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Record W3121850668 · doi:10.1111/1911-3846.12450

The Effect of Environmental Risk on the Efficiency of Negotiated Transfer Prices

2018· article· en· W3121850668 on OpenAlexvenueno aff
Markus C. Arnold, Robert M. Gillenkirch, R. Lynn Hannan

Bibliographic record

VenueContemporary Accounting Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationProfit (economics)MicroeconomicsBusinessEconomicsTransfer (computing)Computer science

Abstract

fetched live from OpenAlex

ABSTRACT This study investigates whether environmental risk affects the efficiency of negotiated transfer prices. We analyze a setting where the buyer faces environmental risk but the seller does not. From the risk‐neutral firm's perspective, the transfer should be made in our setting because the expected value of the buyer's profit is greater than the certain opportunity cost of the seller from the transfer. We develop hypotheses to predict that, as environmental risk increases, it becomes more difficult for buyers and sellers to reach agreement. Such difficulty reduces efficiency in terms of both firm profit and negotiation time. We test our hypotheses via an experiment in which buyer and seller dyads negotiate over the transfer of a resource at six levels of environmental risk. Results show that, as predicted, environmental risk decreases efficiency. Specifically, as environmental risk increases, the frequency of agreement decreases, thereby reducing expected firm profit. Further, environmental risk increases negotiation time for those dyads that are able to reach an agreement. Data suggest that the cause of the decreased efficiency is that buyers and sellers use different reference points for determining a fair transfer price and environmental risk exacerbates the effects of such differences.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.004
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.054
GPT teacher head0.369
Teacher spread0.315 · 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; both teacher heads agree on what is shown here.

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
Published2018
Admission routes1
Has abstractyes

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