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Information Journal Paper

Title

Analysis of Effective Paratextual Elements to Improving Fiction Retrieval by Structural Equation Modeling

Pages

  60-83

Abstract

 Objective: The purpose of this study is to identify Paratextual Elements in retrieving fiction from the readers’,perspective in order to provide the most appropriate access points for the readers and improve access to fictions based on the readers’,needs. Methodology: The approach of this research is mixed method conducted with sequential exploratory strategy. The statistical population in both stages of the study was readers of Fiction Literature. In the first phase of the study, a number of 14 story readers participated in a semistructured interview. The collected data was coded and analyzed based on the six-step content analysis framework of Brown and Clark using MAXQDA software version 2018. In the second phase, a questionnaire designed based on 5-point Likert scale was used to data collection and the collected data was analyzed by Structural equation modeling. Also, SmartPLS software version 3 was used to analyze the data. Findings: According to the first stage results of the study, 12 Paratextual Elements in the form of 4 main themes of bibliographic information (including author name, title, publisher name and date of publication), information about the author (including biography, awards and other works of the author), review/ criticism (including 3 elements of other readers' comments, literary criticism and reviews and summaries about the work) and physical characteristics (including two elements of cover design and number of pages) were identified by themes analysis of the interviews. In the second phase of the study, the results of the Structural equation modeling test confirmed the effect of review and criticism, bibliographic information, and information about physical characteristics of work on improving Fiction Retrieval. Based on the results, reviews and criticism that have been written about the work with path coefficient of 0. 517 more than other Paratextual Elements about the work could help the reader to retrieve fiction. After that, the bibliographic information with path coefficient of 0. 287 and the physical characteristics of the work with path coefficient of 0. 148 are in the next ranking. Since the value of t-statistic for author information is less than 1. 96, the effect of this variable on Fiction Retrieval is not confirmed. Conclusion: Paratextual Elements could be useful in perception, evaluation, making decision, selecting resources and help fiction readers to find relevant fictions. With use of Paratextual Elements of fiction to enrich library catalogues and create several access points, we could develop and improve search and retrieval systems and also increase the efficiency of these systems in retrieving fiction readers need. Therefore, it is suggested that the Paratextual Elements identified in this study, whose effect on improving Fiction Retrieval have been confirmed, be used as part of describing fiction content in library catalogs. It is also suggested that these elements be used in the design and development of fiction storage and retrieval systems in libraries to increase the efficiency of these systems and enable successful search and retrieval for fiction readers.

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    • Cite

    APA: Copy

    GHASEMPOUR, SAKINEH, ABAM, ZOYA, & BARADAR, ROYA. (2021). Analysis of Effective Paratextual Elements to Improving Fiction Retrieval by Structural Equation Modeling. LIBRARY AND INFORMATION SCIENCE, 24(3 (95) ), 60-83. SID. https://sid.ir/paper/1049902/en

    Vancouver: Copy

    GHASEMPOUR SAKINEH, ABAM ZOYA, BARADAR ROYA. Analysis of Effective Paratextual Elements to Improving Fiction Retrieval by Structural Equation Modeling. LIBRARY AND INFORMATION SCIENCE[Internet]. 2021;24(3 (95) ):60-83. Available from: https://sid.ir/paper/1049902/en

    IEEE: Copy

    SAKINEH GHASEMPOUR, ZOYA ABAM, and ROYA BARADAR, “Analysis of Effective Paratextual Elements to Improving Fiction Retrieval by Structural Equation Modeling,” LIBRARY AND INFORMATION SCIENCE, vol. 24, no. 3 (95) , pp. 60–83, 2021, [Online]. Available: https://sid.ir/paper/1049902/en

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