Abstract:
This dissertation critically reviews the recruitment and talent acquisition role of Generative Artificial Intelligence (GAI), comprehensively addressing how operational efficiency, screening fairness, workforce diversity, ethical transparency, and candidate experiences evolve under AI-assisted hiring paradigms. Driven by strict qualitative feedback from the viva voce panel, this study transitions fully away from quantitative assumptions to adopt a rigorous, qualitative exploratory research design rooted in the interpretive paradigm. Guided by the Technology Acceptance Model (TAM) and Stakeholder Theory, this study operationalizes how HR professionals, talent acquisition leads, and AI software engineering specialists experience and interpret the deployment of GAI across localized professional ecosystems. Primary data gathered via in-depth semi-structured interviews with 12 purposively and snowball-sampled participants across three leading, private software development and IT infrastructure firms operating within the Rawalpindi-Islamabad tech cluster. The target industry strictly rationalized due to its rapid, tech-driven talent requirements and early-stage adoption of algorithmic automation. Data processing conducted via programmatic open coding and rigorous thematic analysis aligned with Braun and Clarke’s (2006) six-stage framework. The empirical findings reveal that while GAI serves as a powerful administrative tool—drastically reducing time-to-hire, minimizing operational costs, and automating monotonous tasks like high-volume resume parsing—it introduces significant systemic vulnerabilities. These include algorithmic amplification of historical data biases, opaque black-box architecture, data privacy non-compliance, and severe limitations in evaluating complex interpersonal soft skills and cultural alignment. The study establishes five distinct qualitative propositions demonstrating that GAI must act exclusively as an augmented decision support mechanism rather than an autonomous proxy for human judgment. Ethical governance frameworks, continuous representative dataset auditing, upfront transparent disclosure to candidates, and proactive HR capability building presented as mandatory pre-conditions for responsible algorithmic integration within contemporary strategic human resource management.
