Importance-performance analysis based balanced scorecard for performance evaluation in higher education institutions: an integrated fuzzy approach
Abstract
Recognizing the state of the universities and disrupting their functions by performance evaluation helps them adopt more appropriate educational, research and institutional policies to conduct a university system. In this paper, the importance of the services provided and the activities of the university are determined by means of the balanced scorecard (BSC) approach, and the performance assessment structure is implemented based on an integrated fuzzy multi-criteria decision making (MCDM) approach. For this purpose, interdependencies between BSC aspects and effective indicators weight are determined by Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) and Fuzzy Analytic Network Process (FANP) methods, respectively. Accordingly, the final weight of the effective indexes on the performance evaluation of university is presented and the educational income is recognized as one of the most important indicators. Finally, the priorities of universities are specified in order to improve the performance and policy making by the importance-performance analysis (IPA). Therefore, the growth of the number of students should be considered as one of the most important stages in improving university performance in the future in order to achieve educational income. Moreover, the guidelines for universities and higher education institutions are presented to identify key factors in implementing and improving performance.
Keyword : performance evaluation, importance-performance analysis, balanced scorecard, universities and higher education institutions, fuzzy DEMATEL, fuzzy ANP
How to Cite
Nazari-Shirkouhi, S., Mousakhani, S., Tavakoli, M., Dalvand, M. R., Šaparauskas, J., & Antuchevičienė, J. (2020). Importance-performance analysis based balanced scorecard for performance evaluation in higher education institutions: an integrated fuzzy approach. Journal of Business Economics and Management, 21(3), 647-678. https://doi.org/10.3846/jbem.2020.11940
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Alipour, N., Sangari, M. S., & Nazari-Shirkouhi, S. (2019). Investigating green human resource practices in the healthcare sector: A joint application of balanced scorecard and SIR method. In 2019 15th Iran International Industrial Engineering Conference (IIIEC) (pp. 283–288). IEEE. https://doi.org/10.1109/IIIEC.2019.8720625
Al-Hosaini, F. F., & Sofian, S. (2015). A review of balanced scorecard framework in higher education institution (HEIs). International Review of Management and Marketing, 5(1), 26–35.
Al Kaabi, B. R. H. (2018). The role of a balanced balanced scorecard and its role in improving the efficiency of resources. Iraqi Administrative Sciences Journal, 2(1), 50–92. https://doi.org/10.33013/iqasj.v2n1y2018.pp50-92
Alani, F. S., Khan, M. F. R., & Manuel, D. F. (2018). University performance evaluation and strategic mapping using balanced scorecard (BSC) Case study – Sohar University, Oman. International Journal of Educational Management, 32(4), 689–700. https://doi.org/10.1108/IJEM-05-2017-0107
Atafar, A., Shahrabi, M., & Esfahani, M. (2013). Evaluation of university performance using BSC and ANP. Decision Science Letters, 2(4), 305–311. https://doi.org/10.5267/j.dsl.2013.06.004
Baba, Z., & Shukor, R. A. (2003). Performance indicators for national libraries in Asia/Oceania: Preliminary proposals based on a survey of Asia/Oceania libraries. In World Library and Information Congress: 69th IFLA General Conference and Council (pp. 1–11). IFLA, Berlin.
