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Article

Mobile Participatory Urban Governance in a Developing Country: Women’s Acceptance of City Reporting Apps in Karaj, Iran

by
Afsaneh Dehghanpour-Farashah
1,
Faezeh Behnamifard
2,
Mostafa Behzadfar
2,
Mehran Alalhesabi
3 and
Saeed Mojtabazadeh-Hasanlouei
4,*
1
Faculty of Governance, University of Tehran, Tehran 14176-33461, Iran
2
Smart City Lab, School of Architecture and Environmental Design, Iran University of Science and Technology, Tehran 13114-16846, Iran
3
School of Architecture and Environmental Design, Iran University of Science and Technology, Tehran 13114-16846, Iran
4
Department of Civil Engineering, Zanjan Branch, Islamic Azad University, Zanjan 45156-58145, Iran
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(12), 5388; https://doi.org/10.3390/su17125388
Submission received: 24 April 2025 / Revised: 30 May 2025 / Accepted: 8 June 2025 / Published: 11 June 2025
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Abstract

Citizen engagement in urban planning is vital for democratic governance and sustainable development. While technologies such as e-governance platforms and mobile applications have facilitated participatory processes, their success ultimately hinges on citizen acceptance. This study investigates the factors influencing female citizens’ acceptance of a participatory urban planning application, employing a theoretical model based on the technology acceptance model (TAM) and its associated hypotheses. Data were collected through a survey of 390 women and analyzed using partial least squares structural equation modeling (PLS-SEM) via SmartPLS3.2.8. The results demonstrate that perceived usefulness (β = 0.634, p < 0.001) and perceived ease of use (β = 0.321, p < 0.001) significantly predict intention to use, whereas perceived privacy risk exerts a negative influence (β = −0.190, p < 0.001). Environmental attitude (β = 0.396, p < 0.001) and attitude toward participation (β = 0.315, p < 0.001) also enhance perceived usefulness. Due to the impact of the environmental and participatory attitudes of citizens and their social environment on their acceptance of these apps, there is an urgent need to increase the level of citizen awareness and knowledge through targeted education. These findings offer valuable insights for both theoretical advancement and practical policy development in regards to urban governance.

