Intangible assets : values, measures, and risks
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Notice bibliographique
Résumé
In today's ultra-competitive global economy, intangibles are increasingly taking centre stage in firms' business strategies and investors' valuations. Physical and financial assets are becoming commodities, yielding at best a competitive return on investment. In their place, intangible assets such as patents, brands, unique business processes, breakthrough scientific discoveries, and strategic alliances are what firms are using to create dominant market positions, control risk, generate abnormal profits, and achieve growth and wealth. The dramatic rise and fall of high-technology company valuations over the past five years has brought the unusual economic characteristics of intangible assets into the public arena. The concurrent advantages and vulnerabilities of intangible-intensive companies has highlighted the importance of having an in-depth understanding of the economics of intangibles and developing tools to better manage and evaluate them. This Reader provides that understanding by bringing together the best research and advocacy on intangibles. The chapters provide a comprehensive tableau of both rigorous perspectives and empirical evidence about intangible assets by scholars and policy makers in accounting, economics, finance, and information technology. As such, the Reader both informs and sets a solid foundation for the next generation of challenging questions that need to be addressed. The Reader has four sections: Section I explains why intangibles have become so important in the modern economy. Section II investigates the impact of specific kinds of intangibles on firm performance and equity market values. Section III documents the severe adverse effects of the informational deficiencies that are created by the accounting and financial reporting rules that govern intangibles. Finally, the chapters in Section IV call for improved disclosure and measurement of intangibles in financial statements, and make concrete suggestions for what such solutions should look like. Contributors to this volume - David Aboody (The Anderson School, UCLA) Mary Barth (Stanford Graduate School of Business) Margaret Blair (Georgetown University Law Center) Stephen Bond (Nuffield College, Oxford) Jeff Boone (Mississippi State University) Louis Chan (University of Illinois) Michael Clement (University of Texas at Austin) Jason Cummins (Federal Reserve Board) Michael Darby (The Anderson School, UCLA) Z. Deng (Ph.D. student, New York University) George Foster (Stanford Graduate School of Business) John R. M. Hand (Kenan-Flagler Business School, UNC Chapel Hill) Ron Kasznik (Stanford Graduate School of Business) Josef Lakonishok (University of Illinois) Baruch Lev (Stern School of Business, New York University) Joan Luft (Michigan State University) Randall Morck (University of Alberta) Leonard Nakamura (Federal Reserve Bank of Philadelphia) Francis Narin (President, CHI Research) K. K. Raman (University of North Texas) Paul Romer (Stanford Graduate School of Business) Chandra Seethamraju (Olin School of Business, University of Washington, St. Louis) Carl Shairo (Walter A. Haas School of Business, University of California at Berkeley) Michael Shields (Michigan State University) Theodore Sougiannis (University of Illinois) Hal Varian (University of California at Berkeley) Steven Wallman (Founder and CEO of FOLIOfn) Bernard Yeung (Stern School of Business, New York University) Paul Zarowin (Stern School of Business, New York University) Lynne Zucker (UCLA)
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Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
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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 découle