Validating an AHP-based decision-support framework for AI adoption in public administration
DOI: https://doi.org/10.3846/bmee.2026.27100Abstract
Purpose – This study validates a multi-criteria decision-support framework for assessing organisational readiness for Artificial Intelligence (AI) adoption in public administration, with a specific focus on municipal administration. Validation ensures that the results are conceptually sound, credible, reliable, and robust.
Research methodology – The framework integrates the Technology-Organisation-Environment (TOE) and Unified Theory of Acceptance and Use of Technology (UTAUT) approaches into a hierarchical readiness structure, operationalised through the Analytical Hierarchy Process (AHP). Expert judgments were aggregated using pairwise comparisons to derive criteria’ weights and support group decision-making. Validation combined expert assessment of conceptual coherence, completeness, clarity, interpretability, scale suitability, and practical utility with robust- ness testing across different Multi-Criteria Decision-Making methods (MCDM).
Findings – Voluntariness of use, behavioural intention to use AI, social influence, innovation and readiness for change, skills and expertise, data, and leadership represent central AI-readiness conditions. Expert validation supports the relevance and usability of the proposed hierarchy for AI-readiness assessment. Rankings across alternative MCDM methods showed strong convergence, indicating stable assessment outcomes despite differences in aggregation logic.
Research limitations – The framework was validated using three empirical organisational cases in public administration, along with five synthetically constructed readiness profiles for methodological testing, which may limit the generalisability of the findings.
Practical implications – The framework helps administrations diagnose AI-readiness gaps, benchmark organisational capabilities, and prioritise improvement measures related to AI adoption.
Originality/Value – The study contributes to a validated AI-readiness assessment framework that integrates structured expert judgment with multi-method robustness testing in the context of public administration.
Keywords:
expert validation, MCDM methods, decision-support framework, AHP, AI adoptionHow to Cite
Share
License
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Anders Ericsson, K., & Towne, T. J. (2010). Expertise. WIREs Cognitive Science, 1(3), 404–416. https://doi.org/10.1002/wcs.47
Avramova, T., Peneva, T., & Ivanov, A. (2025). Overview of existing multi-criteria decision-making (MCDM) methods used in industrial environments. Technologies, 13(10), Article 444. https://doi.org/10.3390/technologies13100444
Bączkiewicz, A., Kizielewicz, B., Shekhovtsov, A., Wątróbski, J., & Sałabun, W. (2021). Methodical aspects of MCDM based e-commerce recommender system. Journal of Theoretical and Applied Electronic Commerce Research, 16(6), 2192–2229. https://doi.org/10.3390/jtaer16060122
Carmines, E. G., & Woods, J. A. (2005). Validity assessment. In K. Kempf-Leonard (Ed.), Encyclopedia of social measurement (pp. 933–937). Elsevier. https://doi.org/10.1016/B0-12-369398-5/00418-7
Cinelli, M., Kadziński, M., Miebs, G., Słowiński, R., Gonzalez, M., & Burgherr, P. (2023). MCDA Methods Selection Software (MCDA MSS) [Computer software]. Poznan University of Technology. https://mcda.cs.put.poznan.pl/index.php
Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302. https://doi.org/10.1037/h0040957
Feltovich, P. J., Prietula, M. J., & Ericsson, K. A. (2006). Studies of expertise from psychological perspectives. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (1st ed., pp. 41–68). Cambridge University Press. https://doi.org/10.1017/CBO9780511816796.004
Fengmin, L., Dengfeng, W., Ying, X., Zihao, M., & Jing, C. (2025). Classification, selection of MCDM methods and robust decision-making in multidisciplinary design optimization of automotive structures. Structural and Multidisciplinary Optimization, 68, Article 232. https://doi.org/10.1007/s00158-025-04150-4
Forman, E. H., & Gass, S. I. (2001). The analytic hierarchy process – an exposition. Operations Research, 49(4), 469–486. https://doi.org/10.1287/opre.49.4.469.11231
Hemming, V., Burgman, M. A., Hanea, A. M., McBride, M. F., & Wintle, B. C. (2018). A practical guide to structured expert elicitation using the IDEA protocol. Methods in Ecology and Evolution, 9(1), 169–180. https://doi.org/10.1111/2041-210X.12857
Hien, N. T. T., Quynh, P. H., & Minh, V. Q. (2025). A comparative analysis of multi-criteria decision-making methods. Engineering, Technology & Applied Science Research, 15(5), 26369–26375. https://doi.org/10.48084/etasr.12782
InstaText. (n.d.). InstaText (Version 1.4.3) [AI-assisted writing and editing tool]. https://instatext.io/
Johansen, J., & Fischer-Hübner, S. (2023). Expert opinions as a method of validating ideas: Applied to making GDPR usable. In N. Gerber, A. Stöver, & K. Marky (Eds.), Human factors in privacy research (pp. 137–152). Springer. https://doi.org/10.1007/978-3-031-28643-8_7
Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or not, AI comes – an interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63, 5–20. https://doi.org/10.1007/s12599-020-00676-7
