Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
Henry Assael (“ An Empirical Study of Word-of-Mouth Generation and Consumption ”) is a professor of marketing at the Stern School of Business, New York University. He has written over 30 articles for scholarly journals, and he edited a 33-volume series on the history of marketing and a 30-volume series on the history of advertising. He is the author of three widely used texts: Consumer Behavior: A Strategic Approach (seven editions), Marketing: Principles and Strategy (three editions), and Marketing Management: Strategy and Action. Hemant K. Bhargava (“ Retailer-Driven Product Bundling in a Distribution Channel ”) is an associate dean and the Jerome and Elsie Suran Professor of Technology Management at the Graduate School of Management, University of California, Davis. He studies business strategy and competition for technology products such as information goods, online services, software, electronic gadgets, media and entertainment goods, and alternative energy technologies. Peter Boatwright (“ A Satisficing Choice Model ”) is an associate professor of marketing at the Tepper School of Business at Carnegie Mellon University. He also has a courtesy faculty appointment in mechanical engineering at Carnegie Mellon University. He received his Ph.D. from University of Chicago's Booth School of Business, and his research interests include product development processes and marketing of new products, Bayesian modeling, and consumer response to product assortment. Bryan Bollinger (“ Peer Effects in the Diffusion of Solar Photovoltaic Panels ”) is an assistant professor of marketing at New York University's Stern School of Business. His research interests lie at the intersection of marketing, empirical industrial organization, and economic policy, including empirical methods, dynamics, technology adoption, demand- and supply-side spillover effects, and the effectiveness of marketing mix variables and policy tools in affecting consumer and firm behavior. He received both a B.A. and B.E. in engineering from Dartmouth College, and an M.A. in economics and a Ph.D. in marketing from Stanford University. Xiaohong Chen (“ An Empirical Study of Word-of-Mouth Generation and Consumption ”) is a professor of management science at the Business School of Central South University, China. She received a B.S. in computer science and an M.S. in management science from Central South University, China, and a Ph.D. in management science from the Tokyo Institute of Technology, Japan. She is the principle professor of national first-level key principles “Management Science and Engineering” and “Innovation Group” of the National Natural Science Foundation in China. She is also the winner of “State Science Fund for Outstanding Youth” and named one of China's “National Outstanding Women” and “National Prominent Social Scientists.” Her research has been published in several top journals. John Deighton (“ Editorial—Research Priorities of the Marketing Science Institute: 2012–2014 ”) is the Executive Director of the Marketing Science Institute and the Harold M. Brierley Professor of Business Administration at the Harvard Business School. His Ph.D. is from the Wharton School of the University of Pennsylvania, and he served previously on the faculties of the University of Chicago and Dartmouth College. Kenneth Gillingham (“ Peer Effects in the Diffusion of Solar Photovoltaic Panels ”) is an assistant professor of economics at Yale University, with appointments in the School of Forestry and Environmental Studies (primary) and the Department of Economics (courtesy). He holds a Ph.D. from Stanford University and a B.A. from Dartmouth College. His research focuses on the adoption of new technologies, including renewable energy, energy efficiency, and green transportation technologies. He was a Fulbright Fellow in New Zealand and has worked at the White House Council of Economic Advisers and Resources for the Future. Liang Guo (“ Consumer Deliberation and Product Line Design ”) is an associate professor of marketing and Senior Wei Lun Fellow at Hong Kong University of Science and Technology. He received a Ph.D. in business administration from the University of California, Berkeley, and a B.A. in economics from Beijing University. His research interests include behavioral economics, channel interaction, information acquisition and sharing, and marketing strategy. His research work has been accepted for publication at the Journal of Economics and Management Strategy, Management Science, and Marketing Science; he serves on the editorial boards of Marketing Science and Management Science (associate editor). He was named an MSI Young Scholar in 2009. Mantian (Mandy) Hu (“ An Empirical Study of Word-of-Mouth Generation and Consumption ”) is an assistant professor in the Department of Marketing at the Chinese University of Hong Kong, Hong Kong. She received a B.A. in economics from Fudan