--- title: "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 · AI Fault Line" url: https://aifaultline.com/research/p/doi-10-5281-zenodo-23912898/ description: "AI Fault Line is a neutral, evidence-cited field guide to why experts looking at the same developments disagree about AI's effect on productivity, jobs, inequality, regulation and the pace of change." --- [Research](https://aifaultline.com/research/) # 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](https://aifaultline.com/authors/mary-grace-varnal/), [Florinda Vigonte](https://aifaultline.com/authors/florinda-vigonte/), [Marmelo V. Abante](https://aifaultline.com/authors/marmelo-v-abante/) · [Zenodo (CERN European Organization for Nuclear Research)](https://aifaultline.com/sources/zenodo/) · 3 Nov 2026 [Read original source](https://doi.org/10.5281/zenodo.23912898) Open access Where this sits in the debate - [Will AI actually boost productivity and growth?](https://aifaultline.com/debate/productivity/) · its finding: it depends ([why studies differ](https://aifaultline.com/debate/productivity/#why)) Direction of the finding as read from the abstract by an AI model ([AI-written](https://aifaultline.com/about/)). ## 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. [How sources are ranked](https://aifaultline.com/about/#source-ranking). Published 2026 (recently) Citations 0 (OpenAlex) 0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison. [Chart: published 2026] ## 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](https://aifaultline.com/about/)). [Why studies differ](https://aifaultline.com/debate/productivity/#why). - [Experimental evidence on the productivity effects of generative artificial intelligence](https://aifaultline.com/research/p/doi-10-1126-science-adh2586/) Productivity: its finding supports the claim · Shakked Noy and Whitney Zhang · 2023 - [The simple macroeconomics of AI](https://aifaultline.com/research/p/doi-10-1093-epolic-eiae042/) Productivity: its finding goes against the claim · Daron Acemoğlu · 2024 ## Related papers On the same debate question, key papers first; related by topic, not by method or conclusion. Browse all: [Productivity](https://aifaultline.com/research/?question=productivity). - [Generative AI at Work](https://aifaultline.com/research/p/doi-10-1093-qje-qjae044/) Erik Brynjolfsson et al. · The Quarterly Journal of Economics · 2025 - [The Productivity J-Curve: How Intangibles Complement General Purpose Technologies](https://aifaultline.com/research/p/doi-10-1257-mac-20180386/) Erik Brynjolfsson et al. · American Economic Journal Macroeconomics · 2020 - [Miracle or Myth? Assessing the macroeconomic productivity gains from Artificial Intelligence](https://aifaultline.com/research/p/doi-10-1787-b524a072-en/) Francesco Filippucci et al. · OECD · 2024 - [AI and Growth: Where Do We Stand?](https://aifaultline.com/research/p/title-e78842657b0f08e8/) Philippe Aghion and Simon Bunel · Federal Reserve Bank of San Francisco · 2024 Overview assembled automatically from collected metadata. [How this site is built](https://aifaultline.com/about/).