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<p>Effective internal communication is essential for employee engagement and organizational success, yet prior studies have primarily relied on traditional statistical methods that often fail to capture deeper patterns. This study investigates the influence of formal and informal communication structures on organizational effectiveness and employee engagement. Data were collected from 315 employees in Ghaziabad, Uttar Pradesh, India using a structured questionnaire. The analysis combined statistical and machine learning approaches, applying one-way ANOVA alongside models such as Logistic Regression, Decision Trees, Random Forest, SVM, Gradient Boosting, XGBoost, Naïve Bayes, and KNN. The ANOVA results indicated no significant difference in employee satisfaction between formal and informal communication structures, suggesting that both contribute meaningfully to organizational outcomes. In contrast, machine learning models revealed predictive patterns, with Logistic Regression using regularization achieving 100% accuracy and ensemble methods also performing strongly. These findings demonstrate that while both structures are equally effective, machine learning provides deeper insights, enabling organizations to design adaptive communication strategies that enhance engagement and overall effectiveness.</p>

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Assessing the Impact of Communication Structures on Organizational Effectiveness and Employee Engagement Using Machine Learning

  • 期刊论文

  • 发表日期：

    2026年4月6日

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  • Shaily Tyagi,
  • Aditya Prakash

摘要

Effective internal communication is essential for employee engagement and organizational success, yet prior studies have primarily relied on traditional statistical methods that often fail to capture deeper patterns. This study investigates the influence of formal and informal communication structures on organizational effectiveness and employee engagement. Data were collected from 315 employees in Ghaziabad, Uttar Pradesh, India using a structured questionnaire. The analysis combined statistical and machine learning approaches, applying one-way ANOVA alongside models such as Logistic Regression, Decision Trees, Random Forest, SVM, Gradient Boosting, XGBoost, Naïve Bayes, and KNN. The ANOVA results indicated no significant difference in employee satisfaction between formal and informal communication structures, suggesting that both contribute meaningfully to organizational outcomes. In contrast, machine learning models revealed predictive patterns, with Logistic Regression using regularization achieving 100% accuracy and ensemble methods also performing strongly. These findings demonstrate that while both structures are equally effective, machine learning provides deeper insights, enabling organizations to design adaptive communication strategies that enhance engagement and overall effectiveness.