Wal‐Mart is coming to Guelph: hedonic to utilitarian shoppers' perceptions
Bibliographic record
Abstract
Purpose To date few research studies exist on consumers' responses to the adoption of Wal‐Mart into towns and cities. This paper seeks to examine the expected impact of a Wal‐Mart store in a community before its arrival. Design/methodology/approach Media reviews, participant observations and in‐depth interviews were applied. Positive and negative articles relating to Wal‐Mart as exhibited in the newspapers – theGuelph Tribuneand theGuelph Mercury– were reviewed. Participant observations were conducted in three different shopping areas of Guelph: the Downtown area, the Stone Road mall area and the Willow West mall area. A total of 13 participants from these shopping areas were interviewed. Findings Overall, this study found that the participants were receptive to the notion of Wal‐Mart coming to Guelph despite the negative publicity and strong opposition Wal‐Mart had faced in the media. Additionally, this study offered insights for this marketplace based on the consumption context of hedonic and utilitarian shoppers. The intensity of these shoppers' perceptions and beliefs were found to be different for different contexts such as retail shopping, businesses and social. Research limitations/implications This study demonstrates the importance of wider contextual comprehension when trying to understand what values consumers hold for retailers in the marketplace. However, these findings are restricted by the limited range of opinions captured. A fully holistic view is only possible when taking into account the perspectives of local business owners, future Wal‐Mart employees and managers, activists, or politicians – all of whom have an impact on the situation of Wal‐Mart in Guelph. Practical implications Insights from this study can assist management personnel for their future expansion plans. Originality/value This study extends the application of consumers' value dimensions by focusing not only on consumers' hedonic and utilitarian values but also by incorporating the community context. Furthermore, it offers a multi‐method qualitative market research approach for discovering insights that would not have emerged from utilizing just one method of data collection. This is also the first study to assess consumer responses before a store's construction.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".