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Abstract

Purpose: This study maps global research on generative artificial intelligence (GenAI) and employee productivity, separating workplace outcomes from consumer, student, technical, and financial-market applications.


Research Method: A Scopus search for 2023–2026 identified 237 English-language open-access business and economics journal articles. Title–abstract screening retained 61 articles. Performance analysis was integrated with VOSviewer-compatible author-keyword co-occurrence, overlay, and density mapping.


Results and Discussion: Output increased from 2 articles in 2023 to 27 articles in 2026 as of the search date. The corpus covered 31 journals, 194 authors, and 37 countries; 37.7% of articles involved international collaboration. Five keyword clusters linked workplace adoption and human outcomes; LLM-enabled professional work; creativity, work design, and HRM; productivity realization; and agency in platform labor. Themes shifted from prompts and tools toward productivity, collaboration, well-being, and agency, although objective productivity measures remained peripheral.


Implications: Researchers and managers should evaluate quality-adjusted net productivity, including verification, coordination, learning, governance, and employee well-being, rather than infer performance from adoption or task speed alone.


Originality: The study offers a screened, outcome-centered, multilevel map of a rapidly emerging field and identifies its transition from technology adoption to human and organizational consequences.

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Manueke, K. A. (2026). Generative Artificial Intelligence and Employee Productivity: A Global Bibliometric and Science-Mapping Analysis. Economics and Digital Business Review, 7(1), 641–656. https://doi.org/10.37531/ecotal.v7i2.4061
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