Assessing machine learning adoption at the firm level: the moderating effect of the environmental context
Year: 2023
Type: conference-paper
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.
Source: Procedia Computer Science
Authors: Pedro Filipe, Pedro Ruivo, Tiago Oliveira
Institution: Universidade Nova de Lisboa
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4352978408