1007/s11263-021-01447-x
  14. Cunha, T., Soares, C., and de Carvalho, A. C., "Metalearning and recommender systems: A literature review and empirical study on the algorithm selection problem for collaborative filtering," Information Sciences, Vol. 423, pp. 128-144, 2018. https://doi.org/10.1016/j.ins.2017.09.050
  15. Dahlberg, T. and Nyrhinen, M., "A new instrument to measure the success of IT outsourcing," In Proceedings of the 39th Hawaii International Conference on System Sciences (HICSS'06), Vol. 8, pp. 200a-200a, 2006.
  16. Davenport, T. H., "Competing on analytics," Harvard Business Review, Vol. 84, No. 1, p. 98, 2006.
  17. de Almeda, A. R., Medeiros, P. Y., and Halpern, E. E., "Why internal clients are dissatisfied with the quality of information technology services provided by their organizations?," Procedia Computer Science, Vol. 55, pp. 922-930, 2015. https://doi.org/10.1016/j.procs.2015.07.112
  18. DeLone, W. H. amd McLean, E. R., "The delone and mclean model of information system success," Journal of Management Information System, Vol. 19, No. 4, pp. 9-30, 2003. https://doi.org/10.1080/07421222.2003.11045748
  19. Dennis, A. R., Wixom, B. H., and Vandenberg, R. J., "Understanding fit and appropriation effects in group support systems via meta-analysis understanding fit and appropriation effects in group support systems via meta-analysis," MIS Quaterly, pp. 167-193, 2001.
  20. Dharanikota, S. and Marakas, G. M., "Does AI reliance lead to performance? A task-technology fit theory perspective," 2021.
  21. Dishaw, M. T. and Strong, D. M., "Extending the technology acceptance model with task-technology fit constructs," Information & Management, Vol. 36, No. 1, pp. 9-21, 1999. https://doi.org/10.1016/S0378-7206(98)00101-3
  22. Domberger, S., Fernandez, P., and Fiebig, D. G., "Modelling the price, performance and contract characteristics of it outsourcing," Journal of Information Technology, Vol. 15, No, 2, pp. 107-118, 2000. https://doi.org/10.1080/026839600344302
  23. Elbanna, A., "Top management support in multiple-project environments: An in-practice view," European Journal of Information Systems, Vol. 22, No. 3, pp. 278-294, 2013. https://doi.org/10.1057/ejis.2012.16
  24. Feng, L., Lu, J., and Wang, J., "A Systematic Review of Enterprise Innovation Ecosystems," Sustainability, Vol. 13, No. 10, p. 5742, 2021. https://doi.org/10.3390/su13105742
  25. Fornell, C. and Larcker, D. F., "Structural equation models with unobservable variables and measurement error: Algebra and statistics," Journal of Marketing Research, Vol. 18, No. 3, pp. 382-388, 1981. https://doi.org/10.2307/3150980
  26. Gan, Q. and Cao, Q., "Adoption of electronic health record system: Multiple theoretical perspectives," In: 2014 47th Hawaii International Conference on System Sciences, pp. 2716-2724, 2014.
  27. Garbelli, M. E., "Market-Driven Management, Competitive Markets, and Performance Metrics," Symphonya-Emerging Issues in Management, Vol. 1, pp. 72-87, 2008.
  28. Gebauer, J., Shaw, M. J., Gribbins, M. L., Gebauer, J., Shaw, M. J., and Gribbins, M. L., "Towards a specific theory of task-technology fit for mobile information systems," Journal of Strategic Information Systems, pp. 12-15, 2005.
  29. Goodhue, D. L. and Thompson, R. L., "Task-technology fit and individual performance," MIS Quarterly, Vol. 19, No. 2pp. 213-236, 1995. https://doi.org/10.2307/249689
  30. Goodhue, D. L., "Development and measurement validity of a task-technology fit instrument for user evaluations of information systems," Decision Sciences, Vol. 29, No. 1, pp. 105-138, 1998. https://doi.org/10.1111/j.1540-5915.1998.tb01346.x
  31. Gorry, G. A. and Scott Morton, M. S., "A Framework for Management Information systems," Sloan Management Review Vol. 13, No. 1, pp. 55-70, 1971.
