Personal information management among office support staff in a university environment: an exploratory study
Notice bibliographique
Résumé
Since the late 1960s, several studies have investigated personal information management (PIM) in the workplace. However, very few studies have focused on the behaviour of office support staff in a work environment. The purpose of this exploratory study was to examine the document management behaviour of office support staff in a large Canadian university. The methodological approach used for this study was grounded theory. Fifteen in-depth interviews were conducted in participants' offices, and visual observations of their document structures were made. A pre-interview survey was also administered in order to gather additional information. Participants were chosen according to the principles of theoretical sampling, and simultaneous data collection and analysis continued until theoretical saturation was reached. Transcribed interviews were coded, after which abstract concepts were derived and grouped into categories, using the constant comparison method. A substantive theory was then developed. The findings suggest the existence of several distinct document spaces within workers' document landscape: a main folder, secondary folders, the operating system desktop, e-mail, paper documents and shared environments. Behaviour pertaining to the handling of orphan files and multiple versions, the naming of files and folders as well as searching and browsing were described. Overall, despite several elements in common, significant variation was noted among participants. In order to explain the variation observed, a model of the factors that are likely to influence PIM behaviour was developed. It comprises seven main categories of factors: job content, job status, existing documents, relationship with the superior, worker characteristics, organizational context and document attributes. Several of the factors identified had never been mentioned in the PIM literature, while in other cases, the evidence presented helped confirm previous findings. The proposed model also highlights the inherent complexity of PIM, and the importance of adopting an all-encompassing view when analyzing PIM behaviour.
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 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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,007 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,003 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».