Examining Question-Answering Technology from the Task Technology Fit Perspective

Abstract

The World Wide Web has become a vital supplier of information for organizations in order to carry on such tasks as business intelligence, security monitoring, and risk assessments. By utilizing the task-technology fit (TTF) theory, we investigate the issue of when open-domain question-answering (QA) technology would potentially be superior to general-purpose Web search engines. Specifically, we argue theoretically and back up our arguments with a user study that the presence of fusion (information synthesis) is crucial to warrant the use of QA. At the same time, many information seeking tasks do not require fusion and, thus, are adequately served by traditional keyword search portals (Google, MSN, Yahoo, etc.). This explains why prior attempts to demonstrate the value of QA empirically were unsuccessful. We also discuss methodological challenges to any empirical investigation of QA and present several solutions to those challenges, validated with our user study. In order to carry our study, we created a novel prototype by following the Design Science guidelines. Our prototype is the first of its kind and is capable of answering list questions, such as What companies own low orbit satellites? or In which cities have illegal methyl-methionine labs been found? This investigation is only a precursor to a full-scale empirical study, but it serves as a medium to overview the state of the art QA technologies and to introduce important theoretical and empirical concepts involved. Although we did not find empirical evidence that one technology is uniformly better than the other, we discovered that once the user accumulates experience using QA, he/she can make an intelligent decision whether to use it for a particular task, which leads to the user to be more productive on average with the same tasks compared to when there is no choice of technology.