The uses of personal communication devices in corporate environments
Notice bibliographique
Résumé
The rise of smartphones in the past decade has created situations in which individuals use them in public and private domains. More recently there has been an increase in the adoption of smartphones by corporations; what is not very well understood is their use within meetings. In this dissertation I present quantitative and qualitative data from two online surveys conducted two years apart on the type of smart mobile devices used in meetings, and the attitudes and behaviours of meeting participants towards their usage. The results from the two surveys included four key findings: (1) meeting participants believed that multitasking with a mobile device was a commonly adopted activity; (2) participants took a more accepting attitude towards using certain mobile devices (specifically laptops) in meetings; (3) it was somewhat acceptable to make work-related calls or send text messages regarding work-related emergency matters using smartphones during meetings; and (4) individuals in management tended to think that making important work-related calls during meetings was acceptable. Furthermore, from a list of six types of departments, the operations department tended to rate texting important work-related messages during meetings as acceptable compared with other departments. After reviewing the data from surveys I and II, it was determined that more detailed data were required to observe people’s actual behaviours in live meetings. As a result, a study was devised to simulate a meeting scenario in which one individual would receive and send text messages. Eight video recordings of meeting participants were captured and analyzed to assess their resulting attitudes and behaviours. In four of the meetings text messages arrived in two clusters (i.e., five text messages at the beginning and three at the end of the meeting), while for the remaining four meetings text messages arrived evenly distributed throughout the meeting. The data from those meetings suggest that the participants in the evenly distributed text messages group of meetings interacted with their mobile devices more often but on a less obtrusive level by checking their phone status. The participants in the clustered grouping of text messages group of meetings tended to produce more negative comments (verbal and non-verbal) regarding the actor and their own phone usage. When the actor received a text message, participants tended to give a negative non-verbal gesture, such as gazing at him, or when participants used their own mobile phones they tended to provide a verbal justification of their own use.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».