نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مدیریت فناوری اطلاعات، واحد بین المللی کیش، دانشگاه آزاد اسلامی، کیش، ایران.
2 گروه مدیریت، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران.
3 گروه مدیریت صنعتی،واحد تهران غرب، دانشگاه آزاد اسلامی،تهران، ایران.
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Abstract
Purpose: In today’s competitive and complex environment, digital transformation has emerged as a strategic imperative for organizations. This transformation not only enhances organizational agility, innovation, and resilience, but also paves the way toward achieving sustainable competitive advantage. However, success in implementing digital transformation depends on multiple factors, the identification, analysis, and modeling of which remain a major challenge in the fields of management, information technology, and data science. Despite investing in advanced technologies, many organizations fail to achieve desired outcomes—highlighting the multifaceted and intricate nature of the digital transformation process. In response to this scientific and practical gap, the present study aims to develop a predictive model based on machine learning to examine the moderating role of organizational agility in digital transformation success. The proposed model serves as an analytical tool for assessing organizational readiness and forecasting transformation outcomes, thereby supporting strategic decision-making at both macro and operational levels.
Method: This study introduces a custom-designed Organizational Agility Index (OAI ), which incorporates a set of key variables including organizational size, timing of digital technology adoption, level of digital skill training among employees, type and scope of digital tools in use, and changes in customer interaction patterns. The OAI was integrated into the model as a moderating variable to evaluate its influence on the relationship between digital transformation factors and overall success. Standardized digital transformation data were collected from reliable sources and merged with the OAI. These combined datasets were then fed into a suite of machine learning algorithms, including ensemble methods such as XGBoost, Random Forest, Voting, Bagging, and AdaBoost. The objective was to assess the predictive accuracy of these models and compare their performance across different organizational scenarios. Evaluation metrics such as Accuracy, F1 Score, and Confusion Matrix were employed to measure model effectiveness. Additionally, cross-validation techniques were applied to ensure the generalizability and robustness of the trained models against real-world data.
Findings: The analysis revealed that ensemble learning models—particularly XGBoost—demonstrated superior performance in predicting digital transformation success. XGBoost achieved the highest accuracy (0.9703) and F1 score (0.9703), indicating its strong capability in correctly classifying successful and unsuccessful transformation cases. While other algorithms also performed reasonably well, XGBoost emerged as the most precise and effective model in this study. Furthermore, the moderating role of the Organizational Agility Index was validated, showing that agility significantly enhances model performance. Supplementary analyses indicated that organizations with higher agility levels are more likely to succeed in digital transformation compared to less agile counterparts. These findings underscore the importance of organizational factors alongside technological investments. Sensitivity analysis further revealed that digital skill training and changes in customer engagement were the most influential variables in boosting agility and transformation success.
Conclusion: The findings of this study highlight that integrating organizational indicators with advanced machine learning algorithms can provide a powerful framework for strategic decision-making in digital transformation initiatives. Organizational agility, in particular, plays a critical role in improving the accuracy of predictive models and can help managers better understand internal capabilities to navigate transformation paths more effectively. The proposed model not only offers predictive insights into transformation success but also serves as a practical framework for assessing organizational readiness in the face of technological change. Moreover, it lays the groundwork for developing analytical tools in future research and can inform policy design, resource allocation, and risk management strategies related to digital transformation. Ultimately, this study emphasizes the value of combining data-driven approaches with organizational insight, offering a novel pathway for analysis and decision-making in the dynamic and complex landscape of digital transformation.
Keywords: Digital Transformation, Digital transformation success, Organizational Agility Index, Machine Learning, Digital Technologies, Moderating Variable
کلیدواژهها [English]
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