User diversity: as demonstrated by deep log analysis

The purpose of this paper is to demonstrate a novel form of deep log analysis by linking questionnaire data with transactional server log data generated by the same users; and to provide a richer understanding of the information‐seeking behaviour of a strategic community of virtual scholars.

Usage statistics were obtained from logs for an 18‐month period: 16,865 sessions were covered and 110,029 pages were viewed. Searching behaviour was studied in regard to number of returned hits and number of searches in a session. A questionnaire survey was also conducted to identify ScienceDirect users according to the subject/discipline to which they belonged and attitude towards some scholarly communication issues. The answers of more than 750 ScienceDirect users to the questionnaire were linked to the usage logs of the same users through matching internet protocol (IP) addresses.

The study reveals large differences between scholars in different subjects in terms of information‐seeking behaviour and their interaction with electronic journal systems.

The findings can be utilised to improve electronic journal systems such as ScienceDirect in order to provide more suitable service for users in different subjects.

The originality of the paper lies in its methodology that links questionnaire attitudinal data to the web log data of the same users at individual level to gain a better understanding of users' behaviour.