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Assessing machine learning adoption at the firm level
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Resumo(s)
Granted that Machine learning (ML) can positively impact an organization's performance, it is crucial to understand the technological, organizational, and environmental drivers upon its adoption. Using the technology-organization-environment (TOE) framework and the institutional (INT) theory, a measurement of the determinants of ML adoption and an evaluation of the moderating effects of the environmental context were made in a single framework. Partial least squares, a structural equation modeling technique, was used in a dataset of 319 firms to test the suggested hypotheses. The research empirically sustains the impact of the environment on ML adoption, both as a predictor and as a moderator of the technological context. Moreover, it suggests that external pressures may lead to a rushed adoption of ML when the firm is not yet prepared to accommodate it.
Descrição
Filipe, P., Ruivo, P., & Oliveira, T. (2023). Assessing machine learning adoption at the firm level: The moderating effect of the environmental context. Procedia Computer Science, 219, 1034-1042. https://doi.org/10.1016/j.procs.2023.01.381
Palavras-chave
Environmental context Information technology (IT) adoption Institutional (INT) theory Machine learning Technology-organization-environment (TOE) framework General Computer Science