Mixed-Methods Exploration of Sociotechnical Factors to Improve Adoption of Crime-Reporting Apps in Urban Communities

. 2026 International Conference on Partial Least Squares Structural Equation Modeling 8PLS-SEM | Mauritius | September 2-5

Mixed-Methods Exploration of Sociotechnical Factors to Improve Adoption of Crime-Reporting Apps in Urban Communities

Olalekan David-Banjo, Chinazunwa Uwaoma, Paul Witman

Crime-reporting mobile applications can enhance community safety and engagement in urban areas, yet their adoption remains low, partly because developers prioritize technological features over residents’ social realities. This study used an exploratory sequential mixed-methods design to identify and validate the sociotechnical factors that drive adoption. Two focus groups and an analysis of 660 app reviews revealed that users’ initial sense of usefulness fades, whereas sustained engagement depends on perceived value (PV), comprising six dimensions: safety, anonymity, recency, autonomy, camaraderie, and accessibility. A survey of 505 urban residents, analyzed with PLS-SEM, validated PV as a formative higher-order construct formed by four of these dimensions (safety, anonymity, autonomy, and camaraderie). PV was the strongest predictor of behavioral intention, ahead of effort expectancy, facilitating conditions, and performance expectancy, and behavioral intention strongly predicted actual use. The model explained 75% of the variance in behavioral intention and 51% in actual use. The findings offer design principles for community-centered crimereporting technologies. Keywords: crime-reporting apps; sociotechnical factors; mixed-methods; PLS-SEM; perceived value; technology adoption.

INTRODUCTION Although crime-reporting mobile applications (apps) can enhance safety and foster social cohesion, their persistently low use in urban communities limits their societal impact and excludes vulnerable populations from important safety resources. Developers often prioritize technological sophistication over community needs, resulting in solutions that are misaligned with the technological frames of urban residents (Orlikowski & Gash, 1994; Maedche et al., 2019). The issue is pressing: the National Crime Victimization Survey reports that urban violent victimization remained elevated at 29.6 per 1,000 persons in 2023, while urban property victimization rose from 176.1 to 192.3 per 1,000 households between 2022 and 2023 (Tapp & Coen, 2024). 1

. 2026 International Conference on Partial Least Squares Structural Equation Modeling 8PLS-SEM | Mauritius | September 2-5 We applied an exploratory sequential mixed-methods design to investigate sociotechnical factors influencing crime-reporting app adoption, guided by three questions: (1) How do urban residents perceive the social value of crime-reporting apps, and what social factors influence their willingness to use them? (2) How do urban residents perceive an app’s technological features, and which features matter most to them? (3) From a user perspective, what sociotechnical factors are crucial to app design? This research advances understanding and has practical design implications for community safety technologies. BACKGROUND Sociotechnical systems theory highlights the interplay between technical systems and the social contexts in which they are embedded (Baxter & Sommerville, 2011). However, software developers frequently design crime-reporting apps in isolation, paying little attention to the contexts in which these technologies operate. Consequently, designs are often dominated by designers’ technological frames, thus misaligning with community needs and creating adoption challenges (Orlikowski & Gash, 1994). The conflicting needs, goals, and requirements of stakeholders exacerbate this misalignment (Maedche et al., 2019). Orlikowski and Iacono (2001) critique research that takes the IT artifact for granted and treats technologies as “relatively stable, discrete, independent, and fixed” (p. 121). This reductionist view ignores the social and organizational dynamics that ultimately determine how technologies are interpreted, adapted, and sustained in practice (Orlikowski & Gash, 1994). Similarly, Benbasat and Zmud (2003) emphasize that IT developments are inseparable from the “structures, routines, norms, and values” (p. 186) of the environments in which they are instantiated. Although participatory design research suggests that artifacts can be co-created with end users (Kensing & Blomberg, 1998; Sanders & Stappers, 2008), developers of sociotechnical systems often overlook social or community values as design considerations, as documented in related domains (Dillahunt et al., 2018; Ehsan et al., 2023). This oversight often produces unintended outcomes (Whitworth, 2011). This study addresses these issues by integrating qualitative insights from urban residents with quantitative validation using partial least squares structural equation modeling (PLS-SEM). It examines perceived value (PV) as a multifaceted sociotechnical construct. Six dimensions emerged qualitatively: sense of safety, anonymity, recency, autonomy, camaraderie, and accessibility; four of these were retained in the validated model. To predict adoption, PV was assessed alongside constructs of the unified theory of acceptance and use of technology (UTAUT; Venkatesh et al., 2003). The study’s theoretical contribution extends technology adoption models with user-defined constructs, offering actionable insights for designing trustworthy, relevant crime-reporting tools. METHODOLOGY An exploratory sequential mixed-methods design (Creswell & Plano Clark, 2017) was applied (Fig. 1). In phase 1, two virtual focus groups were conducted with urban residents of Orange County, California (n=5; n=6), recruited via UserInterviews.com. Participants were adult smartphone owners with prior crime-reporting app experience. Discussions followed a semi-structured protocol and were complemented by content

