دانلود رایگان مقاله انگلیسی قابلیت تجزیه و تحلیل کلان داده در چابکی زنجیره تامین - امرالد 2018

Abstract

Purpose – The purpose of this paper is to examine when and how organizations build big data analytics capability (BDAC) to improve supply chain agility (SCA) and gain competitive advantage. Design/methodology/approach – The authors grounded the theoretical framework in two perspectives: the dynamic capabilities view and contingency theory. To test the research hypotheses, the authors gathered 173 usable responses using a pre-tested questionnaire. Findings – The results suggest that BDAC has a positive and significant effect on SCA and competitive advantage. Further, the results support the hypothesis that organizational flexibility (OF) has a positive and significant moderation effect on the path joining BDAC and SCA. However, contrary to the belief, the authors found no support for the moderation effect of OF on the path joining BDAC and competitive advantage. Originality/value – The study makes some useful contributions to the literature on BDAC, SCA, OF, and competitive advantage. Moreover, the results may further motivate future scholars to replicate the findings using longitudinal data.

7. Conclusions

7.1 Implications for theory

Brusset (2016) found empirically that visibility may not have a significant effect on SCA. Contrary to Brusset’s (2016) findings, Dubey, Altay, Gunasekaran, Blome, Papadopoulos and Childe (2018) observed that supply chain visibility has a positive and significant effect on SCA. Srinivasan and Swink (2017) argue that visibility is created via external relations, which may help decision makers to sense changes in customer and competitors markets, including demands, pricing and promotional actions, and product inventories. Hence, the organizations that develop demand and supply visibility are also better positioned to develop and deploy systems and processes supporting analytics capability. Building upon this tautology, we posited that organizations that seek to enhance supply chain visibility would invest in building BDAC, which will help them handle large data derived from various sources to extract useful insights. Building on Srinivasan and Swink’s (2017) arguments, we tested the direct impact of BDAC on SCA. Our study is the first to provide an empirical test of the distinct effects of BDAC on SCA and competitive advantage. The information derived via BDAC provides firms with real-time information regarding changes in future product demand due to changes in downstream inventories, promotions, and sales. Moreover, supplier-sourced data provide information regarding supply shortages and excess inventories resulting from changes in upstream inventories, capacities, and the status of orders and shipments. We have thus answered the research calls of prior literature (see Gunasekaran, Yusuf, Adeleye and Papadopoulos, 2017; Gunasekaran, Papadopoulos, Dubey, Fosso Wamba, Childe, Hazen and Akter, 2017; Dubey, Altay, Gunasekaran, Blome, Papadopoulos and Childe, 2018). Moreover, our study is the first to empirically investigate the moderating effect of OF on the paths connecting BDAC and SCA/competitive advantage. Thus, we contribute to the literature by addressing the need for more holistic understanding of distinct relationships among contingencies (i.e. OF), response alternatives (i.e. BDAC) and multiple performance outcomes (i.e. SCA and competitive advantage). In doing so, we contribute to our understanding of the specific contextual factors under which BDAC can effectively improve SCA. Hence, by integrating the perspectives of the DCV and CT, we provide a solid theoretical grounding for our empirical investigation of CT as a complement to DCV, given its shortcomings in recognizing the complexity involved while bundling resources and capabilities (Eckstein et al., 2015).