Dynamical Gene-Environment Networks Under Ellipsoidal Uncertainty: Set-Theoretic Regression Analysis Based on Ellipsoidal OR

Approaching towards sustainable supply chain under the spotlight of business intelligence

These two main objectives of this study are to present a theoretical model to explain how business intelligence capabilities influence the company's supply chain sustainability and to examine the relationships among different BI and CSCS dimensions. This study was conducted with the use of a standard BI questionnaire along with the United Nations CSCS questionnaire among 234 Iranian pharmaceutical companies, from which 188 were also surveyed. Smart pls3 and partial least squares methods were used for validity as well as reliability evaluation of the measurement model. According to the findings, BI significantly affects the sustainability of the pharmaceutical supply chain and some of its dimensions, including vision, scope, and internal aspects, thereby the hypothesis indicating the effect of BI on these dimensions was accepted. However, there was an insignificantly positive relationship between BI and the other dimensions of CSCS, including expectation, engagement, and goals; hence, the hypothesis indicating the effect of BI on these dimensions was rejected. If the policy of the board is to implement supply chain sustainability, BI can have a greater impact on the company. Otherwise, BI may be implemented with not much effect though it can be indirectly beneficial to these companies. No studies have been performed on direct examination of the relationship of BI and CSCS and their various dimensions with the use of an extensive survey among Iran's pharmaceutical companies. Also, this study reveals some facts about the sustainability of the pharmaceutical supply chain, BI, and relevant issues as significant obstacles against a sustainable supply chain and BI. This article also supports the UN questionnaire on supply chain sustainability and adopts it in the surveys. Furthermore, various social networks such as Facebook, Twitter, and Instagram were compared, and it was concluded that the data required for the pharmaceutical industry was more accessible from Twitter, in comparison to the other social networks.