Predicting E-Leadership Effectiveness Through Emotional Intelligence, Virtual Communication Quality, and Innovation Capability Using Artificial Intelligence
Keywords:
E-leadership effectiveness, emotional intelligence, virtual communication quality, innovation capability, artificial intelligence, digital leadership, virtual organizationsAbstract
The present study aimed to predict e-leadership effectiveness through emotional intelligence, virtual communication quality, and innovation capability using artificial intelligence among managers and employees working in technology-oriented organizations in Tehran. This study was conducted using a descriptive-correlational design with a predictive approach. The statistical population consisted of managers, supervisors, and employees working in technology-oriented organizations and digital enterprises in Tehran during 2026. A total of 368 participants were selected through multistage cluster sampling. Data were collected using the E-Leadership Effectiveness Scale, Emotional Intelligence Questionnaire, Virtual Communication Quality Scale, and Innovation Capability Questionnaire. Descriptive statistics, Pearson correlation coefficients, multiple regression analysis, structural equation modeling, and artificial intelligence-based predictive modeling were used for data analysis. Statistical analyses were performed using SPSS-27 and AMOS-26 software. The findings demonstrated that emotional intelligence, virtual communication quality, and innovation capability were positively and significantly associated with e-leadership effectiveness (p < 0.01). Multiple regression analysis revealed that the predictor variables collectively explained 66% of the variance in e-leadership effectiveness (R² = 0.66). Among the predictors, virtual communication quality showed the strongest predictive effect (β = 0.39), followed by innovation capability (β = 0.35) and emotional intelligence (β = 0.31). Structural equation modeling confirmed the adequacy of the proposed conceptual model, with fit indices indicating satisfactory model fitness (CFI = 0.95, RMSEA = 0.061). Furthermore, the artificial intelligence-based predictive model demonstrated high predictive accuracy, with validation accuracy reaching 88.7%, indicating the effectiveness of machine learning techniques in predicting e-leadership effectiveness in digital organizational environments. The results indicated that emotional intelligence, virtual communication quality, and innovation capability play substantial roles in enhancing e-leadership effectiveness within technology-mediated organizations. Effective digital leadership depends not only on technological proficiency but also on leaders’ emotional competencies, communication quality, and innovation-oriented capabilities. The findings further demonstrated that artificial intelligence-based predictive approaches can provide highly accurate models for understanding leadership performance in virtual environments. Therefore, organizations should prioritize leadership development programs focused on emotional intelligence, communication management, and innovation enhancement to improve leadership effectiveness and organizational adaptability in the digital era.
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Copyright (c) 2026 Amin Nazari, Mehrdad Tahmasebi Borjoui, Mohammad Keshtkar (Author); Majid Esfandiar (Corresponding author); Mohsen Najafi (Author)
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