University, China, an M.A. in economics from Tufts University, and a Ph.D. in marketing from New York University. She is the 2011 recipient of the Best Proposal Award in the Society for Marketing Advances (SMA) Dissertation Proposal Competition. Ganesh Iyer (“ Competition in Consumer Shopping Experience ”) is the Edgar F. Kaiser Professor of Business Administration at the Haas School of Business, University of California, Berkeley. He received his Ph.D. from the University of Toronto and was previously on the faculty at Washington University in St. Louis. His research uses economic theory to study marketing strategy problems; his areas of research are the coordination of product distribution, marketing information, Internet strategy, strategic communication, and bounded rationality in marketing strategy. He is currently an associate editor for Marketing Science, Management Science, and Quantitative Marketing and Economics. He received the 2000 John D. C. Little Award and was a finalist for the Little award on three other occasions, and two of his papers have been finalists for the INFORMS Long Term Impact Award. Gareth M. James (“ Predicting the Path of Technological Innovation: SAW vs. Moore, Bass, Gompertz, and Kryder ”) is an expert on statistical methodology with particular application to marketing problems such as prediction of technology evolution. He teaches both M.B.A. and Ph.D. courses ranging from introductory statistics to advanced modern nonlinear regression techniques. He was recently elected a fellow of the American Statistical Association, the nation's preeminent professional statistical society, in recognition of his outstanding professional contributions to and leadership in the field of statistical science. He has also earned numerous accolades from USC Marshall, including the Evan C. Thompson Faculty Teaching and Learning Innovation Award, and he is a two-time winner of both the Dean's Award for Research Excellence and the Golden Apple Award for teaching excellence in his M.B.A. courses. He has published numerous articles in leading journals such as the Journal of the American Statistical Association, for which he also serves on the editorial review board. Zsolt Katona (“ Contextual Advertising ”) is an assistant professor of marketing at the Haas School of Business, University of California, Berkeley. He has a Ph.D. in management from INSEAD; he also earned a Ph.D. in computer science from Eotvos University, Budapest. His current research focuses on understanding the interaction between websites' online advertising strategies. He also studies the role that link structure of social networks plays in word-of-mouth effects and community formation. Previously, he had analyzed characteristics of different random networks and published his work in such journals as the Journal of Applied Probability, Statistics and Probability Letters, and Random Structures and Algorithms. Susan Keane (“ Editorial—Research Priorities of the Marketing Science Institute: 2012–2014 ”) is the Editorial Director at the Marketing Science Institute, where she manages the development of the Relevant Knowledge book series, the working paper series, and other print and digital content. Dmitri Kuksov (“ Competition in Consumer Shopping Experience ”) is a professor of marketing at the Naveen Jindal School of Management, the University of Texas at Dallas. He previously worked at Washington University in St. Louis, and he holds a Ph.D. in marketing from the Haas Business School of the University of California, Berkeley. His research interests include competitive strategy, markets with incomplete information, consumer communication and networks, branding and product line strategy, and customer satisfaction. His work has appeared in a number of journals, including Marketing Science, Management Science, the Journal of Marketing Research, and the Journal of Economic Theory. He received the 2005 Frank M. Bass Dissertation Award for his work on search costs and product differentiation, which was also a finalist for the INFORMS Long Term Impact Award, and two of his papers were finalists for 2007 John D. C. Little Award. Natalie Mizik (“ Firm Innovation and the Ratchet Effect Among Consumer Packaged Goods Firms ”) is the Shansby Associate Professor of Marketing at the Foster School of Business, University of Washington (UW). She has published research in a broad set of substantive areas including branding, strategy, managerial myopia, customer satisfaction, and direct-to-physician pharmaceutical marketing. An award-winning teacher and researcher, she has served on the faculty of the Columbia Graduate School of Business and UNC Kenan-Flagler Business School, and she was a visiting professor at the MIT Sloan School of Management before she joined
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.
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 | 0,006 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 découle