What drives MLOps adoption? An analysis using the TOE framework

References

• Abed, S.S. (2020). Social commerce adoption using TOE framework: An empirical investigation of Saudi Arabian SMEs. International Journal of Information Management, 53, 102118. https://doi.org/10.1016/j.ijinfomgt.2020.102118
   Web of Science ®Google Scholar
• Ackerman, S., Farchi, E., Raz, O., Zalmanovici, M., & Dube, P. (2020). Detection of data drift and outliers affecting machine learning model performance over time. https://arxiv.org/abs/2012.09258
   Google Scholar
• Ackerman, S., Raz, O., Zalmanovici, M., & Zlotnick, A. (2021). Automatically detecting data drift in machine learning classifiers. https://arxiv.org/abs/2111.05672
   Google Scholar
• Afsharchi, M. (2019). “http://cv.znu.ac.ir/afsharchim/AI/lectures/Decision
   Google Scholar
• Agresti, A. (2007). Building and applying logistic regression models. An introduction to categorical data analysis. (2nd ed., pp. 137–172). Hoboken,NJ: ohn Wiley & Sons. https://doi.org/10.1002/9780470114759.ch5
   Google Scholar
• Ahmad, O.F., Stoyanov, D., & Lovat, L.B. (2020). Barriers and pitfalls for artificial intelligence in gastroenterology: Ethical and regulatory issues. Techniques and Innovations in Gastrointestinal Endoscopy, 22(2), 80–84. https://doi.org/10.1016/j.tgie.2019.150636
   Google Scholar
• Alaskar, T.H., Mezghani, K., & Khalil Alsadi, A. (2021). Examining the adoption of big data analytics in supply chain management under competitive pressure: Evidence from Saudi Arabia. Journal of Decision Systems, 30(2–3), 300–320. https://doi.org/10.1080/12460125.2020.1859714
   Web of Science ®Google Scholar
• Ali, O., Shrestha, A., Osmanaj, V., & Muhammed, S. (2021). Cloud computing technology adoption: An evaluation of key factors in local governments. Information Technology & People, 34(2), 666–703. https://doi.org/10.1108/ITP-03-2019-0119
   Web of Science ®Google Scholar
• Aljawder, A., & Al-Karaghouli, W. (2022). The adoption of technology management principles and artificial intelligence for a sustainable lean construction industry in the case of bahrain. Journal of Decision Systems, 1–30. https://doi.org/10.1080/12460125.2022.2075529
   Web of Science ®Google Scholar
• Alla, S., Adari, S.K. (2021). Beginning MLOps with MLFlow: Deploy models in AWS sageMaker, google cloud, and microsoft azure (pp. 79–124). Berkeley, CA: Apress. https://doi.org/10.1007/978-1-4842-6549-9_3
   Google Scholar
• Antonini, M., Rodrigo Pincheira Caro, M., Vecchio, M., & Antonelli, F. (2022). Tiny-MLOps: A framework for orchestrating ML applications at the far edge of IoT systems. IEEE international conference on evolving and adaptive intelligent systems (EAIS), 1–8. IEEE. https://doi.org/10.1109/EAIS51927.2022.9787703
   Google Scholar
• Arinez, J.F., Chang, Q., Gao, R.X., Chengying, X., & Zhang, J. (2020). Artificial intelligence in advanced manufacturing: Current status and future outlook. Journal of Manufacturing Science and Engineering, 142(11). https://doi.org/10.1115/1.4047855
   Web of Science ®Google Scholar
• Baniecki, H., Kretowicz, W., Piatyszek, P., Wisniewski, J., & Biecek, P. (2021). Dalex: Responsible machine learning with interactive explainability and fairness in python. Journal of Machine Learning Research, 22(1), 9759–9765.
   Google Scholar
• Barani Sundaram, B., Pandey, A., Tirulo Abiko, A., Vijaykumar, J., Rastogi, U., Haile Genale, A., & Karthika, P. (2022). Analysis of machine learning data security in the internet of things (IoT) circumstance. Expert clouds and applications. (pp. 227–236). Singapore: Springer. https://doi.org/10.1007/978-981-16-2126-0_20
   Google Scholar
• Barreno, M., Nelson, B., Joseph, A.D., & Doug Tygar, J. (2010). The security of machine learning. Machine Learning, 81(2), 121–148. https://doi.org/10.1007/s10994-010-5188-5
   Web of Science ®Google Scholar
• Bastani, O., Kim, C., & Bastani, H. (2017). Interpreting blackbox models via model extraction. arXiv preprint arXiv:1705.08504.
