Understanding Smart Divide in a Quantitative Socio-Technical Framework: Perspectives from Rural Communities

The rapid development of Information and Communication Technologies (ICTs) has reshaped how communities access and utilize essential services. Rural communities, however, continue to face significant access barriers due to geographic isolation, ICT infrastructure gaps, and social inequalities. Smart divide, an emerging concept representing the disparities in smart infrastructure penetration, the variation in smart service adoption, and entrenched digital inequalities across communities, is expected to widen in the coming decades. This study aims to examine the causes of this divide quantitatively from the lens of a socio-technical system, emphasizing the interwoven roles of social and technological factors in contributing to the divide. A Structural Equation Modelling (SEM) model is developed to examine the interplay of ICT infrastructures, social infrastructures, and socio-economic factors contributing to smart divide. The model tests three relationships: both insufficient ICT infrastructures and disadvantaged socio-economic factors proportionately correlate to smart divide occurrence, and social infrastructure acts as a mediator reducing the impact of both ICT infrastructures’ deficiencies and socio-economic disadvantages. Mail survey data on households’ digital connectivity, socio-economic indicators, and smart divide metrics (smart education and health) was collected from 262 residents of two remote small towns with racially diverse and underserved communities in Southern Illinois. The model explained 41.3% of variance in the smart divide. Human factors showed the strongest relationship with smart divide (β = 0.407, p = 0.098), while digital connectivity had no significant direct effect (β = 0.161, p > 0.05). Social infrastructure significantly moderated the relationship between digital connectivity and smart divide (β = -0.167, p < 0.01) but not the relationship between human factors and smart divide relationship (p > 0.05). Results indicate that social support can compensate for connectivity gaps but cannot address persistent inequalities rooted in individual characteristics.