Assessing Innovation Strategies in the Digital Economy through Artificial Intelligence-Based Criteria Using CoCoSo method

Alireza  Taghizadeh; Koorosh  Karaminezhad; Nika  Fakhri; Behzad  Moghaddami; Dorsa Charkhian

doi:10.31181/jidmgc11202523

Authors

Alireza Taghizadeh Master of Financial Management, Alborz Campus, University of Tehran, Tehran, Iran Author https://orcid.org/0009-0008-5187-8362
Koorosh Karaminezhad Department of Management, University of Brighton, London, United Kingdom Author https://orcid.org/0009-0004-0335-6747
Nika Fakhri Martin Tuchman School of Management, New Jersey Institute of Technology (NJIT), Newark, NJ, United States Author https://orcid.org/0009-0002-3756-8014
Behzad Moghaddami Department of Informatics, Linnaeus University, Universitetsplatsen 1, 352 52 Växjö, Sweden Author https://orcid.org/0009-0008-3156-8092
Dorsa Charkhian Drexel University Westphal College of Media Arts & Design, Philadelphia, PA 19104, United States Author https://orcid.org/0009-0004-1222-8210

DOI:

https://doi.org/10.31181/jidmgc11202523

Keywords:

Digital Economy, Artificial Intelligence, Strategy Selection, Decision Making, CoCoSo Method

Abstract

The digital economy, as the driving force of development in recent decades, has been directly affected by advances in Artificial Intelligence. The growing penetration of smart technologies has transformed business models while also reshaping decision-making structures. Therefore, the use of strategies that are evaluated and ranked based on AI-based criteria is an unavoidable necessity. This study ranked innovative strategies in the digital economy by reviewing previous studies and using the Combined Compromise Solution (CoCoSo) method. First, relevant criteria were identified through a review of previous studies; in this study, 10 AI-based criteria in the digital economy were identified, which were confirmed by experts. Then, innovative strategies were evaluated and ranked based on these criteria using the CoCoSo method. The results showed that “The expansion of AI in emerging sectors such as fintech (A4)” with a score of 0.291 and the first rank, plays a pivotal role in creating transformative innovations. In second place, “Investing in advanced cloud and computing infrastructure for AI (A5)” with a score of 0.259, serves as the foundation for robust processing capabilities and facilitates data transfer, without which the implementation of other strategies remains inefficient. Also, “Personalizing user experiences with AI (A2)” with a score of 0.194 and third place, focuses on optimizing decision-making and creating added value through customized experiences, which plays a key role in customer retention and increased engagement across digital platforms. From a managerial perspective, it is recommended that executives first focus on implementing the strategy A4 and launch innovative pilot models in collaboration with fintech and smart tech startups to accelerate the growth of the digital economy. While investment in A5 is needed to create scalable cloud infrastructures that facilitate SMEs' access to AI.

Downloads

Download data is not yet available.

References

Lu, L., Yang, S., & Li, Q. (2024). The interaction of digital economy, artificial intelligence and sports industry development --based on China PVAR analysis of provincial panel data. Heliyon, 10(4). https://doi.org/10.1016/j.heliyon.2024.e25688

Faj’ri, F. A., Sahban, M. A., Saragih, H., & Kojongian, P. J. C. (n.d.). Digital Economy and Business Transformation in Indonesia: An Exploration of Opportunities and Challenges. ResearchGate. Retrieved October 23, 2025, from https://www.researchgate.net/publication/384714755_Digital_Economy_and_Business_Transformation_in_Indonesia_An_Exploration_of_Opportunities_and_Challenges

Frolov, D. P., & Lavrentyeva, A. V. (2019). Regulatory Policy for Digital Economy- Holistic Institutional Framework. Montenegrin Journal of Economics, 15(4), 33–44.

