Operational AI Opportunity Formation in Small Industry: A TOE-DOI Explanation

Ikhwan Arief; Prima Fithri; Angelina Ajeng Masayu; A Aisyah

doi:10.37385/jaets.v7i2.10664

Authors

Ikhwan Arief Universitas Andalas
Prima Fithri Universitas Andalas
Angelina Ajeng Masayu Universitas Andalas
A Aisyah Universitas Andalas

DOI:

https://doi.org/10.37385/jaets.v7i2.10664

Keywords:

operational ai opportunity, small industry, technology-organization-environment, diffusion of innovation, partial least squares structural equation modeling

Abstract

Artificial intelligence (AI) is increasingly accessible to small firms, but prior studies mainly explain readiness, adoption, or downstream value rather than the stage at which AI first becomes visible as an operational opportunity. This study examines that upstream stage in small industrial firms by combining technology-organization-environment (TOE) antecedents with diffusion of innovation (DOI) outcomes. A purposive, census-oriented survey was conducted on the official West Sumatra small-industry frame. The final dataset contains 51 usable responses from 69 registered small industrial firms, equal to 73.91% official-frame coverage, and was analyzed with PLS-SEM in SmartPLS 4. A conservative rerun removed one weak infrastructure indicator while retaining the same inner model. The main supported path is human resources and digital literacy to observability (β = 0.609, p = 0.001, f² = 0.318), followed by governance, strategy, and budget support to observability (β = 0.447, p = 0.030, f² = 0.179). Observability reaches R² = 0.496, whereas infrastructure, data management, competitive pressure, and ecosystem support do not show comparable direct effects. The findings indicate that early AI opportunity becomes visible mainly through interpretive workforce capacity and bounded managerial support.

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