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Record W2735615502 · doi:10.5539/ibr.v10n8p161

Logistics Flexibility and Customer Satisfaction in Spain’s Furniture Industry

2017· article· en· W2735615502 on OpenAlexvenueno aff
Gonzalo Maldonado Guzmán, Sandra Yesenia Pinzón Castro, Heira Georgina Valdez-Bocanegra

Bibliographic record

VenueInternational Business Research · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsFlexibility (engineering)Customer satisfactionBusinessMarketingSample (material)Competitive advantageEmpirical researchIndustrial organizationOperations managementProcess managementEconomicsManagement

Abstract

fetched live from OpenAlex

The use of flexibility has been analyzed and discussed in the literature as a strategy that produces many competitive advantages and as an essential resource in enterprises for some decades. However, logistics flexibility is a rather recent construct and a variable that has also been considered as a business strategy that produces not only higher competitive advantages but also a higher level of customer satisfaction. Although logistics flexibility has several benefits in enterprises, there is still in the literature a high percentage of ignorance from a considerable amount of organizations about the effects of logistics flexibility in customer satisfaction. For this reason, the main objective of this empirical research is the analysis of the effects of logistics flexibility on the logistics related to customer satisfaction by using a sample of 322 enterprises in furniture industry in Spain. The results obtained provide empirical evidence of the positive and significant effects that logistics flexibility has on the logistics related to customer satisfaction.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.152
GPT teacher head0.407
Teacher spread0.255 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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