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This paper aims to provide a comprehensive understanding of the supply chain learning (SCL)–performance relationship based on the existing empirical evidence.

We sampled 54 empirical studies on the SCL–performance relationship. We proposed a conceptual research framework and adopted a meta-analytical approach to analyse the SCL–performance relationship.

The results of the meta-analysis confirm the positive effects of SCL on the performance of both firms and supply chains. In addition, building on the knowledge-based view, we found that learning from customers has a stronger positive effect on performance than does learning from suppliers, while joint learning has a stronger positive effect on performance than does absorptive learning. Business knowledge had a greater effect on performance than did general knowledge, process knowledge or technical knowledge, while explicit knowledge had a stronger effect than tacit knowledge. Moreover, the SCL–performance relationship is moderated by performance measure and industry type but not by regional economic development, highlighting the broad applicability of SCL.

This study is the first meta-analysis on the SCL–performance relationship. It differentiates between learning from customers and learning from suppliers, examines a more comprehensive list of performance measures and tests five moderators to the main effect, significantly contributing to the SCL literature.

Following the contextual approach to intellectual capital, this study analyzed the specific types of external relational capital that foster product/service, process and managerial innovativeness in organic farming as key drivers of sustainable food production.

Survey data from 358 organically certified Spanish farmers were analyzed using structural equation modeling based on partial least squares. A total of three models, one for each type of innovativeness, were developed to analyze the impact of external relational capital. These models took into account four specific types of relational capital: vertical relationships, horizontal relationships, relationships with government institutions and relationships with knowledge-intensive institutions.

Although relational capital and innovativeness are clearly underdeveloped, knowledge generated through and embedded in external relationships plays a substantial role in promoting innovativeness in organic farming. Moreover, depending on the type of innovation to be developed, the type of external relational capital that is relevant differs.

This study's findings indicate that organic farmers prioritize process innovation over product/service and managerial innovation. For the latter categories, building relationships with customers, consumers and government institutions is key. Policymakers should encourage farmer-engaging socialization spaces that emphasize family farms and their knowledge contribution.

Past studies have examined the overall degree of association between external relational capital and innovation, often overlooking the nuances of contextual factors. In contrast, this research delves into the unique contributions of knowledge sourced from various external relationships, focusing specifically on how these relationships influence different types of innovation within the specific context of organic farming.

In recent years, counterproductive knowledge behavior (CKB) and its types have received increasing interest in knowledge management as the degree of knowledge sharing and innovation in enterprises continues to increase. A rapidly growing number of studies have shed light on the important antecedents and consequences of employees’ CKB. However, the various labels, conceptualizations and operationalizations of CKB have fragmented this body of research. This study aims to systematically integrate the effects of the six types of organizational characteristics on CKB and further draws more general conclusions based on the results of previous studies.

Based on a survey of 103 effect values responsible for 52 CKB samples, the authors use the ABC theory to explore the effects of the six types of organizational characteristics on CKB. Moderator analysis were performed to resolve inconsistencies in empirical studies and understand the contexts under which CKB has the strongest or weakest effect.

The results showed that task interdependence and a positive organizational atmosphere, in general, negatively affect employees’ CKB in the moderation analysis. In contrast, workplace discomfort, negative organizational atmosphere, internal competition and time pressure positively and partly affect employees’ CKB. The direction and magnitude of these effects were affected by emotional factors, knowledge personnel types and sample sources. Discussing the theoretical, methodological and practical implications of these findings can offer a guiding framework for future research.

Better control of employees’ CKB is not achieved by adjusting organizational characteristics alone but by combining personal characteristics and mood changes with it to balance organizational characteristics and CKB. Furthermore, the large-sample joint study integrated the conceptual definition of CKB. The multivariate data study provided more reliable conclusions and a solid theoretical foundation for CKB research areas.

Previous research has analyzed the consequence of network stability; however, little is known about how partner type diversity influence network stability in R&D network. Based on knowledge-based view and social network theory, the purpose of this paper is to unravel the internal mechanisms between partner type diversity and network stability through the mediating role of knowledge recombination in R&D network.

The authors collected an unbalanced panel patent data set from information communication technology industry for the period 1994–2016. Then, the authors tested the different dimensions of partner type variety and its relevance in the R&D network and the mediating role of knowledge recombination through adopting the multiple linear regression.

Results indicate an inverted U-shaped relationship between partner type diversity (variety and relevance) and network stability, whereas knowledge recombination partially mediate these relationships.

From the perspective of R&D networks, this paper explores that there are the under-researched phenomena the antecedent of network stability through nodal attributes (i.e. partner type variety and partner type relevance). Moreover, this paper empirically examined the mediating role of knowledge recombination in the partner type diversity–network stability relationships. The novel perspective allows focal firm to recognize importance of nodal attributes, which are critical to fully excavate the potential capabilities of cooperating partners in R&D network.