1. Introduction

Citizen participation refers to a process in which ordinary people, whether on a voluntary or obligatory basis and whether acting individually or collectively, aim to influence decisions that involve important choices affecting their community [1]. This approach in urban governance has been widely supported by scholars and practitioners. They emphasize the advantages of active citizen involvement compared to passive public presence. Aligning public policies with citizens’ preferences and promoting a clear understanding of decision-making processes can improve government efficiency and transparency. This alignment often leads to better outcomes at lower costs. Providing opportunities for citizens to participate in local and civic matters is a priority in democratic societies.
Information and communication technology (ICT) has improved social interaction and strengthened citizen engagement in policymaking and governance [2]. Many governments have created e-government infrastructures and launched e-participation programs over recent years. However, digital infrastructure alone is not enough to guarantee citizen engagement. The key factor is the level of acceptance these systems receive from citizens, which is shaped by various contextual and personal factors. Researchers have therefore begun to identify the elements that influence acceptance to improve the design of these platforms [3,4,5,6,7,8,9].
With the growing use of smartphones, e-government and e-participation have progressed toward mobile government and mobile participation. These approaches use mobile applications to facilitate engagement, without requiring formal meetings. They offer continuous and accessible online interaction for citizens [10,11]. Several mobile applications have been developed to support citizen participation in local governance, including city reporting apps (CRAs). Citizen reporting includes any activity in which individuals report observed issues in their environment [12]. These reports ideally contain images and location data and are submitted directly to local authorities on the spot [13].
Recent studies have examined participatory mobile applications in different cities and countries [9,11,14,15,16,17]. However, few studies have focused specifically on CRAs. Some research has focused on app development in certain areas [18]. For example, a study in the Czech Republic evaluated multiple non-emergency reporting platforms, including email, telephone, online forms, WebGIS, and mobile apps. The evaluation used four criteria: searchability, issue coverage, geolocation functionality, and visualization of submitted reports [12]. In Bekasi, Indonesia, a study on the SOROT app showed that 28% of users submitted reports. Most of these reports concerned transportation and traffic issues [19]. In Surabaya, citizens’ attitudes toward a reporting app were shaped mainly by its perceived ease of use and usefulness [20]. A study in Germany based on the TAM found that perceived usefulness and ease of use predicted citizens’ intentions to adopt mobile reporting services [13].
Demographic characteristics also influence technology adoption. Gender is among the factors considered important in behavior prediction theories such as the unified theory of acceptance and use of technology (UTAUT) [21]. Some studies report no significant differences between men and women in the use of mobile apps [22,23]. Other studies have found that women are less likely to adopt new technology and tend to use it less frequently [24,25,26]. Li et al. [23] reported that women’s use of technology was lower compared to that of men, even when adoption occurred.
Since CRAs are a type of urban service, the question of their acceptance falls within the domain of urban studies. Prior research in this field has shown that women are more frequently present in residential areas, green spaces, and neighborhood environments [27,28,29,30]. Consequently, they may possess a more nuanced understanding of their surroundings [31]. However, in many developing countries, women face challenges in participating in formal urban planning processes [32,33,34]. Advances in e-government and digital interaction provide an opportunity to address these challenges. These tools also support gender equality and empower women to participate in civic matters [6]. Understanding how women accept and use digital participatory tools is an important area of inquiry.
Taking this into consideration, this study focuses on the behavior of female citizens in relation to the acceptance of CRAs. Because the use of these applications is relatively new in Iran, the city of Karaj was selected as the research setting. The goal is to identify the factors that influence women’s intention to use CRAs as a participatory mechanism to assist city officials. Achieving this goal contributes to research on urban governance and offers practical insights for improving participatory practices through mobile technology.

