Digital Divide and A Spatial Investigation of Convergence in ICT Development Across Provinces in Indonesia

Ramadhanti, Herdina Dwi; Astuti, Erni Tri

doi:10.34123/jurnalasks.v14i1.388

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…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 [21,[25][26][27][28][29]. SATUM includes the methods of descriptive mapping, cluster analysis, OLS regression, and spatial autocorrelation of dependent factors and model residuals.…”

Section: Conceptual Modelmentioning

confidence: 99%

Digital Inequalities in China in 2020: Spatial and Multivariate Analysis

Pick,

Ren,

Sarkar

2024

Applied Sciences

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China’s digital divide is explored through spatial and multivariate analysis. The dependent variables include general uses of information and communication technologies (ICTs) and mobile devices, measures of ICT infrastructure, purposeful uses for software services, and purposeful uses of e-commerce. Independent variables include a variety of demographic, economic, educational, ethnic, innovation, and knowledge production indicators. Data come from the China Yearbook. Theoretically, the study draws on the Spatially Aware Technology Utilization Model (SATUM). Digital disparities in Chinese provinces are analyzed using regression analysis, spatial autocorrelation, and k-means cluster analysis. The important correlates associated with digital inequality are expenditure for science and technology, income, R&D investment, full-time teachers, non-Han (minority) population, and proportion of urbanization. Longitudinal comparison reveals factors influencing ICT changes to be expenditure on science and technology, the unemployment rate, and college degree. Among the cluster findings are geographic concentrations of digital variables in Shanghai and Beijing and marked spatial pattern differences in central and central–east China between variable groups. Provincial and national policy implications, which are compared to China’s five-year plans, include an emphasis on science and technology, technology education in all provinces, support for higher provincial incomes, and ICT training for the non-Han population. These steps are especially important in ICT-deprived provinces.

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Section: Conceptual Modelmentioning

confidence: 99%

Digital Inequalities in China in 2020: Spatial and Multivariate Analysis

Pick,

Ren,

Sarkar

2024

Applied Sciences

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“…This model considers the spatial distribution and clustering of dependent variables as well as examining the associations of dependent variables with a set of demographic, educational, economic, ethnic/racial, social capital, attitudinal, trust, and proximity factors. The model has been applied to studying digital access, use, and purposeful use [35][36][37][38][39], as well as to densities of properties in the sharing economy [23,24]. The model's unit analysis is a geographic areal unit-for example, it has been applied to units including nations [35], states and provinces [36], U.S. counties [40], and zip codes [24].…”

Section: Conceptual Modelmentioning

confidence: 99%

Spatiotemporal Patterns and Socioeconomic Influences on Host Participation in Short-Term Rental Markets: Airbnb in San Francisco

Sarkar,

Pick,

Jabeen

2023

IJGI

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This paper examines spatiotemporal patterns and socioeconomic influences on host participation in Airbnb’s short-term rental (STR) marketplace in San Francisco during the years 2019–2022, a four-year period that spans the COVID-19 pandemic. This provides the motivation for the study to examine how San Francisco’s demographic and socioeconomic fluctuations influenced Airbnb hosts to rent their properties on the platform. To do so, Airbnb property densities, indicators of host participation, are estimated at the census tract level and subsequently mapped in a GIS along with points of interest (POIs) located all over the city. Mapping unveils spatiotemporal patterns and changes in Airbnb property densities, which are also analyzed for spatial autocorrelation using Moran’s I. Clusters and outliers of property densities are identified using K-means clustering and geostatistical methods such as local indicators of spatial association (LISA) analysis. Locationally, San Francisco’s Airbnb hotspots are not located in the city’s core, unlike other major Airbnb markets in metropolitan areas. Instead, such hotspots are in the city’s northeastern neighborhoods around ethnic enclaves, in close proximity to POIs that are frequented by visitors, and have a higher proportion of hotel and lodging employment and lower median household income. A conceptual model posits associations of Airbnb property densities with sixteen demographic, socioeconomic factors, indicators of trust, social capital, and sustainability, along with proximity to points of interest. Ordinary least squares (OLS) regressions reveal that occupation in professional, scientific, and technical services, hotel and lodging employment, proximity to POIs, and proportion of Asian population are the dominant factors influencing host participation in San Francisco’s shared accommodation economy. The occupational influences are novel findings for San Francisco. These influences vary somewhat for two main types of properties—entire home/apartment and private rooms. Implications of these findings are discussed in relation to supply side motivations of Airbnb hosts to participate in San Francisco’s STR marketplace.

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Analyzing the role of ICT in bridging the digital divide: a transitional analytical framework for ICT access to impact

Hussain

Wen

Jiang

et al. 2023

LHT

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PurposeIn light of the shift in focus from information communication technology (ICT) access (access divide) and skills (skills divide) to the tangible impacts of ICT use (impact divide), a growing number of scholars have called for further investigation into the inter-territorial and multi-dimensional aspects of the digital divide in China. This study aims to address these gaps by examining the disparities across 31 provinces, particularly emphasizing the transition from the traditional access and skills divides to the impact divide.Design/methodology/approachMultivariate regression analysis extensively investigates the transition from the access and skills divides to the impact divide across 31 provinces. Additionally, ArcGIS software is used to analyze spatial agglomeration and the auto-correlation (Moran-i) and predict mapping patterns in the data corresponding to all three levels of the digital divide.FindingsAccording to the study's findings, poverty is a significant factor in the digital divide between different regions in China. The research shows that provinces with advanced administrative systems, such as Guangdong, Shanghai, Beijing, Jiangsu, Shandon and Zhejiang, have high scores on the digital development index (DDI). However, regions with poverty-ridden and rural areas, primarily located in southwest, central and western China, tend to have lower DDI scores.Originality/valueThis study offers a novel contribution to the literature by presenting an innovative conceptual framework that explores the impact divide within China's provinces. The authors also address this lacuna in the literature by developing and testing two dimensions to examine the relationships statistically under a wide range of socioeconomic and ICT indicators.

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Digital Divide and A Spatial Investigation of Convergence in ICT Development Across Provinces in Indonesia

Cited by 3 publications

References 19 publications

Digital Inequalities in China in 2020: Spatial and Multivariate Analysis

Digital Inequalities in China in 2020: Spatial and Multivariate Analysis

Spatiotemporal Patterns and Socioeconomic Influences on Host Participation in Short-Term Rental Markets: Airbnb in San Francisco

Analyzing the role of ICT in bridging the digital divide: a transitional analytical framework for ICT access to impact

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