An empirical study of interest, task complexity, and search behaviour on user engagement

•User engagement (UE) is an important outcome measure in interactive information retrieval.•We asked 144 Amazon Mechanical Turk participants to complete 6 search tasks on different topics.•We used questionnaires and log analysis to investigate the effects of task interest and complexity on UE.•Effort (greater perceived task difficulty, higher SERP exploration) had a negative effect on UE.•Success (greater task determinability, more bookmarked pages) was positively associated with UE. User engagement has become an important outcome measure in interactive information retrieval (IIR) research, as commercial (e.g., search engines and e-commerce companies) and educational (e.g., libraries) enterprises focus on capturing and retaining customers. User engagement pertains to the kind of investment – emotional, cognitive, behavioural – the user is willing to make in an application. While research has shown how characteristics of users (e.g., individual differences and preferences) and the systems and content with which they interact influence engagement, less is understood about how the tasks people perform using digital applications affect their engagement. Drawing upon a wealth of literature in IIR, this study examined the effects of task on search engagement in a within-subjects Amazon Mechanical Turk (MTurk) experiment. Participants completed six search tasks on different task topics using task versions that included or excluded items and dimensions in the task descriptions. Items refer to things being compared (alternatives) and dimensions correspond to attributes by which items may differ. The task topics were meant to influence user interest in the task, and the versions were intended to manipulate the task doer's degree of certainty as they planned and performed the task, with the expectation that these factors would affect their self-reported engagement. We captured self-reported task perceptions (e.g., complexity, difficulty, interest) and logged search behaviours (e.g., querying, bookmarking) to both validate our manipulations and to understand how these variables related to engagement. Using multi-level modelling (MLM) we discovered that task topic affected user engagement, whereas task version had limited effects. However, participants’ perceptions of the tasks as interesting, difficult, and so on affected their engagement. Through the self-report and behavioural data, we observed that effort (more search engine results page exploration, greater percei