Evaluating the Impact of Artificial Intelligence on Employee Engagement and Performance in Pakistan

Authors

  • Syed Intasar Hussain Kazmi PhD Scholar, Faculty of Business and Management Sciences, The Superior University, Lahore
  • Muhammad Farhan Afzal Ph.D.scholar, COMSATS University Islamabad Wah Campus
  • Sadia Gondal University of Engineering and Technology Pakistan Department: Humanities, Social Sciences and Modern Languages
  • Muhammad Umair Ashraf Department of management Sciences, The Islamai University Bahawalpur , Pakistan
  • Muhammad Umair Lecturer business administration National College of Business Administration and Economics, Multan, Punjab, Pakistan

DOI:

https://doi.org/10.69565/jess.v3i1.184

Keywords:

Artificial Intelligence, Employee Performance, Training, AI Awareness, Workplace Dynamics, Quantitative Research, Survey Data

Abstract

This study addresses the pressing issue of how Artificial Intelligence (AI) impacts employee performance in the unique context of Pakistan. With the global integration of AI technologies rapidly reshaping workplaces, understanding its implications on employees becomes imperative. Grounded in the Social Cognitive Theory, this research investigates the complex relationships between AI integration, employee training, AI awareness, and employee performance. Employing quantitative methods and survey data, the study uncovers significant positive associations between these factors and employee performance. These findings underscore the importance of holistic AI adoption strategies that encompass employee development. This study carries vital implications for policymakers, business leaders, and practitioners navigating the evolving AI landscape, offering insights into optimizing workforce performance.

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Published

2024-01-10

How to Cite

Kazmi, S. I. H., Afzal , M. F., Gondal, S., Ashraf, M. U., & Umair, M. (2024). Evaluating the Impact of Artificial Intelligence on Employee Engagement and Performance in Pakistan. Journal of Excellence in Social Sciences, 3(1), 30–42. https://doi.org/10.69565/jess.v3i1.184

Issue

Section

Articles