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This paper analyzes the effect of circular economy practices on firm performance for a circular supply chain and explores the moderating role that big-data-driven supply chain plays within these relationships.

This study uses data collected through an online survey distributed to managers of 378 Italian firms that have adopted circular economy principles. The data are processed using multiple regression analysis.

The results indicate that the three categories of circular economy practices investigated – namely circular economy supply chain management design, circular economy supply chain relationship management and circular economy HR management – play a crucial role in enhancing firm performance from a circular economy perspective. A big-data-driven supply chain acts as a moderator of the relationship between circular economy HR management and firm performance for a circular economy supply chain.

This study makes a number of original contributions to research on circular economy practices in a big-data-driven supply chain and provides useful insights for practitioners. First, it answers the call to capture digital transformation trends and to extend research on sustainability in supply chain management. Second, it enhances the literature by investigating the relationships between three different kinds of circular economy supply chain practices and firm performance. Finally, it clarifies the moderating role of big data in making decisions and implementing circular supply chain solutions to achieve better environmental, social and economic benefits.

India ranks third in the global startup ecosystem in the world incubating more than 50,000 startups and witnessing 15% YoY growth per year. Being a center of innovation and skilled labor, Indian startups have attracted investments from all over the world. This paper aims at exploring the trends that are driving the growth in the Indian startup ecosystem.

Top 200 startups according to valuation are selected as a sample to find out the major trends in the Indian startup ecosystem. This paper includes surveying the sample startups about the implementation of trends such as big data, crowdfunding and shared economy in their startup and its tangible, as well as intangible impacts on their business. The result of the survey is analyzed to get an overview of the emerging trends in the Indian startup ecosystem.

Major ten emerging trends that drive growth in the Indian startup ecosystem are discovered and the areas where these trends can be leveraged are identified.

This research has contributed toward structuring and documenting the growth driving trends, and it will help the budding entrepreneurs to get familiar with the contemporary trends, pros and cons associated with it and the ways to leverage these trends to build a successful startup.

Educational intelligence can be considered a prized asset in political actors’ careful calculations in setting policy agendas for radical educational transformations in the age of the Fourth Industrial Revolution characterized by Big Data, Artificial Intelligence (AI), machine learning, and the Internet of Things (IoT). As an agent of globalization, the European Union (EU) is uniquely positioned to steer the direction of this new wave of digital technologies for two cardinal objectives in the EU’s rhetorical discourse: social cohesion and economic prosperity. Conversely, its complex governance architecture, which restricts its role in educational policy, tempers its ability to drive policy reforms in education for the strategic and coordinated deployment of Big Data in educational systems to support those twin objectives. This chapter examines this burgeoning policy arena in the European Union by interrogating the most recent policies on the “data economy” enacted at the EU-level and the positionality of education in this newest wave of policy formulation. A content and discourse analysis of policy documents on Big Data reveals that the EU is launching multiple initiatives to regulate these novel technologies across its socio-economic sectors. However, the amorphous nature and unpredictable impact of these technologies, along with the jurisdictional barriers in the education sector stemming from the delimitation of governance layers in the EU, pose difficulties in generating a coordinated approach to policy implementation to engender tangible results. Hence, the contours of an educational intelligent economy in the EU needs considerable policy attention and technical resources in its transition from the current ideational stage to its concrete manifestation.

This paper aims to assess how big data–driven supply chain management (BDSCM) influences sustainable supply chain management (SSCM) to achieve sustainable corporate performance (SCP) for small and medium-sized enterprises (SMEs) in an emerging economy such as Vietnam, besides exploring whether Circular Economy Thinking Application (CETA) moderates the relationship between BDSCM and SSCM.

This study collected survey data from 495 SMEs in the food supply chain sector. It employed the PLS-SEM (Partial Least-Squares Structural Equation Modeling) technique to evaluate the hypothesized relationships.

This study found that BDSCM positively, directly and indirectly, impacted SCP. SSCM partially mediated the correlation between BDSCM and SCP. Additionally, CETA moderated the relationship between BDSCM and SSCM. CETA had a direct and positive effect on SSCM.