   Google Scholar
• Bergkvist, L., & Rossiter, J.R. (2007). The predictive validity of multiple-item versus single-item measures of the same constructs. Journal of Marketing Research, 44(2), 175–184. https://doi.org/10.1509/jmkr.44.2.175
   Web of Science ®Google Scholar
• Bertino, E., Kantarcioglu, M., Gurcan Akcora, C., Samtani, S., Mittal, S., & Gupta, M. (2021). “AI for security and security for AI.” In Proceedings of the eleventh ACM conference on data and application security and privacy, Virtual, 333–334. https://doi.org/10.1145/3422337.3450357
   Google Scholar
• Black, J., & Douglas Murray, A. (2019). Regulating AI and machine learning: Setting the regulatory agenda. European Journal of Law and Technology, 10(3).
   Google Scholar
• Borgman, H., Bahli, B., Heier, H., & Schewski, F. (2013). Cloudrise: Exploring cloud computing adoption and governance with the TOE framework. 46th Hawaii international conference on system sciences. Wailea, HI, USA. 01, 4425–4435. https://doi.org/10.1109/HICSS.2013.132
   Google Scholar
• Borg, M., Ronald, J., Simon, °., Arvid, E., Ludwig, H., & August, L. (2021). “Test automation with grad-CAM heatmaps-A future pipe segment in MLOps for vision AI?” In 2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), 175–181. IEEE.
   Google Scholar
• Chang, M.K., Cheung, W., Hung Cheng, C., & Yeung, J. (2008). Understanding ERP system adoption from the user’s perspective. International Journal of Production Economics, 113(2), 928–942. https://doi.org/10.1016/j.ijpe.2007.08.011
   Web of Science ®Google Scholar
• Chen, T., Liu, J., Xiang, Y., Niu, W., Tong, E., & Han, Z. (2019). Adversarial attack and defense in reinforcement learning-from AI security view. Cybersecurity, 2(1), 1–22. https://doi.org/10.1186/s42400-019-0027-x
   Google Scholar
• Clohessy, T., Acton, T., Rogers, N. (2019). Blockchain adoption: Technological, organisational and environmental considerations. In: Treiblmaier, H., Beck, R. Business transformation through blockchain. (pp. 47–76). Palgrave Macmillan. https://doi.org/10.1007/978-3-319-98911-2_2
   Google Scholar
• Das, S.D., & Kumar Bala, P. (2022). Artificial intelligence in telemedicine: A brief survey. In Mishra, S., Tripathy, H.K., Mallick, P., Shaalan, K. (Eds.), Augmented intelligence in healthcare: A pragmatic and integrated analysis (SCI ed., Vol. 1024, pp. 453–461). Singapore: Springer. https://doi.org/10.1007/978-981-19-1076-0_23
   Google Scholar
• Das, S., & Mert Cakmak, U. (2018). Hands-on automated machine learning: A beginner’s guide to building automated machine learning systems using AutoML and Python. Packt Publishing Ltd.
   Google Scholar
• de la Rua Martınez, J. (2020). “Scalable architecture for automating machine learning model monitoring.”
