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The purpose of this paper is to employ a quantitative approach to explore the intellectual structure of the market orientation (MO) field over the course of its development.

This research was conducted by using the bibliometric techniques of citation and co-citation analyses to investigate 1,892 publications in the MO field from 1990 to 2016, as well as factor analysis and multidimensional scaling to present a clear visual experience of the knowledge structure of the MO filed.

This study reveals meaningful outputs to assist in: delineating the critical authors, institutions and countries related to the study of MO; identifying the published documents that have had a significant influence on the field; clarifying the subfields that have developed from the MO field; and mapping the intellectual structure of the field in a two-dimensional space that allows for the visual representation of different themes.

Given the sheer volume of works that exist, these bibliometric techniques cannot completely measure, describe and present the entire intellectual structure of the MO field. Instead, co-citation analysis was performed using the data from only the top publications to identify the level of integration of the field, the changes of each knowledge group and the maturity of its evolution.

First, this study extends the approach to identify the subject of MO from a quantitative perspective. Second, our analysis shows the intersection between the marketing discipline and management discipline in the MO literature. Finally, this study reveals the development tendency of the MO field in recent years. The results of this study are valuable to readers interested in MO research, especially those newly interested in this field.

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.