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. 2026 International Conference on Partial Least Squares Structural Equation Modeling 8PLS-SEM | Mauritius | September 2-5 analysis of 660 Google Play reviews of the crime-reporting apps Citizen (n=209) and SpotCrime (n=451). In phase 2, an online Zoho survey of residents of densely populated U.S. cities yielded 505 valid responses from 748 submitted questionnaires (67.5%); 53.1% were women, 54.1% were aged 40 or older, and 47.5% held undergraduate degrees. Items used five-point Likert scales. UTAUT items were adapted from Venkatesh et al. (2003), and PV items were developed from phase 1 insights (Tables 1 and 2). PV was specified as a second-order formative construct formed from reflective first-order dimensions and estimated using the disjoint two-stage approach (Sarstedt et al., 2019). Following UTAUT2 (Venkatesh et al., 2012), facilitating conditions predicted behavioral intention. Six UTAUT indicators (PE2, PE4, FC3, FC4, SI1, SI2) and two PV dimensions (recency, accessibility) were removed owing to discriminant validity violations; consequently, social influence was excluded from the final model.

Fig. 1. Exploratory sequential mixed-methods design

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. 2026 International Conference on Partial Least Squares Structural Equation Modeling 8PLS-SEM | Mauritius | September 2-5 Table 1. Measurement items for perceived value dimensions and item retention Construct

Indicators

Questions

Sense of safety

SS1

The notifications and incident reports I receive from the crime-reporting app make me feel safe. Using the crime-reporting app makes me feel less likely to be targeted by crime. The crime-reporting mobile app empowers me to recognize and respond appropriately to suspicious activities. I decide how and when to use the crimereporting app. When using the crime-reporting mobile app, I can customize the notifications and incident radius to suit my needs. I am confident that other users cannot identify me through the app. When I report an incident, my identity remains protected and anonymous. The crime-reporting mobile app provides timely and accurate crime updates. The crime-reporting app keeps me informed about the latest incidents in my neighborhood. When using the crime-reporting mobile app, I feel a sense of shared responsibility with others. I am more likely to use the crime-reporting mobile app if others in my neighborhood use it as well. I enjoy engaging with other users on the crime-reporting app platform. The crime-reporting mobile app is accessible to people with little experience. The crime-reporting mobile app’s design accommodates users with different abilities and backgrounds.

SS2 SS3 Sense of autonomy

SAU1 SAU2

Sense of anonymity

SAN1 SAN2

Sense of recency

SR1 SR2

Sense of camaraderie

SC1 SC2 SC3

Sense of accessibility

SAC1 SAC2

4

Retained in model? (Yes or No) Yes Yes Yes Yes Yes Yes Yes No No Yes Yes Yes No No

. 2026 International Conference on Partial Least Squares Structural Equation Modeling 8PLS-SEM | Mauritius | September 2-5 Table 2. Measurement items, sources, and retention status Construct

Performance expectancy

Perception that the system will improve task performance

Indicators

Survey items and codes

Sources

PE1

Using the crime-reporting mobile app makes it easier for me to report crimes or suspicious activities. This crime-reporting mobile app helps me quickly notify people about incidents in my area. By using this app, I can contribute to making my community safer. The crime-reporting mobile app is useful for staying informed about crime and safety issues nearby. Learning to use the crimereporting mobile app is easy for me. It is easy to report incidents using the crime-reporting mobile app. It is easy for me to navigate the crime-reporting mobile app and find the features I need. The instructions provided in the crime-reporting mobile app are clear and understandable. I have access to a smartphone and an internet connection to use the crime-reporting mobile app. I have the knowledge necessary to use the crime-reporting mobile app. If I encounter any issues while using the crime-reporting mobile app, I can easily get help or support. The crime-reporting mobile app works well with other features or apps on my phone (e.g., camera, GPS). People in my community think I should use this app for reporting crime. I would use the app if many of my friends or neighbors recommended it. I am likely to recommend the crime-reporting mobile app to others in my community. I will regularly use the crimereporting mobile app when I need to report an incident. I intend to use the crimereporting mobile app to report

