1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Data-driven capabilities, supply chain integration and competitive performance : Evidence from the food and beverages industry in Pakistan

      ,
      British Food Journal
      Emerald

      Read this article at

      ScienceOpenPublisher
      Bookmark
          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

          Purpose

          The purpose of this paper is to analyze the effects of data-driven capabilities on supply chain integration (SCI) and competitive performance of firms in the food and beverages (F & B) industry in Pakistan.

          Design/methodology/approach

          The authors adopt the structural equation modeling approach to test the proposed hypotheses using AMOS 23. Survey data were collected from 240 firms in the F & B industry in Pakistan.

          Findings

          The results revealed that SCI (i.e. internal integration (II) and external integration (EI)) significantly mediates the effect of data-driven capabilities (i.e. flexible information technology resources and data assimilation) on a firm’s competitive performance. In addition to the direct effects, II also has an indirect effect on competitive performance through EI.

          Practical implications

          The study has several implications for managers in the context of big data application in food supply chain management (FSCM) in a developing country context. The study posits that firms can achieve excellence in performance by governing data-driven supply chain operations. The study also has implications for distributors and importers in the F & B industry. The cloud-based sharing of data can improve the operational performance of channel members while reducing their overall cost of operations. In practice, food franchises largely get the advantage of shared resources of their suppliers in managing orders, payments, inventory and after-sales services.

          Originality/value

          The study is novel and deepens the understanding about the use of big data in FSCM keeping in view the industry trends and stakeholder’s priorities in a developing country context.

          Related collections

          Most cited references89

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

          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.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

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

              Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models

                Bookmark

                Author and article information

                Journal
                British Food Journal
                BFJ
                Emerald
                0007-070X
                September 23 2019
                October 23 2019
                September 23 2019
                October 23 2019
                : 121
                : 11
                : 2708-2729
                Article
                10.1108/BFJ-02-2019-0131
                86acfa85-63c0-4d69-803e-b58350396611
                © 2019

                https://www.emerald.com/insight/site-policies

                History

                Comments

                Comment on this article