  32. Grant, A. M., "The significance of task significance: Job performance effects, relational mechanisms, and boundary conditions," Journal of Applied Psychology, Vol. 93, No. 1, pp. 108-124, 2008. https://doi.org/10.1037/0021-9010.93.1.108
  33. Hair, J. F., Anderson, R. E., Tatham, R. L., and Black, W. C., "Multivariate data analysis prentice hall," Upper Saddle River, NJ, 730, 1998.
  34. Hayduk, L. A., "Structural equation modeling with LISREL: Essentials and advances," Social Forces, Vol. 69, No. 1, pp. 338, 1987.
  35. Ho, V. T., Ang, S., and Straub, D., "When subordinates become it contractors: persistent managerial expectations in IT outsourcing," Information System Research, Vol. 14, No. 1, pp. 66-125, 2003. https://doi.org/10.1287/isre.14.1.66.14764
  36. Hu, L. T. and Bentler, P. M., "Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification," Psychological Methods, Vol. 3, No. 4, pp. 424-453, 1998. https://doi.org/10.1037/1082-989X.3.4.424
  37. Ifinedo, P., "Measuring africa's e-readiness in the global networked economy: A nine-country data analysis," International Journal of Education and development using ICT, Vol. 1, No. 1, pp. 53-71, 2005.
  38. Jeffery, M., "Data-Driven marketing: The 15 metrics everyone in marketing should know," John Wiley & Sons, 2010.
  39. Jubraj, R., Graham, T., and Ryan, E., "Redefine banking with artificial intelligence," Intell. Bank, pp. 1-20, 2018.
  40. Junglas, I., Abraham, C., and Watson, R. T., "Task-technology fit for mobile locatable information systems," Decision Support Systems, Vol. 45, No. 4, pp. 1046-1057, 2008. https://doi.org/10.1016/j.dss.2008.02.007
  41. Karimi-Alaghehband, F. and Rivard, S., "IT outsourcing success: A dynamic capability-based model," Journal of Strategic Information Systems, Vol. 29, No. 1, pp. 101599, 2020. https://doi.org/10.1016/j.jsis.2020.101599
  42. Khan, I. U., Hameed, Z., Yu, Y., Islam, T., Sheikh, Z., and Khan, S. U., "Predicting the Acceptance of MOOCs in a developing country: Application of task-technology fit model, social motivation, and self-determination theory," Telematics and Informatics, Vol. 35, No. 4, pp. 964-978, 2018. https://doi.org/10.1016/j.tele.2017.09.009
  43. Kim, N. J., Kim, J. O., Lee, J. E., Mydin, O., and Marzuki, A., "An influence of outdoor recreation participants' perceived restorative environment on wellness effect, satisfaction and loyalty," SHS Web of Conferences, Vol. 12, p. 01082, 2014. https://doi.org/10.1051/shsconf/20141201082
  44. Klopping, I. M. and Mckinney, E., "Extending the technology acceptance model and the task-technology fit model to consumer e-commerce," Information Technology, Learning and Performance Journal, Vol. 22, No. 1, pp. 35-48, 2004.
  45. Koh, C., Ang, S., and Straub, D. W., "IT outsourcing success: A psychological contract perspective," Information Systems Research, Vol. 15, No. 4, pp. 356-373, 2004. https://doi.org/10.1287/isre.1040.0035
  46. Koo, C., Watia, Y., and Jungb, J.J., "Examination of how social aspects moderate the relationship between task characteristics and usage of social communication technologies (SCTs) in organizations," International Journal of Information Management, Vol. 31, No. 5, pp. 445-459, 2011. https://doi.org/10.1016/j.ijinfomgt.2011.01.003
  47. Kuo, R. Z. and Lee, G. G., "KMS adoption: The effects of information quality", Management Decision, Vol. 47 No. 10, pp. 1633-1651, 2009. https://doi.org/10.1108/00251740911004727
  48. Lee, C. C., Cheng, H. K., and Cheng, H. H., "An empirical study of mobile commerce in insurance industry: task-technology fit and individual differences," Decision Support Systems, Vol. 43, No. 1, pp. 95-110, 2007. https://doi.org/10.1016/j.dss.2005.05.008
  49. Lee, I. and Shin, Y. J., "Machine learning for enterprises: Applications, algorithm selection, and challenges," Business Horizons, Vol. 63, No. 2, pp. 150-170, 2020.
