In search of sustainable strategies to improve employees’ work engagement without harming their long-term performance in the VUCA era, this study examines the impact path of subordinate moqi on employees’ work engagement based on leader–member exchange theory and organizational support theory. A total of 322 R&D staff from high-tech enterprises in Shanghai, Beijing, Shenzhen, and other cities participated in the study. The data analysis reveals that the leader–member exchange partially mediates between subordinate moqi and work engagement. Moreover, both supervisor’s organizational embodiment and perceived organizational support can positively moderate the impact of subordinate moqi on leader–member exchange. The empirical results enrich the intermediary mechanism between subordinate moqi and work engagement. Employees with subordinate moqi have better chances of meeting their supervisors’ implicit expectations, leading to a higher relationship quality with followers and increasing enthusiasm for work. The current study clarifies the theoretical and practical significance of subordinate moqi in establishing high-quality work relationships and sustainable development of organizations’ human resources.

Hearing impairments affect approximately 1.5 billion individuals worldwide, highlighting the critical need for effective communication tools between deaf and hearing populations. Traditional sign language translation (SLT) models predominantly rely on gloss-based methods, which convert visual sign language inputs into intermediate gloss sequences before generating textual translations. However, these methods are constrained by their reliance on extensive annotated data, susceptibility to error propagation, and inadequate handling of low-frequency or unseen sign language vocabulary, thus limiting their scalability and practical application. Drawing upon multimodal translation theory, this study proposes the open-vocabulary sign language translation (OVSLT) method, designed to overcome these challenges by integrating open-vocabulary principles. OVSLT introduces two pivotal modules: Enhanced Caption Generation and Description (CGD), and Grid Feature Grouping with Advanced Alignment Techniques. The Enhanced CGD module employs a GPT model enhanced with a Negative Retriever and Semantic Retrieval-Augmented Features (SRAF) to produce semantically rich textual descriptions of sign gestures. In parallel, the Grid Feature Grouping module applies Grid Feature Grouping, contrastive learning, feature-discriminative contrastive loss, and balanced region loss scaling to refine visual feature representations, ensuring robust alignment with textual descriptions. We evaluated OVSLT on the PHOENIX-14T and CSLDaily datasets. The results demonstrated a ROUGE score of 29.6% on the PHOENIX-14T dataset and 30.72% on the CSLDaily dataset, significantly outperforming existing models. These findings underscore the versatility and effectiveness of OVSLT, showcasing the potential of open-vocabulary approaches to surpass the limitations of traditional SLT systems and contribute to the evolving field of multimodal translation.