   Google Scholar
• De Souza, A., Alexandre, N., & de Brito Guirardello, E. (2017). Propriedades psicométricas na avaliação de instrumentos: avaliação da confiabilidade e da validade. Epidemiologia e Serviços de Saúde, 26(3), 649–659. https://doi.org/10.5123/S1679-49742017000300022
   PubMedGoogle Scholar
• Fan, S.K.S., Lin, W.K., & Jen, C.H. (2022). Data-driven optimization of accessory combinations for final testing processes in semiconductor manufacturing. Journal of Manufacturing Systems, 63, 275–287. https://doi.org/10.1016/j.jmsy.2022.03.014
   Web of Science ®Google Scholar
• Fathian, M., Akhavan, P., & Hoorali, M. (2008). E-readiness assessment of non-profit ICT SMEs in a developing country: The case of Iran. Technovation, 28(9), 578–590. https://doi.org/10.1016/j.technovation.2008.02.002
   Web of Science ®Google Scholar
• Ferraiolo, D., Janet Cugini, D.R.K., & Cugini, J. (1995). “Role-based access control (RBAC): Features and motivations.” Proceedings of 11th annual computer security application conference, online (pp. 241–248). National Institute of Standards and Technology. https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=916537
   Google Scholar
• Floridi, L. (2021). The European legislation on AI: A brief analysis of its philosophical approach. Philosophy & Technology, 34(2), 215–222. https://doi.org/10.1007/s13347-021-00460-9
   PubMedGoogle Scholar
• Gangwar, H., Date, H., & Ramaswamy, R. (2015). Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. Journal of Enterprise Information Management, 28(1), 107–130. https://doi.org/10.1108/JEIM-08-2013-0065
   Web of Science ®Google Scholar
• Gangwar, H., Date, H., & Raoot, A.D. (2014). Review on IT adoption: Insights from recent technologies. Journal of Enterprise Information Management, 27(4), 488–502. https://doi.org/10.1108/JEIM-08-2012-0047
   Google Scholar
• Garg, S., Pundir, P., Geetanjali Rathee, P.G., Garg, S., & Ahlawat, S. (2021). “On continuous integration/continuous delivery for automated deployment of machine learning models using mlops.” In 2021 IEEE fourth international conference on artificial intelligence and knowledge engineering (AIKE), 25–28. IEEE.
   Google Scholar
• Gift, N., & Deza, A. (2021). Practical MLOps. O’Reilly Media, Inc.
   Google Scholar
• Gizelis, C.A., Nestorakis, K., Misargopoulos, A., Nikolopoulos-Gkamatsis, F., Kefalogiannis, M., Palaiogeorgou, P., Christonasis, A.M., Boletis, K., Giamalis, T., & Charisis, C. (2022). Decision support using AI: The data exploitation at telecoms in practice. Journal of Decision Systems, 1–19. https://doi.org/10.1080/12460125.2022.2078554
   Web of Science ®Google Scholar
• Gomez-Uribe, C., & Neil, H. (2015). The Netflix recommender system. ACM Transactions on Management Information Systems, 6(4), 1–19. https://doi.org/10.1145/2843948
   Web of Science ®Google Scholar
• Goode, S., & Stevens, K. (2000a). An analysis of the business characteristics of adopters and non-adopters of world wide web technology. Information Technology and Management, 1(1), 129–154. https://doi.org/10.1023/A:1019112722593
   Google Scholar
• Goode, S., & Stevens, K. (2000b). An analysis of the business characteristics of adopters and non-adopters of world wide web technology. Information Technology and Management, 1(1/2), 129–154.
   Google Scholar
• Google Cloud. (2020). “MLOps: Continuous delivery and automation pipelines in machine learning.” Retrieved from https://cloud.google.com/solutions/machine-learning/mlopscontinuousdelivery-and-automation-pipelines-in-machine-learning.
   Google Scholar
• Granlund, T., Kopponen, A., Stirbu, V., Myllyaho, L., & Mikkonen, T. (2021). “MLOps challenges in multi-organization setup: experiences from two real-world cases.” In 2021 IEEE/ACM 1st Workshop on AI Engineering-Software Engineering for AI (WAIN), 82–88. IEEE.
   Google Scholar
• Haakman, M., Luıs, C., Hennie, H., & Arie van, D. (2021). AI lifecycle models need to be revised. Empirical Software Engineering, 26(5), 1–29. https://doi.org/10.1007/s10664-021-09993-1
   Web of Science ®Google Scholar
• Haimerl, M. (2019 March 13th). Current regulatory issues of AI in the field of medical technology. Artificial intelligence: From research to application: The upper-rhine artificial intelligence symposium UR-AI 2019, Offenburg, Germany, (Karlsruhe: Hochschule Karlsruhe - Technik und Wirtschaft. 31. https://arxiv.org/abs/1903.08495#
   Google Scholar
• Hair, J.F. (2009). Multivariate data analysis.
   Google Scholar
• Hair, J. F. Jr., Anderson, R. E., Tatham, R. L. and Black, W. C. (1998). Multivariate data analysis (5th ed.). New Jersey, Prentice-Hall.
   Google Scholar
• Hewage, N., & Meedeniya, D. (2022). Machine learning operations: A survey on MLOps tool support. arXiv preprint arXiv:2202.10169.
