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Using fuzzy AHP to develop intellectual capital evaluation model for assessing their performance contribution in a university
Expert Systems with Applications 37 (2010) 4941–4947
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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Using fuzzy AHP to develop intellectual capital evaluation model for assessing their performance contribution in a university Shyh-Hwang Lee * Graduate School of Business Administration, Shu-Te University, No. 59, Hengshan Rd., Yanchao, Kaohsiung County 82445, Taiwan, ROC
a r t i c l e
i n f o
Keywords: University assessment Intellectual capital Performance evaluation Fuzzy analytic hierarchy process
a b s t r a c t The university performance assessment has become one of the most important debates for the universities in Taiwan. This study aims at developing an intellectual capital (IC) evaluation model to facilitate the understanding of their contribution to the university performances. A conceptual framework that incorporates IC with the university assessment scheme in Taiwan is proposed to construct the IC components as the central intangible resources of universities that are concentrated on its link to university performance ultimately. Analytic hierarchy process (AHP) is applied to formulate and prioritize the IC measurement indicators for constructing the IC evaluation model as decision guidelines under which the development and productive use of investments in intangible assets can be made. In this paper, a fuzzy approach is integrated with AHP method to make up the vagueness about the degree of importance of decision-makers on judgment. An illustrative example is provided of the proposed model for developing a visualized form of Shu-Te University distinction tree. Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction There has been enormous growth in the number of universities (including colleges) over the past decade in Taiwan since a higher educational reform is promoted in 1991. There were just about 50 universities ten years ago, had nowadays increased triple times to 163 dramatically. Subsequently, the number of students in universities is multiplied by more than four times from 1996 to 2007 (Ministry of Education, 2008). Moreover, the domestic birthrate drops year by year. The new born population, only 204,000, has been much less than the freshman enrollment, 310,000, in 2007. The rapidly increasing universities have gone beyond the social demands. Increased pressures from executive and legislative departments for accountability and floods of reports critical of higher education have called into question the quality and efficiency of higher education (Mu, 2004; Tai, 1999). Recently, the significance of the quality of higher education has been recognized by government and enterprise in Taiwan. The idea of New Public Management has influenced the way universities should be governed and managed with more autonomy regarding the organization and budget allocation. University assessment mechanisms seem to be the logic choice for quality control because of the great social changes associated with mass higher education (Neave, 1998). Commencing in 2005, the Higher Education Evaluation and Accreditation Council of Taiwan (HEEACT) has developed * Tel.: +886 7 6158000x4706; fax: +886 7 6158000x3199. E-mail address: [email protected]. 0957-4174/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2009.12.020
and initiated the university assessment, conducted by external peers every four years, for quality assurance in higher education in terms of the Higher Education Act 1994. The Council requires that all of the de-accredited departments in universities be phased out and no further enrolments be permitted. Meanwhile, selfassessment was encouraged within many universities to be responsible for quality. These political and managerial challenges require the development of new strategies and input measurements, in the circumstances of result-based steering that focuses on quantity, within many universities to allocate their public budgets more effectively. It is explicitly recognized in the competitive business environment that knowledge creation and sharing can be an essential source of organizational advantages (Beamish & Armistead, 2001; Drucker, 1993). However, there are not many organizations have a clear idea of how to proceed with it. Intellectual capital (IC) is suggested to be the pursuit of effective use of knowledge as opposed to information (Bontis, 1998; Edvinsson & Malone, 1997) and used to emphasize the importance of knowledge management as essential to the growth and development of organizations in a knowledge society (Eckstein, 2004; Van der Meer-Kooistra & Zijlstra, 2001). Intellectual capital is the resource that comes from the knowledge, experience and transferable competencies of its staff, from the organization’s ability to innovate and manage change, from its infrastructure, and from relationships between stakeholders and partners (Stewart, 1999). The IC-approach originally introduced by Edvinsson and Sullivan (1996) divides intellectual capital
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into human capital and structural capital, such that the latter is again divided into organizational capital and customer capital and relationship capital. Some recent studies tend to rename customer and relationship capital as relational capital only (Fernandez, Montes, & Vazquez, 2000). Commonly, IC is categorized with three main constructs including human capital, organizational capital and relational capital. In the literature, the illustrative definitions of these constructs are summarized as follows: 1. Human capital is the individual-level knowledge, such as professional skills, experience, and innovativeness that each employee possesses. It is the human capital that provides the most valuable assets (Stewart, 1999). 2. Organizational capital is the sum of all assets pertaining to the firm which make the creative ability of the organization possible. The vision of the firm, management philosophy, organization culture, strategies, processes, working systems, and information technology can be mentioned among these assets (Edvinsson & Malone, 1997). 3. Relational capital is the sum of all assets that arrange and manage the firm’s relations with the environment. The relational capital contains the relations with customers, suppliers, shareholders, the rival, community, the official institutions, and society (Roos & Roos, 1997).
Criteria
Descriptions
Administration
Curriculum Technology transfer
Research
Teaching
Service
2. Methodology 2.1. Conceptual framework It is not the categories of IC with its manifestation in the activities but the assessment process between them that directly influence the performances in university. Therefore, a research framework employs the principle of conventional input-output conversion process, derived from the model for IC reporting of Austrian universities (Leitner, 2004), is proposed to demonstrate the performance relationships as shown in Fig. 1.
Performance Evaluation Process
Human Capital
Administration Curriculum Technology Transfer
Organizational Capital Relational Capital
Administrative efficiency Development of non-scientific staff Design based on the industrial requirements Contribution to students’ competence Knowledge management effectively to innovate upon new technology Commercializing of university research, such as external research income Publications count Tangible outputs of university research (patents, licenses, spin-offs) Professional oriented Quality dimensions include the attributes of students Spending on student services, especially libraries and IT Campus committee, student advising, employment rate
have been recognized to be crucial for the performances of research institutions (Bueno, 2002; Hargreaves, 2001; Kelly, 2004; Leitner, 2004). A model that incorporates IC in the performance process of universities was developed by Leitner (2004) for Austrian universities. The model conceptualizes the transformation process of intangible resources when carrying out different activities (research, teaching, service, etc.) resulting in different outputs. However, those have little coalesced to form the concept of IC based on a long debate that focuses on the performance measurements for internal resource allocation decisions (Belfield & Thomas, 2000; Casper & Henry, 2001; Ewell, 1999; Liefner, 2003). Hence, an IC reporting model is proposed in this paper that concentrates on Taiwanese university assessment and its link to the prioritized IC measurement indicators as an understanding of their relative importance of contribution to university performances. Prioritization of IC measurement indicators, using AHP pair-wise comparison method with fuzzy approach, is conducted at Shu-Te University as an application of the proposed model for Taiwanese universities.
Intellectual Capital Inputs
Evaluation criteria
IC indicators
In the context of universities, human capital is the knowledge of the scholar researchers and staff of the university. Organizational capital comprises the processes and routine management within the university. Relational capital comprises the relationships and networks of the entire university (Leitner, 2004). For many practitioners, it is difficult to define the IC concept and to fit its notion into the performance management due to the absence of applicable IC reporting model as a means of measuring the IC contribution to the performances. Hence, it initiates a conceptual framework for relevant exploration of identifying IC measurements among the candidates as a measure of core competency and competitive advantage of an organization and has been adopted by some private companies for the investments on IC-such as training, research, and innovation (Fruin, 1997). An IC reporting model that incorporates the knowledge-production value creation processes seems to be a rich source for the development of IC measurement indicators (Bukh, Larsen, & Mouritsen, 2001; Peppard & Rylander, 2001). Moreover, a practical interest has been developed in the prioritization of those indicators, using analytic hierarchy process (AHP), which helps companies for resources allocation by providing a guideline to decide in which activity they will invest first (Bozbura & Beskese, 2007; Han & Han, 2004). Nevertheless, few theories have been initiated and developed to manage the intangible resources of universities even though they
Table 1 University assessing criteria (adopted from the university assessment guidelines provided by HEEACT in Taiwan).
Research Teaching Service
Impacts
Stakeholder: Ministry Students Industry Public Community etc.
Fig. 1. Conceptual framework for incorporating IC in university evaluation in Taiwan.
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The figure itself is fairly straightforward in conception. The IC measurement indicators (INDs) constructed the measures of the intangible input resources of a university. Based on this research framework, the inputs and outputs are quantified by indicators and assessing criteria. Then, it is possible to link inputs and outputs to facilitate the understanding of not only how the different forms of intangible assets influence the outputs but also how these investments can be allocated by the university. The outputs of IC model in the case of universities and research organizations are also knowledge-based by nature or even intangible assets themselves, such as patents or new technologies developed. The logic is similar to that of models or research processes developed by the innovation literature, which frequently separate inputs, processes and outputs (Fernandez et al., 2000; Herremans & Isaac, 2004; Roessner, 2000; Rothwell, 1994). 2.2. University assessing criteria The comparative assessing criteria are adopted from the university assessment guidelines provided by HEEACT in Taiwan. It differentiates six performance domains with assessing criteria including Administration, Curriculum, Technology transfer, Research, Teaching, and Service that are explicitly used for the prioritization of IC measurement indicators in this paper. A synopsis of which is summarized in Table 1. 2.3. Hierarchical structure The research framework in Fig. 1 is formulated with a hierarchical structure of defined goal, university assessing criteria with the IC evaluation points. Evaluation approaches are linked to the long deliberation about how to estimate the impacts of IC to university performances. Fig. 2 illustrates the hierarchical structure explained above. This structure serves to prioritize IC measurement indicators which ultimately contribute to the goal for maximizing university performances. In this paper, the university assessing criteria (as shown in Table 1), provided by HEEACT in Taiwan, were used to prioritize IC the measurement indicators in a university. 2.4. Analytic hierarchy process AHP was developed by Saaty (1980) to structure complex multicriteria decision-making in business and it has been applied to
IC Measurement Indicators:
Mw ¼ kmax w;
kmax P n:
It indicates that the eigenvector corresponding to the largest eigenvalue (kmax) of the pair-wise comparisons matrix reflects the relative importance of the decision elements. This conventional AHP approach gives reasonably good approximation only when the decision-maker’s preferences are consistent. However, the descriptions of linguistic variable (such as ‘judgment’ or ‘preference’) are usually vague and the verbal attitudes of decision-maker’s requirements on evaluation process always contain ambiguity and multiplicity of meaning. AHP is ineffective when applied to ambiguous problem. Thus, fuzzy sets could be incorporated with the pair-wise comparison, as an extension of AHP, to solve this kind of uncertainty.
2.5. Fuzzy AHP method Zadeh (1965) first introduced the fuzzy set theory to deal with the uncertainty due to imprecision or vagueness. A fuzzy set e ¼ fðx; l ðxÞÞjx 2 Xg is a set of ordered pairs, let the universe of A eA discourse X be the subset of real number R, where le ðxÞ is called A the membership function which assigns to each object x a grade of membership ranging between zero and one. Triangular fuzzy number is the most widely used membership function in many application fields because of its intuitive appeal and computational efficiency. A triangular fuzzy number, defined to be a normal and e ¼ ða; b; cÞ, has the folconvex fuzzy subset of X and denoted as A lowing membership function (graphically represented in Fig. 3) (Kaufmann & Gupta, 1991):
IND2
Service
Teaching
Research
Technology Transfer
Administration
IND1
Curriculum
Prioritizing IC Measurement Indicators to Maximize University Performance
Goal:
University Evaluation Criteria:
many management decision-making situations (Hung, Yang, Ma, & Yang, 2006; Liberatore & Nydick, 1997; Wang & Shu, 2005). With this method, a complicated system is converted to a hierarchical system of elements. In each hierarchical level, pair-wise comparisons of n elements are made by using a nominal scale and the value mij is assigned to represent the judgment concerning the relative importance of decision element ei over ej. These comparisons compose a pair-wise comparison matrix M = {mij}. In order to find the weight of each element, or the score of each alternative, the priority vector (or eigenvector) w = (w1, w2, . . ., wn)T of this comparison matrix is calculated based on solving the equation:
………
Fig. 2. The hierarchical structure for prioritizing the IC measurement indicators.
IND7
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8 > < 1; VðM 1 P M 2 Þ ¼ hgtðM 2 \ M 1 Þ ¼ 0; > :
μÃ
a2 c1 ; ðb1 c1 Þðb2 a2 Þ
if b1 P b2 if a2 P c1 otherwise
The degree of possibility for a convex fuzzy number to be greater than k convex fuzzy number Mi (i = 1, 2, . . ., k) can be defined by
1.0
VðM P M 1 ; M2 ; . . . ; M k Þ ¼ V½ðM P M1 Þ and ðM P M2 Þ and and ðM P M k Þ ¼ min VðM P Mi Þ; i ¼ 1; 2; 3; . . . ; k:
ð2Þ
Assume that 0
0.0
a
b
d ðAi Þ ¼ min VðSi P Sk Þ
c
For k = 1, 2, . . ., n; k – i. Then the weight vector is given by
e ¼ ða; b; cÞ. Fig. 3. A triangular fuzzy number, A
0
0
0
W 0 ¼ ðd ðA1 Þ; d ðA2 Þ; . . . ; d ðAn ÞÞT
8 xa > < ba ; a 6 x 6 b cx leA ðxÞ ¼ cb ; b6x6c > : 0; otherwise
where Ai (i = 1, 2, . . ., n) are n decision elements. Via normalization, the normalized weight vectors are
The parameter ‘‘b” gives the maximal grade of le ðxÞ, the paramA eters ‘‘c” and ‘‘a” are the upper and lower bounds which limit the field of possible evaluation. In this research, triangular fuzzy numbers are used to represent subjective pair-wise comparisons of experts’ judgments among the options such as just equal, equally, weakly, moderately, strongly, and extremely. The triangular fuzzy conversion scale used to convert such linguistic values into fuzzy scales in the evaluation model of this paper is shown in Table 2. There are several fuzzy AHP methods explained in the literature (Buckley, 1985; Chang, 1996; Cheng, 1996; Van Laarhoven & Pedrycz, 1983). This paper apply Chang’s extent analysis method (Chang, 1996) since the steps of this approach is similar to the conventional AHP and relatively easier than the other fuzzy AHP approaches. According to Chang’s extent analysis method, the value of fuzzy synthetic extent is defined, using the standard fuzzy arithmetic, as below:
where W is a non-fuzzy number. Compared to conventional AHP, The fuzzy AHP approach allows a more accurate description of the decision making process.
Si ¼
m X
M ji
" n X m X
j¼1
i¼1
W ¼ ðdðA1 Þ; dðA2 Þ; . . . ; dðAn ÞÞT
#1 M ji
ð1Þ
j¼1
where the symbol represents standard fuzzy operator and M ji is a triangular fuzzy number representing the extent analysis value for decision element i with respect to goal j. Basically, M ji is the generic element of a fuzzy pair-wise comparison matrix like the one used in the AHP method. The degree of possibility of M1 P M2 is defined as:
VðM 1 P M 2 Þ ¼ sup½minðlM1 ðxÞ; lM2 ðyÞÞ
3. A case application Above, a fuzzy AHP approach to prioritize IC measurement indicators that are concentrated on their link with the performance measurements has been presented. In this section, a case study at Shu-Te University is realized to make this approach more understandable for practitioners to establish their IC reporting model. As follows are outlined the steps to prioritize IC measurement indicators according to their relative importance of contribution to university performances. 1. Establishing IC measurement indicators: a list of IC measurement indicators provides the evaluation points that are evaluated against the hierarchy. 2. Pair-wise comparison of decision-making: according to the constructed hierarchical structure as shown in Fig. 2, the IC measurement indicators are compared in pair-wise with respect to the comparative assessing criteria, which adopted from the university assessment scheme in Taiwan. 3. Prioritization of IC measurement indicators: the fuzzy AHP is used to synthesize the data using Chang’s extension method to arrive at a prioritized list of IC measurement indicators according to their relative importance to satisfy the goal. They are elaborated in the following sub-section.
xPy
and can be equivalently expressed as follows:
Table 2 Triangular fuzzy conversion scale (Chang, 1996). Linguistic scale
Triangular fuzzy scale
Triangular fuzzy reciprocal scale
Just equal Equally important Weakly more important Moderately more important Strongly more important Extremely more important
(1, 1, 1) (1/2, 1, 3/2) (1, 3/2, 2) (3/2, 2, 5/2)
(1, 1, 1) (2/3, 1, 2) (1/2, 2/3, 1) (2/5, 1/2, 2/3)
(2, 5/2, 3)
(1/3, 2/5, 1/2)
(5/2, 3, 7/2)
(2/7, 1/3, 2/5)
3.1. Establishing IC measurement indicators IC measurement indicators of university have been rarely addressed in the literature. Some exemplary IC indicators are used for Austrian universities such as number of full-time professors, number of scientific staff, number of student assistants, and international scientists at the university (Leitner, 2004). Nevertheless the meaning and IC value of higher education is contingent on the cultural context and highly relevant for each case university. In this case study, we define the IC measurement indicators and its supporting concepts, drawing heavily upon a practice oriented research approach, based on: 1. proposed indicators in the intellectual capital literature, and
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2. the findings in the assessing process within Taiwanese universities. This also allowed the IC perspective to be highly actionable on performance processes from a managerial perspective. Seven of the IC measurement indicators for university are selected in Table 3 among candidates. 3.2. Pair-wise comparison of decision-making Five college deans at Shu-Te were invited to be the decisionmakers because they are academic professors and experienced practitioners in university management. It can be assumed that the judgments of the group were highly relevant to Shu-Te performance processes from a managerial perspective. The weight values of assessing criteria are self-defined by each university within the lower and upper limits provided by the university assessment guidelines for Taiwan universities. The weights of assessing criteria defined by Shu-Te University are 0.10, 0.25, 0.15, 0.15, 0.20, and 0.15 for Administration, Curriculum, Technology transfer, Research, Teaching, and Service, respectively. The research component is an essential element in the academic promotion process and is considered to be the most important criterion in the self-assessment at Shu-Te University. In most cases, universities assign the highest weight to research and less weight to teaching and service (Alpert, 1985; Seldin, 1984). The IC measurement indicators are then compared in pair-wise by each group member for their relative importance with respect to each university assessing criterion using words such as Equally, Weakly, Moderately, Strongly, and Extremely. In an attempt to avoid discrepancies occurring, the authors carefully explained the definition of terms and the required procedure to the decisionmaking group. The verbal responses are then quantified and translated by the triangular fuzzy conversion scale as shown in Table 2.
Table 3 Exemplary IC measurement indicators for university performance. Human capital IND1: Scholar index; professional scholars recruited in a long-term plan such as total number, fluctuation, and average duration of scientific scholars IND2: Staff index; such as attitude, competence, and expenses for training, etc. Organizational capital IND3: Vision, its strategic deployments, processes, and routine management IND4: Information technology in service for supporting the research, teaching and industrial cooperative practice Relational capital IND5: Academic relationship; such as conference activities, international scientists IND6: Public relationship with social environments IND7: Industrial relationship to enhance the student’s employment; number of new co-operation partners
All entries in the cells of the matrix M are replaced with single triangular fuzzy number representing the judgment of the group on each pair of IC indicators. 3.3. Prioritization of IC measurement indicators Chang’s extension method is then used to synthesize the data. The values of fuzzy synthetic extents with respect to the assessing criteria for each indicator are calculated. Taking the data entered by respondent 1 as an example, the values of fuzzy synthetic extents with respect to the assessing criterion Administration for IND1 are calculated as below (see Eq. (1)):
SIND1 ¼ ð3:91; 5:17; 7:17Þ ð0:0126; 0:0156; 0:0202Þ ¼ ð0:0492; 0:0809; 0:1448Þ The minimum of the degrees of possibility are calculated as below (see Eq. (2)):
min VðSIND1 P SIND2 ; SIND3 ; . . . ; SIND7 Þ ¼ 0:3249 Same calculations are performed for the other indicators and these yield the following weights vector:
W 0 ¼ ð0:3249; 0:7323; 1; 0:6992; 0:4785; 0:6303; 0:7022ÞT Via normalization, the local priority weights of all indicators with respect to assessing criterion Administration are calculated for respondent 1 and the results are obtained as below by taking the geometric mean of individual evaluation of the five respondents:
ðIND1; IND2; IND3; IND4; IND5; IND6; IND7Þ ¼ ð0:0421; 0:2452; 0:2372; 0:1719; 0:0843; 0:1177; 0:1014Þ: The calculations of local priority weights of all indicators with respect to the other assessing criteria will not be given in the paper because they are similar. Finally, the overall priority weight of each indicator is calculated by multiplying its local priority weight with its corresponding weight along the hierarchy (as shown in Table 4). The proposed model therefore provides a critical link of university performances to the intangible resource inputs by ranking the IC measurement indicators within each assessing criterion according to their relative importance of contribution to the overall performances for universities. All the intangible resources necessary to improve the overall performances for the stakeholders are identified and visualized in the form of a context specific distinction tree as shown in Fig. 4. It ensures that Shu-Te management team has the same understanding of IC contribution to the university performances for developing knowledge-based strategies. This way also corresponds to the previous IC models (Castro & Sáez, 2008; Peppard & Rylander, 2001) where this distinction was roughly made by Navigator.
Table 4 Priority weights of IC indicators for their performance contribution at Shu-Te University.
IND1 IND2 IND3 IND4 IND5 IND6 IND7
Administration 0.10
Curriculum 0.20
Tech. transfer 0.15
Research 0.25
Teaching 0.15
Service 0.15
Weights
0.0421 0.2452 0.2372 0.1719 0.0843 0.1177 0.1014
0.3600 0.0314 0.0505 0.1303 0.2306 0.0449 0.1522
0.3081 0 0.1281 0.0577 0.2073 0.0248 0.2741
0.3665 0.0103 0.0492 0.1855 0.2556 0.0099 0.1231
0.3504 0.0119 0.0416 0.2718 0.1887 0.0264 0.1091
0.0477 0.2123 0.1645 0.1712 0.0883 0.1054 0.2107
0.2737 0.0670 0.0963 0.1647 0.1911 0.0467 0.1605
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Intellectual Capital
Human Capital 34%
Scholar 27.37%
Staff 6.70%
Total Number
Expense for Training
Fluctuation
Attitude
Average Duration
Competence
Relational Capital 40%
Academic 19.11%
Industrial 16.05%
International Scientists
Organizational Capital 26%
Public 4.67%
Society
Number of Partners
Conferences Visited
Employment Programs
Research Programs
IT 16.47%
Library
Official Institution
Strategies 9.63%
Culture
Routine Processes
Image
Electronic Media
Community
Co-operative Education
Research grants abroad Fig. 4. The Shu-Te distinction tree.
4. Conclusions and implications University assessment has become one of the most important challenges for many Taiwanese universities to develop new strategies which could help to improve their performances within the limited resources. Nevertheless, few applicable models have been addressed that concentrates on the management of intangible capital, even though it has been recognized to be crucial, for the performances of universities and research organizations. A decision model of university performance, adopted from the university assessment scheme in Taiwan, linking to the constructed IC measurement indicators of a university is proposed. AHP method integrated with fuzzy approach is used to develop a hierarchical structure for prioritizing the IC measurement indicators in a university as a better comprehension of the critical intangible assets with the manifestation in activities according to their relative importance of contribution to overall performances ultimately. Based on this coherent model for IC evaluation for university performance it would be possible to predict the consequences of the decisions made based upon the information provided by university assessment, such as re-allocation resources. A visualized form of a context specific distinction tree is developed based on the proposed model, where this distinction was roughly made by Navigator in previous IC model, to facilitate the
understanding of what resources are actually important for the overall performances. An illustrative example is provided of the proposed model at the Shu-Te University. Although the assessing criteria may be affected by the contingent factors of different universities, it is the adaptation of the IC evaluation model to those factors for the assessment of their performance contribution in a university. It would certainly go a long way towards addressing a number of criticisms of IC aspects of university. However, with the research supporting the value of IC measurement indicators should continue to be considered the important facilitators of the knowledge-based university. The coherent model for university assessment could thus provide an insight into the improvement of the development and application of IC reporting for university performances. Acknowledgement This research is partially supported by project from Shu-Te University. References Alpert, D. (1985). Performance and paralysis: The organizational context of the American research university. Journal of Higher Education, 56(3), 241–281.
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Hung, M. L., Yang, W. F., Ma, H. W., & Yang, Y. M. (2006). A novel multi-objective programming approach dealing with qualitative and quantitative objectives for environmental management. Ecological Economics, 56(4), 584–593. Kaufmann, A., & Gupta, M. M. (1991). Introduction to fuzzy arithmetic theory and applications. New York: Van Nostrand Reinhold. Kelly, A. (2004). The intellectual capital of schools: Analysing government policy statements on school improvement in light of a new theorization. Journal of Education Policy, 19(5), 609–629. Leitner, K. H. (2004). Intellectual capital reporting for universities: Conceptual background and application for Austrian universities. Research Evaluation, 13(2), 129–140. Liberatore, M. J., & Nydick, R. L. (1997). Group decision making in higher education using the analytic hierarchy process. Research in Higher Education, 38(5), 593–614. Liefner, I. (2003). Funding, resource allocation, and performance in higher education systems. Higher Education, 46, 469–489. Ministry of Education, Republic of China (2008). Education in Taiwan 2007. Retrieved 21.02.08. Mu, T.-T. (2004). The practice and challenge of university evaluation. Retrieved 15.12.07 (in Chinese). Neave, G. (1998). The evaluative state reconsidered. European Journal of Education, 33, 265–284. Peppard, J., & Rylander, A. (2001). Using an intellectual capital perspective to design and implement a growth strategy: The case of APiON. European Management Journal, 19(5), 510–525. Roessner, D. (2000). Quantitative and qualitative methods and measures in the evaluation of research. Research Evaluation, 8(2), 125–132. Roos, G., & Roos, J. (1997). Measuring your company’s intellectual performance. Long Range Planning, 30(3), 413–426. Rothwell, R. (1994). Industrial innovation: Success, strategy, trends. In M. Dodgson & R. Rothwell (Eds.), The handbook of industrial innovation. Cheltenham: Edward Elgar. Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resources allocation. London: McGraw-Hill. Seldin, O. (1984). Changing practices in faculty evaluation: A critical assessment and recommendations for improvement. San Francisco, CA: Jossey-Bass. Stewart, T. A. (1999). Intellectual capital: The new wealth of organizations. New York: Currency Doubleday. Tai, H. H. (1999). The privatization of higher education and its impact on Taiwan’s universities. Bulletin of Education Research, 42, 233–254 (in Chinese). Van der Meer-Kooistra, J., & Zijlstra, S. M. (2001). Reporting on intellectual capital. Accounting, Auditing and Accountability Journal, 14(4), 456–476. Van Laarhoven, P. J. M., & Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11, 229–241. Wang, J., & Shu, Y. F. (2005). Fuzzy decision modeling for supply chain management. Fuzzy Sets and Systems, 150, 107–127. Zadeh, L. A. (1965). Fuzzy sets. Information Control, 8, 338–353.
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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Using fuzzy AHP to develop intellectual capital evaluation model for assessing their performance contribution in a university Shyh-Hwang Lee * Graduate School of Business Administration, Shu-Te University, No. 59, Hengshan Rd., Yanchao, Kaohsiung County 82445, Taiwan, ROC
a r t i c l e
i n f o
Keywords: University assessment Intellectual capital Performance evaluation Fuzzy analytic hierarchy process
a b s t r a c t The university performance assessment has become one of the most important debates for the universities in Taiwan. This study aims at developing an intellectual capital (IC) evaluation model to facilitate the understanding of their contribution to the university performances. A conceptual framework that incorporates IC with the university assessment scheme in Taiwan is proposed to construct the IC components as the central intangible resources of universities that are concentrated on its link to university performance ultimately. Analytic hierarchy process (AHP) is applied to formulate and prioritize the IC measurement indicators for constructing the IC evaluation model as decision guidelines under which the development and productive use of investments in intangible assets can be made. In this paper, a fuzzy approach is integrated with AHP method to make up the vagueness about the degree of importance of decision-makers on judgment. An illustrative example is provided of the proposed model for developing a visualized form of Shu-Te University distinction tree. Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction There has been enormous growth in the number of universities (including colleges) over the past decade in Taiwan since a higher educational reform is promoted in 1991. There were just about 50 universities ten years ago, had nowadays increased triple times to 163 dramatically. Subsequently, the number of students in universities is multiplied by more than four times from 1996 to 2007 (Ministry of Education, 2008). Moreover, the domestic birthrate drops year by year. The new born population, only 204,000, has been much less than the freshman enrollment, 310,000, in 2007. The rapidly increasing universities have gone beyond the social demands. Increased pressures from executive and legislative departments for accountability and floods of reports critical of higher education have called into question the quality and efficiency of higher education (Mu, 2004; Tai, 1999). Recently, the significance of the quality of higher education has been recognized by government and enterprise in Taiwan. The idea of New Public Management has influenced the way universities should be governed and managed with more autonomy regarding the organization and budget allocation. University assessment mechanisms seem to be the logic choice for quality control because of the great social changes associated with mass higher education (Neave, 1998). Commencing in 2005, the Higher Education Evaluation and Accreditation Council of Taiwan (HEEACT) has developed * Tel.: +886 7 6158000x4706; fax: +886 7 6158000x3199. E-mail address: [email protected]. 0957-4174/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2009.12.020
and initiated the university assessment, conducted by external peers every four years, for quality assurance in higher education in terms of the Higher Education Act 1994. The Council requires that all of the de-accredited departments in universities be phased out and no further enrolments be permitted. Meanwhile, selfassessment was encouraged within many universities to be responsible for quality. These political and managerial challenges require the development of new strategies and input measurements, in the circumstances of result-based steering that focuses on quantity, within many universities to allocate their public budgets more effectively. It is explicitly recognized in the competitive business environment that knowledge creation and sharing can be an essential source of organizational advantages (Beamish & Armistead, 2001; Drucker, 1993). However, there are not many organizations have a clear idea of how to proceed with it. Intellectual capital (IC) is suggested to be the pursuit of effective use of knowledge as opposed to information (Bontis, 1998; Edvinsson & Malone, 1997) and used to emphasize the importance of knowledge management as essential to the growth and development of organizations in a knowledge society (Eckstein, 2004; Van der Meer-Kooistra & Zijlstra, 2001). Intellectual capital is the resource that comes from the knowledge, experience and transferable competencies of its staff, from the organization’s ability to innovate and manage change, from its infrastructure, and from relationships between stakeholders and partners (Stewart, 1999). The IC-approach originally introduced by Edvinsson and Sullivan (1996) divides intellectual capital
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into human capital and structural capital, such that the latter is again divided into organizational capital and customer capital and relationship capital. Some recent studies tend to rename customer and relationship capital as relational capital only (Fernandez, Montes, & Vazquez, 2000). Commonly, IC is categorized with three main constructs including human capital, organizational capital and relational capital. In the literature, the illustrative definitions of these constructs are summarized as follows: 1. Human capital is the individual-level knowledge, such as professional skills, experience, and innovativeness that each employee possesses. It is the human capital that provides the most valuable assets (Stewart, 1999). 2. Organizational capital is the sum of all assets pertaining to the firm which make the creative ability of the organization possible. The vision of the firm, management philosophy, organization culture, strategies, processes, working systems, and information technology can be mentioned among these assets (Edvinsson & Malone, 1997). 3. Relational capital is the sum of all assets that arrange and manage the firm’s relations with the environment. The relational capital contains the relations with customers, suppliers, shareholders, the rival, community, the official institutions, and society (Roos & Roos, 1997).
Criteria
Descriptions
Administration
Curriculum Technology transfer
Research
Teaching
Service
2. Methodology 2.1. Conceptual framework It is not the categories of IC with its manifestation in the activities but the assessment process between them that directly influence the performances in university. Therefore, a research framework employs the principle of conventional input-output conversion process, derived from the model for IC reporting of Austrian universities (Leitner, 2004), is proposed to demonstrate the performance relationships as shown in Fig. 1.
Performance Evaluation Process
Human Capital
Administration Curriculum Technology Transfer
Organizational Capital Relational Capital
Administrative efficiency Development of non-scientific staff Design based on the industrial requirements Contribution to students’ competence Knowledge management effectively to innovate upon new technology Commercializing of university research, such as external research income Publications count Tangible outputs of university research (patents, licenses, spin-offs) Professional oriented Quality dimensions include the attributes of students Spending on student services, especially libraries and IT Campus committee, student advising, employment rate
have been recognized to be crucial for the performances of research institutions (Bueno, 2002; Hargreaves, 2001; Kelly, 2004; Leitner, 2004). A model that incorporates IC in the performance process of universities was developed by Leitner (2004) for Austrian universities. The model conceptualizes the transformation process of intangible resources when carrying out different activities (research, teaching, service, etc.) resulting in different outputs. However, those have little coalesced to form the concept of IC based on a long debate that focuses on the performance measurements for internal resource allocation decisions (Belfield & Thomas, 2000; Casper & Henry, 2001; Ewell, 1999; Liefner, 2003). Hence, an IC reporting model is proposed in this paper that concentrates on Taiwanese university assessment and its link to the prioritized IC measurement indicators as an understanding of their relative importance of contribution to university performances. Prioritization of IC measurement indicators, using AHP pair-wise comparison method with fuzzy approach, is conducted at Shu-Te University as an application of the proposed model for Taiwanese universities.
Intellectual Capital Inputs
Evaluation criteria
IC indicators
In the context of universities, human capital is the knowledge of the scholar researchers and staff of the university. Organizational capital comprises the processes and routine management within the university. Relational capital comprises the relationships and networks of the entire university (Leitner, 2004). For many practitioners, it is difficult to define the IC concept and to fit its notion into the performance management due to the absence of applicable IC reporting model as a means of measuring the IC contribution to the performances. Hence, it initiates a conceptual framework for relevant exploration of identifying IC measurements among the candidates as a measure of core competency and competitive advantage of an organization and has been adopted by some private companies for the investments on IC-such as training, research, and innovation (Fruin, 1997). An IC reporting model that incorporates the knowledge-production value creation processes seems to be a rich source for the development of IC measurement indicators (Bukh, Larsen, & Mouritsen, 2001; Peppard & Rylander, 2001). Moreover, a practical interest has been developed in the prioritization of those indicators, using analytic hierarchy process (AHP), which helps companies for resources allocation by providing a guideline to decide in which activity they will invest first (Bozbura & Beskese, 2007; Han & Han, 2004). Nevertheless, few theories have been initiated and developed to manage the intangible resources of universities even though they
Table 1 University assessing criteria (adopted from the university assessment guidelines provided by HEEACT in Taiwan).
Research Teaching Service
Impacts
Stakeholder: Ministry Students Industry Public Community etc.
Fig. 1. Conceptual framework for incorporating IC in university evaluation in Taiwan.
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The figure itself is fairly straightforward in conception. The IC measurement indicators (INDs) constructed the measures of the intangible input resources of a university. Based on this research framework, the inputs and outputs are quantified by indicators and assessing criteria. Then, it is possible to link inputs and outputs to facilitate the understanding of not only how the different forms of intangible assets influence the outputs but also how these investments can be allocated by the university. The outputs of IC model in the case of universities and research organizations are also knowledge-based by nature or even intangible assets themselves, such as patents or new technologies developed. The logic is similar to that of models or research processes developed by the innovation literature, which frequently separate inputs, processes and outputs (Fernandez et al., 2000; Herremans & Isaac, 2004; Roessner, 2000; Rothwell, 1994). 2.2. University assessing criteria The comparative assessing criteria are adopted from the university assessment guidelines provided by HEEACT in Taiwan. It differentiates six performance domains with assessing criteria including Administration, Curriculum, Technology transfer, Research, Teaching, and Service that are explicitly used for the prioritization of IC measurement indicators in this paper. A synopsis of which is summarized in Table 1. 2.3. Hierarchical structure The research framework in Fig. 1 is formulated with a hierarchical structure of defined goal, university assessing criteria with the IC evaluation points. Evaluation approaches are linked to the long deliberation about how to estimate the impacts of IC to university performances. Fig. 2 illustrates the hierarchical structure explained above. This structure serves to prioritize IC measurement indicators which ultimately contribute to the goal for maximizing university performances. In this paper, the university assessing criteria (as shown in Table 1), provided by HEEACT in Taiwan, were used to prioritize IC the measurement indicators in a university. 2.4. Analytic hierarchy process AHP was developed by Saaty (1980) to structure complex multicriteria decision-making in business and it has been applied to
IC Measurement Indicators:
Mw ¼ kmax w;
kmax P n:
It indicates that the eigenvector corresponding to the largest eigenvalue (kmax) of the pair-wise comparisons matrix reflects the relative importance of the decision elements. This conventional AHP approach gives reasonably good approximation only when the decision-maker’s preferences are consistent. However, the descriptions of linguistic variable (such as ‘judgment’ or ‘preference’) are usually vague and the verbal attitudes of decision-maker’s requirements on evaluation process always contain ambiguity and multiplicity of meaning. AHP is ineffective when applied to ambiguous problem. Thus, fuzzy sets could be incorporated with the pair-wise comparison, as an extension of AHP, to solve this kind of uncertainty.
2.5. Fuzzy AHP method Zadeh (1965) first introduced the fuzzy set theory to deal with the uncertainty due to imprecision or vagueness. A fuzzy set e ¼ fðx; l ðxÞÞjx 2 Xg is a set of ordered pairs, let the universe of A eA discourse X be the subset of real number R, where le ðxÞ is called A the membership function which assigns to each object x a grade of membership ranging between zero and one. Triangular fuzzy number is the most widely used membership function in many application fields because of its intuitive appeal and computational efficiency. A triangular fuzzy number, defined to be a normal and e ¼ ða; b; cÞ, has the folconvex fuzzy subset of X and denoted as A lowing membership function (graphically represented in Fig. 3) (Kaufmann & Gupta, 1991):
IND2
Service
Teaching
Research
Technology Transfer
Administration
IND1
Curriculum
Prioritizing IC Measurement Indicators to Maximize University Performance
Goal:
University Evaluation Criteria:
many management decision-making situations (Hung, Yang, Ma, & Yang, 2006; Liberatore & Nydick, 1997; Wang & Shu, 2005). With this method, a complicated system is converted to a hierarchical system of elements. In each hierarchical level, pair-wise comparisons of n elements are made by using a nominal scale and the value mij is assigned to represent the judgment concerning the relative importance of decision element ei over ej. These comparisons compose a pair-wise comparison matrix M = {mij}. In order to find the weight of each element, or the score of each alternative, the priority vector (or eigenvector) w = (w1, w2, . . ., wn)T of this comparison matrix is calculated based on solving the equation:
………
Fig. 2. The hierarchical structure for prioritizing the IC measurement indicators.
IND7
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8 > < 1; VðM 1 P M 2 Þ ¼ hgtðM 2 \ M 1 Þ ¼ 0; > :
μÃ
a2 c1 ; ðb1 c1 Þðb2 a2 Þ
if b1 P b2 if a2 P c1 otherwise
The degree of possibility for a convex fuzzy number to be greater than k convex fuzzy number Mi (i = 1, 2, . . ., k) can be defined by
1.0
VðM P M 1 ; M2 ; . . . ; M k Þ ¼ V½ðM P M1 Þ and ðM P M2 Þ and and ðM P M k Þ ¼ min VðM P Mi Þ; i ¼ 1; 2; 3; . . . ; k:
ð2Þ
Assume that 0
0.0
a
b
d ðAi Þ ¼ min VðSi P Sk Þ
c
For k = 1, 2, . . ., n; k – i. Then the weight vector is given by
e ¼ ða; b; cÞ. Fig. 3. A triangular fuzzy number, A
0
0
0
W 0 ¼ ðd ðA1 Þ; d ðA2 Þ; . . . ; d ðAn ÞÞT
8 xa > < ba ; a 6 x 6 b cx leA ðxÞ ¼ cb ; b6x6c > : 0; otherwise
where Ai (i = 1, 2, . . ., n) are n decision elements. Via normalization, the normalized weight vectors are
The parameter ‘‘b” gives the maximal grade of le ðxÞ, the paramA eters ‘‘c” and ‘‘a” are the upper and lower bounds which limit the field of possible evaluation. In this research, triangular fuzzy numbers are used to represent subjective pair-wise comparisons of experts’ judgments among the options such as just equal, equally, weakly, moderately, strongly, and extremely. The triangular fuzzy conversion scale used to convert such linguistic values into fuzzy scales in the evaluation model of this paper is shown in Table 2. There are several fuzzy AHP methods explained in the literature (Buckley, 1985; Chang, 1996; Cheng, 1996; Van Laarhoven & Pedrycz, 1983). This paper apply Chang’s extent analysis method (Chang, 1996) since the steps of this approach is similar to the conventional AHP and relatively easier than the other fuzzy AHP approaches. According to Chang’s extent analysis method, the value of fuzzy synthetic extent is defined, using the standard fuzzy arithmetic, as below:
where W is a non-fuzzy number. Compared to conventional AHP, The fuzzy AHP approach allows a more accurate description of the decision making process.
Si ¼
m X
M ji
" n X m X
j¼1
i¼1
W ¼ ðdðA1 Þ; dðA2 Þ; . . . ; dðAn ÞÞT
#1 M ji
ð1Þ
j¼1
where the symbol represents standard fuzzy operator and M ji is a triangular fuzzy number representing the extent analysis value for decision element i with respect to goal j. Basically, M ji is the generic element of a fuzzy pair-wise comparison matrix like the one used in the AHP method. The degree of possibility of M1 P M2 is defined as:
VðM 1 P M 2 Þ ¼ sup½minðlM1 ðxÞ; lM2 ðyÞÞ
3. A case application Above, a fuzzy AHP approach to prioritize IC measurement indicators that are concentrated on their link with the performance measurements has been presented. In this section, a case study at Shu-Te University is realized to make this approach more understandable for practitioners to establish their IC reporting model. As follows are outlined the steps to prioritize IC measurement indicators according to their relative importance of contribution to university performances. 1. Establishing IC measurement indicators: a list of IC measurement indicators provides the evaluation points that are evaluated against the hierarchy. 2. Pair-wise comparison of decision-making: according to the constructed hierarchical structure as shown in Fig. 2, the IC measurement indicators are compared in pair-wise with respect to the comparative assessing criteria, which adopted from the university assessment scheme in Taiwan. 3. Prioritization of IC measurement indicators: the fuzzy AHP is used to synthesize the data using Chang’s extension method to arrive at a prioritized list of IC measurement indicators according to their relative importance to satisfy the goal. They are elaborated in the following sub-section.
xPy
and can be equivalently expressed as follows:
Table 2 Triangular fuzzy conversion scale (Chang, 1996). Linguistic scale
Triangular fuzzy scale
Triangular fuzzy reciprocal scale
Just equal Equally important Weakly more important Moderately more important Strongly more important Extremely more important
(1, 1, 1) (1/2, 1, 3/2) (1, 3/2, 2) (3/2, 2, 5/2)
(1, 1, 1) (2/3, 1, 2) (1/2, 2/3, 1) (2/5, 1/2, 2/3)
(2, 5/2, 3)
(1/3, 2/5, 1/2)
(5/2, 3, 7/2)
(2/7, 1/3, 2/5)
3.1. Establishing IC measurement indicators IC measurement indicators of university have been rarely addressed in the literature. Some exemplary IC indicators are used for Austrian universities such as number of full-time professors, number of scientific staff, number of student assistants, and international scientists at the university (Leitner, 2004). Nevertheless the meaning and IC value of higher education is contingent on the cultural context and highly relevant for each case university. In this case study, we define the IC measurement indicators and its supporting concepts, drawing heavily upon a practice oriented research approach, based on: 1. proposed indicators in the intellectual capital literature, and
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2. the findings in the assessing process within Taiwanese universities. This also allowed the IC perspective to be highly actionable on performance processes from a managerial perspective. Seven of the IC measurement indicators for university are selected in Table 3 among candidates. 3.2. Pair-wise comparison of decision-making Five college deans at Shu-Te were invited to be the decisionmakers because they are academic professors and experienced practitioners in university management. It can be assumed that the judgments of the group were highly relevant to Shu-Te performance processes from a managerial perspective. The weight values of assessing criteria are self-defined by each university within the lower and upper limits provided by the university assessment guidelines for Taiwan universities. The weights of assessing criteria defined by Shu-Te University are 0.10, 0.25, 0.15, 0.15, 0.20, and 0.15 for Administration, Curriculum, Technology transfer, Research, Teaching, and Service, respectively. The research component is an essential element in the academic promotion process and is considered to be the most important criterion in the self-assessment at Shu-Te University. In most cases, universities assign the highest weight to research and less weight to teaching and service (Alpert, 1985; Seldin, 1984). The IC measurement indicators are then compared in pair-wise by each group member for their relative importance with respect to each university assessing criterion using words such as Equally, Weakly, Moderately, Strongly, and Extremely. In an attempt to avoid discrepancies occurring, the authors carefully explained the definition of terms and the required procedure to the decisionmaking group. The verbal responses are then quantified and translated by the triangular fuzzy conversion scale as shown in Table 2.
Table 3 Exemplary IC measurement indicators for university performance. Human capital IND1: Scholar index; professional scholars recruited in a long-term plan such as total number, fluctuation, and average duration of scientific scholars IND2: Staff index; such as attitude, competence, and expenses for training, etc. Organizational capital IND3: Vision, its strategic deployments, processes, and routine management IND4: Information technology in service for supporting the research, teaching and industrial cooperative practice Relational capital IND5: Academic relationship; such as conference activities, international scientists IND6: Public relationship with social environments IND7: Industrial relationship to enhance the student’s employment; number of new co-operation partners
All entries in the cells of the matrix M are replaced with single triangular fuzzy number representing the judgment of the group on each pair of IC indicators. 3.3. Prioritization of IC measurement indicators Chang’s extension method is then used to synthesize the data. The values of fuzzy synthetic extents with respect to the assessing criteria for each indicator are calculated. Taking the data entered by respondent 1 as an example, the values of fuzzy synthetic extents with respect to the assessing criterion Administration for IND1 are calculated as below (see Eq. (1)):
SIND1 ¼ ð3:91; 5:17; 7:17Þ ð0:0126; 0:0156; 0:0202Þ ¼ ð0:0492; 0:0809; 0:1448Þ The minimum of the degrees of possibility are calculated as below (see Eq. (2)):
min VðSIND1 P SIND2 ; SIND3 ; . . . ; SIND7 Þ ¼ 0:3249 Same calculations are performed for the other indicators and these yield the following weights vector:
W 0 ¼ ð0:3249; 0:7323; 1; 0:6992; 0:4785; 0:6303; 0:7022ÞT Via normalization, the local priority weights of all indicators with respect to assessing criterion Administration are calculated for respondent 1 and the results are obtained as below by taking the geometric mean of individual evaluation of the five respondents:
ðIND1; IND2; IND3; IND4; IND5; IND6; IND7Þ ¼ ð0:0421; 0:2452; 0:2372; 0:1719; 0:0843; 0:1177; 0:1014Þ: The calculations of local priority weights of all indicators with respect to the other assessing criteria will not be given in the paper because they are similar. Finally, the overall priority weight of each indicator is calculated by multiplying its local priority weight with its corresponding weight along the hierarchy (as shown in Table 4). The proposed model therefore provides a critical link of university performances to the intangible resource inputs by ranking the IC measurement indicators within each assessing criterion according to their relative importance of contribution to the overall performances for universities. All the intangible resources necessary to improve the overall performances for the stakeholders are identified and visualized in the form of a context specific distinction tree as shown in Fig. 4. It ensures that Shu-Te management team has the same understanding of IC contribution to the university performances for developing knowledge-based strategies. This way also corresponds to the previous IC models (Castro & Sáez, 2008; Peppard & Rylander, 2001) where this distinction was roughly made by Navigator.
Table 4 Priority weights of IC indicators for their performance contribution at Shu-Te University.
IND1 IND2 IND3 IND4 IND5 IND6 IND7
Administration 0.10
Curriculum 0.20
Tech. transfer 0.15
Research 0.25
Teaching 0.15
Service 0.15
Weights
0.0421 0.2452 0.2372 0.1719 0.0843 0.1177 0.1014
0.3600 0.0314 0.0505 0.1303 0.2306 0.0449 0.1522
0.3081 0 0.1281 0.0577 0.2073 0.0248 0.2741
0.3665 0.0103 0.0492 0.1855 0.2556 0.0099 0.1231
0.3504 0.0119 0.0416 0.2718 0.1887 0.0264 0.1091
0.0477 0.2123 0.1645 0.1712 0.0883 0.1054 0.2107
0.2737 0.0670 0.0963 0.1647 0.1911 0.0467 0.1605
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Intellectual Capital
Human Capital 34%
Scholar 27.37%
Staff 6.70%
Total Number
Expense for Training
Fluctuation
Attitude
Average Duration
Competence
Relational Capital 40%
Academic 19.11%
Industrial 16.05%
International Scientists
Organizational Capital 26%
Public 4.67%
Society
Number of Partners
Conferences Visited
Employment Programs
Research Programs
IT 16.47%
Library
Official Institution
Strategies 9.63%
Culture
Routine Processes
Image
Electronic Media
Community
Co-operative Education
Research grants abroad Fig. 4. The Shu-Te distinction tree.
4. Conclusions and implications University assessment has become one of the most important challenges for many Taiwanese universities to develop new strategies which could help to improve their performances within the limited resources. Nevertheless, few applicable models have been addressed that concentrates on the management of intangible capital, even though it has been recognized to be crucial, for the performances of universities and research organizations. A decision model of university performance, adopted from the university assessment scheme in Taiwan, linking to the constructed IC measurement indicators of a university is proposed. AHP method integrated with fuzzy approach is used to develop a hierarchical structure for prioritizing the IC measurement indicators in a university as a better comprehension of the critical intangible assets with the manifestation in activities according to their relative importance of contribution to overall performances ultimately. Based on this coherent model for IC evaluation for university performance it would be possible to predict the consequences of the decisions made based upon the information provided by university assessment, such as re-allocation resources. A visualized form of a context specific distinction tree is developed based on the proposed model, where this distinction was roughly made by Navigator in previous IC model, to facilitate the
understanding of what resources are actually important for the overall performances. An illustrative example is provided of the proposed model at the Shu-Te University. Although the assessing criteria may be affected by the contingent factors of different universities, it is the adaptation of the IC evaluation model to those factors for the assessment of their performance contribution in a university. It would certainly go a long way towards addressing a number of criticisms of IC aspects of university. However, with the research supporting the value of IC measurement indicators should continue to be considered the important facilitators of the knowledge-based university. The coherent model for university assessment could thus provide an insight into the improvement of the development and application of IC reporting for university performances. Acknowledgement This research is partially supported by project from Shu-Te University. References Alpert, D. (1985). Performance and paralysis: The organizational context of the American research university. Journal of Higher Education, 56(3), 241–281.
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