Examining the use of multiple cognitive load measures in evaluating online shopping convenience: an EEG study
Bibliographic record
Abstract
Purpose In the past decade, the use of neurophysiological measures as a complementary source of information has contributed to our understanding of human–computer interaction. However, less attention has been given to their capability in providing measures with high temporal resolution. Two studies are designed to address the challenge of measuring users’ cognitive load in an online shopping environment and investigate how it is related to task difficulty, task uncertainty and shopping convenience. Design/methodology/approach Two experiments using behavioral and neurophysiological measures are conducted to investigate how various types of the cognitive load construct can be measured and used in an online shopping context. Findings Results of the first study suggest that although all cognitive load measures are influenced by task difficulty, only accumulated load (i.e. total cognitive load experienced during a task) is sensitive to task uncertainty. Results of the second study show that convenience negatively influences accumulated load, and the latter negatively influences user satisfaction. Practical implications Our research offers practical value by providing designers with a validated method to measure users’ cognitive load, enabling the identification of usability issues and design improvement. Originality/value This study contributes to the literature by developing a rich and temporally high-resolution measurement of the cognitive load construct and examining how it can inform us about users’ cognitive state in an online shopping environment.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".