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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
ZENODO
Dataset . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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Global Artificial Intelligence Adoption Survey – Greece: Anonymized Dataset and Codebook for Public Sector Employees' Perceptions

Authors: Drosatos, George; Maltezou, Helena; Bellou, Victoria; Mystakidis, Stylianos; Papadakis, Stamatios; Stefanouli, Vasiliki; Sindakis, Stavros; +1 Authors

Global Artificial Intelligence Adoption Survey – Greece: Anonymized Dataset and Codebook for Public Sector Employees' Perceptions

Abstract

This dataset contains the anonymized Greek survey data collected as part of the Greek participation in the Global Artificial Intelligence Adoption Survey: Perceptions of Public Sector Employees (Aristovnik et al., 2026). The survey examines how public sector employees perceive, use, and evaluate artificial intelligence tools in their work. The questionnaire and the broader theoretical background of the study were developed on the basis of the common research framework of the Global Artificial Intelligence Adoption Survey, as well as related work on artificial intelligence adoption and applications in public administration (Aristovnik et al., 2024; Babšek et al., 2025). The dataset includes responses from public sector employees in Greece and covers sociodemographic characteristics, organizational context, use of artificial intelligence tools, frequency and experience of use, intention to continue using AI, perceived work-related effects, learning and training, ethical concerns, trust, organizational support, readiness, adaptability, work experience, accountability, transparency, legal compliance, and the perspectives of non-users. The data were collected through an online questionnaire during the period October 2025 – February 2026. The target population was employees working in the public sector in Greece, including public services, local government organizations, public organizations, educational and research institutions, and other entities of the broader public sector, depending on the dissemination channels used by the Greek research team. The deposited files include: Readme.pdf: documentation explaining the dataset, file structure, anonymization approach, and recommended use; Greek_Dataset.xlsx: anonymized Greek dataset in Excel format; Greek_Dataset.csv: anonymized Greek dataset in CSV format, UTF-8 encoded; Greek_Dataset.json: anonymized Greek dataset in JSON format; Codebook.xlsx: Greek codebook describing variable names, variable labels, value labels, and coding scheme; questionnaire_el.pdf: Greek version of the survey questionnaire; questionnaire_en.pdf: English version of the survey questionnaire. Before publication, the dataset was anonymized to reduce the risk of identification or re-identification of respondents. The anonymization process included the removal of organization names and free-text responses, grouping of exact age and work-experience values into ranges, and suppression of low-frequency categories where needed. The dataset should therefore be used as an anonymized research dataset and interpreted as a descriptive sample of participating public sector employees in Greece, not as a statistically weighted or representative estimate of the entire Greek public sector workforce. The dataset is linked to the Greek national report: Δροσάτος, Γ., Μαλτέζου, Ε., Μπέλλου, Β., Μυστακίδης, Σ., Παπαδάκης, Σ., Στεφανούλη, Β., Συνδάκης, Σ., & Φιτσιλής, Π. (2026). «Αντιλήψεις εργαζομένων του δημόσιου τομέα στην Ελλάδα για τη χρήση της τεχνητής νοημοσύνης». Ελληνική συμμετοχή στο Global Artificial Intelligence Adoption Survey. Σελ. 1–67. https://doi.org/10.5281/zenodo.20377539 References Aristovnik, A. et al. (2026). Global Artificial Intelligence Adoption Survey: Perceptions of Public Sector Employees. Forthcoming. Aristovnik, A., Umek, L., & Ravšelj, D. (2024). Artificial Intelligence in Public Administration: A Bibliometric Review in Comparative Perspective. In M. Trajanovic, N. Filipovic, & M. Zdravkovic (Eds.), Disruptive Information Technologies for a Smart Society (Vol. 872, pp. 126–140). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-50755-7_13 Babšek, M., Ravšelj, D., Umek, L., & Aristovnik, A. (2025). Artificial Intelligence Adoption in Public Administration: An Overview of Top-Cited Articles and Practical Applications. AI, 6(3), 44. https://doi.org/10.3390/ai6030044

Keywords

Public Sector Employees, Public Sector, Artificial Intelligence, Training, Accountability, Digital Transformation, Organizational Readiness, Trust, Transparency, Artificial Intelligence Adoption, Data protection

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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average