E-Procurement in International Paper Industry. Adoption and Benefits of e-Procurement and its Models among Paper and Board Producers
Notice bibliographique
Résumé
The main aim of this research was to analyze the adoption and benefits of e-Procurement and its models among paper and board producers. The study aimed to analyze both the current use of e-Procurement and to review paper and board producers’ intended future actions in utilizing e-Procurement. Additionally, the gained benefits through using e-Procurement were also to be examined. \n\nThe theoretical part of the research was based on earlier researches and literature. Based on the sources, hypotheses related to the adoption and benefits of e-Procurement were drawn and a model of e-Procurement adoption and its benefits among paper and board producers was developed. The model covered also the background factors that were expected to have an effect on the e-Procurement adoption based on earlier researches. In the empirical part of the research, the hypotheses were tested trough quantitative methods, and the model was adjusted based on empirical observations. \n\nThe empirical part of the research analyzed the adoption and benefits of e-Procurement and its models among paper and board producers in Finland, Sweden, Germany, USA and Canada. Over 700 questionnaires were sent to the paper and board production units in the destination countries. The findings of the study pointed out that the majority of paper and board producers were not yet applying e-Procurement. E-Procurement was currently used significantly more in indirect than in direct materials purchasing. The most used e-Procurement model both in direct and indirect materials purchasing was buy-side marketplace. \n\nAccording to the research results, the use of e-Procurement in the future can be expected to increase among paper and board producers, though any extreme growth is not likely to happen. North American paper and board production units were currently using e-Procurement more than their European counterparts, though the difference in the e-Procurement usage between the regions can not be stated as significant. E-Procurement was mostly used in on-going supplier relationships, in where its usage was significantly higher than the usage of e-Procurement in other supplier relationships.
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 | 0,004 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».