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How does university-firm collaboration affect the performance of both universities and firms? The purpose of this paper is to evaluate university-firm collaborations aimed at expanding the treatment effects of collaboration ambition on university academic performance as well as collaboration ambition focused on the firm’s production of innovation and financial performance for the top 110 US universities and the top 200 US R&D performing firms.

“Two studies, based on the three archival data sets (National Bureau of Economic Research-Rensselaer Scientific Papers Database and the Harvard Dataverse Network (DVN) US Patent Citations database and Compustat database), are undertaken in the top 110 US universities and the top 200 US R&D performing firms.” The study introduces a theoretical model that explicitly addresses collaboration diversity, number of collaborations, knowledge stock and the endogeneity problem that is generated by self-selection of collaboration ambition in university and firm’s performance.

The results suggest that the effects of adopting proactive collaboration decision on academic performance are insignificant in the firm subsample. However, more interestingly, the authors find supporting evidence of the negative impact of collaboration on university groups. The authors also find that collaboration diversity, knowledge stock and collaboration ambition lead to stronger firm performance but the number of collaborations is smaller on firm performance. Furthermore, the authors find that collaboration ambition moderates the positive effect of the number of collaborations on firm performance.

University-firm collaboration is a multifaceted relationship, suggesting that the empirical analysis can be interpreted through the university and the firm view to enhance the understanding of the collaboration for performance creation. This study articulates the positive role of collaboration diversity, knowledge stock and collaboration ambition and the negative role of the number of collaborations on university-firm collaboration in terms of university and firm performance. Moreover, proactive collaboration ambition has the positive effect of a higher number of collaborations on firm performance. The authors conclude that policy should refrain from overly focusing on collaboration diversity, number of collaborations, knowledge stock and collaboration ambition, and the authors consider the interactions between the number of collaborations and collaboration ambition on university-firm collaboration when discussing their effects on mutual performance.

This study demonstrates the effects of university-firm collaboration on academic performance. In addition, the authors discuss the factors that influence collaboration to help the firm to increase its innovation and financial performance. Therefore, it would be interesting to see simultaneously how university-firm collaboration affects the performance of both partners.

This paper aims to explore the determinants of firms’ attitudes to R&D cooperation with universities with the goal to propose a model of knowledge transfer university-firm.

By understanding university-industry cooperation as a process, the model in this study is based on two factors: the “promoter of university-industry relations” (PUIR) and a technological system that is able to match the research supply of universities with the demand for innovation of the firms. A total of 375 firms, 420 research groups and 18 experts in knowledge transfer from Spain, Portugal and France have been involved in this study.

This study provides the first evidences of the relation between the number of matches’ demand-supply of research, the number of university-firm relations and the willingness to cooperate; all of these presented in the knowledge transfer model. Results also reinforce the utility of the role of PUIR and the matching system in the open innovation process.

However, more evidences are necessary to get a complete validation of the model. In the future, the continuous utilization of the matching system by the PUIRs of the selected regions will allow the authors to evaluate how well the system is working by analysing the possible increases in formal collaboration university-firm in the area of R&D.

This study contributes to the literature through aligning the open innovation and intellectual capital theories in the specific and complex context of university-firm collaboration. Also, little research regarding this topic has been noticed in SUDOE European Union (EU) countries (SUDOE is an EU territorial cooperation programme that supports regional development by funding transnational projects).

The present study intends to foster understanding of how a traditional manufacturer can utilize the “simple rules” approach of managerial heuristics to facilitate its smart solution development (SSD) process.

The study uses an in-depth single case research strategy and 25 senior manager interviews to understand the application of simple rules in smart solution development.

The findings reveal process, boundary, preference, schedule, and stop rules as the dominant managerial heuristics in the case and identify how the manufacturer applies these rules during the innovation process phases of ideation, incubation, transformation, and industrialization for attaining project outcomes.

The study contributes to the new service development (NSD) literature by shedding light on simple rules and how managers may apply them to facilitate SSD. The main limitations stem from applying the qualitative case study approach and the interpretative nature of the study, which produces novel insights but prevents direct generalization to other empirical cases.

The resulting framework provides guidelines for managers on how to establish formal and clear simple rules that enable industrial solution providers to approach decision-making in smart solution development in a more agile manner.

The study comprises one of the first attempts to investigate managerial heuristics in the context of SSD and puts forward a plea for further NSD research applying psychological conceptualizations to enrich the simple rules perspective.

The purpose of this study is to introduce a conceptual framework for knowledge transfer between Chinese firms and universities around the world, based on a joint learning process. The transfer of knowledge is important to increase the innovative capabilities of Chinese firms by accessing more sources of new knowledge not available locally.

The framework is derived from the resource‐based perspective. Its main characteristics are defined, and the required stages are explained. The framework stresses the dynamic relationship among all stages, and the active interaction between interorganizational and intraorganizational learning processes.

Having a suitable process for selecting a partner is the pre‐condition for achieving effective knowledge transfer between university and firm, because it determines similar capabilities and how both partners can enhance the requirements of each other. In addition, the joint learning experience between university and firm will be rewarding to a large extent if high transparency, collaborative intent, informal communication and organizational commitment are adopted as the basis for joint learning.

The study proposes a conceptual framework for knowledge transfer between Chinese firms and universities around the world, based on previous research in this domain. An empirical study will be beneficial to support this framework and to suggest needed modifications.

The study provides a greater understanding of the importance of a joint learning process between university and firm to support the innovative capacity of a firm. It emphasizes that the successful collaboration with a university can make a firm function as an external R&D hub.

