Submitted:
07 January 2025
Posted:
08 January 2025
You are already at the latest version
Abstract
Old-Smart Renewal Community is a crucial agenda for urban renewal and urban governance. Successful implementation of old-smart renewal communities requires the active participation of residents in smart city development. The residents’ sense of gain is a critical metric for evaluating the success of old-smart renewal communities. From the institutional and utilitarian perspectives, institutional elements and technical values significantly influence residents’ perceptions of gain in the smart renewal projects for older communities. Drawing on the Technology Acceptance Model (TAM), this study investigates the impact of the institutional environment, facilitating conditions, perceived usefulness, and perceived ease of use on residents’ sense of gain in the old-smart renewal communities. A questionnaire was crafted to gather data, yielding 384 valid responses for path analysis. The results indicate that residents’ objective gains directly shape their subjective feelings. Facilitating conditions and institutional environments influence objective gains through the mediating role of perceived ease of use. Perceived ease of use and objective gains are identified as a sequential mediating link affecting the relationships between facilitating conditions, institutional environments, and residents’ subjective feelings. Additionally, institutional environments are found to influence community residents’ subjective feelings through the sequential mediation of perceived usefulness and objective gains. Our findings enrich the literatures on urban governance by identifying the antecedents of community residents’ sense of gain in old-smart renewal communities. It also enriches the TAM by expanding the relationships between institutional environment, convenient conditions, and community residents’ sense of gain. Meanwhile, it offers insights for formulating the governance mechanisms in the old-smart renewal community development.
Keywords:
1. Introduction
2. Theoretical Foundation and Hypotheses Development
2.1. An Overview of Resident’s Sense of Gain
2.2. Effects of Institutional Elements on Technical Values in Old-Smart Renewal Communities
2.3. The Mediating Effects of Technical Values in the Old-Smart Renewal Communities
3. Research Methodology
3.1. Research Procedure and Sample Characteristics
3.2. Measurements
3.3. Analytical Strategy
4. Results
4.1. Measurement Validation
4.2. Hypotheses Testing
4.2.1. The Direct Effect Testing
4.2.2. The Mediating Effect Testing
5. Discussions
6. Theoretical and Practical Implications
6.1. Theoretical Implications
6.2. Practical Implications
6.3. Limitations and Future Research Directions
7. Conclusions
Author Contributions
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Coe, A.; Paquet, G.; Roy, J. E-Governance and Smart Communities. Soc. Sci. Comput. Rev. 2001, 19, 80–93. [CrossRef]
- Batty, M., et al., Smart cities of the future. The European Physical Journal Special Topics, 2012. 214(1): p. 481-518. [CrossRef]
- Calzada, I.; Cobo, C. Unplugging: Deconstructing the Smart City. J. Urban Technol. 2015, 22, 23–43. [CrossRef]
- Meijer, A.; Bolívar, M.P.R. Governing the smart city: a review of the literature on smart urban governance. Int. Rev. Adm. Sci. 2015, 82, 392–408. [CrossRef]
- O’brien, E.; Kassirer, S. People Are Slow to Adapt to the Warm Glow of Giving. Psychol. Sci. 2018, 30, 193–204. [CrossRef]
- Okulicz-Kozaryn, A.; Mazelis, J.M. Urbanism and happiness: A test of Wirth’s theory of urban life. Urban Stud. 2016, 55, 349–364. [CrossRef]
- Qi, L.; Guo, J. Development of smart city community service integrated management platform. Int. J. Distrib. Sens. Networks 2019, 15. [CrossRef]
- Zhang, C.; Lu, B. Residential satisfaction in traditional and redeveloped inner city neighborhood: A tale of two neighborhoods in Beijing. Travel Behav. Soc. 2016, 5, 23–36. [CrossRef]
- Zhang, S.; Zhang, F.; Xue, B.; Wang, D.; Liu, B. Unpacking resilience of project organizations: A capability-based conceptualization and measurement of project resilience. Int. J. Proj. Manag. 2023, 41. [CrossRef]
- Zhu, S.; Li, D.; Jiang, Y. The impacts of relationships between critical barriers on sustainable old residential neighborhood renewal in China. Habitat Int. 2020, 103. [CrossRef]
- Shen, L.; Huang, Z.; Wong, S.W.; Liao, S.; Lou, Y. A holistic evaluation of smart city performance in the context of China. 2018, 200, 667–679. [CrossRef]
