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This study aims to examine direct effects of qualified team gatekeepers on absorptive capacity (AC), and the mediating roles of combinative capabilities – knowledge integration capability (KIC) and interteam coordination.

A social networking analysis was used to analyze a unique data set collected from all members of 32 Japanese research and development (R&D) teams to identify key individuals who perform daily gatekeeping functions. This study analyzed the data through partial least squares structural equation modeling with higher-order latent variables. Finally, cross-validation tests were used with holdout samples to test the model’s predictive validity.

Qualified gatekeepers directly contribute to teams’ realized AC but not to their potential AC. Furthermore, qualified gatekeepers can improve their teams’ capability to absorb and exploit external knowledge by facilitating their capability to consolidate knowledge, that is, its KIC and interteam coordination.

Unlike prior research that asks top managers to identify team gatekeepers, this study used social network analysis to identify these vital individuals. This study provides a new framework indicating how qualified gatekeepers impact the AC of R&D teams through the examination of both the direct and indirect paths of gatekeeping abilities, two combinative capabilities as mediators and team AC.

The purpose of this paper is to analyze the mediating effect of the identification of valuable external knowledge on the relationship between the development of inter-organizational ties (structural social capital) and the acquisition of external knowledge.

Using a sample of 87 firms from Spanish biotechnology and pharmaceutics industries, the authors have tested the proposed mediation hypothesis by applying the partial least squares technique to a structural equations model.

The study results show that those firms with stronger, more frequent and closer inter-relationships are able to increase the amount of intentionally acquired knowledge, partly due to the greater level of development of their knowledge identification capability. Thus, firms with a higher capability to recognize the value of the knowledge embedded in their inter-organizational networks will be more likely to design better strategies to acquire and integrate such knowledge into their current knowledge bases for either present or future use.

This research contributes to knowledge management and social capital literature by means of the study of two key determinants of knowledge acquisition – structural social capital and knowledge identification capability – and the explanation of their relationships of mutual influence. The paper thus tries to fill this literature gap and connects the relational perspective of social capital with the knowledge-based view from a strategic point of view.

Considering the vital role of resource-constraint innovation in developing countries, the aim of the study is to examine the mechanism of internal and external heterogeneous knowledge sharing (HKS) in the relationship between sustainable leadership (SL) and frugal innovation (FI). The social exchange theory was used to develop a research framework.

This study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis to examine the relationship among several latent factors based on 263 participants from Pakistani SMEs.

The current findings support the significant positive impact of SL on both internal and external HKS. Moreover, this study also confirms the mediating effect of both types of HKS in the relationship between SL and FI.

To delve further into the benefits and vital role of HKS, it is recommended to conduct further research that would examine the potential impact of heterogeneous knowledge sources on the “SL–FI relationship” and to apply the presented research methodology in other countries and organizations beyond Pakistani SMEs.

This study is one of the first documented attempts to demonstrate HKS as a mechanism in the relationship between a specific type of leadership and FI.

In organisational and innovation research, the term “open innovation” refers to the inflow and outflow of knowledge to and from organisations: with open innovation theory suggesting active exchanges of knowledge with external actors leads to the development of exploitable new ideas. In the field of education, however, the exchange of knowledge with external parties represents a paradigm shift. In response, this article presents findings from research design to explore the nature and composition of school innovation networks, and the effects of such these networks on knowledge mobilisation.

The study draws on data from a representative random sample of 411 German school leaders. Respondents were asked to detail their engagement in open and closed innovation activity and their school's external collaborations during the last 12 months. A latent class distal outcome model was developed to examine whether different types of collaboration associate with different knowledge mobilisation processes.

The study findings suggest that schools in Germany mainly use internal knowledge for innovation, with external knowledge exchange taking place on a very limited basis. Knowledge mobilisation varies depending on the innovation network. The authors use the findings to indicate new insights for how schools can further innovate learning and teaching in future.

Although there is increasing discussion on Professional Learning Networks in schools, the discourse on knowledge mobilisation within educational networks is limited, making concept of open innovation so far completely absent from discourses on school improvement. This paper initiates the population of this new research space.

This paper aims to test the influence of external information search (EIS) on knowledge elaboration and group cognitive complexity (GCC) under the moderating effect of absorptive capacity (AC is indicated by prior knowledge base and gender diversity).

The results of three studies (one field study and two experimental studies) are reported. The first study tests the interaction between EIS and the two dimensions of AC on group knowledge elaboration in a sample of 65 organizational groups. In the second study, EIS was directly manipulated and the interaction with AC in a sample of 65 groups was tested. In the last experimental study, the AC of the boundary spanner (highest level of expertise versus lowest level of expertise) was manipulated and the effects of EIS in a sample of 37 groups were tested.

The first study reveals a significant interaction between EIS and prior knowledge base on knowledge elaboration and points toward a compensatory interplay of EIS and AC on GCC. The results of the second study indicate that EIS increases the time spent on task, as well as the efficiency of knowledge integration (GCC per unit of time). Furthermore, EIS has the strongest positive effect on GCC in groups in which at least one of the AC dimensions is average or high. The results of the last study show that the AC of the boundary spanner compensates for the lack of absorptive capacity of the group and also show that the cognitive distance between the boundary spanner and the rest of the group has a negative influence on the efficiency of knowledge integration in groups.

The limitations of Study 1, common to non-experimental research (related to causality), are dealt with in the second and third studies that establish causality between EIS and GCC.

The paper has important implications for the management of information search effort in organizational groups, in particular the groups are advised to: engage in EIS to increase their cognitive repertoire and cognitive complexity, delegate, when possible, their most competent members to engage in boundary spanning activities as they will maximize the cognitive benefits of EIS and finally minimize the cognitive dissimilarity between the boundary spanner and the rest of the group to facilitate the effective integration of novel insights into the group cognition.

