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Record W2578461401 · doi:10.1108/jmh-09-2016-0056

The overlooked influence of personality, idiosyncrasy and eccentricity in corporate mergers and acquisitions

2017· article· en· W2578461401 on OpenAlexaff
Kathleen Park, Anthony M. Gould

Bibliographic record

VenueJournal of Management History · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPersonality psychologyPersonalityOriginalityValue (mathematics)IdiosyncrasySociologyPerspective (graphical)Public relationsPositive economicsEpistemologySocial psychologyEconomicsPsychologyPolitical scienceCreativityComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Purpose Merger waves have typically been viewed through the prism of either corporate strategy or macro-economics. This paper aims to broaden debate about factors that cause – or are associated with – mergers/merger waves over a 120-year period. It ascribes “personalities” to six distinct waves and draws an overarching conclusion about how merger architects are viewed. Design/methodology/approach Databases and interviews are used to piece together detail about CEOs associated with six distinct and recognized merger-waves during a 120-year focal period. The study establishes and defends, a priori, principles for interrogating data to get a sense of each wave-era’s corporate personality/idiosyncrasy. For each era, two exemplar CEO-profiles are presented and – through inductive-reasoning – held out as representative. Findings Distinct personalities are associated with six merger waves. Each wave is given a summary anthropomorphic description which conveys a sense that it may be viewed as the non-rationale expression of aggregate and historically distinct CEO behavior within a circumscribed timeframe. Research limitations/implications The work’s key limitation – explicitly acknowledged – is that it amassed data/evidence from disparate historical sources. However, the authors have developed and defended principles for addressing this concern. Practical implications Improved investment analyses, in particular. The work prefigures formal establishment of a new variable-set impacting share-price prediction. Social implications The paper offers a perspective on how psychological/personality-related variables impact management decision-making, creating something of a bridge between mostly non-overlapping research disciplines. Originality/value The paper broadens debate about how and why merger waves occur. It removes the exclusive analysis of merger waves from the hands of economic historians and strategic management theorists.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.000
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 head0.023
GPT teacher head0.210
Teacher spread0.187 · 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 designQualitative
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

Citations28
Published2017
Admission routes1
Has abstractyes

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