CHAPTER 5 RESEARCH METHODOLOGY
5.8 Data Analysis Procedures: Within-case and Cross-case analyses
Cases are examined as using within-case and cross-case techniques. The textual material was
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subjected to multiple forms of analysis: axieal coding, latent semantic analysis and classical
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content analysis.
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The ‘within-case’ analysis utilizes the theoretical concepts from process metaphysics, while the
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cross-case analysis is analyzed via assemblage theory. The within-case analysis is a preparatory
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step for the cross-case analyses. The primary purpose of within-case analysis is to become
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intimately familiar with the data and with each case as a stand-alone entity. Furthermore, the
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within-case analysis identified key process elements, and data patterns from each case, thus
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providing a solid foundation for the cross-case analysis. Once the key processes were identified,
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they were examined via the concepts from assemblage theory in the cross-case analysis. The
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purpose of conducting the cross-case analysis was to identify and investigate similarities and
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differences between the cases along two dimensions: vendor neutral vs. vendor-led CCs and
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established functioning CCs vs. abandoned or nonfunctioning CCs.
A generic depiction of my data analysis approach diagramed as a three-staged, multi-method
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approach is found in Figure 5.8.1. This approach allowed for a kind of triangulation and
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faciliated further analysis of the discovered categories. My initial coding during T1 was informed 3
by my research questions and the concepts from assemblage theory. Thus I did not take a
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Grounded Theory approach in this analysis. However, when issues were found to emerge from
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the data they were not ignored. The richer, ongoing interpretation was folded into my analysis.
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The definitions of the concepts from Assemblage Theory were the focus of my early analysis.
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Having established an initial set of broad categories of assemblages, I further drilled down
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during the second stage, T2, of coding using latent semantic analysis. With the aid of a software 9
tool, Leximancer, I identified common themes and idioms arising in the respondent’s narratives.
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The third stage, T3 is a synthesis and re-examination of the data (See Fig. 11). 11
Figure 5.8.1. Iterative data collection and analysis method (Adapted from (Woodside 2010))
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Overview of the Analysis Method: I conducted the Interviews later transcribed, cleaned, and
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annotated the MS Word files. The annotation is a basis for manual concepts coding and assists in
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later validation of the machine coding and the synthesis of discovered concepts. The data
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processing was divided into three parts: coding procedures (sorting), data reduction techniques
(categorizing), and drawing conclusions (mapping). Coding procedures dealt with strategies to
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handle the semi-structured interview data as well as the more open-ended interviews and
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documents. Interviews were coded using “Open Coding (Glaser and Strauss 1971)”, which
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enables the examination, comparison, conceptualization, and categorization of data. I mapped
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the results of the open coding concepts to the theoretical concepts of Assemblage. The Analysis
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also deployed software machine coding, using both Leximancer, and nVivo. I utilized nVivo as
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the main research database that contains all research related files, such as recording, manual
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coding, interview notes, and secondary text documents. Leximancer’s latent semantic analysis
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generated themes, and interpretations of those themes provided further insights, which might
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otherwise have been missed(Crofts and Bisman 2010) . The technique is described more fully
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below.
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Figure 5.8.2. Overall Data Analysis Process utilizing Leximance and nVivo (Adapted from(Penn-Edwards 2010) )
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Content Analysis 13
Content analysis is a method for extracting the contextual meanings and concepts from text
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documents. I performed a specific type of content analysis called Latent Semantic Analysis
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(LSA). The first step in LSA is to read an input text file. In doing so, the researcher typically
transforms words that contain many spelling variants (e.g., organize, organization, organizing,
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etc.) into “word stems” – so that various grammatical and spelling variations are recognized as
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having the same meaning. The second step is the creation of a document matrix-vector – which
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is comprised of two elements: words and documents being analyzed (see Figure 5.8.3).
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Documents are anything with a “semantic structure” that an analyst seeks to interpret. For
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example, documents may be abstracts from research papers, blog posts, or advertising copy.
6 W o rd f req u en cy Documents D1 D2 D3 . . . DN Word 1 1 0 1 0 Word 2 0 1 1 0 .. Word N 1 0 1 0
Figure 5.8.3. Document Matrix
The third step in LSA is dimension reduction. The document matrix yields a large vector that
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needs to be reduced to smaller sets of meaningful concepts. One of the simplest and powerful
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dimension reduction approaches is Singular Value Decomposition (SVD). SVD is based on
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linear algebra, details of which are explained in earlier studies (Landauer et al. 1998; Martin and
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Berry 2007). SVD finds the obvious patterns and trends within the document matrix by
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analyzing which words frequently appear in specific documents (frequency count), as well as
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other words that often appear nearby (known as co-occurrences). These patterns are then
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presented as concepts.
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While there are many available software tools for performing LSA, a popular and recognized tool
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within the IS and computer science literature is Leximancer. Several IS studies employing this toolset
have recently appeared (Crawford and Hasan 2006; Debuse and Lawley 2009; Mindel and
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Mathiassen 2015; Ridley and Young 2012). In Leximancer, concepts are identified via words that are
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weighted according to how frequently they occur within two-sentence “chunks” of text containing the
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focal concept, compared to how frequently they occur elsewhere. The concepts then are clustered into
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higher-level themes. The themes are comprised of concepts that appear together often in the same
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chunks of texts. Leximancer provides results in the form of “overall” visual maps, where the analyst
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can view the concepts, sub-concepts (keywords used in creating a concept), or themes (see Figure
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5.8.4). Once the initial overall map is created, the analyst can change the theme size to adjust the
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grouping of concepts on the map. For example, in order to select fewer but broader themes, or
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conversely, to drill down into more detailed themes, the analyst has the ability to select the
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desired level of granularity.
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Figure 5.8.4. Leximancer processing: transforming words to themes
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Leximancer produces visual diagrams, with certain key terms appearing in different-sized
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circles. Not only is the size of the key term important, but the color of the circle encasing it is
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important as well. Specifically, the “hot” colors (hues including red, orange, and yellow)
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depict that the theme has a stronger relationship with the concepts (many or similar concepts
clustering to make a theme).
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The strength of Leximancer is not merely the identification of concept tokens and patterns,
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but in its ability to query, retrieve and further drill down into the texts. During this process, it
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helps in identifying and excluding from the analysis extraneous terms and false concepts. Of
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course this is an iterative and human guided process, and the researcher, like a pilot using fly-
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by-wire guided avionics, manages the entire research process system-machine and manual-
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and is responsible for the interpretation and sense-making of these analyses.
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