Bibliographic record
Abstract
Purpose This paper aims to augment traditional investigations of consumer‐brand relationships and suggest alternative ways to consider these interactions. Specifically, the paper employs consumer associative networks for the purpose of uncovering how various stakeholders perceive the Pharmacy Guild of New Zealand brand subsequent to the implementation of a programme designed to enhance consistency of its brand. Design/methodology/approach The paper utilises semi‐structured interviews in a focus group setting to solicit attitudes, opinions and general feedback regarding a new service concept called the Supporting Independent Living (SIL) Program, recently developed by the Pharmacy Guild of New Zealand (PGNZ). Results are interpreted utilising community branding and network approaches, such as associative network theory. Findings The importance of utilising an associative network approach in investigating brand‐customer relationships is supported. This helps to identify the relationships between firms and their brands and the impact that this has on the brand development of existing, or newly‐created services. Originality/value The managerial implications include the suggestion of using a stakeholder approach once the SIL concept is fully operational; particularly focusing on how the brand association information is flowing back to the PGNZ parent brand and how various stakeholders (based on their salience) perceive their relationship with the brand.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.052 | 0.006 |
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".