Web site navigability refers to the degree to which a visitor can follow a Web site's hyperlink structure to successfully find information with efficiency and ease. In this study, we take a data-driven approach to measure Web site navigability using Web data readily available in organizations. Guided by information foraging and information-processing theories, we identify fundamental navigability dimensions that should be emphasized in metric development. Accordingly, we propose three data-driven metrics-namely, power, efficiency, and directness-that consider Web structure, usage, and content data to measure a Web site's navigability. We also develop a Web mining-based method that processes Web data to enable the calculation of the proposed metrics. We further implement a prototype system based on the Web mining-based method and use it to assess the navigability of two sizable, real-world Web sites with the metrics. To examine the analysis results by the metrics, we perform an evaluation study that involves these two sites and 248 voluntary participants. The evaluation results show that user performance and assessments are consistent with the analysis results revealed by our metrics. Our study demonstrates the viability and practical value of data-driven metrics for measuring Web site navigability, which can be used for evaluative, diagnostic, or predictive purposes.
The increasing popularity of Web 2.0 has led to exponential growth of user-generated content in both volume and significance. One important type of user-generated content is the blog. Blogs encompass useful information (e.g., insightful product reviews and information-rich consumer communities) that could potentially be a gold mine for business intelligence, bringing great opportunities for both academic research and business applications. However, performing business intelligence on blogs is quite challenging because of the vast amount of information and the lack of commonly adopted methodology for effectively collecting and analyzing such information. In this paper, we propose a framework for gathering business intelligence from blogs by automatically collecting and analyzing blog contents and bloggers' interaction networks. Through a system developed using the framework, we conducted two case studies with one case focusing on a consumer product and the other on a company. Our case studies demonstrate how to use the framework and appropriate techniques to effectively collect, extract, and analyze blogs related to the topics of interest, reveal novel patterns in the blogger interactions and communities, and answer important business intelligence questions in the domains. The framework is sufficiently generic and can be applied to any topics of interest, organizations, and products. Future academic research and business applications related to the topics examined in the two cases can also be built using the findings of this study.