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Introduction Since the earliest transaction monitoring studies, researchers have encountered the boundaries that define transaction log analysis as a methodology for studying the use of online information retrieval systems. Because, among other reasons, transaction log databases contain relatively few fields and lack sufficient retrieval tools, students of transaction log data have begun to ask as many questions about what transaction logs cannot reveal as they have asked about what transaction logs can reveal. Researchers have conducted transaction monitoring studies to understand the objective phenomena embodied in this statement: “Library patrons enter searches into online information retrieval systems.” Transaction log data effectively describe what searches patrons enter and when they enter them, but they don't reflect, except through inference, who enters the searches, why they enter them, and how satisfied they are with their results.

The purpose of this article is to present an overview of the history and development of transaction log analysis (TLA) in library and information science research. Organizing a literature review of the first twenty‐five years of TLA poses some challenges and requires some decisions. The primary organizing principle could be a strict chronology of the published research, the research questions addressed, the automated information retrieval (IR) systems that generated the data, the results gained, or even the researchers themselves. The group of active transaction log analyzers remains fairly small in number, and researchers who use transaction logs tend to use this method more than once, so tracing the development and refinement of individuals' uses of the methodology could provide insight into the progress of the method as a whole. For example, if we examine how researchers like W. David Penniman, John Tolle, Christine Borgman, Ray Larson, and Micheline Hancock‐Beaulieu have modified their own understandings and applications of the method over time, we may get an accurate sense of the development of all applications.

To provide a background for this special section on transaction log analysis, the following discussion proposes a definition of transaction log analysis and briefly introduces some of the issues involved in the methodology.

Most automated library systems include a transaction logging component. Yet this fact may be among the best kept secrets in the automated library arena. Often only a few people within a library are aware of its existence, and even fewer have access to the transaction log data. This is unfortunate, since the concrete data garnered by transaction logs can provide bibliographic instructors, reference staff members, systems librarians, and system designers with unique and valuable insights into the patron/system interaction.

Introduction The central goal of transaction log analysis is to acquire new knowledge, which will help library managers, systems designers/developers, and researchers better understand how online information systems are used by library patrons and staffs. This knowledge can then be used to improve library services and operations through better designed systems and better allocation (or reallocation) of library resources. An example of reallocation of resources is changing schedules of public service staff to more closely match when and where patrons need help (e.g., in nonfiction, on weekends) and the relocation of terminals to where they are needed the most (e.g., to the science reference area). The system's resources (access points, online storage, databases) can also be distributed for the most effective and efficient operations through the use of transaction log data. A clearer understanding of human/computer interaction within the library setting can also be used to improve the design and utilization of other online systems and services used within libraries and other organizations.

The article presents a methodology that can be used to analyze data from the transaction log of EBSCO Discovery Service searches recorded in Google Analytics. It explains the steps to follow for exporting the data, analyzing the data, and recreating searches. The article provides suggestions to improve the quality of research on the topic. It also includes advice to vendors on improving the quality of transaction log software.

Case study

Although Google Analytics can be used to study transaction logs accurately, vendors still need to improve the functionality so librarians can gain the most benefit from it.

The research is applicable to the usage of Google Analytics with EBSCO Discovery Service.

The steps presented in the article can be followed as a step-by-step guide to repeating the study at other institutions.

The methodology in this article can be used to assess how library instruction can be improved.

This article provides a detailed description of a transaction log analysis process that other articles have not previously described. This includes a description of a methodology for accurately calculating statistics from Google Analytics data and provides steps for recreating accurate searches from data recorded in Google Analytics.

This study aims to explore the evidence-based patterns of e-journal usage, such as the most used and least used databases, at a public-sector university in Pakistan, by analysing scientists’ usage of databases over time.

Through transaction log analysis, the frequencies of page views, sessions, session duration and size of the used data were calculated through SAWMILL software and entered into MS Excel.

The results revealed that the broad databases of science and engineering were being used more by users as compared to the narrower e-journal databases. Furthermore, the users were mostly accessing the e-journal databases from the university’s central library and its various academic departments. Early morning hours, working days and start of the academic year were found to be the most active timings of e-journal database utilisation.

The results of the study will help the Higher Education Commission (HEC) of Pakistan and information professionals in better access management of databases.

This study was conducted to check the feasibility of a PhD project’s first phase and presents the frequencies of HEC e-journal databases’ usage by using transaction log analysis method. The results will be used in preparing interview guide and sample selection for interview. Other Central Asian studies used COUNTER reports provided by publishers for log analysis instead of using raw log data.

For the purposes of library and information science research, transaction log analysis can be narrowly defined as the study of electronically recorded interactions between online information retrieval systems and the persons who search for the information found in those systems.

The ability to conduct unobtrusive observation of user searching is a potential strength of the method of information retrieval system analysis known as transaction log analysis (TLA). Transaction logs supply unequivocal information about what a user typed while searching. All other methods rely on self‐reporting, which, as Nielsen points out, is not always corroborated by the logs. Regardless of where in an institution information retrieval (IR) system evaluation takes place, TLA is a method that enables library staff at all levels to examine a variety of system and user‐related activities that are recorded on the log. Dominick suggested that TLA can enable the examination of three broad categories of activity: 1) system performance and resource utilization, 2) information retrieval performance, and 3) user interaction with the IR system. This article has been divided into several sections corresponding to functional areas in a library to suggest useful applications of TLA.

The purpose of this article is to alert researchers to software for web tracking of information seeking behaviour, and to offer a list of criteria that will make it easier to select software. A selection of research projects based on web tracking as well as the benefits and disadvantages of web tracking are also explored.

An overview of the literature, including clarification of key concepts, a brief overview of studies of web information seeking behaviour based on web tracking, identification of software used, as well as the strengths and short‐comings noted for web tracking is used as a background to the identification of criteria for the selection of web tracking software.

Web tracking can offer very valuable information for the development of websites, portals, digital libraries, etc. It, however, needs to be supplemented by qualitative studies, and researchers need to ensure that the tracking software will collect the data required.

The criteria is not applied to any software in particular.

The criteria can be used by researchers working on web usage and web information seeking behaviour to select suitable tracking software.

Although there are many reports on the use of web tracking (also reported in this article), nothing could be traced on criteria for the evaluation of web tracking software.