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Record W4402798880 · doi:10.1111/1475-679x.12574

Internalizing Peer Firm Product Market Concerns: Supply Chain Relations and M&A Activity

2024· article· en· W4402798880 on OpenAlexaff
Farzana Afrin, Jinhwan Kim, Sugata Roychowdhury, Benjamin Yost

Bibliographic record

VenueJournal of Accounting Research · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicAuction Theory and Applications
Canadian institutionsKellogg's (Canada)
FundersNorthwestern UniversityStanford University
KeywordsBusinessSupply chainProduct (mathematics)Product marketIndustrial organizationMicroeconomicsEconomicsMarketing

Abstract

fetched live from OpenAlex

ABSTRACT We explore whether firms internalize the product market concerns of their economically linked peers by examining merger and acquisition decisions in the context of customer–supplier relations. Given the extensive transfer of capital, knowledge, and information between merging parties, we hypothesize that customers’ competition concerns discourage their suppliers from engaging in vertically conflicted transactions (i.e., acquisitions of their customers’ rivals or suppliers to those rivals). Consistent with our hypothesis, we find that suppliers are less likely to engage in such transactions when their customers are subject to higher product market competition. Moreover, the effect is more pronounced when suppliers and customers have greater relationship‐specific investments and when customers face heightened proprietary information concerns. Using plausibly exogenous variation in common ownership between customers and their rivals as a shock to customers’ competition concerns, we conclude that the link between customers’ competition concerns and supplier acquisitions is likely causal. Our findings suggest that firms alter their investment and strategic decisions in response to the product market competition concerns of their economically related peers.

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)0.030
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0300.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.258
GPT teacher head0.515
Teacher spread0.257 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations6
Published2024
Admission routes1
Has abstractyes

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