On the relationship between competitive intelligence and innovation
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Notice bibliographique
Résumé
Innovation research suggests customer, competitor and market knowledge are important requirements for innovation. Researchers in competitive intelligence (CI) have proposed that there should be a relationship between CI and innovation. Yet despite both fields recognising the need for CI and related areas for innovation in their theories, there have not been many empirical studies that look at CI and innovation and those few studies that do exist have limited focus and have only looked at a small subset of CI variables (for example collection sources). The aim of this study is to examine if there is a relationship between CI and innovation. This was done by surveying Strategic and Competitive Intelligence Professional (SCIP) members and those attending SCIP events, and asking them about their intelligence practices and how innovative their company was. Ninety-five questions were asked about CI structure and organization, intelligence focus, information sources used, analytical techniques used, communication methods, and the management of the intelligence efforts. Of the 95 competitive intelligence measures used in this study, 56 (59%) were significantly correlated with the study’s measure of innovation. The measures within the CI organizational elements and CI management categories had the highest percentage of measures significantly correlated with innovation (90% and 89%). Four of the CI measures had statistically significant correlations above .300. These included the extent to which business decisions in the organization were better facilitated/supported as a result of intelligence efforts (.355), the number of performance measures used in assessing CI’s performance (.322) and decision depth (.313), which is a measure of the number of decisions that utilized CI. As a study of this nature measuring the relationship between CI and innovation has not been conducted previously, the findings can be beneficial to organisations using innovation to succeed in the competitive environment.
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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 | 0,001 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,000 | 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) | 0,000 | 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