Davis, 1989; Davis et al., 1989, as cited in Venkatesh et al., 2003

PE2 PE3 PE4

Effort expectancy

Degree of ease associated with system use

EE1 EE2 EE3 EE4

Facilitating conditions

Extent to which the individual believes that technical infrastructure exists to support system use

FC1 FC2 FC3

FC4

Social Influence

Behavioral intention

Belief that people important to the user think they should use the app Individual’s intention to use the system

SI1 SI2 BI1 BI2 BI3

5

Retained in model? (Y(es) or N(o)) Yes

No Yes No

Davis, 1989; Davis et al., 1989, as cited in Venkatesh et al., 2003

Yes Yes Yes Yes

Thompson et al. 1991, as cited in Venkatesh et al. 2003

Yes Yes No

No

Venkatesh et al. (2003); Fishbein & Ajzen (1975)

No

Davis et al., 1989, as cited in Venkatesh et al. 2003

Yes

No

Yes Yes

. 2026 International Conference on Partial Least Squares Structural Equation Modeling 8PLS-SEM | Mauritius | September 2-5

Actual use

Extent of system use

AU1

crimes or suspicious activities in the future. I use the crime-reporting mobile app.

Venkatesh et al., 2000, as cited in Venkatesh et al. 2003

Fig. 2. First-order reflective measurement model

Fig. 3. Second-order perceived value model Notes: Values in parentheses are p-values. AU1 is a single-item measure (loading fixed at 1.000).

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Yes

. 2026 International Conference on Partial Least Squares Structural Equation Modeling 8PLS-SEM | Mauritius | September 2-5 RESULTS AND DISCUSSION Phase 1: Qualitative results Thematic analysis of the focus group transcripts revealed two central themes. First, transient usefulness, the initial impression that the app served its purpose, waned when the app failed to meet evolving needs. Second, PV, a more enduring, context-driven judgment of the app’s relevance, was shaped by app features and users’ social realities. Six PV dimensions emerged. Sense of safety was foundational. Participants expected reassurance, vigilance, and support for preventive action (Farrall et al., 2000). Sense of anonymity was a precondition for reporting without fear of retaliation, consistent with greater selfdisclosure under anonymity (Joinson, 2001; Suler, 2004). Sense of recency, the timeliness and accuracy of incident information, underpinned credibility, in line with DeLone and McLean’s (2003) emphasis on information quality. Sense of autonomy reflected users’ need to control interactions by filtering content and adjusting notifications; users with such control typically engage proactively (Spreitzer, 1995; Deci & Ryan, 2000). Sense of camaraderie captured expectations of connectedness and collective vigilance, consistent with Putnam’s (1995) work on social capital. Sense of accessibility emphasized usability across devices, abilities, and languages. A directed content analysis (Hsieh & Shannon, 2005) of app reviews largely reinforced these dimensions. Recency, accessibility, safety, and autonomy were most frequently mentioned, while anonymity (6 of 660) and camaraderie (14) were rare in reviews but salient in the focus groups (Table 3). Table 3. Comparison of thematic mentions between Citizen and SpotCrime Theme Sense of recency Sense of anonymity Sense of autonomy Sense of camaraderie Sense of safety Sense of accessibility Other

Citizen (n=209)

SpotCrime (n=451)

48 0 65 1 21 60 14

114 6 71 13 124 86 37

Total mentions 162 6 136 14 145 146 51

Phase 2: PLS-SEM results Measurement model assessment The reflective measurement model (Fig. 2) showed adequate reliability and validity: loadings exceeded 0.708 (0.829–0.936), Cronbach’s α exceeded 0.70 (0.739–0.916), composite reliability remained below 0.95 (0.885–0.940), and AVE exceeded 0.50 (0.759–0.872) (Table 4) (Hair et al., 2019). No HTMT bootstrapped confidence interval included 1.0 (Henseler et al., 2015), although several values approached or exceeded 0.90 (e.g., performance expectancy–effort expectancy = 0.908), so results involving these constructs warrant caution (Franke & Sarstedt, 2019) (Table 5). For the formative higher-order PV construct, collinearity was not a concern (VIF = 2.469–3.425). All outer weights were positive and significant (p ≤ .001): camaraderie (w = 0.414), autonomy (0.250), safety (0.235), and anonymity (0.214). All outer loadings exceeded 0.86 (p < .001) (Table 6).

