Artificial Intelligence-Driven Talent Analytics and Employee Performance in Emerging Economies: A Conceptual and Opinion-Based Framework

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@article{1724242,
      author = {Chika Omukpai PhD},
      title = {Artificial Intelligence-Driven Talent Analytics and Employee Performance in Emerging Economies: A Conceptual and Opinion-Based Framework},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {4630-4639},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724242.pdf},
      abstract = {This conceptual paper examines the theoretical relationship between Artificial Intelligence (AI)-driven Talent Analytics and employee performance in the context of emerging economies. Drawing on the Resource-Based View (RBV), Dynamic Capabilities Theory, and the Technology Acceptance Model (TAM), the paper argues that organisations in developing markets that strategically deploy AI-driven talent analytics – encompassing predictive workforce planning, AI-based recruitment analytics, and performance forecasting systems – are better positioned to enhance key dimensions of employee performance, namely task efficiency, goal achievement rate, and productivity output. The paper contends that while AI-driven talent analytics holds transformative potential, its efficacy in emerging economies is conditioned by structural factors unique to these settings, including infrastructural deficits, talent ecosystem immaturity, and sociocultural dynamics. The paper advances a conceptual framework, proposes empirically testable hypotheses, and offers theoretical contributions alongside practical implications for policymakers, HR practitioners, and organisational leaders.},
      keywords = {AI-driven talent analytics; employee performance; emerging economies; predictive workforce planning; AI-based recruitment analytics; performance forecasting; task efficiency; goal achievement; productivity output},
      month = {October},
  }