This study is among the first empirical attempts to uncover the causal effect of EIS on knowledge elaboration and GCC in groups and to uncover the role of the boundary spanner in the EIS efforts.

This paper examines the innovation behavior of family-owned firms versus non-family-owned firms. The role of internal family governance and the influence of external stimuli (competition) on innovation are also considered.

The data of 20,995 family and non-family firms across 38 countries are derived from the World Bank Enterprise Survey during the period 2019–2020. Probit models are used to examine the impact of family ownership, family governance, and competition on innovation outcomes.

Family firms are more likely to make R&D investments, acquire external knowledge, engage in product innovation (including innovations that are new to the market) and process innovation, relative to non-family firms. However, a high propensity of family member involvement in top management positions can reduce innovation. Competition has a negative impact on innovation outcomes for both family and non-family firms, but it has a positive moderating effect on the innovation activities of family firms where a higher level of family member involvement in management is present.

This paper provides novel insights into family firm innovation dynamics by identifying family firms as more innovative than non-family firms for all types of indicators, debunking the idea that family firms are conservative, reluctant to change, and averse to the risks in innovation activities. However, too much family involvement in decision making may stifle some innovation activities in family firms, except in cases where the operating environment is highly competitive; this provides new insights into the ownership-management dynamic of family firms.

Professional service firm (PSF) performance depends on the accumulation and application of specialist knowledge to find customised solutions to customer problems. However, available research has not examined whether knowledge acquired from external sources affects PSF outcomes by strengthening professionals’ beliefs rather than only by increasing technical competency. Drawing on self-efficacy theory, this study tests a model that links the quality of content acquired from external sources and the credibility of those sources to professionals’ self-efficacy and, in turn, to PSF outcomes (solution quality and firm performance). In particular, this paper aims to consider the case of professional content exchanged through professional social media.

A cross-sectional research design was applied. Data were collected from a sample of 208 accountants, auditors and lawyers who used professional social media and were analysed using covariance-based structural equation modelling.

When accessing professional content from external sources, source credibility and content quality are significant antecedents of professionals’ self-efficacy, which, in turn, has positive effects on PSF outcomes (solution quality and PSF performance).

Self-efficacy plays a key role in the link between knowledge acquired from external sources (professional content) and PSF outcomes.

This study provides recommendations and actionable insights for PSFs, professionals and other actors who create and exchange professional content. Professional associations may also take an active role by contributing and sharing credible and high-quality content, using, for example, professional social media.

This paper advances the current understanding of the effects of professionals’ access to content from external sources on PSF outcomes. It provides an explanation of these effects based on the enhancement of professionals’ beliefs instead of their technical competencies, as indicated in previous research. In addition, it is the first research effort to consider professional social media as a communication channel to exchange content that affects the self-efficacy of PSF professionals.

This paper aims to investigate the impact of Industry 4.0 (I4.0) technologies on knowledge creation for innovation purposes by assessing the relationships among the variety of I4.0 technologies adopted (breadth I4.0), the penetration of these technologies within the firm’s value chain activities (depth I4.0) and the mediating role of both internal (inter-functional (IF)) and external [with knowledge-intensive business services (KIBS)] collaborations in this process.

The study employed a quantitative research design. By administering a survey to entrepreneurs, chief operation officers or managers in charge of the operational and technological processes of Italian manufacturing firms, the authors collected 137 useful questionnaires. To test this study's theoretical framework and hypotheses, the authors ran regression and mediation analyses.

First, the results highlight the positive link between breadth I4.0 and depth I4.0. Moreover, the results show the key role played by increased collaboration among the firm’s business functions and by relationships with KIBS in creating knowledge to innovate processes and products when I4.0 technologies are adopted.

The variety of I4.0 technologies adopted enables a firm to use such technologies in various value chain activities. However, the penetration of I4.0 into the firm’s value chain activities (depth I4.0) does not per se directly imply the production of new knowledge, for which a firm needs internal collaboration among different business functions, in particular with the production area, or collaboration with external partners that favor I4.0 implementation, such as KIBS.

To achieve innovation goals by creating new knowledge, especially in the manufacturing industries, firms should encourage internal and external collaboration when I4.0 technologies are adopted. Moreover, policy makers should not only consider fiscal incentives for the adoption of such technologies, but also encourage the building of networks between adopting firms and external actors.

The study is one of the first attempt that provides empirical evidence of how I4.0 enables the creation of knowledge to innovate processes and products, highlighting the relevance of collaboration both within the company and with external partners.

Through a comprehensive review of the literature on open innovation (OI), this study aimed to achieve two objectives: (1) to identify the main thematic areas discussed in the past and track their evolution over time; and (2) to provide recommendations for future research avenues.

To achieve the first objective, a method based on text mining was implemented, with the analysis focusing on 1,772 journal articles published between 2003 and 2018. For the second objective, a review based on recent and relevant papers was conducted for each thematic area.

The paper identified nine thematic areas explored in existing research: (1) context-dependency of OI, (2) collaborative frameworks, (3) organizational dimensions of OI, (4) performance and OI, (5) external search for OI, (6) OI in small and medium-sized enterprises, (7) OI in the pharmaceutical industry, (8) OI and intellectual property rights, and (9) technology. The analysis of the most recent papers belonging to the more investigated areas offers suitable suggestions for future research avenues.

To the best of the authors’ knowledge, no review has yet been undertaken to reorganize the OI literature.

Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.

In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.

The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.

To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.