Predicting the Level of Fundamental Changes in the Digital Transformation Process Using Deep Learning Methods
Keywords:
Fundamental changes, digital transformation process, deep learning, classification algorithmsAbstract
This study aims to develop a data-driven predictive framework to determine the level of fundamental organizational changes required for digital transformation using machine learning and deep learning techniques. The research employed a quantitative data-mining approach using a standardized corporate digital transformation dataset obtained from Kaggle. The dataset consisted of 2,000 samples and 23 features representing critical digital transformation indicators, including R&D expenditure, technological intensity, managerial expertise, digital infrastructure, and customer interaction metrics. After data cleaning, normalization, and feature extraction, the dataset was processed using multiple classification algorithms including KNN, SVM, Naive Bayes, Decision Tree, and Deep Multilayer Perceptron (DMLP). Model performance was evaluated through inferential metrics such as Accuracy, Balanced Accuracy, Recall, Precision, F1-score, Specificity, Matthews Correlation Coefficient, Cohen’s Kappa, Log-Loss, AUROC, and AUPRC. All results were calculated based on the mean performance of twenty independent experimental runs to ensure statistical reliability. Inferential analysis demonstrated that the DMLP deep learning model significantly outperformed conventional classification approaches. The proposed model achieved an accuracy of 99.84%, balanced accuracy of 99.94%, F1-score of 99.91%, and AUROC of 99.95%, indicating superior predictive capability. The model also exhibited the lowest Log-Loss values and minimal standard deviation across evaluation metrics, confirming strong statistical stability and generalization ability on unseen data. Comparative results revealed that traditional algorithms, particularly KNN, showed limited effectiveness in capturing complex nonlinear relationships, whereas the deep multilayer architecture effectively extracted hidden patterns associated with organizational digital transformation readiness. The findings indicate that deep learning provides a reliable and high-precision mechanism for predicting organizational readiness and required structural changes in digital transformation initiatives. The proposed model functions not only as a predictive system but also as a strategic decision-support framework capable of guiding digital transformation planning, optimizing resource allocation, and assisting organizations in managing complex digital transition processes.
Downloads
References
Ahmad, M. F., Husin, N. A. A., Ahmad, A. N. A., Abdullah, H., Wei, C. S., & Nawi, M. (2022). Digital transformation: An exploring barriers and challenges practice of artificial intelligence in manufacturing firms in Malaysia. Journal of Advanced Research in Applied Sciences and Engineering Technology. https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/939
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
Butt, A., Imran, F., Helo, P., & Kantola, J. (2024). Strategic design of culture for digital transformation. Long Range Planning, 57(2), 102415. https://doi.org/10.1016/j.lrp.2024.102415
Chen, W., Zhang, L., Jiang, P., Meng, F., & Sun, Q. (2022). Can digital transformation improve the information environment of the capital market? Evidence from the analysts' prediction behaviour. Accounting & Finance, 62(2), 2543-2578. https://doi.org/10.1111/acfi.12873
Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1), 6. https://doi.org/10.1186/s12864-019-6413-7
Chiu, M. C., Huang, Y. J., & Wei, C. J. (2024). Enhancing SMEs digital transformation through machine learning: A framework for adaptive quality prediction. Journal of Industrial Information Integration, 41, 100666. https://doi.org/10.1016/j.jii.2024.100666
colabsss. (2025). Corporate Digital Transformation Dataset. https://www.kaggle.com/datasets/colabsss/corporate-digital-transformation-dataset
Çorbacıoğlu, Ş. K., & Aksel, G. (2023). Receiver operating characteristic curve analysis in diagnostic accuracy studies: A guide to interpreting the area under the curve value. Turkish Journal of Emergency Medicine, 23(4), 195-198. https://doi.org/10.4103/tjem.tjem_182_23
Corso, G., Stark, H., Jegelka, S., Jaakkola, T., & Barzilay, R. (2024). Graph neural networks. Nature Reviews Methods Primers, 4(1), 17. https://doi.org/10.1038/s43586-024-00294-7
Côté, P.-O., Nikanjam, A., Ahmed, N., Humeniuk, D., & Khomh, F. (2024). Data cleaning and machine learning: a systematic literature review. Automated Software Engineering, 31(2), 54.
