Highlighted here are the output maps for the initial analysis utilizing Set 1 data

(VRT-1, primacy and termism). The first map highlighted (Fig 5.5) is for the corporate bi scenario, and without any filter.2

1_{Corporate websites were initially included in the corporate corpus but these tended to disrupt the later more} complex analyses. The reason for this, it is believed, was that websites are more geared to a different audience and are more marketing-oriented rather than appealing to a commercially-minded audience, so skewing the data – or indeed, making it more difficult to extract a result. As all the other corpuses assessed were commercially- focussed it made more sense to be consistent and focus on the annual reports and related documents of all the companies in the corporate corpus, and in this way making the range of analyses across corpuses for different stakeholder organizations more comparable (see also Chapter 4, Section 4.3.1.ii).

2_{Dim l and Dim ll respectively have percentages (in brackets) denoting 100% and 0% variance. This is because} the maps are uni-dimensional; hence, 100% variance has to be on the horizontal dimension – and as such, a manual exploding of the combos is from the horizontal axis, as Chapter 4 showed. Consequently, variance in these maps - while having use as demonstrated above in 2D and 3D maps - can be ignored here and in the following scenarios, as there is no effective Dim ll for any practical purpose. Dim l, however, with the utility’s graphics harnessed in the way they have been, indicates pre to post-Crash splits, where found, for the

stakeholder organization examined. For example, as is observable in Fig 5.5 for the corporate domain. Fig 5.52

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What is observable is that shareholder primacy, denoted in blue, is the same pre and post-Crash, two instances of each. However, whereas pre-Crash there are more stakeholder instances than shareholder instances (3 green:2 blue), this is reversed post-Crash (2 blue:1 green). There is a movement, therefore, pre to post-Crash, stakeholder to shareholder. Similarly, of short-termism (orange), pre-Crash, to long-termism (purple), post-Crash. Addressed, therefore, are research questions Q1 and Q2.i; though hypotheses H1a and H1b are not supported - ie the movement is not occurring in the proposed fashion. The fact that the bias is apparently dependent on only one combo difference in some cases has been found to be far less of an issue than might be thought and is discussed further on in relation to the usefulness of tri and quad terms. Green and orange, stakeholder and short-termism respectively, are in equal proportions in the pre and post-Crash scenarios too (1:1); which simply means that there is no change in short-term stakeholder perception over the period, although this does change a bit as is observable with filtering.

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If a filter is added such that any combo values are excluded that are less than plus or minus 20% of zero (using a minimax difference value between pre and post-Crash data, see Table 5.1), similar results are found, as shown in Fig 5.6 (see also Chapter 4 concerning how this was done).

Though it is not a given, in this case, and with the absence of any stakeholder combo post-Crash (no green), the movement from short-term stakeholder primacy, pre-Crash, to long-term shareholder primacy, post-Crash, is maintained.

Bi terms, of the form AB, above suggest a change in corporate perception. However, the question was whether this effect was maintainable for tri terms, those of the form ABC.

There are more combos present, quite obviously in the tri map (Fig 5.7). And very apparently the predominating colours pre-Crash are green and orange, while post-Crash the

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predominating colours are blue and purple. Hence, as with the bi analysis, the movement over time is from a short-term stakeholder perception to a long-term shareholder perception. It is of note that whereas with bi terms there has to be an element of each of a VRT and a DimSyn, with tri terms it is feasible to end up with two of either - ie two out of the three. Chapter 4 (Section 4.6.3) discusses the rationale for the handling of this.

Fig 5.8 shows that even with a 20% filter applied the same predominating movement is observed ie short-term stakeholder, pre-Crash, to long-term shareholder, post-Crash.

For quad terms of the form ABCD the findings are maintained. Fig 5.9 shows the result without filtering. Also looked at here are results without partials. Partials, as discussed in Chapter 4, refer to the way the different combinations form with increased options of what to include with the more elements there are – hence, more colour content too for the distribution if included. An example of a partial is: p1srm, where p1, s, and r, are all VRTs, and there is

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only one DimSyn for m (manager) present. Purely from an objective analytical standpoint, and without attempting to explain the finding, Figs 5.9 and 5.10 show respectively the results

Fig 5.9

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without and with partials where it is similarly observable that the movement towards post- Crash, long-term shareholder perceptual bias is maintained.

There is a generation of comparable results with the application of a 20% filter to the quad analysis, both without partials (Fig 5.11) and with partials (Fig 5.12).

Fig 5.11

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What has been demonstrated is that though variables are manipulated, Q1, predicting change over time of the perceptions of value creation in relation to the relative merits of shareholder primacy and stakeholder primacy - and Q2.i, predicting this change in relation to termism - are addressed. But hypotheses H1a and H1b, relating to movement pre to post- Crash, short-term shareholder primacy to long-term stakeholder primacy, are not supported. This is contrary to expectations. However, these findings only utilize Set 1 variables.