Effective techniques for the segmentation of academic library users. Issue 8 (14th November 2016)

• Record Type: Journal Article
• Authors: Yi, Zhixian
• Is Part Of: Library management. Volume 37:Issue 8/9(2016)
• Page Start: 454
• Page End: 464
• Publication Date: 2016-11-14
• Subjects: Survey -- Segmentation -- Factors -- Academic library users -- Effective techniques -- Ordinal regression; Library administration -- Periodicals; 025.005
• DOI: 10.1108/LM-07-2016-0052
• Languages: English
• ISSNs: 0143-5124

Abstract

Abstract : Purpose: The purpose of this paper is to examine how Australian academic librarians perceived effective techniques used to segment library users into smaller groups and the influencing factors for their perceptions of the used techniques. Design/methodology/approach: An online survey was sent to 400 academic librarians in 37 Australian universities and 230 (57.5 per cent) respondents completed and returned surveys. The descriptive and inferential statistics and content analysis method were used to analyse the collected data. Findings: A variety of effective techniques were utilised to segment library users. Library variables such as number of staff and number of library branches and human capital variables such as education level, years of present position, formally studying marketing and attending a workshop on marketing in the last five years were significant predictors of perceptions of the effective segmentation techniques used, but this study indicates that other predictors such as number of different library professional positions and years involved in all library services and demographical variables made no difference. Practical implications: This paper provides a useful overview of the effective techniques used to segment library users. Originality/value: The value of this study is that librarians may utilise the results to better understand different techniques, to reflect on the effectiveness of the used techniques, and to balance the weight of the influencing factors. This will enable them to segment library users more effectively in the future.