Can supply chain risk management practices mitigate the disruption impacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

<p class="first" id="d10503943e130">This study investigates the role of supply chain risk management (SCRM) in mitigating the effects of disruptions impacts on supply chain resilience and robustness in the context of COVID-19 outbreak. Using structural equation modeling on a survey data from 470 French firms, the results confirm the basic tenets of resource-based view and organizational information processing theories regarding the combination of dynamic resources to face disruptions’ uncertainty. Furthermore, the findings reveal the mediating role of SCRM practices and the prominent role they play in fostering supply chain resilience and robustness. Overall, by providing empirical assessment of a comprehensive SCRM framework, this research contributes to the extant literature and suggests further avenues for research. </p>

Related collections

Most cited references 155

Record: found
Abstract: found
Article: not found

Common method biases in behavioral research: A critical review of the literature and recommended remedies.

Philip M Podsakoff, Scott MacKenzie, Jeong-Yeon Lee … (2003)

Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.

0 comments Cited 10262 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

Franz Faul, Edgar Erdfelder, Axel Buchner … (2009)

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

0 comments Cited 5392 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Firm Resources and Sustained Competitive Advantage

Jay Barney (1991)

Understanding sources of sustained competitive advantage has become a major area of research in strategic management. Building on the assumptions that strategic resources are heterogeneously distributed acrossfirms and that these differences are stable over time, this article examines the link betweenfirm resources and sustained competitive advantage. Four empirical indicators of the potential of firm resources to generate sustained competitive advantage-value, rareness, imitability, and substitutability-are discussed. The model is applied by analyzing the potential of severalfirm resourcesfor generating sustained competitive advantages. The article concludes by examining implications of this firm resource model of sustained competitive advantage for other business disciplines.