Staining Technique
4.2 Casting a different approach
Investigating the financial crisis over time presented considerable challenges to determine the way primacy and temporality variables required specification and examination, as well as mapping how they changed over the period of investigation. Standard linguistic analysis techniques, as discussed, while providing insights about how to go about the research were nonetheless unsatisfactory for the task it was found. As a result of the lack of suitable methodologies for the needs of the present research, therefore, that the approach of
narrative staining was devised.
Narrative staining - itself based on perceptual mapping found in marketing - is
essentially a qualitative approach. As an addition to the family of corpus linguistic analysis techniques, through using frequencies too, narrative staining also has a quantitative element. Additionally, it is a type of advanced graphical methodology. This narrative-based
analytical method is, consequently, useful for analysing multivariate research questions from high volume frequency data – which is to say, where more than one conceptual phenomenon is simultaneously under investigation and the requirement is to look at the interplay. More specifically, the method allows a visual assessment of complex (interacting, term-based) variables in large corpuses. Comparisons can then be made of multiple
corpuses, both contemporary or at different time points. If taken to its conclusion for this
of TV shows in different decades to see how any shift in communitarianism versus individualism might be operating.
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extensive purpose, the approach is also a type of qualitative data reduction method. Chapter 6 explores the far-reaching implications of this, as there is an impact on many academic domains as well as other areas of scientific and business importance.
The name ‘narrative staining’ is inspired from the variables (as combined terms) when processed appearing much like bacteria in a Petri dish once treated in some manner to change their colour. The name apart, from this central analysis, other secondary analyses are then possible – generally by making frequency counts of the data in different ways – that allows for further research questions to be addressed.
Applied to analysing the way a complex (combined) construct, corporate value, operates over time, narrative staining, rather than describing the construct, as with content analysis, focuses on its dynamic form in the context of primacy and temporality as it is impacted over the period of the financial crisis. Indeed, it is not how the construct is used per se, rather it is the objective change in its use that is to be assessed. Q1, for example, seeks an answer about a change in the relative merits of shareholder versus stakeholder primacy. Moreover, there is an avoidance of absolute conceptions of the construct of corporate value. The only interest is in the perception of relative change in corporate value in some larger comparative sense as a focus over time. In that regard, no qualitative method appears to date to have used a combinatory approach for assessing multiple variables. Hence, perceptual change, expressed as a combination of variables, reflects the manner managerial bias impacts on, firstly, who those executives favour (shareholders or stakeholders) and, secondly, their temporal horizon (short-term or long-term) in creating corporate value.
Additionally, in contrast to content analysis and the centring on what Hacker (2004) called the ‘visible communication content’ in applying narrative staining the theme of corporate value – and its specification as VRTs – is chosen in advance of an analysis of the text. It is not an extracted or ‘visible’ theme, or text-dependent in that sense, but a pre-
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selected one. The purpose of this is to make the investigation research led rather than text content led.
Colour coding based on themes is routinely a part of content analysis. However, narrative staining utilizes a different type of colour coding. Crucially, instead, the basis of the colour coding applied is on the combinations of the variables of VRTs and DimSyns1. And it is the distribution of these coloured combos, rather than any themes – as understood by content analysis - that generates the mapping effect over time. Hence, this was not achievable with currently available content analysis or alternative linguistic analysis methodologies.
Whilst the focus of qualitative analytical approaches is on a limited number of datasets, narrative staining is, rather, a qualitative data reduction method. Due to this there is great flexibility when it comes to examining multiple corpuses - and with particular applicability to multivariate research questions from high volume frequency data for complex variables. The observation may be made of a small number of data insufficiencies in the dataset of occasionally missing data points (see Appendix 3). However, the scale of the analysis – one of the features made possible by the approach – has helped to smooth out anomalies,
particularly from the large primary corporate and regulatory corpuses.
The technique has not been easy to develop but the reward is that narrative staining has proven highly effective in analysing a very large body of data in an original way; and beyond the parameters of this study, it is also has broader relevance. Indeed, narrative staining, where terms are extracted and handled dissimilarly to other methodological techniques, and with its ability therefore to look at complex bias, can be understood as having a different purpose to a collocation analysis or other related corpus data extraction
1And as seen below, coloured according to what DimSyn (primacy or temporality) the combo contains, not the VRT, as this gives a more effective result.
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methods. On that basis, narrative staining appears to fit as another form of corpus linguistic analysis, and with consequently extensive application.
Furthermore, something that does not appear to have previously been undertaken is to look at multiple corpuses in the way conducted for this investigation. Yet the method
developed has provided a window into the multi-actor, value-creating outlook of companies. Coupled with looking at other stakeholder organizations it provides a view across the
economy of business thinking in regard to value creation apparently never before observed.
4.2.1 From simple perceptual mapping to more sophisticated possibilities: In using the
narrative staining technique its underlying analytical methodology, based on perceptual, or positioning mapping1 as it is also called, was found to be connected with correspondence analysis (see also Lebart, et al, 1998). This opens up other areas of possible investigation – as well as potentially further quantitative analysis if a study requires it.
Important corpus linguistic studies using correspondence analysis and that bear on the current research include: measuring the changing usage of scientific terms over time (Degaetano-Ortlieb et al, 2013); how gender difference relate to spoken English (Brezina, 2013); and comparing English and Japanese professional writers of English language, such as journalists, to assess how they each express their discourse (Yasuhiro, 2013).2
The key point is how the methods are geared to analysing high volume frequency data in visual form; perceptual mapping (largely used in marketing) and correspondence analysis (with wider academic use). This is in contrast to SPSS3, which appears never to have been a
1The use of these names is interchangeable. See: MEXL from DecisionPro Inc, http://www.decisionpro.biz/, the utility used in the present research
2Statistics Package for Social Scientists; often the first choice for conducting analyses but apparently not for graphic requirements.
3See also: van der Veen, et al (2008) on diachronic analysis – a time-based data comparison. Their work is relevant to the current research, even though theirs is a study in the field of environmental archaeology. It is also important for using Conoco, another correspondence analysis utility. In addition: Abdi and Bera (2014) on the relationship between types of music favoured and colour preference; and the extensive work on
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favoured option in any easily identifiable research, despite its significance-testing capabilities and the numerical statistics it can generate.
Furthermore, while much of this material was not detectable early on – as there was no one to furnish the information directly – the extensive effort to find it was eventually rewarding. To date, the current research appears to be the first to look at corporate behaviour and perceptual bias with respect to value creation, and to attempt it from the angle of complex variables. In addition, the achievement of this is within the context of narrative, which has been key to the analytical focus taken.
Nevertheless, having gone through this investigative process, the multidisciplinary usage of these techniques highlighted is here observed not only to be germane to the present work, and how narrative staining could evolve, but could also be said to similarly reflect the type of cross-boundary research this study embodies.