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Record W2056178918 · doi:10.1108/10878570910941172

Using a value creation compass to discover “Blue Oceans”

2009· article· en· W2056178918 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStrategy and Leadership · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCompetitor analysisValue (mathematics)RevenueMarketingBusinessProduct (mathematics)LoyaltyMarket segmentationIndustrial organizationComputer science

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.167
GPT teacher head0.305
Teacher spread0.138 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it