In the era of digital transformation, the role of digital leadership in companies is continuously demanded and its importance is increasingly emphasized. In relation to such research background, this study focuses on digital leadership, a new form of leadership rather than traditional leadership as suggested in previous studies, as a way to promote organizational sustainability and provide the moderated mediation model for organizational sustainability in a rapidly changing innovative business environment in the digital age. The main contribution of this study is to fill the gap in previous research and expand the research field on leadership types and organizational sustainability. To achieve our research goals, we conduct empirical study and the questionnaire was distributed to 552 organizational members from Chinese small and medium-sized information technology enterprises. The results presented that digital leadership had positive influence on digital organizational culture, digital capability, and Organizational sustainability. In addition, digital organizational culture and digital capability significantly mediated the relationship between digital leadership and organizational sustainability. Absorptive capacity significantly moderated the mediating effect of digital capability between digital leadership and organizational sustainability. Finally, innovation atmosphere significantly moderated the impact of digital organizational culture on organizational sustainability. The result indicates that digital leadership significantly enhances organizational sustainability both directly and indirectly through digital capabilities and digital organizational culture. Furthermore, absorptive capacity and innovation atmosphere positively moderate these relationships, with high levels of absorptive capacity and innovation atmosphere amplifying the impact of digital leadership on sustainability. This study contributes to the literature by providing a comprehensive understanding of the mechanisms through which digital leadership drives organizational sustainability, offering practical insights for SMEs undergoing digital transformation.

The direct positive relationship between empowering leadership and subordinate empowerment is well established. However, leader–member exchange (LMX) research, which concerns a leader’s relationship-building with subordinates in a work unit, suggests that the direct leader empowering–subordinate empowerment association may be more complex than understood in the current literature. Accordingly, this study examined LMX theory-based mediation and moderation processes occurring between empowering leadership and subordinate empowerment. In a field study employing 132 administrative workers in 26 work groups, as expected, an individual subordinate’s perceived LMX mediated the positive effects of empowering leadership on the subordinate’s psychological empowerment. In addition, LMX differentiation cross-level moderated the linkage between empowering leadership and perceived LMX. Together, study findings suggest that subordinates’ perceived LMX in a dyadic relationship with a leader and in a work group needs to be carefully considered in empowering leadership processes.

This study focuses on the field of voice forgery detection, which is increasing in importance owing to the introduction of advanced voice editing technologies and the proliferation of smartphones. This study introduces a unique dataset that was built specifically to identify forgeries created using the “Mix Paste” technique. This editing technique can overlay audio segments from similar or different environments without creating a new timeframe, making it nearly infeasible to detect forgeries using traditional methods. The dataset consists of 4665 and 45,672 spectrogram images from 1555 original audio files and 15,224 forged audio files, respectively. The original audio was recorded using iPhone and Samsung Galaxy smartphones to ensure a realistic sampling environment. The forged files were created from these recordings and subsequently converted into spectrograms. The dataset also provided the metadata of the original voice files, offering additional context and information that could be used for analysis and detection. This dataset not only fills a gap in existing research but also provides valuable support for developing more efficient deep learning models for voice forgery detection. By addressing the “Mix Paste” technique, the dataset caters to a critical need in voice authentication and forensics, potentially contributing to enhancing security in society.