Case Study: Competitive Advantage at All Costs - An Inside Look at Five Pharmaceutical Industry Practices Which Undermined Customer Relationships
Bibliographic record
Abstract
This study attempts to answer the question: Over the past 35 years, what competitive business practices instituted by the pharmaceutical industry intended to drive increased competitiveness and business efficiencies had the unintended results of increasing the publics’ general distrust and dissatisfaction with the industry? This paper takes a unique perspective by analyzing five apparently disparate business practices designed to improve business performance but which also increased customer distrust and dissatisfaction of the industry. Specifically the five business practices were:Reliance on pharmaceutical marketers with MBAs but limited or no practical pharmaceutical selling experience.The loss of independent pharmacies and pharmacists as customers due to the rise in national pharmacy chains.Purchase and use of physician-specific prescribing dataUse of direct to consumer advertising for prescription drugsDelaying generic competition and providing industry support for the Affordable Healthcare Act in exchange for U. S. government prohibition against the importation of low cost prescription drugs into the U.S. and prohibiting negotiated Medicare drug prices.The paper uses a case study methodology. The results section details the practices reviewed in this case study and suggests they did improve competitiveness and efficiencies, but also contributed to erosion in customer confidence which includes key customer groups: physicians, pharmacists, patients and payers. Consumer groups such as American Association of Retired Persons (AARP) are focusing both financial resources and membership efforts to advocate for changes in pharmaceutical industry pricing and marketing practices. To see if this phenomenon has occurred in other industries, it was observed that the airlines, fast food and information technology industries also pursued greater business efficiencies. The discussion section suggests they too experienced and continue to experience significant customer relationship issues resulting from the drive for greater efficiencies, including government intervention to address customer concerns. The conclusions review some of the limitations of this study and suggest areas for additional research.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".