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      Industry 4.0 technology capabilities, resilience and incremental innovation in Australian manufacturing firms: a serial mediation model

      , , ,
      Supply Chain Management: An International Journal

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          Abstract

          Purpose

          There is a well-documented trend among businesses for applying continuously improving, technologically-supported processes. This trend, in part, responds to evolving and challenging business environments and competitive pressures. It also increasingly mandates the need for businesses to invest in improving their digital capabilities and is driven by the expectation that such investments will better equip them for uncertain times. The COVID-19 pandemic presented disruptions to the supply chain, logistics, operations, market demand and labour supply, with industry reports providing evidence that businesses with digital capabilities were better able to respond to such disruptions promptly and appropriately. The study aims to investigate the effects of Industry 4.0 (I4.0) technologies on business operations and supply chain resilience.

          Design/methodology/approach

          The authors surveyed 117 Australian manufacturing firms using an online survey and analysed the data by using the partial least square structural equation modelling method.

          Findings

          The authors found I4.0 capabilities directly and positively impact supply chain resilience and that incremental innovation acts as a complementary mediator for the I4.0 technologies’ relationship with supply chain resilience. I4.0 technology capability needs to first transfer to incremental innovation for operations resilience. The authors also found that incremental innovation and operations resilience are serial mediators in the relationship between I4.0 technologies and supply chain resilience.

          Originality/value

          This research linked the three research areas of I4.0 implementations, innovation capabilities and resilience. To the best of the authors’ knowledge, there has not been a previous study that investigated all three constructs together. Also, this study considered operations resilience and supply chain resilience as two distinct constructs and found I4.0 technologies had differential effects on them. The findings, thus, provide a novel contribution to the resilience, organizational capability and innovation literature. The investigations make clear to business practitioners how investments in technology and innovation capabilities translate into the resilience that is required in periods of disruption to business certainty.

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          Most cited references63

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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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            • Record: found
            • Abstract: not found
            • Article: not found

            Using PLS path modeling in new technology research: updated guidelines

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              • Abstract: found
              • Article: not found

              Confidence Limits for the Indirect Effect: Distribution of the Product and Resampling Methods.

              The most commonly used method to test an indirect effect is to divide the estimate of the indirect effect by its standard error and compare the resulting z statistic with a critical value from the standard normal distribution. Confidence limits for the indirect effect are also typically based on critical values from the standard normal distribution. This article uses a simulation study to demonstrate that confidence limits are imbalanced because the distribution of the indirect effect is normal only in special cases. Two alternatives for improving the performance of confidence limits for the indirect effect are evaluated: (a) a method based on the distribution of the product of two normal random variables, and (b) resampling methods. In Study 1, confidence limits based on the distribution of the product are more accurate than methods based on an assumed normal distribution but confidence limits are still imbalanced. Study 2 demonstrates that more accurate confidence limits are obtained using resampling methods, with the bias-corrected bootstrap the best method overall.
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                Author and article information

                Journal
                Supply Chain Management: An International Journal
                SCM
                1359-8546
                1359-8546
                January 10 2023
                April 28 2023
                January 10 2023
                April 28 2023
                : 28
                : 4
                : 760-772
                Article
                10.1108/SCM-08-2022-0325
                7cbec741-df7a-4faf-900a-de8a155663da
                © 2023

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