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Cited by 21Smart Cities and TechnologiesTechnology Adoption and User BehaviourE-Government and Public Services

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The Acceptance Model of Smart City Service: Focused on Seoul

Open access
Lee, Seulki·2023·Sustainability
DOI 10.3390/su15032695
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Abstract

The goal of providing smart city services is not only simply building smart technology and infrastructure but also improving the quality of life of citizens who use smart city services and have positive experiences. This requires establishing service supply strategies considering citizens’ satisfaction levels by identifying the factors that affect the will or behavior of citizens who use smart city services. However, decision making regarding smart city policies and service supply in Korea is conducted through the central government-centered top-down mode, which lacks the consideration of how to improve citizens’ satisfaction levels or their intention to use technology. Thus, we proposed an acceptance model for smart city services, which is a theoretical model that offers a foundation for a model to evaluate the interaction levels of citizens toward smart city services based on the technology acceptance model, which is the most widely used tool to evaluate what factors affect the acceptance and use of information technology and system-based services. In addition, we defined research models by discussing previous studies that proposed factors that affect the acceptance of smart city and U-City services and urban public services. To empirically verify the research models proposed herein, we surveyed citizens in Seoul and conducted structural equation modeling using AMOS 28. As a result, we derived a total of eight factors (Quality, User Characteristics, Positive Attitude, Perceived Risk, Social Influence, Perceived Ease of Use, Perceived Usefulness, and Acceptance Intention) that affected the acceptance of smart city services and 42 assessment items from these factors. The results of this study are expected to be foundational data for establishing policies and systems for the improvement of citizens’ interaction level and continuous use of smart city services.

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  • Increased interaction with users plays a pivotal role in enhancing smart city services and smart city governance by fostering a citizen-centered approach.

    “Increased interaction with users (IwU) plays a pivotal role in enhancing smart city services (SC) and smart city governance (SCG) by fostering a citizen-centered approach that values user engagement as a driving force for service usage and collaborative governance (Lee, 2023).”

    From Smart city governance and interoperability: enhancing human security in Yogyakarta and Makassar, Indonesia (2025) · cites this paper as Lee, 2023

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Citations

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Research works printed but not linked: 33

