What is user engagement? A conceptual framework for defining user engagement with technology
Bibliographic record
Abstract
Abstract The purpose of this article is to critically deconstruct the term engagement as it applies to peoples' experiences with technology. Through an extensive, critical multidisciplinary literature review and exploratory study of users of Web searching, online shopping, Webcasting, and gaming applications, we conceptually and operationally defined engagement. Building on past research, we conducted semistructured interviews with the users of four applications to explore their perception of being engaged with the technology. Results indicate that engagement is a process comprised of four distinct stages: point of engagement, period of sustained engagement, disengagement, and reengagement. Furthermore, the process is characterized by attributes of engagement that pertain to the user, the system, and user‐system interaction. We also found evidence of the factors that contribute to nonengagement. Emerging from this research is a definition of engagement—a term not defined consistently in past work—as a quality of user experience characterized by attributes of challenge, positive affect, endurability, aesthetic and sensory appeal, attention, feedback, variety/novelty, interactivity, and perceived user control. This exploratory work provides the foundation for future work to test the conceptual model in various application areas, and to develop methods to measure engaging user experiences.
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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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".