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Record W2193285558 · doi:10.1111/caim.12156

The Role of Non‐Technological Innovations in Services: The Case of Food Retailing

2015· article· en· W2193285558 on OpenAlexaff
Beatrice D’Ippolito, Francesco Timpano

Bibliographic record

VenueCreativity and Innovation Management · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsYork University
Fundersnot available
KeywordsBusinessMarketingCore productProduct (mathematics)Service (business)Database transactionProduct innovationCore (optical fiber)Industrial organizationGoods and servicesCommerceTransaction costObject (grammar)Focus (optics)EconomicsMarket economy

Abstract

fetched live from OpenAlex

A growing proportion of innovation, especially in consumer‐based industries, is linked to both aesthetic and symbolic components, yet there is still wide uncertainty as to how consumers respond to the design of products and whether their product choices are consistent across product categories. We draw attention to instances whereby less technology‐intensive initiatives can convey innovation in services industries. The focus is on the case of Eataly, a food retailer in which, it is argued, non‐technological innovations have shaped the firm's core values and triggered consumer interest towards a supermarket where, besides physical goods, experience has become the object of transaction. By emphasizing the importance for retailers of focusing not only on single products, but also on other dimensions of the firm's organization, we intend to contribute to the literature that explores changing facets of innovation in service industries.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0080.014
Scholarly communication0.0070.004
Open science0.0010.005
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.048
GPT teacher head0.274
Teacher spread0.226 · 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 designObservational
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

Citations17
Published2015
Admission routes1
Has abstractyes

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