Journal of Supply Chain Management最新文献

Supply chain management researchers are increasingly using archival data to push boundaries of existing knowledge. Archival data provide opportunities to address new research questions and offer fresh perspectives on old questions. This editorial seeks to establish a common ground regarding research design, measurement validity, and endogeneity to help both authors and reviewers fully utilize archival data to advance supply chain management knowledge.

Recent research highlights opportunities to study collaborations between established firms and startups from a buyer–supplier relationship perspective. However, before firms can leverage startups’ resources and capabilities in a buyer–supplier relationship, they need to identify, evaluate, and select suitable startups as their suppliers. As prior research has only studied how buying firms select established firms as suppliers, it is unclear which processes, tools, or organizational approach firms use when selecting startup firms as suppliers. These suppliers are markedly different in that they lack organizational routines, financial resources, and operational capabilities, posing a significant risk for buying companies. This inductive, qualitative case study research collects data from 20 established buying firms and investigates how they select startups as suppliers. We first identify five design themes distinguishing buying firms’ selection strategies. Based on these themes, we develop a typology of three supplier selection archetypes. The results suggest that firms that are willing and able to adapt their selection approach to startups are expected to show a higher selection performance, meaning that they are more likely to select suitable startups as suppliers. The results extend the supplier selection literature and provide new insights into the emerging field of new venture suppliers.

The risk created by government policymaking can be daunting, but little is known about the extent to which this risk is disruptive to business in general and to supply chain operations in particular. Because government is a powerful and omnipresent entity, scholars and executives alike could benefit from greater understanding of how firms react to risk emanating from the policymaking process. To help address this gap, we use resource dependence theory to develop hypotheses concerning the accumulation of inventory by firms to buffer against their exposure to potential policy changes and how such a link might be moderated by macro-level and industry-level factors. Data from 19,634 firm-year observations reveal that firms accumulate more inventory as a buffer against policy risk under conditions of high policy uncertainty and high industry dynamism. Overall, our findings support the predictions of resource dependence theory and refine understanding of supply chain responses to macro-level uncertainty by demonstrating the contingent influence of government policy. In doing so, our study provides a foundation for future research to explore the intersections between government actions and supply chain activities and offers insights for managers and policymakers about how to factor government into their decision calculi.

Global apparel brands that source production from less developed countries are increasingly exposed to risks that arise from human rights violations. Despite widely publicized factory safety failures, reactionary regulatory efforts remain inadequate. This situation is commonly attributed to unyielding downward price pressure imposed on factories by Western brands. To address this problem, this research develops a comprehensive cost model for low-cost apparel production following a total cost of ownership approach. Comprehensive production data for cotton T-shirts from nine Bangladeshi factories provide the basis for model construction. The model reveals that materials generate the bulk of production costs (77%) for a T-shirt, in contrast to worker wages which generate only 2 percent of the total production cost per unit (e.g., 16 cents out of a $8USD retail price). The TCO provides fundamental insights, grounded in real data, to guide policy and regulation among stakeholders in high-risk, low-cost supply chains. Based on the research context, results are considered through the lens of agency theory to provide managerial implications for relevant stakeholders.

The Journal of Supply Chain Management’s 2020 Emerging Discourse Incubator hopes to stimulate the development of supply chain specific theory. Well-executed case-based research will always be an appropriate means for developing supply chain theory. However, this EDI seeks to highlight emerging approaches to theory building that provide alternatives to case-based research or can be used as a source of triangulation with it. To start that discourse, this issue offers three emergent approaches. In “Theorizing Supply Chains with Qualitative Big Data and Topic Modeling,” Tima Bansal, Jury Gualandris, and Nahyun Kim explore the application of topic modeling to develop supply chain theory from qualitative textual big data evidence. Anne Touboulic, Lucy McCarthy, and Lee Matthews illustrate the use of critical engaged research to develop supply chain theory in “Re-Imagining Supply Chain Challenges Through Critical Engaged Research.” In “A New Methodology for Supply Chain Management: Discourse Analysis and its Potential for Theoretical Advancement,” Cynthia Hardy, Vikram Bhakoo, and Steve Maguire describe the potential for discourse analysis for developing supply chain management theory. These are but a few examples of potential approaches to developing supply chain theory. We welcome submissions on additional approaches and sources of data that are used in other disciplines, but have yet to be applied in the context of developing supply chain theory, and submissions on approaches that are emergent in the social sciences, in general. JSCM welcomes submissions for this EDI through the end of 2020.

In this manuscript, we explore how engaged research can support the development of the theory and practice of supply chain management (SCM) and present critical engaged research as an extended form of engaged research. The article’s main purpose is to examine more closely the relationship between critical engaged research and the process of theorizing. This essay presents an expanded model of knowledge production for the field of SCM and explores the opportunities for the production and co-production of new knowledge types, with an emphasis on knowledge produced through a critical engagement with practice. We offer a discussion on how critical engaged research may be applied in SCM research to build, elaborate and test theory.

The availability of Big Data has opened up opportunities to study supply chains. Whereas most scholars look to quantitative Big Data to build theoretical insights, in this paper we illustrate the value of qualitative Big Data. We begin by describing the nature and properties of qualitative Big Data. Then, we explain how one specific method, topic modeling, is particularly useful in theorizing supply chains. Topic modeling identifies co-occurring words in qualitative Big Data, which can reveal new constructs that are difficult to see in such volume of data. Analyzing the relationships among constructs or their descriptive content can help to understand and explain how supply chains emerge, function, and adapt over time. As topic modeling has not yet been used to theorize supply chains, we illustrate the use of this method and its relevance for future research by unpacking two papers published in organizational theory journals.

Governments play important roles as focal organizations in many interorganizational networks. However, the government perspective has largely been overlooked in the literature on supply networks, including research on humanitarian operations and logistics. So far, little attention has been devoted to how government agencies and other actors interact within complex networks. In this study, we use a qualitative research approach to study interorganizational interaction in the context of a major U.S. disaster: Hurricane Sandy. Specifically, we investigate the relatively successful Sandy response effort conducted by the New Jersey state government in interaction with other humanitarian actors. We find that the government took three main roles in interacting with other actors within the disaster response network: organizer, facilitator, and supply network member. Moreover, we develop a grounded model that provides a theoretical explanation of the interaction process and highlights the practices used by the government during the response stage. In addition to contributing to the humanitarian research domain, our study advances the emerging discourse on networks whose focal actors are not for-profit firms.

This study develops and tests theory about different forms of absorptive capacity that support radical, differentiation and imitation product development strategies. Absorptive capacity theory provides a generalized explanation for how firms exploit their embeddedness in relationships with buyers and suppliers. We develop and test theory that relates combinations of four components of absorptive capacity (R&D capability, product development capability, cooperative embeddedness, and competitive embeddedness) to success rates in three product development strategies. We used data from the American pharmaceutical industry to estimate generalized linear mixed models. Our results confirm known relationships between R&D capability, alliance network position, and the development of radically new products, but reveal different sets of factors that influence differentiation and imitation. We describe a previously undetected influence of competitive embeddedness on the development of radically new products, a contrasting absorptive capacity structure for generic product development, and a mixed structure for differentiated product development.