IDEAS home Printed from https://ideas.repec.org/p/ipt/iptwpa/jrc138684.html
   My bibliography  Save this paper

What factors influence perceived artificial intelligence adoption by public managers?

Author

Listed:

Abstract

The adoption of artificial intelligence (AI) in the public sector is now reaching a stage where, drawing on the experience of early pilots and adoptions, EU public administrations are starting to face the challenges of implementing AI solutions. In response, this study investigates AI adoption in the public sector with a twofold goal: Add evidence to the existing body of knowledge to have a better understanding of the dynamics underlying AI adoption in the EU. We do this by providing quantitative (survey) insights into AI readiness and adoption in the public sector, across different country contexts. By offering a picture of the status of AI adoption and readiness in public administrations, we identify the main challenges and drivers of AI adoption, which are required for ensuring AI’s trustworthy use. Define recommendations for managers in the public sector and public administrations. Based on the insights from the first aim, we formulate ways forward to inform policymakers. We surveyed 576 public managers in seven countries: Germany, Spain, France, the Netherlands, Austria, Poland and Sweden. The sample was diverse in age, job level, organisation size and geographical origin. We asked each of them about the level of AI adoption in their organisation. This was measured in two ways: we asked specifically about the extent to which they thought that their organisation had implemented AI projects in service delivery, internal operations and policy decision-making. Next, we asked about the exact number of projects that were either planned or implemented, with the response options of 0, 1, 2–5 or more than 5. Building on the latest scientific insights, we look at what combination of technological, organisational, environmental and individual-level factors contributes to AI adoption. Based on our research, we have three key conclusions: 1. AI adoption is no longer a promise; it is a reality, in particular for service delivery and internal operations. 2. Soft factors and in-house expertise are important internal factors for AI adoption. 3. Citizen needs are an important external factor for AI adoption.

Suggested Citation

  • GRIMMELIKHUIJSEN Stephan & TANGI Luca, 2024. "What factors influence perceived artificial intelligence adoption by public managers?," JRC Research Reports JRC138684, Joint Research Centre.
    • Handle: RePEc:ipt:iptwpa:jrc138684
    as

