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Informed by the job characteristics model (JCM) and job crafting theory (JCT), this study aims to investigate the mediating role of meaningfulness at work in the relationship between a growth mindset and in-role performance and moderating role of job crafting in this indirect effect.

To this end, the authors examined the moderated mediation model with 271 corporate trainers enrolled in the largest online community for adult educators in South Korea.

Results showed that the relationship between a growth mindset and in-role performance is positively mediated by meaningfulness at work. Furthermore, job crafting acted as a moderator in this relationship, such that trainers with high levels of job crafting showed a greater in-role performance, while trainers with low levels of job crafting reported a negative indirect effect of a growth mindset.

The current study contributes to the JCM and JCT by suggesting a growth mindset as individual characteristics to promote meaningfulness at work and in-role performance. The study also responds to the calls to expand the mediation mechanisms and boundary conditions of a growth mindset in the workplace. The authors provide important insights into how corporate trainers’ job crafting is crucial in enhancing or impeding their performance and meaningful work.

Technological tools for knowledge management (KM) actively support and enhance knowledge acquisition and sharing in organizations. However, technology for KM has been understudied, especially in terms of disruptive technologies (DTs). There is a need to identify how DTs, which are becoming increasingly important in industry and society, are applied to KM and their impact. This paper aims to examine the current state of technology and DT adoption in KM.

The analysis involves four steps. First, we examine the current status of DT in academia through a keyword co-occurrence network of literature. Second, we analyze the technological convergence (TC) of KM technology through the cooperative patent classification code co-classification analysis of patents. Third, we explore the main topics of KM technologies using BERTopic, and finally, we explore the introduction of DT into KM technologies and suggest potential TC combinations for the future.

KM technologies can be categorized into four main topics (knowledge acquisition, sharing, searching, and transfer), and DT is most often applied to knowledge transfer and acquisition. The DTs that are attracting attention from academia and industry are artificial intelligence, augmented and virtual reality, and blockchain, which have applications in healthcare, supply chain management, and human resource management.

The findings provide useful insights for organizations to build a technology roadmap for KM. They can also improve the rigid mindset of organization employees toward DT adoption and innovation. By adopting a KM system that leverages DT, organizations will be able to manage and operate efficiently and systematically.