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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.

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 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 mainly relies on the surveillance capitalism business model, enabling companies to monetize their data. The surveillance allows for transforming private human experiences into behavioral data that can be harnessed in the marketing sphere. This study aims to focus on investigating the domain of data economy with the methodological lens of quantitative bibliometric analysis of published literature.

The bibliometric analysis seeks to unravel trends and timelines for the emergence of the data economy, its conceptualization, scientific progression and thematic synergy that could predict the future of the field. A total of 591 data between 2008 and June 2021 were used in the analysis with the Biblioshiny app on the web interfaced and VOSviewer version 1.6.16 to analyze data from Web of Science and Scopus.

This study combined findable, accessible, interoperable and reusable (FAIR) data and data economy and contributed to the literature on big data, information discovery and delivery by shedding light on the conceptual, intellectual and social structure of data economy and demonstrating data relevance as a key strategic asset for companies and academia now and in the future.

Findings from this study provide a steppingstone for researchers who may engage in further empirical and longitudinal studies by employing, for example, a quantitative and systematic review approach. In addition, future research could expand the scope of this study beyond FAIR data and data economy to examine aspects such as theories and show a plausible explanation of several phenomena in the emerging field.

The researchers can use the results of this study as a steppingstone for further empirical and longitudinal studies.

This study confirmed the relevance of data to society and revealed some gaps to be undertaken for the future.

Recently, the concept of the circular economy (CE) has witnessed significant momentum in academic and professional circles. However, there is a dearth of research that studies the enabling factors of the CE in the era of digital transformation. The existing research aimed to identify the impact of Industry 4.0 readiness on the CE in manufacturing firms operating in Jordan, as well as to identify the mediating role of the industrial Internet of things and big data analytics.

For this work objectives, 380 questionnaires were analyzed. Convergent validity and discriminant validity tests were performed through partial least squares-structural equation modelling (PLS-SEM) in the Smart-PLS programme. Data reliability was confirmed. A bootstrapping technique was used to analyze the data and then hypothesis testing was performed.

The results indicate that Industry 4.0 readiness, industrial Internet of things (IIoT) and big data analytics positively enable CE, also the IIoT and big data analytics positively mediate the nexus between Industry 4.0 readiness and CE.

This study promotes the idea of focusing on Industry 4.0 readiness to enhance CE in the Jordanian manufacturing sector and knowing the effect of IIoT and big data analytics in this relationship.

This research developed a theoretical model to understand how Industry 4.0 readiness might enhance the CE in manufacturing firms by invoking the IIoT and big data analytics as mediating constructs in the relationship between Industry 4.0 readiness and CE. This paper offers new theoretical and practical contributions that add value to industry 4.0 and CE literature by testing these constructs' mediation models in the manufacturing sector.

This study aims to construct mechanisms of big data-driven business model innovation from the market, strategic and economic perspectives and core logic of business model innovation.

The authors applied deductive reasoning and case analysis method on manufacturing firms in China to validate the mechanisms.

The authors have developed an integrated framework to deduce the elements of big data-driven business model innovation. The framework comprises three elements: perspectives, business model processes and big data-driven business model innovations. As we apply the framework on to three Chinese companies, it is evident that the mechanisms of business model innovation based on big data is a progressive and dynamic process.

The case sample is relatively small, which is a typical trade-off in qualitative research.

A robust infrastructure that seamlessly integrates internet of things, front-end customer systems and back-end production systems is pivotal for companies. The management has to ensure its organization structure, climate and human resources are well prepared for the transformation.

When provided with a convenient crowdsourcing platform to provide feedback and witness their suggestions being implemented, users are more likely to share insights about their use experience.

Extant studies of big data and business model innovation remain disparate. By adding a new dimension of intellectual and economic resource to the resource-based view, this paper posits an important link between big data and business model innovation. In addition, this study has contributed to the theoretical lens of value by contextualizing the value components of a business model and providing an integrated framework.

In the tide of the sharing economy, food and beverage sharing services (FBSS) are gradually drawing public attention. Many comments about FBSS are posted and discussed online, and this information may suggest the key factors in the operation of FBSS. This study aims to identify the key success factors (KSFs) of FBSS from online communities and media, potential consumers, customers and experts.

This study utilizes Internet big data analytics (IBDA) to identify the key FBSS factors and then examines the KSFs of FBSS through conducting an analysis of the importance of key factors for potential consumers, confirmatory factor analysis of customer satisfaction of key factors, multiple regression analysis of customer satisfaction of key factors influencing the customers' intentions to continue participating in FBSS and a decision-making trial and evaluation laboratory of experts' opinions.

The results showed that the 15 key FBSS factors through IBDA were screened out. Among them, four KSFs that influence the operation of FBSS were identified. These four KSFs are discussed in detail in the text.

The findings of this study provide references for FBSS providers in the future to enhance customer value, service quality and business competitive advantages of FBSS.