Capability Development through Just-in-Time Access to Knowledge in Document Repositories: A Longitudinal Examination of Technical Problem Solving

With knowledge and expertise increasingly being recognized as important, firms have made significant investments in document repositories as part of their knowledge management initiatives. Document repositories are intended to enhance the ability to access codified knowledge and help improve task performance through knowledge reuse. However, it is not clear what effects they have on how knowledge workers perform their tasks. Using longitudinal data on repository accesses and calls to technical support by field technicians in an engineering firm, we examine how just-in-time access to codified knowledge affects patterns of help-seeking from technical support. We find evidence that greater accessing of codified knowledge reduces calls for help to technical support. The type of codified knowledge accessed from the repository affects field technicians’ calling behavior. Accessing general knowledge reduces calls to support for low-complexity problems, while accessing procedural knowledge reduces calls related to high-complexity problems. Further, accessing procedural knowledge is significantly associated with promotion, suggesting that the use of document repositories can help individuals build firm-specific human capital. Building on the insights of cognitive load theory, this study suggests that making information available just in time through document repositories reduces the cognitive load involved for task performance and enables learning. This work contributes to a greater understanding of the value of knowledge management systems and suggests that, beyond the efficiencies gained from knowledge reuse, just-in-time access to knowledge repositories builds problem solving capabilities and contributes to human capital development.

Reinforcement Learning Page Prediction for Hierarchically Ordered Municipal Websites

Public websites offer information on a variety of topics and services and are accessed by users with varying skills to browse the kind of electronic document repositories. However, the complex website structure and diversity of web browsing behavior create a challenging task for click prediction. This paper presents the results of a novel reinforcement learning approach to model user browsing patterns in a hierarchically ordered municipal website. We study how accurate predictor the browsing history is, when the target pages are not immediate next pages pointed by hyperlinks, but appear a number of levels down the hierarchy. We compare traditional type of baseline classifiers’ performance against our reinforcement learning-based training algorithm.

Trends in anesthesiology research: a machine learning approach to theme discovery and summarization

Objectives Traditionally, summarization of research themes and trends within a given discipline was accomplished by manual review of scientific works in the field. However, with the ushering in of the age of “big data,” new methods for discovery of such information become necessary as traditional techniques become increasingly difficult to apply due to the exponential growth of document repositories. Our objectives are to develop a pipeline for unsupervised theme extraction and summarization of thematic trends in document repositories, and to test it by applying it to a specific domain. Methods To that end, we detail a pipeline, which utilizes machine learning and natural language processing for unsupervised theme extraction, and a novel method for summarization of thematic trends, and network mapping for visualization of thematic relations. We then apply this pipeline to a collection of anesthesiology abstracts. Results We demonstrate how this pipeline enables discovery of major themes and temporal trends in anesthesiology research and facilitates document classification and corpus exploration. Discussion The relation of prevalent topics and extracted trends to recent events in both anesthesiology, and healthcare in general, demonstrates the pipeline’s utility. Furthermore, the agreement between the unsupervised thematic grouping and human-assigned classification validates the pipeline’s accuracy and demonstrates another potential use. Conclusion The described pipeline enables summarization and exploration of large document repositories, facilitates classification, aids in trend identification. A more robust and user-friendly interface will facilitate the expansion of this methodology to other domains. This will be the focus of future work for our group.