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Record W1558261592 · doi:10.18438/b8d90k

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

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

Bibliographic record

VenueEvidence Based Library and Information Practice · 2012
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionPhoneMedical educationCoding (social sciences)PsychologyThe artsLibrary scienceMedicineSociologyComputer scienceSocial science

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.009
Science and technology studies0.0010.000
Scholarly communication0.0020.395
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.171
GPT teacher head0.429
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2012
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