Charting relationship marketing practice: it really didn’t emerge in the 1970s
Bibliographic record
Abstract
Purpose – This paper aims to provide a history of relational perspectives in marketing practice from the nineteenth through to the twentieth century. Design/methodology/approach – This paper engages in a systematic reading of published histories of retailing practice using the key attributes of transaction and relationship marketing as a conceptual framework to interrogate whether earlier practitioners were committed to either approach. Findings – This paper supplements the studies conducted in other domains that undermine the idea that relational practices were rejected in favor of transaction-type approaches during the industrialization of the USA and Canada. Practical implications – The content of this paper provides textbook authors with a means to fundamentally revise the way they discuss relationship marketing. It has a similar pedagogic utility. Originality/value – This paper studies the writings of practitioners known to be pioneers of retailing to unravel their business philosophies, comparing and contrasting these to known attributes of relationship marketing. It deals with an historical period that has not previously been studied in this level of detail by marketing historians.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".