This chapter makes a case for extending institutional preservation strategies to the entire landscape of knowledge capital. First, the authors define the three primary types of capital – physical, financial, and knowledge. Knowledge capital is further broken down into three categories – human, structural, and relational. The individual types of knowledge capital are defined, along with their variant economic properties and behaviors. The challenges these variations present for preservation are discussed. The authors also highlight these assets’ significant opportunities for curating new knowledge. Each type of knowledge capital is described, along with the preservation challenges and the curation opportunities.

The purpose of this paper is to identify the different aspects of knowledge and how they associate with information security awareness (ISA). The paper also explores how ISA differs based on demographic characteristics.

Survey data was collected from 609 respondents in Malaysia.

The results show that increasing access to informal, multimedia learning mediums, declarative, schematic and strategic knowledge positively impacts an individual's ISA, whereas textual learning medium decreases the ISA. Respondents with different education levels significantly prefer different types of knowledge. Males learn better for ISA with schematic and strategic knowledge compared to females.

The research provides implications for governments and organizations in designing effective ISA campaigns.

Studies show that ISA is crucial in improving information systems policy compliance behavior. The literature has examined various topics ranging from the factors influencing the ISA to how ISA impacts information security behavior. However, there is a lack of study on how different aspects of knowledge impact ISA. This study identified various knowledge aspects from the literature and grouped them into the source, type of knowledge, emotion toward knowledge and learning medium.

This study aims to adopt the microfoundation perspective to investigate undesirable knowledge rejection by individuals in organizations in the context of counterproductive knowledge behavior (CKB). The paper advances a conceptual framework of the conditions of knowledge rejection by individuals and their respective knowledge rejection behavior types.

This study reviews the limited literature on knowledge rejection and outline a set of antecedents leading to rejecting knowledge as well as a set of different types of knowledge rejection behaviors. This study reviews and synthesizes articles on knowledge rejection from a microfoundation perspective.

The proposed conceptual framework specifies four particular conditions for knowledge rejection and outlines four respective knowledge rejection behavior types resulting from these conditions. Recipients’ lack of capacity leads to ineptitude, lack of motivation leads to dismissal of knowledge, lack of alignment with the source leads to disruption and doubts about the validity of external knowledge lead to resistance. The authors treat these behaviors as variants of CKB, as they can hinder the productive use of knowledge resources in the organization.

Further investigation of both knowledge rejection causes and the resulting knowledge rejection behaviors will ensure a more thorough grasp of the relationships between them, both in terms of the inherent nature of these relationships and their dynamics that would likely be context-sensitive. Although this study focuses only on the individual level, future studies can conduct multi-level analyses of undesirable knowledge rejection, including team and organizational levels.

Practitioners can use the framework to identify, diagnose and manage knowledge rejection more meaningfully, accurately and purposefully in their organizations. This study offers valuable insights for managers facing undesirable knowledge rejection, and provides recommendations on how to address this behavior, improves the constructive use of knowledge resources and the effectiveness of knowledge processes in their organizations. Managers should be aware of undesirable knowledge rejection, its potential cost or concealed cost to their organizations and develop strategies to reduce or prevent it.

The paper contributes toward understanding the relatively neglected topic of knowledge rejection in the knowledge management field and offers a new way of conceptualizing the phenomenon. It proposes that there are two types of knowledge rejection – undesirable and desirable – and advances a more precise and up-to-date definition of undesirable knowledge rejection. Responding to calls for more research on CKBs, the study examines a hitherto unresearched behavior of knowledge rejection and provides a foundation for further study in this area.

Informal learning networks are critical to response to calls for practitioners to reskill and upskill in off-site construction projects. With the transition to the coronavirus disease 2019 (COVID-19) pandemic, social media-enabled online knowledge communities play an increasingly important role in acquiring and disseminating off-site construction knowledge. Proximity has been identified as a key factor in facilitating interactive learning, yet which type of proximity is effective in promoting online and offline knowledge exchange remains unclear. This study takes a relational view to explore the proximity-related antecedents of online and offline learning networks in off-site construction projects, while also examining the subtle differences in the networks' structural patterns.

Five types of proximity (physical, organizational, social, cognitive and personal) between projects members are conceptualized in the theoretical model. Drawing on social foci theory and homophily theory, the research hypotheses are proposed. To test these hypotheses, empirical case studies were conducted on two off-site construction projects during the COVID-19 pandemic. Valid relational data provided by 99 and 145 project members were collected using semi-structured interviews and sociometric questionnaires. Subsequently, multivariate exponential random graph models were developed.

The results show a discrepancy arise in the structural patterns between online and offline learning networks. Offline learning is found to be more strongly influenced by proximity factors than online learning. Specifically, physical, organizational and social proximity are found to be significant predictors of offline knowledge exchange. Cognitive proximity has a negative relationship with offline knowledge exchange but is positively related to online knowledge exchange. Regarding personal proximity, the study found that the homophily effect of hierarchical status merely emerges in offline learning networks. Online knowledge communities amplify the receiver effect of tenure. Furthermore, there appears to be a complementary relationship between online and offline learning networks.

Proximity offers a novel relational perspective for understanding the formation of knowledge exchange connections. This study enriches the literature on informal learning within project teams by revealing how different types of proximity shape learning networks across different channels in off-site construction projects.

This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.