Theoretical Framework and Modeling

Accepting or refusing to use CRAs is considered a form of behavioral intention, which is a central concept to several models that predict human behavior, including the theory of planned behavior (TPB) and the theory of reasoned action (TRA). According to these theories, an individual’s behavioral intention is shaped by their attitude toward the behavior and by subjective norms (SNs). SNs refer to the perceived social pressure regarding whether others believe a particular behavior is appropriate [35]. These norms influence the selection and performance of a wide range of individual and collective actions, including those related to environmental responsibility and technology use. In the context of technology adoption, SNs—also referred to as social influence [36]—are shaped by the extent to which others expect an individual to use a smartphone application or a new technology [37]. Studies have shown that subjective norms positively influence behavioral intention to use (IU) such systems [38,39,40]. Research has also shown that subjective norms influence participatory environmental decision making and the selection of environmental activities/apps, both in theoretical models and in empirical studies [41].
Perceived privacy risk (PPR) is another construct that may influence an individual’s behavioral intention to accept or reject a given technology. Previous studies have confirmed that trust and perceived trustworthiness are critical factors in the adoption of online commercial and governmental services [4,42]. Online users face several types of risks during internet activities [43]. One of the most significant risks is the potential disclosure of personal data, including location information, to third parties, which may shape an individual’s willingness to use mobile-based services [44]. In this study, PPR is defined as the perceived likelihood of encountering privacy threats while using a mobile application. Users must weigh the potential risks of disclosing personal information against the anticipated benefits when using CRAs [44,45]. PPR has been shown to have a negative influence on an individual’s intention to use (IU) such technologies [42,43,46].
In addition to the constructs discussed, the theoretical framework of this study is grounded in the technology acceptance model (TAM), which was developed by Davis in 1989 [47]. TAM has been widely applied in research on information and communication technologies, including studies on e-government adoption [4,5], m-government systems [48,49,50], and the acceptance of mobile applications by specific user groups [13,20,50,51,52]. According to TAM, the two principal determinants of users’ behavioral intention to adopt or reject technological innovations are perceived usefulness (PU) and perceived ease of use (PEU) [47].
PU refers to an individual’s assessment of how beneficial a particular technology is within a specific task context [42]. In the case of urban sensing, PU extends beyond individual benefits and encompasses collective advantages for the urban environment. Using mobile devices to report local issues is generally more efficient and convenient than traditional reporting methods. It contributes to improved living conditions and supports more effective urban governance. PU also reflects communal benefit, which may enhance public understanding of civic engagement and urban well-being [13,21,53,54].
PU can be influenced by two additional constructs: environmental attitude (EA) and attitude toward participation (ATP) [13]. Urban sensing is based on detecting and resolving local environmental problems such as traffic congestion, noise pollution, and infrastructure deficiencies, as explained by Burke et al. [55]. Since an individual’s attitude reflects their evaluation of behavior [35], their perception of urban issues may vary significantly. EA captures the extent to which individuals care about the condition of their urban surroundings [56]. It is also associated with the likelihood of performing environmentally responsible actions [57]. People who are more environmentally aware may perceive CRAs as more useful because they are motivated to respond to irregularities and issues in their local environment [13]. As a result, EA can directly affect PU and the intention to use CRAs. ATP, the second factor influencing PU, reflects an individual’s altruistic motivation to participate in urban sensing. According to Alford [53], this motivation arises from a desire to serve the public good. Therefore, ATP is determined by the degree to which individuals wish to be involved in civic policy and governance processes.
PEU refers to the extent to which a user believes a given technology is simple to use, as defined by Davis [47]. In the field of human–computer interaction, ease of use is a primary goal in system and interface design [58]. This concept applies to both the mobile device and the user interface of apps used in urban sensing. Users’ previous experiences play an important role in shaping their usability perceptions, alongside objective usability factors [21,58]. In TAM, PEU is positively associated with both IU and PU.
PEU can itself be influenced by smartphone literacy (SPL). As mobile technologies become increasingly advanced, users are expected to possess a minimum level of technical competence and digital confidence [59]. Despite this, the role of digital literacy is often underexplored in technology adoption research [21]. SPL refers to an individual’s ability to use smartphones and mobile technologies effectively and efficiently [13,21]. It includes self-efficacy in performing tasks such as navigating touchscreen interfaces, installing apps, and using mobile internet tools. Users with higher levels of mobile literacy are more likely to find urban sensing technologies easy to use.
In summary, this study draws upon theoretical literature to propose a research model in which citizens’ intentions to use CRAs are predicted by seven constructs. The model is operationalized through eight research hypotheses (Figure 1, Table 1).

2. Materials and Methods

2.1. Instrument Development

A structured questionnaire was developed based on a review of the relevant literature and was administered to a sample of female citizens. The first section of the questionnaire presented the research objectives and introduced the mobile application under investigation. It then collected demographic information, including the participant’s age, occupation, education level, and place of birth.
The second section used a five-point Likert scale to assess 21 items, ranging from 1 (strongly disagree) to 5 (strongly agree). Five of the constructs (EA, ATP, SPL, PEU, and SN) were each measured by three items, while three of them (PU, PPR, and IU) were each measured by two items. The full list of questionnaire items and their corresponding sources is presented in Table 2.

