MétaCan
Menu
Back to cohort
Record W4394709834 · doi:10.1111/joms.13071

A Contingency View of Impression Management: Heterogeneous Investor Responses to CEO Positive Portrayal of Mergers and Acquisitions

2024· article· en· W4394709834 on OpenAlexaff
Conor Callahan, Ruixiang Song, Wei Shi, Kevin Veenstra, Gerry McNamara

Bibliographic record

VenueJournal of Management Studies · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsMcMaster University
Fundersnot available
KeywordsContingencyMergers and acquisitionsImpression managementBusinessImpressionContingency theoryContingency planIndustrial organizationAccountingAdvertisingPsychologyEconomicsManagementSocial psychologyFinance

Abstract

fetched live from OpenAlex

Abstract Existing research has suggested seemingly contradictory conclusions about the efficacy of impression management (IM) tactics. While a growing body of research highlights the potential benefits of IM, other studies imply that the effectiveness of these tactics in shaping stakeholder perceptions may be limited. Our study advances theory on IM by drawing upon expectancy violations theory to develop a contingency theory of IM efficacy. Concentrating on CEOs’ positive portrayal of merger and acquisition (M&A) activity, we hypothesize that the effectiveness of this IM tactic hinges on factors related to the communicator (CEO duality), context (acquisition foreshadowing), and audience (investor type). Our results indicate that investor reactions to CEOs’ positive portrayal are more favourable when M&A activity has been foreshadowed or when the institutional investor is transient. Conversely, reactions are less favourable for CEOs also serving as board chair. Our findings provide novel insights into IM theory, suggesting that potential expectancy violations associated with IM tactics could be shaped by the attributes of communicator, context, and audience.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.025
GPT teacher head0.276
Teacher spread0.251 · 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 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

Citations8
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Management StudiesSame topicCorporate Finance and GovernanceFrench-language works237,207