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Record W4402964291 · doi:10.3390/jrfm17100438

Exploring the Usefulness of Real Options Theory for Foreign Affiliate Divestments: Real Abandonment Options’ Applications

2024· article· en· W4402964291 on OpenAlexvenueno aff
Andrejs Čirjevskis

Bibliographic record

VenueJournal of risk and financial management · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsDivestmentAbandonment (legal)BusinessFinancePolitical scienceLaw

Abstract

fetched live from OpenAlex

Scholars propose that future research on real options theory should shift attention away from option buying during the first investment stage and toward option execution after investment. Researchers maintain that it would be interesting to explore the circumstances under which investors decide to withdraw their investments, thereby exercising the option to abandon their investments. The present research seeks to fill the gap in the literature and investigate the applicability of real options theory when an organization enhances sustainability policies while focusing on disciplined capital allocation through exit strategies. With case study data on Natura &Co’s divestment strategy for the Body Shop in November 2023, a real options analysis revealed the method’s practical advantages and disadvantages. This paper investigates real options theory in the context of the divestments of foreign affiliates, providing unique viewpoints and enhancing the theory beyond previous knowledge while also increasing our understanding of the divestiture phenomenon. This study concludes with a review of this paper’s theoretical contributions to real options theory, the managerial and practical/social implications of real options applications in general, and the valuation methods of abandonment options in particular, shedding light on the potential of future research.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.053
GPT teacher head0.234
Teacher spread0.181 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2024
Admission routes1
Has abstractyes

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