Taking an original line as suggested in Chapter 4, the form of positioning (perceptual) map used represents a summary of much of the collected data. Indeed, though differential biasing in the results was observed (eg Table 5.1), it is hard to make sense of, particularly across multiple corpuses. As such, in order to answer the research questions about the nature of perceived corporate value, pre to post-Crash, some type of more detailed – and more visual – consolidation is required in order to gain an overall sense of specific biases.
Useful insights were provided by 4-elelment sets (see eg Appendices 7, 8; Table 5.1). But the study’s main focus is on 8-element sets with DimSyns (Table 5.1). Applying a perceptual mapping approach (Figs 5.1 and 5.2), what is found is that there is a type of conceptual space in which the twelve stakeholder organizations distribute in relation to one another. And the (bi) term combinations take up particular positions relative to the stakeholder organizations.
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Looking at pre and post-Crash separately, what is clear is that a definite movement is observable over time for both stakeholders and term combinations relative to them; a finding that addresses the original research question Q1, that proposes movement will occur.
Fig 5.1
Fig 5.2
1This is in fact a 2D view of a 3D graph, rotated so that Dim II and Dim III appear to represent horizontal and vertical axes (Dim=dimension, referring to axes); hence Dim I is not shown. This rotation is made to give the best output view. Percentages in dimension brackets on axes refer to variance explained by the Dim or factor, ie its strength. This factor may be unknown but with several organizations and combos associating it suggests a common element – or depending on distance from Dims, a mixture of elements from both factors. Here, the Dims are attempting to characterize some combination of primacy and temporality in relation to stakeholder organizations, though variance is fairly low on these axes. But of real interest, only, is movement over time.
1
1
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While absolute positioning cannot be taken as meaningful on these maps as a rule, ICSA, for example, can nevertheless be seen to move from a pre-Crash position high on the vertical and in proximity to rp2 (return, permanent) to, post-Crash, a more central point, while rp2 has moved to the lower left quadrant. This suggests that the (relative) affinity for this combo has moved over the period (a fact also supported by its ICSA’s minimax difference value of - 100, indicating a strong pre-Crash bias). In looking at the other stakeholder organizations and combos, a similar logic is applied. Clustering is also observable, specifically as it changes over time round the central axis. Though difficult to see or interpret on these maps, it
suggests some type of alignment in operation. Further investigation to reveal any underlying effect, however, would be required.
Note, too, how stakeholder positions are also reflected in a correlation analysis (Appendix 13) where, for example, r=0.771 for the CBI and TUC, and which in both the pre and post- Crash conditions (Figs 5.1 and 5.2 respectively), though again hard to see, are in close proximity.
5.2.1 Multiple stakeholder v single stakeholder maps: While providing useful snapshots
across time, the above maps, however, do not allow a sufficient assessment of all the potential biases and effects required for this investigation, and hence to address fully the research questions. Fig 5.1 and Fig 5.2, for example, allow a sense of what is happening pre and post-Crash. But while the maps can be combined they can also become more difficult to interpret – a pre-Crash/post- Crash map of the frequencies may be plotted (Fig 5.3), or a difference measure (as from -Table 5.1) may be similarly plotted, to produce a single map but interpretation is difficult with overlapping combos. If attempting to examine all twelve stakeholder corpuses simultaneously, for a consideration of the wider views of the business community, this difficulty only enhances.
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Nevertheless, it is apparent in Fig 5.3, simply looking at the corporate and regulatory corpuses rather than all twelve, that the upper half of the map appears to be associated with the pre-Crash corpus conditions and the lower half with the post-Crash corpus conditions. Indeed, particular combos are then visible associating more with the different pre and post- Crash conditions. For example, 10) rm (return, manager) is clearly within the upper pre- Crash area, though roughly equally between the regulatory and corporate corpuses. More specifically, 13) si (share, investor) and 5) p1i (price, investor) are associated with the post- Crash regulatory corpus, and in the lower half; while 14) sm (share, manager) appears to be somewhere between corporate, pre-Crash, and regulatory, post-Crash. The possible meaning of these associations are considered more fully in Chapter 6, although it can be said here that it only goes some way to provide the necessary information about overall biasing.
In a sense, maps with many stakeholder organizations and combos contain too much compressed information – and the above examples are also two dimensions of a 3D
Fig 5.3
1Dim II and Dim III account for 13.7% and 7.6% respectively. Again very low variances but anyway not of much interest in comparison to the relative positioning of combos in relation to stakeholder corpuses over time where biasing effects are depicted.
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representation, which similarly adds to the complexity. Though, in Fig 5.4, it can be seen that 14) sm (share, manager) is in fact more associated with the corporate corpus, pre-Crash. The limited information at this point suggested therefore that for the primary corpuses combos with the term manager (a stakeholder term) are more prevalent pre-Crash and those combos with the term investor (a shareholder term) are more prevalent post-Crash. In terms of hypothesis H1a and H1b, with respect to predicted shareholder and stakeholder orientations as they might change over time, this is the reverse of what was expected. But there was a need for more supporting evidence for these findings - including for the termism dimensions, which are harder to see.
Fig 5.4: 3D positioning map of corporate and regulatory corpuses, pre to post-Crash1
1Fig 5.4 shows the corporate and regulatory corpuses in a space taking relative positions in relation to combos. Figs 5.3 and 5.4 both depict the same corpuses over time, though in 2D and 3D respectively. However, these maps, highlighting the affinity of combos to stakeholder corpuses, show heavy clustering (particularly evident with the 3D image) making it difficult to interpret the maps. Considered together the maps reduce ambiguity. Eg 14)sm is more clearly associated with the pre-Crash corporate corpus in the 3D image; whereas 5)p1i and 13)si, both with the DimSyn ‘investor’, are more differentiated in the 2D image in Fig 5.3, and associating particularly with the post-Crash regulatory corpus.
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In the context of these perceptual maps, it is worth mentioning that other researchers using similar approaches in their fields are often faced with comparable challenges and may assess data in partial steps to generate a series of practical maps – as looked at above with just the corporate and regulatory corpuses. And, with a view to making additional simultaneous comparisons, if it is required to look at another temporal dimension - which this study has done when considering a ‘sense of urgency’, and the results of which are given further on – there are a lot of variables to handle and display. For this research, too, the approach is adapted in a manner not previously attempted – ie employing combinations of terms - so further interpretation is difficult on that basis alone. Partly, though, difficulties are also due to the way in which the utilities are designed to work, which is to say whether their function is for marketing purposes or a deeper statistical analysis as used, say, in ecological studies. The novel solution therefore applied is to break the process down, first deconstructing the maps into a uni-dimensional form, so allowing biasing effects to be shown using narrative
staining (as detailed in Chapter 4) for individual stakeholder organizations. From there, and
based on a reconstitution of the results, secondary analyses may then be applied to more fully address the research questions.