Identification of profiles in the satisfaction of users of digital repositories through a regression tree
Abstract
In recent years, universities have sought to provide access to digital repositories in order to ease the location of information sources that aid the scientific research process. Nevertheless, there are few studies that address their user satisfaction. This paper presents the results of a research that was conducted with the aim of understanding the satisfaction of university students on the use of digital repositories. A 26-item questionnaire organized in seven dimensions was admnistered to 219 participants from the Autonomous University of Tamaulipas, from the Nuevo Laredo and Ciudad Victoria, Tamaulipas, Mexico campuses. The following variables were studied as possible predictors of user satisfaction: a) related to the interface of the repository: interactivity, confidence, access opportunity, ease of use, visual attractiveness, innovation and b) related to the student: Sex, maximum level of education, and campus location. SPSS statistical package was used to perform the data mining technique called "regression tree" with the Classification and Regression Tree (CRT) growth method. We obtained a tree describing three profiles with low, medium and high levels of satisfaction. Participants with low levels of satisfaction were those who found that repositories were not easy to use. An average level of satisfaction was observed in people who perceived the repositories as easy to use, not dependable, and with a low level of innovation. The highest levels of satisfaction were observed in students who perceived repositories as dependable and easy to use. The results contribute to the understanding of user satisfaction in terms of the studied variables with the objective of prioritizing them in the design and implementation of new institutional repositories to provide better user experiences and optimal exploitation of these resources.
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References
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