  50. Li, L., Wang, Y., Xu, Y., and Lin, K. Y., "Meta-learning based industrial intelligence of feature nearest algorithm selection framework for classification problems," Journal of Manufacturing Systems, 2021.
  51. Li, Y. H., "An empirical investigation on the determinants of e-procurement adoption in Chinese manufacturing enterprises," In International Conference on Management Science & Engineering 15th Conference Proceedings, IEEE, pp. 32-37, 2008.
  52. Markus, M. L., "Power, politics and MIS implementation," Communication of the ACM, Vol. 26, No. 6, pp. 430-444, 1983. https://doi.org/10.1145/358141.358148
  53. McAfee, A., "The impact of enterprise information technology adoption on operational performance: An empirical investigation," Production and Operations Management, Vol. 11, No. 1, pp. 33-53, 2002. https://doi.org/10.1111/j.1937-5956.2002.tb00183.x
  54. Mihet, R. and Thomas, P., "The economics of big data and artificial intelligence," Disruptive Innovation in Business and Finance in the Digital World, Vol. 20, pp. 29-43, 2019. https://doi.org/10.1108/S1569-376720190000020006
  55. Mohamed, M. S., Khalifa, G. S. A., and Hamoud, A., "The mediation effect of innovation on the relationship between creativity and organizational productivity: An empirical study within public sector organizations in the UAE," Journal of Engineering and Applied Sciences, Vol. 14, No. 10, pp. 3234-3242, 2019. https://doi.org/10.36478/jeasci.2019.3234.3242
  56. Muller, R. and Jugdev, K., "Critical success factors in projects," International Journal of Managing Projects in Business, Vol. 5, No. 4, pp. 757-775, 2012. https://doi.org/10.1108/17538371211269040
  57. Munoz, M. A., Kirley, M., and Halgamuge, S. K., "The algorithm selection problem on the continuous optimization domain," In Computational Intelligence in Intelligent Data Analysis, pp. 75-89, 2013.
  58. Narasimhaiah and Toni, "The impact of IT outsourcing on information systems success," Information & Management, Vol. 51, No. 3, pp. 320-335, 2014. https://doi.org/10.1016/j.im.2013.12.002
  59. Nelson, R. R., Todd, P. A., and Wixom, B. H., "Antecedents of information and system quality: An empirical examination within the context of data warehousing," Journal of Management Information Systems, Vol. 21, No. 4, pp. 199-235, 2005. https://doi.org/10.1080/07421222.2005.11045823
  60. Ooka, R., Miyoshi, T., and Yamazaki, T., "Unit traffic classification and analysis on P2P video delivery using machine learning," IEICE Communications Express, Vol. 8, No. 12, pp. 640-645, 2019. https://doi.org/10.1587/comex.2019xbl0115
  61. Operskalski, J. T. and Barbey, A. K., "Risk literacy in medical decision-making," Science, Vol. 352, No. 6284, pp. 413-414, 2016. https://doi.org/10.1126/science.aaf7966
  62. Park Y. J. and Rim, M. H., "The relationship analysis of RFID adoption and organizational performance," In ICSNC 2011, The Sixth International Conference on Systems and Networks Communications, pp. 76-82, 2011.