  1. Han, 2014, A Study on the Affecting Factors of U-City Service Acceptance, J. Soc. E-Bus. Stud., 19, 53, 10.7838/jsebs.2014.19.2.053 · DOI
  2. Beak, 2017, Smart City Infrastructure Construction Strategy: Focusing on Performance Indicators for Investment Expansion, Mon. KOTI Mag. Transp., 228, 13
  3. Lee, 2022, Key Factors Affecting Acceptance of Smart City Service: Focused on Seoul, J. Archit. Inst. Korea, 38, 233
  4. Ju, 2020, Analyzing Smart City research trends, J. Korean Soc. Reg. Inf. Chem., 23, 149
  5. Park, 2019, A Study on the Research Trends for Smart City using Topic Modeling, J. Internet Comput. Serv., 20, 119
  6. Jeong, 2020, Policy Direction Setting through Comparative Analysis of Foreign Smart City Policies, J. Korea Acad.-Ind. Coop. Soc., 21, 151
  7. Cho, 2019, Factors that affect user satisfaction toward continuous usage of AI speakers—Focusing on the mediation effect of emotional attachment, J. Korea Des. Forum, 24, 87
  8. Venkatesh, 2003, User acceptance of information technology: Toward a unified view, MIS Q., 27, 425, 10.2307/30036540 · DOI · Paper page
  9. Anderson, 1988, Structural equation modeling in practice: A review and recommended two-step approach, Psychol. Bull., 103, 411, 10.1037/0033-2909.103.3.411 · DOI · Paper page
  10. Yang, 2021, Analysis of Sensible Factors on Service Acceptance Intention of Smart City, J. Korea Inst. Intell. Transp. Syst., 20, 146, 10.12815/kits.2021.20.5.146 · DOI
  11. Lee, C.H. (2012). A Empirical Study on the Citizens’ Perception Factors to the U-City Service Acceptance. [Ph.D. Thesis, Dongeui University].
  12. Kim, 2011, Spreading out the Citizen’s Acceptance on the u-Public Service: A Case of u-Green in Seoul Metropolitan Government, Korean Policy Sci. Rev., 15, 105
  13. Lee, 2012, An Empirical Study on Intentions to Use of Smart TV, J. Digit. Converg., 10, 107
  14. Han, 2013, An Empirical Study on the Influencing Factors of Intention to Adoption of Mobile Government Service, Asia Pac. J. Inf. Syst., 23, 78
  15. Oh, 2012, Factors Influencing Acceptance of E-government Self Service Technology (SST), E-Bus. Stud., 13, 441, 10.15719/geba.13.1.201203.441 · DOI
  16. Jin, 2017, An Empirical Study on the Factors Influencing the Acceptance of Mobile Easy Payment Services: A Case of Chinese User, J. Korea Contents Assoc., 17, 1
  17. Darmawan, A.K., Siahhan, D., Susanto, T.D., and Uman, B. (2019, January 16–17). Identifying success factors in Smart City readiness using a structure equation modelling approach. Proceedings of the 2019 ICOMITEE, Jember, Indonesia. · DOI
  18. Habib, 2020, Factors that determine residents’ acceptance of smart city technologies, Behav. Inf. Technol., 39, 610, 10.1080/0144929X.2019.1693629 · DOI
  19. Sepasgozara, 2019, Implementing citizen centric technology in developing smart cities: A model for predicting the acceptance of urban technologies, Technol. Forecast. Soc. Chang., 142, 105, 10.1016/j.techfore.2018.09.012 · DOI
  20. Pallant, J. (2001). SPSS Survival Manual, Open University Press.
  21. Nunnalyy, J.C. (1978). Psychometric Theory, McGraw-Hill.
  22. Bartlett, 1954, A note on the multiplying factors for various chi square approximations, J. R. Stat. Soc., 16, 296, 10.1111/j.2517-6161.1954.tb00174.x · DOI
  23. Kaiser, 1970, A second-generation little jiffy, Psychometrika, 35, 401, 10.1007/BF02291817 · DOI
  24. Aksorn, 2008, Critical success factors influencing safety program performance in Thai construction projects, Saf. Sci., 46, 709, 10.1016/j.ssci.2007.06.006 · DOI
  25. Bae, 2018, A Study on the Determinant Factors of Innovation Resistance and Innovation Acceptance on Internet Primary Bank Services: Combining the Theories of Innovation Diffusion and Innovation Resistance, E-Bus. Stud., 19, 91, 10.20462/TeBS.2018.4.19.2.91 · DOI
  26. DeLone, 1992, Information system success: The Quest for the Dependent Variable, Inf. Syst. Res., 3, 60, 10.1287/isre.3.1.60 · DOI · Paper page
  27. Park, 2017, Intention to Use Mobile Delivery Application Services, Depending on Personal Innovativeness and Self-Efficacy, J. Korea Contents Assoc., 17, 440
  28. Han, D.K. (2015). A Study on Factors Affecting Fintech Acceptance and Revitalization. [Ph.D. Thesis, Yonsei University].
  29. Baumgartner, 1996, Application of Structural Equation Modeling in Marketing and Consumer Research: A review, Int. J. Res. Mark., 13, 139, 10.1016/0167-8116(95)00038-0 · DOI
  30. Jiang, 2002, Reducing user-related risks during and prior to system development, Int. J. Proj. Manag., 20, 507, 10.1016/S0263-7863(01)00049-7 · DOI
  31. Wang, 2008, Assessing e-Government systems success: A validation of the DeLone and McLean model of information systems success, Gov. Inf. Q., 25, 717, 10.1016/j.giq.2007.06.002 · DOI
  32. Fornell, 1981, Evaluating structural equation models with unobservable variable and measurement error, J. Mark. Res., 18, 39, 10.1177/002224378101800104 · DOI · Paper page
  33. Barclay, 1995, The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computer Adoption and Use an Illustration, Technol. Stud., 2, 285

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Metadata

Authors
Lee, S.
Affiliations
Kwangwoon University (KR)
Corresponding author
Lee, Seulki
ORCID
Lee, Seulki
Venue
Sustainability
Publisher
MDPI AG
Published
2023-02-02
Volume
15
Issue
3
Pages
2695
ISSN
2071-1050 (ISSN-L)
DOI
10.3390/su15032695
OpenAlex
W4319031873
Cited by
21
References
2
Citations by year
2026: 3 · 2025: 6 · 2024: 7 · 2023: 5
FWCI
4.54
Type
journal article
Language
en
Open access
Licence
Locations
  • Sustainability (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
  • National Research Foundation of Korea (NRF-2020R1I1A1A01071545)
  • Kwangwoon University (NRF-2020R1I1A1A01071545)
Topics
  • Social Sciences › Urban Studies › Smart Cities and Technologies
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Social Sciences › Political Science and International Relations › E-Government and Public Services
Keywords
smart city services, technology acceptance model, citizen acceptance, acceptance intention, perceived usefulness, perceived ease of use, risk perception, social influence, user characteristics, public service quality, U-City, top-down policy, citizen satisfaction, Seoul

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