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Paper Type
ERF
Abstract
With the popularity of the Internet and web-based business model, web analytics tools have increasingly drawn attention. This study aims to discover the design principles of web analytics tools from the analysis of users’ feedback from the actual use of the tools. Specifically, we employed text mining (clustering algorithm) to extract the design principles from online user reviews of web analytics. Overall, the results highlight the necessity to incorporate the product, technical, and experience design principles into web analytics tools design. Keywords Web analytics, design features, text mining, clustering, users reviews.
Recommended Citation
Harb, Yousra; Shang, Yanyan; and Al-Musa, Lamar, "Discovering Design Principles of Web Analytics Tools: A Text Mining Approach" (2020). AMCIS 2020 Proceedings. 20.
https://aisel.aisnet.org/amcis2020/data_science_analytics_for_decision_support/data_science_analytics_for_decision_support/20
Discovering Design Principles of Web Analytics Tools: A Text Mining Approach
With the popularity of the Internet and web-based business model, web analytics tools have increasingly drawn attention. This study aims to discover the design principles of web analytics tools from the analysis of users’ feedback from the actual use of the tools. Specifically, we employed text mining (clustering algorithm) to extract the design principles from online user reviews of web analytics. Overall, the results highlight the necessity to incorporate the product, technical, and experience design principles into web analytics tools design. Keywords Web analytics, design features, text mining, clustering, users reviews.
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