   Google Scholar
• Hildebrand, C., & Bergner, A. (2019). AI-Driven sales automation: using chatbots to boost sales. NIM Marketing Intelligence Review, 11(2), 36–41. https://doi.org/10.2478/nimmir-2019-0014
   Google Scholar
• Hirmer, S., & Cruickshank, H. (2014). The user-value of rural electrification: An analysis and adoption of existing models and theories. Renewable & Sustainable Energy Reviews, 34, 145–154. https://doi.org/10.1016/j.rser.2014.03.005
   Web of Science ®Google Scholar
• Hsu, P. (2013). Integrating ERP and e-business: Resource complementarity in business value creation. Decision Support Systems, 56, 334–347. https://doi.org/10.1016/j.dss.2013.06.013
   Web of Science ®Google Scholar
• Huang, Y., Zhang, H., Yuanming, L., Lau, C., & You, Y. (2022). ActiveLearning-as-a-service: an efficient MLOps system for data-centric AI. 07.
   Google Scholar
• Hughes, L., Dwivedi, Y.K., Misra, S.K., Rana, N.P., Raghavan, V., & Akella, V. (2019). Blockchain research, practice and policy: Applications, benefits, limitations, emerging research themes and research agenda. International Journal of Information Management, 49, 114–129. https://doi.org/10.1016/j.ijinfomgt.2019.02.005
   Web of Science ®Google Scholar
• Jacobsen, D.I. (2018). Adopting and refining e-services—the role of organization size. Public Organization Review, 18(1), 111–123. https://doi.org/10.1007/s11115-016-0364-0
   Google Scholar
• Jaradat, Z., Al-Dmour, A., Alshurafat, H., Al-Hazaima, H., & Obeid Al Shbail, M. (2022). Factors influencing business intelligence adoption: Evidence from Jordan. Journal of Decision Systems, 1–21. https://doi.org/10.1080/12460125.2022.2094531
   Web of Science ®Google Scholar
• Jeyaraj, A., Rottman, J.W., & Lacity, M.C. (2006). A review of the predictors, linkages, and biases in IT innovation adoption research. Journal of Information Technology, 21(1), 1–23. https://doi.org/10.1057/palgrave.jit.2000056
   Web of Science ®Google Scholar
• John, M.M., Olsson, H.H., & Jan, B. (2021). “Towards mlops: A framework and maturity model.” In 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 1–8. IEEE.
   Google Scholar
• Julies, B., & Zuva, T. (2021). A review on TAM and TOE framework progression and how these models integrate. Advances in Science, Technology and Engineering Systems Journal, 6(3), 137–145. https://doi.org/10.25046/aj060316
   Google Scholar
• Karamitsos, I., Albarhami, S., & Apostolopoulos, C. (2020). Applying DevOps practices of continuous automation for machine learning. Information, 11(7), 363. https://doi.org/10.3390/info11070363
   Google Scholar
• Katz, G., Shabtai, A., & Rokach, L. (2014). Adapted features and instance selection for improving co-training. In Holzinger, A., Jurisica, I. (Eds.), Interactive knowledge discovery and data mining in biomedical informatics (pp. 81–100). Springer.
   Google Scholar
• Khan, R., & Qudrat-Ullah, H. (2021). Technology adoption theories and models. 27–48.
   Google Scholar
• Kouhizadeh, M., Saberi, S., & Sarkis, J. (2021). Blockchain technology and the sustainable supply chain: Theoretically exploring adoption barriers. International Journal of Production Economics, 231, 107831. https://doi.org/10.1016/j.ijpe.2020.107831
   Web of Science ®Google Scholar
• Kreuzberger, D., Kuhl, N., & Sebastian, H. (2022). Machine learning operations (MLOps): Overview, definition, and architecture. IEEE Access, 11, 31866–31879. arXiv preprint arXiv:2205.02302. https://doi.org/10.1109/ACCESS.2023.3262138
   Web of Science ®Google Scholar
• Kshetri, N., & Dholakia, N. (2002). Determinants of the global diffusion of B2B e-commerce. Electronic Markets, 12(2), 120–129. https://doi.org/10.1080/10196780252844562
   Google Scholar
• Kumar, P.R., Herbert Raj, P., & Jelciana, P. (2018). Exploring data security issues and solutions in cloud computing. Procedia Computer Science, 125, 691–697. https://doi.org/10.1016/j.procs.2017.12.089
   Google Scholar
• Kumar, D., & Sampath, A. (2022). Orchestration of ML/AI models using MLOps/AIOps frameworks. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4140494
   Google Scholar
• Kumar, A., Singh, R., & Swain, S. (2022). Adoption of technology applications in organized retail outlets in India: A TOE model. Global Business Review, 097215092110723. https://doi.org/10.1177/09721509211072382
   Web of Science ®Google Scholar
• Lechesa, M., Seymour, L., & Schuler, J. (2012). ERP software as service (SaaS): Factors affecting adoption in South Africa. In Møller, C., Chaudhry, S.(Eds.), Re-conceptualizing enterprise information systems (pp. 152–167). Springer.
