(En) act your age! Marketing and the marketization of history in young SMEs
Bibliographic record
Abstract
Purpose This paper aims to examine how wineries used history in their marketing communications to overcome the liability of newness in a settled field that valorizes duration and longevity. Design/methodology/approach A multiple-case study investigated the treatment of history in marketing by young wineries in a new wine region. Data included interviews, site visits and marketing communications. Findings Wineries worked to communicate stakeholder legitimacy and authenticity by constructing organizational histories through bricolage, communicating history in symbolic, material and practice forms. Research limitations/implications Young organizations can communicate field legitimacy and projections of organizational and product authenticity through constructed histories. Results may not be generalizable to other jurisdictions as wine marketing is normatively subject to government regulation. The importance of history in marketing communications also varies across sectors. Practical implications Young businesses in sectors where tradition, place and longevity are venerated can establish authenticity and legitimacy through the marketization of history by following practices that demonstrate adherence to tradition and making thoughtful choices in the construction of the symbolic and material aspects of their organizations. Originality/value This study demonstrates that new/young organizations can use bricolage to create their own marketized histories as proxies for age.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".