Using a value creation compass to discover “Blue Oceans”
Bibliographic record
Abstract
Purpose Researchers Kim and Mauborgne argue that firms seeking to grow in mature markets need to create new buyer value, thereby entering Blue Ocean markets, where they don't have rivals. In contrast, firms fighting rivals in bloody, Red Oceans will struggle to remain profitable. To facilitate the search for Blue Oceans the paper aims to offer managers a new tool to uncover new points of buyer differentiation. Design/methodology/approach This paper draws from the strategy, marketing and economics literatures to illustrate how firms can enhance performance by creating Blue Oceans. Findings This paper suggests that one way to generate Blue Ocean strategies is to use the fundamental building blocks of value creation. Based on extensive work with value creation logics, it proposes that there are three types of value firms can offer customers: lower prices using an industrial efficiency logic; increase user connectivity with a network services logic; or enhance the offering's fit with the user needs using a knowledge intensive logic. By combining parts of two or more of the value creation logics, managers may construct innovative bundles of attributes. Practical implications Blue Ocean strategies are most appropriate for companies in the mature/decline phase of the product life cycle that are suffering from declining revenues and decreasing customer loyalty. Organizations facing these pressures typically attempt to increase the bottom line by increasing marketing and branding efforts while cutting costs and trying to dodge price wars. These value renovations usually meet with little success as competitors are attempting the same moves in what is largely a zero sum game. Instead of focusing on besting rivals, Kim and Mauborgne argue firms should aim for value innovation by redefining their offerings to compete in niches where there is no competition. Applying value creation logics helps managers redefine their offerings. Originality/value This is the first paper to outline how combining value creation logics leads to discovering Blue Oceans.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".