Relationships Between Librarians and Faculty Still Need Further Investigation
Notice bibliographique
Résumé
Objective – To examine how the Key Mediating Variable (KMV) model of Morgan and Hunt’s Commitment-Trust Theory of Relationship Marketing can be used to look at the relationships between librarians and faculty as reported in the literature. Relationship marketing stresses customer retention and long-term customer relationships, rather than focusing on the product. To also identify: 1) the methods reported in the literature to evaluate relationships between librarians and faculty; 2) the elements reported in the literature that lead to commitment and trust in librarian-faculty relationships; and 3) the elements reported in the literature that prevent commitment and trust in librarian-faculty relationships. Design – A systematic review. Setting – A university in the United States. Subjects – 304 journal articles on librarian-faculty relationships were read and analyzed for variables included in the KMV model of relationship marketing. Methods – The authors searched 20 databases to find publications in various disciplines. Their search strategy included, but was not limited, to the following keywords: faculty, librarian*, relationships, library users, information professionals, liaisons, academic, university, college*, collaboration, and perceptions. They initially selected 389 references based on the occurrence of search terms in the title or abstract, as well as the presence of related subject headings. The authors then read the abstracts and included/excluded references based on the following criteria: Inclusion criteria: Academic libraries or special libraries. English language, any instance of collaboration or cooperation, subject term or mention of relationship, the words trust or commitment or antecedents or outcomes from the model included in the abstract. Exclusion criteria: blogs, books, emails, or any article that could not meet the subject inclusion criteria (p. 14). Additional articles were identified by scanning the bibliographies of the articles selected at the abstract stage, searching the Directory of Open Access Journals (DOAJ) and Google Scholar, as well as conducting a cited reference search in Web of Science and Google Scholar. Among the 304 journal articles that the authors selected, read and analyzed, only 13 of these satisfied the last inclusion criteria of the systematic review in that they contained “a high presence of the KMV model’s antecedents and outcomes” (p. 15). Many articles concentrated on a service or project that librarians worked on with faculty and did not discuss the librarian-faculty relationship. Main Results – 77 out of the 304 analyzed articles discussed research methodologies. The methods used in these articles to evaluate relationships between librarians and faculty were: surveys (53%); literature reviews (26%); interviews (18%); and focus groups (5%). The 13 articles containing variables from the KMV model indicated the following positive antecedents as leading to commitment and trust in librarian-faculty relationships: communication (7/13 articles); shared values (7/13 articles); and relationship benefits (7/13 articles). The negative antecedent that hindered commitment and trust in librarian-faculty relationships was reported as opportunistic behavior in 4 articles (e.g., librarians seen as having an ulterior motive when they market their services to faculty). Cooperation (12/13 articles); functional conflict (2/13 articles); and uncertainty, i.e., faculty uncertain about the teaching ability of librarians (2/13 articles), were found to be the outcomes of relationships between librarians and faculty. Conclusion – The authors found that “a focus on communication, shared values and benefits of the relationship would build stronger ties and foster commitment and trust with teaching faculty” (p. 17). Whereas the literature shows that collaborations between librarians and faculty are important to librarians’ work, very few studies have actually examined the librarian-faculty relationship. Future studies should explore in-depth the basics of relationship building between librarians and faculty.
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 | 0,028 | 0,085 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,004 | 0,009 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,011 | 0,023 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,002 |
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 ».