Côté, P. O., Nikanjam, A., Ahmed, N., Humeniuk, D., & Khomh, F. (2024). Data cleaning and machine learning: a systematic literature review. Automated Software Engineering, 31(2), 54. https://doi.org/10.1007/s10515-024-00453-w
Cui, J., Wan, Q., & Shin, S. (2025). Human-AI integration and sound-vibration technology-driven enterprise digital transformation: The mediating role of technological innovation. Sound & Vibration, 59(1), 1733. https://doi.org/10.59400/sv1733
Derakhshan Barjoei, P., Zamani Boroujeni, F., Davami, F., & Habibinikou, H. (2025). A 3D approach design for aircraft landing on runways using Convolutional Neural Networks. Computational Algorithms and Numerical Dimensions. https://www.journal-cand.com/article_221731.html
Einy, S., Oz, C., & Navaei, Y. D. (2021a). The anomaly‐and signature‐based IDS for network security using hybrid inference systems. Mathematical Problems in Engineering, 2021(1), 6639714. https://doi.org/10.1155/2021/6639714
Einy, S., Oz, C., & Navaei, Y. D. (2021b). Network Intrusion Detection System Based on the Combination of Multiobjective Particle Swarm Algorithm‐Based Feature Selection and Fast‐Learning Network. Wireless Communications and Mobile Computing, 2021(1), 6648351. https://doi.org/10.1155/2021/6648351
Einy, S., Saygin, H., Hivehchi, H., & Dorostkar Navaei, Y. (2022). Local and deep features based convolutional neural network frameworks for brain MRI anomaly detection. Complexity, 2022(1), 3081748. https://doi.org/10.1155/2022/3081748
Einy, S., Sen, E., Saygin, H., Hivehchi, H., & Dorostkar Navaei, Y. (2023). Local Binary Convolutional Neural Networks′ Long Short‐Term Memory Model for Human Embryos′ Anomaly Detection. Scientific Programming, 2023(1), 2426601. https://doi.org/10.1155/2023/2426601
Elia, G., Solazzo, G., Lerro, A., Pigni, F., & Tucci, C. L. (2024). The digital transformation canvas: A conceptual framework for leading the digital transformation process. Business Horizons, 67(4), 381-398. https://doi.org/10.1016/j.bushor.2024.03.007
Eom, T., Woo, C., & Chun, D. (2024). Predicting an ICT business process innovation as a digital transformation with machine learning techniques. Technology Analysis & Strategic Management, 36(9), 2271-2283. https://doi.org/10.1080/09537325.2022.2132927
Fu, T., Zhang, J., Sun, R., Huang, Y., Xu, W., Yang, S., & Chen, H. (2024). Optical neural networks: progress and challenges. Light: Science & Applications, 13(1), 263. https://doi.org/10.1038/s41377-024-01590-3
Gołąb-Andrzejak, E. (2023). AI-powered digital transformation: Tools, benefits and challenges for marketers-case study of LPP. Procedia Computer Science, 219, 397-404. https://doi.org/10.1016/j.procs.2023.01.305
Guarda, T., Balseca, J., García, K., González, J., Yagual, F., & Castillo-Beltran, H. (2021). Digital transformation trends and innovation. IOP Conference Series: Materials Science and Engineering,
Hendrawan, S. A., Chatra, A., Iman, N., Hidayatullah, S., & Suprayitno, D. (2024). Digital transformation in MSMEs: Challenges and opportunities in technology management. Jurnal Informasi Dan Teknologi, 141-149. https://doi.org/10.60083/jidt.v6i2.551
Kalinina, I., Gozhyj, A., Bidyuk, P., Gozhyi, V., Korobchynskyi, M., & Nadraga, V. (2025). A Systematic Approach to Data Normalization and Standardization in Machine Learning Problems. Lecture Notes in Data Engineering, Computational Intelligence, and Decision-Making, Volume 2: 2024 International Scientific Conference" Intelligent Systems of Decision-Making and Problems of Computational Intelligence", Proceedings,
Kim, K., & Kim, B. (2022). Decision-making model for reinforcing digital transformation strategies based on artificial intelligence technology. Information, 13(5), 253. https://doi.org/10.3390/info13050253
Kitsios, F., & Kamariotou, M. (2021). Artificial intelligence and business strategy towards digital transformation: A research agenda. Sustainability, 13(4), 2025. https://doi.org/10.3390/su13042025
Klopov, I., Shapurov, O., Voronkova, V., Nikitenko, V., Oleksenko, R., Khavina, I., & Chebakova, Y. (2023). Digital Transformation of Education Based on Artificial Intelligence. Tem Journal, 12(4), 2625. https://doi.org/10.18421/TEM124-74
