Research Article

INTEGRATION OF ARTIFICIAL INTELLIGENCE IN ADMINISTRATIVE MANAGEMENT: A FRAMEWORK FOR SUSTAINABLE ORGANIZATIONAL PERFORMANCE

1 Bayero University Kano, Department of Public Administration, Faculty of Management Sciences
* Corresponding author: Get2abusalihu@gmail.com
Published: Dec, 2023
Pages: 189-204

Abstract

This research proposal seeks to investigate and develop a comprehensive framework for the strategic integration of Artificial Intelligence (AI) in administrative management, with a focus on enhancing sustainable organizational performance. The study aims to explore the potential benefits, challenges, and strategic considerations associated with the adoption of AI technologies in administrative functions. The objectives of the study encompass an examination of the current state of AI adoption in administrative management, identification of key factors influencing successful AI integration, assessment of the impact of AI on organizational performance metrics, and the development of a strategic framework for sustainable AI integration. The literature review establishes a theoretical foundation, identifying gaps and informing the research design. The research adopt content analysis as the methodology that guide the study. Some of the findings of the study reveals that AI integration is transforming Administrative Management across various countries, offering efficiency gains, data-driven decision-making, and improved customer experiences. AI is applied in numerous Administrative functions, such as Planning, Organizing, supply chain management, and finance, with practical examples and case studies demonstrating its impact. finally, the study recommends that Organizations should articulate a clear strategic vision for the integration of AI within administrative management. This vision should be aligned with broader organizational goals and sustainability objectives. Leaders should actively communicate this vision throughout the organization to foster a shared understanding of the role of AI in achieving sustainable performance, more so, Prioritize robust data security and privacy measures to safeguard an sensitive information handled by AI systems. 