The insights into how BDSCM influences SSCM to achieve SCP for SMEs in the food value chain in an emerging economy like Vietnam provides an original value. Moreover, the novelty of this study is further reinforced by the coverage of the newfound mechanism, where CETA moderates the relationship between BDSCM and SSCM, directly and positively enhancing SSCM. These contributions could interest business practitioners and academics.

The implications and impacts of the educational intelligent economy from the vantage point of digital frontierism is explored using a decolonial framework, with a specific focus on Big Data and data sharing in Comparative and International Education (CIE). Recent debates are reviewed about CIE’s past histories and its current directions to tease out their implications for data sharing. The authors demonstrate how data sharing continues to reinforce imperialism through control, dissemination, and application of data, and how electronic and digital colonialism preserve current intellectual and structural hegemonies. Then, we give an example of how donors and funding agencies, including the National Science Foundation, engage in neoliberal scientism and control of data, and how it affects the future of social sciences, including CIE. Our inquiry is at the intersections of economic intelligence and educational intelligence in a rapidly evolving technocentric, data-dominated, and networked economy. The authors demonstrate how educational intelligence in the global economy may exacerbate the asymmetric access to data between the global North and the South, as educational data are increasingly becoming global commodities to be traded between various public and private actors. Finally, the authors argue that decolonial participatory research designs that aim at positive, sustained transformations, as opposed to the stagnancy of Big Data and data mining, should be used to address the problems inherent to the Educational Intelligent Economy.

The purpose of the research is to assess the risk of the financial market in the digital economy through the quantitative analysis model in the big data era. It is a big challenge for the government to carry out financial market risk management in the big data era.

In this study, a generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) model is constructed to analyze the big data financial market in the digital economy. Additionally, the correlation test and stationarity test are carried out to construct the best fit model and get the corresponding VaR value.

Owing to the conditional heteroscedasticity, the index return series shows the leptokurtic and fat tail phenomenon. According to the AIC (Akaike information criterion), the fitting degree of the GARCH model is measured. The AIC value difference of the models under the three distributions is not obvious, and the differences between them can be ignored.

Using the GARCH-VaR model can better measure and predict the risk of the big data finance market and provide a reliable and quantitative basis for the current technology-driven regulation in the digital economy.

The purpose of this paper is to examine how managers orchestrate, bundle and leverage resources from big data for value creation in emerging economies.

The authors grounded the theoretical framework in two perspectives: the resource management and entrepreneurial orientation (EO). The study utilizes an inductive, multiple-case research design to understand the process of creating value from big data.

The findings suggest that EO is vital through which companies based in emerging economies can create value through big data by bundling and orchestrating resources thus improving performance.

This is one of the first studies to have integrated resource orchestration theory and EO in the context of big data and explicate the utility of such theoretical integration in understanding the value creation strategies through big data in the context of emerging economies.

In the context of circular economy, tourism resources development and integrated marketing need to interact with multiple businesses, data processing with big data technology, and in the information age, the best means of integrated marketing is to integrate business through an information platform. The research purpose of this article is to improve the intelligence of the tourism platform by constructing an OR model, integrate information and improve the data processing effect in an all-round way through data mining.

This study builds the business interaction in the form of OR (operations research model) model and combines this model with the e-commerce platform to build an online travel consumption model based on the user's online operation mode.

With the development of the times and the upgrading of industries, tourism and other industries have provided new tourism resources and tourism methods for human beings by virtue of the beautiful environment, special experiences and colorful stories and related industries have been related to the tourism industry based on natural resources, customs and folklore and agricultural experiences. By identifying important nodes with high connectivity in the social network, the weighted centrality algorithm can mine the importance of interest tags.

In addition, this research builds a test platform for model performance analysis and integrates various factors. The results show that the model constructed in this study can further promote the development of tourism, improve the efficiency of tourism resources, provide marketing effects and meet the circular economy theory.

The data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation.

The authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach.

The study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration.

Practically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy.

Socially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society.

This is the first paper exploring the data economy opportunity for business value creation from a critical perspective.