Strategic account management as a value co-creation selling model in the pharmaceutical industry
Bibliographic record
Abstract
Purpose This study aims to explore the dynamics enabling strategic account management (SAM) to function as a value co-creation selling model in the pharmaceutical industry. Design/methodology/approach Using an inductive qualitative research design, data are collected within 11 industry customers in Canada. This work focuses on hospitals as strategic accounts of pharmaceutical companies, exploring SAM value co-creation in the “hospital-pharmaceutical company” relationship. Findings The findings suggest the presence of two key dimensions that can enable a value co-creation SAM model in the hospital-pharmaceutical relationship: “customer-tailored value-added initiatives” and “relationship enhancers”. Customer-tailored value-added initiatives explain the activities that are central to the hospital-pharmaceutical company relationship and can lead to the provision of value added that is unique to the hospital. Relationship enhancers explain the activities that can help strengthen hospital-pharmaceutical company relations in the pursuit of enhanced value-added interactions between the two parties. The research demonstrates a cyclical relationship between “customer-tailored value-added initiatives” and “relationship enhancers”, leading to value co-creation through a SAM model. Practical implications The study informs pharmaceutical industry practitioners on how to improve their value proposition through new, more sustainable selling practices. It offers information on implementing a value co-creation SAM model, which can enable pharmaceutical companies to sustain long-lasting value-added relationships with key accounts such as hospitals. Originality/value The study contributes to the field of SAM by conceptualizing SAM as a value co-creation system. It introduces new knowledge in pharmaceutical marketing by offering empirical insight on the applicability and use of SAM in the hospital-pharmaceutical company dyad.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".