Using the Online Search Volume to Predict Performance: An Abstract

Abstract

This study attempts to examine how online search data related to the change of MNE’s performance. By using both cross-sectional and longitudinal panel studies, a positive relationship between online search interest related to an MNE’s product and corporation names and its financial performance is hypothesized and tested, and its managerial and theoretical implication is discussed. As a result of the extensive use of search engines, consumers and managers increasingly rely on online information in decision making. Information about search frequency reveals consumer interest and intention to make a transaction. Search data serve as a predictor for future demand, which means data from search engines not only reflect a different degree of interest of a product or corporation but also indicate a fundamental change in how to explain the present and predict future business (Wu and Brynjolfsson 2015). This study attempts to reveal the relationship between Google Trends data and MNEs financial performance, both cross-sectionally and longitudinally. These findings have theoretical contributions and invaluable managerial implications as well. The established correlation between Google Trends data and business performance reassures managers of the importance of aggregate consumer interest. The present study extends findings by Du et al. (2015) who highlight the importance of feature keyword search in the automobile industry in the United States. This study adds a new perspective on existing MNE performance predictive model. Online search data represented by Google Trends provide a promising research avenue for International business. Krugman (2009) argues that social science has focused on developing complex statistic models to make business predictions. Even so, all the models have shown limited predictability of disruptive economic fluctuations. Simon (1984) advises that developing tools to explain and observe economic phenomena is better than focusing on models and noisy data in social science research. Search engine technology represented by Google Trends answers this call and provides useful aggregate data reflecting invaluable information such as consumers’ interest in a product feature and that feature’s importance as well as their buying intentions (Du et al. 2015). Analyzing search data helps researchers explain consumers’ intentions and improve the effectiveness and efficiency of predicting future economic activities (Wu and Brynjolfsson 2015).