2.2. Sampling and Data Collection

The study population consisted of women residing in Karaj, a major metropolitan area in Iran located near the capital city, Tehran. Given the focus on urban participatory governance and the adoption of CRAs, a convenience sampling method was used. Stratification by age and education level was applied to improve the diversity of the sample. Participants were recruited from two major women’s cultural centers in Karaj, which attract a broad demographic, including working professionals, students, and homemakers.
Prior to finalizing the questionnaire, a pilot test was conducted with 18 female residents, leading to minor phrasing adjustments. A total of 600 questionnaires were distributed, and after a ten-day collection period, 437 were returned. Following the removal of incomplete or incorrectly filled-out forms, 390 valid responses were retained for analysis.
This sample size exceeds the minimum requirements for SEM, which recommends a respondent-to-indicator ratio of at least 10:1 [68]. Additionally, using Westland’s statistical algorithm [69], the required minimum sample size for SEM—given 8 latent variables, 21 indicator variables, a power level of 0.80, and a significance level of 0.05—is estimated at 264. Therefore, the final sample of 390 participants ensures robust sampling adequacy.
To control for potential confounding variables, age, education level, and employment status were included as control variables in the analysis. These variables were selected due to their potential impact on technology adoption and participatory behavior [21]. Table 3 presents the demographic distribution of respondents in terms of age, education, employment, and place of birth. Overall, 58.72% of respondents were under the age of 41, 53.07% held at least an undergraduate degree, 52.3% were not currently employed, and 46.15% were born outside of Karaj (immigrants). Compared to the most recent national data [70], the sample closely mirrors the characteristics of the female population in Karaj, thereby supporting the generalizability of the findings within the study’s context.

2.3. Data Analysis

To assess common method bias (CMB), Harman’s single-factor test was conducted, as CMB is a potential concern in all self-reported datasets. A principal component factor analysis without rotation was performed. The analysis extracted three factors with initial eigenvalues greater than 1.00, jointly explaining 69.039% of the total variance. The first factor accounted for only 28.913% of the variance, indicating that common method bias is not a significant issue in this study [71]. Inter-correlation values presented in Table 4 further support this finding, as none exceeded 0.90. The highest observed correlation was 0.823, which falls within acceptable thresholds.
Descriptive statistics for all questionnaire items were calculated using IBM SPSS26. The theoretical model was evaluated using PLS-SEM with SmartPLS3.2.8. This modeling approach has been widely adopted in prior studies [13,20,50] and is especially suitable for constructs involving non-normal data distributions and small to medium sample sizes [72].

3. Results

The research model was evaluated in three stages:
  • An analysis of indicator loadings was conducted to assess reliability. As shown in Table 4, all loadings exceeded the threshold of 0.60, confirming the reliability of the indicators [73].
  • Convergent validity was assessed using Cronbach’s alpha (Alpha), composite reliability (CR), and average variance extracted (AVE). Table 4 shows that all values exceeded the recommended thresholds (Alpha > 0.70, CR > 0.60, AVE > 0.50, and CR > AVE), demonstrating that the items adequately represent their respective constructs and confirming the model’s convergent validity [72].
  • The Fornell–Larcker criterion was used to evaluate discriminant validity. As indicated in Table 5, the square root of the AVE (√AVE) for each construct (shown in the diagonal) is greater than its correlations with other constructs, supporting the model’s discriminant validity [74].
Based on these evaluations, the measurement model demonstrates strong psychometric properties and meets all necessary reliability and validity criteria.
To assess the structural model, key parameters, including the R² values, beta coefficients, and corresponding t-values, were examined. These values can be interpreted in the same manner as that used in simple regression analysis [13]. A bootstrapping procedure with 5000 resamples was applied, following established methodological recommendations [72]. Additional indicators such as predictive relevance (Q2) and effect sizes (f2) were also evaluated. The results of the structural model assessment are presented in Figure 2.
SN, PU, PEU, and PPR together explain 49.2% of the variance in respondents’ intention to use CRAs (R2 = 0.492), reflecting a substantial and consistent level of explanatory power. These results are comparable to findings in earlier studies based on TAM constructs [50,51,52] and exceed those reported in some other research [13,20,48]. The results of the hypothesis testing are summarized in Table 6.
These results confirm that all research hypotheses are supported (p < 0.05). In terms of effect sizes (f2), measurements using Cohen’s guideline (considering 0.02 to represent a small effect, 0.15 a medium effect, and 0.35 a large effect) [75] show that, except for PEU, PPR, and SN effects, which are small, all other constructs display medium to large effects (Table 6). Furthermore, Q2, which represents the predictive relevance of the model for values higher than 0 [73], confirms an acceptable predictive relevance for all three endogenous variables in this study (Figure 2).