Baker, R. L. (2002). Evaluating quality and effectiveness: regional accreditation principles and practices. The Journal of Academic Librarianship, 28(1), 3–7. https://doi.org/10.1016/S0099-1333(01)00279-8
Beheshtinia, M. A., & Omidi, S. (2017). A hybrid MCDM approach for performance evaluation in the banking industry. Kybernetes, 46(8), 1386–1407. https://doi.org/10.1016/j.jsm.2017.12.006
Chen, S.-H., Yang, C.-C., & Shiau, J.-Y. (2006). The application of balanced scorecard in the performance evaluation of higher education. The TQM Magazine, 18(2), 190–205. https://doi.org/10.1108/09544780610647892
Chen, S. H., Wang, H. H., & Yang, K. J. (2009). Establishment and application of performance measure indicators for universities. The TQM Journal, 21(3), 220–235. https://doi.org/10.1108/17542730910953004
Dai, L., & Li, J. (2016). Study on the quality of private university education based on analytic hierarchy process and fuzzy comprehensive evaluation method1. Journal of Intelligent & Fuzzy Systems, 31(4), 2241–2247. https://doi.org/10.3233/JIFS-169064
Dinçer, H., Hacıoğlu, Ü., & Yüksel, S. (2017). Balanced scorecard based performance measurement of European airlines using a hybrid multicriteria decision making approach under the fuzzy environment. Journal of Air Transport Management, 63, 17–33. https://doi.org/10.1016/j.jairtraman.2017.05.005
Ding, L., & Zeng, Y. (2015). Evaluation of Chinese higher education by TOPSIS and IEW – The case of 68 universities belonging to the Ministry of Education in China. China Economic Review, 36, 341–358. https://doi.org/10.1016/j.chieco.2015.05.007
Dizaji, M., Mazdeh, M., & Makui, A. (2018). Performance evaluation and ranking of direct sales stores using BSC approach and fuzzy multiple attribute decision-making methods. Decision Science Letters, 7(2), 197–210. https://doi.org/10.5267/j.dsl.2017.5.003
Dyson, R. G. (2000). Strategy, performance and operational research. Journal of the Operational Research Society, 51(1), 5–11. https://doi.org/10.1057/palgrave.jors.2600916
Farid, D., Nejati, M., & Mirfakhredini, H. (2008). Balanced scorecard application in universities and higher education institutes: implementation guide in an Iranian context. Universitatii Bucuresti. Economic and Administrative, 2, 29–42.
Fijałkowska, J., & Oliveira, C. (2018). Balanced scorecard in universities. Journal of Intercultural Management, 10(4), 57–83.
Gabus, A., & Fontela, E. (1972). World problems, an invitation to further thought within the framework of DEMATEL. Battelle Geneva Research Center, Geneva, Switzerland.
Gamal, A., & Soemantri, A. I. (2017). The effect of balanced scorecard on the private college performance (Case study at the University of WR Supratman Surabaya). Archives of Business Research, 5(5). https://doi.org/10.14738/abr.55.3093
Gitinavard, H., & Akbarpour Shirazi, M. (2018). An extended intuitionistic fuzzy modified group complex proportional assessment approach. Journal of Industrial and Systems Engineering, 11(3), 229–246.
Gitinavard, H., & Zarandi, M. H. F. (2016). A mixed expert evaluation system and dynamic intervalvalued hesitant fuzzy selection approach. International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 10, 337–345.
Ha, M.-H., & Yang, Z. (2018). Modelling interdependency among attributes in MCDM: Its application in port performance measurement multi-criteria decision making in maritime studies and logistics (pp. 323–354). Springer. https://doi.org/10.1007/978-3-319-62338-2_12
Hassan, S. A. H. S., Tan, S. C., & Yusof, K. M. (2016). MCDM for engineering education: Literature review and research issues engineering education for a smart society (pp. 204–214). Springer. https://doi.org/10.1007/978-3-319-60937-9_16
Hung, Y.-H., Chou, S.-C. T., & Tzeng, G.-H. (2006). Using a fuzzy group decision approach-knowledge management adoption. Paper presented at the APRU DLI 2006 Conference.
Iranmanesh, S. H., Tavakoli, M., Heydari, K., Bastan, M., & Yazdanparast, R. (2019). An integrated resilience engineering algorithm for performance optimisation of electricity distribution units. International Journal of Computer Applications in Technology, 60(3), 254–266. https://doi.org/10.1504/IJCAT.2019.100303
Kai, J. (2009). A critical analysis of accountability in higher education: Its relevance to evaluation of higher education. Chinese Education & Society, 42(2), 39–51. https://doi.org/10.2753/CED1061-1932420204
Kaplan, R. S., & Norton, D. P. (1995). Putting the balanced scorecard to work. Performance Measurement, Management, and Appraisal Sourcebook, 66, 17511.
Kaplan, R. S., & Norton, D. P. (2004). Strategy maps: Converting intangible assets into tangible outcomes. Harvard Business Press.
