Machine Learning for Public Sector Performance Prediction: The Role of Communication, Decision-Making, and Strategy Alignment
DOI:
https://doi.org/10.37385/jaets.v7i2.9624Keywords:
Organizational Performance, Decision Making, Communication Quality, Machine Learning, Public SectorAbstract
This study explores the relationship between communication quality, participatory decision-making, and organizational performance in the public sector using machine learning techniques. Data were collected from government agencies, employing stratified random sampling to survey civil servants at various levels of tenure. A decision-tree classification model was used to identify key predictors of organizational performance, with the model achieving 71% accuracy and a weighted F1-score of 0.71. The results highlight that interpersonal communication quality, employee involvement in decision-making, and strategic alignment were the most significant factors influencing performance. This study demonstrates the value of machine learning in capturing complex, nonlinear relationships in organizational data and provides practical insights for enhancing communication systems and decision-making structures in public institutions. The findings offer a framework for improving public sector performance by promoting participatory governance and aligning strategic priorities.
Downloads
References
AL-Inizi, M. S. M. (2025). Enhancing governmental decision-making through predictive analytics with machine learning-based data-driven framework. Babylonian Journal of Machine Learning, 2025, 86-96.
Bernardin, H.J. and Russell, J.E. (1993) Human Resource Management: An Experiential Approach. McGraw-Hill, New York.
Blockeel, H., Devos, L., Frénay, B., Nanfack, G., and Nijssen, S. (2023). Decision trees: From efficient prediction to responsible AI. Frontiers in Artificial Intelligence, 6, Article 1124553. https://doi.org/10.3389/frai.2023.1124553
Chaabane, S., Guermazi, S., and Hammami, M. (2019). Enhancing techniques for learning decision trees from imbalanced data. SpringerLink. https://doi.org/10.1007/s11634-019-00354-x
Farhadpour, S., Warner, T. A., and Maxwell, A. E. (2024). Selecting and interpreting multiclass loss and accuracy assessment metrics for classifications with class imbalance: Guidance and best practices. Remote Sensing, 16(3), 533. https://doi.org/10.3390/rs16030533
Fiedler, F. E. (1964). A contingency model of leadership effectiveness. Advances in Experimental Social Psychology, 1, 149–190. https://doi.org/10.1016/S0065-2601(08)60051-9
Getnet Agazu, B., and Kero, C. A. (2024). Innovation strategy and firm competitiveness: A systematic literature review. Journal of Innovation and Entrepreneurship, 13, Article 24. https://doi.org/10.1186/s13731-024-00381-9
Ghofar, A., Akbar, M., and Luddin, M. (2018). The effect of interpersonal communication, self-engagement and organizational commitment toward the employee performance. International Journal of Human Capital Management, 2(2), 30–44. https://doi.org/10.21009/IJHCM.02.02.04
Ghonim, M. A., Khashaba, N. M., Al-Najaar, H. M., and Khashan, M. A. (2022). Strategic alignment and its impact on decision effectiveness: a comprehensive model. International Journal of Emerging Markets, 17(1), 198-218.
