One of the advantages of e-retailers is their capability to provide a large amount of information to consumers. However, when the amount of information exceeds consumers’ information processing capacities, it will lead to worse decision quality and experience, causing the information overload effect. In this study, the event-related potentials (ERPs) were applied to examine the hidden neural mechanism of the impact of information overload on consumers’ decision processes. Behavioral data showed that people would spend more time making decisions when faced with information overload. Neurophysiologically, consumers would invest less attentional resources in the high amount of information (HAI) condition than those in the low amount of information (LAI) condition and lead to less positive P2 amplitudes. The HAI condition would increase decision difficulty than would the LAI condition and result in smaller P3 amplitudes. In addition, an increased late positive component (LPC) was observed for the HAI condition in contrast to the LAI condition, indicating that consumers were more inclined to have decision process regret when consumers were overloaded. We further investigated the dynamic information processing when consumers got over information overload by mining the brain’s time-varying networks. The results revealed that during the decision process and the neural response stage, the central area controlled other brain regions’ activities for the HAI condition, suggesting that people may still consider and compare other important information after the decision process when faced with information overload. In general, this study may provide neural evidence of how information overload affects consumers’ decision processes and ultimately damages decision quality.

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With the advent of the era of big data, data analysis can objectively explain many events and behaviors with scientific results. China is actively promoting the development of the service industry and opening up to the outside world. Guangdong is the largest foreign trade province. It is of great significance to analyze the impact of the tertiary industry on the export trade by means of data analysis. Based on Michael Porter diamond model, the data of six variables from 2010 to 2019 are selected under the collection of a large number of data, such as the number of employment in the tertiary industry, research and development expenditure. Unit root test and co-integration test are used to analyze.At last, using the method of stepwise regression, the regression equation model of Guangdong Province’s export trade volume under the influence of the tertiary industry is established. Through the model, it is concluded that the research expenditures and development level of the tertiary industry can significantly promote the export trade of goods in Guangdong Province, and then improve the competitiveness of export trade. In addition, this paper also stands on the perspective of the tertiary industry, for the development of commodity export trade in Guangdong Province to put forward relevant suggestions.

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