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Supplier–supplier coopetition is vital to buyer innovation in reality. However, it has not received enough attention in prior research. Integrating the ability-motivation-opportunity framework of organizational learning perspective and the awareness-motivation-capability framework of competitive dynamics theory, this paper investigates the effect of supplier–supplier coopetition within supplier network on buyer innovation, as well as the contingent role of the relational attributes -- duration and tie strength dispersion of buyer–suppliers relationship at the supplier network level.

Testing this model on the secondary data of supply networks formed by 204 US listed buyer firms in SIC code 28, 35, 36 during 2008–2019, the authors utilize a fixed-effect regression model to investigate the relationship between supplier–supplier coopetition and the focal buyer's innovation.

The authors provide support for the positive influence of supplier–supplier cooperation on buyer innovation and an inverted U-shaped relationship between supplier–supplier competition and the focal buyer's innovation. The buyer–suppliers tie strength dispersion amplified the above two effects, and supplier–supplier cooperation mitigates the effect of supplier–supplier competition on the focal buyer's innovation.

Extending the traditional dyadic view to a network-level view via linking the supplier–supplier dyad and the buyer–suppliers dyad, this paper contributes to a better understanding of supplier–supplier coopetition and its impact on buyer innovation with learning and competitive tension as the underlying explanations, and validates the contingent role of buyer–suppliers relational attributes.

This paper investigates third-party relationships in project networks in the construction industry and seeks increased understanding on how such relationships can be strengthened. The purpose of this paper is to develop a framework and propositions on enhanced relationship strength between component suppliers and designers as third parties.

A conceptual approach is used in this paper. Previous empirical research on business relationships, project networks and relationship strength is reviewed systematically, to identify factors required for strengthening the relationship in project networks.

Cooperative practices needed for strengthening the relationships in project networks were identified. The framework on how such practices are associated with relationship strength between supplier and designers was developed. Propositions on strengthening the relationship between component suppliers and designers were stated. These propositions can be developed further and tested in a hypothetic-deductive study.

The research was delimited by the choice of designers as third parties. The authors used some excerpts from the earlier interview study with over 20 designers, to illustrate the issues. Empirical analysis was not included in this paper which causes an evident limitation to validity. Additional research is proposed on analyzing the contractors’ and suppliers’ viewpoints to third-party relationships.

The paper suggests cooperative practices for construction component suppliers to enhance their relationship strength with third parties in project networks.

Limited research attention has been directed at the third-party relationships of suppliers in project networks. This paper offers important knowledge about these less salient relationships in project networks, beyond a simple dyadic relationship in the direct supply chain.

Recent management literature demonstrates a growing interest in strategic networking. The aim of this paper is to explore the adoption of strategic networks using Hong Kong clothing industry as the context because the influence of Asian cultures is under‐researched.

Three key research questions about asset specificity, years of relationship, and size of the supplier base were developed from the literature. Qualitative data were collected from in‐depth interviews with senior executives at five large clothing companies in Hong Kong.

The qualitative findings confirm that the specific asset investment, reciprocal or one‐way, has a positive impact on the development of strategic networking; that enterprises strive hard to maintain longer relationships with key supplier members; and that a small supplier base is widely adopted by the sample firms.

The implications for managerial practice are that strong strategic networking is needed as transaction‐specific assets can safeguard the network relationship; that a long‐term relationship enables effective transactions; and that managing a small number of suppliers helps to stabilize network relationships.

This paper represents an initial attempt to include Asian cultures in the study of strategic network concepts within one globalized industry – the Hong Kong clothing industry. The paper also demonstrates to practicing managers how strategic networks made up of manufacturers and their suppliers are adopted and maintained, and in turn, guides practicing managers on how to allocate resources appropriately to develop a strategic network.

Prior research on supply chain management has advanced substantially our understanding of how suppliers’ knowledge affects manufacturers’ green innovation. However, overlooking the suppliers’ diverse green knowledge in supplier networks, namely, green knowledge diversity, has limited our understanding of both supply chain management and green innovation development. To address this important issue, this study aims to rely on social network theory as the overarching framework and knowledge-based view as the underlying theoretical foundation to examine how green knowledge diversity contributes to manufacturers’ green innovation performance, while considering three types of supplier network properties (network strength, network heterogeneity and network density).

This study collects both survey and secondary proxy data from 209 manufacturing firms over three time periods (mid-2018, mid-2019 and mid-2020). PROCESS macro is applied to test the research hypotheses.

The results provide compelling evidence that green knowledge management processes partially mediate the effect of green knowledge diversity on manufacturers’ green innovation performance. The effect of green knowledge diversity is strengthened by supplier network strength and supplier network heterogeneity, but hindered by supplier network density.

This study provides a practical guide to help manufacturers enhance green innovation performance by properly managing and leveraging their suppliers’ diverse green knowledge domains in supplier networks.

This study contributes to the supply chain management and green innovation literature by offering novel theoretical and empirical insights into how manufacturers can use their supplier networks to strengthen green innovation.

The purpose of this paper is aimed at understanding and identifying the context, capability requirements, and configurations (3Cs) of modular supply networks in the Chinese automotive industry.

In an attempt to specify the main elements of modular supply networks, a case study and 3C framework approach analyze different network contexts. The framework considers not only the capabilities of product design, production, inbound logistics, and information sharing, but also the configuration of the role structure, process structure, and information structure that helps achieve these capabilities.

