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Record W2901485662 · doi:10.3917/mana.212.0803

Discursive struggles between bidding and target companies: an analysis of press releases issued during hostile takeover bids

2018· article· en· W2901485662 on OpenAlex
Emmanuelle Nègre, Marie-Anne Verdier, Charles H. Cho

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueM n gement · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsYork University
Fundersnot available
KeywordsLegitimationBiddingLegitimacyOffensiveSample (material)Reciprocity (cultural anthropology)Law and economicsBusinessPublic relationsSociologyLawPolitical scienceEconomicsMarketingManagementPoliticsSocial science

Abstract

fetched live from OpenAlex

What are the types of interactions in the discursive struggles between the two parties involved in a hostile takeover bid? How is the legitimacy of the bid (de) constructed by both the bidding and target companies during their discursive struggles? This paper aims at addressing these research questions. Discursive struggles between the bidding and target companies are studied in a sample of 66 press releases related to seven hostile takeover bids approved by the French Market Regulator between December 2006 and December 2014. A study of the sequence followed by each party in issuing their press releases confirms the existence of strong interactions in all the hostile takeover bids studied. Using a manual content analysis methodology, we find that the disclosures made by the bidding and target companies consist of a series of attacks and defenses in which target companies are particularly offensive. We also give evidence that the two companies use legitimation, (de) legitimation and (re) legitimation arguments during discursive struggles, revealing the reciprocity of the communication between the two protagonists. We underline the symbolic or strategic dimensions of these legitimacy strategies in the view of the outcome of bids. Finally, we discuss the implications of our findings for regulators and make suggestions for future research. Based on the metaphor of ventriloquism, our research highlights the importance of considering disclosures as a dynamic and mutual influence process.

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.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.023
GPT teacher head0.241
Teacher spread0.218 · 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