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Neighborhood Disparities in Smart City Service Adoption

Shahaf Donio, Eran Toch

TL;DR

This study investigates how neighborhood context shapes the adoption of resident-facing smart city services in Tel Aviv, extending e-Government Adoption Models to include place-based factors. Using a four-neighborhood random-sample survey and structural equation modeling, the authors link neighborhood residency to digital proficiency, privacy perceptions, and perceived utility, which in turn influence adoption of municipal digital services ($AoMDS$). The results show strong neighborhood effects: northern, higher-SES areas display higher technology proficiency and perceived utility but also greater privacy concerns, while southern neighborhoods exhibit lower adoption and literacy levels. The findings highlight the need for place-aware design and targeted digital-literacy and privacy-education programs to bridge digital inequality and improve uptake of smart city services.

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.

Neighborhood Disparities in Smart City Service Adoption

TL;DR

This study investigates how neighborhood context shapes the adoption of resident-facing smart city services in Tel Aviv, extending e-Government Adoption Models to include place-based factors. Using a four-neighborhood random-sample survey and structural equation modeling, the authors link neighborhood residency to digital proficiency, privacy perceptions, and perceived utility, which in turn influence adoption of municipal digital services (). The results show strong neighborhood effects: northern, higher-SES areas display higher technology proficiency and perceived utility but also greater privacy concerns, while southern neighborhoods exhibit lower adoption and literacy levels. The findings highlight the need for place-aware design and targeted digital-literacy and privacy-education programs to bridge digital inequality and improve uptake of smart city services.

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.
Paper Structure (25 sections, 2 equations, 4 figures, 2 tables)

This paper contains 25 sections, 2 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Theoretical Background
  3. Research Scope and Hypotheses
  4. Hypotheses Development
  5. Hypotheses Development
  6. Methodology
  7. Procedure and participants
  8. Sample Validity
  9. Tel Aviv Smart City Services
  10. Measures
  11. Dependent Variable
  12. Independent Variables
  13. Factor Analysis
  14. Structural equation modeling
  15. Results
  16. ...and 10 more sections

Figures (4)

  • Figure 1: Research model and hypotheses
  • Figure 2: Tel Aviv-Yafo city map by statistical area divisions, with the sampled neighborhoods highlighted. Color represents the socioeconomic index (according to Israel's Central Bureau of Statistics): red represents low socioeconomic status, and green represents high socioeconomic status.
  • Figure 3: Model results with SEM implementation
  • Figure 4: Mapping the Facebook posts that include address data (red spots) in each statistical area of Tel Aviv.