The Effects of Consumer Personality Types on the Attitudes and Usage of Self-Checkout Technology in the Retail Sector among 18–22 Years Old
Bibliographic record
Abstract
The aim of the research is to understand the relationship between personality types and the use of self-checkout machines (SCO) in retail. Understanding this relationship will provide different perspectives of how and why consumers interact with this technology in order to implement the technology for improved use. This research presents the theory behind technology acceptance, consumer personalities, technology anxiety and human interaction before creating a questionnaire to understand the relationship between SCO use and personality types. The findings show a relationship between personality types and attitudes towards and usage of SCO. A number of situational factors are also found to have a significant effect on consumers’ decision to use SCO, of which speed and item quantity had a greater influence on attitudes and usage than personality type. As one of the first papers comparing personality types and the adoption of self-checkout technology, it provides a unique insight into how such technologies are used in retail. By understanding how different personalities view, and use, self-checkouts, they will be better able to optimise the customer experience when preparing to leave the store, and ultimately encourage them to return later.Research already exists that looks at self-service technology in different situations but little research exists that looks specifically at self-checkouts in retail environments. This paper addresses that lack by not only looking at attitudes towards self-check-outs, but also comparing those attitudes to personality types.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".