This paper analyzes two types of modular supply network, directly reflecting the degrees of involvement in the modularity logic of the module supplier and the automaker, which largely depend on their corporate strategy toward technology innovation. In a fully integrated module supply network, the module supplier needs sufficient capability for upstream supply network integration and modularization, whereas in the partly integrated module supply network, a third‐party logistics provider plays an important role with regard to production capability.

This paper investigates four modules. The practical tools for configuring the modular supply network can be specified in more detail in further research.

The 3C framework described herein contributes to theory in the fields of supply chain management and modularity, and it assists in further expanding the theory of SCM.

The purpose of this paper is to analyze the evolution of a supply network and the causes of network change and stability. It presents the study of a supply network of Delphi Braga (Portugal), one of the largest European plants producing car-radios, navigation system and other related products, in order to understand how it changed over time. The paper contributes to the understanding of network management.

The case study was carried out on a network of five suppliers of Delphi Braga adopting a relational view of networks meaning that both parties of the relationships were studied. Data and results are based on document sources and interviews with managers both from Delphi and the studied suppliers.

The causes of network change and stability are both exogenous and endogenous. They are multiple and have different impacts on network structures and processes. The paper analyses several causes of network change and their impact on the studied supplier network.

The research is based on a single case study. Although the studied supplier network comprises several firms, it should be pointed out that each network has its own dynamics and structures. Therefore, though the paper makes a relevant contribution for network management, the results cannot be generalized for other networks and firms.

Managers should be aware of the dynamics of their supplier networks and understand the different impacts of both their firm’s decisions as well as the environment changes. By doing this, managers can anticipate changes in the structure and dynamics of their supplier networks.

This case study contributes to our current understanding of relationship development both at the dyadic and network level by analyzing the case of a supply network in a major European plant of car-radios and navigation systems. It discusses implications and proposes further research to advance knowledge on the topic of network change.

This paper attempts to address three key objectives. The primary aim is to enhance sourcing strategy for a centralized and coordinated multitier multiple suppliers networks with uncertain demand and supplier failure risks. The second objective is to enumerate all possible practical supplier(s) failure scenarios and quantify expected loss of demand cost. Finally, the work illustrates statistical experimentation to identify “influential” variables that can significantly impact the expected supply network and loss costs.

A seven-step solution framework is proposed to derive an optimal sourcing strategy for the specific network configuration with varied supplier failure scenarios. Five distinct models are formulated to address all possible scenarios of supplier failure events. Mixed-integer nonlinear programming technique is used to derive expected supply network cost and loss cost. The solution framework is verified using a real-life case.

A cross-case analysis indicates that an increase in suppliers' failure risk (SFR) probabilities or customer demand rate increases the expected loss of demand costs for a multitier supply network. Besides, an increase in unit component prices increases the expected supply network cost.

A two-tier automotive supply network for a single product is considered for all case studies.

The enhanced strategy can facilitate practitioners enumerate different supply network failure scenarios and implement the best solution.

There is no evidence of earlier research to derive optimal sourcing strategy for a centralized, coordinated multitier multiple supplier's network, considering demand uncertainties and SFR.

The purpose of this paper is to provide a methodology for benchmarking supplier risks through the creation of Bayesian networks. The networks are used to determine a supplier's external, operational, and network risk probability to assess its potential impact on the buyer organization.

The research methodology includes the use of a risk assessment model, surveys, data collection from internal and external sources, and the creation of Bayesian networks used to create risk profiles for the study participants.

It is found that Bayesian networks can be used as an effective benchmarking tool to assist managers in making decisions regarding current and prospective suppliers based upon their potential impact on the buyer organization, as illustrated through their associated risk profiles.

A potential limitation to the use of the methodology presented in the study is the ability to acquire the necessary data from current and potential suppliers needed to construct the Bayesian networks.

The methodology presented in this paper can be used by buyer organizations to benchmark supplier risks in supply chain networks, which may lead to adjustments to existing risk management strategies, policies, and tactics.

This paper provides practitioners with an additional tool for benchmarking supplier risks. Additionally, it provides the foundation for future research studies in the use of Bayesian networks for the examination of supplier risks.

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.

To counteract the effects of global competition, many organizations have extended their enterprises by forming supply chain networks. However, as organizations increase their dependence on these networks, they become more vulnerable to their suppliers' risk profiles. The purpose of this paper is to present a methodology for modeling and evaluating risk profiles in supply chains via Bayesian networks.

Empirical data from 15 casting suppliers to a major US automotive company are analyzed using Bayesian networks. The networks provide a methodological approach for determining a supplier's external, operational, and network risk probability, and the potential revenue impact a supplier can have on the company.

Bayesian networks can be used to develop supplier risk profiles to determine the risk exposure of a company's revenue stream. The supplier risk profiles can be used to determine those risk events which have the largest potential impact on an organization's revenues, and the highest probability of occurrence.

A limitation to the use of Bayesian networks to model supply chain risks is the proper identification of risk events and risk categories that can impact a supply chain.

The methodology used in this study can be adopted by managers to formulate supply chain risk management strategies and tactics which mitigate overall supply chain risks.

The methodology used in this study can be used by organizations to reduce supply chain risks which yield numerous societal benefits.

As part of a comprehensive supplier risk management program, organizations along with their suppliers can develop targeted approaches to minimize the occurrence of supply chain risk events.