   Google Scholar
• Lee, G., & Xia, W. (2006). Organizational size and IT innovation adoption: A meta-analysis. Information Management, 43(8), 975–985. https://doi.org/10.1016/j.im.2006.09.003
   Google Scholar
• Leukel, J., Gonzalez, J., & Martin, R. (2021). Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review. Journal of Manufacturing Systems, 61, 87–96. https://doi.org/10.1016/j.jmsy.2021.08.012
   Web of Science ®Google Scholar
• Li, B., Peng, Q., Liu, B., Di, S., Liu, J., Pei, J., Jinfeng, Y., & Zhou, B. (2023). Trustworthy ai: From principles to practices. ACM Computing Surveys, 55(9), 1–46. https://doi.org/10.1145/3569085
   Web of Science ®Google Scholar
• Li, R.C., & Tee, M.L. (2021). “Developing an implementation framework for automated customer support service in collaborative customer relationship management systems.” In 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 1092–1096. IEEE.
   Google Scholar
• Lloyd, J.W. (1995). Surviving the AI winter. In Lloyd, J.W. (Ed.), Logic programming: The 1995 international symposium (pp. 33–47). MIT Press.
   Google Scholar
• Lundberg, S.M., & Lee, S.I. (2017). A unified approach to interpreting model predictions. Proceedings of the 31st international conference on neural information processing systems (pp. 30). Long Beach, California, USA: Curran Associates Inc.
   Google Scholar
• Makinen, S., Skogstrom, H., Laaksonen, E., & Mikkonen, T. (2021). Who needs MLOps: what data scientists seek to accomplish and how can MLOps help? 109–112.
   Google Scholar
• Malhotra, Y. (2022). “How you can implement well-architected ‘zero trust’hybrid-cloud computing beyond ‘lift and shift’: cloud-enabled digital innovation at scale with infrastructure as code (IaC), DevSecOps and MLops.” In 2022 New York State Cyber Security Conference: Invited Presentations, Albany, New York: https://its.ny.gov/2022-nyscsc.
   Google Scholar
• Mariani, M.M., Perez-Vega, R., & Wirtz, J. (n.d.). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), 755–776. https://doi.org/10.1002/mar.21619
   Web of Science ®Google Scholar
• Markus, A.F., Kors, J.A., & Rijnbeek, P.R. (2021). The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies. Journal of Biomedical Informatics, 113, 103655. https://doi.org/10.1016/j.jbi.2020.103655
   PubMed Web of Science ®Google Scholar
• McKendrick, J. (2021). AI adoption skyrocketed over the last 18 months. Harvard Business Review. https://hbr.org/2021/09/ai-adoption-skyrocketed-over-the-last-18-months
   Google Scholar
• Mishra, A.N., & Kumar Pani, A. (2020). Business value appropriation roadmap for artificial intelligence. VINE Journal of Information and Knowledge Management Systems, 51(3), 353–368. https://doi.org/10.1108/VJIKMS-07-2019-0107
   Web of Science ®Google Scholar
• Mucha, T.M., Sijia, M., & Abhari, K. (2022). Beyond MLOps: The lifecycle of machine learning-based solutions.
   Google Scholar
• Nagpal, A., & Gabrani, G. (2019). “Python for data analytics, scientific and technical applications.” In 2019 Amity International Conference on Artificial Intelligence (AICAI) (pp. 140–145). IEEE.
