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      Dynamic supply chain capabilities : How market sensing, supply chain agility and adaptability affect supply chain ambidexterity

      , , ,
      International Journal of Operations & Production Management
      Emerald

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          Abstract

          Purpose

          This paper positions market sensing, supply chain agility and supply chain adaptability as a coherent cluster of dynamic supply chain capabilities. The purpose of this paper is to understand how dynamic supply chain capabilities interrelate and their effect on supply chain ambidexterity.

          Design/methodology/approach

          Based on a survey of Pakistani manufacturing firms, a theoretically-derived model was tested in a structural equation model.

          Findings

          The results of the study show that a market-sensing capability is an antecedent of supply chain agility and supply chain adaptability. Furthermore, supply chain agility, directly, and supply chain adaptability, indirectly, affect supply chain ambidexterity. Supply chain agility, therefore, mediates the relationship between supply chain adaptability and supply chain ambidexterity.

          Originality/value

          The contribution of this study lies in: first, identifying dynamic capability clusters relevant for achieving supply chain ambidexterity; second, evaluating performance implications of dynamic capabilities in the supply chain, specifically supply chain agility and adaptability; and third, proposing a unique measurement of supply chain ambidexterity in the light supply chain theory, and empirically evaluating the relationship between dynamic capabilities and supply chain ambidexterity.

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          Most cited references98

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          Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

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

            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.
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              Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

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                Author and article information

                Journal
                International Journal of Operations & Production Management
                IJOPM
                Emerald
                0144-3577
                June 28 2018
                October 24 2018
                June 28 2018
                October 24 2018
                : 38
                : 12
                : 2266-2285
                Article
                10.1108/IJOPM-09-2017-0555
                63f5226e-100b-4c26-8d0a-5b82973b8953
                © 2018

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