Xue, L., & Pang, Z. (2022). Ethical governance of artificial intelligence: An integrated analytical framework. Journal of Digital Economics, 1(1), 44–52. https://doi.org/10.1016/j.jdec.2022.08.003

Gagulina, N., Samoylov, A., Novikov, A., & Yanova, E. (2020). Innovation-driven development and quality of living under conditions of digital economy. E3S Web of Conferences, 157, 04037. https://doi.org/10.1051/e3sconf/202015704037

Dahlman, C., Mealy, S., & Wermelinger, M. (n.d.). Harnessing the digital economy for developing countries. OECD. Retrieved October 23, 2025, from https://www.oecd.org/en/publications/harnessing-the-digital-economy-for-developing-countries_4adffb24-en.html

Rong, K. (2022). Research agenda for the digital economy. Journal of Digital Economics, 1(1), 20–31. https://doi.org/10.1016/j.jdec.2022.08.004

Haenlein, M., & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0

Costello, A. M., Down, A. K., & Mehta, M. N. (2020). Machine + man: A field experiment on the role of discretion in augmenting AI-based lending models. Journal of Accounting and Economics, 70(2), 101360. https://doi.org/10.1016/j.jacceco.2020.101360

Cui, R., Li, M., & Zhang, S. (2022). AI and Procurement. Manufacturing & Service Operations Management, 24(2), 691–706. https://doi.org/10.1287/msom.2021.0989

Wang, L., & Zhao, L. (2022). Digital Economy Meets Artificial Intelligence: Forecasting Economic Conditions Based on Big Data Analytics. Mobile Information Systems, 2022(1), 7014874. https://doi.org/10.1155/2022/7014874

Tapscott, D. (1999). The digital economy, promise and peril in the age of networked intelligence. McGraw-Hill.

Bukht, R., & Heeks, R. (2017). Defining, Conceptualising and Measuring the Digital Economy. SSRN. https://doi.org/10.2139/ssrn.3431732

Meidyasari, S. (2024). The Impact of Digital Economy in Driving Economic Growth and Development in Indonesia. INJURITY: Journal of Interdisciplinary Studies, 3(11), 777–783.

Onitsuka, K., Hidayat, A. R. T., & Huang, W. (2018). Challenges for the next level of digital divide in rural Indonesian communities. The Electronic Journal of Information Systems in Developing Countries, 84(2), e12021. https://doi.org/10.1002/isd2.12021

Ariansyah, K., Anandhita, V. H., & Sari, D. (2019). Investigating the Next Level Digital Divide in Indonesia. In *2019 4th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON)* (pp. 1–5). https://doi.org/10.1109/TIMES-iCON47539.2019.9024668

Mohammadabadi, S. M. S., Seyedkhamoushi, F., Mostafavi, M., & Peikani, M. B. (2024). Examination of AI’s Role in Diagnosis, Treatment, and Patient Care. In Transforming Gender-Based Healthcare with AI and Machine Learning. CRC Press.

Seifi, N., Ghoodjani, E., Majd, S. S., Maleki, A., & Khamoushi, S. (2025). Evaluation and prioritization of artificial intelligence integrated block chain factors in healthcare supply chain: A hybrid Decision Making Approach. Computational Decision Making International Journal, 2, 374–405. https://doi.org/10.59543/comdem.v2i.11029

Shoeibi, M., Oskouei, A. E., & Kaveh, M. (2024). A novel six-dimensional chimp optimization algorithm—deep reinforcement learning-based optimization scheme for reconfigurable intelligent surface-assisted energy harvesting in batteryless IoT networks. Future Internet, 16(12), 460.

Golkarieh, A., Kiashemshaki, K., & Boroujeni, S. R. (2025). Advanced Deep Learning Techniques for Classifying Dental Conditions Using Panoramic X-Ray Images. arXiv. https://doi.org/10.48550/arXiv.2508.21088

Basirat, S., Raoufi, S., Bazmandeh, D., Khamoushi, S., & Entezami, M. (2025). Ranking of AI-Based Criteria in Health Tourism Using Fuzzy SWARA Method. Computational Decision Making International Journal, 2, 530–545. https://doi.org/10.59543/comdem.v2i.13795

Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M. (2024). AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact. Sustainability, 16(5), 1790. https://doi.org/10.3390/su16051790

Entezami, Z., Davis, C. H., & Entezami, M. (2025). An AI-Assisted Topic Model of the Media Literacy Research Literature. Media Literacy Academic Research, 8(1), 5–28. https://doi.org/10.34135/mlar-25-01-01

Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577

Lee, W. J., Wu, H., Yun, H., Kim, H., Jun, M. B. G., & Sutherland, J. W. (2019). Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data. Procedia CIRP, 80, 506–511. https://doi.org/10.1016/j.procir.2018.12.019

Boroujeni, S. R., Abedi, H., & Bush, T. (2025). Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control. Computational Decision Making International Journal, 2, 687–707. https://doi.org/10.59543/comdem.v2i.14391

Shoeibi, M., Nevisi, M. M. S., Khatami, S. S., Martín, D., Soltani, S., & Aghakhani, S. (2024). Improved IChOA-Based Reinforcement Learning for Secrecy Rate Optimization in Smart Grid Communications. Computer Materials & Continua, 81(2). Retrieved October 23, 2025, from https://www.researchgate.net/profile/Mehrdad-Shoeibi/publication/385123676_Improved_IChOA-Based_Reinforcement_Learning_for_Secrecy_Rate_Optimization_in_Smart_Grid_Communications/links/67db50a9e2c0ea36cd7063d5/Improved-IChOA-Based-Reinforcement-Learning-for-Secrecy-Rate-Optimization-in-Smart-Grid-Communications.pdf

Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of Artificial Intelligence in Transport: An Overview. Sustainability, 11(1), 189. https://doi.org/10.3390/su11010189

Golkarfard, A., Sadeghmalakabadi, S., Talebian, S., Basirat, S., & Golchin, N. (2025). Ethical challenges of AI integration in architecture and built environment. Current Opinion, 5(2), 1136–1147.

Afandizadeh, S., Sedighi, F., Kalantari, N., & Mirzahossein, H. (2024). Modeling of the effect of transportation system accessibility on residential real estate prices: The case of Washington metropolitan area, USA. Case Studies on Transport Policy, 18, 101277.

Joseph, J. (n.d.). Retracted: Innovative Analysis of Precision Farming Techniques with Artificial Intelligence. Retrieved October 23, 2025, from http://ouci.dntb.gov.ua/en/works/4kp5q8Wl/

Miškufová, M., Košíková, M., Vašaničová, P., & Kiseľáková, D. (2025). Digitalization and Artificial Intelligence: A Comparative Study of Indices on Digital Competitiveness. Information, 16(4), 286. https://doi.org/10.3390/info16040286

Wang, X., Zhu, X., Anwar, M. K., Meng, Q., & Zhong, N. (2025). Evaluating the role of AI and empirical models for predicting regional economic growth and transportation dynamics: an application of advanced AI approaches. International Journal of Transportation Science and Technology, 19, 156–174. https://doi.org/10.1016/j.ijtst.2024.08.007

Kelley, S., Ovchinnikov, A., Hardoon, D. R., & Heinrich, A. (2022). Antidiscrimination Laws, Artificial Intelligence, and Gender Bias: A Case Study in Nonmortgage Fintech Lending. Manufacturing & Service Operations Management, 24(6), 3039–3059. https://doi.org/10.1287/msom.2022.1108

Mishra, S., Ewing, M. T., & Cooper, H. B. (2022). Artificial intelligence focus and firm performance. Journal of the Academy of Marketing Science, 50(6), 1176–1197. https://doi.org/10.1007/s11747-022-00876-5

Padigar, M., Pupovac, L., Sinha, A., & Srivastava, R. (2022). The effect of marketing department power on investor responses to announcements of AI-embedded new product innovations. Journal of the Academy of Marketing Science, 50(6), 1277–1298. https://doi.org/10.1007/s11747-022-00873-8

Kim, J. H., Kim, M., Kwak, D. W., & Lee, S. (2022). Home-Tutoring Services Assisted with Technology: Investigating the Role of Artificial Intelligence Using a Randomized Field Experiment. Journal of Marketing Research, 59(1), 79–96. https://doi.org/10.1177/00222437211050351

Luo, X., Qin, M. S., Fang, Z., & Qu, Z. (2021). Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions. Journal of Marketing, 85(2), 14–32. https://doi.org/10.1177/0022242920956676

Miklós-Thal, J., & Tucker, C. (2019). Collusion by Algorithm: Does Better Demand Prediction Facilitate Coordination Between Sellers? Management Science, 65(4), 1552–1561. https://doi.org/10.1287/mnsc.2019.3287