  63. Patterson, M. G. and West, M. A., "Validating the organizational climate measure: Links to managerial practices, productivity and innovation," Journal of Organizational Behavior, Vol. 26, No. 4, pp. 379-408, 2005. https://doi.org/10.1002/job.312
  64. Perrow, C., "A framework for the comparative analysis of organizations," American Sociological Review, Vol. 32, No. 2, pp. 194-208, 1967. https://doi.org/10.2307/2091811
  65. Poppo, L. and Zenger, T., "Do formal contracts and relational governance function as substitutes or complements?," Strategic Management Journal, Vol. 23, No. 8, pp. 707-725, 2002. https://doi.org/10.1002/smj.249
  66. Preacher, K. J. and Hayes, A. F., "Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models," Behavior Research Methods, Vol. 40, No. 3, pp. 879-891, 2008. https://doi.org/10.3758/BRM.40.3.879
  67. Rejikumar, G., Asokan A. A., and Sreedharan, V. R., "Impact of data-driven decision-making in lean six sigma: An empirical analysis," Total Quality Management & Business Excellence, Vol. 31, No. 3, pp. 279-296, 2020. https://doi.org/10.1080/14783363.2018.1426452
  68. Rice, J. R., "The algorithm selection problem-abstract models," Department of Computer Science Technical Reports. Paper 99, 1975.
  69. Rizwan, M., Hussain, S., Nawaz, M. S., and Hameed, W. U., "Impact of effective training program, job satisfaction and reward management system on the employee motivation with mediating role of employee commitment," Journal of Public Administration and Governance , Vol. 3, No. 3, p. 278, 2013. https://doi.org/10.5296/jpag.v3i3.6222
  70. Ryan, R. M. and Deci, E. L., "Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being," American Psychologist, Vol. 55, No. 1, pp. 68-78, 2000. https://doi.org/10.1037/0003-066X.55.1.68
  71. Saunders, C., Gebelt, M., and Hu, Q., "Achieving success in information systems outsourcing," California Management Review, Vol. 39, No. 2, pp. 63-79, 1997. https://doi.org/10.2307/41165887
  72. Schein, E. H., "Organizational Culture and Leadership," John Wiley & Sons, Vol. 2, 2010.
  73. Sedera, D. and Gable, G., "A factor and structural equation analysis of the enterprise systems success measurement model," In Systems Success Measurement Model. International Conference of Information, p. 449, 2004.
  74. Shahbaz, M., Gao, C., Zhai, L. L., Shahzad, F., and Hu, Y., "Investigating the adoption of big data analytics in healthcare: The moderating role of resistance to change," Journal of Big Data, Vol. 16, No. 1, pp. 1-20, 2019.
  75. Simon, H., "The new science of management decision," New York: Harper & Row, 1960.
  76. Smith-Miles, K. A., "Cross-disciplinary perspectives on meta-learning for algorithm selection," ACM Computing Surveys, Vol. 41, No. 1, pp. 1-25, 2008. https://doi.org/10.1145/1456650.1456656
  77. Soon, K. W. K., Lee, C. A., and Boursier, P., "A study of the determinants affecting adoption of big data using integrated technology acceptance model (TAM) and diffusion of innovation (DOI) in Malaysia," Internation Journal of Applied Business and Economic Research, Vol. 14, No. 1, pp. 17-47, 2016.
  78. Sultan, F. and Chan, L., "The adoption of new technology: The case of object-oriented computing in software companies," IEEE Transactions on Engineering Management, Vol. 47, No. 1, pp. 106-126, 2000. https://doi.org/10.1109/17.820730
  79. Sultana, S., Akter, S., and Kyriazis, E., "How data-driven innovation capability is shaping the future of market agility and competitive performance?," Technological Forecasting and Social Change, Vol. 174, 2022.
  80. Tam, C. and Oliveira, T., "Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective," Computers in Human Behavior, Vol. 61, pp. 233-244, 2016. https://doi.org/10.1016/j.chb.2016.03.016
  81. Tanaka, M., Bloom, N., David, J. M., and Koga, M., "Firm Performance and Macro Forecast Accuracy," Journal of Monetary Economics, Vol. 114, pp. 26-41, 2020. https://doi.org/10.1016/j.jmoneco.2019.02.008
  82. Van de Ven, A. H. and Drazen, R., "Alternative forms of in contingency theory," Administrative Science Quarterly, Vol. 30, No. 4, pp. 514-539, 1985. https://doi.org/10.2307/2392695
  83. Vansteenkiste, M., Lens, W., and Deci, E. L., "Intrinsic versus extrinsic goal contents in self-determination theory: Another look at the quality of academic motivation," Educational Psychologist, Vol. 41, No. 1, pp. 19-31, 2006. https://doi.org/10.1207/s15326985ep4101_4
  84. Venkatesh, V., Ramesh, V., and Massey, A. P., "Understanding usability in mobile commerce," Communications of the ACM, Vol. 46, No. 12, pp. 53-56, 2003. https://doi.org/10.1145/953460.953488
  85. Victor, J. G., Francisco, J. L., and Antonio, J. V., "Antecedents and Consequences of Organizational Innovation and Organizational Learning in Entrepreneurship," Industrial Management & Data Systems, Vol. 106, No. 1, 2006.