Koli, S. (2025). Shree-L1: A dynamic CNN architecture for efficient tumor classification in medical imaging. Big Data and Computing Visions, 5(2), 94-101. https://doi.org/10.22105/bdcv.2024.491412.1219
Korobchynskyi, M., & Nadraga, V. (2025). A Systematic Approach to Data Normalization and Standardization in Machine Learning Problems. Lecture Notes in Data Engineering, Computational Intelligence, and Decision-Making, Volume 2: 2024 International Scientific Conference" Intelligent Systems of Decision-Making and Problems of Computational Intelligence”, Proceedings,
Kraus, S., Durst, S., Ferreira, J. J., Veiga, P., Kailer, N., & Weinmann, A. (2022). Digital transformation in business and management research: An overview of the current status quo. International Journal of Information Management, 63, 102466. https://doi.org/10.1016/j.ijinfomgt.2021.102466
Kusuma, A. R., Syarief, R., Sukmawati, A., & Ekananta, A. (2024). Factors influencing the digital transformation of sales organizations in Indonesia. Heliyon, 10(5). https://www.researchgate.net/publication/378702985_Factors_influencing_the_digital_transformation_of_sales_organizations_in_Indonesia
Lamtar-Gholipoor, M., Fakheri, S., & Alimoradi, M. (2024). Artificial neural network TSR for optimization of actinomycin production. Big Data and Computing Visions, 4(1), 57-66. https://doi.org/10.22105/bdcv.2024.474793.1184
Lamtar Gholipoor, M., Alimoradi, M., & Fakheri, S. (2024). A Novel Metaheuristic Approach Inspired by Trees Social Relationships and Models for Fermentation Medium. Metaheuristic Algorithms with Applications, 1(1), 1-11. https://www.researchgate.net/publication/383038719_Metaheuristic_Algorithms_with_Applications_A_Novel_Metaheuristic_Approach_Inspired_by_Trees_Social_Relationships_and_Models_for_Fermentation_Medium_Citation
Lemieux, F. (2023). Digital transformation and artificial intelligence: opportunities and challenges. In Digital Strategies And Organizational Transformation (pp. 103-117). https://doi.org/10.1142/9789811271984_0006
Manzari Vahed, N., Chaharsoughi, S. K., & Ashnavar, H. (2025). The Fairness Analysis of the Supply Chain in the Saipa Automotive Group: Examining Deviations and Supplier Performance Using a Neural Network Approach. Annals of Process Engineering and Management, 2(3), 131-142. https://www.apem.reapress.com/journal/article/view/39
Mao, A., Mohri, M., & Zhong, Y. (2023). Cross-entropy loss functions: Theoretical analysis and applications. International conference on Machine learning,
Mhlanga, D. (2023). Digital transformation education, opportunities, and challenges of the application of ChatGPT to emerging economies. Education Research International, 2023(1), 7605075. https://doi.org/10.1155/2023/7605075
Mienye, I. D., Swart, T. G., & Obaido, G. (2024). Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications. Information, 15(9), 517. https://doi.org/10.3390/info15090517
Momeni, A., Rahmani, B., Scellier, B., Wright, L. G., McMahon, P. L., Wanjura, C. C., & Fleury, R. (2025). Training of physical neural networks. Nature, 645(8079), 53-61. https://doi.org/10.1038/s41586-025-09384-2
Momeni, A., Rahmani, B., Scellier, B., Wright, L. G., McMahon, P. L., Wanjura, C. C., Li, Y., Skalli, A., Berloff, N. G., Onodera, T., Oguz, I., Morichetti, F., del Hougne, P., Le Gallo, M., Sebastian, A., Mirhoseini, A., Zhang, C., Marković, D., Brunner, D., . . . Fleury, R. (2025). Training of physical neural networks. Nature, 645(8079), 53-61. https://doi.org/10.1038/s41586-025-09384-2
Naidu, G., Zuva, T., & Sibanda, E. M. (2023). A review of evaluation metrics in machine learning algorithms. https://doi.org/10.1007/978-3-031-35314-7_2
Naidu, G., Zuva, T., & Sibanda, E. M. (2023). A review of evaluation metrics in machine learning algorithms. Computer science on-line conference,
Nanehkaran, Y., Licai, Z., Chen, J., Jamel, A. A., Shengnan, Z., Navaei, Y. D., & Aghbolagh, M. A. (2022). Anomaly Detection in Heart Disease Using a Density‐Based Unsupervised Approach. Wireless Communications and Mobile Computing, 2022(1), 6913043.
