Bibliographic record
Abstract
Purpose The purpose of this paper is to introduce a template to guide practitioners in the creation of multiple marketing plans that are intended to target different groups of stakeholders – some of whom are supportive, others adversarial, namely, the business-to-business (B2B) marketer’s agenda. Design/methodology/approach The methodology involved a combination of purposeful sampling, real-time participatory observation, action research and secondary data analysis. The main method of this research is analytical and conceptual with the objective of identifying the diverse groups of stakeholders with whom business marketers must interact. Findings In cases where multiple marketing plans were used for different stakeholder groups, B2B firms encountered lower levels of negative attribution from social network systems, mass media and subsequently public and governmental stakeholders. Originality/value This paper suggests the need for multiple marketing plans that target not only supportive customers but also neutral and adversarial stakeholders who represent a source of negative attribution because they have the potential to derail or even destroy the B2B firm’s marketing agenda. It is suggested that practitioners must also address those stakeholders who distrust or even dislike their firm and its marketing objectives.
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 | 0.051 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".