Malefaki, S., Markatos, D., Filippatos, A., & Pantelakis, S. (2025). A comparative analysis of multi-criteria decision-making methods and normalization techniques in holistic sustainability assessment for engineering applications. Aerospace, 12(2), Article 100. https://doi.org/10.3390/aerospace12020100
Mergel, I., Edelmann, N., & Haug, N. (2019). Defining digital transformation: Results from expert interviews. Government Information Quarterly, 36(4), Article 101385. https://doi.org/10.1016/j.giq.2019.06.002
Microsoft. (n.d.). Microsoft Copilot (GPT‑5.5) [Large language model]. https://copilot.microsoft.com/
Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), Article 103434. https://doi.org/10.1016/j.im.2021.103434
Morgan, M. G. (2014). Use (and abuse) of expert elicitation in support of decision making for public policy. Proceedings of the National Academy of Sciences, 111(20), 7176–7184. https://doi.org/10.1073/pnas.1319946111
Nasyuha, A. H., Tujantri, H., Veza, O., Nurarif, S., & Chung, M.-Y. (2025). Comparison of WSM and weight product methods with WSM-Score and vector approaches. Sinkron: Jurnal Dan Penelitian Teknik Informatika, 9(2), 948–956. https://doi.org/10.33395/sinkron.v9i2.14817
Neumann, O., Guirguis, K., & Steiner, R. (2024). Exploring artificial intelligence adoption in public organizations: A comparative case study. Public Management Review, 26(1), 114–141. https://doi.org/10.1080/14719037.2022.2048685
O’Hagan, A. (2019). Expert knowledge elicitation: Subjective but scientific. The American Statistician, 73(sup1), 69–81. https://doi.org/10.1080/00031305.2018.1518265
Paradowski, B., Wątróbski, J., & Sałabun, W. (2025). Novel coefficients for improved robustness in multi-criteria decision analysis. Artificial Intelligence Review, 58, Article 298. https://doi.org/10.1007/s10462-025-11307-6
Petkov, D., Petkova, O., Andrew, T., & Nepal, T. (2007). Mixing multiple criteria decision making with soft systems thinking techniques for decision support in complex situations. Decision Support Systems, 43(4), 1615–1629. https://doi.org/10.1016/j.dss.2006.03.006
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98. https://doi.org/10.1504/IJSSCI.2008.017590
Sahoo, S. K., & Goswami, S. S. (2023). A comprehensive review of Multiple Criteria Decision-Making (MCDM) methods: Advancements, applications, and future directions. Decision Making Advances, 1(1), 25–48. https://doi.org/10.31181/dma1120237
Salomon, V. A. P., & Gomes, L. F. A. M. (2024). Consistency improvement in the analytic hierarchy process. Mathematics, 12(6), Article 828. https://doi.org/10.3390/math12060828
Siksnelyte-Butkiene, I., Zavadskas, E. K., & Streimikiene, D. (2020). Multi-Criteria Decision-Making (MCDM) for the assessment of renewable energy technologies in a household: A review. Energies, 13(5), Article 1164. https://doi.org/10.3390/en13051164
Strauss, M. E., & Smith, G. T. (2009). Construct validity: Advances in theory and methodology. Annual Review of Clinical Psychology, 5(1), 1–25. https://doi.org/10.1146/annurev.clinpsy.032408.153639
Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). The processes of technological innovation. Lexington Books.
Triantaphyllou, E. (2000). Multi-criteria decision making methods. In Multi-criteria decision making methods: A comparative study (pp. 5–21). Springer. https://doi.org/10.1007/978-1-4757-3157-6_2
Vassoney, E., Mammoliti Mochet, A., Desiderio, E., Negro, G., Pilloni, M. G., & Comoglio, C. (2021). Comparing multi-criteria decision-making methods for the assessment of flow release scenarios from small hydropower plants in the Alpine area. Frontiers in Environmental Science, 9, Article 635100. https://doi.org/10.3389/fenvs.2021.635100
Velasquez, M., & Hester, P. (2013). An analysis of multi-criteria decision making methods. International Journal of Operations Research, 10(2), 56–66.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3), 425–478. https://doi.org/10.2307/30036540
Wiangkham, A., & Vongvit, R. (2025). Comparative analysis of multi-criteria decision making methods for prioritizing influential factors of ChatGPT adoption in higher education. Expert Systems with Applications, 287, Article 128188. https://doi.org/10.1016/j.eswa.2025.128188
Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial intelligence and the public sector – applications and challenges. International Journal of Public Administration, 42(7), 596–615. https://doi.org/10.1080/01900692.2018.1498103
Wu, J.-Z., & Tiao, P.-J. (2018). A validation scheme for intelligent and effective multiple criteria decision-making. Applied Soft Computing, 68, 866–872. https://doi.org/10.1016/j.asoc.2017.04.054
Yannis, G., Kopsacheili, A., Dragomanovits, A., & Petraki, V. (2020). State-of-the-art review on multi-criteria decision-making in the transport sector. Journal of Traffic and Transportation Engineering (English Edition), 7(4), 413–431. https://doi.org/10.1016/j.jtte.2020.05.005
Zlaugotne, B., Zihare, L., Balode, L., Kalnbalkite, A., Khabdullin, A., & Blumberga, D. (2020). Multi-criteria decision analysis methods comparison. Environmental and Climate Technologies, 24(1), 454–471. https://doi.org/10.2478/rtuect-2020-0028
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.