Nanehkaran, Y., Licai, Z., Chen, J., Zhongpan, Q., Xiaofeng, Y., Navaei, Y. D., & Einy, S. (2022). Diagnosis of chronic diseases based on patients' health records in IoT healthcare using the recommender system. Wireless Communications and Mobile Computing, 2022(1), 5663001. https://doi.org/10.1155/2022/5663001
Nanehkaran, Y., Licai, Z., Chen, J., Zhongpan, Q., Xiaofeng, Y., Navaei, Y. D., & Einy, S. (2022). Diagnosis of chronic diseases based on patients’ health records in IoT healthcare using the recommender system. Wireless Communications and Mobile Computing, 2022(1), 5663001.
Navaei, Y. D., Rezvani, M. H., & Moghaddam, A. M. E. (2024). A novel neighborhood-based importance measure for social network influence maximization using NSGA-III. 2024 10th International Conference on Artificial Intelligence and Robotics (QICAR),
Omol, E. J. (2024). Organizational digital transformation: from evolution to future trends. Digital Transformation and Society, 3(3), 240-256. https://doi.org/10.1108/DTS-08-2023-0061
Paul, J., Ueno, A., Dennis, C., Alamanos, E., Curtis, L., Foroudi, P., & Marvi, R. (2024). Digital transformation: A multidisciplinary perspective and future research agenda. International Journal of Consumer Studies, 48(2), e13015. https://doi.org/10.1111/ijcs.13015
Perifanis, N. A., & Kitsios, F. (2023). Investigating the influence of artificial intelligence on business value in the digital era of strategy: A literature review. Information, 14(2), 85. https://doi.org/10.3390/info14020085
Rau, G., & Shih, Y. S. (2021). Evaluation of Cohen's kappa and other measures of inter-rater agreement for genre analysis and other nominal data. Journal of English for Academic Purposes, 53, 101026. https://doi.org/10.1016/j.jeap.2021.101026
Sadr, H., Zahiri, Z., Nazari, M., Bahadori, M. H., Ashoobi, M. T., & Hoseini, A. (2025). Optimizing clinical decisions in reproductive medicine with a hybrid AI predictive model. Big Data and Computing Visions, 5(4), 287-306. https://www.bidacv.com/article_233609.html
Scabini, L. F., & Bruno, O. M. (2023). Structure and performance of fully connected neural networks: Emerging complex network properties. Physica A: Statistical Mechanics and its Applications, 615, 128585.
Shehadeh, M. (2024). Digital transformation: a catalyst for sustainable business practices. In Technological Innovations for Business, Education and Sustainability (pp. 29-45). https://doi.org/10.1108/978-1-83753-106-620241003
Vial, G. (2021). Understanding digital transformation: A review and a research agenda. In Managing digital transformation (pp. 13-66). https://doi.org/10.4324/9781003008637-4
Xinxian, C., & Jianhui, C. (2022). Digital transformation and financial risk prediction of listed companies. Computational Intelligence and Neuroscience, 2022(1), 7211033. https://doi.org/10.1155/2022/7211033
Zhang, J., & Chen, Z. (2024). Exploring human resource management digital transformation in the digital age. Journal of the Knowledge Economy, 15(1), 1482-1498. https://doi.org/10.1007/s13132-023-01214-y
Zhang, X., Xu, Y. Y., & Ma, L. (2023). Information technology investment and digital transformation: the roles of digital transformation strategy and top management. Business Process Management Journal, 29(2), 528-549. https://doi.org/10.1108/BPMJ-06-2022-0254
Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., & Parmar, M. (2024). A review of convolutional neural networks in computer vision. Artificial Intelligence Review, 57(4), 99. https://doi.org/10.1007/s10462-024-10721-6
Zhu, C., Liu, X., & Chen, D. (2024). Prediction of digital transformation of manufacturing industry based on interpretable machine learning. PLoS One, 19(3), e0299147. https://doi.org/10.1371/journal.pone.0299147
