Customer value disclosure and analyst forecasts: the influence of environmental dynamism
Bibliographic record
Abstract
Purpose – The purpose of this paper is to study the economic benefits of a pro-active disclosure strategy in a dynamic environment. More specifically, the paper explores the relationships between customer value disclosure, analyst following, and earnings forecasts, taking into account environmental dynamism as captured by R&D intensity, sales variability, and the reverse of industry concentration. Design/methodology/approach – The paper considers the possibility that a firm's information dynamics may simultaneously affect disclosure strategy, analyst following, and analyst forecasts. Regression models are used in the testing of the hypotheses. Findings – First, results show that customer value disclosure is positively associated with analyst following and consensus in analyst earning forecasts. Second, environmental dynamism enhances the association between customer value disclosure and analyst following as well as consensus among analysts. Those results suggest that customer metrics attract analysts and improve their ability to forecast earnings. Moreover, customer value disclosure appears particularly relevant for forecasting earnings of firms involved in dynamic environments. Practical implications – Customer value disclosure would allow financial analysts to better assess future earnings in a context of uncertainty. Moreover, analysts may be reluctant to follow a firm facing high environmental dynamism without a clear corporate disclosure commitment. In such a context, managers may consider disclosing strategic information in an attempt to attract financial analysts. Originality/value – The findings reveal that the relations between customer value disclosure, analyst following, and analyst forecasts are not straightforward but are affected by a firm's environmental uncertainty.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".