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

机译：对用户参与度的兴趣，任务复杂性和搜索行为的实证研究

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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 perceived task difficulty) had a negative effect on engagement, while bookmarking pages and the ability to understand the task and how to complete it was associated with positive engagement. These results have implications for designing search tasks, deciphering the relationship between user experience and task complexity in IIR experiments, and aligning self-reports and search behaviours in evaluating online search engagement.
机译：随着商业（例如搜索引擎和电子商务公司）和教育（例如图书馆）企业专注于捕获和留住客户，用户参与已成为交互式信息检索（IIR）研究的重要成果指标。用户参与度涉及用户愿意在应用程序中进行的投资类型（情感，认知，行为）。尽管研究表明用户的特征（例如，个体差异和偏好）以及与之交互的系统和内容如何影响参与度，但人们对人们使用数字应用程序执行的任务如何影响其参与度的了解较少。这项研究基于IIR的大量文献，在一项主题内的Amazon Mechanical Turk（MTurk）实验中研究了任务对搜索参与度的影响。参与者使用在任务描述中包括或排除项目和维度的任务版本，完成了针对不同任务主题的六个搜索任务。项目指的是被比较的事物（替代项），而维度则对应于项目可能有所不同的属性。任务主题旨在影响用户对任务的兴趣，而版本旨在在计划和执行任务时操纵任务执行者的确定性程度，并期望这些因素会影响他们的自我报告参与度。我们捕获了自我报告的任务感知（例如，复杂性，难度，兴趣）和记录的搜索行为（例如，查询，添加书签），以验证我们的操作并了解这些变量与参与度之间的关系。使用多级建模（MLM），我们发现任务主题影响用户参与度，而任务版本的影响有限。但是，参与者对任务的兴趣，难度等方面的看法影响了他们的参与度。通过自我报告和行为数据，我们发现工作量（更多的搜索引擎结果页面探索，更大的感知任务难度）对参与度有负面影响，同时为页面添加书签以及理解任务和如何完成任务的能力积极参与。这些结果对设计搜索任务，在IIR实验中破译用户体验和任务复杂性之间的关系以及在评估在线搜索参与度时调整自我报告和搜索行为的含义具有重要意义。

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