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STRATEGIC TRANSFORMATION IN THE AGE OF AI LINKING DIGITAL INNOVATION TO ORGANIZATIONAL EFFECTIVENESS

Authors
  • Ali Abdulhussein Gaber Aljashaam

    Al-Furat Al-Awsat Technical University, Iraq
Keywords:
Artificial intelligence, Digital innovation, Strategic transformation, Organizational effectiveness, Developing economies, Iraq, Intelligent information systems, Dynamic capabilities, Resource-based view, Structural Equation Modeling (SEM)
Abstract

Objective: Artificial Intelligence (AI) is a radically new force that is changing organizational strategies in the rapidly changing digital world, especially in developing economies such as Iraq. The paper seeks to examine the correlation between competences of AI and numerical improvement and administrative function regarding the cultural limitations and economic stability and their distinct features in Iraqi institutions. The application of Resource-inclined View and Dynamic Capabilities Theory, the framework that operates with the ambit of digital innovation by AI is linked with planned revolution and performance findings. Method: This study uses a Structural Equation Modeling (SEM) to test hypotheses via the application of a numerical cross-sectional study with data offered by 200 institutions. Results: Findings of this study indicate that AI capacities have an excessive effect on the promotion of digital innovation, which, subsequently, intercedes the association between AI and institutional performance. The findings also show that digital revolution is a main tool to apply AI technologies in improving planned agility and function of chaotic environment. Novelty: The study does not only fill the existing gaps in empirical research in the Middle East but also offers real-world inferences to potential policymakers and executives interested in endorsing invention determined by AI. It has been suggested to invest in AI, become digitally literate, implement smart systems, and have an innovation-oriented organizational culture. Finally, the paper ends with the future research directions, which would help to contextualize the AI integration in the countries with emerging economies even better.

References

A. T. Othman and M. M. Al-Kassab, “Analysis of Challenges and Obstacles to Artificial Intelligence Adoption in the Banking Sector in Iraq: Opportunities and Challenges,” in Studies in Systems, Decision and Control, 2025, pp. 251–268. doi: 10.1007/978-3-031-90271-0_19.

S. J. Nisrina, R. Masviansyah, A. W. Adiwana, I. R. Kusumasari, and R. Hidayat, “The Role of Information Systems in Improving the Efficiency of Business Decision Making,” J. Bisnis Dan Komun. Digit., vol. 2, no. 2, p. 10, 2024, doi: 10.47134/jbkd.v2i2.3470.

B. Domini, A. S. Dewi, and G. P. Cesna, “The Effects of Artificial Intelligence (AI) on Company Performance Including the Value of AI-based Business Transformation Initiatives,” IAIC Trans. Sustain. Digit. Innov., vol. 5, no. 1, pp. 24–38, 2023, doi: 10.34306/itsdi.v5i1.606.

S. Wang and H. Zhang, “Digital Transformation and Innovation Performance in Small- and Medium-Sized Enterprises: A Systems Perspective on the Interplay of Digital Adoption, Digital Drive, and Digital Culture,” Systems, vol. 13, no. 1, p. 43, 2025, doi: 10.3390/systems13010043.

A. Abdurrahman, A. Gustomo, and E. A. Prasetio, “Impact of Dynamic Capabilities on Digital Transformation and Innovation to Improve Banking Performance: A TOE Framework Study,” J. Open Innov. Technol. Mark. Complex., vol. 10, no. 1, p. 100215, 2024, doi: 10.1016/j.joitmc.2024.100215.

M. I. Tabash, U. Farooq, S. S. Issa, M. Alnahhal, S. A. Prestianawati, and M. Varma, “Empirical assessment of digital transformation and renewable energy adoption in the Middle East,” Discov. Sustain., vol. 6, no. 1, 2025, doi: 10.1007/s43621-025-01758-w.

M. M. Mariani, I. Machado, V. Magrelli, and Y. K. Dwivedi, “Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions,” Technovation, vol. 122, p. 102623, 2023, doi: 10.1016/j.technovation.2022.102623.

J. Li and X. Jin, “The Impact of Artificial Intelligence Adoption Intensity on Corporate Sustainability Performance: The Moderated Mediation Effect of Organizational Change,” Sustainability, vol. 16, no. 21, p. 9350, 2024, doi: 10.3390/su16219350.

U. S. Kesmodel, “Cross-sectional Studies - What Are They Good for?,” Acta Obstet. Gynecol. Scand., vol. 97, no. 4, pp. 388–393, 2018, doi: 10.1111/aogs.13331.

P. Mannadhan, J. R. Szymański, M. Zurek-Mortka, and M. Sathiyanarayanan, “A Novel Framework for the Iraqi Manufacturing Industry Towards the Adoption of Industry 4.0,” Sustainability, vol. 16, no. 20, p. 9045, 2024, doi: 10.3390/su16209045.

V. Zamanzadeh, A. Ghahramanian, M. Rassouli, A. Abbaszadeh, H. Alavi-Majd, and A.-R. Nikanfar, “Design and implementation content validity study: Development of an instrument for measuring patient-centered communication,” J. Caring Sci., vol. 4, no. 2, pp. 165–178, 2015, doi: 10.15171/jcs.2015.017.

A. Hamza, Z. Ali, S. Dudley, K. Saleem, M. Uneeb, and N. Christofides, “A multi-stage review framework for AI-driven predictive maintenance and fault diagnosis in photovoltaic systems,” Appl. Energy, vol. 393, p. 126108, 2025, doi: 10.1016/j.apenergy.2025.126108.