   Google Scholar
• Nalchigar, S., & Eric, Y. (2020). Designing business analytics solutions. Business & Information Systems Engineering, 62(1), 61–75. https://doi.org/10.1007/s12599-018-0555-z
   Web of Science ®Google Scholar
• O’Leary, K., & Uchida, M. (2020). Common problems with creating machine learning pipelines from existing code.
   Google Scholar
• Oliveira, T., & Martins, M.F. (2010). Understanding e-business adoption across industries in European countries. Industrial Management & Data Systems, 110(9), 1337–1354. https://doi.org/10.1108/02635571011087428
   Web of Science ®Google Scholar
• Oliveira, T., Martins, R., Sarker, S., Thomas, M., & Popoviˇc, A. (2019). Understanding SaaS adoption: The moderating impact of the environment context. International Journal of Information Management, 49, 1–12. https://doi.org/10.1016/j.ijinfomgt.2019.02.009
   Web of Science ®Google Scholar
• Papernot, N., McDaniel, P., Sinha, A., & Wellman, M. (2016). Towards the science of security and privacy in machine learning. arXiv preprint arXiv:1611.03814.
   Google Scholar
• Perera, P., Bandara, M., & Perera, I. (2016). “Evaluating the impact of DevOps practice in Sri Lankan software development organizations.” In 2016 Sixteenth International Conference on Advances in ICT for Emerging Regions (ICTer), 281–287. IEEE.
   Google Scholar
• Polyzotis, N., Roy, S., Euijong Whang, S., & Zinkevich, M. (2018). Data lifecycle challenges in production machine learning: A survey. ACM SIGMOD Record, 47(2), 17–28. https://doi.org/10.1145/3299887.3299891
   Web of Science ®Google Scholar
• Premkumar, G., & Ramamurthy, K. (1995). The role of interorganizational and organizational factors on the decision mode for adoption of interorganizational systems. Decision Sciences, 26(3), 303–336. https://doi.org/10.1111/j.1540-5915.1995.tb01431.x
   Web of Science ®Google Scholar
• Premkumar, G., & Roberts, M. (1999). Adoption of new information technologies in rural small businesses. Omega, 27(4), 467–484. https://doi.org/10.1016/S0305-0483(98)00071-1
   Web of Science ®Google Scholar
• Rajan, C., & Baral, R. (2015). Adoption of ERP system: An empirical study of factors influencing the usage of ERP and its impact on end user. IIMB Management Review, 27(2), 77. https://doi.org/10.1016/j.iimb.2015.04.004
   Google Scholar
• Ramdani, B., Kawalek, P., & Lorenzo, O. (2009). Predicting SMEs’ adoption of enterprise systems. Journal of Enterprise Information Management, 22(1/2), 10–24. https://doi.org/10.1108/17410390910922796
   Google Scholar
• Recupito, G., Pecorelli, F., Catolino, G., Moreschini, S., DiNucci, D., Palomba, F., & Tamburri, D. (2022). A multivocal literature review of MLOps tools and features. 48th Euromicro conference on software engineering and advanced applications (SEAA), August 31 - September, Spain. (pp. 06). IEEE. https://doi.org/10.1109/SEAA56994.2022.00021
   Google Scholar
• Reddy, M., Dattaprakash, B., Kammath, S., Subramanya, K., Manokaran, S., & Rangaswamy, B. (2022). Application of mlops in prediction of lifestyle diseases. ECS Transactions, 107(1), 1191. https://doi.org/10.1149/10701.1191ecst
   Google Scholar
• Reis, C., Ruivo, P., Oliveira, T., & Faroleiro, P. (2020). Assessing the drivers of machine learning business value. Journal of Business Research, 117, 232–243. https://doi.org/10.1016/j.jbusres.2020.05.053
   Web of Science ®Google Scholar
• Renggli, C., Luka, R., Gurel, N.M., Karlas, B., Wentao, W., & Ce, Z. (2021). A data quality-driven view of mlops. IEEE Data engineering bulletin. 44(1). https://doi.org/10.3389/fnhum.2021.659410
   Google Scholar
• Ribeiro, M.T., Singh, S., & Guestrin, C. (2016). ” Why should i trust you?” Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135–1144). New York, NY, USA: Association for Computing Machinery.
