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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.

This study focuses on the self-organized cooperative consumption of platform participants on social media platform, and reveals how the brand owner cooperates with two-sided customers to achieve value co-creation.

The authors adopted a case study approach to explore how a Chinese beauty startup developed collaborative networks from 2013 to 2022, and tracked the the changes of network structure and cooperation mechanism.

The study finds that the brand owner cooperates with two-sided customers to integrate resources and establish diverse relational trust, which enhances the evolution of a heterogeneous collaborative network for value co-creation.

The study builds upon traditional dyadic actor-to-actor interactions between providers and customers, develops a novel interaction framework of actor-to-network to explain the value co-creation by collaborative networking, reveals the self-organized mechanism of cooperative consumption on social media.

Previous studies have shown that the application of information technology (IT) can help break through the innovation boundaries of firms and has undoubtedly become a key enabler of collaborative innovation. These studies, however, are mainly based on theoretical analysis and case studies, and little is empirically known about the relationship between IT investments and collaborative innovation. Therefore, the purpose of this study is to empirically explore how firms' IT investments affect the firms' collaborative innovation performance. The authors also examine the moderating roles of the top management team's (TMT's) educational background and absorptive capacity in this relationship.

The authors collected data on 2,097 listed Chinese manufacturing companies and used the ordinary least squares (OLS) method to perform regression analysis. In addition, the authors conducted robustness tests using the propensity score matching (PSM) method and the instrumental variable method.

The results show that the relationship between IT investments and collaborative innovation is inverted, U-shaped and curvilinear. In addition, the TMT's educational background and absorptive capacity positively moderate the inverted U-shaped relationship between IT investments and collaborative innovation.

The study's findings on the relationship between IT investments and collaborative innovation differ from previous mainstream findings that recognized a positive linear relationship. The authors' findings deepen the understanding of the dual role of IT investments. Moreover, this research helps expand the contingency perspective in IT investments and collaborative innovation research.

Collaborative Logistics (CL) and merging operations are crucial strategies for reducing costs and improving service in transportation companies. This study proposes a model for optimizing efficiency in supply chain networks through inbound and outbound Collaborative Logistics implementation among the carriers in centralized, coordinated networks with cross-docking.

A mixed-integer non-linear programming model is developed to determine the optimal truck-goods assignment while gaining economies of scale through mixing multiple less-than-truckload (LTL) products with different weight-to-volume ratios. Unlike the previous studies that have considered Collaborative Logistics from the cost and profit-sharing perspective, the proposed model seeks to determine an appropriate form of Collaborative Logistics in the VRP.

This article shows that in a three-echelon supply chain consisting of a set of suppliers, a set of customers and a cross-docking terminal, partial collaboration among the inbound carriers and outbound carriers outperforms no/complete collaboration. This approach enhances the supply chain efficiency by minimizing the total transportation costs, the total transportation miles and the total number of trucks and maximizing fleet utilization. While addressing the four points, the role of collaborative logistics among the carriers was discussed. In a three-echelon SC consisting of a set of suppliers, a set of customers and a cross-docking terminal, partial collaboration among the inbound carriers and outbound carriers outperforms no/complete collaboration. Using a combination of experimental analysis and optimization process, it was recommended that managers be cautious that too much (full or complete) or no collaboration can result in SC performance deterioration.

The suggested approach enhances the supply chain efficiency by minimizing the total transportation costs, the total transportation miles and the total number of trucks and maximizing fleet utilization. While addressing the four points, the role of Collaborative Logistics among the carriers was discussed.

This article aims to identify the capabilities supporting the development of collaborative innovation within knowledge‐intensive environments.

Re‐considering the history of the ARPANET project as a vivid example of collaborative innovation, the article presents qualitative research from a historical case.

Within this framework, the article shows that benefiting from collaboration in innovation entails that the innovative organisation is capable of achieving (at least) the following tasks: to leverage complementarities between internal and external sources of innovation (design capability); to codify, capitalise and disseminate knowledge outcomes (knowledge management capability); and to align product and organisations in a dynamic way (adaptive governance capability).

This contribution is limited by looking at a single case. On the premise that model generalization depends on extensive empirical data, the current article should be considered as preliminary/exploratory research that aims at identifying the capabilities supporting collaborative innovation within knowledge‐intensive environments.

The originality of this article is to look at a historical case to elaborate on a typology of collaborative innovation capabilities.

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.

