Generative-AI and sustainable innovation among artisanal firms in the extractive industry: does evolutionary sense-making and pro-environmental behavior matter?

Stewart Selase Hevi, Gladys Nkrumah, Esther Asiedu, Clemence Dupey Agbenorxevi and Ebenezer Malcalm

Technological Sustainability, 2025, vol. 4, issue 3, 199-213

Abstract: PurposeThis study explores the moderated mediation roles of evolutionary sense-making and pro-environmental behaviors between generative-AI adoption and sustainable innovation among owner/managers of extractive-based artisanal firms in Ghana.Design/methodology/approachA stratified sampling method was used to select 391 owner/managers of extractive-based artisanal firms in Ghana. The study relied on regression test statistics to measure the conjectured paths.FindingsThrough the deployment of hierarchical regression, generative-AI adoption was found to be a positive predictor of sustainable innovation among extractive-based artisanal firms. Further, pro-environmental behaviors moderate the mediated link between evolutionary sense-making and sustainable innovation.Research limitations/implicationsThe study relied on cross-sectional design to explore the hypothesized paths mapped out to address the research objectives. Although cross-sectional design is effective in helping assess thoughts and behaviors of respondents, it is restricted methodologically in reflecting fluctuations in thoughts and behaviors overtime. The study suggests future investigations to be undertaken through longitudinal surveys.Originality/valueThe study is one of the first to use Industry 5.0 generative-AI applications to extend empirical literature on sustainable development among artisanal firms within the extractive industry in sub-Saharan Africa.

Keywords: Generative-AI adoption; Evolutionary sense-making; Pro-environmental behaviors; Sustainable innovation; Artisanal firms; Ghana (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eme:techsp:techs-01-2025-0001

DOI: 10.1108/TECHS-01-2025-0001

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