Evaluating the Impact and Value of Competitive Intelligence From The users Perspective - The Case of the National Research Council’s Technical Intelligence Unit
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
Understanding and being able to measure and prove the impact and value of intelligence is of significant importance. The objective of this study was to develop an evaluation instrument that the users of intelligence could fill in that could be used to assess both the impact and value of the intelligence they received. Starting with an evaluation instrument based on lists of benefits identified in the competitive intelligence literature, measures of these benefits and client satisfaction/service quality metrics, the study researchers interviewed clients of one large government competitive technical intelligence organization asking them to articulate the benefits they obtained from the intelligence they received and methods for evaluating these benefits. All users of intelligence identified benefits they had received from the intelligence received. Additional benefits beyond those that are in the current literature were identified by those interviewed. In terms of measurement of these benefits, intelligence users (the clients) understood why hard financial type measures for example ROI or dollar impact on performance was important (especially in their organization) they felt that assessing these for the intelligence they received would be difficult but that softer, more subjective measurement such as extent to which the user agrees that the intelligence provided the intended benefit could be used. Additional perceptual based indicators of service quality and customer satisfaction measures were also suggested by intelligence clients. Based onthe results of the literature review and interviews, an intelligence evaluation instrument was developed that asks the clients to assess the extent to which they have realized one or more of 27 impacts identified in this study as well as assessing 10 elements of service quality.
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 | 0,019 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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