   Google Scholar
• Rudd, J., & Priestley, J. (2017). A comparison of decision tree with logistic regression model for prediction of worst non-financial payment status in commercial credit. Grey literature from PhD candidates. https://digitalcommons.kennesaw.edu/dataphdgreylit/5/
   Google Scholar
• Ruf, P., Madan, M., Reich, C., & Ould Abdeslam, D. (2021). Demystifying mlops and presenting a recipe for the selection of open-source tools. Applied Sciences, 11(19), 8861. https://doi.org/10.3390/app11198861
   Google Scholar
• Sanchez-Fernandez, R., & Angeles Iniesta-Bonillo, M. (2007). The concept of perceived´ value: A systematic review of the research. Marketing Theory, 7(4), 427–451. https://doi.org/10.1177/1470593107083165
   Google Scholar
• Scarpello, V., & Campbell, J.P. (1983). Job satisfaction: Are all the parts there? Personnel Psychology, 36(3), 577–600. https://doi.org/10.1111/j.1744-6570.1983.tb02236.x
   Web of Science ®Google Scholar
• Schmidt, P., Biessmann, F., & Teubner, T. (2020). Transparency and trust in artificial intelligence systems. Journal of Decision Systems, 29(4), 260–278. https://doi.org/10.1080/12460125.2020.1819094
   Web of Science ®Google Scholar
• Shaheen, M.Y. (2021). Adoption of machine learning for medical diagnosis. ScienceOpen preprints. https://doi.org/10.14293/S2199-1006.1.SOR-.PPHMKA6.v1
   Google Scholar
• Shankar, S., Garcia, R., Hellerstein, J.M., & Parameswaran, A.G. (2022). Operationalizing machine learning: an interview study. arXiv. https://doi.org/10.48550/arXiv.2209.09125
   Google Scholar
• Shankar, S., & Parameswaran, A. (2021). Towards observability for machine learning pipelines. CoRr, abs/2108.13557. https://arxiv.org/abs/2108.13557
   Google Scholar
• Smith-Renner, A., Fan, R., Birchfield, M., Tongshuang, W., Boyd-Graber, J., Weld, D.S., & Findlater, L. (2020). No explainability without accountability: An empirical study of explanations and feedback in interactive ml. Proceedings of the 2020 chi conference on human factors in computing systems (pp. 1–13). New York, NY, USA: Association for Computing Machinery.
   Google Scholar
• Smuha, N.A. (2021). From a ‘race to AI’to a ‘race to AI regulation’: Regulatory competition for artificial intelligence. Law, Innovation and Technology, 13(1), 57–84. https://doi.org/10.1080/17579961.2021.1898300
   Google Scholar
• Subramanya, R., Sierla, S., & Vyatkin, V. (2022). From DevOps to MLOps: Overview and application to electricity market forecasting. Applied Sciences, 12(19), 9851. https://doi.org/10.3390/app12199851
   Google Scholar
• Symeonidis, G., Nerantzis, E., Kazakis, A., & Papakostas, G.A. (2022). MLOps–definitions, tools and challenges. IEEE 12th Annual computing and communication workshop and conference (CCWC), Las Vegas, NV, USA. IEEE. https://doi.org/10.1109/CCWC54503.2022.9720902
   Google Scholar
• Tamburri, D.A. (2020). “Sustainable mlops: Trends and challenges.” In 2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 17–23. IEEE.
   Google Scholar
• Tankard, C. (2012). Big data security. Network Security, 2012(7), 5–8. https://doi.org/10.1016/S1353-4858(12)70063-6
   Google Scholar
• Testi, M., Ballabio, M., Frontoni, E., Iannello, G., Moccia, S., Soda, P., & Vessio, G. (2022). “MLOps: A taxonomy and a methodology.” IEEE Access.
   Google Scholar
• Thessen, A. (2016). Adoption of machine learning techniques in ecology and earth science. One Ecosystem, 1, e8621. https://doi.org/10.3897/oneeco.1.e8621
   Google Scholar
• Thompson, R., Higgins, C., & Howell, J. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125–143. https://doi.org/10.2307/249443
   Web of Science ®Google Scholar
• Thong, J., & Yap, C.S. (1995). CEO characteristics, organizational characteristics and information technology adoption in small businesses. Omega, 23(4), 429–442. https://doi.org/10.1016/0305-0483(95)00017-I
   Web of Science ®Google Scholar
• Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington, MA: Lexington Books.