The purpose of this paper is to investigate the academic side of university–firm linkages, reporting the results of research (called the “BR Survey”, a primary database) conducted in Brazil with leaders of research groups that interacted with firms. The authors analysed the answers from 662 research groups (from both universities and research institutes) to investigate whether the intensity of private funds affects the results of the interactions. The main intent is to answer the following question: Is there a difference between funding sources and the type of results achieved by research groups when interacting with firms?

To verify the impact of some variables on the perception of the main results of university–firm interactions, highlighting the impact of funding sources, the authors present a Logit Model defined with binary dependent variables. The null value is categorized as a “scientific result” (new scientific discoveries and research projects; publications, theses and dissertations; human resources’ and students’ education) and the value 1 is classified as an “innovative/technological result” (new products, artefacts and processes; improvement of industrial products and processes; patents, software, design and spin-off firms).

The authors found that the modes of interaction (relationship types) and some knowledge transfer channels, besides the number of interactions with firms, have statistically significant coefficients, so their values present different impacts on the results of the interaction. The results suggest that the Brazilian innovation policy towards a more active and entrepreneurial role of universities is fostering innovative/technological results from university–firm interactions.

The originality of the study lies on the results found that given the fact that private funding sources do not affect the conventional mission of Brazilian universities – teaching and research – university research groups should be even more incentivized to search for private funds to carry out their research. This may be a solution to the public fund scarcity and may help in reducing the historical distance between universities and firms in Brazil.

The purpose of this paper is to explore open innovation (OI) collaborations between high-tech small and medium-sized enterprises (SMEs) and large customers. The research aims to add new insights into how smaller firms attract and build trusting relationships with larger customers for the purpose of innovation, and to highlight customers’ contribution in SMEs’ innovation process.

This exploratory research is based on three case studies and adopts a process perspective to gather qualitative data on OI collaborations, focusing on the inherent dynamics, and evolution in long-term relationships.

The study provides insights into how SMEs develop OI relationships with both industry and research customers by building trust through various mechanisms. Motivated by the potential benefits of OI in strengthening the firms’ technological edge, the SME managers proactively and strategically developed and managed their OI relationships. The results proved that large customers contributed greatly to the SMEs’ innovation processes both directly and indirectly.

The research provides advice for smaller firms which are considering adopting an OI strategy with customers through mechanisms such as trust building and enhancing legitimacy.

The research adds to the OI literature on SMEs by exploring how smaller firms manage OI challenges, exploit benefits, and develop trusting relationships with larger customers and research institutions.

The paper develops a model for enhancing R&D productivity for Indian public funded laboratories. The paper utilizes the productivity data of five Council of Scientific and Industrial Research (CSIR) laboratories for analysis and to form the constructs of the model.

The weighted average method was employed for analyzing the rankings of survey respondents pertaining to the significant measures enhancing R&D involvement of researchers and significant non-R&D jobs. The authors have proposed a model of productivity. Various individual, organizational and environmental constructs related to the researchers working in the CSIR laboratories have been outlined that can enhance R&D productivity of researchers in Indian R&D laboratories. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to find the predictability of the productivity model.

The organizational factors have a crucial role in enhancing the R&D outputs of CSIR laboratories. The R&D productivity of researchers can be improved through implementing the constructs of the proposed model of productivity.

The R&D productivity model can be adapted by the R&D laboratories to enhance researchers’ R&D involvement, increased R&D outputs and achieving self-sustenance in long run.

The R&D laboratories can initiate exercises to explore the most relevant factors and measures to enhance R&D productivity of their researchers. The constructs of the model can function as a guideline to introduce the most preferable research policies in the laboratory for overall mutual growth of laboratory and the researchers.

Hardly any studies have been found that have focused on finding the measures of enhancing R&D involvement of researchers and the influence of significant time-intensive jobs on researchers’ productivity.

This paper aims to address the gap that, to date, no systematic review has been carried out on the role that structural social capital (SC) plays for knowledge transfer and innovation at the interpersonal, inter-unit and inter-firm levels. Individuals and organisations are becoming increasingly involved in collaboration networks to share knowledge and generate innovation. SC theory has been adopted in several areas of study to explain how individuals, groups and organisations manage relationships to generate innovation.

This review covers studies of SC in organisational behaviour, strategy and management over a period of 20 years.

The literature review shows that knowledge types and knowledge transfer processes are the missing links in the relationship between structural SC and innovation. Moreover, the paper demonstrates that seemingly opposite configurations of SC are complementary to each other (structural holes vs dense networks; strong vs weak ties) and that contextual factors should be considered when discussing the effects of SC on knowledge transfer and innovation. In addition, it is the balance of different configurations of SC which enables an individual or a company to explore, access, assimilate and combine different knowledge types, which will lead to improved innovation outcomes.

This review facilitates understanding of the role of SC for knowledge transfer processes and the mediating role of knowledge transfer processes and knowledge types in the relationship between structural SC and innovation.

The purpose of this paper is to examine the conceptualization of knowledge transfer and technology transfer to seek answers to the question: Why should an organizational manager need to know the difference between knowledge transfer (KT) and technology transfer (TT)?

An extensive literature review method was used to identify and analyze relevant international publications. The literature sources are categorized as follows: literature on KT only, literature on TT only and literature on comparative analysis on KT and TT. The conceptualization of KT and TT is based on signaling theory.

The authors identified differences between KT and TT based on six dimensions, namely knowledge versus technology characteristics, usage of KT and TT in national development, sender versus receiver, intra-firm versus inter-firm transfer, foreign direct investment (FDI) and workers’ mobility.

This is a conceptual analysis that should contribute to the existing literature by comprehensively reviewing the processes of KT and TT based on selected research conducted worldwide as well as suggest practical guidelines to organizational managers in managing KT and TT.

This review could shed new insights for future researchers to validate and examine the identified differences between KT and TT so that managers could make use of the findings to manage KT and TT successfully in their organizations.