## 3 Conceptual Model  
Approaches to modeling digital inequalities for China range from econometric models [15; 16] to simple frameworks of ICT use [12; 17] to spatial analytics [3; 18]. Apart from China, more complex conceptual models of digital inequalities and divides include digital divide multi-level behavioral modeling [6; 9], digital divide units of analysis and access-use progression [20], and spatial modeling [8; 21]. Economic approaches tend to focus on digital inequalities among economic indicators, e.g., a study that analyzed and confirmed the effects of ICT on penetration levels of GDP,technology cost, trade, and education [20]. Other studies have examined ICT impacts on economic growth based on time series, fixed effects, and econometric techniques [15; 16]. Complex causal-sequential models incorporate into a multi-level model the digital inequality dimensions of digital access, capital and human resources, individual characteristics, and impacts/outcomes [6]. This holistic framework has the advantage of simulating sequential aspects over varied units of analysis (individual, organization, or society). However, obtaining sufficient data to test it is sometimes challenging. Consideration of technology development over time can employ the approach of adoption-diffusion modeling, which originated from [PERSON] [22], but this model fails to consider the broader social, economic, and cultural forces impacting events and for which longitudinal data often are not available or limited [21]. The same is true for technology acceptance theories, such as the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Model of Adoption of Technology in Households (MATH), which are often deployed to model ICT use, including internet use [23], or to study transformational government in the United States [24]. The Spatially Aware Technology Utilization Model (SATUM) posits associations induced from the digital divide/inequality literature or justified by author reasoning. It proposes relationships of independent socioeconomic, infrastructural, innovation, and social capital variables with dependent factors that directly or indirectly include ICT and the internet [25; 26; 27; 21; 28; 29]. SATUM includes the methods of descriptive mapping, cluster analysis, OLS regression, and spatial autocorrelation of dependent factors and model residuals. It is suitable for digital inequality investigations that focus on geographic disparities but does not provide for multi-level or bi-directional analysis. The SATUM model is adopted for the present research due to its geographic approach, its feasibility for studying national states or provinces, its inclusion of spatial clustering for understanding digital inequalities, and its incorporation of testing for spatial bias and longitudinal analysis. The modified operational SATUM model for this study is presented in Figure 1. The figure on the left shows the exploratory analysis of spatial patterns for ICT variables as conducted using two methods [21]. One is k-means cluster analysis, which measures the groupings of geographical units with the least distance between provinces based on a given k number of clusters, while the second, Moran's Index, tests to what extent, for a single variable, like units aggregate together (positive value of the index) or are repelled from each other (negative value of the index). At the figure's lower right, the confirmatory analysis of ordinary least squares regression appears, which connotes the association of the independent variables with each dependent one. The Moran's I test, computed based on the regression residuals, evaluates if the model removes a spatial bias, leading to random residuals. If they are not random, regression findings must be treated cautiously, as the model has spatial bias [25]. The residuals also undergo standard OLS residual testing through the Joint Wald, Koenker, and Jarque Bera statistics.