- Zhou, Y. and P.D. Lund, Peer-to-peer energy sharing and trading of renewable energy in smart communities─ trading pricing models, decision-making and agent-based collaboration. Renewable Energy, 2023. 207: p. 177-193. [CrossRef]
- Tyagi, N.; Bhushan, B. Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions. Wirel. Pers. Commun. 2023, 130, 857–908. [CrossRef]
- Vanolo, A. Smartmentality: The Smart City as Disciplinary Strategy. Urban Stud. 2013, 51, 883–898. [CrossRef]
- He, Y.; Wang, Y.; Song, Z.; Yu, H.; Xue, Y. Study on Carbon Emissions from the Renovation of Old Residential Areas in Cold Regions of China. Sustainability 2023, 15, 3018. [CrossRef]
- Huda, N.U.; Ahmed, I.; Adnan, M.; Ali, M.; Naeem, F. Experts and intelligent systems for smart homes’ Transformation to Sustainable Smart Cities: A comprehensive review. Expert Syst. Appl. 2023, 238. [CrossRef]
- Venkatesh, V.; Davis, F.D. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Manag. Sci. 2000, 46, 186–204. [CrossRef]
- Karahanna, E.; Straub, D.W.; Chervany, N.L. Information Technology Adoption Across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs. MIS Q. 1999, 23, 183. [CrossRef]
- Ciasullo, M.V.; Troisi, O.; Grimaldi, M.; Leone, D. Multi-level governance for sustainable innovation in smart communities: an ecosystems approach. Int. Entrep. Manag. J. 2020, 16, 1167–1195. [CrossRef]
- Florida, R.; Mellander, C.; Rentfrow, P.J. The Happiness of Cities. Reg. Stud. 2013, 47, 613–627. [CrossRef]
- Angelidou, M. The Role of Smart City Characteristics in the Plans of Fifteen Cities. J. Urban Technol. 2017, 24, 3–28. [CrossRef]
- Abusaada, H.; Elshater, A. Competitiveness, distinctiveness and singularity in urban design: A systematic review and framework for smart cities. 2021, 68, 102782. [CrossRef]
- Aggarwal, S.; Chaudhary, R.; Aujla, G.S.; Kumar, N.; Choo, K.-K.R.; Zomaya, A.Y. Blockchain for smart communities: Applications, challenges and opportunities. 2019, 144, 13–48. [CrossRef]
- Alshamaila, Y.; Papagiannidis, S.; Alsawalqah, H.; Aljarah, I. Effective use of smart cities in crisis cases: A systematic review of the literature. Int. J. Disaster Risk Reduct. 2023, 85. [CrossRef]
- Legris, P.; Ingham, J.; Collerette, P. Why do people use information technology? A critical review of the technology acceptance model. Inf. Manag. 2003, 40, 191–204. [CrossRef]
- Li, R.; Kido, A.; Wang, S. Evaluation Index Development for Intelligent Transportation System in Smart Community Based on Big Data. Adv. Mech. Eng. 2014, 7. [CrossRef]
- Luperto, M.; Monroy, J.; Renoux, J.; Lunardini, F.; Basilico, N.; Bulgheroni, M.; Cangelosi, A.; Cesari, M.; Cid, M.; Ianes, A.; et al. Integrating Social Assistive Robots, IoT, Virtual Communities and Smart Objects to Assist at-Home Independently Living Elders: the MoveCare Project. Int. J. Soc. Robot. 2022, 15, 517–545. [CrossRef]
- Lye, G.X.; Cheng, W.K.; Tan, T.B.; Hung, C.W.; Chen, Y.-L. Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment. Sensors 2020, 20, 2098. [CrossRef]
- Mital, M.; Pani, A.K.; Damodaran, S.; Ramesh, R. Cloud based management and control system for smart communities: A practical case study. Comput. Ind. 2015, 74, 162–172. [CrossRef]
- Neirotti, P.; De Marco, A.; Cagliano, A.C.; Mangano, G.; Scorrano, F. Current trends in Smart City initiatives: Some stylised facts. Cities 2014, 38, 25–36. [CrossRef]
- Yu, S.; Liu, Y.; Cui, C.; Xia, B. Influence of Outdoor Living Environment on Elders’ Quality of Life in Old Residential Communities. Sustainability 2019, 11, 6638. [CrossRef]
- Anderson, T.E.; Melchior, A. Assessing telecommunications technology as a tool for urban community building. J. Urban Technol. 1995, 3, 29–44. [CrossRef]
- Angelidou, M. Smart city policies: A spatial approach. Cities 2014, 41, S3–S11. [CrossRef]
- Beck, D.; Ferasso, M.; Storopoli, J.; Vigoda-Gadot, E. Achieving the sustainable development goals through stakeholder value creation: Building up smart sustainable cities and communities. J. Clean. Prod. 2023, 399. [CrossRef]
- Ji, T.; Chen, J.-H.; Wei, H.-H.; Su, Y.-C. Towards people-centric smart city development: Investigating the citizens’ preferences and perceptions about smart-city services in Taiwan. Sustain. Cities Soc. 2021, 67, 102691. [CrossRef]
- Lee, L.N. and M.J. Kim, A Critical Review of Smart Residential Environments for Older Adults With a Focus on Pleasurable Experience. Frontiers in Psychology, 2020. 10.
