How an In-Store Self-Service Technology Impacts Customer Shopping Experience, Satisfaction and WOM Intentions
Bibliographic record
Abstract
Retailers are increasingly using self-service technologies (SSTs) in-store. However, their impacts are not well known. The objective of this study is to establish whether the use of SST and its key characteristics (perceived ease of use (PEOU) and perceived usefulness (PU)) has a significant effect on the customer experience (i.e., cognitive, affective, sensory, behavioral and social dimensions). The effect of these dimensions on satisfaction and positive word-of-mouth (WOM) intent were also investigated. We conducted an in-store experiment in which half of the 102 participants chose sports shoes using an interactive wall while a salesperson exclusively assisted the other half. The results demonstrate that the cognitive/positive affective and sensory dimensions of the experience are positively influenced by use of SST while the social dimension is diminished. Further, when SST was used, the negative affect increased. For customers who used the SST, PEOU and PU contribute to enhance all dimensions of the experience, except for PEOU which lowers the social experience. Customer experience positively impact consumer’s satisfaction and WOM intent.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".