An Analysis of Satisfaction with University Library Learning Spaces Based on SPSS Regression Model
- DOI
- 10.2991/978-94-6463-504-1_22How to use a DOI?
- Keywords
- post-use evaluation; college libraries; learning spaces; satisfaction evaluation
- Abstract
Adopting the method of post occupancy evaluation (POE), the study space in the library of Zhuhai College of Science and Technology (ZCST) is taken as the research object, representative research items are selected, and the research and information feedback are conducted in the form of questionnaires and interviews. Optimization suggestions for college library learning space satisfaction are proposed through user character analysis, factor analysis, and regression analysis. Among them, the five research items of space capacity, color atmosphere, online reservation, digital resource satisfaction, service professionalism, and service timeliness can explain 42.1% of the variation of overall user satisfaction, and optimization suggestions are made for the four factors corresponding to these five research items: physical space, psychological environment, information resources, and supporting facilities and services, respectively.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Changzhi Niu AU - Chun Zhou AU - Qiqi Huang AU - Mengyao Wang AU - Yisi Chen AU - Yujia Huo AU - Sijie Lie AU - Jiayi Zhou PY - 2024 DA - 2024/08/31 TI - An Analysis of Satisfaction with University Library Learning Spaces Based on SPSS Regression Model BT - Proceedings of the 2024 3rd International Conference on Information Economy, Data Modelling and Cloud Computing (ICIDC 2024) PB - Atlantis Press SP - 228 EP - 239 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-504-1_22 DO - 10.2991/978-94-6463-504-1_22 ID - Niu2024 ER -