- Macke, J.; Sarate, J.A.R.; Moschen, S.d.A. Smart sustainable cities evaluation and sense of community. J. Clean. Prod. 2019, 239. [CrossRef]
- Mahmood, D.; Javaid, N.; Ahmed, I.; Alrajeh, N.; Niaz, I.A.; Khan, Z.A. Multi-agent-based sharing power economy for a smart community. Int. J. Energy Res. 2017, 41, 2074–2090. [CrossRef]
- Stübinger, J.; Schneider, L. Understanding Smart City—A Data-Driven Literature Review. Sustainability 2020, 12, 8460. [CrossRef]
- Venkatesh, V., et al., User acceptance of information technology: Toward a unified view. MIS quarterly, 2003: p. 425-478.
- He, Q.; Dong, S.; Rose, T.; Li, H.; Yin, Q.; Cao, D. Systematic impact of institutional pressures on safety climate in the construction industry. Accid. Anal. Prev. 2016, 93, 230–239. [CrossRef]
- Wang, J.; Ding, S.; Song, M.; Fan, W.; Yang, S. Smart community evaluation for sustainable development using a combined analytical framework. J. Clean. Prod. 2018, 193, 158–168. [CrossRef]
- Guetterman, T.C.; Sakakibara, R.V.; Clark, V.L.P.; Luborsky, M.; Murray, S.M.; Castro, F.G.; Creswell, J.W.; Deutsch, C.; Gallo, J.J. Mixed methods grant applications in the health sciences: An analysis of reviewer comments. PLOS ONE 2019, 14, e0225308. [CrossRef]
- O'Leary-Kelly, S.W.; Vokurka, R.J. The empirical assessment of construct validity. J. Oper. Manag. 1998, 16, 387–405. [CrossRef]
- Arthur Jr, W., D.J. Woehr, and R. Maldegen, Convergent and discriminant validity of assessment center dimensions: A conceptual and empirical reexamination of the assessment center construct-related validity paradox. Journal of Management, 2000. 26(4): p. 813-835. [CrossRef]
- Edwards, J.R.; Lambert, L.S. Methods for integrating moderation and mediation: A general analytical framework using moderated path analysis.. Psychol. Methods 2007, 12, 1–22. [CrossRef]
- Madan, K. and R. Yadav, Understanding and predicting antecedents of mobile shopping adoption: A developing country perspective. Asia Pacific Journal of Marketing and Logistics, 2018. 30(1): p. 139-162. [CrossRef]
- Yu, B.; Vahidov, R.; Kersten, G.E. Acceptance of technological agency: Beyond the perception of utilitarian value. Inf. Manag. 2021, 58, 103503. [CrossRef]
- Capodaglio, A.G.; Callegari, A.; Lopez, M.V. European Framework for the Diffusion of Biogas Uses: Emerging Technologies, Acceptance, Incentive Strategies, and Institutional-Regulatory Support. Sustainability 2016, 8, 298. [CrossRef]
- Stratigea, A.; Papadopoulou, C.-A.; Panagiotopoulou, M. Tools and Technologies for Planning the Development of Smart Cities. J. Urban Technol. 2015, 22, 43–62. [CrossRef]
- Zavratnik, V.; Podjed, D.; Trilar, J.; Hlebec, N.; Kos, A.; Duh, E.S. Sustainable and Community-Centred Development of Smart Cities and Villages. Sustainability 2020, 12, 3961. [CrossRef]

