In this paper, we examine how different ranking and personalization mechanisms on product search engines influence consumer online search and purchase behavior. To investigate these effects, we combine archival data analysis with randomized field experiments. Our archival data analysis is based on a unique dataset containing approximately 1 million online sessions from Travelocity over a 3-month period. Using a hierarchical Bayesian model, we first jointly estimate the relationship among consumer click and purchase behavior, and search engine ranking decisions. To evaluate the causal effect of search engine interface on user behavior, we conduct randomized field experiments. The field experiments are based on a real-world hotel search engine application designed and built by us. By manipulating the default ranking method of search results, and by enabling or disabling a variety of personalization features on the hotel search engine website, we are able to empirically identify the causal impact of search engines on consumers’ online click and purchase behavior.
The archival data analysis and the randomized experiments are consistent in demonstrating that ranking has a significant effect on consumer click and purchase behavior. We find that hotels with a higher reputation for providing superior services are more adversely affected by an inferior screen position. In addition, a consumer utility-based ranking mechanism yields the highest click and purchase propensities in comparison to existing benchmark systems such as ranking based on price or customer ratings. Our randomized experiments on the impact of active vs. passive personalization mechanisms on user behavior indicate that although active personalization (wherein users can interact with the recommendation algorithm) can lead to a higher click-through rate compared to passive personalization, it leads to a lower conversion rate when consumers have a planned purchase beforehand. This finding suggests that active personalization strategies should not be adopted ubiquitously by product search engines. On a broader note, our inter-disciplinary approach provides a methodological framework for how econometric modeling, randomized field experiments, and IT-based artifacts can be integrated in the same study towards deriving causal relationships between variables of interest.