IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i16p7281-d1722823.html
   My bibliography  Save this article

Smart Skills for Smart Cities: Developing and Validating an AI Soft Skills Scale in the Framework of the SDGs

Author

Listed:
  • Nuriye Sancar

    (Department of Mathematics, Near East University, 99138 Nicosia, Turkey
    Computer Information Systems Research and Technology Centre, Near East University, 99138 Nicosia, Turkey)

  • Nadire Cavus

    (Computer Information Systems Research and Technology Centre, Near East University, 99138 Nicosia, Turkey
    Department of Computer Information Systems, Near East University, 99138 Nicosia, Turkey)

Abstract

Artificial intelligence (AI) soft skills have become increasingly vital in today’s technology-driven world, as they support decision-making systems, strengthen collaboration among stakeholders, and enable individuals to adapt to rapidly changing environments—factors that are fundamental for achieving the sustainability goals of smart cities. Even though AI soft skills are becoming more important, no scale specifically designed to identify and evaluate individuals’ AI soft skills has been found in the existing literature. Therefore, this paper aimed to develop a reliable and valid scale to identify the AI soft skills of individuals. A sample of 685 individuals who were employed in AI-active sectors, with a minimum of a bachelor’s degree, and at least one year of AI-related work experience, participated in the study. A sequential exploratory mixed-methods research design was utilized. Exploratory factor analysis (EFA) identified a five-factor structure that accounted for 67.37% of the total variation, including persuasion, collaboration, adaptability, emotional intelligence, and creativity. Factor loadings ranged from 0.621 to 0.893, and communalities ranged from 0.587 to 0.875. Confirmatory factor analysis (CFA) supported this structure, with strong model fit indices (GFI = 0.940, AGFI = 0.947, NFI = 0.949, PNFI = 0.833, PGFI = 0.823, TLI = 0.972, IFI = 0.975, CFI = 0.975, RMSEA = 0.052, SRMR = 0.035). Internal consistency for each factor was high, with Cronbach’s alpha values of dimensions ranging from 0.804 to 0.875, with a value of 0.921 for the overall scale. Convergent and discriminant validity analyses further confirmed the construct’s robustness. The finalized AI soft skills (AISS) scale, consisting of 24 items, offers a psychometrically valid and reliable tool for assessing essential AI soft skills in professional contexts. Ultimately, this developed scale enables the determination of the social and cognitive skills needed in the human-centered and participatory governance structures of smart cities, supporting the achievement of specific Sustainable Development Goals such as SDG 4, SDG 8, and SDG 11, and contributes to the design of policies and training programs to eliminate the deficiencies of individuals in these areas. Thus, it becomes possible to create qualified human resources that support sustainable development in smart cities, and for these individuals to take an active part in the labor market.

