The adoption of digital technologies (DTs) in the workplace has emerged as a core driver of organizational effectiveness, and many studies have explored the intrinsic connection between the two. However, due to the wide range of subdivisions of employee performance, existing studies present inconsistent research conclusions on the implementation effects of DTs and lack a systematic review of their impact on employee psychology and behavior for large sample data. To address this issue, employing a random-effects model and a psychometric meta-analysis approach based on subgroup and meta-regression analyses, this study examines 106 empirical studies, comprising 119 effect sizes. The findings reveal that DTs exhibit a “double-edged sword” effect. On the bright side, DTs significantly enhance task performance, innovation performance, employee engagement, job satisfaction, and job efficacy. On the dark side, DTs aggravate service sabotage, withdrawal behavior, job burnout, and work anxiety and have a suppressive effect on job well-being, while their influence on turnover intention is non-significant. Furthermore, this study identifies the moderating effects of industry characteristics, technology usage types, and demographic factors on the relationships between DTs and behavioral and psychological outcomes. The research conclusions help clarify the logical relationship between DTs and employee psychology and behavior and provide explanations for the differentiated research conclusions of previous studies. This study provides information for scientific management decisions regarding DTs in the workplace.

As digitalization and environmental sustainability advance globally, small and medium-sized enterprises (SMEs) are facing transformative pressures as well as emerging opportunities. Rapid digital innovation promotes intelligent production, cost reduction, efficiency gains, and improved management practices, while green development mandates emphasize energy conservation, emissions reduction, and sustainable supply chains. Amid concurrent digital and green transformations, SMEs are leveraging digital technologies to bolster green learning and enhance sustainable product development. This study investigates the digital–green learning orientation (DGLO) and its influence on ambidextrous research and development (R&D) capabilities, which in turn shape sustainable product development performance (SPDP). Drawing on survey data from 306 SMEs in eastern and southern China, multiple regression analysis was employed to assess the relationships between DGLO, ambidextrous R&D capabilities, and SPDP. The findings reveal that DGLO significantly enhances SPDP. Moreover, DGLO promotes SPDP through both exploitative and exploratory R&D capabilities, with each playing a complementary role.

With the rapid expansion of green innovation and the increasing emphasis on innovation quality, concerns have emerged regarding whether firms engage in symbolic green innovation activities rather than substantive technological advancement. Understanding how policy interventions can curb green innovation bubbles (GIB) has therefore become an important research issue. Using data on Chinese listed companies from 2005 to 2024, this study employs a difference-in-differences (DID) model to investigate the impact of the patent pledge policy (PPP) on green innovation bubbles and its underlying mechanisms. The results show that the patent pledge policy reduces green innovation bubbles by restraining the TMT promotion focus (TMTpro), strengthening the TMT prevention focus (TMTpre), and alleviating financing constraints (FC). Heterogeneity analyses further indicate that the inhibitory effect of the patent pledge policy on green innovation bubbles is more pronounced among firms located in eastern regions, central region, non-heavily polluting firms, and firms in the mature stage and decline stage of the life cycle. This study extends research on the economic consequences of the patent pledge policy from the perspectives of green innovation authenticity and resource allocation efficiency and provides theoretical implications for improving intellectual property finance policy, optimizing green innovation evaluation systems, and enhancing the quality of corporate green technological innovation.