   Google Scholar
• Treveil, M., Nicolas, O., Stenac, C., Kenji, L., Du, P., Joachim, Z., Adrien, L., Makoto, M., & Lynn, H. (2020). Introducing MLOps. O’Reilly Media.
   Google Scholar
• Trizano-Hermosilla, I., & Alvarado, J.M. (2016). Best alternatives to Cronbach’s alpha reliability in realistic conditions: Congeneric and asymmetrical measurements. Frontiers in Psychology, 7, 769. https://doi.org/10.3389/fpsyg.2016.00769
   PubMed Web of Science ®Google Scholar
• Truby, J., Brown, R., & Dahdal, A. (2020). Banking on AI: Mandating a proactive approach to AI regulation in the financial sector. Law and Financial Markets Review, 14(2), 110–120. https://doi.org/10.1080/17521440.2020.1760454
   Web of Science ®Google Scholar
• Tuzun, E., Bedir, T., Yagup, M., & Ince, K. (2019). Adopting integrated˙ application lifecycle management within a large-scale software company: An action research approach. The Journal of Systems & Software, 149, 63–82. https://doi.org/10.1016/j.jss.2018.11.021
   Web of Science ®Google Scholar
• Valdebenito, J., & Quelopana, A. (2019). “Conceptual model for software as a service (SaaS) enterprise resource planning (ERP) systems adoption in small and medium sized enterprises (SMEs) using the technology-organization-environment (TOE) framework.” In International Conference on Information Technology & Systems, 143–152. Springer.
   Google Scholar
• van Doorn, J., Lemon, K., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. (2010). Customer engagement behavior: Theoretical foundations and research directions. Journal of Service Research - J SERV RES, 13(3), 253–266.
   Web of Science ®Google Scholar
• Wang, Y.S., Hsien-Ta, L., Ci-Rong, L., & Zhang, D.Z. (2016). Factors affecting hotels’ adoption of mobile reservation systems: A technology-organization-environment framework. Tourism Management, 53, 163–172. https://doi.org/10.1016/j.tourman.2015.09.021
   Web of Science ®Google Scholar
• Wang, Y., Pyhajarvi, M., & Mantyla, M.V. (2020). “Test automation process improvement in a DevOps team: Experience report.” In 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), 314–321. IEEE.
   Google Scholar
• Weaver, J.F. (2018). America’s first AI legislation. RAIL, 3(1), 201.
   Google Scholar
• Wiens, J., & Shenoy, E.S. (2018). Machine learning for healthcare: On the verge of a major shift in healthcare epidemiology. Clinical Infectious Diseases, 66(1), 149–153. https://doi.org/10.1093/cid/cix731
   PubMed Web of Science ®Google Scholar
• Xu, J. (2022). Future and FinTech, The: ABCDI and Beyond (pp. 451–488). World Scientific https://doi.org/10.1142/9789811250903_0014
   Google Scholar
• Xu, S., Kevin, Z., & Gibbs, J. (2004). Global technology, local adoption: A CrossCountry investigation of internet adoption by companies in the United States and China. Electronic Markets, 14(1), 13–24. https://doi.org/10.1080/1019678042000175261
   Google Scholar
• Zaharia, M., Chen, A., Davidson, A., Ghodsi, A., Ann Hong, S., Konwinski, A., & Murching, S., Nykodym, T., Ogilvie, P., Parkhe, M., Xie, F., & Zumar, C. (2018). Accelerating the machine learning lifecycle with MLflow. IEEE Data Engineering Bulletin, 41(4), 39–45.
   Google Scholar
• Zhao, Y., Belloum, A.S., da Costa, G. A., & Zhao, Z. (2022). MLOs scaling machine learning lifecycle in an industrial setting. International Journal of Industrial and Manufacturing Engineering, 16(5), 143–153.
   Google Scholar
• Zhou, Y., Yu, Y., & Ding, B. (2020). “Towards mlops: A case study of ml pipeline platform.” In 2020 International conference on artificial intelligence and computer engineering (ICAICE), 494–500. IEEE.
   Google Scholar
• Zhu, K., & Kraemer, K.L. (2005). Post-adoption variations in usage and value of e-business by organizations: Cross-country evidence from the retail industry. Information Systems Research, 16(1), 61–84. https://doi.org/10.1287/isre.1050.0045
   Web of Science ®Google Scholar