Factors Impacting the Adoption of Automated Machine Learning by Small and Medium Enterprises in Saudi Arabia: An Exploratory Interpretive Study

Abstract

The qualitative dissertation sought to explore the inhibiting and enabling adoption factors of AutoML by SMEs in Saudi Arabia. The dissertation shed light on current knowledge and existing gaps in literature, made the case for interpretivism against positivism, where the literature review showed saturation of quantitative methods, where positivists synthesis existing knowledge (AI/ML adoption factors) into a new questionnaire. A practice argued to leave researchers in a loop of simile, analogical meanings with different numbers, without the ability to uncover new insights into AI/ML social science literature that interpretivism offer, especially when the case is novel technologies such as AI.