This study aims to examine the effect of supplier supply network (SSN) resources on buyer–supplier collaborative product innovation. The existing relevant studies from the network perspective are focused more horizontally on the mobilization of the firm’s entire supplier network resources and less vertically on the utilization of the individual supplier's supply network resources. Therefore, this paper takes a contingency perspective, regards the buyer as the receiver of a supplier’s supply network resources and, based on the theoretical motivation–opportunity–ability framework, explores the important motivation and ability factors that may weaken or enhance the product innovation value of the network resources.

This paper develops a new research model that assesses how the innovation utilization effectiveness of SSN resources is contingent on a buyer’s perception of the SSN effects and relative absorptive capacity on the SSN. A large sample questionnaire is designed and collected from 300 Chinese high-tech manufacturing firms and their suppliers. An empirical test is carried out in which multiple regression analysis is applied to 246 valid sample data.

The results show that SSN resources can significantly enhance buyer–supplier collaborative product innovation; however, the innovation utilization effectiveness is limited by the buyer’s relative absorptive capacity for the SSN and perception of the SSN effects. Specifically, if a buyer perceives that a greater innovation effect of a supplier’s supply network is correlated with an improved relative position in the SSN or with higher network structural equivalence and network cognitive congruence between the buyer and the supplier on the SSN, then the buyer will be more motivated and capable of transforming the SSN resources into actual product innovation value.

The research results provide useful guidance for firms to effectively mobilize their SSN resources to tap into the supplier innovation value for a sustainable competitive advantage.

This paper extends the research on supplier innovation value to the SSN field. Through linking dyadic and network levels of analysis, this paper reveals the value and uniqueness of product innovation utilization of a specific supplier’s supply network resources in the buyer–supplier relationship and provides a new research perspective for future studies on related issues.

During the past decade, fast-paced changes created a new environment organisations need to adapt to in an agile way. To support their transformation, organisations are rethinking their approach to learning. They are moving away from traditional instructor-centred, standardised classroom-based learning settings. Instead, learning needs to be tailored to the individuals’ needs, available anywhere at any time and needs to enable learners to build their network. The development of digital tools, specifically network technology and social collaboration platforms, has enabled these new learning concepts.

The use of these new learning concepts in organisations also has implications for higher education. The present case study, therefore, investigates how universities can best prepare future employees and leaders for these new working environments, both on a content level and a methodological level. It also investigates if these new learning concepts can support universities in dealing with a changing environment.

The investigated case is a traditional face-to-face leadership lecture for a heterogeneous group of students. It was reconceptualised as a personalised and social collaborative learning setting, delivered through a social collaboration platform as the primary learning environment. Initial evaluation results indicate positive motivational effects, experience sharing and changes in perception of the student − lecturer relationship. The findings also supported previous challenges of computer-supported collaborative learning settings, such as the perception of a higher cognitive load. The implications of these results for the future teaching and business models of higher education are discussed. In addition, the potential of these computer-supported social collaborative learning settings is outlined.

The purpose of this paper is to investigate the impact of two dimensions of technological relatedness, namely technological similarity and complementarity, on collaborative performance, plus the mediating role of collaboration network stickiness and the moderating role of partner expertise and geographical distance in interfirm collaboration contexts.

This study takes Chinese Scientific and Technological Achievements (STA) of inter-firm collaboration in five high-tech fields in 2010–2020 as the sample and uses OLS regression to test the hypothesis.

Technological similarity and complementarity positively affect collaborative performance. Partner expertise negatively moderates the relationship between similarity, complementarity and collaborative performance. Geographical distance positively moderates the relationship between similarity and collaborative performance while negatively moderates that between complementarity and collaborative performance. Collaboration network stickiness partly mediates the relationship between similarity and collaborative performance.

This study expands literature on inter-firm collaboration, especially research on the antecedents of collaborative performance. Moreover, this study not only compensates for lack of empirical analysis in partner selection research, but also utilizes second-hand data to enhance the objectivity of analysis. Additionally, we enrich the research on the moderating role of partner expertise and geographical distance as well as the mediating role of collaboration network stickiness.

Infrastructure projects lend themselves quite naturally to the domain of collaborative networks, because they are not feasible without the coordinated efforts of several independent and heterogeneous actors. This chapter analyzes the nature and role of multilateral collaboration in the early stages of local infrastructure projects.

After having developed a conceptual model of collaborative approaches to early project stages, a survey was designed and targeted at experienced and informed managers of utilities that develop infrastructural facilities in Italy.

The empirical analysis reveals that collaboration with local governments and other stakeholders is sought by managers mainly to acquire knowledge about project options and feasibility, and to improve public acceptance.