The knowledge demands of expertise seekers in two different contexts: Knowledge allocation versus knowledge retrieval

This paper explores the knowledge demands of expertise seekers for the purpose of designing effective expertise locator systems. We conduct an empirical investigation, using conjoint analysis and within-subject tests, to determine the relative importance assigned to different expert attributes under two expertise seeking contexts: knowledge allocation and knowledge retrieval. Our results show that when choosing an expert to retrieve knowledge from (knowledge retrieval), expertise seekers will assign greater importance to the person's level of expertise. When selecting an expert to transfer knowledge to (knowledge allocation), attributes representing the network ties between the expert and the seeker as well as the benevolence of the expert will be perceived as more important. These results are important for the design of expertise locator systems that are better customized to fit the knowledge needs of their users, and to serve the organization as a whole.

► Expertise seekers select experts based on their attributes. ► The relative importance of attributes varies depending on the search context. ► Expertise is more important for knowledge retrieval. ► Reciprocity is more important for knowledge allocation.