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. 2026 International Conference on Partial Least Squares Structural Equation Modeling 8PLS-SEM | Mauritius | September 2-5 Table 4. Reliability and validity results Constructs

Indicators

Loadings >0.7

AU1

1.000

BI1 BI2 BI3

0.899 0.908 0.894

PE1 PE3

0.932 0.936

EE1 EE2 EE3 EE4

0.882 0.894 0.898 0.899

FC1 FC2

0.880 0.911

SS1 SS2 SS3

0.891 0.829 0.892

SAN1 SAN2

0.914 0.933

SC1 SC2 SC3

0.900 0.890 0.846

SAU1 SAU2

0.880 0.900

Cα >0.7

rho_A >0.7

CR >0.7

AVE >0.5

0.883

0.884

0.928

0.810

0.853

0.854

0.932

0.872

0.916

0.917

0.940

0.798

0.754

0.765

0.890

0.802

0.841

0.853

0.904

0.759

0.828

0.836

0.920

0.853

0.853

0.856

0.911

0.773

0.739

0.743

0.885

0.793

Actual use Behavioral intention

Performance expectancy

Effort expectancy

Facilitating conditions

Sense of safety

Sense of anonymity

Sense of camaraderie

Sense of autonomy

Notes: Actual use is a single-item construct; reliability and AVE are not applicable.

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. 2026 International Conference on Partial Least Squares Structural Equation Modeling 8PLS-SEM | Mauritius | September 2-5 Table 5. Discriminant validity (bootstrapping): HTMT inference Original sample (O)

Sample mean (M)

Bias

5.0%

95.0%

Behavioral intention ↔ actual use

0.759

0.759

-0.000

0.712

0.802

Effort expectancy ↔ actual use

0.673

0.673

-0.001

0.615

0.725

Effort expectancy ↔ behavioral intention Facilitating conditions ↔ actual use Facilitating conditions ↔ behavioral intention Facilitating conditions ↔ effort expectancy Performance expectancy ↔ actual use Performance expectancy ↔ behavioral intention Performance Expectancy ↔ effort expectancy Performance expectancy ↔ facilitating conditions

0.894

0.894

-0.000

0.857

0.927

0.609

0.608

-0.000

0.540

0.674

0.880

0.880

-0.000

0.816

0.936

0.890

0.891

0.001

0.822

0.948

0.613

0.612

-0.001

0.546

0.674

0.891

0.891

-0.000

0.847

0.930

0.908

0.907

-0.000

0.870

0.939

0.882

0.882

0.000

0.815

0.941

Structural model assessment The model explained substantial variance in behavioral intention (R² = 0.751) and moderate variance in actual use (R² = 0.51). Inner VIF values (2.445–4.156; Table 7) indicated no critical collinearity, although values above 3 call for cautious interpretation (Hair et al., 2019). PV was the strongest predictor of behavioral intention (β = 0.402, p < .001), exceeding effort expectancy (β = 0.247, p < .001), facilitating conditions (β = 0.170, p = .001), and performance expectancy (β = 0.125, p = .023). Behavioral intention strongly predicted actual use (β = 0.714, p < .001) (Table 8). PLSpredict yielded Q²predict > 0 for all indicators. Behavioral intention indicators outperformed the linear model benchmark, indicating high predictive power, whereas actual use did not (RMSE = 1.014 vs. 0.997), indicating no predictive power for that single item (Shmueli et al., 2019) (Table 9).

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. 2026 International Conference on Partial Least Squares Structural Equation Modeling 8PLS-SEM | Mauritius | September 2-5 Table 6. Outer weights and outer loadings of perceived value dimensions: bootstrapping results Dimension → perceived value Sense of anonymity Sense of autonomy Sense of camaraderie Sense of safety

O 0.214 0.250 0.414 0.235

Outer weights STDEV t 0.064 3.345 0.059 4.223 0.078 5.280 0.067 3.504

p 0.001 0.000 0.000 0.000

Outer loadings O t p 0.869 37.129 0.000 0.862 34.435 0.000 0.940 59.601 0.000 0.888 35.655 0.000

Notes: Stage 2 of the disjoint two-stage approach; bootstrapping results. O = original sample; STDEV = standard deviation.

