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Enregistrement W219863710

Value Creation and Games of Innovation: The Competencies That Lead Firms to Business Success Depend on the Demands of the Particular "Game" in Which They Compete, a Study Reveals

2004· article· en· W219863710 sur OpenAlex

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Notice bibliographique

RevueResearch-Technology Management · 2004
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueEconomic and Business Development Strategies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésProfitability indexValue (mathematics)MarketingBusinessIndustrial organizationSection (typography)Knowledge managementEconomicsComputer scienceAdvertising
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Many people believe that is unmanageable, given the uncertainties involved, the tendency toward unduly optimistic forecasts and the difficulty of predicting consumers' responses. The solution they often propose, however, is to foster many entrepreneurial flowers and let markets select the best products. This romantic view of is flawed because it allows too many resources to be allocated to hot sectors, thereby creating bubbles. The research we conducted, and which is explained in this article, has led us to a contrary view; namely, that is manageable and is not totally a gamble. Our study was carried out with senior R&D leaders from around the world, as part of an Industrial Research Institute subcommittee investigation on Managing R&D for Growth. Its original purpose was to identify: 1) the effective R&D strategies to manage for growth and (2) the best practices to achieve superior performance. Our central finding is that firms achieve high levels of performance in terms of profitability and growth not so much by adopting best practices but by adapting their capabilities and practices to the requirements of value creation and capture in the particular game, or games, in which they have elected to compete. The competitive and technological contexts orient and structure each game differently. In the first section of this paper, we outline the research methods, data sources and data analysis. Next, we describe the eight games of innovation that were identified by understanding value creation. Then, in the third section, the generic practices for managing are shown to vary with value creation activities in each game of innovation. In the fourth section, the specific capabilities and practices that are significantly associated with high levels of sales growth are presented for four games. We conclude by summarizing our key findings and their strategic implications. How the Study Was Conducted As this research began, the context created by the New Economy challenged many received practices and theories related to the management of innovation. Novel ways of managing R&D were identified that included cross-functional teamwork, speed in bringing products to markets, technology outsourcing, and so forth (1,2). For our part, instead of relying on the management literature to identify best practices, we decided to build from reality and ask CTOs and R&D vice presidents in the United States, Canada and Europe which strategies and practices they had developed to face the new situation. From our extensive discussions, a theoretical model was elaborated and a survey instrument was designed to quantify value creation and capture best practices for managing innovation. Then, the same executives as well as a broader group were invited to respond to the instrument in personal or virtual meetings. Seventy-three CTOs and R&D VPs in Europe (25), Canada (20) and the United States (28) agreed. From the data they provided, we identified 125 best practices for managing in such areas as exploration, portfolio and project management, transfer to business units, and market shaping. We then asked our respondents to rate the extent to which these practices were actually used within their firms. Ratings were obtained on each of the 125 dimensions. Our sample was not random but designed for learning by including a substantial number of firms in fast-moving, R&D-intensive, sectors. The industries covered are both capital- and knowledge-intensive, but only 20 percent of our sample is from the IRI membership. Data on the financial performance of firms were gathered independently from public databanks and corporate websites. The wealth of information we gathered made it possible to undertake statistical analyses. First, factor analyses were made to identify the underlying vectors that characterize the responses of the CTOs and VPs to their use of management practices. …

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,565
Score d'incertitude au seuil0,327

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,003
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,085
Tête enseignante GPT0,297
Écart entre enseignants0,212 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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