Staining Technique
4.1 Comparing qualitative techniques
Discourse analysis was initially considered for the investigation but quickly found to be unsuitable. Primarily the reasons for this were: its unit of coding was too large, utilizing the sentence or categorical level; its focus tends to be on a fairly small particular text or two rather than being easily used comparatively across large multiple corpus datasets; and it is concerned with how individuals construct categories, and is hence a subjective approach depending largely on the context and discursive qualities of the writer of the text, as well as the interpretation by the investigator. Meaning can be fluid. Indeed, discourse analysis aims at revealing socio-psychological characteristics of people involved rather than text structure. It is an approach, therefore, too abstract. For this research, an approach was required that
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readily homed in on the text structure itself. Data had to be inherent in the text and not a result of an exploration of how any categories were socially constructed. Hence, the need was for a far more objective analysis to consider how data might change over time to reflect managerial bias.
By contrast, content analysis, which seemed applicable to the textual focus of the study, attempts to describe the characteristics or properties of documents and communication objects, such as textual data, pictures, audio, or video, in some manner. As such, it may have a number of purposes and cover a variety of techniques (Neuendorf, 2002). It sometimes gets used, as a result, as a catch-all term, including for both qualitative and quantitative methods. Yet the procedure can lead to being able to make valid inferences from the text (Weber, 1990, p.9) and in a systematic manner (see Krippendorf, 2004, p.3). However, as a descriptive technique inference is limited to that text and by the scope of the content. Hence, it is not causal, nor can in some form, one document be related to another, which was a requirement for making comparisons over the crisis period.
At its simplest, the use of content analysis to examine frequencies of word occurrences in a text or corpus - which is where this research similarly takes its lead. Another use is to extract an underlying theme from an examination of the spoken or written material of individuals – a procedure eliciting data examinable qualitatively or quantitatively. The procedure often involves thematic coding, so building up a picture of the distinctive, often subtle, perceptions reflected in the material that may be prevalent. For example, looking at organizational or stakeholder behaviours.
Yet while thematic features were relevant, the conclusion was that content analysis was inadequate as an approach because it could not handle all the analytical requirements of the
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current research.1 Besides the above issues, any social or organizational constructs identified in a content analysis are in a singular thematic form, whereas this research required the functioning of bias to be understood when corporate value - even as a theme - is viewed in the context of different dimensions on which it impacts (ie components of primacy and temporality). This is a far more complex construct. A theme of stakeholder behavior – or any other variable including that of corporate value – on its own would not therefore provide the necessary data to address the hypotheses, which consider combined constructs (eg H1a: Pre Crash, value-related terms will dominate in the space representing short-term
ShP).
Likewise, when using content analysis more thematically it may be concerned with meaning, intention and consequence (Downe-Wamboldt, 1992) to try to make sense of the construct found and examined. While this may help theoretically to describe the corporate value phenomenon of interest and provide the flexibility to make abstractions (see eg Robson, 1993), data in this research does not require consideration in that way. Instead, and in line with basic content analysis, a more elementary frequency-based approach is used. Examining frequencies is fundamental to corpus linguistic analysis too. And, in further comparing content analysis with corpus linguistics, a corpus linguistic search to explore term use can, by contrast, be as simple or as complex as one wants it to be (see eg Gries, 2016). Such analyses may thus have a much larger scale of focus and assessment2 than content analysis, making it potentially of use. However, though having more flexibility with corpus sizes3 than content analysis - where a very limited number of texts or other objects
1There is also thematic analysis as a separate but similar technique to content analysis though similarly found to be too abstract for the needs of this research (see Vaismoradi, et al, 2013).
2Where term frequencies underlie the use of not only corpus linguistic analysis, but also perceptual mapping and correspondence analysis (See also Lebart et al, 1998).
3This refers to the number of documents within a corpus. One document, however, can contain millions of words.
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such as transposed interviews may be used – corpus linguistics can similarly have limitations.
