a b s t r a c tIn this study, we examine the structural characteristics of supply networks and investigate the relationship between a firm's supply network accessibility and interconnectedness and its innovation output. We also examine potential moderating effects of absorptive capacity and supply network partner innovativeness on innovation output. We hypothesize that firms will experience greater innovation output from (1) higher levels of supply network accessibility and supply network interconnectedness, (2) the interaction between the levels of these two structural characteristics, (3) the moderating role of absorptive capacity on supply network accessibility and the moderating role of supply network partner innovativeness on supply network interconnectedness. Supply network partner relationships are drawn in the context of the electronics industry using data from multiple sources. We use social network analysis to create measures for each supply network structural characteristic. Using regression techniques to test the relationship between these structural characteristics and firm innovation for a sample of 390 firms, our findings suggest that supply network accessibility has a significant association with a firm's innovation output. The results also indicate that interconnected supply networks strengthen the association between supply network accessibility and innovation output. Moreover, the influence of the two structural characteristics on innovation output can be enhanced by a firm's absorptive capacity and level of supply network partner innovativeness. By addressing the need for deeper structural analysis, this study contributes to supply chain research by accounting for the embedded nature of ties in supply networks, and showing how these structural characteristics influence the knowledge and information flows residing within a firm's supply network.

Understanding and managing supply chain risks is a critical functional competency for today's global enterprises. A lack of this competency can have significant negative outcomes, including costly production and delivery delays, loss of future sales, and a tarnished corporate image. The ability to identify and mitigate risks, however, is complicated as supply chains are becoming increasingly global, complex, and interconnected. Drawing on the complex systems and epidemiology literature, and using a computational modeling and network analysis approach, we examine the impact of global supply network structure on risk diffusion and supply network health and demonstrate the importance of supply network visibility. Our results show a significant association between network structure and both risk diffusion and supply network health. In particular, our results indicate that small‐world supply network topologies consistently outperform supply networks with scale‐free characteristics. Theoretically, our study contributes to our understanding of risk management and supply networks as complex networked systems using a computational approach. Managerially, our study illustrates how decision makers can benefit from a network analytic approach to develop a more holistic understanding of system‐wide risk diffusion and to guide network governance policies for more favorable health level outcomes. The article concludes by highlighting the main findings and discussing possibilities of future research directions.

Supply chains are continuously evolving and adapting systems driven by complex sociotechnical interfirm interactions. Traditional engineering and operations management modeling approaches have primarily focused on technical issues and are not well suited to effectively capture the many complex structural and behavioral aspects of supply chain systems (SCSs). There is growing recognition by the supply chain community of the significant benefits a network analytic lens can provide to understand, design, and manage SCSs. We systematically review and analyze the relevant literature and, drawing on a multidisciplinary theoretical foundation, develop an integrative framework. Our framework identifies three distinct, but interdependent themes that characterize the study of SCSs: SCS network structure (i.e., system architecture), SCS network dynamics (i.e., system behavior), and SCS network strategy (i.e., system policy and control). We elaborate on these themes, review key findings, identify the current limitations and knowledge gaps, and discuss the fundamental benefits derived from adopting an integrated SCSs perspective. We conclude with future research directions for network analysis in SCS design and management, in particular, and complex enterprise systems, in general. and Sussman, 2011; Bartolomei et al., 2012], a comprehensive understanding of the performance and behavior of supply chain systems (SCSs) therefore requires consideration of both technical and social issues. Traditional engineering and operations management modeling approaches have primarily focused on technical issues [Min and Zhou, 2002]. These approaches, however, are not well suited to effectively capture and describe the structural and behavioral complexities inherent in SCSs.An emerging interdisciplinary lens promising to overcome this theoretical and methodological gap is the use of network analysis approaches [Basole et al., 2011]. Network analysis draws on theories from the social, organizational, and complexity sciences and leverages graph theoretic methods to model, analyze, and visualize the structure, dynamics, and strategies that shape SCSs. There has been a surge in scholarly studies modeling an SCS as a complex network of interactions between system entities since the seminal work by Choi and colleagues [Choi, Dooley, and Rungtusanatham, 2001;Choi and Hong, 2002] and more recently Borgatti and Li [2009]. However, there is no organizing framework to facilitate an understanding of the plethora of supply chain management (SCM) issues examined using network analysis. Moreover, previous work on the use of network analysis in systems engineering (SE) is quite sparse. Notable exceptions include the work by Batallas and Yassine [2006], Braha and Bar-Yam [2006], Collins, Yassine, and Borgatti [2009], and Bartolomei et al. [2012], where the focus is almost entirely on product development; there are no SE studies that have used network analysis for the study of SCSs. Consequently, there is a window of opportunity to review and illustrate the va...

