Library log analysis and its implications for studying online information seeking behavior of cultural groups

The potential of Google Analytics for tracking the reading behavior in web books

Purpose

The purpose of this paper is to introduce Google Analytics as a format suitable for advanced tracking of reading behavior within web books, set the metrics for measuring the reading behavior of web books and describe the first results of a pilot study. This paper offers suggestions for further deployment of web books and web analytics in digital libraries and evaluating web books' performance.

Design/methodology/approach

To understand the reading behavior of web book users, researchers use quantitative research methods based on custom and advanced metrics at Google Analytics.

Findings

Google Analytics is a valuable tool for tracking access to individual books and tracking entire web book collections, mainly if researchers use the combination of unique custom and advanced metrics. A pilot study with 190 users uncovered significant results on reading behavior, for example, the strong preference for scrolling over navigation buttons.

Research limitations/implications

This pilot study is limited to measuring two web books and 190 users. This study demonstrated a workable setup of metrics for measuring reading behavior; it would be helpful to continue measurement with a larger sample of books and users.

Originality/value

Researchers in library and information science currently use web analytics mainly to understand user behavior on the website and in the catalog. This paper presents the possibilities of deploying Google Analytics directly in web books to understand reading behavior.

To pre-filter, or not to pre-filter, that is the query: A multi-campus big data study

Library discovery platforms, which provide searchable user interfaces as their front-facing layer, aggregate tremendous amounts of metadata from multiple data streams describing a wide variety of print and electronic resources. Complicating the matter further, resources may differ in availability or delivery time depending not only on their media but also upon the source of the data stream describing them. How should libraries structure end users’ options for searching discovery platforms in light of the many options available? This study used a nonexperimental design and quantitative methods to analyze users’ revealed preferences for query type in twenty-four academic libraries in a data set containing metadata, sans queries, for over 64 million searches. Libraries studied were all located in California, used the same discovery layer software, and served similar user and faculty constituencies; however, the number of query types and pre-filtering options available differed between institutions. Results show that, when users were presented with the choice between search options, most conducted simple, more broad searches rather than complex and specific searches. When search options were highly constrained by the default choice architecture, but complex searches were possible, few users opted out of the default simple search. Implications for usability of discovery layers and the motivations of librarians in choice architecture are nontrivial and are discussed. The desires of librarians and “power user” faculty must be balanced with the fact that most users are novices and users of all abilities are largely habituated to commercial search products which emphasize post-search results filtering.