Suggested Citation

  • Nuriye Sancar & Nadire Cavus, 2025. "Smart Skills for Smart Cities: Developing and Validating an AI Soft Skills Scale in the Framework of the SDGs," Sustainability, MDPI, vol. 17(16), pages 1-26, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7281-:d:1722823
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/16/7281/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/16/7281/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sebastian Krakowski & Johannes Luger & Sebastian Raisch, 2023. "Artificial intelligence and the changing sources of competitive advantage," Strategic Management Journal, Wiley Blackwell, vol. 44(6), pages 1425-1452, June.
    2. Bai, Caiquan & Yao, Di & Xue, Qihang, 2025. "Does artificial intelligence suppress firms' greenwashing behavior? Evidence from robot adoption in China," Energy Economics, Elsevier, vol. 142(C).
    3. Ayala, Néstor Fabián & Rodrigues da Silva, Jassen & Cannarozzo Tinoco, Maria Auxiliadora & Saccani, Nicola & Frank, Alejandro G., 2025. "Artificial Intelligence capabilities in Digital Servitization: Identifying digital opportunities for different service types," International Journal of Production Economics, Elsevier, vol. 284(C).
    4. Smaldone, Francesco & Ippolito, Adelaide & Lagger, Jelena & Pellicano, Marco, 2022. "Employability skills: Profiling data scientists in the digital labour market," European Management Journal, Elsevier, vol. 40(5), pages 671-684.
    5. Vitalii Kruhlov & Jaroslav Dvorak, 2025. "Social Inclusivity in the Smart City Governance: Overcoming the Digital Divide," Sustainability, MDPI, vol. 17(13), pages 1-20, June.
    6. Bone, Matthew & González Ehlinger, Eugenia & Stephany, Fabian, 2025. "Skills or degree? The rise of skill-based hiring for AI and green jobs," Technological Forecasting and Social Change, Elsevier, vol. 214(C).
    7. Xuesong Zhai & Xiaoyan Chu & Ching Sing Chai & Morris Siu Yung Jong & Andreja Istenic & Michael Spector & Jia-Bao Liu & Jing Yuan & Yan Li & Ning Cai, 2021. "A Review of Artificial Intelligence (AI) in Education from 2010 to 2020," Complexity, Hindawi, vol. 2021, pages 1-18, April.
    8. Jeon, Hongjun & Seo, Wonchul & Park, Eunjeong & Choi, Sungchul, 2020. "Hybrid machine learning approach for popularity prediction of newly released contents of online video streaming services," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    9. Nicoleta Valentina Florea & Gabriel Croitoru, 2025. "The Impact of Artificial Intelligence on Communication Dynamics and Performance in Organizational Leadership," Administrative Sciences, MDPI, vol. 15(2), pages 1-33, January.
    10. Kenneth Bollen, 1986. "Sample size and bentler and Bonett's nonnormed fit index," Psychometrika, Springer;The Psychometric Society, vol. 51(3), pages 375-377, September.
    11. Sohrabpour, Vahid & Oghazi, Pejvak & Toorajipour, Reza & Nazarpour, Ali, 2021. "Export sales forecasting using artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zirar, Araz & Ali, Syed Imran & Islam, Nazrul, 2023. "Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda," Technovation, Elsevier, vol. 124(C).
    2. Robertson, Jeandri & Ferreira, Caitlin & Botha, Elsamari & Oosthuizen, Kim, 2024. "Game changers: A generative AI prompt protocol to enhance human-AI knowledge co-construction," Business Horizons, Elsevier, vol. 67(5), pages 499-510.
    3. Christoph Riedl & Eric Bogert, 2024. "Effects of AI Feedback on Learning, the Skill Gap, and Intellectual Diversity," Papers 2409.18660, arXiv.org.
    4. Benedetta Montanaro & Annalisa Croce & Elisa Ughetto, 2024. "Venture capital investments in artificial intelligence," Journal of Evolutionary Economics, Springer, vol. 34(1), pages 1-28, January.
    5. Golob, Thomas F. & Regan, A C, 2002. "Trucking Industry Preferences for Driver Traveler Information Using Wireless Internet-enabled Devices," University of California Transportation Center, Working Papers qt40q8h6sf, University of California Transportation Center.
    6. Randi Hammervold & Ulf Olsson, 2012. "Testing structural equation models: the impact of error variances in the data generating process," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(5), pages 1547-1570, August.
    7. Abdalmajeed Selmi Arrooqi & Mutlaq Miqad Alruqi, 2025. "Academic leadership attitudes toward employing artificial intelligence applications in developing administrative processes," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-11, December.
    8. Zheng Zhao & Yaoqi Zhang & Yali Wen, 2018. "Residents’ Support Intentions and Behaviors Regarding Urban Trees Programs: A Structural Equation Modeling-Multi Group Analysis," Sustainability, MDPI, vol. 10(2), pages 1-11, January.
    9. Asif Ali Wagan & Abdullah Ayub Khan & Yen-Lin Chen & Por Lip Yee & Jing Yang & Asif Ali Laghari, 2023. "Artificial Intelligence-Enabled Game-Based Learning and Quality of Experience: A Novel and Secure Framework (B-AIQoE)," Sustainability, MDPI, vol. 15(6), pages 1-12, March.
    10. Gheţie Romina-Medea & Cuc Lavinia-Denisia & Rad Dana & Pantea Mioara-Florina & Szentesi Silviu-Gabriel, 2025. "Can AI Redefine Best Practices in Human Resources?," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 19(1), pages 3936-3949.
    11. Panteha Farmanesh & Niloofar Solati Dehkordi & Asim Vehbi & Kavita Chavali, 2025. "Artificial Intelligence and Green Innovation in Small and Medium-Sized Enterprises and Competitive-Advantage Drive Toward Achieving Sustainable Development Goals," Sustainability, MDPI, vol. 17(5), pages 1-20, March.
    12. Yi WANG & Yiting XU & Yufei ZHONG, 2025. "Does Artificial Intelligence Invariably Enhance ESG Performance?," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 24-40, July.
    13. Uršič, Dejan & Čater, Tomaž, 2025. "Digital innovation in management and business: A comprehensive review, multi-level framework, and future research agenda," Journal of Business Research, Elsevier, vol. 197(C).
    14. Xiu Guan & Xiang Feng & A.Y.M. Atiquil Islam, 2023. "The dilemma and countermeasures of educational data ethics in the age of intelligence," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    15. Ogasawara, Haruhiko, 2016. "Bias correction of the Akaike information criterion in factor analysis," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 144-159.
    16. repec:plo:pone00:0087071 is not listed on IDEAS
    17. Wala Bagunaid & Naveen Chilamkurti & Prakash Veeraraghavan, 2022. "AISAR: Artificial Intelligence-Based Student Assessment and Recommendation System for E-Learning in Big Data," Sustainability, MDPI, vol. 14(17), pages 1-22, August.
    18. Giustina Secundo & Claudia Spilotro & Johanna Gast & Vincenzo Corvello, 2025. "The transformative power of artificial intelligence within innovation ecosystems: a review and a conceptual framework," Review of Managerial Science, Springer, vol. 19(9), pages 2697-2728, September.
    19. Yuqin Yang & Ying Zhang & Daner Sun & Wenmeng He & Yantao Wei, 2025. "Navigating the landscape of AI literacy education: insights from a decade of research (2014–2024)," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-12, December.
    20. Abirami Raja Santhi & Padmakumar Muthuswamy, 2022. "Pandemic, War, Natural Calamities, and Sustainability: Industry 4.0 Technologies to Overcome Traditional and Contemporary Supply Chain Challenges," Logistics, MDPI, vol. 6(4), pages 1-32, November.
    21. Ma, Dechao & Wu, Weiwei, 2024. "Does artificial intelligence drive technology convergence? Evidence from Chinese manufacturing companies," Technology in Society, Elsevier, vol. 79(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7281-:d:1722823. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.