{"title":"Mapping representations in qualitative case studies: can we adapt Boisot’s I-Space model?","authors":"C. Spinuzzi","doi":"10.1108/jwl-01-2023-0013","DOIUrl":null,"url":null,"abstract":"\nPurpose\nThis paper aims to consider ways to visually model data generated by qualitative case studies, pointing out a need for visualizations that depict both synchronic relations across representations and how those relations change diachronically. To develop an appropriate modeling approach, the paper critically examines Max Boisot’s I-Space model, a conceptual model for understanding the interplay among knowledge assets used by a population. I-Space maps information in three dimensions (abstraction, codification and diffusion). It is not directly adoptable for case study methodology due to three fundamental disjunctures: in theory, methodology and unit of analysis. However, it can be adapted for qualitative research by substituting analogues for abstraction, codification and diffusion.\n\n\nDesign/methodology/approach\nUsing an example from early-stage technology entrepreneurship, this paper first reviews network, flow and matrix models used to systematically visualize case study data. It then presents Boisot’s I-Space model and critiques it from the perspective of qualitative workplace studies. Finally, it adapts the model using measures that have been used in qualitative case studies.\n\n\nFindings\nThis paper notes three limitations of the I-Space model when applied to empirical cases of workplace learning. Its theory of information does not account well for how people use representations synchronically for learning. It is a conceptual framework, and the tentative attempts to use it for mapping representations have been used in workshops, not for systematically collected data. It does not adequately bound a case for analysis. Thus, it can be applied analogically but not directly for mapping representations in qualitative case studies.\n\n\nPractical implications\nThis paper identifies a possible way to develop I-Space for strategically mapping representations in qualitative case studies, using measures analogous to the I-Space axes to reflect observable behavior.\n\n\nOriginality/value\nIn providing a methodological critique for one model of knowledge management, this paper also develops criteria for appropriate modeling of meaningful artifacts in the context of qualitative studies of workplaces.\n","PeriodicalId":47077,"journal":{"name":"Journal of Workplace Learning","volume":" ","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Workplace Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/jwl-01-2023-0013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
This paper aims to consider ways to visually model data generated by qualitative case studies, pointing out a need for visualizations that depict both synchronic relations across representations and how those relations change diachronically. To develop an appropriate modeling approach, the paper critically examines Max Boisot’s I-Space model, a conceptual model for understanding the interplay among knowledge assets used by a population. I-Space maps information in three dimensions (abstraction, codification and diffusion). It is not directly adoptable for case study methodology due to three fundamental disjunctures: in theory, methodology and unit of analysis. However, it can be adapted for qualitative research by substituting analogues for abstraction, codification and diffusion.
Design/methodology/approach
Using an example from early-stage technology entrepreneurship, this paper first reviews network, flow and matrix models used to systematically visualize case study data. It then presents Boisot’s I-Space model and critiques it from the perspective of qualitative workplace studies. Finally, it adapts the model using measures that have been used in qualitative case studies.
Findings
This paper notes three limitations of the I-Space model when applied to empirical cases of workplace learning. Its theory of information does not account well for how people use representations synchronically for learning. It is a conceptual framework, and the tentative attempts to use it for mapping representations have been used in workshops, not for systematically collected data. It does not adequately bound a case for analysis. Thus, it can be applied analogically but not directly for mapping representations in qualitative case studies.
Practical implications
This paper identifies a possible way to develop I-Space for strategically mapping representations in qualitative case studies, using measures analogous to the I-Space axes to reflect observable behavior.
Originality/value
In providing a methodological critique for one model of knowledge management, this paper also develops criteria for appropriate modeling of meaningful artifacts in the context of qualitative studies of workplaces.
期刊介绍:
The Journal of Workplace Learning aims to provide an avenue for the presentation and discussion of research related to the workplace as a site for learning. Its scope encompasses formal, informal and incidental learning in the workplace for individuals, groups and teams, as well as work-based learning, and off-the-job learning for the workplace. This focus on learning in, from and for the workplace also brings with it questions about the nature of interventions that might assist the learning process and of the roles of those responsible directly or indirectly for such interventions. Since workplace learning cannot be considered without reference to its context, another aim of the journal is to explore the organisational, policy, political, resource issues and other factors which influence how, when and why that learning takes place.