Investor visits to corporate sites and cost stickiness
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".