S. K. Kwak and J. H. Kim, “Statistical data preparation: Management of missing values and outliers,” Korean J. Anesthesiol., vol. 70, no. 4, pp. 407–411, 2017, doi: 10.4097/kjae.2017.70.4.407.

M. W. Watkins, “Exploratory Factor Analysis: A Guide to Best Practice,” J. Black Psychol., vol. 44, no. 3, pp. 219–246, 2018, doi: 10.1177/0095798418771807.

M. S. Zeb, “Confirmatory Factor Analysis (CFA) for Testing Validity And Reliability Instrument in the Study of Education,” Aust. J. Basic Appl. Sci., vol. 5, no. 12, pp. 1098–1103, 2011.

R. Hidayat and P. Wulandari, “Structural Equation Modelling (SEM) in Research: Narrative Literature Review,” Open Access Indones. J. Soc. Sci., vol. 5, no. 6, pp. 852–858, 2022, doi: 10.37275/oaijss.v5i6.141.

J. G. Zheng, “Data Visualization in Business Intelligence,” in Global Business Intelligence, 2017, pp. 67–81. doi: 10.4324/9781315471136-6.

D. Bhushan, S. Hooda, and P. Mondal, “Co-pyrolysis of biomass and plastic wastes and application of machine learning for modelling of the process: A comprehensive review,” J. Energy Inst., vol. 119, p. 101973, 2025, doi: https://doi.org/10.1016/j.joei.2025.101973.

B. Wang, P.-L. P. Rau, and T. Yuan, “Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale,” Behav. & Inf. Technol., vol. 42, no. 9, pp. 1–14, 2022, doi: 10.1080/0144929x.2022.2072768.

S. Guo, L. Shi, and X. Zhai, “Developing and validating an instrument for teachers’ acceptance of artificial intelligence in education,” Educ. Inf. Technol., 2025, doi: 10.1007/s10639-025-13338-6.

M. Gebremedhin, E. Gebrewahd, and L. K. Stafford, “Validity and reliability study of clinician attitude towards rural health extension program in Ethiopia: exploratory and confirmatory factor analysis,” BMC Health Serv. Res., vol. 22, no. 1, pp. 1–11, 2022, doi: 10.1186/s12913-022-08470-9.

J. Rosak-Szyrocka and S. Tiwari, “Structural Equation Modeling (SEM) to Test Sustainable Development in University 4.0 in the Ultra-Smart Society Era,” Sustainability, vol. 15, no. 23, p. 16167, 2023, doi: 10.3390/su152316167.

B. Y. Kassa and E. K. Worku, “The Impact of Artificial Intelligence on Organizational Performance: The Mediating Role of Employee Productivity,” J. Open Innov. Technol. Mark. Complex., vol. 11, no. 1, p. 100474, 2025, doi: 10.1016/j.joitmc.2025.100474.

N. Setiyowati and A. Zabidi, “Perceived Leadership Styles and Academicians’ Job Performance: Teaching, Research, and Community Services in Indonesia,” Unkn. J., vol. 7, no. 1, pp. 11–26, 2020.

V. S. Litvinenko, “Digital Economy as a Factor in the Technological Development of the Mineral Sector,” Nat. Resour. Res., vol. 29, no. 3, pp. 1521–1541, 2019, doi: 10.1007/s11053-019-09568-4.

“Adopting AI in Iraq,” 2024.

M. L. Sheng, “A dynamic capabilities-based framework of organizational sensemaking through combinative capabilities towards exploratory and exploitative product innovation in turbulent environments,” Ind. Mark. Manag., vol. 65, pp. 28–38, 2017, doi: 10.1016/j.indmarman.2017.06.001.

D. O. Olutimehin, O. C. Ofodile, I. Ejibe, O. G. Odunaiya, and O. T. Soyombo, “Implementing AI in Business Models: Strategies for Efficiency and Innovation,” Int. J. Manag. & Entrep. Res., vol. 6, no. 3, pp. 863–877, 2024, doi: 10.51594/ijmer.v6i3.940.

J. Alwali and W. Alwali, “Linking AI-driven HRM and emotional intelligence to leadership effectiveness and employee performance,” Leadersh. & Organ. Dev. J., pp. 1–21, 2025, doi: 10.1108/lodj-05-2025-0358.

N. Adel, “The Impact of Digital Literacy and Technology Adoption on Financial Inclusion in Africa, Asia, and Latin America,” Heliyon, vol. 10, no. 24, p. e40951, 2024, doi: 10.1016/j.heliyon.2024.e40951.

A. A. Moustapha, M. L. Akanbi, G. O. Bello, A. I. Yakub, R. D. Akanji, and B. Raji, “Artificial Intelligence in Developing Nation: Bridging the Execution Gap with Potential,” Int. J. Latest Technol. Eng. Manag. & Appl. Sci., vol. 14, no. 8, pp. 1497–1510, 2025, doi: 10.51583/ijltemas.2025.1408000188.

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2025-12-12
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How to Cite

STRATEGIC TRANSFORMATION IN THE AGE OF AI LINKING DIGITAL INNOVATION TO ORGANIZATIONAL EFFECTIVENESS. (2025). International Journal of Business, Law and Political Science, 2(12), 633-648. https://doi.org/10.61796/ijblps.v2i12.435

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