Karra, E. D., & Papadopoulos, D. L. (2008). The evaluation of an academic institution using the balanced scorecard (academic scorecard): The case of University of Macedonia, Thessaloniki, Greece. Social Science Research Network (SSRN). https://doi.org/10.2139/ssrn.492783
Lin, C.-J., & Wu, W.-W. (2008). A causal analytical method for group decision-making under fuzzy environment. Expert Systems with Applications, 34(1), 205–213. https://doi.org/10.1016/j.eswa.2006.08.012
Ling Sim, K., & Chye Koh, H. (2001). Balanced scorecard: A rising trend in strategic performance measurement. Measuring Business Excellence, 5(2), 18–27. https://doi.org/10.1108/13683040110397248
Liou, T.-S., & Wang, M.-J. J. (1992). Ranking fuzzy numbers with integral value. Fuzzy Sets and Systems, 50(3), 247–255. https://doi.org/10.1016/0165-0114(92)90223-Q
Martilla, J. A., & James, J. C. (1977). Importance-performance analysis. The Journal of Marketing, 41(1), 77–79. https://doi.org/10.1177/002224297704100112
Mourato, J., Patrício, M. T., Loures, L., & Morgado, H. (2019). Strategic priorities of Portuguese higher education institutions. Studies in Higher Education, 1–13. https://doi.org/10.1080/03075079.2019.1628202
Nazarko, J., & Šaparauskas, J. (2014). Application of DEA method in efficiency evaluation of public higher education institutions. Technological and Economic Development of Economy, 20(1), 25–44. https://doi.org/10.3846/20294913.2014.837116
Nurcahyo, R., Wardhani, R. K., Habiburrahman, M., Kristiningrum, E., & Herbanu, E. A. (2018). Strategic formulation of a higher education institution using balance scorecard. In 2018 4th International Conference on Science and Technology (ICST). IEEE. https://doi.org/10.1109/ICSTC.2018.8528294
Nuut, A. (2006). Evaluation of library performance: current developments in Estonia. Performance Measurement and Metrics, 7(3), 163–172. https://doi.org/10.1108/14678040610713129
Nuut, A., Lepik, A., & Liivamägi, T. (2002). Developing performance measurement and quality – evaluation in Estonian research libraries: Survey of current situation. In Meaningful Measurement for Emerging Realities: Proceedings of 4th Northumbria International Conference on Performance Measurement in Libraries and Information Services (pp. 159−170). Association of Research Libraries.
Opricovic, S., & Tzeng, G.-H. (2003). Defuzzification within a multicriteria decision model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(05), 635–652. https://doi.org/10.1142/S0218488503002387
Özdemir, A., & Tüysüz, F. (2017). An integrated fuzzy DEMATEL and fuzzy ANP based balanced scorecard approach: Application in Turkish Higher Education Institutions. Journal of Multiple-Valued Logic & Soft Computing, 28(2), 251–287.
Papenhausen, C., & Einstein, W. (2006). Implementing the Balanced Scorecard at a college of business. Measuring Business Excellence, 10(3), 15–22. https://doi.org/10.1108/13683040610685757
Rezaie, K., Dalfard, V. M., Hatami-Shirkouhi, L., & Nazari-Shirkouhi, S. (2013). Efficiency appraisal and ranking of decision-making units using data envelopment analysis in fuzzy environment: A case study of Tehran stock exchange. Neural Computing and Applications, 23(1), 1–17. https://doi.org/10.1007/s00521-012-1209-6
Ramasamy, N., Rajesh, R., Pugazhendhi, S., & Ganesh, K. (2016). Development of a hybrid BSC-AHP model for institutions in higher education. International Journal of Enterprise Network Management, 7(1), 13–26. https://doi.org/10.1504/IJENM.2016.075174
Saaty, T. L. (1988). What is the analytic hierarchy process? Mathematical models for decision support (pp. 109–121). Springer. https://doi.org/10.1007/978-3-642-83555-1_5
Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process (Vol. 4922). RWS publications Pittsburgh.
Sarrico, C. S., Rosa, M. J., Teixeira, P. N., & Cardoso, M. F. (2010). Assessing quality and evaluating performance in higher education: Worlds apart or complementary views? Minerva, 48(1), 35–54. https://doi.org/10.1007/s11024-010-9142-2
Slizyte, A., & Bakanauskiene, I. (2007). Designing performance measurement system in organization. In Organizacijų vadyba: sisteminiai tyrimai (T. 43, pp. 135–148). Vytauto Didžiojo universiteto leidykla.
