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This study is aimed at making an inspection of the effects of collaborative innovation network characteristics on firm innovation performance, and the intermediary roles of knowledge transfer efficiency is taken into account.

This study used a convenient sampling method to obtain population and samples. Using data obtained by publishing online and paper questionnaires, and using on-site interviews in Anhui Province in the Yangtze River Delta region of China, descriptive analysis, regression analysis and correlation analysis are utilized to study the direct influence of collaborative innovation network characteristics on knowledge transfer efficiency as well as firm innovation performance, and the intermediary roles of knowledge transfer efficiency on firm innovation performance, respectively. In this study, 3,000 questionnaires were distributed to the employees of enterprises engaged in research and development (R&D) activities, of which 2,560 were valid. With the help of SPSS24.0 software, the reliability and validity of the questionnaire was analyzed.

The results are indicative of that network centrality and relationship strength positively affect knowledge transfer efficiency and firm innovation performance. Nevertheless, network scale has no significant correlation with knowledge transfer efficiency and enterprise innovation performance. In addition, knowledge transfer efficiency is an intermediary between collaborative innovation network characteristics and enterprise innovation performance, and positively affects enterprise innovation performance, which demonstrated that managers should take advantage of collaborative innovation network characteristics to elevate knowledge transfer efficiency because well-realized transferals of knowledge can help accelerate the coordination of resources in knowledge, and finally bring about the advancement of firm's innovation abilities and performance.

There are few previous studies that fully examined the relationships among collaborative innovation network characteristics, knowledge transfer efficiency and firm innovation performance. This paper developed previous researches on the relationships between collaborative innovation network characteristics, knowledge transfer efficiency and firm innovation performance. The mediation of knowledge transfer efficiency on the relationship between collaborative innovation network characteristics and firm innovation performance is analyzed. Further, studies on collaborative innovation network characteristics using data obtained from employees engaged in R&D activities are very limited in the literature. On account of that, the findings in this study may make sense to the innovation ability of innovative enterprise and expand the literature in the field of enterprise strategic management and knowledge management.

This analysis shows that collaborative innovation network characteristics have both positive and negative effects on firm innovation performance. Therefore, business managers should pay attention to their position in the collaborative innovation network and maintain the relationship strength with other innovation subjects. Special consideration should be given to the knowledge transfer of innovative enterprises, so as to improve firm innovation performance practically.

The study may provide additional understandings for researchers, government managers, universities and enterprises with regard to strategic management from the visual angle of innovation ecosystems. It is instrumental in the exploration of the mechanisms enabling firm innovation performance.

The purpose of this paper is to develop the concept of a high performance alliance macro-culture as a multilevel construct reflective of resilient collaborative systems of exchange within strategic alliances and explores the distinct capabilities of this multilevel approach in predicting alliance outcomes.

The hypotheses developed in this study are tested using primary data collected from 650 members of 15 non-profit organizations in two multi-organizational collaborative networks. Considering the multilevel nature of the study the structural hypotheses are tested using a multilevel confirmatory factor analysis and the predictive hypotheses are tested using multilevel structural equation modeling.

All but one structural hypothesis are supported and all predictive hypotheses are supported suggesting that a multilevel macro-cultural conceptualization is effective in exploring the relationship between collaborative exchange systems and their outcomes.

Limitations stem from the generalizability of the data collected as the alliances formed by non-profit firms may not be wholly reflective of the alliance structures and goals of other firm types.

This study primarily contributes to multilevel study of strategic alliances and the study of collaborative norms and structures of allied groupings. The results of this study lend support to the importance of taking a network governance perspective and illustrate the limitations of traditional single-level approaches when studying interfirm collaborative networks and structural resilience therein.

The purpose of this paper is to examine how a performance measurement system (PMS) can be designed for a collaborative network and to identify which factors affect such a design.

This is a single-case study of a collaborative network. The data have been collected from semi-structured interviews conducted during 2008-2009 and after the design process in 2010 and 2012, respectively.

The research results present a five-step process model for designing a PMS for a collaborative network. The findings show that a participatory development style that enhances socialisation, the positive development of a network culture and an outside facilitator all have beneficial effects on the design process.

The practical contribution of this study is related to knowledge about the PMS design process for a collaborative network to support its measurement-related development projects. This knowledge involves the phases of such a process as well as the various factors supporting or hindering it.

