The Impact of Pre-Defined Terms on the Vocabulary of Collaborative Indexing Systems

Collaborative indexing systems have attracted an increasing amount of attention over the last three years. One fundamental limitation to such a system is the uncontrolled nature of its vocabulary, as this consists of terms users freely choose to index resources. As a result, the vocabulary can be poorly structured, making it difficult to harvest knowledge from the user community. Pre-defined terms are suggested to reduce this uncontrolled vocabulary. However, this suggestion has not yet been proven. This work therefore focuses on an empirical study of the adoption of pre-defined terms and its impact on the community’s vocabulary by implying innovation diffusion theory. A research model is formulated to explain the relationship between the degree of adoption and its impact on the vocabulary in order to indicate consolidated term usage. For this purpose, constructs of social network analysis are applied. The model is then validated by one lab experiment (n=172), before being cross-validated by two open web experiments (n=254, n=160). Results indicate that to a remarkable extent, pre-defined terms are appropriate for reducing the uncontrolled nature of the community’s vocabulary, such that the utility of collaborative indexing systems can be increased.