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Record W3171846995 · doi:10.5430/ijfr.v12n4p259

The Impact of Corporate Performance on Mental Accounting and Loss Aversion in Financial Decisions

2021· article· en· W3171846995 on OpenAlexvenueno aff
Mona Hassabelrasoul Mohammad, Dalal Mohamed Ebrahim Mohamed, Elsaid Abd Elazim Tolba Elsharkawi

Bibliographic record

VenueInternational Journal of Financial Research · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsnot available
Fundersnot available
KeywordsMental accountingLoss aversionAccountingBusinessReal estateAffect (linguistics)Financial accountingFinanceActuarial scienceAccounting information systemEconomicsPsychology

Abstract

fetched live from OpenAlex

This study investigates the effect of the organization performance on two psychological biases, mental accounting and aversion to loss, on financial decisions to both investors and managers. To achieve this, two experiments are conducted. The first experiment consists of 40 graduate students as investors, while the second one consists of 40 accountants in a real estate company as managers. The results of the study indicate that the performance of companies impacts both mental accounting and aversion to loss of investors, whereas the performance of companies affects the mental accounting of managers in making their financial decisions but does not affect the aversion to loss.

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)0.003
metaresearch head score (Gemma)0.022
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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.276
GPT teacher head0.509
Teacher spread0.233 · 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
Published2021
Admission routes1
Has abstractyes

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