Pictures Are Worth a Thousand Words: Utilizing Photographic Narrative Inquiry to Identify Retail Atmospherics
Bibliographic record
Abstract
Purpose of Study: In the past, studies of retail environments have explored number of atmospheric stimuli to influence consumer shopping behavior. One of the major challenges for retail educators is getting students to differentiate the diverse functions of retail atmospherics. Our teaching innovation focuses on increasing student engagement and understanding retail atmospheric through photographic narrative inquiry. Thus, we propose the Photographic Narrative Inquiry Retail Atmospheric (PNIRA) exercise. This is a field-based exercise that requires students to visit a retail store, take notes, pictures and synthesize information into a short essay, using the concepts/theories related to the course as a way to understand the components of retail atmospherics. Method/Design and Sample: Seventy-one upper year undergraduate marketing concentration majors were given a survey to evaluate the PNIRA exercise. Results: The students seemed to have a favorable impression towards the exercise. Students reported that the retail atmospheric exercise achieved its learning objectives, as well as enhanced their learning experience, increased their creative input, bettered their knowledge of marketing principles, increased their interest in the topic, and increased their enthusiasm for the course. Value to Marketing Educators: The paper benefits marketing educators by providing an experiential exercise that helps students understand the concept of retail atmospherics. The technique (photographic narrative inquiry) can be applied to other courses or formats (i.e., online). The PNIRA exercise also meets the learning objectives (5 components of retail atmospherics) via experiential learning. Assessment strategies and limitations are also discussed.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".