Journal article
Auto-hermeneutics: A phenomenological approach to information experience
Library & information science research, v 39(1), pp 1-7
Jan 2017
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The need for methodologically rigorous approaches to the study of human experience in LIS has emerged in recent years. Auto-hermeneutics is a research approach that offers a systematic way to study one’s own experiences with information, allowing investigators to explore yet-undocumented contexts, setting precedents for further work in these areas and ultimately deepening our understanding of information experiences. This articulation of auto-hermeneutics is based on the phenomenological method of Heidegger and draws principles from systematic self-observation and interpretative phenomenological analysis. Similarities and differences among auto-hermeneutics and other automethodologies are discussed, along with guidelines for assessing auto-hermeneutic research. Finally, an example of an auto-hermeneutic study illustrates the unique contributions this approach affords.
•A need has emerged in LIS for a methodologically rigorous and flexible approach to studying information experience.•Auto-hermeneutics is a research approach suitable for exploring one's own experiences with information.•Auto-hermeneutics can contribute to the ontological characterization of information phenomena.•It draws methodological principles from systematic self-observation and interpretative phenomenological analysis.•It is differentiated from other auto-methodologies used in LIS, including auto-ethnography and self-study.
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Details
- Title
- Auto-hermeneutics: A phenomenological approach to information experience
- Creators
- Tim Gorichanaz - Drexel University
- Publication Details
- Library & information science research, v 39(1), pp 1-7
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000395850800001
- Scopus ID
- 2-s2.0-85013776620
- Other Identifier
- 991019167678804721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Information Science & Library Science