| Variables | Items | Factor Loading | Cronbach’s Alpha | AVE | C.R. |
|---|---|---|---|---|---|
| PU | PU1 | 0.772 | 0.885 | 0.607 | 0.885 |
| PU2 | 0.792 | ||||
| PU3 | 0.781 | ||||
| PU4 | 0.779 | ||||
| PEU | PEU1 | 0.761 | 0.862 | 0.568 | 0.866 |
| PEU2 | 0.793 | ||||
| PEU3 | 0.824 | ||||
| PEU4 | 0.793 | ||||
| CC | CC1 | 0.741 | 0.819 | 0.533 | 0.820 |
| CC2 | 0.709 | ||||
| CC3 | 0.671 | ||||
| CC4 | 0.793 | ||||
| IE | IE1 | 0.845 | 0.915 | 0.729 | 0.915 |
| IE2 | 0.889 | ||||
| IE3 | 0.830 | ||||
| IE4 | 0.850 | ||||
| OG | OG1 | 0.876 | 0.876 | 0.654 | 0.882 |
| OG2 | 0.875 | ||||
| OG3 | 0.830 | ||||
| OG4 | 0.628 | ||||
| SF | SF1 | 0.861 | 0.883 | 0.665 | 0.887 |
| SF2 | 0.872 | ||||
| SF3 | 0.695 | ||||
| SF4 | 0.822 |
| Mean | SD | PEU | PU | CC | IE | OG | SF | |
|---|---|---|---|---|---|---|---|---|
| PEU | 3.853 | 0.670 | (0.858) | |||||
| PU | 3.929 | 0.663 | 0.651*** | (0.884) | ||||
| CC | 3.883 | 0.697 | 0.599*** | 0.753*** | (0.816) | |||
| IE | 3.994 | 0.713 | 0.583*** | 0.599*** | 0.588*** | (0.914) | ||
| OG | 4.135 | 0.686 | 0.628*** | 0.561*** | 0.533*** | 0.628*** | (0.870) | |
| SF | 3.917 | 0.784 | 0.563*** | 0.557*** | 0.581*** | 0.619*** | 0.712*** | (0.883) |
| Variables | Perceived Ease of Use | Perceived Usefulness | Objective Gain | Subjective Feeling |
|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | Model 4 | |
| Constant | 0.780*** | 0.781*** | 1.261*** | 0.028 |
| Convenient conditions | 0.583***(0.038) | 0.161**(0.055) | ||
| Institutional environment | 0.222***(0.037) | 0.249***(0.044) | ||
| Perceived ease of use | 0.369***(0.058) | 0.272***(0.052) | 0.221***(0.055) | |
| Perceived usefulness | 0.468***(0.052) | 0.116*(0.058) | ||
| Objective gain | 0.622***(0.052) | |||
| R2 | 0.604 | 0.493 | 0.434 | 0.548 |
| Adjusted R2 | 0.602 | 0.489 | 0.432 | 0.544 |
| F | 291.068*** | 123.281*** | 146.363*** | 153.555*** |
| Path | Estimate | SE | 95% LLCI | 95%ULCI |
|---|---|---|---|---|
| Mediation | ||||
| CC→PU→SF | 0.019 | 0.014 | 0.000 | 0.053 |
| CC→PEU→SF | 0.129** | 0.039 | 0.056 | 0.209 |
| CC→PEU→PU→SF | 0.025(P=0.05) | 0.013 | 0.001 | 0.051 |
| CC→PU→OG→SF | 0.047* | 0.022 | 0.008 | 0.093 |
| CC→PEU→OG→SF | 0.099*** | 0.024 | 0.053 | 0.147 |
| CC→PEU→PU→OG→SF | 0.063*** | 0.014 | 0.036 | 0.093 |
| IE→PU→SF | 0.029 | 0.016 | 0.001 | 0.063 |
| IE→PEU→SF | 0.049** | 0.017 | 0.020 | 0.087 |
| IE→PEU→PU→SF | 0.010 | 0.006 | 0.000 | 0.022 |
| IE→PU→OG→SF | 0.073** | 0.022 | 0.034 | 0.120 |
| IE→PEU→OG→SF | 0.038** | 0.013 | 0.017 | 0.067 |
| IE→PEU→PU→OG→SF | 0.024** | 0.008 | 0.011 | 0.042 |
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