4. Discussion

The results confirm hypotheses H1 and H2, demonstrating that citizens’ environmental attitudes (βEA→PU = 0.396, p = 0.000) and attitudes toward participation (βATP→PU =0.315, p = 0.000) positively influence their perceptions of the usefulness of CRAs. Together, these two constructs explain 78.1% of the variance in PU, indicating a high level of explanatory power (R² = 0.781). This finding aligns with the view of Damianus and Racoma [76], who emphasized that uncertainty or ambivalence regarding environmental attitudes can significantly influence environmental behavior. Citizens’ awareness, attitudes, and behaviors regarding environmental protection are therefore crucial for the success of environmental policies [77], and they also shape perceptions of the usefulness of digital tools such as CRAs. Some researchers argue that women’s environmental attitudes are particularly influential, as women may exhibit a stronger biospheric orientation compared to men, which may increase their tendency toward environmentalism [78].
As emphasized by Shareef et al. [8], a lack of practical experience with technology can lead to unfavorable attitudes toward adoption from technological, behavioral, economic, and organizational perspectives. Consistent with this argument, the present findings support H3, confirming that smartphone literacy positively affects perceived ease of use (βSPL→PEU = 0.635, p = 0.000). This result is also aligned with Davis’s original findings regarding the development of TAM [47], as well as with later research on e-government [5], m-government [14], and mobile application adoption [13,51,52,79]. The study also supports H4 (βPEU→PU =0.264, p = 0.000), indicating that PEU contributes to the PU of CRAs.
H5, which posits a positive effect of SN on the intention to use CRAs, is also supported (βSN→IU = 0.140, p = 0.043). This finding diverges from those of some earlier studies [14,48,51,79], although other research has also underscored the significance of subjective norms and social influence in shaping behavior [14]. In addition to affecting behavioral intention, subjective norms are believed to influence actual usage behavior. The correlation between PU and IU has consistently been identified as one of the most reliable relationships in TAM-based studies [5,13,14,20,48,50,51,79]. The current study confirms H6 (βPU→IU = 0.634, p = 0.000), reinforcing this established association. However, not all studies have found this relationship to be significant [4,49,52]. For example, Kim and Lee [7] found that in the context of active e-participation, women’s engagement was more strongly influenced by perceived intrinsic value rather than instrumental value, the latter being closely associated with perceived usefulness.
The findings also support H7, indicating that PEU has a positive effect on the intention to use CRAs (βPEU→IU = 0.321, p = 0.000). This aligns with earlier research concerning mobile reporting services [13,20], car-sharing platforms [51], health and fitness apps [79], SMS-based systems [48], and m-government [9,14,49,80] or e-government services [4,5].
Consistent with H8, the study confirms that perceived privacy risk negatively affects the intention to use CRAs (βPPR→IU = -0.190, p = 0.010), in line with prior studies [9]. While some researchers have examined trust as a proxy for privacy risk [4,5,14,49,52,79], privacy concerns remain central in technology adoption due to the uncertainties associated with data misuse and the lack of guaranteed positive outcomes. As suggested by previous research [79], perceived trust in a system is inversely related to perceived privacy risk [52]. It is notable, however, that some studies have found privacy risk to be a non-significant factor in the adoption mobile applications, including mobile reporting services [13] and mobile banking platforms [52]. These inconsistencies may be attributed to cultural variations across societies.
To address these cultural differences, it is important to recognize that user attitudes toward mobile environmental apps like CRAs are significantly shaped by sociocultural contexts. In collectivist societies, social norms may have a stronger influence on behavioral intention than in individualist cultures, where perceived usefulness and ease of use tend to be more dominant [81,82]. Comparative studies across national contexts—such as between South Korea and the United States—demonstrate that the importance of TAM constructs varies substantially with cultural orientation [83]. Moreover, in emerging economies, factors such as infrastructural limitations and trust in government institutions play a more prominent role in mobile service adoption [50,84]. In light of these observations, further cross-national analysis is recommended to improve the generalizability of findings and to deepen the understanding of the interplay between cultural values, technological perceptions, and environmental behavior.

