Document Type : Research Paper
Authors
1
Department of Urbanism, Faculty of Architecture and Urbanism, Ferdowsi University of Mashhad, Mashhad, Iran
2
Department of Geography and Urban Planning, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran
10.22034/jsc.2025.525145.1848
Abstract
A B S T R A C T
Artificial intelligence (AI), as a key technology of the digital era, offers extensive capacities to enhance the quality of urban services and improve crisis-management processes. Through big-data analytics, real-time processing, and accurate predictive modeling, AI can reduce costs, accelerate decision-making, and increase the efficiency of public services. Nevertheless, concerns such as privacy violations, data security risks, overreliance on intelligent systems, and job displacement introduce new socio-cultural challenges. This study was designed and conducted to examine the public perceptions of residents of the metropolis of Mashhad regarding AI applications in two domains—urban services and crisis management. The analysis focused on four principal constructs: “perceived risks,” “user comfort and trust,” “application in urban services,” and “role in crisis management.” Data were collected in 2024 through a cross-sectional survey using a seven-point Likert-scale questionnaire; after screening, 380 valid responses were analyzed. Instrument validity was supported by a review of the theoretical literature, and reliability was confirmed with Cronbach’s alpha exceeding 0.80. Factor analysis and multivariate regression indicated that these four constructs jointly explained 68% of the variance in public perceptions. The findings show that, although AI applications in urban services and crisis management were received favorably, serious concerns persist regarding privacy breaches, potential medical errors, and system robustness. Moreover, hands-on experience with AI increased user trust but reduced satisfaction with urban services, and demographic variables exhibited no significant effects. We conclude that the sustainable adoption of AI requires strengthening technical infrastructure, enhancing algorithmic transparency, providing technology-literacy training, and enacting equitable policies aligned with social justice.
Exended Abstract
Introduction
The accelerating integration of Artificial Intelligence (AI) into urban management has redefined the dynamics of service delivery, crisis response, and citizen–government relations. As a transformative technology, AI enables predictive analytics, automation, and real-time decision-making that can enhance the efficiency of public services, optimize resource use, and improve urban resilience. However, it also introduces concerns regarding privacy, ethical accountability, algorithmic bias, job displacement, and overreliance on automated systems. The duality of opportunities and risks makes public perception a decisive factor in AI adoption within urban contexts.
The present study situates this debate within Mashhad, Iran’s second largest metropolis, which is characterized by high population density, substantial urban infrastructure challenges, and a rapidly expanding smart city agenda. Mashhad’s recent investments in digital infrastructures, smart governance initiatives, and fiber optic expansion provide a fertile ground for examining citizen perspectives. The research is driven by the recognition that while technological capacity is growing, the sustainability and legitimacy of AI integration depend upon citizen acceptance, trust, and perceived benefits.
Drawing on theoretical frameworks such as the Technology Acceptance Model (TAM) and ethical AI principles (AI4People), the study conceptualizes citizen perception along four principal dimensions: (1) perceived threats, including privacy violations and misuse; (2) comfort and trust, reflecting confidence in institutions and AI providers; (3) applications of AI in urban services, such as transportation, safety, and efficiency improvements; and (4) the role of AI in crisis management, encompassing disaster preparedness and emergency response. By exploring these dimensions, the study provides insights into how citizens negotiate between optimism about efficiency gains and caution regarding social risks.
Methodology
The research employed a quantitative survey-based design, focusing on Mashhad’s residents as the target population. Using Cochran’s formula for indeterminate populations, a sample size of 385 was estimated; 380 valid responses were ultimately collected in 2023. The sample consisted of residents aged 18 and above, distributed through online questionnaires.
The survey instrument was carefully developed through literature review and validation by urban studies experts. It included structured items measuring the four key constructs: perceived threats, comfort/trust, urban service applications, and crisis management roles of AI. Items were designed on a 7-point Likert scale, ranging from strong disagreement to strong agreement. The questionnaire also included demographic variables (age, gender, education, employment, and income), along with self-assessed AI knowledge and experience.
Reliability tests confirmed high internal consistency, with Cronbach’s alpha values exceeding 0.80 across dimensions. Validity was assessed through exploratory factor analysis (EFA) using SPSS, supported by the Kaiser–Meyer–Olkin (KMO = 0.879) and Bartlett’s test of sphericity (χ² = 4059.70, p < 0.001). Principal Component Analysis (PCA) with Varimax rotation extracted the four anticipated factors, which together explained 68% of the total variance.