References

  1. Akter, S., Michael, K., Uddin, M. R., McCarthy, G., and Rahman, M. 2020. ―Transforming business using digital innovations: the application of AI, blockchain, cloud and data analytics,‖ Annals of Operations Research, pp. 1-33.
  2. Alon-Barkat, S., & Busuioc, M. (2021). Decision-makers processing of AI algorithmic advice: Automation bias versus selective adherence. arXiv.
  3. Alsheibani, S., Cheung, Y., and Messom, C. 2018. ―Artificial Intelligence Adoption: AI-readiness at Firm Level,‖ PACIS 2018 Proceedings, p. 37.
  4. Alsheibani, S., Messom, C., Cheung, Y., and Alhosni, M. 2020b. ―Winning AI strategy: Six-steps to create value from artificial intelligence,‖ AMCIS 2020 Proceedings, p. 1.
  5. Alsheibani, S., Messom, Cheung, Y., and Alhosni, M. 2020a. ―Reimagining the Strategic Management of Artificial Intelligence: Five Recommendations for Business leaders,‖ AMCIS 2020 Proceedings, p. 4.
  6. Ambati, L. S., Narukonda, K., Bojja, G. R., and Bishop, D. 2020. ―Factors Influencing the Adoption of Artificial Intelligence in Organizations - From an Employee's Perspective,‖ MWAIS 2020 Proceedings, p. 20.
  7. Aoki, N. (2020). An experimental study of public trust in AI chatbots in the public sector. Government Information Quarterly, 37(4), 101490.
  8. Asatiani, A., Malo, P., Nagbøl, P. R., Penttinen, E., Rinta-Kahila, T., and Salovaara, A. 2021. ―Sociotechnical Envelopment of Artificial Intelligence: An Approach to Organizational Deployment of Inscrutable Artificial Intelligence Systems,‖ Journal of the Association for Information Systems (22:2), p. 8.
  9. Atkinson, K., Bench-Capon, T., & Bollegala, D. (2020). Explanation in AI and law: Past, present and future. Artificial Intelligence, 289, 103387.
  10. Beierle, C., & Kern-Isberner, G. (2019). Regelbasierte Systeme. In C. Beierle, & G. Kern-Isberner, Methoden wissensbasierter Systeme. Springer.
  11. Berger, C., & Kolain, M. (2021). Recht digital: schwer verständlich »by design« und allenfalls teilweise automatisierbar? kompetenzzentrum öffentliche IT.
  12. Brynjolfsson, E., & McAfee, A. (2017). "The Business of Artificial Intelligence." Harvard Business Review.
  13. Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., & Trench, M. (2017). "Artificial Intelligence: The Next Digital Frontier?" McKinsey Global Institute.
  14. Calo, R., Froomkin, A. M., & Kerr, I. (2017). "Robotics and Artificial Intelligence." Cambridge Handbook of Consumer Privacy.
  15. Cheng, M. M., & Hackett, R. D. (2021). A critical review of algorithms in HRM: Definition, theory, and practice. Human Resource Management Review, 31(1), 100698.
  16. Chopra, S., & Meindl, P. (2020). "Supply Chain Management: Strategy, Planning, and Operation." Pearson.
  17. Chui, M., Löffler, M., & Roberts, R. (2016). "The Internet of Things." McKinsey Quarterly.
  18. Daoanis, L. E. (2012). Performance Appraisal System: It‗s Implication to Employee Performance. International Journal of Economics and Management Sciences, 2(3), 55-62.
  19. Davenport, T. H., & Ronanki, R. (2018). "Artificial Intelligence for the Real World." Harvard Business Review.
  20. Devanna, M. F. (1984). A framework for strategic human resource management. Strategic Human, 33–51.
  21. Douglas, P. H. (1919). Plant Administration of Labor. Journal of Political Economy, 27, 544-560.
  22. Drucker, P. F. (1954). The practice of management. New York: Harper & Brothers.
  23. Duan, Y., Edwards, J. S., and Dwivedi, Y. K. 2019. ―Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda,‖ International Journal of Information Management (48), pp. 63-71.
  24. Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., Roubaud, D., and Hazen, B. T. 2020. ―Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations,‖ International Journal of Production Economics (226), p. 107599.
  25. Duşa, A. 2018. QCA with R: A comprehensive resource, Cham, CH: Springer.
  26. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., and Eirug, A. 2019. ―Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,‖ International Journal of Information Management (57), p. 101994.
  27. Eilbirt, H. (1959). The Development of Personnel Management in the United States. The Business History Review, 33(3), 345–364. doi:doi:10.2307/3111950
  28. Eisinga, R., Te Grotenhuis, M., and Pelzer, B. 2013. ―The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown?‖ International journal of public health (58:4), pp. 637-642.
  29. Evans, R., and Gao, J. 2016. DeepMind AI Reduces Google Data Centre Cooling Bill by 40%. https://deepmind.com/blog/article/deepmind-ai-reduces-google-data-centre-cooling-bill-40. Accessed 30 April 2021.
  30. Floridi, L., & Cowls, J. (2019). "A Unified Framework of Five Principles for AI in Society." Harvard Data Science Review.
  31. Francis H, Keegan A . (2006). The changing face of HRM in search of balance. Human Resource Management journal, 16(3):231–249.
  32. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2016). "Where machines could replace humans—and where they can‘t (yet)." McKinsey Quarterly.
  33. Oliveira, T., and Martins, M. F. 2011. ―Literature review of information technology adoption models at firm level,‖ Electronic Journal of Information Systems Evaluation (14:1), p. 110.
  34. Park, Y., Fiss, P. C., and El Sawy, O. A. 2020. ―Theorizing the Multiplicity of Digital Phenomena: The Ecology of Configurations, Causal Recipes, and Guidelines for Applying QCA,‖ MIS Quarterly (44:4), pp. 1493-1520.
  35. Paschen, U., Pitt, C., and Kietzmann, J. 2020. ―Artificial intelligence: Building blocks and an innovation typology,‖ Business Horizons (63:2), pp. 147-155.
  36. Pérez-Luño, A., Wiklund, J., and Cabrera, R. V. 2011. ―The dual nature of innovative activity: How entrepreneurial orientation influences innovation generation and adoption,‖ Journal of business Venturing (26:5), pp. 555-571.
  37. Perrault, R., Shoham, Y., Brynjolfsson, E., Clark, J., Etchemendy, J., Grosz, B., Lyons, T., Manyika, J., Mishra, S., and Niebles, J. C. 2019. ―The AI Index 2019 Annual Report,‖ AI Index Steering Committee, Human-Centered AI Institute, Stanford University, Stanford, CA.
How to Cite

Salihu, A. (2023). INTEGRATION OF ARTIFICIAL INTELLIGENCE IN ADMINISTRATIVE MANAGEMENT: A FRAMEWORK FOR SUSTAINABLE ORGANIZATIONAL PERFORMANCE. Bayero Journal of Management Sciences, 5(2), 189-204. https://doi.org/10.67894/bjms.2023.dj028g28

A. Salihu, "INTEGRATION OF ARTIFICIAL INTELLIGENCE IN ADMINISTRATIVE MANAGEMENT: A FRAMEWORK FOR SUSTAINABLE ORGANIZATIONAL PERFORMANCE," Bayero Journal of Management Sciences, vol. 5, no. 2, pp. 189-204, December 2023. doi: 10.67894/bjms.2023.dj028g28

Share this article:
Facebook X / Twitter LinkedIn