5. Conclusions

This study examined how individual attitudes and the social environment shape the acceptance of reporting applications as tools of participatory urban governance among female citizens in Karaj, Iran. The findings show that women’s intentions to use these apps are influenced by subjective norms and individual perceptions of ease of use, usefulness, and privacy risks. The results also underscore the importance of women’s attitudes toward the environment and their involvement in local affairs in shaping their perceptions of app usefulness. Smartphone literacy contributes to a greater sense of usefulness, indicating that citizen education plays a key role in promoting the adoption of participatory tools. In this regard, beyond public education aimed at encouraging pro-environmental behaviors through traditional and social media [85], as Behnamifard et al. [50] suggest, embedding educational content within CRAs may further enhance their perceived usefulness.
This exploratory study contributes to the TAM literature by incorporating sociocultural and gender-specific variables in the context of mobile participatory urban governance. By demonstrating the influence of subjective norms and environmental attitudes on perceived usefulness, the study expands the TAM framework to reflect collective and contextual factors in technology adoption. In addition, the emphasis on privacy concerns aligns with recent research on digital trust and offers a foundation for future investigations into participatory technologies in transitional societies.
The findings offer practical implications for policymakers, urban planners, and app developers. Given the impact of perceived usefulness and privacy risks, local authorities should implement targeted education campaigns using both traditional media and social platforms to explain app functionalities, security features, and civic benefits. Many users remain uncertain about whether their mobile service providers offer secure and reliable access to e-government platforms, particularly in countries where such services are still developing [14]. Thus, local authorities and service providers should focus on increasing transparency around app functionality to alleviate privacy concerns.
Embedding in-app tutorials or gamified features can enhance user confidence and engagement. Studies have shown that perceived playfulness significantly influences user behavior online. Developers are encouraged to adopt user-centered design principles by incorporating elements such as rewards and interactive features, reinforcing findings that link playfulness with technology adoption. Transparent communication regarding data protection and service reliability is also essential for building public trust, especially in contexts like Iran, where mobile governance is still emerging. Pilot initiatives involving local women’s groups may help foster trust and provide iterative feedback for continuous improvement.
While this study offers meaningful contributions, several limitations must be acknowledged. First, the relative novelty of mobile participatory applications in the study area may affect the generalizability of results, as user attitudes may evolve over time. Second, the cross-sectional design limits the ability to establish causal relationships. Third, the use of self-reported data introduces the possibility of social desirability and recall biases. Lastly, while the exclusive focus on women helps highlight gender-specific factors, it limits the generalizability of the findings to the broader population.
Despite these limitations, the study provides valuable insights into the development and acceptance of mobile participation within Iran’s distinct social and cultural setting. Future research in other developing countries is needed to explore broader factors influencing the adoption of participatory apps. Longitudinal studies using alternative theoretical models beyond TAM can offer deeper insights into evolving user behaviors. Comparative studies across demographic variables such as gender, ethnicity, age, and socioeconomic status would further clarify differences in acceptance patterns. Because this study focused on non-users—due to the early stage of participatory apps in Iran—future research should examine factors influencing current or sustained usage to better understand long-term engagement in mobile participatory planning.

Author Contributions

Conceptualization, A.D.-F., F.B., and S.M.-H.; methodology, A.D.-F., F.B., M.B., M.A., and S.M.-H.; software, F.B., A.D.-F., and S.M.-H.; validation, F.B., A.D.-F., M.B., M.A., and S.M.-H.; formal analysis, F.B., A.D.-F., and S.M.-H.; investigation, F.B., A.D.-F., and, S.M.-H.; data curation, A.D.-F., F.B., M.B., M.A., and S.M.-H.; writing—original draft preparation A.D.-F., F.B., M.B., M.A., and S.M.-H.; writing—review and editing, F.B., A.D.-F., M.B., M.A., and S.M.-H.; visualization, A.D.-F., F.B., and S.M.-H. All authors have read and agreed to the published version of the manuscript.

