Consumer information search behavior and purchasing decisions: Empirical evidence from Korea

• Empirical data analysis showed a strong correlation between searches and purchases.
• Search traffic explains purchases variably, depending on product classification.
• Hypotheses positing differences in searches by product were generally supported.
• Searches for new products can distort the explanation of this correlation.
• PC searches remain a stronger predictor of sales than mobile searches.

Recently, Internet activity by consumers adopting innovation or purchasing products has increased markedly. To understand this phenomenon, our study focuses on the correlation between purchase behavior and search activity. Utilizing the product classifications established in previous studies, we classify physical products into durable, nondurable, and industrial goods. We then empirically analyze case studies to determine the correlation between Internet searches and product purchases. Our research results show that the correlation between sales and search traffic is more significant for consumer goods than for industrial goods; furthermore, in the consumer goods category, search traffic is a particularly strong predictor of sales in the case of consumer durable goods. These results may be self-evident, implicit in the definition of each product category. However, the presented findings confirm that even among nondurable goods, search traffic can be a significant predictor of purchases, depending on both price and frequency of purchases. In contrast, for durable goods, search traffic may not be strongly indicative of actual purchases for new products, for which traffic simply reflects rising interest. We also show that PC searches are a stronger predictor of sales than mobile searches. The conclusions drawn from this study provide an important foundation for effectively using search statistics in technology business management to formulate marketing strategies as well as to forecast and analyze the adoption of new technology based on real-time monitoring of the changing involvement with each product.