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Enregistrement W1558261592 · doi:10.18438/b8d90k

Perception and Information Behaviour of Institutional Repository End-Users Provides Valuable Insight for Future Development

2012· article· en· W1558261592 sur OpenAlexvenueno aff
Lisa Shen

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2012
Typearticle
Langueen
DomaineDecision Sciences
Thématiquescientometrics and bibliometrics research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPerceptionPhoneMedical educationCoding (social sciences)PsychologyThe artsLibrary scienceMedicineSociologyComputer scienceSocial science

Résumé

récupéré en direct d'OpenAlex

Objective – To determine the perceptions and information behavior of institutional repository (IR) end-users.
 
 Design – Semi-structured interviews. 
 
 Setting – The interviews were conducted over the telephone. 
 
 Subjects – Twenty end-users of five different IRs were interviewed for the study. Seventeen of the interviewees were recruited via recruitment forms the researchers placed on IR homepages and the other three interviewees were referred to researchers by IR managers. 
 
 The interviewees’ academic backgrounds varied, including six undergraduates, four masters’ students, three doctorial students, five faculty, and two library or museum staff members. They represented disciplines in Arts and Humanities (5), Science and Health Sciences (10), and Social Sciences (5). Fifteen of the 20 interviewees were recruited through their own institution’s IR. All except two of the interviewees had used the IR for which they were recruited less than six times.
 
 Methods – Forty-three potential interviewees were recruited using web recruitment forms and IR manager recommendations. Researchers subsequently excluded 23 (53.5%) of the interviewees because they were primarily IR contributors rather than end-users, or could not be reached by phone. 
 
 Twenty interviews ranging from 17 to 60 minutes were conducted between January and June 2008. The average interview time was 34 minutes. The recordings were transcribed then analyzed using qualitative data analysis software NVivo7. Coding categories were developed using both the original research questions and emerging themes from the actual transcripts. The final coding scheme had a Holsi Coefficient of Reliability of 0.732 for inter-coder reliability.
 
 Main Results – Researchers identified six common themes from the results:
 
 How do end-users characterize IRs?
 While most interviewees recognized that there is a relationship between the IR and its host institution, their understandings of the function and content of IRs varied widely. Interviewees likened the IRs they used to a varying array of information resources and tools, including databases, interface, server, online forums, and “static Wikipedia” (p. 27). Furthermore, six of the interviewees had never heard of the actual term “Institutional Repository” (p. 27). 
 
 How do end-users access and use IRs?
 The most common methods of accessing IRs included selecting the link on their institution library’s website and Google searches. Many interviewees found out about the IRs they are using through recommendations from professors, peers, or library workshops. Other interviewees found out about particular IRs “simply because a Google search had landed them there” (p. 29). 
 
 Interviewees’ preferred method of interacting with an IR were divided between browsing and keyword searching. However, these preferences may have been the result of an IR’s content or interface limitations. For instance, some interviewees expressed difficulties with browsing a particular IR, while another interviewee preferred browsing because “there wasn’t much going on” when searching for a specific topic of interest (p. 30).
 
 For what purposes do end-users use IRs?
 Interviewees commonly cited keeping abreast with research projects from their own university as a reason to access their institutions’ IRs. Student interviewees also used IRs to find examples of theses and dissertations they would be expected to complete. Identifying people doing similar work across different departments in the same institution for collaboration and networking opportunities was another unique purpose for using IRs.
 
 How do end-users perceive the credibility of information from IRs?
 Many interviewees perceived IRs to be more “trustworthy” than Google Scholar (p. 33). In their view, an IR’s credibility was assured by the reputation of its affiliated institution. On the other hand, many interviewees viewed a lack of comprehensiveness in content negatively when judging the credibility of an information source, which placed most IRs in a less favorable light.
 
 Additionally, researchers noted conflicting assumptions made by interviewees about IRs in the evaluation process for their content. Some interviewees believed all the content of an IR has been vetted through an approval process, while others distrusted all IR content that was not peer-reviewed. 
 
 To what extent are end-users willing to return to an IR or recommend it to their peers?
 The great majority of interviews indicated they were likely to use IRs again in the future, and nearly all indicated they would recommend IRs to their peers. However, most interviewees did not know of any people using IRs. The few interviewees who did often knew of IR contributors rather than end-users. 
 
 How do IRs fit into end-users’ information seeking behavior?
 Many interviewees noted that IRs provided them with content that was not commonly available through traditional publishing channels, including conference papers and dissertations. Others felt IRs made content available more quickly than other information sources. However, the results also suggested that most interviewees did not include IRs in their routine research process.
 
 Conclusion – This study identified current end-users’ perceptions of IRs and highlighted several areas for future IR development. Areas of improvement for IRs included intensifying publicity efforts; increasing content recruitment; making content recruitment policies more transparent; and improving appearance and navigation functionalities. The findings also suggested new directions for IR marketing, such as emphasizing on the networking and collaborating benefits of using IR.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,013
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Communication savante
Catégories consensuellesCommunication savante
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,710
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0070,013
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0070,009
Études des sciences et des technologies0,0010,000
Communication savante0,0020,395
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,171
Tête enseignante GPT0,429
Écart entre enseignants0,258 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeAutre devis
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2012
Routes d'admission1
Résumé présentoui

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