  86. Voola, R., Casimir, G., Carlson, J., and Agnihotri, M. A., "The effects of market orientation, technological opportunism, and e-business adoption on performance: A moderated mediation analysis," Australasian Marketing Journal, Vol. 20, No. 2, pp. 136-146, 2012. https://doi.org/10.1016/j.ausmj.2011.10.001
  87. Wade, M. and Hulland, J., "Review: The resource-based view and information systems research: Review, extension, and suggestions for future research," MIS Quarterly, Vol. 28, No. 1, pp. 107-142, 2004. https://doi.org/10.2307/25148626
  88. Wang, R. Y. and Strong, D. M., "Beyond accuracy: What data quality means to data consumers," Journal of Management Information Systems, Vol. 12, No. 4, pp. 5-33, 1996. https://doi.org/10.1080/07421222.1996.11518099
  89. Webb, M., "The Impact of Artificial Intelligence on the Labor Market," Available at SSRN 3842150, 2019.
  90. Wells, J. D., Sarker, S., Urbaczewski, A., and Sarker, S. U., "Studying customer evaluations of electronic commerce applications: A review and adaptation of the task-technology fit perspective," In 36Th Annual Hawaii International Conference On System Sciences, p. 10, 2003.
  91. Wen, B., Jin, Y., and Kwon, O., "Effects of artificial intelligence functionalities on online store's image and continuance intention: A resource-based view perspective," The Journal of Society for e-Business Studies Vol.25, No.2, pp. 65-98, 2020.
  92. Yang, Z., Sun, J., Zhang, Y., and Wang, Y., "Understanding SaaS adoption from the perspective of organizational users: A tripod readiness model," Computers in Human Behavior, Vol. 45, pp. 254-264, 2015. https://doi.org/10.1016/j.chb.2014.12.022
  93. Yoo, K. W., Hwang, K., and Kwon, O., "The effects of vr-based cultural heritage experience on visit intention," The Journal of Society for e-Business Studies Vol. 26, No. 2, pp. 95-122, May 2021. https://doi.org/10.7838/JSEBS.2021.26.2.095
  94. Yuan, Y., Archer, N., Connelly, C. E., and Zheng, W., "Identifying the ideal fit between mobile work and mobile work support," Information & Management, Vol. 47, No. 3, pp. 125-137, 2010. https://doi.org/10.1016/j.im.2009.12.004
  95. Yuce, A., Abubakar, A. M., and Ilkan, M., "Intelligent tutoring systems and learning performance," Online Information Review, Vol.43, No. 4, pp. 600-616, 2019. https://doi.org/10.1108/oir-11-2017-0340
  96. Zepeda, L., "Simultaneity of technology adoption and productivity," Journal of Agriculture and Resource Economics, pp. 46-57, 1994.
  97. Zha, X., Yang, H., Yan, Y., Liu, K., and Huang, C., "Exploring the effect of social media information quality, source credibility and reputation on informational fit-to-task: Moderating role of focused immersion," Computers in Human Behavior, Vol. 79, pp. 227-237, 2018. https://doi.org/10.1016/j.chb.2017.10.038
  98. Zhai, C., "Research on post-adoption behavior of B2B e-marketplace in China," In 2010 International Conference on Management and Service Science, pp. 1-5, 2010.
  99. Zhou, T., Lu, Y., and Wang, B., "Integrating TTF and UTAUT to explain mobile banking user adoption," Computers in Human Behavior, Vol. 26, No. 4, pp. 760-767, 2010. https://doi.org/10.1016/j.chb.2010.01.013