Gregory, R. W., Henfridsson, O., Kaganer, E., & Kyriakou, H. (2021). The Role of Artificial Intelligence and Data Network Effects for Creating User Value. Academy of Management Review, 46(3), 534–551. https://doi.org/10.5465/amr.2019.0178

Krakowski, S., Luger, J., & Raisch, S. (2023). Artificial intelligence and the changing sources of competitive advantage. Strategic Management Journal, 44(6), 1425–1452. https://doi.org/10.1002/smj.3387

Sesana, R., Abyaneh, M. D., Golabchi, M., Corsaro, L., Sheibanian, N., & Ozbilen, S. (2025). Machine learning applied to high-entropy alloy coatings process parameters and composition optimization–A case study. In High-Entropy Alloy Coatings-Fundamentals and Applications (pp. 1–33). Routledge, Taylor and Francis. Retrieved October 23, 2025, from https://iris.polito.it/handle/11583/3003222

Kvamsdal, S. F., Belik, I., Hopland, A. O., & Li, Y. (2021). A Machine Learning Analysis of the Recent Environmental and Resource Economics Literature. Environmental and Resource Economics, 79(1), 93–115. https://doi.org/10.1007/s10640-021-00554-0

Li, Q., Yu, C., & Yan, G. (2022). A New Multipredictor Ensemble Decision Framework Based on Deep Reinforcement Learning for Regional GDP Prediction. IEEE Access, 10, 45266–45279. https://doi.org/10.1109/ACCESS.2022.3170905

Talebian, S., Golkarieh, A., Eshraghi, S., Naseri, M., & Naseri, S. (2025). Artificial intelligence impacts on architecture and smart built environments: a comprehensive review. Advances in Civil Engineering and Environmental Science, 2(1), 45–56.

Grennan, J., & Michaely, R. (2021). FinTechs and the Market for Financial Analysis. Journal of Financial and Quantitative Analysis, 56(6), 1877–1907. https://doi.org/10.1017/S0022109020000721

Shafik, W. (2024). The role of artificial intelligence in the emerging digital economy era. Artificial Intelligence Enabled Management Emerging Economic Perspectives, 33, 33–50.

Lee, C.-C., Fang, Y., Quan, S., & Li, X. (2024). Leveraging the power of artificial intelligence toward the energy transition: The key role of the digital economy. Energy Economics, 135, 107654. https://doi.org/10.1016/j.eneco.2024.107654

Zhang, Z. (2023). The impact of the artificial intelligence industry on the number and structure of employments in the digital economy environment. Technological Forecasting and Social Change, 197, 122881. https://doi.org/10.1016/j.techfore.2023.122881

Lim, L., & Siripipatthanakul, S. (2023). A Review of Artificial Intelligence (AI) and ChatGPT Influencing the Digital Economy. International Conference on Research and Development, 2(2), 29–42. https://doi.org/10.47841/icorad.v2i2.139

Hang, H., & Chen, Z. (2022). How to realize the full potentials of artificial intelligence (AI) in digital economy? A literature review. Journal of Digital Economics, 1(3), 180–191. https://doi.org/10.1016/j.jdec.2022.11.003

Cheng, C., & Huang, H. (2022). Evaluation and Analysis of Regional Economic Growth Factors in Digital Economy Based on the Deep Neural Network. Mathematical Problems in Engineering, 2022(1), 1121886. https://doi.org/10.1155/2022/1121886

Zhu, L., Yu, Z., & Zhan, H. (2021). Impact of Industrial Agglomeration on Regional Economy in a Simulated Intelligent Environment Based on Machine Learning. IEEE Access, 9, 20695–20702. https://doi.org/10.1109/ACCESS.2020.3047830

Yazdani, M., Zarate, P., Zavadskas, E. K., & Turskis, Z. (2018). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501–2519. https://doi.org/10.1108/MD-05-2017-0458

Assessing Innovation Strategies in the Digital Economy through Artificial Intelligence-Based Criteria Using CoCoSo method

Authors

DOI:

Keywords:

Abstract

Downloads

References

Downloads

Published

Issue

Section

License

How to Cite

Cover page

template

The Journal is indexed or abstracted in:

Other information: