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Given recent prominent ethical scandals (e.g. Tesla, Uber) and the increasing demand for ethical management, the importance of business ethics has recently surged. One area that needs further research regards how ethical leaders can foster followers’ organizational commitment. Drawing upon social exchange theory, the current research proposes that ethical leadership relates to follower affective and normative commitment through perceived organizational support (POS). Moreover, based on self-determination theory, we expected follower psychological empowerment to positively moderate the relationship between ethical leadership and commitment components.

Data were collected using a three-wave study among employees from multiple organizations (N = 297) in Canada. Structural equations modeling and bootstrapping analyses were applied to test the hypotheses.

The results showed that ethical leadership was positively related to follower affective and normative commitment through POS. Furthermore, the relationship between ethical leadership and POS was stronger at high levels of empowerment. This moderating effect extended to the indirect relationship between ethical leadership and commitment components.

This study counts among the few investigations that have examined the mechanisms linking ethical leadership to followers’ organizational commitment and boundary conditions associated with this relationship. Moreover, our findings were obtained while controlling for transformational leadership, which highlights the incremental validity of ethical leadership.

This study aims to enhance the classification and processing of online appeals by employing a deep-learning-based method. This method is designed to meet the requirements for precise information categorization and decision support across various management departments.

This study leverages the ALBERT–TextCNN algorithm to determine the appropriate department for managing online appeals. ALBERT is selected for its advanced dynamic word representation capabilities, rooted in a multi-layer bidirectional transformer architecture and enriched text vector representation. TextCNN is integrated to facilitate the development of multi-label classification models.

Comparative experiments demonstrate the effectiveness of the proposed approach and its significant superiority over traditional classification methods in terms of accuracy.

The original contribution of this study lies in its utilization of the ALBERT–TextCNN algorithm for the classification of online appeals, resulting in a substantial improvement in accuracy. This research offers valuable insights for management departments, enabling enhanced understanding of public appeals and fostering more scientifically grounded and effective decision-making processes.