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Record W2509480690 · doi:10.1108/sl-04-2016-0026

The value matrix: a tool for assessing the future of a business model

2016· article· en· W2509480690 on OpenAlexaboutno aff
Vladyslav Biloshapka, Oleksiy Osiyevskyy, Marc H. Meyer

Bibliographic record

VenueStrategy and Leadership · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitor analysisMarketingBusiness modelProduct-service systemBusinessValue captureNew product developmentCompetitive advantageNew business developmentValue (mathematics)Business valueProduct innovationProfit (economics)Industrial organizationEconomicsComputer scienceMicroeconomics

Abstract

fetched live from OpenAlex

Purpose Good companies innovate. In the process, they consider target markets, target customers, new product or service offerings, and the positioning of these relative to competitors. This forms a basic strategy for the innovation. However, the lesson of competitive dynamics today is that innovation effort stops short of its ultimate potential if it does not also embrace the business model possibilities provided by the innovation itself. This short article provides a strategic lens for considering the efficacy and power of a business model for a product or service innovation. Design/methodology/approach The current paper is grounded in the empirical results of an ongoing longitudinal study (undertaken by the authors team in the U.S., Canada and Ukraine) aimed at exploring the structure, characteristics, evolution, and performance outcomes of organizational business models. Findings The business model’s key characteristics are customer value (the “effectiveness side” of the equation, i.e., doing right things for customers that the latters are ready to appreciate and pay for, but not always to the focal firm) and business value (the “efficiency side” of the equation, reflecting translation of the customer value into profit). Importantly, our evidence strongly reveals the dynamic nature of the business model construct, implying that the companies evolve in terms of these two dimensions. Practical implications The recommendations part of the article is primarily based on the in-depth analysis of the recent history of large companies that were struggling to: sustain customer value, and develop and apply internal product and production platforms to increase operating efficiency, and hence business value. All these firms had either slipped into or were in the danger of slipping into Impostor status, and were seeking ways to regain and sustain their Innovation advantage, often over newer entrants in their respective industries. Originality/value Introduction of the Business Model Value Matrix allowing to analyze the current company’s business model; practical recommendations regarding getting to and remaining in the Winner quadrant

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.021
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.064
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0210.010
Science and technology studies0.0020.003
Scholarly communication0.0090.014
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.002

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.

Opus teacher head0.087
GPT teacher head0.282
Teacher spread0.195 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations16
Published2016
Admission routes1
Has abstractyes

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