Keywords
Intelligence cycle
Intelligence governance
Dynamic capabilities
Decision intelligence
Technology-adoption decision
PLS-SEM
Public-sector artificial intelligence
How to Cite
Abstract
Purpose: This study operationalises competitive intelligence (CI) as a measurable organisational capability for strategic technology-adoption decisions. It uses the intelligence cycle to structure how a public-sector organisation collects, analyses, governs, and acts on signals about a new technology, here artificial intelligence (AI), and tests this capability model empirically.
Methodology/Approach: A reflective partial least squares structural equation model (PLS-SEM) is estimated on an open primary survey of 203 mid and senior government officials across the six Gulf Cooperation Council (GCC) countries. The analysis reports exploratory factor analysis, KMO and Bartlett tests, reliability and convergent validity, discriminant validity by the Fornell-Larcker and HTMT criteria with bootstrap inference, collinearity diagnostics, 5,000-sample bootstrap path estimation, mediation, effect sizes, and cross-validated predictive relevance.
Originality/Relevance: The study moves CI from an interpretive frame to an empirically validated capability model in a genuine organisational, non-educational setting, connecting the intelligence cycle, intelligence governance, dynamic capabilities, and decision intelligence to a strategic technology-adoption outcome.
Key findings: The capability dimensions explain 70 percent of the variance in strategic AI outcomes, with strong predictive relevance (Q-squared = 0.68). Technical infrastructure, the collection backbone, is the dominant driver (beta = 0.66) and governance contributes significantly (beta = 0.21), while organisational readiness is a condition built by infrastructure and governance rather than an independent driver. All constructs show high reliability (alpha 0.92 to 0.96) and convergent validity (AVE 0.87 to 0.94), and discriminant validity holds under the HTMT inference criterion.
Theoretical/methodological contributions: The paper contributes a transferable operationalisation of the intelligence cycle as a validated structural model, a governance-centred reading of technology-adoption decisions, and a reproducible CI measurement template applicable across organisational contexts.
References
Ackermann, A., Henke, J., Chevalère, S., et al. (2025). The effects of adaptive learning technologies on student performance. npj Science of Learning, 10, 12. https://doi.org/10.1038/s41539-024-00293-z
Ali, M. R., Fatemi, S. H., Boskabadi, A., Nikfar, M., Ugwuoke, J., & Ali, H. (2024). Generative AI in engineering and STEM education: A review of opportunities and challenges. Education Sciences, 14(6), 643. https://doi.org/10.3390/educsci14060643
AlShloul, S., Mazhar, T., Abbas, Q., et al. (2024). Artificial intelligence-enabled adaptive learning systems: A review. Computers and Education: Artificial Intelligence, 7, 100219. https://doi.org/10.1016/j.caeai.2024.100219
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108
Biloš, S., & Budimir, B. (2024). Understanding generative AI adoption among students: An extended technology acceptance model. Journal of Theoretical and Applied Electronic Commerce Research, 19(2), 863-880. https://doi.org/10.3390/jtaer19020045
Calof, J. L., & Wright, S. (2008). Competitive intelligence: A practitioner, academic and inter-disciplinary perspective. European Journal of Marketing, 42(7/8), 717-730. https://doi.org/10.1108/03090560810877114
Celik, I. (2023). Exploring the determinants of artificial intelligence (AI) literacy: Digital divide, computational thinking, cognitive absorption. Computers in Human Behavior, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228-239. https://doi.org/10.1080/14703297.2023.2190148
Dake, D. K., Bada, G. K., & Dadzie, A. E. (2023). Internet of Things (IoT) applications in education: Benefits and implementation challenges in tertiary institutions. Journal of Information Technology Education: Research, 22, 311-338. https://doi.org/10.28945/5183
Dervenis, K., Kyriatzis, P., Stoufis, S., & Fitsilis, P. (2022). Assessing the readiness of educational institutions for AI adoption. In Proceedings of the 6th International Conference on Algorithms, Computing and Artificial Intelligence (pp. 1-6). https://doi.org/10.1145/3564982.3564990
Dishman, P. L., & Calof, J. L. (2008). Competitive intelligence: A multiphasic precedent to marketing strategy. European Journal of Marketing, 42(7/8), 766-785. https://doi.org/10.1108/03090560810877141
Duong, C. T. (2024). Factors influencing the continuance intention to use ChatGPT among university students. Journal of Research in Innovative Teaching & Learning. https://doi.org/10.1108/JRIT-01-2024-0006
Gandolfi, E. (2025). Generative AI as an adaptive tutoring layer in intelligent learning environments. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-024-00403-3
Gao, M., Cheah, J.-H., Lim, X.-J., & Luo, X. (2024). Determinants of generative AI adoption and continued use among university students. International Journal of Management Education, 22(3), 100958. https://doi.org/10.1016/j.ijme.2024.100958
