Type of institution and the development of accurate processes

Hypothesis H8 - There is a significant positive association between the type of institution (i.e. pre- and post-1992) in terms of (a) strategy and (b) IT employed and perceived accuracy.

Pre- and post-1992 institutions have differing characteristics relating to their finances (Moody’s, 2014) and their management, resources and performance (McCormack, Propper & Smith, 2014). An analysis was therefore undertaken of respondents divided between these two groups (excluding colleges of higher education). The first part of this analysis was an attempt to understand if there were any significant differences between the two groups in terms of institutional strategy and links to the accuracy of budgeting, student number estimates and forecasting.

The survey questionnaire asked if respondents agreed or disagreed with four statements on strategy using a scale of 1 for strongly agree to 6 for strongly disagree.

An EFA was conducted on the sub-construct for strategy which considered respondents views on the linking of the budget to strategic objectives, reflecting on strategic objectives, the use of feedback from the budget process and the expectation of closing the gap between desired and actual performance. The analysis in Appendix VI identified one factor and it was therefore viewed as appropriate to combine these four items for strategy for the purpose of assessing the relationship between accuracy and strategy.

In order to test H8 the factors for accuracy and strategy were sub-divided between pre- and post- 1992 institutions, with the results shown in the Table 6.7 below:

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Table 6.7 EFA of perceived accuracy and strategy factors for pre- and post-1992 institutions

Pre-1992 KMO Bartlett's

test of sphericity Anti-image correlation Eigenvalue Factor loadings Extraction sums of squared Rotation sums of squared Cronbach's alpha Construct Code Items ≥0.5 <0.05 ≥0.5 >1.0

Accuracy BACC Budgeting Accuracy 0.610 0.002 0.581 1.728 0.820 57.607% 0.625 SACC Accuracy of student number estimates 0.591 0.797

FACC Forecasting accuracy 0.723 0.649 Strategy OBJE The budget process is explicitly linked to

strategic objectives/targets within your

0.697 0.000 0.648 2.301 0.841 57.513% One factor

0.739 TALK Setting the budget causes us to talk about and

reflect upon our strategy

0.650 0.822 CHAN Feedback from the budgeting process can result

in a change in our strategy/tactics

0.811 0.682 CGAP Managers are expected to identify initiatives to

close the gap between current and desired

0.794 0.672

Post-1992 KMO Bartlett's

test of sphericity Anti-image correlation Eigenvalue Factor loadings Extraction sums of squared loadings Rotation sums of squared loadings Cronbach's alpha Construct Code Items ≥0.5 <0.05 ≥0.5 >1.0

Accuracy BACC Budgeting Accuracy 0.702 0.000 0.722 2.100 0.708 55.176% 0.783 SACC Accuracy of student number estimates 0.710 0.726

FACC Forecasting accuracy 0.678 0.792 Strategy OBJE The budget process is explicitly linked to

strategic objectives/targets within your

0.741 0.000 0.740 2.596 0.749 55.471% One factor

0.817 TALK Setting the budget causes us to talk about and

reflect upon our strategy

0.701 0.888 CHAN Feedback from the budgeting process can result

in a change in our strategy/tactics

0.768 0.881 CGAP Managers are expected to identify initiatives to

close the gap between current and desired

0.794 0.461

One factor One factor

The statistical analysis in Table 6.2 shows a correlation between the accuracy factor and the strategy factor (r = -0.252; p = 0.021). Analysing pre- and post-1992 institutions separately shows that there was no significant correlation for pre-1992 institutions (r = 0.015; p = 0.928), but post- 1992 institutions demonstrate a negative correlation (r = -0.448; p = 0.003) indicating that as post- 1992 institutions became more cautious they were less likely to agree that attaining accuracy assisted in achieving strategy. Comparing the budgeting accuracy variable only with the strategy factor produces a similar negative correlation for post-1992 institutions (r = -0.366; p = 0.017), and (r = 0.121; p = 0.462) for pre-1992 institutions. However, this association was not revealed by a multiple regression analysis of post-1992 institutions.

An earlier study by Newton (1997) concluded that post-1992 universities had more sophisticated computer systems because of their enhanced reporting practices. However, the current study shows little significant difference between the software used by pre- and post-1992 institutions.

Pre-1992 universities were more likely to employ sophisticated software for student number planning, such as Cognos Planning tools (seven pre-1992 and two post-1992) or Corporate Planner

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(seven pre-1992 and five post-1992), particularly at the more cautious end of the budgeting accuracy spectrum, but the numbers were relatively small.

The leading financial software in the sector, Agresso/Coda supplied by Unit4, was used by both pre- and post-1992 universities for budgeting, although slightly more common in pre-1992 institutions (20 pre-1992 and 16 post-1992).

The use of a ‘funds checking’ mechanism within the financial software to automatically ensure that expenditure budgets were not exceeded (thus preventing adverse variances) was rarely used, irrespective of the degree of budgeting accuracy achieved, despite its widespread availability. Post- 1992 universities (ten) were slightly more predisposed to using funds checking than pre-1992 institutions (four).

Institutions regularly employed other mechanisms to monitor and warn of impending overspends which can be addressed at an early stage. These included providing a mechanism for managers and budget holders to drill down to successive levels of detail from summary reports and investigate budget profile variances. A total of 30 respondents from post-1992 institutions said that managers and budget holders could drill down, compared with 25 from the pre-1992 universities (three colleges of higher education also used the facility). These institutions were not grouped within any particular area along the budgeting accuracy scale.

Those respondents who indicated that the ability to drill down was available commonly responded that budget holders and managers made mixed use of the facility. 30 of the 47 respondents said that this was the case (16 post-1992 and 14 pre-1992). Only 15 claimed that it was used regularly (8 post-1992 and 7 pre-1992) and just 2 (both pre-1992) said it was not used. It might therefore be concluded that although monitoring facilities are in place to allow managers and budget holders to identify the early warning signs of potential under or over-spending, they are not necessarily effectively used and that this is the case for both pre- and post-1992 institutions across the range of cautious, accurate and optimistic budgeting.

In terms of how the new fee regime had affected budgeting accuracy, most responded that there had either been no effect or that the process was now less accurate. In might have been anticipated

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that the new £9,000 tuition fee regime would not make it any more difficult for old and traditional universities to recruit students as they were already popular destinations for undergraduates. Indeed, the removal of the student number control, allowing unrestricted recruitment, might permit such institutions to increase their number of students. However, attracting and retaining students at newer institutions would be more problematic, particularly if greater reliance is placed on the Clearing process for recruitment of students who may be less likely to complete their studies, and therefore increase the difficulty of setting accurate budgets. However, differences between pre- and post-1992 universities in terms of their views on the impact of the new fee regime on budgetary accuracy (with a Likert scale of 1 for less accurate and 10 for more accurate) failed to reveal a pattern of significant differences between the two types of institution. Statistical testing of the correlation between the measure of accuracy and impact of the new fee regime shows little correlation for either pre-1992 institutions (r = -0.117; p = 0.461) and post-1992 (r = -0.103; p = 0.516).

Overall, there does appear to be some evidence of differences between pre- and post-1992 universities in terms of the correlation between strategy and accuracy. However, H8 is rejected for part (a) on strategy as although there is an association when using Pearson’s correlation coefficient this association does not also arise under multiple regression analysis. Part (b) on IT employed is also rejected as newer universities do not make greater use of the latest technology compared with older institutions, in contrast to the earlier study by Newton (1997).