MétaCan
Menu
Back to cohort
Record W4321087938 · doi:10.1111/jifm.12170

Investor visits to corporate sites and cost stickiness

2023· article· en· W4321087938 on OpenAlexaff
Wenyun Yao, Hanwen Xu, Yuling Fan, Zefeng Xu

Bibliographic record

VenueJournal of International Financial Management and Accounting · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsMcGill University
Fundersnot available
KeywordsEndogeneityRobustness (evolution)BusinessEnterprise valueInstrumental variableCorporate financeMonetary economicsEconomicsAccountingFinanceEconometrics

Abstract

fetched live from OpenAlex

Abstract A corporate site visit is an effective way to obtain information on a firm. Most studies focus on the information advantages of corporate site visits, but evidence of their impact on firm operations is limited. In this paper, we investigate whether investors’ corporate site visits affect cost stickiness. Using data on investor corporate site visits to Chinese listed firms from 2013 to 2018, we find that these visits can inhibit cost stickiness. This finding holds in robustness tests and when controlling for endogeneity, including firm fixed effects, and using the Heckman selection model and the instrumental variables method. Further analyses reveal this inhibition is more pronounced for nonstate‐owned enterprises and the results are more significant regarding cost stickiness in firms consuming nonlabor materials and firms visited by institutional investors. Moreover, we explore plausible mechanisms through which corporate site visits inhibit cost stickiness, such as through a monitoring channel and a learning channel. Our study contributes to academic evidence on the benefit and value of corporate site visits to firm operations, showing these visits can be a useful way to build connections between investors and firms.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.615

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 communication0.0000.002
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 head0.028
GPT teacher head0.235
Teacher spread0.207 · 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

Citations16
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of International Financial Management and AccountingSame topicCorporate Finance and GovernanceFrench-language works237,207