The Relationships between Cognitive Motivational Factors of Users of Social Networking Sites SNSs
Notice bibliographique
Résumé
The objective of this pilot study was to apply the hard laddering technique [1], [2], embedded in means-end chain (MEC) theory [3], to understand why users utilize the various features and functionalities of social networking sites (SNSs). A convenience sample of 72 SNSs users in Brazil took part in the study. The study focused on Facebook as it is one of the primary means of social networking in this developing country. MEC theory has been developed in order to understand how consumers link attributes (A) of products with particular consequences (C), and how these consequences satisfy their personal values (V). The associations in the mind of the consumer between A’s, C’s, and V’s are labeled means-end chains. They are often seen as a representation of the basic drive that motivates consumer behaviour, for they link attributes of a product (such as Facebook), through the consequences (e.g., to Facebook users) stemming from these attributes, and, ultimately, to the personal values (e.g., of Facebook users) that underlie these consequences. Respondents were initially asked to write down up to three features (the attributes A) of Facebook that they consider the most important. For this purpose, respondents were presented with three text boxes to type in the attributes, which then were referred to in the subsequent questions. Next, respondents were asked why the first attribute they have just identified was important to them (the consequence C). Respondents subsequently were asked to give a reason (the personal value V) why they indicated that this consequence was important to them. After completing the above process for the first attribute, respondents were then prompted to fill in text boxes for the second and third most important attributes as well. Two researchers familiar with the topic coded the data. The first step of the data coding consisted of the content analysis of the attribute and consequence levels. Then the values were coded using the Schwartz’s list of values [4]. Cases where there were disagreements were resolved by the third, independent, researcher. In the end, four key attributes (Information search, Wider availability, User friendliness, and Social engagement), six consequences (Information access, Perceived usefulness, Socialisation, Ease of navigation, Perceived risk, and Satisfaction/ Entertainment), and five personal values (Intellectual/ Broadminded, Self-controlled/ Responsible, True friendship, Social recognition/ Sense of Accomplishment, and Comfortable life/ Happiness) were elicited. The most meaningful links between the attributes (A), consequences (C) and personal values (V) were presented in the form of a so-called hierarchical value map (HVM) [5]. The HVM constitutes the most popular approach for presenting MEC data [6]. The HVM is a graphical representation of the most meaningful relationships (means-end chains) between the A, C, and V categories. In the resulting HVM map, the users’ knowledge about Facebook’s functional attributes (features or physical characteristics) are linked with their knowledge about consequences (tangible benefits or risks) as well as personal values (high level reasons such as social recognition, self-control or happiness).
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 | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».