Effort-Accuracy Trade-Off in Using Knowledge Management Systems

Effort-Accuracy Trade-Off in Using Knowledge Management Systems

Robin S. Poston (University of Memphis, USA) and Cheri Speier (Michigan State University, USA)
DOI: 10.4018/978-1-60566-687-7.ch001
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Abstract

To solve complicated problems, people often seek input from others. Knowledge management systems (KMSs) provide help in this activity by offering a computer-mediated approach to information sharing. However, if the KMS contains content that is obsolete or incomplete, those using the system may expend greater amounts of effort to detect what content is worthwhile or they risk relying on poor inputs, which may lead to less accurate solutions to their problems. As a result, most KMSs include rating schemes as part of the user interface designed to help those using the system identify high-quality content. Rating schemes depend on current users rating the quality of the existing content, guiding subsequent users in future content searches. If specific ratings are low in validity, then they may not reflect the true content quality (unintentionally or intentionally). This chapter provides a robust summary of the KMS literature and draws on the effort-accuracy trade-off framework to offer the results of a research study. The research study examines how rating validity influences how KMS users employ their limited cognitive resources to search and evaluate KMS content, with the goal of finding and using the highest-quality content. Through an experimental design, the study described herein manipulates rating validity and content quality in a replicated KMS setting and examines how users trade off search and evaluation effort. The results of the study demonstrate that rating validity differentially influences how KMS search and evaluation effort relates to decision accuracy. The chapter concludes with a discussion of the study findings and ideas for future research.
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Knowledge Management System Usage

KMSs are technology-based systems that help employees make future use of the tacit and explicit knowledge of others (Alavi & Leidner, 2001). This chapter focuses on the “repository” type of KMS which emphasizes the documentation and storage of knowledge (i.e., KMS content) to facilitate its reuse through access to the codified expertise (Grover & Davenport, 2001; Jones & Price, 2004). Research has discussed social and technical limitations of KMS usage; however this chapter specifically examines how end users interact with KMSs to locate content to use in knowledge tasks (Alavi & Leidner, 2001). KMSs often include design features such as search algorithms and rating schemes to help users find relevant and reliable content (Fisher et al., 2003). A research stream examining search algorithms exists (Fang, 2000; Park & Kim, 2000); yet little is known about how users use rating schemes, especially in the KMS environment.

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