Sohrabvandi, S., Gitinavard, H., & Ebrahimnezhad, S. (2017). A new extended analytical hierarchy process technique with incomplete interval-valued information for risk assessment in IT outsourcing. International Journal of Engineering, 30(5), 739–748.
Solgi, E., Husseini, S. M. M., Ahmadi, A., & Gitinavard, H. (2019). A hybrid hierarchical soft computing approach for the technology selection problem in brick industry considering environmental competencies: A case study. Journal of Environmental Management, 248, 109219. https://doi.org/10.1016/j.jenvman.2019.06.120
Sun, C.-C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37(12), 7745–7754. https://doi.org/10.1016/j.eswa.2010.04.066
Tesfamariam, S., & Sadiq, R. (2006). Risk-based environmental decision-making using fuzzy analytic hierarchy process (F-AHP). Stochastic Environmental Research and Risk Assessment, 21(1), 35–50. https://doi.org/10.1007/s00477-006-0042-9
Tippins, M. J., & Sohi, R. S. (2003). IT competency and firm performance: is organizational learning a missing link? Strategic Management Journal, 24(8), 745–761. https://doi.org/10.1002/smj.337
Tseng, M. L. (2010). Implementation and performance evaluation using the fuzzy network balanced scorecard. Computers & Education, 55(1), 188–201. https://doi.org/10.1016/j.compedu.2010.01.004
Tzeng, G.-H., Chiang, C.-H., & Li, C.-W. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, 32(4), 1028–1044. https://doi.org/10.1016/j.eswa.2006.02.004
Vroon, R. (2010). How to effectively manage performance in a university. In 12th Twente Student Conference on IT. University of Twente. Twente, The Netherlands.
Wu, S.-I., & Hung, J.-M. (2007). The performance measurement of cause-related marketing by balance scorecard. Total Quality Management & Business Excellence, 18(7), 771–791. https://doi.org/10.1080/14783360701349831
Wu, W.-W., & Lee, Y.-T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499–507. https://doi.org/10.1016/j.eswa.2005.12.005
Wu, H. Y., Lin, Y. K., & Chang, C. H. (2011). Performance evaluation of extension education centers in universities based on the balanced scorecard. Evaluation and Program Planning, 34(1), 37–50. https://doi.org/10.1016/j.evalprogplan.2010.06.001
Yao, W., Xu, X., & Chen, W. (2014) Towards evaluating the performance of higher education: From a non-financial perspective. In Z. Huang, C. Liu, J. He, & G. Huang (Eds.), Web Information Systems Engineering – WISE 2013 Workshops. WISE 2013: Lecture Notes in Computer Science, Vol. 8182. (pp. 109–119). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54370-8_10
Yüksel, İ., & Dağdeviren, M. (2010). Using the fuzzy analytic network process (ANP) for Balanced Scorecard (BSC): A case study for a manufacturing firm. Expert Systems with Applications, 37(2), 1270–1278. https://doi.org/10.1016/j.eswa.2009.06.002
Zolfani, S. H., & Ghadikolaei, A. S. (2013). Performance evaluation of private universities based on balanced scorecard: Empirical study based on Iran. Journal of Business Economics and Management, 14(4), 696–714. https://doi.org/10.3846/16111699.2012.665383
Al-Hosaini, F. F., & Sofian, S. (2015). A review of balanced scorecard framework in higher education institution (HEIs). International Review of Management and Marketing, 5(1), 26–35.
Al Kaabi, B. R. H. (2018). The role of a balanced balanced scorecard and its role in improving the efficiency of resources. Iraqi Administrative Sciences Journal, 2(1), 50–92. https://doi.org/10.33013/iqasj.v2n1y2018.pp50-92
Alani, F. S., Khan, M. F. R., & Manuel, D. F. (2018). University performance evaluation and strategic mapping using balanced scorecard (BSC) Case study – Sohar University, Oman. International Journal of Educational Management, 32(4), 689–700. https://doi.org/10.1108/IJEM-05-2017-0107
Atafar, A., Shahrabi, M., & Esfahani, M. (2013). Evaluation of university performance using BSC and ANP. Decision Science Letters, 2(4), 305–311. https://doi.org/10.5267/j.dsl.2013.06.004
Baba, Z., & Shukor, R. A. (2003). Performance indicators for national libraries in Asia/Oceania: Preliminary proposals based on a survey of Asia/Oceania libraries. In World Library and Information Congress: 69th IFLA General Conference and Council (pp. 1–11). IFLA, Berlin.
