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Employee – Job profile matching challenges: AI/ML-based recommender systems solutions for knowledge workers

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posted on 2024-03-21, 23:20 authored by Anju Pradhan
The application of Artificial Intelligence (AI) is significantly increasing in many Human Resources (HR) functions. This research aims to understand how diverse experts from distinct organisations, such as Project Managers, Managers, Supervisors and Human Resource Managers, perceive the potential of artificial intelligence-based recommender systems to match job profiles with employee profiles. This study employs a Delphi study-based methodology specifically, organising an expert panel that provides their opinions through their ratings and comments of a set of propositions. Based on the online Delphi study results and participant opinions, this research aims to identify the challenges related to employee-job profile matching through artificial intelligence and machine learning tools in the form of recommender systems. In this study, we have delved into the various challenges of matching employee profiles to job profiles and the current problems faced by executives, human resource personnel or supervisors such as project managers in an organisation. The study also sheds light on the potential or feasibility solutions of artificial intelligence in the form of recommender systems where we also test a couple of propositions that focus on potential solutions and various challenges for matching employee profiles to job profiles in an organisation.

History

Table of Contents

Chapter One: Introduction -- Chapter Two: Literature Review -- Chapter Three: Research Methodology -- Chapter Four: Analysis -- Chapter Five: Discussion -- Chapter Six: Limitations, Future Directions and Conclusion -- References -- Appendix A: Participants Recruitment Advertisement Email, and Social Media -- Appendix B: Participant Information and Consent Form -- Appendix C: Propositions for Online Delphi Study -- Appendix D: Ethics Approval -- Appendix E: Codes and Themes

Awarding Institution

Macquarie University

Degree Type

Thesis MRes

Degree

Master of Research

Department, Centre or School

Department of Management

Year of Award

2024

Principal Supervisor

Roger Moser

Additional Supervisor 1

Meena Chavan

Rights

Copyright: The Author Copyright disclaimer: https://www.mq.edu.au/copyright-disclaimer

Language

English

Extent

100 pages

Former Identifiers

AMIS ID: 340881

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