A SYSTEMATIC LITERATURE REVIEW ON PRESCRIBED ARTIFICIAL INTELLIGENCE (AI) TECHNOLOGIES UTILIZATION AND EMPLOYEE PRODUCTIVITY AMONG TAX ADMINISTRATION PERSONNEL: BASIS FOR AN AI ADOPTION AND CAPABILITY DEVELOPMENT FRAMEWORK
Mary Grace Varnal, Florinda Vigonte, Marmelo V. Abante · Zenodo (CERN European Organization for Nuclear Research) · 3 Nov 2026
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Where this sits in the debate
- Will AI actually boost productivity and growth? · its finding: it depends (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
Abstract
ABSTRACT: This systematic literature review (SLR) examines the use of prescribed AI technology and employee productivity in tax administration and forms the basis of an AI Adoption and Capability Development Framework. The review follows the PRISMA 2020 guidelines and synthesizes 40 relevant references from academic, government, standards, and institutional sources identified through searches in major academic databases and targeted searches of relevant government and international sources. It applies the UTAUT model to examine performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, and use behavior, and the IWPQ to assess task performance, contextual performance, and counterproductive work behavior. Findings indicated that AI has the potential to improve task speed, output quality, information access, learning, and collaboration, but the extent of this is dependent on the nature of the task, employee capability, organizational context, implementation, and the quality of the human-AI interaction. Despite the extensive research on these variables, there is very little evidence specifically addressing the relationship between prescribed AI use and employee productivity in a tax administration context. The findings provide an evidence-based foundation for developing an AI Adoption and Capability Development Framework to support structured, responsible, and effective AI adoption among tax administration personnel. The review concludes that AI adoption and employee productivity are related but distinct concepts and calls for empirical evidence from tax administration personnel in the Philippines to determine the extent to which prescribed AI use is linked to multi-dimensional employee productivity.
About this publication
Preprint. Posted on a preprint server or repository (arXiv, SSRN, Zenodo and others) before or without peer review. Anyone can post there.
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- 2026 (recently)
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Reaches a different conclusion
Papers on the same question whose finding points the other way, as read from the abstracts by an AI model (AI-written). Why studies differ.
- Experimental evidence on the productivity effects of generative artificial intelligence
Productivity: its finding supports the claim · Shakked Noy and Whitney Zhang · 2023 - The simple macroeconomics of AI
Productivity: its finding goes against the claim · Daron Acemoğlu · 2024
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