Neighborhood Disparities in Smart City Service Adoption

2025-01-08

AI Summary

This study investigates spatial disparities in smart-city service adoption across four neighborhoods in Tel Aviv to uncover the geographical drivers of digital inequality. Employing a telephone-based random survey (n=489), integrated municipal digital service usage data, and structural equation modeling (SEM), it provides the first empirical evidence that a “place effect”—independent of demographic attributes—significantly shapes adoption behavior. Specifically, neighborhood affiliation exerts influence through two mediating pathways: digital literacy and privacy concerns. Moving beyond conventional individual-centered approaches, the study proposes a novel analytical framework for digital inequality that integrates spatial context with psychological perception. It establishes spatial location as a critical structural determinant of technology adoption. These findings offer both theoretical grounding and empirical support for designing spatially responsive digital inclusion policies and implementing targeted interventions to bridge the digital divide.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

While local governments have invested heavily in smart city infrastructure, significant disparities in adopting these services remain in urban areas. The success of many user-facing smart city technologies requires understanding barriers to adoption, including persistent inequalities in urban areas. An analysis of a random sample telephone survey (n=489) in four neighborhoods of Tel Aviv merged with digital municipal services usage data found that neighborhood residency influences the reasons why residents adopt resident-facing smart city services, as well as individual-level factors. Structured Equation Modeling shows that neighborhood residency is related to digital proficiency and privacy perceptions beyond demographic factors and that those influence the adoption of smart-city services. We summarize the paper by discussing why and how place effects must be considered in further research in smart cities and the study and mitigation of digital inequality.

Problem

• Smart City Services
• Digital Inequality
• Usage Factors

Innovation

• Smart City Services
• Geographical Impact Analysis
• Digital Inequality Mitigation