This study contributes to a theoretical and empirical understanding of whether and how administrative environmental innovations (AEIs)-implemented to help track and manage a firm's environmental impacts-are related to environmental disclosure. Drawing on the Belief-Action-Outcome framework, we posit that the motivation of individuals (employees, managers, and the leadership) within the firm to access, use, and act on the environmental information available to them would be enhanced by the firm's implementation of AEIs, resulting in more extensive environmental disclosure by the firm. Additionally, building on the literature on supply chain networks, we posit that the structural position of the firm vis-à-vis its supply network-reflecting information flows, network learning, and status-moderates the AEI implementationenvironmental disclosure relationship. To test our hypotheses, we build a multi-industry dataset of 3,106 firm-year observations based on 67,809 dyadic cost-of-goods-sold-based relationships obtained from Bloomberg's supply chain relationships database to construct the supply networks of focal firms. We also draw on Bloomberg's environmental, social, and governance (ESG) data for our AEI implementation and environmental disclosure measures. We find significant evidence to support our hypothesis that AEI implementation is positively associated with the extent of environmental disclosure. However, the implementation of both internal and external forms of AEIs has a more pronounced positive relationship with the extent of environmental disclosure, compared to the implementation of either form alone. With regard to supply network structure, we identify three principal variables-accessibility, control, and interconnectedness-that influence network learning and status of the focal firm and find that they moderate the AEI implementation-environmental disclosure relationship. We provide insights for theory and practice based on our findings.

No abstract

This methodological note identifies and describes a data‐driven visualization approach to study innovations in supply chain networks (ISCN). We demonstrate its value and applicability with illustrative examples to pertinent structure‐related ISCN research questions in the global electronics industry. Our visualization approach can be used to reveal and understand important clusters, patterns, trends, and outliers of ISCN not necessarily identified with traditional methods. The broader aim of this note is to demonstrate the complementary value of emerging visual analytic approaches in managerial decision‐making contexts and describe how actionable insights can be achieved.

No abstract

We investigate the evolution of supply chain alliance networks with a focus on the influence of structural, firm‐, and industry‐level mechanisms. While several structural supply chain characteristics have been found to be significant drivers of firm innovation and performance, a dearth of studies exists examining how these characteristics change over time by influencing one another. We develop and empirically test hypotheses on the impact of prior structural configurations and the moderating roles of absorptive capacity and industry growth on the temporal trajectory of supply chain alliance network structures. Adopting a multi‐method approach, we jointly use econometric analyses and simulation experiments to examine our hypotheses from complementary angles. Specifically, we characterize the dynamic relationship between the structural mechanisms on a longitudinal dataset of 2221 unique firms and 13,668 firm‐year observations spanning 25 years. We find empirical support for negative crossover effects between two key structural properties of supply chain alliance networks, a positive moderation of a firm's absorptive capacity, and a negative moderation of industry growth on the structural reinforcement. We conduct corresponding simulation experiments based on a separable temporal exponential random graph model (STERGM) to track the temporal changes in the simulated networks' key measures. The simulation results concur with most of our empirical findings and provide additional insights complementary to our econometric analysis results. By focusing on the mechanism of temporal changes in network structural properties, our study contributes to supply chain management research with a supply network perspective and interfirm alliance network research by broadening its scope into structural dynamism. Our multi‐method approach demonstrates how multiple complementary methodologies can foster a more nuanced understanding of managing supply chain alliance network management.