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Servitization meets sustainability

2018· article· en· W2896210821 on OpenAlexaboutno aff
Henrique Luiz Corrêa

Bibliographic record

VenueFuture Studies Research Journal Trends and Strategies · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityProduct (mathematics)BusinessIncentiveService (business)Product-service systemValue (mathematics)Quarter (Canadian coin)Goods and servicesMarketingEconomicsBusiness modelComputer scienceMarket economy

Abstract

fetched live from OpenAlex

Companies that have traditionally sold physical products have begun to increase “services” offered alongside the product. Sometimes businesses have even gone to the extreme of selling only the “benefit” of the physical good as a service, while retaining ownership of the service-supporting good, such as the Xerox copier in your office. This movement is frequently referred to as “servitization” (Baines et al. 2009), which is sometimes illustrated by the popular saying: “people don't want to buy a quarter-inch drill. They want a quarter-inch hole.” We argue that, with servitization, because the supplying company retain ownership of the physical good supporting the service provided, the company is incentivized to put increased efforts, from design to disposal of the physical good, into designing in processes that will maximize the value that can be recouped from the good after the end of its economic life. This would be consistent with efforts toward increased sustainability in supply chains. In the traditional transactional sale of physical goods companies does not have an incentive to make decisions aimed at keeping the value of the product throughout its economic life. Companies would not be interested in extending its economic life either, or even being concerned about how reusable, remanufacture-able, refurbish-able or recyclable the product is. This has implications for the level of environmental sustainability achieved.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.002
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 head0.084
GPT teacher head0.412
Teacher spread0.329 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations6
Published2018
Admission routes1
Has abstractyes

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