Bibliographic record
Abstract
Purpose This study investigates the validity of the relationship paradigm in contrast with the marketing‐mix paradigm with respect to modern Russian markets. Moreover, the specifics of Russian marketing practices are outlined and a comparison with marketing practices in other countries is provided. Design/methodology/approach The paper is rooted in the theoretical framework of the “Contemporary Marketing Practices” project. Data about marketing practices are gathered with standardised questionnaires and groups of organisations are identified using cluster analysis, the gap criterion, and canonical discriminant analysis. By comparing scores for relational marketing as well as transactional marketing, the marketing practices are described and contrasted with those in Argentina and Canada. To assess the success of different combinations of marketing activities, association rules are computed. Findings Contemporary Russian marketing practices cover only a narrow spectrum of the diversity of marketing practices observed in other nations, and the overall intensity of marketing activities is low in comparison with international benchmarks. Overall, the relevance of the traditional transactional marketing concept holds for current practices and market conditions in Russia. Relational activities are considered as merely additional rather than as alternative options of developing organisations' marketing. Practitioners can adjust their marketing to the patterns of profitable activities revealed by this investigation. In particular, the new possibilities arising from IT‐based marketing are found to be not utilised by vendors who are already established in Russian markets. Originality/value The paper brings Russian markets into the academic discussion. Additionally, the use of association analysis for the evaluation of patterns of marketing activities and their success is introduced.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".