Goklas, A., Riyanto, S., Sihombing, J., and others. (2021). Management of organizational performance: The role of human resource management strategy. Jurnal Administrare: Jurnal Pemikiran Ilmiah dan Pendidikan Administrasi Perkantoran, 8(1), 245–254. http://ojs.unm.ac.id/index.php/administrare/index
Grant, A. M., and Parker, S. K. (2009). Redesigning work design theory: The rise of relational and proactive perspectives. Academy of Management Annals, 3(1), 317–375. https://doi.org/10.1080/19416520903047327
Gunathilaka, M. D. K. L., & Wijeratne, V. P. I. S. (2022). Pro-Environmental Communication and Behavior in Sustainable Use of Environmental Resources: A Study in Kalpitiya Fisheries Inspector Division, Sri Lanka. International Journal of Environment, Engineering and Education, 4(2), 55–59. https://doi.org/10.55151/ijeedu.v4i2.74
Gustyawan, W., and others. (2020). The effect of interpersonal communication and organizational trust on employee engagement and their impact on job performance. Journal of HR Management. https://doi.org/10.38035/dijefa.v3i5.1485
Johansson, C., Grandien, C., and Strandh, K. (2019). Roadmap for a communication maturity index for organizations—Theorizing, analyzing and developing communication value. Public Relations Review, 45(4), 101791. https://doi.org/10.1016/j.pubrev.2019.05.012
Johnson, J. M., and Khoshgoftaar, T. M. (2019). Survey on deep learning with class imbalance. Journal of Big Data, 6, Article 27. https://doi.org/10.1186/s40537-019-0192-5
Kalua, A., Wibowo, M. A., & Latumakulita, L. A. (2026). Design and Evaluation of a Decision Support System for Classifying Tourism Site Crowding and Recommending Governance Responses in Bunaken National Park. Jurnal Testing Dan Implementasi Sistem Informasi, 4(1), 49-59. https://doi.org/10.55583/jtisi.v4i1.2232
Kang, I. G., Kim, N., Loh, W. Y., and Bichelmeyer, B. A. (2021). A machine-learning classification tree model of perceived organizational performance in US federal government health agencies. Sustainability, 13(18), 10329. https://doi.org/10.3390/su131810329
Kaplan, R.S. and Norton, D.P. (1996) Strategic Learning: The Balanced Scorecard. Strategy and Leadership, 24, 18-24. https://doi.org/10.1108/eb054566
Katz, D., and Kahn, R. (2015). The social psychology of organizations. In Organizational behavior 2 (pp. 152-168). Routledge.
Kerrissey, M. J., and Novikov, S. (2024). Joint problem-solving orientation, mutual value recognition, and performance in fluid teamwork environments. Journal of Organizational Behavior, 45(4), 532–554. https://doi.org/10.3389/fpsyg.2024.1288904
Kim, J. S. (2022). An empirical analysis of the relationships among participatory decision making and employees’ task performance and personal growth. Sustainability, 14(19), 12392. https://doi.org/10.3390/su141912392
Locke, E. A., and Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705–717. https://doi.org/10.1037/0003-066X.57.9.705
Lokeshwaran, K., Komal Kumar, N., Senthil Murugan, J., Elanangai, V., & Sathya, S. (2025). Benchmarking Transformer Models Against Classical Approaches for Fake Review Detection on the Deceptive Opinion Spam Corpus. International Journal of Environment, Engineering and Education, 7(3), 182–195. https://doi.org/10.55151/ijeedu.v7i3.334
Lu, M. (2025). Automated machine learning and data-driven decision support system for strategy management in organizational activities. Journal of computing and information technology, 33(1), 57-71. https://doi.org/10.20532/cit.2025.1005877
Maslikha, I., Fauzi, A., Sutomo, D., and Fakhruddin, M. R. (2022). The effect of interpersonal communication and organizational trust on employee engagement and their impact on job performance. Dinasti International Journal of Economics, Finance and Accounting. https://dinastipub.org/DIJEFA/article/view/1485/1077
McPhee, R. D., and Zaug, P. (2000). The communicative constitution of organizations. Building theories of organization: The constitutive role of communication, 10(1-2), 21.
Mintzberg, H. (1993). Structure in fives: Designing effective organizations. Prentice-Hall.
Mykkänen, M. (2023). The Elements of Advocacy: Finnish PR Consultants' Media Strategies in Lobbying. In (Re) discovering the Human Element in Public Relations and Communication Management in Unpredictable Times (pp. 193-209). Emerald Publishing Limited.
Nugroho, A. D., and Wahjoedi, T. (2024). The influence of the work environment and communication on work morale and its impact on employee performance. World Journal Of Advanced Research And Reviews, 21(3), 2534-2546. https://doi.org/10.30574/wjarr.2024.21.3.0922
Okereka, P., and Aliogo, E. (2023). Participatory decision making and organizational productivity: A study of the Delta State public service. International Journal of Management and Economics, 7(2), 105–120. https://doi.org/10.29103/mrbj.v3i2.14421
Park, E. Y., Yi, M., Kim, H. S., and Kim, H. (2021). A decision tree model for breast reconstruction of women with breast cancer: a mixed method approach. International Journal of Environmental Research and Public Health, 18(7), 3579. https://doi.org/10.3390/ijerph18073579
Permatasari, J., and Ratnawati, I. (2021). Work climate and employee performances: A literature observation. International Research Journal of Management, IT and Social Sciences, 8(2), 184–195. https://doi.org/10.21744/irjmis.v8n2.1425
Porter, M. E. (1985). Competitive advantage: Creating and sustaining superior performance. Free Press.