The study presents a PMS design process for the case network, which can be utilised in other collaborative networks in a similar context. It also highlights the most essential practical experiences related to this process.

This paper proposes a multi-layer hybrid decision-making approach to evaluate the capability alternatives for developing a collaborative network to operate in the international market.

The present study is contextualised in the Iranian pistachio export industry. An extensive review of the state-of-the-art literature on supplier collaboration was conducted to identify key capabilities that are essential to establish a collaborative network. The set of defined capabilities were then optimised through interviews with 14 experts from the relevant industry, academics and export authorities. A combination of the fuzzy Delphi method and the best–worst method (BWM) approach was, respectively, used to reduce the number of capability alternatives and assign priority weights to these alternatives. Subsequently, a weighted aggregated sum product assessment method (WASPAS) was employed to rank and evaluate the ability to creating a collaborative network for the export of pistachio.

From the extant literature review, 18 capabilities for the formation of coordination networks in the international markets were identified. Then, the prominent indicators in forming a global network were extracted. After ranking the top pistachio export countries/regions to formalise an efficient collaborative network, it was revealed that although Iran exports approximately 30% of the global market, it falls behind the USA and European Union. The competitors have scored higher in critical criteria, including “trust and commitment”, “strategy and management”, “managerial control and standardization” and “financial resources”.

The proposed hybrid approach encompassing fuzzy Delphi–BWM–WASPAS offers to solve the capability evaluation and selection as well as ranking the possible alternative to formalise a collaborative network in an integrated fashion. This combination of methods is capable to first identify the most important factors, then measuring their importance and eventually rank the possible alternatives. The suggested framework provides an approach to deal with the uncertainty of global collaborative network formation.

Progress in theory building in the field of collaborative networks in manufacturing is preponderantly seen in contributions from disciplines outside manufacturing science. Interdisciplinary research is one way of accelerating the development of appropriate theory for this emerging domain where industrial practice has moved beyond the state of the art of scientific knowledge for establishing workable, competitive solutions. The purpose of this paper is to examine to what extent interdisciplinary research has contributed to a better understanding of collaborative (manufacturing) networks.

To find out more about provenances of on‐going studies, to identify clusters of contributions and to provide direction for future work of researchers in this domain, publications of the past 22 years have been evaluated. To retrieve these contributions, a structured literature review has been undertaken by applying keywords to selected databases and using a strictly defined stepwise procedure. In total, 202 publications of all kinds have been evaluated.

From the analysis of the results, it appears that most interdisciplinary contributions to collaborative (manufacturing) networks rely on one original outside discipline for either developing solutions or advancing theoretical insight. Consequently, and after further analysis, it seems that researchers in collaborative networks hardly resort to multi‐disciplinary approaches, unless “natural”; further advances might arrive from stimulating these multi‐disciplinary avenues rather than sticking to more mono‐disciplinary, and less risky, takes on both applications and theoretical insight. A more detailed investigation of the value of contributions reveals that efforts to make interdisciplinary advances are either difficult or limited. Also, the findings indicate that researchers tend to follow a more “technical” approach to decision making by actors in networks rather than searching for a shift in paradigm.

While setting out these directions for future research and guiding research, this first‐of‐its‐kind review introduces the collaboration model as a systematic approach to collaborative (manufacturing) networks. This model might serve as a reference model to integrate disciplines for addressing the characteristics of Collaborative Networks. Its use in the review led to the finding that typical traits of networks, such as changeability, supplementary assets and decentralisation of decision making, are under‐researched.

The aim of this study is to investigate the relationship between heterogeneous collaborative networks and firm performance, using the resource-based view (RBV) and its extension through the knowledge-based view (KBV) as theoretical lens. Moreover, the authors examine family management and intellectual property rights (IPRs) as contingent factors that enhance the effectiveness of heterogeneous collaborative networks in achieving superior firm performance.

The hypotheses are developed and checked by using a panel data sample of 10,985 firm-year observations from 1,766 Spanish manufacturing firms over the period 2007–2016.

The results indicate that heterogeneous collaborative networks positively influence firm performance. Furthermore, the positive impact of these innovation networks on firm performance is reinforced by high levels of family management, and such effect is even stronger when there exists high levels of IPRs.

This research is the first, to our knowledge, to provide important new insights into the manner in which the effect of both family management and IPRs have the potential to amplify the performance gains attained from heterogenous collaborative networks.