    Download full text from publisher

    File URL: https://publications.jrc.ec.europa.eu/repository/handle/JRC138684
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aboelmaged, Mohamed Gamal, 2014. "Predicting e-readiness at firm-level: An analysis of technological, organizational and environmental (TOE) effects on e-maintenance readiness in manufacturing firms," International Journal of Information Management, Elsevier, vol. 34(5), pages 639-651.
    2. Helen Margetts & Cosmina Dorobantu, 2019. "Rethink government with AI," Nature, Nature, vol. 568(7751), pages 163-165, April.
    3. Jan Jöhnk & Malte Weißert & Katrin Wyrtki, 2021. "Ready or Not, AI Comes— An Interview Study of Organizational AI Readiness Factors," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 5-20, February.
    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. Maike Müller & Dirk Stegelmeyer & Rakesh Mishra, 2023. "Development of an augmented reality remote maintenance adoption model through qualitative analysis of success factors," Operations Management Research, Springer, vol. 16(3), pages 1490-1519, September.
    2. Karim, Sitara & Naz, Farah & Naeem, Muhammad Abubakr & Vigne, Samuel A., 2022. "Is FinTech providing effective solutions to Small and Medium Enterprises (SMEs) in ASEAN countries?," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 335-344.
    3. Srivastava, Deepak Kumar & Kumar, Vikas & Ekren, Banu Yetkin & Upadhyay, Arvind & Tyagi, Mrinal & Kumari, Archana, 2022. "Adopting Industry 4.0 by leveraging organisational factors," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    4. Tan Yigitcanlar & Rashid Mehmood & Juan M. Corchado, 2021. "Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    5. Nick Lüthi & Christian Matt & Thomas Myrach & Iris Junglas, 2023. "Augmented Intelligence, Augmented Responsibility?," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(4), pages 391-401, August.
    6. Elisa Giampietri & Samuele Trestini, 2020. "Analysing farmers' intention to adopt web marketing under a technology-organisation-environment perspective: A case study in Italy," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 66(5), pages 226-233.
    7. Dian Prihadyanti & Karlina Sari & Dudi Hidayat & Nur Laili & Budi Triyono & Chichi Shintia Laksani, 2022. "The Changing Nature of Expatriation: The Emerging Role of Knowledge Transfer Readiness," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(2), pages 1496-1541, June.
    8. BARCEVICIUS Egidijus & CIBAITE Guonda & CODAGNONE Cristiano & GINEIKYTE Vaida & KLIMAVICIUTE Luka & LIVA Giovanni & MATULEVIC Loreta & MISURACA Gianluca & VANINI Irene, 2019. "Exploring Digital Government transformation in the EU," JRC Research Reports JRC118857, Joint Research Centre.
    9. Fernandes, Marta & Canito, Alda & Bolón-Canedo, Verónica & Conceição, Luís & Praça, Isabel & Marreiros, Goreti, 2019. "Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry," International Journal of Information Management, Elsevier, vol. 46(C), pages 252-262.
    10. Muhammad Salar Khan & Hamza Umer & Farhana Faruqe, 2024. "Artificial intelligence for low income countries," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    11. Charlene H. Chu & Simon Donato-Woodger & Shehroz S. Khan & Rune Nyrup & Kathleen Leslie & Alexandra Lyn & Tianyu Shi & Andria Bianchi & Samira Abbasgholizadeh Rahimi & Amanda Grenier, 2023. "Age-related bias and artificial intelligence: a scoping review," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    12. Yi-Hsiang Lu & Ching-Chiang Yeh & Yu-Mei Kuo, 2024. "Exploring the critical factors affecting the adoption of blockchain: Taiwan’s banking industry," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-25, December.
    13. Peter Buxmann & Thomas Hess & Jason Bennett Thatcher, 2021. "AI-Based Information Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 1-4, February.
    14. Radzivon Marozau & Hanna Aginskaja & Maribel Guerrero, 2024. "ICT-related dynamic capabilities and SMEs’ performance: An analysis during the economic stagnation in Belarus," The Journal of Technology Transfer, Springer, vol. 49(5), pages 1696-1718, October.
    15. Ramendra Verma & Shikha Kapoor, 2024. "Exploring barriers to acceptance of artificial intelligence in social welfare schemes of governments in India – a systematic literature review," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(11), pages 5139-5156, November.
    16. Michael Weber & Martin Engert & Norman Schaffer & Jörg Weking & Helmut Krcmar, 2023. "Organizational Capabilities for AI Implementation—Coping with Inscrutability and Data Dependency in AI," Information Systems Frontiers, Springer, vol. 25(4), pages 1549-1569, August.
    17. Jithesh Arayankalam & Satish Krishnan, 2023. "ICT-Based Country-Level Determinants of Social Media Diffusion," Information Systems Frontiers, Springer, vol. 25(5), pages 1881-1902, October.
    18. Deng, Shichang & Zhang, Jingjing & Lin, Zhengnan & Li, Xiangqian, 2024. "Service staff makes me nervous: Exploring the impact of insecure attachment on AI service preference," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    19. Mai-Lun Chiu & Tsung-Sheng Cheng & Chun-Nan Lin, 2024. "Driving Open Innovation Capability Through New Knowledge Diffusion of Integrating Intrinsic and Extrinsic Motivations in Organizations: Moderator of Individual Absorptive Capacity," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 3685-3717, March.
    20. D’Amico, Elettra & Belitski, Maksim & Colombelli, Alessandra, 2023. "Evaluating Internal and External Knowledge Sources in Adopting Artificial Intelligence," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202315, University of Turin.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ipt:iptwpa:jrc138684. 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: Publication Officer (email available below). General contact details of provider: https://edirc.repec.org/data/ipjrces.html .

    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.