Glickman, M., & Sharot, T. (2025). How human-AI feedback loops alter human perceptual and decision-making behavior. Nature Human Behaviour, 9, 1-12. https://doi.org/10.1038/s41562-024-02077-2
Hair, J. F., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027
Kassab, M., DeFranco, J., & Laplante, P. (2020). A systematic literature review on Internet of Things in education: Benefits and challenges. Journal of Computer Assisted Learning, 36(2), 115-127. https://doi.org/10.1111/jcal.12383
Kavitha, M., & Joshith, V. P. (2025). Generative AI adoption in higher education: An expectation-confirmation perspective. Journal of Educational Technology Systems. https://doi.org/10.1177/00472395251380294
Kayalı, E., Yavuz, M., Balat, Ş., & Çalışan, M. (2023). Investigating students' use of ChatGPT through the technology acceptance model. Australasian Journal of Educational Technology, 39(5), 1-18. https://doi.org/10.14742/ajet.8915
Koch, K., Carolus, A., Wienrich, C., & Latoschik, M. E. (2024). The influence of AI literacy on perceived usefulness and trust in generative AI. Heliyon, 10(20), e39686. https://doi.org/10.1016/j.heliyon.2024.e39686
Liengaard, B. D., et al. (2021). Prediction: Coveted, yet forsaken? Introducing a cross-validated predictive ability test in PLS-SEM. Decision Sciences, 52(2), 362-392. https://doi.org/10.1111/deci.12445
Lintner, M. (2024). A systematic review of AI literacy scales. npj Science of Learning, 9, 50. https://doi.org/10.1038/s41539-024-00264-4
Ma, X., & Chen, Y. (2024). Exploring the relationship between generative AI use and student engagement using structural equation modeling. IEEE Access, 12, 1-14. https://doi.org/10.1109/ACCESS.2024.3468378
Mansoor, A., et al. (2024). Students' perceptions and adoption of generative AI tools in higher education. Frontiers in Communication, 9, 1478476. https://doi.org/10.3389/fcomm.2024.1478476
Mohiuddin, G., et al. (2023). Edge-enabled mobile learning for the Internet of Things in smart education. SAGE Open, 13(3). https://doi.org/10.1177/21582440231190337
Naqvi, R. A., Ganjoo, R., Rowe, N., Pashine, S., & Mishra, S. (2025). Generative AI in education: Adoption, perceptions, and outcomes. Frontiers in Artificial Intelligence, 8, 1571527. https://doi.org/10.3389/frai.2025.1571527
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
Ng, D. T. K., et al. (2024). Reviewing the impact of generative AI on student learning outcomes in higher education. British Journal of Educational Technology, 55(5), 1-22. https://doi.org/10.1111/bjet.13411
Pellissier, R., & Nenzhelele, T. E. (2013). Towards a universal competitive intelligence process model. South African Journal of Information Management, 15(2), a567. https://doi.org/10.4102/sajim.v15i2.567
Rohrbeck, R., & Kum, M. E. (2018). Corporate foresight and its impact on firm performance: A longitudinal analysis. Technological Forecasting and Social Change, 129, 105-116. https://doi.org/10.1016/j.techfore.2017.12.013
Sandu, C., Gide, A., & Elkhodr, M. (2024). The role of the Internet of Things and artificial intelligence in education: Opportunities and challenges. Discover Education, 3, 126. https://doi.org/10.1007/s44217-024-00126-6
Shahzad, M. F., Xu, S., An, X., & Asif, M. (2025). Effects of ChatGPT usage on students' academic performance and continuance: A structural model. SAGE Open, 15(1). https://doi.org/10.1177/21582440251368594
Sharma, P. N., Liengaard, B. D., Hair, J. F., Sarstedt, M., & Ringle, C. M. (2022). Predictive model assessment and selection in composite-based modeling using PLS-SEM. European Journal of Marketing, 56(6), 1662-1677. https://doi.org/10.1108/EJM-08-2020-0636
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
Wang, X., & Yu, Z. (2025). A meta-analysis of the effects of ChatGPT on students' learning. PLOS ONE, 20(2), e0299018. https://doi.org/10.1371/journal.pone.0299018
Wang, Y., Li, T., Cheung, A. C. K., & Wong, G. K. W. (2025). The effects of generative AI on academic writing and learning autonomy. International Journal of Educational Technology in Higher Education, 22, 9. https://doi.org/10.1186/s41239-025-00547-9
Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: A systematic review from 2011 to 2021. International Journal of STEM Education, 9(1), 59. https://doi.org/10.1186/s40594-022-00377-5
Yin, M., Goh, E., & Hu, X. (2024). Learning analytics for self-regulated learning in online environments. International Journal of Educational Technology in Higher Education, 21, 60. https://doi.org/10.1186/s41239-024-00480-3
Zapata-Rivera, J., Torre, I., Lee, J., Sarasa-Cabezuelo, A., Ghergulescu, I., & Libbrecht, P. (2024). Adaptive learning analytics and intelligent feedback systems. Frontiers in Artificial Intelligence, 7, 1532896. https://doi.org/10.3389/frai.2024.1532896
Zhang, L., et al. (2024). Integrating sensors and artificial intelligence in smart classrooms: A review of engagement-aware learning environments. Sensors, 24(17), 5487. https://doi.org/10.3390/s24175487