Although prior studies have identified several technologies related to Industry 4.0 and their individual potential, it is still unclear how these technologies could be integrated to achieve better results. Based on this, we propose several collaborative networks combining technologies associated with Industry 4.0.

A literature review was performed using a research model to support the evaluation and identification of key and collaborative technologies related to Industry 4.0. We examined these technologies using hierarchical cluster analysis and principal components analysis, based on their characteristics.

The study identified big data, cloud computing, the internet of Things and cyber-physical systems as key technologies for Industry 4.0, and a further eight collaborative technologies that are strongly related to industrial performance. We found five collaborative networks with distinct goals in the context of Industry 4.0: (1) smart manufacturing; (2) technological platforms; (3) market reactiveness; (4) smart products and (5) flexibility.

The findings allowed us to create five pathways for future work on Industry 4.0 technologies via collaborative networks. In practice, this will help managers to improve their focus on priorities regarding the implementation of Industry 4.0 technologies.

This study provides insights into how to establish links between technologies through collaborative networks for certain purposes. In addition, we propose five future directions for these collaborative networks that require further investigation by researchers.

The aim of this paper is to propose a genetic algorithms approach to develop a collaborative supply chain network, i.e. a supply chain network with genetic algorithms embedded (GA‐SCN), so as to increase the efficiency and effectiveness of a supply chain network.

The methodologies of the GA‐SCN are illustrated through a case study of a supply chain network of a Hong Kong lamp manufacturing company involving 10 entities, whose roles range from suppliers, purchasers, designers and manufacturers, to sales and distributors. A GA‐SCN is developed according to the information provided by the company, the performance results in the case study are discussed, and the concepts of network analysis are then introduced to analyze the equivalence structure of the developed GA‐SCN.

The genetic algorithms approach is a suitable approach for developing an efficient and effective supply chain network in terms of shortening the processing time and reducing operating time in the network: the processing time and operating cost are reduced by around 45 percent and 35 percent per order, respectively, in the case study.

This paper is the first known study to apply genetic algorithms for the development of a collaborative supply chain network to increase the competitiveness of a supply chain.

This research paper explores the value of collaborative inquiry networks of headteachers and curriculum coordinators to cope with 2020's coronavirus pandemic in Chile. Specifically, the authors describe the main challenges that networks identify in their contexts, the collaborative practices performed by different schools to address these challenges, and the influence of the networks on the innovative responses of teachers in their own schools.

This is a mixed-method study from a complementary stance using different methods and data of a project implemented with a total of 54 headteachers and curriculum coordinators. The data sources were participants' individual reports, the network teams' reports of their collaborative inquiry projects, and a short open-ended questionnaire responded by teachers that did not participate directly in the networks but benefited from their work. The data were analyzed using content analysis, categories were created to organize and describe the main findings.

Participants of the networks reported that their active participation in the collaborative inquiry allows them to share knowledge among different schools and has helped them to support innovative practices in their own schools. Specifically, they have reported that collaborating has permitted them to maintain a pedagogical focus, foster distributed leadership within the school communities, provide them with greater autonomy, and develop skills to favor the emotional containment of their teams. Inquiry teams perform diverse collective practices; they designed and applied virtual surveys, planned and implemented virtual workshops with teachers, and generated meaningful reflection about formative assessment and pedagogical practices.

This work offers insights into how the Chilean school system has responded to COVID-19 challenges and shows how despite the negative aspects of the pandemic, it has become an opportunity to recognize and enhance teachers' professional development through the collaboration among different schools. Most headteachers and curriculum coordinators reported that an active collaborative inquiry changed how they used to think about their leadership and strengthened the value of professional relationships to address extremely difficult challenges as a result of the pandemic. These lessons can be taken for the future, to rethink and rebuild educational systems.

This paper seeks to propose an overall model of collaborative forecasting for networked manufacturing enterprises.

Contributions by several authors to collaborative forecasting have been analysed from different viewpoints. A collaborative‐forecasting model for networked manufacturing enterprises has been proposed and validated by means of a simulation study.

This model significantly reduces the inventory levels of the whole network and improves customer service.

Simulation experiments were done with the enterprise network herein described. Future research will include the simulation of more complex enterprise network scenarios with different characteristics.

The model can be implemented node‐to‐node, since not all the companies in the network have to participate, thus facilitating implementation and propagation throughout the network.

The paper proposes a new structured planning and forecasting collaboration model for networked manufacturing enterprises.