“Investigating Online Information Search Strategy: A Predictive Modeling Approach” by Prof. Chee-Wee Tan
Prof. Chee-Wee Tan
Department of Digitization
Copenhagen Business School
Information search plays an increasingly pivotal role in consumers’ online experience. To aid consumers in locating desired products or services, digital platforms are offering a multitude of search features that allow consumers to express their preferences in the form of search criteria and rearrange the resulting options. However, due to the concurrent presence of multiple search features, it is not uncommon for consumers to be confronted with the dilemma of having to chart an efficacious path towards attaining desired outcomes. Consequently, building on information foraging theory, we endeavor to shed light on how the configuration of search features would shape consumers’ search strategy and their eventual search outcomes. Specifically, we leverage on process mining and predictive modeling techniques to capture searchers’ transitional probabilities among search tactics as key indicators of search strategy in an experimental setting. This enables us to not only illuminate how search strategy shifts in response to the provision of search features, but to also uncover the impact of search strategies in influencing search performance.