Store Atmospherics and Experiential Marketing: A Conceptual Framework and Research Propositions for An Extraordinary Customer Experience
Bibliographic record
Abstract
The components of a store atmosphere that can be manipulated to generate answers on individuals are related tosensory factors. Experiential marketing that enhances the sensory aspects of consumption helps in understandingthe impact of retail environment on consumer behavior. Retailers around the world have embraced the concept ofcustomer experience management, with many incorporating the notion into their mission statements, searchingfor the creation of a distinctive customer experience for their customers (Verhoef et al., 2009). The mainobjective of this paper is to propose a conceptual framework for an extraordinary customer experience. Theconstruction of this theoretical paper was possible through the usage of desk research methodology. We reviewedthe theory on store atmospherics and customer experience, both related to the retail setting, beginning on the1950’s and ending on 2011. We also provide some research propositions aiming to develop the knowledge in thisfield. It is concluded that it is imperative for retailers today to take in account customers’ holistic experience as arelevant tool to manage the retail operation in a scenario of global competition.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".