Content analysis might also be considered contrived, as constructed themes in analyses are dependent on the investigator’s evaluation of what constitutes a theme. Corpus
linguistics however is more objective, and with a concern on language use in real contexts (Adolphs and Lin, 2011). On that basis, one important linguistic analytic method is
colocation analysis. And with its mapping facility, it was initially thought applicable for this research. Work on climate change (Fig 4.1) by Grundmann and Krishnamurthy (2010), for example, is a foremost study demonstrating how a visual assessment can be made of a discourse through examining the collocation of words; that is how one particular word is present in relation to another. It also showed, significntly, how colour could be used to indicate changes in perception. And a method of visualization was specifically a requirement for the present research in mapping how bias with respect to primacy and temporality might alter over time.
In applying collocation, an examination is made of each side of the discourse to see if, or how, it differs from the other. In one part of their study, drawing on twenty years of newspapers in the US and the UK, Grundmann and Krishnamurthy compared how the terms ‘change’, ‘warming’ and ‘greenhouse’ varied in relation to other high frequency terms within a five-word proximity. These terms were then colour coded according to a specific area of interest, or frame as they identify it, though in essence a theme.
It is an interesting study as it shows a variation between the UK and the US in the way each country thinks – whether by a political, scientific or an action frame. For example, the UK (with more purple) is seen as far more action-oriented than the US on climate change. Based on term frequencies the approach in general demonstrates a visual mapping of how
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the thinking about a concept – in this case climate change – can be compared between corpuses and mapped.
In relation to the present research, this type of linguistic analysis, designed to explore language use for a given corpus, seemed to offer analytical possibilities. It was feasible, for example, to examine frequencies over time, one corpus against another, by such an
approach and map the effect. Independently, too, the idea of using terminology relating to ‘action’, had been thought an interesting notion to explore. For this study, it took expression as a sense of urgency to act – and in comparison to other research, finessed more in
conception as part of a larger temporal construct.
Fig. 4.1: Simple mapping by colour. Source Grundmann and Krishnamurthy (2010)
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Yet while all that was the starting point for this investigation, a standard corpus linguistic analytical approachwas not suited to the requirements. It would not have been possible, it was felt, to examine a complex variable as had been conceived in this way (ie primacy and temporality together – see Introduction). Indeed, looking at the frequencies of each part of such a combination individually for mapping was not an option. In many ways corpus linguistics, though often sophisticated for identifying patterns, as Grundmann and Krishnamurthy (2010) demonstrated very well, did not allow the addressing in some
combined or multivariate manner the impact of one frequency on another as it changed over time; rather a purely corpus linguistic approach would take the research in a different direction. Such methodology also generally confines itself to two or three corpuses at a time, whereas in this research there were two primary corpuses and ten secondary ones. Furthermore, this type of corpus linguistic analysis lacked a sufficiently advanced visualization tool. Hence, one that could handle the multiple variables this research employed (ie two time periods, themselves variables, for: shareholder primacy bias, stakeholder primacy bias, short-term time horizon, long-term time horizon, along with the use of value-related terms or VRTs)1. And it meant that, while providing considerable ideas for tackling the research challenge faced, a corpus linguistic analysis approach was not feasible.
In sum, the design of most qualitative analytical techniques, whether content analysis or others2, and differing with respect to thematic verses pattern evaluation, enables the
1See Appendix 4 for examples
2For applications and their limitations see: Timmis, 2015: p.4, on what can be done with corpuses; Scott, 1996, p53, on semantic prosody, how a word can take on a positive or negative orientation by virtue of its
association with another word (eg ‘sure’: as in definitely sure v not sure); corpus stylistics, the exploration of literary texts, (see eg the CLiCDickens project at Nottingham); and Uhis and Greenfield (2011) - utilizing Greenfield's Theory of Social Change and Human Development (2009) in conjunction with Kasser and Ryan’s Aspiration Index (1996), for generating a list of aspirational terms for experimental testing, such as how the desire for ‘fame’ or ‘power’ changes over time, http://faculty.knox.edu/tkasser/aspirations.html. It is about changes in a value system. The application of this corpus linguistic orientated approach is to a content analysis
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handling of only a limited number of datasets. And colocation analysis apart, all have inadequate or non-existent mapping capabilities, a component the present research required. A colocation analysis while not appropriate for this research, however, is nevertheless a related methodology to what was ultimately to become narrative staining.