Managers’ stock-based compensation and disclosures of high proprietary cost information
Notice bibliographique
Résumé
Purpose The purpose of this study is to examine whether chief executive officers’ (CEOs’) stock-based compensation has any relationship with disclosure of high proprietary information. Design/methodology/approach Drawing on agency and proprietary cost theory, this study examines whether compensating CEOs based on equity value through the grants of stock option and restricted stock will affect different firms with high proprietary costs versus general costs of disclosures. The authors further explore the cross-sectional variation on the relationship between stock-based compensation and disclosures of high proprietary cost information. In particular, the authors examine certain circumstances under which stock-based compensation has a stronger effect in discouraging managers to make disclosures of product-related information. This study conducts an empirical investigation on the relationship by using hand-collected data on the product-related disclosures of biotechnology firms and by developing new disclosure indices to capture the product developments in the preclinical and clinical stages. Findings The authors find that on average, managers’ stock-based compensation does not have any significant relationship with the proxy of high proprietary disclosure index. More importantly, the authors find that managers with more equity-based compensation (in the total pay) make fewer disclosures of high proprietary cost information when they have a stronger need to protect such information. Specifically, the authors find a negative relationship between equity-based compensation and managers’ disclosure of high proprietary cost information when their firms’ product development is in early stage, when the corporate board mainly consists of directors with lack of sufficient knowledge on technology, and when firms are a leader in an industry in terms of market share. Research limitations/implications The authors acknowledge two limitations of the current study. First, the authors cannot completely rule out the possibility that the results are still subject to endogeneity issues such as reverse causality or omitted correlated variables even though the authors control for other important variables that affect disclosures and granting of stock-based compensation (including firm size, leverage, analyst following, institutional ownership and corporate governance) and use the lagged variable of stock-based compensation in the regression model. Second, given that the authors examine a small sample (only 10 per cent of firms in the biotechnology industry) due to the required hand-collection of product-related information, the generalizability of the results may be limited. Originality/value The study contributes to the literature in two important ways. First, the findings can add to the literature on the effect of stock-based compensation on managers’ disclosures. While previous studies suggest that compensating via stock options and restricted stocks can incentivize managers in enhancing firm disclosures in general (e.g. Nagar et al ., 2003), the authors provide evidence suggesting that it may not always be the case. When disclosing information involves high proprietary cost, stock-based compensation can sometimes motivate managers not to reveal information. The study also complements Erkens (2011), who finds that firms offer stock-based compensation to their managers as an attempt to prevent the leakage of research and development (R&D)-related information to competitors. Second, the study can contribute to the extant literature that examines the importance of proprietary costs on firms’ disclosure decisions. The authors attempt to respond to the call for more research in this area (Beyer et al., 2010) by focusing on one specific industry, the biotech industry and by using a novel proxy for the proprietary costs based on the stage of product development for a drug-related product in that industry. As it has been challenging for researchers to properly measure proprietary costs of disclosures, the setting of the biotech industry provides a particularly strong empirical identification to potentially pinpoint the proprietary costs.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».