This robust methodological approach ensured that the constructs accurately reflected citizens’ cognitive and affective orientations toward AI. The data analysis involved descriptive statistics, correlation matrices, and regression modeling to explore relationships between perceptions and demographic or experiential predictors.
Results and discussion
The results reveal a nuanced and ambivalent perception of AI among Mashhad’s citizens.
Perceived threats emerged as a salient concern. Respondents expressed apprehension regarding privacy breaches, unauthorized surveillance, misdiagnosis in medical AI applications, and the potential for widespread unemployment due to automation. The mean scores for threat items were relatively high (around 3.9 on a 7-point scale), indicating a cautious or skeptical orientation. Concerns about misuse in terrorism, biased decision-making, and even existential risks such as “machines surpassing humans” further reflected anxiety about uncontrolled technological expansion.
Comfort and trust showed moderate levels. While some citizens expressed confidence in governmental and private sector use of AI, trust remained fragile, averaging just above the mid-point (mean ≈ 3.9). Factors such as familiarity with AI and prior experience correlated positively with trust, suggesting that exposure mitigates fear. Conversely, older age correlated negatively with trust, reflecting generational divides. Gender differences were also noted, with men reporting slightly higher trust levels than women, though not statistically significant.
Applications in urban services generated comparatively more optimism. Citizens acknowledged AI’s potential to reduce costs, optimize resource allocation, enhance transportation systems, and improve safety monitoring. The mean scores in this dimension exceeded 4.2, highlighting relative acceptance. However, regression analysis revealed a paradox: practical experience with AI was negatively associated with perceived usefulness in urban services. This suggests that unmet expectations or skepticism about AI’s readiness may temper enthusiasm among experienced users.
Crisis management roles of AI were widely endorsed. Respondents valued AI for disaster prediction, emergency response, rescue operations, and dissemination of accurate crisis information. Items such as AI’s ability to issue early warnings and identify high-risk areas scored above 4.0, reflecting strong agreement. Nearly half of respondents “strongly agreed” that AI could significantly improve emergency response efficiency. Interestingly, despite limited firsthand experience, citizens were willing to place trust in AI during high-stakes scenarios, demonstrating reliance on its perceived objectivity and rapid analytical capabilities.
Regression models further indicated that AI knowledge and practical familiarity significantly influenced perceptions. Knowledge positively affected trust and comfort, while experience had dual effects—enhancing trust but lowering optimism about urban service applications. Demographic variables such as education and income showed negligible impact, underscoring the cross-cutting nature of perceptions.
Conclusion
The study highlights a complex blend of optimism and skepticism among Mashhad’s citizens toward AI in urban management. On one hand, there is clear recognition of AI’s potential to enhance efficiency, reduce costs, improve public services, and strengthen disaster resilience. Citizens appear especially receptive to AI applications in crisis management, perceiving them as life-saving tools with tangible societal benefits.
On the other hand, apprehensions about privacy invasion, job displacement, and opaque decision-making remain prominent. The negative relationship between perceived threats and trust underscores the necessity of transparent governance, accountability frameworks, and public education. Citizens’ mixed attitudes suggest that acceptance of AI is conditional upon safeguarding social justice, ensuring fairness, and aligning technological innovation with ethical principles.
The findings carry important policy implications. Urban managers and policymakers must prioritize transparent communication, participatory design, and equitable access to AI-enabled services. Building trust requires not only technical reliability but also visible efforts to address ethical, social, and cultural concerns. Education campaigns to raise technological literacy can reduce uncertainty, while pilot projects demonstrating concrete benefits can bridge the gap between expectations and lived experiences.
Ultimately, the Mashhad case reveals that the success of AI in urban management depends less on technical capabilities and more on cultivating trust, mitigating risks, and embedding AI within a framework of inclusive governance. AI can indeed become a catalyst for sustainable and resilient urban futures, but only if its deployment is socially responsive, ethically grounded, and transparent to the citizens whose lives it seeks to transform.
Funding
There is no funding support.
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.
Conflict of Interest
Authors declared no conflict of interest.
Acknowledgments
We are grateful to all the scientific consultants of this paper.
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