Information technology / artificial intelligence use and labor productivity in firms

Edmunds Čižo (), Vera Komarova (), Anita Kokarēviča (), Jānis Kudiņš (), Oksana Ruža () and Elena Fedorova ()
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Edmunds Čižo: Daugavpils University, Latvia
Vera Komarova: Daugavpils University, Latvia
Anita Kokarēviča: Riga Stradins University, Latvia
Jānis Kudiņš: Daugavpils University, Latvia
Oksana Ruža: Daugavpils University, Latvia
Elena Fedorova: Daugavpils University, Latvia

Entrepreneurship and Sustainability Issues, 2025, vol. 12, issue 4, 232-250

Abstract: This study aims to develop conceptual frameworks and propose an appropriate research methodology for analyzing the relationship between information technology (IT) and artificial intelligence (AI) use and labor productivity in firms, particularly within the context of Latvian ones. Drawing from a comprehensive literature review, it identifies key theoretical models, including the Technology Acceptance Model, Diffusion of Innovations, Resource-Based View, and Socio-Technical Systems Theory, as foundational for understanding IT/AI use in firms and its effects on productivity. The study also proposes a hybrid research methodology combining quantitative causal inference techniques (such as panel data regressions, difference-in-differences, instrumental variable regressions, and Stochastic Frontier Analysis) with qualitative approaches (including case studies, expert interviews, and content analysis) to capture both the measurable impacts and organizational dynamics of IT/AI use. Furthermore, it outlines how advanced tools like machine learning models and Bayesian networks can model complex interdependencies. The conceptual framework integrates theoretical insights with empirical indicators from Latvian official statistics, illustrating how IT/AI contributes to productivity via automation, augmentation, and labor reallocation. The study concludes by identifying limitations (such as its conceptual focus and country-specific scope) and recommends future empirical studies that apply the proposed framework across sectors and regions using firm-level data to validate and expand upon its findings.

Keywords: information technology (IT); Artificial Intelligence (AI); enterprises; labor productivity; conceptual framework; correlation vs. causality; research methodology (search for similar items in EconPapers)
JEL-codes: C81 D24 J24 L23 O33 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:ssi:jouesi:v:12:y:2025:i:4:p:232-250

DOI: 10.9770/x3348726554

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