Notice bibliographique
Résumé
The role of knowledge as a crucial asset for and enterprise’s survival and advancement has been recognized by several researchers (e.g., von Krogh, Ichijo, & Nonaka, 2000). Moreover, by having knowledge (intellectual resources), an organization can understand how to exploit and develop its traditional resources better than its competitors can, even if some or all of those traditional resources are not unique (Zack, 1999). Therefore, knowledge management (KM-) strategy has to be solidly linked (aligned) to business (B-) strategy in order to create economic value and competitive advantage. Several authors clearly indicate the importance of mutually aligning business strategy and KM efforts and how this alignment helps enhance organizational performance (e.g., Earl, 2001; Ribbens, 1997). For example, Maier and Remus (2001, 2002, 2003) propose a process-oriented approach that considers market-oriented factors in a KM strategy. In this approach KM strategies can be described according to the process focus and type of business processes supported (Maier & Remus, 2001). The process focus can extend from a single business process to an organization-wide perspective, including all relevant business processes (core and service). The type of process is related to the identification of knowledge- intensive business processes. In addition, Sabherwal and Sabherwal (2003) empirically found that the cumulative abnormal stock market return (in the five-day event window) due to a KM announcement is positively associated with the alignment between the firm’s business strategy and the attributes of the KM initiative announced. They use four attributes to characterize KM initiatives: KM level, KM process, KM means, and knowledge source. KM level concerns the hierarchical grouping of individuals upon which the KM effort described in the announcement is focused. The KM processes (or K-manipulating processes) involve the sharing, utilization, or creation of knowledge, while KM means involve organizational structural arrangements and technologies that used to enable KM processes (Earl, 2001; Hansen, Nohria, & Tierney, 1999). Finally, knowledge source reflects from where the knowledge originates. However, realizing the importance of aligning B- and KM-strategies in creating value and in gaining competitive advantage is only the first and the easiest step in any KM initiative. The second and almost as important step is to answer how and where to begin questioning (Earl, 2001). In fact this link has not been widely implemented in practice (see Zack, 1999, and the empirical studies cited there), and “many executives are struggling to articulate the relationship between their organization’s competitive strategy and its intellectual resources and capabilities (knowledge)” (Zack, 1999). This is due to the lack of strategic models to link KMstrategy (knowledge [K-] scope, K-systemic competencies, K-governance, K-processes, K-infrastructures, and K-skills) and business strategy. As Zack (1999) argued, they a need pragmatic yet theoretically sound model. It has been highly accepted that a pragmatic and theoretically sound model should meet at least two criteria. First, it should explicitly include the external domains (opportunities/threat) and internal domains (capabilities/arrangements) of both B- and KM-strategies and the relationships between them. Second, it should provide alternative strategic choices. In order address this issue a “KM strategic alignment model (KMSAM)” is presented. It stems from the premise that the realization of business value gained from KM investment requires alignment between the B- and KM-strategies of the firm and is based on the Henderson-Venkatraman (1993) Strategic Alignment Model for information technology (IT).
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,010 | 0,006 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».