Modern Management Administration: Predictive Behavior
Article Sidebar
Main Article Content
Abstract
The article “Modern Management: Predictive Behavior” aims to explain the concept of Predictive Behavior as an effective managerial tool for Behavior analyzing and behavior forecasting analyzing in contemporary dynamic contexts. It serves as a mechanism that supports strategic decision-making through the utilization of big data, artificial intelligence, and behavioral modeling to predict trends among consumers, employees, and other stakeholders. The findings indicate that the application of predictive behavior in marketing enhances accuracy in responding to customer needs, in human resource management it facilitates the prediction of employee turnover and potential, and in organizational policy it helps reduce risks while increasing resilience to uncertainty. Nevertheless, certain limitations remain, particularly concerning data quality, transparency, and ethical considerations, especially the protection of personal information. Therefore, managers are encouraged to develop reliable data systems, establish clear governance mechanisms, and foster an organizational culture open to innovation in order to balance strategic effectiveness with social responsibility.
Article Details
References
Argyris, C., & Schön, D. A. (1996). Organizational learning II: Theory, method, and practice. Addison-Wesley.
Cameron, E., & Green, M. (2019). Making sense of change management: A complete guide to the models, tools and techniques of organizational change (5th ed.). Kogan Page.
Cascio, W. F., & Aguinis, H. (2019). Applied psychology in human resource management (8th ed.). Pearson Education.
Cascio, W. F., & Boudreau, J. W. (2016). The search for global competence: From international HR to talent management. Journal of World Business, 51(1), 103–114. https://doi.org/10.1016/j.jwb.2015.10.002
Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868–1889. https://doi.org/10.1111/poms.12838
Chongwatpol, J., & Sharda, R. (2019). Predictive analytics and business performance: The role of organizational readiness. Information & Management, 56(8), 103-110. https://doi.org/10.1016/j.im.2019.04.004
Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538(7625), 311–313. https://doi.org/10.1038/538311a
Crossan, M. M., Lane, H. W., & White, R. E. (1999). An organizational learning framework: From intuition to institution. Academy of Management Review, 24(3), 522–537. https://doi.org/10.5465/amr.1999.2202135
Davenport, T. H., Guenole, N., & Green, D. (2016). The predictive power of people analytics. Harvard Business Review.
Davenport, T. H., & Harris, J. G. (2017). Competing on analytics: The new science of winning (2nd ed.). Harvard Business Review Press. Note: The two separate entries for the 2017 "Competing on Analytics" book were merged and the edition was clarified. APA 7 uses (2nd ed.) instead of (Updated ed.) if the edition number is available.
Davenport, T. H., & Patil, D. J. (2012). Data scientist: The sexiest job of the 21st century. Harvard Business Review, 90(10), 70–76.
Garvin, D. A., Edmondson, A. C., & Gino, F. (2008). Is yours a learning organization? Harvard Business Review, 86(3), 109–116.
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
Kijsanayotin, B. (2019). Digital transformation readiness in Thai organizations. International Journal of Information Management, 45, 41-49. https://doi.org/10.1016/j.ijinfomgt.2018.11.002
Kotler, P., & Keller, K. L. (2022). Marketing management (16th ed.). Pearson Education.
Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.
McShane, S. L., & Von Glinow, M. A. (2018). Organizational behavior (8th ed.). McGraw-Hill Education.
Office of the National Digital Economy and Society Commission. (2022). Thailand digital economy and society development plan (2023–2027). https://www.onde.go.th/
Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. O’Reilly Media.
Riley, W. T., Glasgow, R. E., Etheredge, L., & Abernethy, A. (2019). Rapid, responsive, relevant (R3) research: A call for a rapid learning health research enterprise. Clinical and Translational Medicine, 8(1), 10. https://doi.org/10.1186/s40169-019-0232-0
Robbins, S. P., & Judge, T. A. (2019). Organizational behavior (18th ed.). Pearson Education.
Schiffman, L. G., & Wisenblit, J. L. (2019). Consumer behavior (12th ed.). Pearson Education.
Senge, P. M. (2006). The fifth discipline: The art and practice of the learning organization. Doubleday.
Sheffi, Y. (2005). The resilient enterprise: Overcoming vulnerability for competitive advantage. MIT Press.
Shmueli, G., Bruce, P. C., Gedeck, P., & Patel, N. R. (2020). Data mining for business analytics: Concepts, techniques, and applications in Python (3rd ed.). Wiley.
Siegel, E. (2016). Predictive analytics: The power to predict who will click, buy, lie, or die (Rev. ed.). Wiley.
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910
Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121. https://doi.org/10.1509/jm.15.0413
Wonglimpiyarat, J. (2019). Building data-driven culture: Challenges and strategies in Thai enterprises. Asia Pacific Journal of Innovation and Entrepreneurship, 13(2), 150-163. https://doi.org/10.1108/APJIE-12-2018-0084
