User engagement (UE) and its measurement have been of increasing interest in human-computer interaction (HCI). The User Engagement Scale (UES) is one tool developed to measure UE, and has been used in a variety of digital domains. The original UES consisted of 31-items and purported to measure six dimensions of engagement: aesthetic appeal, focused attention, novelty, perceived usability, felt involvement, and endurability. A recent synthesis of the literature questioned the original six-factors. Further, the ways in which the UES has been implemented in studies suggests there may be a need for a briefer version of the questionnaire and more effective documentation to guide its use and analysis. This research investigated and verified a four-factor structure of the UES and proposed a Short Form (SF). We employed contemporary statistical tools that were unavailable during the UES’ development to re-analyze the original data, consisting of 427 and 779 valid responses across two studies, and examined new data (N=344) gathered as part of a three-year digital library project. In this paper we detail our analyses, present a revised long and short form (SF) version of the UES, and offer guidance for researchers interested in adopting the UES and UES-SF in their own studies.

A practical approach to measuring user engagement with the refined user engagement scale (UES) and new UES short form

Combining virtual reality and mobile eye tracking to provide a naturalistic experimental environment for shopper research

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.

An Empirical Study of Interest, Task Complexity, and Search Behaviour on User Engagement

Increasingly, users are interacting with information retrieval (IR) systems with the goal of addressing complex, ill-defined search tasks. As a result, the interest in deconstructing and predicting search tasks has grown among IR researchers. Given the multidimensionality of search tasks, researchers usually focus on one or multiple dimensions and study the associations between implicit task dimensions and observable predictors. Synthesizing the knowledge of tasks and predictors learned from individual user studies can clarify the progresses we have made as a research community and provide an intellectual benchmark for further explorations of task-based search interactions. This article presents an overview of 76 task-based interactive IR (IIR) studies published between 2000 and 2020 and systematically coded the papers using features such as task dimensions (definitions and operationalizations), task-predictor associations, as well as task prediction models. Results include 1) data illustrating the growth and interdisciplinarity of IIR studies; 2) a comprehensive typology of task dimensions along with the associated measures; 3) a summary of the statistically significant correlations between task dimensions and predictors; 4) a list of the task dimensions being predicted, ground truth labels, and the feature sets employed. In addition, our bibliography of IIR works can be used by students and junior researchers who are new to the area. The results of our review can facilitate the growth of knowledge in IIR community and serve as the basis for future research on new modalities of user-task interactions.

Deconstructing search tasks in interactive information retrieval: A systematic review of task dimensions and predictors

Virtual Customer Assistants in finance: From state of the art and practices to design guidelines

It is an honour to present the program for the second ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR 2017), held in Oslo, Norway from March 7 to March 11, 2017. CHIIR offers the opportunity to present and discuss research on user-centered approachesto information retrieval, information access and information interaction, including studies of interactive systems, searcher behaviour, novel interaction paradigms, interface design, new evaluation methods, and a range of related areas. CHIIR is an interdisciplinary event in which researchers, students and practitioners working across several areas, including information retrieval, information science and human computer interaction, come together to think creatively about the challenges and opportunities afforded by digital information systems. Welcome to all the CHIIR-ists joining us this year!

The CHIIR 2017 program consists of Full Papers (10 pages), Perspectives Papers (10 pages) and Short Papers (4 pages). We received a total of 112 submissions: 48 Full Papers, 9 Perspectives Papers and 55 Short Papers. Of these, we accepted 19 Full Papers (40%), 5 Perspectives Papers (56%) and 36 Short Papers (65%). We also accepted one Full Paper as a Short Paper. We were pleased to receive so many strong Perspectives Papers, which are meant to present novel ideas or insights, and we hope these will inspire some great discussions. Each Full and Perspectives paper is allocated a 30-minute presentation slot in the program. Short papers are presented as posters during a special evening session, beginning with a short boaster presentation.

In addition to contributed papers, the CHIIR program features three keynote talks by prominent academic and industry researchers, representing the breadth of interests in the CHIIR community: Norbert Fuhr (University of Duisburg-Essen, Germany), Ann Blandford (University College London, UK) and Hakon Wium Lie (Opera Software, Norway). In addition to these speakers and the main conference program, CHIIR features a Doctoral Consortium, two Tutorials, and three Workshops.

We would like to acknowledge the excellent work of the Organizing Committee, including the Doctoral Consortium Chair, Katriina Bystrom (Oslo and Akershus University College of Applied Sciences, Norway); the Workshops and Tutorials Chair, Preben Hansen (Stockholm University, Sweden), and the Proceedings Chair, Sampath Jayarathna (California State Polytechnic University Pomona, USA). The paper reviewing and selection process was enabled by an outstanding 12 member Senior Program Committee and a diverse and international general Program Committee consisting of 77 members. Our thanks and the credits for creating a high quality technical program go to them.

Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval

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Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval