# Framework of Measuring Key Performance Indicators for Decision Support in Higher Education Institution

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3 Fig. 1: Some results of the evaluation of the importance of the factors. Fig. 2: Hierarchy of KPI KPI Tree: The next step is to build KPI tree, which is basically composed by three levels : the goals, the criteria and the KPIs (figure 2). In the evolution of the AHP system that we are presenting, we build the hierarchy which consists of: The goal (1st level) : Total score of HEI performance The criteria (2nd level): we can find three criteria; academic(teaching), research and supporting. The rating scale (3rd level): contains KPIs related to each criteria, and its rating scale. Criteria and KPI Weighting: In the second level (criteria), the three criteria ( academic, research and supporting ) are weighted using pairwaise [6] comparison proposed by AHP approach. Results of questionnaire survey are translated into pair wise comparison matrix and then it is followed by weighting process. AHP method provides a fundamental scale to assign pairwise comparison judgment, as shown on tabel 1. The meaning of the table is that criterion A is strongly more important than criterion B. For the criteria weighting, the fundamental scale is not sharp enough to assign relevant pairwise judgements. AHP method proposes to create as many refinements as needed for the specific problem, and to estimate verbally the value of each new point of the scale. This work has to be done by the evaluation team, in order to obtain a consensus about the evaluation scale. The evaluation team had created a scale divided 1 to 9. Also evaluators use their own comparison ruler, but using the same principle than the basic one. 1691

4 Table 1: Fundamental scale for AHP pairwise comparison Level Definition Explanation 1 Equal importance Two factors contribute equally to the objective 3 Moderate importance Experience and judgment slightly favor one factor over another 5 Strong importance Experience and judgment strongly favor one criterion over another 7 Very strong or demonstrated importance A factor is favored very strongly over another; its dominance demonstrated in practice 9 Extreme importance The evidence favoring one factor over another is of the highest possible order of affirmation Table 2: Pairwise comparison judgment matrix (example)criteria AC RE SU Priority vector Academic (AC) Reasearch (RE) Supporting (SU) Table 3: Inconsistency matrix Criteria AC (0.518) RE (0.195) SU (0.287) Line sum Academic (AC) Reasearch (RE) Supporting (SU) Each evaluator has to compare elements of the same hierarchy level. First, academic is compared to research. Then, on one hand, academic compared to supporting, and on the other hand three couples have to be compared. Results of these judgments are summarized in pairwise comparison judgement matrices (PCJM) as shown on Tabel 2. The meaning of this matrix is that, according to the evaluation team, the most important criterion, is Academic (priority: 0.518) followed by Supporting (priority: 0.287) and finally Research (priority: 0.195). Priorities are computed by this way (example for Academic criterium): = ( ) ( ) The number of criteria divides result of the sum, in our case there are three. So priority for the first criteria is: = The last cell of the PCJM is related to consistency ratio (CR). According to Saaty, AHP method can introduce some inconsistency in judgement because experts not only use rational ways to compare criteria but also their feelings, so results can be not totally consistent. For example, with three criteria (A, B and C), if the first line of the matrix is A = 1/2 B and A = 1/4 C, the logical following should be: B = 1/2 C. But it is possible to find B 1/2 Cand it means there is some inconsistency. So evaluators have to compute a consistency ratio to determine if their judgements are good or too inconsistent.first, each column of PCJM is multiplied by the corresponding priority and lines are summed as show below in Tabel 3. After that line some vector is divided by priority vector: And so evaluators ë max = = can compute 3 The next step is to compute the consistency index: ë max CI = = = 0.007, n-1 2 where n is the number of criteria.ahp method provides a random index (RI), which depends of the matrix size. This index is used to compute the consistency ratio (CR). For a 3x3 matrix RI= So finally: CR = CI/RI = 0.007/0.58 = According to Saaty, if CR 0.10, it means that judgment is consistent enough and can be approved. KPIs are wighted using the same way as criteria weighting described above. KPI Scoring: KPIs are measured based on principles of trends and comparison dimensions. Trends consist of current level and last year performances. For example, if current level is performance of year of 2006, so trends consist of performances of years 2005 and This shows growth of two latest years. On the other hand, comparison shows position current level performances compared to its competitor performances or benchmarks (Table 3). 1692

5 Fig. 3: Basic pairwise comparison ruler Fig. 4: Wheel model for HEI Performance and its KPIs How to score KPI? There are three values of KPI, as described below: 2005 (3.41) and KPI 2006 (3.54) is less than competitor (3.62). If KPI trend is growing and current level is better than competitor/benchmark, then score is 100. It means that the organisation is growing and better than competitor/benchmark; see student satisfaction to opportunity for personal involvement (Table 3), in which KPI 2006 (3.81) is better than KPI 2005 (3.76) and KPI 2006 (3.81) is better than competitor (3.65). If KPI trend is growing and current level is less than competitor/benchmark, or if KPI is declining and current level is better than competitor/benchmark, then score is 50. For example, as shown by KPI Student government, KPI 2006 (3.53) is better than KPI 2005 (3.41) and KPI 2006 (3.53) is less than competitor (3.66). It means that the organisation is growing but not better than competitor/benchmark. On the other hand, as shown by KPI Concern for you as an individual, KPI 2006 (3.50) is better than competitor (3.39), but less than KPI 2005 (3.62). It means that the organisation is better than competitor/benchmark, but internally there is no growth compared to historical performance. If KPI trend is declining and current level is less than competitor/benchmark, then score is 0. It means that the organisation does not grow and does not have any competitive advantage. For example, as shown by KPI Attitude of Non Teaching Staff, KPI 2006 (3.21) is less than KPI Total Score of HEI Performance: Total score of HEI performance is calculated with the following formula: Total HEI Performance score = (s ij x w ij); for i= 1 to k and j= 1 to n where, i is index for HEI performance criteria (academic, research and supporting); j is index for KPI-j of criterion-i W i is weight of criterion-i w is weight of KPI-j related to criterion -i ij Wheel Model of HEI Performance: A simple analogy would be to look at an HEI performance as a wheel and the individual KPIs are the spokes to the wheel (figure 4). Having just one or two spokes loose, can make a wheel out-ofbalance. The longer a wheel runs out of balance the more damaging the effect to the organization. When the wheel on a cart becomes so unstable that its primary function fails, you would simply replace the wheel. Obviously, an organization cannot simply replace itself. but your customer can and will replace the wheel (you the Supplier) if you fail to perform to the Customers' needs and expectations. 1693

7 Focus on the Future Growth and Sustainability: In today s education environment, a focus on the future requires understanding the short- and longer-term factors that affect your organization and the education market. Pursuit of educational excellence requires a strong future for orientation and a willingness to make long-term commitments to key stakeholders your community, employers, faculty, and staff. Organization s planning should anticipate many factors, such as changes in educational requirements, instructional approaches, resource availability, student/stakeholder expectations, new partnering opportunities, faculty and staff development and hiring needs, technological developments, new student and market segments, demographics and community/ societal expectations. Managing for Innovation: Innovation means making meaningful change to improve an organization s programs, services, and processes and to create new value for the organization s stakeholders. Innovation should lead the organization to new dimensions of performance. Innovation is no longer strictly the purview of research; innovation is important for providing ever-improving educational value to students and for improving all educational and operational processes. Management by Fact: Organizations depend on the measurement and analysis of performance. Such measurements should derive from the organization s needs and strategy, and they should provide critical data and information about key processes and results. Many types of data and information are needed for performance management. Performance measurement should focus on teaching and learning, research and supporting processes. Focus on Results and Creating Value: An organization s performance measurements need to focus on key results. Results should be used to create and balance value for students and for key stakeholders the community, employers, faculty and staff, suppliers and partners, and the public. By creating value for students and stakeholders, the organization contributes to improving overall education performance and builds loyalty. benchmarks. This is related to knowledge management based on internal historical performance achievements and external (competitor) performance achievenents. REFERENCES 1. Conway, G.P., Higher education st trends in the 21 century, 2. Kettunen, J., Strategic evaluation of institutions by students in higher education, Perspectives, 7(1): [Liberatore 87] Liberatore M.J., An extension of the analytic hierarchy process for industrial R&D project selection and resource allocation IEEE Transaction of Engineering Management, EM-34(1): [Liberatore 89] Liberatore M.J., A decision support approach for R&D project selection. In: Golden B.L., E.A. Wasil and P.T. Harker, editors. The analytic hierarchy process applications and studies. New York: Springer, p: Liberatore, M.J., R.L. Nydick and P.M. Sanchez, The evaluation of research papers (or how to get an academic committee to agree on something). Interfaces, Saaty, T.L., The analytic hierarchy process. New York : McGrawl-Hill. 7. Saaty, T.L. and L.G. Vargas, Decision making in economic, political, social, and technological environments with the analytic hierarchy process. Pittsburgh: RWS Publications. 8. Suryadi, K., Decision Model for Material Stock vs non Stock using combination of AHP and Cut off Point Method, Proceedings of International Symposium on Analytic Hierarchy Process, Bali August Tam C.Y., An application of the analytic hierarchy process in vendor selection of a telecommunication system. MSc Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong. 10. Tam, C.Y., V.M. Rao Tummala, An application of the AHP in vendor selection of a telecommunications system. Omega, Webber, G., Funding in UK universities: living at the edge, Perspectives, 7(4): Conclusion: The proposed framework is standing on the following principle: there is no the best performance; but there are always better performances to achieve. Better performance of HEI is reflected by growth of organisation results which are demonstrated by current level compared to historical performances; and besides, it is reflected by comparison between current level and the competitor performances or 1695

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