Putra, F. P., Mahendra, I. G. A. P., Tedyyana, A., & Noor, M. . (2026). Improving FAQ Retrieval for Academic Regulations Using Semantic Embeddings and LLM Question Augmentation . Jurnal Testing Dan Implementasi Sistem Informasi, 4(1), 1-19. https://doi.org/10.55583/jtisi.v4i1.2176
Rahmawati, M., Hayati, H. D., Syahril, S., and Kadri, H. A. (2024). Enhancing organizational success through a positive communication climate: Concept, influential factors, and practical approaches. International Journal of Educational Dynamics, 7(1), 145–151. https://garuda.kemdiktisaintek.go.id/documents/detail/4664985
Raj J, J. S., & Gopalan, A. (2025). Compact Bi-slot Patch Antenna with Tapered Edges for Ka-Band Applications Featuring Machine Learning-Assisted Performance Prediction. International Journal of Environment, Engineering and Education, 7(3), 196–207. https://doi.org/10.55151/ijeedu.v7i3.326
Rajhans, K. (2012). Effective organizational communication: A key to employee motivation and performance. Interscience Management Review, 2(2), 81-85. https://www.interscience.in/imr/vol2/iss2/13
Sanjaya, M. R., Khoiriah, A., Heroza, R. I., & Putra, B. W. (2026). Analysis of User Reviews for The Mytelkomsel App Using Naïve Bayes and Random Forest Methods. Jurnal Testing Dan Implementasi Sistem Informasi, 4(1), 32-48. https://doi.org/10.55583/jtisi.v4i1.2220
Saxena, N. C. (2023). Using Machine Learning to improve the performance of Public Enterprises. Public Enterprise, 27(1), 39-51.
Severgnini, E., Galdaméz, E. V. C., and Moraes, R. D. O. (2018). Satisfaction and contribution of stakeholders from the performance prism model. BBR. Brazilian Business Review, 15, 120-134. https://doi.org/10.15728/bbr.2018.15.2.2
Shafie, M. R., Farhadpour, S., Das, S., and Ahmed, I. (2024). Enhancing Employee Turnover Prediction with Optimized Anns and Data Augmentation: A Cluster-Based Approach in Hr Analytics. Sarah and Das, Srinjoy and Ahmed, Imtiaz, Enhancing Employee Turnover Prediction with Optimized Anns and Data Augmentation: A Cluster-Based Approach in Hr Analytics. https://doi.org/10.1016/j.dajour.2024.100461
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Sibuea, P. (2021). The effect of manager–employee interpersonal communication effectiveness and organizational communication climate on employee performance in the working area of the Directorate General of State’s Asset Office of North Sumatera. Budapest International Research and Critics Institute (BIRCI) Journal, 4(4), 11536–11547. https://doi.org/10.33258/birci.v4i4.3217
Singh, A. K. (2014). Role of interpersonal communication in organizational effectiveness. International Journal of Research in Management and Business Studies, 1(4), 36-39. ISSN : 2348-6503 (Online)
Sulistiani, I., Syarifuddin, and Sipakoly, S. (2024). Dynamics of interpersonal communication in improving teamwork efficiency. Jurnal Komunikasi dan Manajemen. https://jkm.my.id/index.php/komunikasi/article/view/56
Theng, D., and Bhoyar, K. K. (2024). Feature selection techniques for machine learning: A survey of more than two decades of research. Knowledge and Information Systems, 66, 1575–1637. https://doi.org/10.1007/s10115-023-02010-5
Vijayakumar, V. S. R., and Padma, R. N. (2014). Impact of perceived organizational culture and learning on organizational identification. International Journal of Commerce and Management, 24(1), 40–62. https://doi.org/10.1108/IJCoMA-01-2012-0003
Yang, Q., Li, J., Zhang, Z., Wu, X., Liao, T., Yu, S., ... and Sun, J. (2021). Clinical characteristics and a decision tree model to predict death outcome in severe COVID-19 patients. BMC infectious diseases, 21(1), 783.
CITEDNESS IN SCOPUS
CITEDNESS IN WOS