Funding

This study received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the Faculty of Governance, University of Tehran (approval code IRB-10734644).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets generated and analyzed during this research are available from the corresponding author and can be provided upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 17 05388 g001
Figure 1. Research model based on the TAM developed by Davis [47].
Figure 1. Research model based on the TAM developed by Davis [47].
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Figure 2. Findings from the structural model analysis (* p < 0.05; ** p < 0.01; n = 390).
Figure 2. Findings from the structural model analysis (* p < 0.05; ** p < 0.01; n = 390).
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Table 1. Research hypotheses and their supporting literature.
Table 1. Research hypotheses and their supporting literature.
HypothesesSupporting Literature
H1: Environmental attitude (EA) may positively influence the perceived usefulness (PU) of CRAs.[13,60,61,62,63,64]
H2: Attitude toward participation (ATP) may positively influence the perceived usefulness (PU) of CRAs.[13,17,61,64,65]
H3: Smart phone literacy (SPL) may positively influence the perceived ease of use (PEU) of CRAs.[13]
H4: Perceived ease of use (PEU) may positively influence the perceived usefulness (PU) of CRAs.[13,47,66]
H5: Subjective norms (SN) may positively influence the intention to use (IU) CRAs.[17,64,66,67]
H6: Perceived usefulness (PU) may positively influence the intention to use (IU) CRAs.[13,47,64,66]
H7: Perceived ease of use (PEU) may positively influence the intention to use (IU) CRAs.[13,47,61,64,66,67]
H8: Perceived privacy risk (PPR) may negatively influence the intention to use (IU) CRAs.[13,17,66]
Table 2. Questionnaire items.
Table 2. Questionnaire items.
ConstructAbb.ItemsRef.
EAEA1I am concerned about environmental issues.[13]
EA2I believe everyone should protect the environment.[60,64]
EA3I care about my city’s environment.[60]
ATPATP1I want to contribute to the protection of the environment.[64]
ATP2I want to have an influence in my city.[13]
ATP3I enjoy being a part of the problem-solving process in my city.[61,63,65]
SPLSPL1I know how to use applications on a smartphone.Self-developed
SPL2I am familiar with using internet services on a smartphone.[13,61,64]
SPL3Internet services on my smartphone are easy to use for me.[13]
SNSN1People who influence my behavior would recommend using the CRA.[64,66,67]
SN2People who I consider important would think I should use the CRA.[64,66,67]
SN3If many people used the CRA, I would be more likely to use it.Self-developed
PEUPEU1It would be easy for me to understand how the CRA works.[13,63,67]
PEU2It would not take me much effort to use the CRA.[13,66]
PEU3Overall, I believe that the CRA would be easy to use.[13,64,66,67]
PUPU1The CRA allows people to report more infrastructure issues.[13]
PU2It would be beneficial to have a CRA in my city.[13,50,61,64]
PPRPPR1Users’ data may be misused by mobile service providers.[13]
PPR2I am hesitant to share personal information with a mobile service.[13]
IUIU1I am considering using the CRA.[13]
IU2I plan to use the CRA.[13,63,66,67]
Table 3. Descriptive statistics of the participants.
Table 3. Descriptive statistics of the participants.
Individual CharacteristicCategoryN[%]
Age20–3010927.95
31–4012030.77
