From the Editors: Introduction to the Emerging Discourse Incubator on the Topic of Emerging Approaches for Developing Supply Chain Management Theory

IF 10.2 2区 管理学 Q1 MANAGEMENT Journal of Supply Chain Management Pub Date : 2020-03-16 DOI:10.1111/jscm.12227
Barbara Flynn, Mark Pagell, Brian Fugate
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引用次数: 12

Abstract

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.

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编者:关于发展供应链管理理论的新兴方法主题的新兴话语孵化器介绍
《供应链管理杂志》2020年新兴话语孵化器希望能够促进供应链具体理论的发展。执行良好的基于案例的研究将永远是发展供应链理论的合适手段。然而,这个EDI试图强调理论构建的新兴方法,这些方法为基于案例的研究提供了替代方案,或者可以作为三角测量的来源。为了开始这个讨论,这个问题提供了三种新兴的方法。在“用定性大数据和主题建模理论化供应链”一文中,Tima Bansal、Jury Gualandris和Nahyun Kim探讨了主题建模在从定性文本大数据证据发展供应链理论中的应用。Anne Touboulic, Lucy McCarthy和Lee Matthews在“通过批判性参与研究重新想象供应链挑战”中阐述了使用批判性参与研究来发展供应链理论。在《供应链管理的新方法论:话语分析及其理论进步的潜力》一书中,Cynthia Hardy、Vikram Bhakoo和Steve Maguire描述了话语分析在发展供应链管理理论方面的潜力。这些只是发展供应链理论的几个潜在方法的例子。我们欢迎提交在其他学科中使用的其他方法和数据来源,但尚未在发展供应链理论的背景下应用,以及提交在社会科学中出现的方法。JSCM欢迎在2020年底之前提交此EDI。
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来源期刊
CiteScore
16.00
自引率
6.60%
发文量
18
期刊介绍: ournal of Supply Chain Management Mission: The mission of the Journal of Supply Chain Management (JSCM) is to be the premier choice among supply chain management scholars from various disciplines. It aims to attract high-quality, impactful behavioral research that focuses on theory building and employs rigorous empirical methodologies. Article Requirements: An article published in JSCM must make a significant contribution to supply chain management theory. This contribution can be achieved through either an inductive, theory-building process or a deductive, theory-testing approach. This contribution may manifest in various ways, such as falsification of conventional understanding, theory-building through conceptual development, inductive or qualitative research, initial empirical testing of a theory, theoretically-based meta-analysis, or constructive replication that clarifies the boundaries or range of a theory. Theoretical Contribution: Manuscripts should explicitly convey the theoretical contribution relative to the existing supply chain management literature, and when appropriate, to the literature outside of supply chain management (e.g., management theory, psychology, economics). Empirical Contribution: Manuscripts published in JSCM must also provide strong empirical contributions. While conceptual manuscripts are welcomed, they must significantly advance theory in the field of supply chain management and be firmly grounded in existing theory and relevant literature. For empirical manuscripts, authors must adequately assess validity, which is essential for empirical research, whether quantitative or qualitative.
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