The role of interactive technologies in the physical retail fashion store
Bibliographic record
Abstract
Numerous fashion brands have integrated interactive technology into their retail spaces to offer consumers immersive digital experiences. This technology empowers customers, acting as store visitors, to actively engage with the brand's digital realm while simultaneously experiencing the physical store environment. However, the existing literature reveals a significant research gap in the integration of interactive technology within physical retail environments, particularly in fashion stores. The purpose of this research was to explore the role of interactive technology in retail design. More specifically, it focuses on investigating the impact that interactive technology has on customer engagement and shopping experiences within fashion retail environments. The research adopted a case study approach, selecting five cases including Canada Goose, Burberry, Ralph Lauren, Lily, and Uniqlo. Data were collected via semi-structured interviews with 27 experts directly involved in the selected cases. The data were analysed using a thematic analysis approach with six stages: data familiarisation, initial code generation, searching for themes, reviewing themes, defining and naming themes, and producing the report. The findings are presented thematically, with three main themes and twelve subthemes. These provide insights and narratives for responding to the research questions. The contributions indicate the role of interactive technology in enhancing the retail store, which are: (1) stimulating dynamic and multi- dimensional experiences, (2) supporting channel integration and gamification, (3) encouraging social media engagement and promotional footprints, and (4) extending in-store engagement and strengthening the relationship between brands and customers. By embedding interactive technology, retailers can create an environment that not only meets but exceeds the expectations of modern consumers, blending physical and digital realms into a cohesive and engaging retail experience.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".