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Enregistrement W1482192873

Linkage, Leverage and Leadership Drive Successful Technological Innovation

2004· article· en· W1482192873 sur OpenAlexaboutno aff
Edward B. Roberts

Notice bibliographique

RevueResearch-Technology Management · 2004
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueBusiness Strategy and Innovation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOfficerLeverage (statistics)BusinessLinkage (software)BenchmarkingMarketingTyingManagementPublic relationsPolitical scienceEconomics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Recently, I carefully reviewed the work that I had done during the last decade in carrying out two global benchmarking activities in 1992 (1) and 1999 (2) of the world's largest R&D spenders. These were the major companies in the U.S. and Canada, Western Europe and Japan that spent at least $100 million annually on research and development. In re-looking at my earlier data and findings, I have concluded that three primary issues matter in managing technology successfully in the large firm: what I call linkage, leverage and technological leadership. 1. Linkage Linking technology to business strategy has turned out to be one of the most critical factors that help generate success in managing technological innovation for the large firm. My research highlighted two primary subtopics in regard to linkage. One is the role of key and senior officers of the company. Without any further thought, of course, the Chief Executive Officer and the Chief Technology Officer are obviously major linchpins who matter and who show up in all of the data globally as tying the business strategy of the company together with its technology strategy and technology endeavors. The CEO is inevitably the strategic linchpin for all major undertakings of the firm, while the CTO no doubt even has this goal in his/her job description. But I am quite concerned about the weaknesses that have shown up in the data in the roles of two other key players, on which I suggest major companies need to focus. First and perhaps most important in weaknesses is the relatively low participation rate of the senior marketing people in providing technology ties to the market and the like. Organizationally, most large firms are deficient, according to my studies, in bringing the Marketing Vice President and the marketing organization into a comparable and parallel role with the CTO in delivering the goods for the company and its stockholders in regard to how technology impacts the firm. Yes, everyone does agree that companies need to be market-driven, at least to some extent. But the evidence indicates that those who run marketing need to see technology more clearly as a critical bridge to achieving company strategic goals and need to foster the internal alliances with their technology partners to help achieve such bridging. The second in regard to linkage is the Chief Financial Officer. The CFO presides over what for many companies is the single largest aggregation of expenditures, namely R&D expenditures. In most firms, R&D expenditures rival in magnitude and sometimes exceed capital expenditures, expenditures that in most firms, and certainly in the ones represented in my research, are considerably larger than the dividend flow. These metrics all indicate how significant from a financial standpoint is R&D. It is the company's largest investment in its future and needs investment guidance from senior finance officers. Yet my survey information from around the world shows only minor participation by Chief Financial Officers in providing direction to link technology and business strategy. Occasional exceptions, such as Judith Lewent, the CFO of Merck, have demonstrated consistent engagement in tying the R&D side of the organization to all maj or aspects of corporate strategy and action, indicating that the role weakness is not universal. The second aspect of vital linkage that I round is in the R&D resource allocations themselves, the outcome of budgeting processes in the firms we examined. My research provides unfortunate testimony as to how even designated corporate research labs have shrunk in the percentage of their work that is actually research-focused. The data show a shrinkage over the 1991-97 period from 41 percent of corporate-level expenditures focusing upon research to only 32 percent of those expenditures. Thus, even the most far-out organizational structure of the firm has been turning its attention more to nearer-term product, process and technical support activities that in my opinion should not be the highest priority of the company at that level. …

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,025
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,032

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,025
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0040,003
Études des sciences et des technologies0,0020,004
Communication savante0,0100,005
Science ouverte0,0010,007
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

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 Opus0,147
Tête enseignante GPT0,317
Écart entre enseignants0,170 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2004
Routes d'admission1
Résumé présentoui

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