Assessing machine learning adoption at the firm level: the moderating effect of the environmental context

Abstract

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.

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