Machine learning Evaluation on Effects of Transformational Judgement and Performance metrics in Information Industry
Keywords:
Machine Learning, Transformational Judgment, Performance Metrics, Information Industry, Regression, Classification, Feature Selection,Abstract
In the rapidly evolving landscape of the information industry, understanding the impact of transformational judgment on performance metrics is crucial for organizational success. This study employs machine learning techniques to evaluate the effects of transformational judgment on various performance metrics within the information industry. Transformational judgment, defined as the ability to envision and enact transformative strategies, is examined as a predictor variable, while performance metrics such as efficiency, innovation, and customer satisfaction serve as outcome variables.Using a dataset encompassing a diverse range of information industry organizations, this study applies regression and classification algorithms to analyze the relationships between transformational judgment and performance metrics. Feature selection and engineering techniques are employed to enhance model accuracy and interpretability. Additionally, model evaluation metrics such as accuracy, precision, recall, and F1-score are utilized to assess the predictive performance of the machine learning models. This research contributes to both theoretical understanding and practical applications within the information industry by elucidating the importance of transformational judgment in achieving organizational success. By leveraging machine learning techniques for predictive analysis, organizations can identify and cultivate transformational leadership qualities to optimize performance outcomes and gain a competitive edge in the dynamic information landscape.
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References
Avolio, B. J., & Bass, B. M. (1991). The Full Range Leadership Development Programs: Basic and Advanced Manuals. Binghamton, NY: Bass, Avolio & Associates.
Bass, B. M. (1985). Leadership and Performance Beyond Expectations. Free Press.
Burns, J. M. (1978). Leadership. Harper & Row.
Northouse, P. G. (2019). Leadership: Theory and Practice (8th ed.). SAGE Publications.
Podsakoff, P. M., MacKenzie, S. B., Moorman, R. H., & Fetter, R. (1990). Transformational Leader Behaviors and Their Effects on Followers' Trust in Leader, Satisfaction, and Organizational Citizenship Behaviors. The Leadership Quarterly, 1(2), 107-142.
Wang, H., Law, K. S., Hackett, R. D., Wang, D., & Chen, Z. X. (2005). Leader-Member Exchange as a Mediator of the Relationship between Transformational Leadership and Followers' Performance and Organizational Citizenship Behavior. Academy of Management Journal, 48(3), 420-432.
Gartner. (2021). Gartner Glossary: Information Industry. Retrieved from https://www.gartner.com/en/information-technology/glossary/information-industry
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
Scikit-learn: Machine Learning in Python. (n.d.). Retrieved from https://scikit-learn.org/stable/
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer.
Aggarwal, C. C. (2018). Data Mining: The Textbook. Springer.
Haleblian, J., & Finkelstein, S. (1993). Top Management Team Size, CEO Dominance, and Firm Performance: The Moderating Roles of Environmental Turbulence and Discretion. Academy of Management Journal, 36(4), 844-863.
Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209.
Davenport, T. H., & Harris, J. (2007). Competing on Analytics: The New Science of Winning. Harvard Business Press.
Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.
McKinsey Global Institute. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey & Company.
Cukier, K., & Mayer-Schönberger, V. (2013). The Rise of Big Data: How It's Changing the Way We Think about the World. Foreign Affairs, 92, 28.
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