DLSU Libraries is composed of one main library and five satellite libraries located at four campuses, serving more than 25,000 patrons. In September 2020, the Libraries launched AnimoSearch, a next-generation discovery service powered by Ex Libris Primo that offers a unified search interface for accessing diverse library resources. AnimoSearch allows users to find relevant materials quickly and easily, utilizing multiple databases and advanced search features like relevance ranking, filtering, and saving searches.
In thi study I investigated the use of transaction log analysis to study the information behavior of AnimoSearch users. My goal was to learn how users interacted with the service as well as what information needs and behaviors they exhibited during the information-seeking process. Specifically, I attempted to answer the following questions:
• What are the most commonly used search terms in a discovery service?
• What are the most frequently used facets and how do they affect the success of a user’s search? and,
• How do users’ search behavior and resource access patterns vary by user segment?
I utilized transaction log analysis to investigate user behavior and information needs. Analysis of transaction logs is a non-obtrusive approach to gather data from a significant number of users with the aim of comprehending the behavior of online users (Jansen, 2006; Philip, 2004). Log studies take the most natural observations of how people use systems without experimenters or observers changing what people do. In Primo, transaction logs refer to a record of all the activities that occur within the system such as searches, clicks on search results, and access to specific resources. I quantitatively and qualitatively analyzed the transaction logs from the academic year 2021-2022 , with an emphasis on identifying patterns of user behavior as a way to understand the context and motivations behind these behaviors. I obtained relevant data from the Primo usage reports such as actions, devices, facets, sessions, and popular searches as well as zero result searches. I used descriptive statistics to calculate frequency counts, means, and standard deviations to summarize the characteristics of the user population, such as their search behavior and the sorts of resources accessed. I also analyzed various user segments to identify common patterns in user activity. In addition, I undertook a content analysis of the search queries to discover prevalent themes and subjects sought by users. These data will aid in identifying popular subject areas and informing collection development decisions.
The study’s findings provided important insights into the information behavior of AnimoSearch users. Results reveal users typically conducted a large number of searches and interacted with a wide range of different resources during a single search session. Most initial searches were conducted in the “Everything” category, while the “resource type” filter was the most commonly utilized for refining results. In contrast, filtering by author was the least frequently employed. Zero search results often occurred when users entered a long string of keywords in the search box. According to the logs, users frequently modified their search queries and employed a variety of search strategies to find the information they required. The study’s findings had important implications for the design of discovery services. It emphasized the importance of systems that support complex and iterative search processes, provide users with a variety of relevant resources, and ensure the reliability and relevance of search results.
This study added to the growing body of research on transaction log analysis as a method of studying information behavior. This approach can provide valuable insights into users’ information needs, behaviors, and preferences by analyzing interactions between users and information systems. The study also emphasized the significance of taking into account the context of use when designing and evaluating information systems. Results of the study will also help developers and vendors to gain insights into how users engage with the system that can be used to improve the design and functionality of the service to better meet user needs.
Reference
- Jansen, B. J. (2006). Search log analysis: What it is, what’s been done, how to do it. Library & Information Science Research, 28(3), 407–432. https://doi.org/10.1016/J.LISR.2006.06.005
- Philip, D. M. (2004). Information-seeking behavior of chemists: A transaction log analysis of referral URLs – ProQuest. Journal of the American Society for Information Science and Technology, 55(4), 326–332.
Janice De Castro Peñaflor
De La Salle University, Manila, Philippines
English 
French