Bibliographic record
Abstract
Retaillance is surveillance in a brick-and-mortar retail setting. In today's competitive landscape, retailers have moved beyond using surveillance practices and technologies for security reasons to using them to compete for consumers' personal and shopping data, even if this information is not voluntarily reported. Retaillance raises ethical questions regarding the differences between the public and the private spheres. Using a pragmatic mixed research methods design that includes an MTurk survey and semi-structured interviews, this exploratory research examines the different retaillance channels and systems, and explores retail consumers' awareness of the presence and scope of retaillance and of the relevant laws and regulations, consumers' behavioural reaction towards retaillance, and the attitudinal and behavioural outcomes of using various surveillance technologies in retailing. Demonstrating the multiplicity and complexity of influences, this research brings together past research by leading scholars from the fields of marketing, consumer behaviour, political science, communications, media studies, science studies, war studies, law, cultural studies, sociology, criminology, and literature. This research has various contributions. Conceptually, it integrates published literature, synthesizes prior studies, provides definitional clarity and creates a conceptual retaillance model that works as a roadmap and opens new avenues for future research. Theoretically, it embraces a multidisciplinary perspective by borrowing theories from other disciplines and integrating them to reveal novel insights when looking at retaillance, offers a new theoretical model, and reconciles contradictory reactions to surveillance. In addition, foreseen contributions encompass helping scholars, retail managers, consumers, and policy makers gain a better understanding of the impact of both traditional surveillance and smart retail technologies on consumer behaviour in a brick-and-mortar setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".