The development and evaluation of a survey to measure user engagement
Bibliographic record
Abstract
Abstract Facilitating engaging user experiences is essential in the design of interactive systems. To accomplish this, it is necessary to understand the composition of this construct and how to evaluate it. Building on previous work that posited a theory of engagement and identified a core set of attributes that operationalized this construct, we constructed and evaluated a multidimensional scale to measure user engagement. In this paper we describe the development of the scale, as well as two large‐scale studies (N=440 and N=802) that were undertaken to assess its reliability and validity in online shopping environments. In the first we used Reliability Analysis and Exploratory Factor Analysis to identify six attributes of engagement: Perceived Usability, Aesthetics, Focused Attention, Felt Involvement, Novelty, and Endurability. In the second we tested the validity of and relationships among those attributes using Structural Equation Modeling. The result of this research is a multidimensional scale that may be used to test the engagement of software applications. In addition, findings indicate that attributes of engagement are highly intertwined, a complex interplay of user‐system interaction variables. Notably, Perceived Usability played a mediating role in the relationship between Endurability and Novelty, Aesthetics, Felt Involvement, and Focused Attention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".