Supply Risk Management: Model Development and Empirical Analysis

  • Daniel Kern
  • Roger Moser
  • Evi Hartmann
  • Marco Moder
Part of the Essays on Supply Chain Management book series (ESCM)


Purpose – The purpose of this paper is to develop a model for upstream supply chain risk management linking risk identification, risk assessment and risk mitigation to risk performance and validate the model empirically. The effect of a continuous improvement process on identification, assessment, and mitigation is also modeled.

Design/methodology/approach – A literature review is undertaken to derive the hypotheses and operationalize the included constructs. We then test the path analytical model using partial least squares analysis on survey data from 162 large and mid-sized manufacturing companies located in Germany.

Findings – All items load high on their respective constructs and the data provides robust support to all hypothesized relationships. Superior risk identification supports the subsequent risk assessment and this in turn leads to better risk mitigation. The model explains 46% of the variance observed in risk performance.

Research limitations/implications – This study empirically validates the effect of the three risk management steps on risk performance and the influence of continuous improvement activities. Limitations of this study can be seen in the use of perceptional data from single informants and the focus on German manufacturing firms.

Practical implications – The detailed operationalization of the constructs sheds further light on the problem of measuring risk management efforts. Clear measurement of the performance of risk management helps to justify investments into risk management.

Originality/value – This is one of the first large-scale, empirical studies on the process dimensions of upstream supply chain risk management.


Supply Chain Risk Management Partial Little Square Risk Performance Risk Mitigation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Gabler Verlag | Springer Fachmedien Wiesbaden 2011

Authors and Affiliations

  • Daniel Kern
  • Roger Moser
  • Evi Hartmann
  • Marco Moder

There are no affiliations available

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