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Record W2808800471 · doi:10.3390/jrfm11030031

What Makes Management Control Information Useful in Buyer–Supplier Relationships?

2018· article· en· W2808800471 on OpenAlexvenueno aff
Juan Manuel Ramón‐Jerónimo, Raquel Flórez‐López

Bibliographic record

VenueJournal of risk and financial management · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
FundersMinisterio de Asuntos Económicos y Transformación Digital, Gobierno de EspañaConsejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía
KeywordsPurchasingScope (computer science)Control (management)Information sharingBusinessDatabase transactionKnowledge managementAsset (computer security)Transactional leadershipManagement control systemTransaction dataComputer scienceProcess managementMarketingDatabaseEconomicsWorld Wide WebComputer security

Abstract

fetched live from OpenAlex

Extensive research results in inconsistent findings regarding the advantages and risks of exchanging management control information in collaborative relationships between buyers and suppliers. This inconsistency is due, in part, to ignoring whether the information shared is useful. This study analyzes the influence of transaction characteristics (asset specificity, opportunistic behaviors, and resource control) on the usefulness of the format and the content of such sharing. Samples of purchasing and sales managers differ in how their assessment of this usefulness reflects the influence of transactional characteristics, as well as the format (timeliness, aggregation, and integration) and content (scope and symmetry) of the management control information itself.

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)0.016
metaresearch head score (Gemma)0.115
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 score0.016
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.115
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.003
Scholarly communication0.0100.011
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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