Table 7. Collinearity assessment (inner model) VIF Effort expectancy → behavioral intention Facilitating conditions → behavioral intention Perceived value → behavioral intention Performance expectancy → behavioral intention

4.156 2.445 4.068 3.773

Table 8. Significance and relevance of path coefficients Original sample (O)

Sample mean (M)

Standard deviation (STDEV)

T statistics (|O/STDEV|)

fsquare

P values

Behavioral intention → actual use Effort expectancy → behavioral intention

0.714

0.714

0.027

26.194

1.041

0.000

0.247

0.246

0.063

3.899

Facilitating conditions → behavioral intention

0.170

0.169

0.053

3.214

Perceived value → behavioral intention

0.402

0.407

0.059

6.754

Performance expectancy → behavioral intention

0.125

0.123

0.055

2.266

0.059 0.048 0.159

0.000 0.001 0.000 0.023

0.017

Table 9. PLSpredict MV summary Q²predict

PLSSEM_RMSE

PLSSEM_MAE

LM_RMSE

LM_MAE

IA_RMSE

IA_MAE

BI1

0.647

0.642

0.459

0.651

0.462

1.081

0.841

BI2

0.570

0.734

0.511

0.735

0.513

1.120

0.882

BI3

0.578

0.708

0.495

0.724

0.504

1.090

0.834

AU1

0.469

1.014

0.765

0.997

0.723

1.392

1.173

CONCLUSIONS AND IMPLICATIONS FOR THEORY AND PRACTICE This study extends technology adoption research on community-based artifacts by showing that PV, formed by senses of safety, anonymity, autonomy, and camaraderie, 10

. 2026 International Conference on Partial Least Squares Structural Equation Modeling 8PLS-SEM | Mauritius | September 2-5 is the strongest driver of intention to adopt crime-reporting apps. Theoretically, it extends UTAUT with community-defined sociotechnical factors and demonstrates the importance of aligning social and technical systems in community safety innovative design. Practically, developers, civic technologists, and public safety agencies should design crime-reporting apps that prioritize community connectedness, user control, identity protection, and intuitive interfaces. Qualitative findings also suggest that timely and accurate incident updates matter to users. These transferable design principles can improve adoption, trust, and sustained engagement. This study’s limitations include cross-sectional, self-reported data, including actual use, raising the possibility of common method bias, and PV dimensions derived from a small single-county sample; recency and accessibility could not be validated, and social influence was excluded. Future research should examine longitudinal usage dynamics, use refined measures to retest the excluded dimensions, and evaluate featurespecific interventions that support community-centered safety outcomes.

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. 2026 International Conference on Partial Least Squares Structural Equation Modeling 8PLS-SEM | Mauritius | September 2-5

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. 2026 International Conference on Partial Least Squares Structural Equation Modeling 8PLS-SEM | Mauritius | September 2-5 Orlikowski, W. J., & Iacono, C. S. (2001). Research commentary: Desperately seeking the “IT” in IT research—A call to theorizing the IT artifact. Information Systems Research, 12(2), 121-134. https://doi.org/10.1287/isre.12.2.121.9700 Putnam, R. D. (1995). Bowling alone: America’s declining social capital. Journal of Democracy, 6(1), 65-78. https://doi.org/10.1353/jod.1995.0002 Sanders, E. B.-N., & Stappers, P. J. (2008). Co-creation and the new landscapes of design. CoDesign, 4(1), 5-18. https://doi.org/10.1080/15710880701875068 Sarstedt, M., Hair, J. F., Jr., Cheah, J. H., Becker, J. M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197-211. https://doi.org/10.1016/j.ausmj.2019.05.003 Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322-2347. https://doi.org/10.1108/EJM-02-2019-0189 Spreitzer, G. M. (1995). Psychological empowerment in the workplace: Dimensions, measurement, and validation. Academy of Management Journal, 38(5), 1442-1465. https://doi.org/10.2307/256865 Suler, J. (2004). The online disinhibition effect. CyberPsychology & Behavior, 7(3), 321-326. https://doi.org/10.1089/1094931041291295 Tapp, S. N., & Coen, E. J. (2024). Criminal victimization, 2023 (NCJ 309335). U.S. Department of Justice, Bureau of Justice Statistics. https://bjs.ojp.gov/library/publications/criminal-victimization-2023 Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540 Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412 Whitworth, B. (2011). The social requirements of technical systems. In B. K. Daniel (Ed.), Virtual communities: Concepts, methodologies, tools and applications (pp. 1461-1481). IGI Global. https://doi.org/10.4018/978-1-60960-100-3.ch114

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