Revealing the Role of Intra-household Dynamics in Computer Adoption: An Inductive Theorization Approach Using Machine Learning in the Indian Context

Abstract

Research on technology adoption has focused on individual, organizational, and institutional factors, yet adoption within households in developing countries like India remains underexplored. To address this gap, we utilized a large-scale national household survey and a machine learning-based inductive approach to uncover the complex relationship between intra-household dynamics and computer adoption. Our study identified household education externalities, the education level of women in the family, and the presence of teenage children as key factors influencing computer adoption. Our decision tree analysis revealed the intricate combinations of predictors that impact adoption, offering nuanced explanations of this complex phenomenon. Our findings can inform the development of customer-oriented marketing strategies and customized intervention programs that address cost, access, and education inequalities hindering household computer adoption, benefiting computer makers and government policymakers.