Customer value extraction vs. co-creation at self-service checkout
Bibliographic record
Abstract
Purpose Self-service checkout systems (SCSs) receive mixed customer evaluations – some see them as convenient, others as unpaid labour. Although these divergent value perceptions can drive customer satisfaction or dissatisfaction, prior research has not examined customer satisfaction with SCSs through the lens of value perception, particularly in relation to the distinction between value co-creation and value extraction. This study aims to address that gap. Design/methodology/approach This research used two scenario-based, between-subjects online experiments, with data gathered via an online research panel. To test the hypotheses, the data were analysed using ANOVA and the PROCESS macro. Findings The results reveal that a high cognitive workload elicits a sense of value extraction, whereas a low cognitive workload elicits value co-creation. Additionally, experiencing a high cognitive workload under intense time pressure can evoke a higher sense of value extraction. Value extraction is a potent mediator that explains the effects of cognitive workload and time pressure on customer satisfaction. Originality/value This study advances the understanding of customer satisfaction with self-service checkout systems (SCSs) by conceptualising value perception as both co-creation and extraction, shaped by key situational and cognitive factors during the self-checkout process. The findings offer strategic insights for effectively implementing SCSs in-store to enhance customer satisfaction.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".