This study aims to examine the influence of Big Data analytics, innovation & capabilities in the supply chain as well as to find the moderating effect of organisational flexibility on the performance of the supply chain (SCP) and find the effect of competitive intensity as a controlling variable on the performance of supply chain. This research aims to present a theoretical model based on the resource-based theory's relational view (RBV). The data was collected through a survey questionnaire and 25 manufacturer firms managers participate in this survey. According to the findings of this study BDA has a favourable and strong association with SCI and SCC, as well as SCP. The majority of the manufacturers' firms in this research employed BDA to speed up their standing algorithms quicker with massive data sets.

The purpose of this paper is to investigate potential links between Blockchain technology (BCT) and big data analytics (BDA) with supply chain risk management (SCRM) and supply chain performance (SCP) in the Jordanian Chemical and Cosmetic Industries Sector. Additionally, the paper tests a conceptual model that links SCRM to indirect effects. To test our proposition, data were collected from 364 employees working in Jordanian Chemical and Cosmetic Industries Sector. The data were analyzed using structural equation modeling with aid of the Lavaan R package. The results show that the influences of blockchain technology and big data analytics on supply chain performance do occur directly, and indirectly through the cascading of a supply chain risk management.

The objective of the study is to identify the impact of big data on sustainable supply chain management. The current research was conducted on hypermarkets in Jordan. Many of these hypermarket brands are widely scattered in Jordan, for instance, Carrefour, Kareem, Safeway and more. Accordingly, the target population in the current research was hypermarkets managers in Jordan as they are responsible for formulating such strategies in the companies they work for. A convenience sample was selected from the target population that included 770 managers based on the sample size formula. The study hypotheses were tested by covariance based structural equation modeling (CB-SEM). The study results showed the impact of each big data dimension on sustainable supply chain management. Based on this result, the researchers recommend the hypermarkets in Jordan to use modern and diverse methods for accurate collection of reliable data and save it in organized ways, and to employ advanced programs to analyze it and extract information of high value.

The aim of this study is to explore the impact of supply chain 4.0 technologies (e.g., big data and Internet-of-things) of the strategic outcomes of supply chain 4.0. The required data was collected via a questionnaire from a sample consisting of 211 employees from construction companies in Jordan. The results point out that big data and the Internet of things have significant impacts on the strategic outcomes of supply chain 4.0. Hence, it was concluded that supply chain technologies are pivotal drivers of supply chain strategic outcomes. Based on these results companies are called to adopt and integrate Industry 4.0 technologies into their supply chains to improve supply chain long-run performance. Scholars were also encouraged to investigate the impact of supply chain 4.0 technologies on other constructs such as supply chain resilience.

Adopting the Supply Chain Operations Reference (SCOR) model, this study aims at investigating the impact of big data (volume, velocity, variety, veracity, and value) on supply chain performance through the mediating role of supply chain management (plan, source, make, deliver, and return) assuming four hypotheses. Data were collected using a questionnaire from managers of food processing companies. The results showed that big data affected supply chain management significantly and positively, which in turn affected supply chain performance significantly and positively. In addition, big data exerted a significant and positive impact on supply chain performance. Based on these links, it was found that supply chain management mediated significantly the effect of big data on supply chain performance. The study contributes to the literature showing that big data plays a pivotal role in improving supply chain performance and supply chain performance from the SCOR model perspective is critical for the relationship between these two constructs.

The organization needs to improve on a steady basis. For this purpose, organizations must gauge their performance regularly. To achieve this purpose, the agility of the supply chain may play a key role. Therefore, this study was designed to explore the relationship between big data analytics, organizational flexibility, supply chain agility, and organizational performance. This study assessed the mediation effect of supply chain agility as well. The research design of the cross-sectional and research approach was quantitative. The data of this study was gathered from the retail sector employees. In total, 516 questionnaires were distributed using simple random sampling. The usable response rate of the study was 54.90%. The gathered data was examined through smart PLS 3.3.2. The findings of the study revealed that Supply chain agility plays a crucial role in improving the performance of the organization. The study also confirmed the mediating effect of supply chain agility. The findings of the study are helpful for the policymakers of the retail sector.

It is essential to improve the efficiency of hospitals, especially in the situation of Covid-19. Therefore, the primary purpose of this paper was to examine the effect of supply chain connectivity, perceived ease of use, and task technology fit on big data analytics and efficiency. This study assessed the mediating role of big data analytics as well. A survey questionnaire was developed to collect the data. The data was collected from the employees working in the hospitals of the UAE. For this purpose, convenient sampling was adopted. The questionnaire was adapted from past studies. The questionnaire was distributed among 426 employees. The usable response rate was 73.22%. The gathered data was assessed using PLS 3. The study's findings confirmed the direct effect of supply chain connectivity, perceived ease of use, task technology fit on big data analytics, and efficiency. This study also revealed the significant effect of big data analytics on efficiency. Moreover, the mediating role of big data is also confirmed in the present study. This study fills several theoretical and managerial gaps mentioned. The study's findings are helpful for the policymakers and academicians of the health sector.

The usage of Artificial Intelligence (AI) technique under retail industry will bring glorious outcome and flourishing benefits for both the retailers and the distinguished customers. The multiple platforms of AI usage in retail arena are discussed under two different cluster classified as online and offline, based on the terms of execution of retail activity. The present research was conducted with the objectives of evaluating the contribution of quality, customer relationship management and big data in designing futuristic retail model and analyzing the intention of retailers and shoppers in experiencing the emergence of AI. Disproportionate multistage judgement sampling method was em-ployed. A sample of 610 shoppers from four different capital regions of southern part of states in India was considered for the statistical analysis. Data was collected during the first quarter period of 2018. Descriptive research design was used to describe the intention of shoppers towards the emer-gence of AI in the Indian retail sector. The usages of AI technologies in online and offline retail are grouped separately and its effect on building the quality, customer relationship management and big data was evolved. Finally, its impact on the retailers intention and customers delight was studied through Structural Equation Modeling with testing of appropriate hypothesis.

Information System (IS) is a strife to exploit Business Intelligence (BI) in an organization. In the Malaysian Halal Food Manufacturing, a league of Information Technology (IT) professional and decision makers is the architect of the perspective in IT. There are numerous research studies on utilizing and investigating strategic effects of environmental factors augmentation on the organizations, but compact information is acknowledged prevailing how the subjective conception for the strategic source of deci-sions is transformed into the objective principle. Hence, general interpretation of the IT professional and the decision makers is crucial for a comprehensive and collaborative decision-making process. Therefore, prosper stimulate assimilation of the environmental factors that persuade the knowledge integration between IT professionals and decision makers is compulsory. BI and Big Data (BD) help organizations derive enhance decision-making process and knowledge creation. The objective of this research study is to emerge knowledge from organizing BD and to utilize BI together with perceiving MIT90s framework and environmental factors for the analysis of decision-making process of halal food manufacturers in Malaysia. The study applied regression analysis to predicted 103 responses to determine decision making of business performance. The results indicate that halal market demand played important role in predicting business performance of halal manufactures. This study provides some insights into decision making perspectives of business performance management among halal food manufacturers in Malaysia.

This article examines the nexus between Big data governance, dynamic capability and decision-making. Survey data was garnered from firms in Jordan and fuzzy sets (fsQCA) approach was applied. Fuzzy set configural modeling revealed two causal models for high decision-making effectiveness conditions. First, Big data governance, number of employees and firm age are necessary for higher levels of effective decision-making. Second, dynamic capability, number of employees and firm age are necessary for higher levels of effective decision-making. Insights for practitioners in an emerging economy and possible areas of future research are highlighted.