41–508922.82
>507211.58
Education Level≤High School Diploma18346.93
Undergraduate Degree15840.51
≥Graduate Degree4912.56
OccupationEmployed9925.39
Student8722.31
Retired6917.69
Unemployed13534.61
Place of BirthKaraj21053.84
Other Cities18046.15
Table 4. Convergent validity of constructs and descriptive statistics of items.
Table 4. Convergent validity of constructs and descriptive statistics of items.
ConstructConvergent Validity CriteriaItemsMSDLoading
AlphaCRAVE
ATP0.8350.9010.751ATP12.961.3760.891
ATP22.921.4670.837
ATP33.041.4160.872
EA0.8030.8840.717EA12.531.3900.870
EA23.241.3820.848
EA33.021.3970.823
IU0.9260.9640.931IU12.501.4540.963
IU22.411.6590.967
PEU0.6440.8080.585PEU12.851.4140.799
PEU22.681.3740.790
PEU32.221.2480.701
PPR0.7490.8710.773PPR13.031.4100.968
PPR23.171.3420.780
PU0.9380.9700.941PU12.971.3950.969
PU22.791.4640.972
SN0.7270.8440.645SN12.801.3260.811
SN22.421.3920.707
SN32.701.4180.881
Smart Phone Literacy (SPL)0.7900.8300.621SPL12.181.3490.685
SPL22.861.1110.854
SPL33.021.3570.815
Table 5. Discriminant validity measures (construct correlations and √AVE).
Table 5. Discriminant validity measures (construct correlations and √AVE).
ConstructATPEAIUPEUPPRPUSNSPL
ATP0.867
EA0.8190.847
IU0.5670.4960.965
PEU0.6960.6470.5940.765
PPR0.7510.8070.3670.6200.879
PU0.8230.8250.6530.7390.7020.970
SN0.5970.6450.3020.5520.6140.6030.803
SPL0.5740.5040.6460.6350.4820.6280.3810.788
Table 6. Hypothesis testing results.
Table 6. Hypothesis testing results.
HypothesesT Valuep ValueDecisionF Square
H1Environmental Attitude → Perceived Usefulness5.0800.000Supported0.228
H2Attitude Toward Participation → Perceived Usefulness3.7260.000Supported0.128
H3Smart Phone Literacy → Perceived Ease of Use13.7880.000Supported0.675
H4Perceived Ease of Use → Perceived Usefulness5.1980.000Supported0.158
H5Subjective Norms → Intention to Use2.0200.043Supported0.021
H6Perceived Usefulness → Intention to Use7.6490.000Supported0.272
H7Perceived Ease of Use → Intention to Use3.6780.000Supported0.086
H8Perceived Privacy Risk → Intention to Use2.5670.010Supported0.031
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Dehghanpour-Farashah, A.; Behnamifard, F.; Behzadfar, M.; Alalhesabi, M.; Mojtabazadeh-Hasanlouei, S. Mobile Participatory Urban Governance in a Developing Country: Women’s Acceptance of City Reporting Apps in Karaj, Iran. Sustainability 2025, 17, 5388. https://doi.org/10.3390/su17125388

AMA Style

Dehghanpour-Farashah A, Behnamifard F, Behzadfar M, Alalhesabi M, Mojtabazadeh-Hasanlouei S. Mobile Participatory Urban Governance in a Developing Country: Women’s Acceptance of City Reporting Apps in Karaj, Iran. Sustainability. 2025; 17(12):5388. https://doi.org/10.3390/su17125388

Chicago/Turabian Style

Dehghanpour-Farashah, Afsaneh, Faezeh Behnamifard, Mostafa Behzadfar, Mehran Alalhesabi, and Saeed Mojtabazadeh-Hasanlouei. 2025. "Mobile Participatory Urban Governance in a Developing Country: Women’s Acceptance of City Reporting Apps in Karaj, Iran" Sustainability 17, no. 12: 5388. https://doi.org/10.3390/su17125388

APA Style

Dehghanpour-Farashah, A., Behnamifard, F., Behzadfar, M., Alalhesabi, M., & Mojtabazadeh-Hasanlouei, S. (2025). Mobile Participatory Urban Governance in a Developing Country: Women’s Acceptance of City Reporting Apps in Karaj, Iran. Sustainability, 17(12), 5388. https://doi.org/10.3390/su17125388

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