The Impact of Conceptual Modeling on Dataset Completeness: A Field Experiment

Friday, Nov. 28, 2014, 1:30-2:30 p.m.
BN-3010

Authors: Roman Lukyanenko, Jeffrey Parsons*, Yolanda Wiersma   (*=presenting author)

Abstract: This paper investigates the impact of conceptual modeling on the information completeness dimension of information quality in the context of user-generated content. We present a theoretical relationship between conceptual modeling approaches and information completeness and hypothesize that traditional class-based conceptual modeling negatively affects information completeness. We conducted a longitudinal field experiment in the context of citizen science in biology. The empirical evidence demonstrates that users assigned to an instantiation that is based on class-based conceptual modeling report fewer observations compared with the alternative instance-based condition. Users in the instance-based condition also reported greater number of new classes of interest. The findings support the proposed hypotheses and establish conceptual modeling as an important factor in evaluating and increasing information completeness.


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