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Record W4386617998 · doi:10.3390/jrfm16090404

Measuring Value in Development with Advanced Real Options for International Sequential Acquisitions

2023· article· en· W4386617998 on OpenAlexvenueno aff
Andrejs Čirjevskis

Bibliographic record

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsInternational businessValue (mathematics)Volatility (finance)Order (exchange)Value captureStrategic managementBusinessCorporate financeValue creationComputer scienceIndustrial organizationFinanceEconomicsMarketingManagement

Abstract

fetched live from OpenAlex

Even though the application of real options theory in international business research has increased substantially, the measurement of collaborative synergies in international sequential acquisition by means of advanced real options is still the theoreticall gap which needs to be filled by new research and promising area for empirical research. This paper moves the field forward and highlights the fact that strategic synergies can be considered as compound real options for growth that are opened by an international sequential acquisition and, thus, add market value to the acquirer. Our research is presented as a theory-building inductive case-based study. This paper aims to integrate sequential compound real options with real options that have changing volatility in order to evaluate collaborative tacit synergies (a value in development) of sequential international acquisitions; thus, it contributes to corporate finance, international business, and strategic management theory with a fresh evaluation of the applications of advanced real options.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.030
GPT teacher head0.226
Teacher spread0.197 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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