Behavior intention in coping cloud adaptation

Authors

  • ALAMIR COSTA LOURO UFES
  • Mariana Melo UFES
  • Marcelo Brandão UFES

Keywords:

Cloud, Data Scientists, Technology Adaptation, Coping Theory

Abstract

This study investigates the indirect impact of perceived technology complexity on the adoption of cloud platforms by data scientists in Brazil. Specifically, it examines the mediating role of perceived work-related opportunities and behavioral intentions, as well as the use of exploration-to-innovate coping strategies. The research employs an online questionnaire survey and uses Confirmatory Factor Analysis, SEM, and OLS to analyze the data. The findings suggest that a serial mediation effect focused on behavioral aspects can better explain the adoption of cloud computing technology. The study identifies the need for further investigation into the potential moderating effects of voluntariness and self-efficacy on the relationship between perceived technology complexity and cloud platform adaptation. Moreover, incorporating other exogenous constructs such as new adaptation strategies, job outcomes, and job satisfaction may provide a more comprehensive understanding of the factors influencing technology adoption in the context of data science. The study highlights that reducing technology complexity can lead to increased adoption, better user experience, better retention rates, more innovation possibilities, and better competition position. However, additional training and support requirements for data scientists can increase the cost and time required for onboarding and maintenance. A cloud platform that is easy to use can enable remote work and collaboration, opening up opportunities for job growth and flexible work arrangements. This can be particularly beneficial for Brazilian professionals who have strong innovation skills but limited training and support. By transcending geographic barriers and infrastructure limitations, a user-friendly cloud platform can help bridge the digital divide. On the other hand, a complex cloud platform can widen the digital divide, particularly in developing countries, exacerbating existing inequalities. The research contributes to theory building by providing a serial mediation framework that better explains technology adaptation phenomena by combining coping theory and technology acceptance model.

References

Acito, F., & Khatri, V. (2014). Business analytics: Why now and what next? Business Horizons, 57(5), 565–570. https://doi.org/10.1016/j.bushor.2014.06.001

Bagozzi, R. P. (2007). The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift. The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift. Journal of the Association for Information Systems, 8(7), 244–254. https://doi.org/Article

Bala, H., & Venkatesh, V. (2013). Changes in employees’ job characteristics during an enterprise system implementation: A latent growth modeling perspective. MIS Quarterly: Management Information Systems, 37(4), 1113–1140. https://doi.org/10.25300/MISQ/2013/37.4.06

Bala, H., & Venkatesh, V. (2015). Adaptation to Information Technology: A Holistic Nomological Network from Implementation to Job Outcomes. Management Science, 61(1), 156–179. https://doi.org/https://doi.org/10.1287/mnsc.2014.2111

Beaudry, A., & Pinsonneault, A. (2005). Understanding User Responses to Information Technology: A Coping Model of User Adaptation. MIS Quarterly, 29(3), 493–524. https://doi.org/https://doi.org/10.2307/25148693

Byrne, B. M. (2012). Structural equation modeling with Mplus : basic concepts, applications, and programming (1st ed.). New York, NY: Taylor & Francis Group.

Davenport, T. H., & Patil, D. J. (2012). Data Scientist : The Sexiest Job of the 21st Century. Harvard Business Review, pp. 8–12.

Folkman, S., Lazarus, R. S., Gruen, R. J., & DeLongis, A. (1986). Appraisal, coping, health status, and psychological symptoms. Journal of Personality and Social Psychology, 50(3), 571–579. doi:10.1037/0022-3514.50.3.571

Grewal, D., Roggeveen, A. L., & Nordfält, J. (2017). The Future of Retailing. Journal of Retailing, 93(1), 1–6. https://doi.org/10.1016/j.jretai.2016.12.008

Gudergan, S. P., Ringle, C. M., Wende, S., & Will, A. (2008). Confirmatory tetrad analysis in PLS path modeling. Journal of Business Research, 61(12), 1238–1249. https://doi.org/10.1016/j.jbusres.2008.01.012

Hair, J. F., Black, B., Babin, B., Anderson, R. E., & Tatham, R. L. (2009). Análise multivariada de dados (6th ed.). Bookman.

____, Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks: Sage.

Hayes, A. (2013). Introduction to mediation, moderation, and conditional process analysis. New York, NY: The Guilford Press. https://doi.org/978-1-60918-230-4

Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3).

________, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of Partial Least Squares Path Modeling in International Marketing. Advances in International Marketing, 20(2009), 277–319. https://doi.org/10.1016/0167-8116(92)90003-4

Lazarus, R. S., & Folkman, S. (1984). Stress, Appraisal, and Coping. Springer Publishing Company, NewYork.

Louro, A. C., Brandão, M. M., & Sincorá, L. A. (2020). Understanding the self-efficacy of data scientists. International Journal of Human Capital and Information Technology Professionals (IJHCITP), 11(2), in press.

MacKenzie, Podsakoff, & Podsakoff. (2011). Construct Measurement and Validation Procedures in MIS and Behavioral Research: Integrating New and Existing Techniques. MIS Quarterly, 35(2), 293. https://doi.org/10.2307/23044045

M'rhaouarh, I., Okar, C., Namir, A., & Chafiq, N. (2018). Cloud Computing adoption in developing countries: A systematic literature review. 2018 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2018, 73–79. https://doi.org/10.1109/ITMC.2018.8691295

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

Preacher, K. J., & Selig, J. P. (2012). Advantages of Monte Carlo Confidence Intervals for Indirect Effects. Communication Methods and Measures, 6(2), 77–98. https://doi.org/10.1080/19312458.2012.679848

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.1080/1097198X.2010.10856507

Wang, N., Liang, H., Jia, Y., Ge, S., Xue, Y., & Wang, Z. (2016). Cloud computing research in the IS discipline: A citation/co-citation analysis. Decision Support Systems, 86, 35–47. https://doi.org/10.1016/j.dss.2016.03.006

Published

2024-09-30

How to Cite

COSTA LOURO, A., Melo, M., & Brandão, M. (2024). Behavior intention in coping cloud adaptation. International Journal of Business Marketing, 9(1), 62–74. Retrieved from https://ijbmkt.org/ijbmkt/article/view/275

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Section

Articles