Baker, R. L. (2002). Evaluating quality and effectiveness: regional accreditation principles and practices. The Journal of Academic Librarianship, 28(1), 3–7. https://doi.org/10.1016/S0099-1333(01)00279-8
Beheshtinia, M. A., & Omidi, S. (2017). A hybrid MCDM approach for performance evaluation in the banking industry. Kybernetes, 46(8), 1386–1407. https://doi.org/10.1016/j.jsm.2017.12.006
Chen, S.-H., Yang, C.-C., & Shiau, J.-Y. (2006). The application of balanced scorecard in the performance evaluation of higher education. The TQM Magazine, 18(2), 190–205. https://doi.org/10.1108/09544780610647892
Chen, S. H., Wang, H. H., & Yang, K. J. (2009). Establishment and application of performance measure indicators for universities. The TQM Journal, 21(3), 220–235. https://doi.org/10.1108/17542730910953004
Dai, L., & Li, J. (2016). Study on the quality of private university education based on analytic hierarchy process and fuzzy comprehensive evaluation method1. Journal of Intelligent & Fuzzy Systems, 31(4), 2241–2247. https://doi.org/10.3233/JIFS-169064
Dinçer, H., Hacıoğlu, Ü., & Yüksel, S. (2017). Balanced scorecard based performance measurement of European airlines using a hybrid multicriteria decision making approach under the fuzzy environment. Journal of Air Transport Management, 63, 17–33. https://doi.org/10.1016/j.jairtraman.2017.05.005
Ding, L., & Zeng, Y. (2015). Evaluation of Chinese higher education by TOPSIS and IEW – The case of 68 universities belonging to the Ministry of Education in China. China Economic Review, 36, 341–358. https://doi.org/10.1016/j.chieco.2015.05.007
Dizaji, M., Mazdeh, M., & Makui, A. (2018). Performance evaluation and ranking of direct sales stores using BSC approach and fuzzy multiple attribute decision-making methods. Decision Science Letters, 7(2), 197–210. https://doi.org/10.5267/j.dsl.2017.5.003
Dyson, R. G. (2000). Strategy, performance and operational research. Journal of the Operational Research Society, 51(1), 5–11. https://doi.org/10.1057/palgrave.jors.2600916
Farid, D., Nejati, M., & Mirfakhredini, H. (2008). Balanced scorecard application in universities and higher education institutes: implementation guide in an Iranian context. Universitatii Bucuresti. Economic and Administrative, 2, 29–42.
Fijałkowska, J., & Oliveira, C. (2018). Balanced scorecard in universities. Journal of Intercultural Management, 10(4), 57–83.
Gabus, A., & Fontela, E. (1972). World problems, an invitation to further thought within the framework of DEMATEL. Battelle Geneva Research Center, Geneva, Switzerland.
Gamal, A., & Soemantri, A. I. (2017). The effect of balanced scorecard on the private college performance (Case study at the University of WR Supratman Surabaya). Archives of Business Research, 5(5). https://doi.org/10.14738/abr.55.3093
Gitinavard, H., & Akbarpour Shirazi, M. (2018). An extended intuitionistic fuzzy modified group complex proportional assessment approach. Journal of Industrial and Systems Engineering, 11(3), 229–246.
Gitinavard, H., & Zarandi, M. H. F. (2016). A mixed expert evaluation system and dynamic intervalvalued hesitant fuzzy selection approach. International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 10, 337–345.
Ha, M.-H., & Yang, Z. (2018). Modelling interdependency among attributes in MCDM: Its application in port performance measurement multi-criteria decision making in maritime studies and logistics (pp. 323–354). Springer. https://doi.org/10.1007/978-3-319-62338-2_12
Hassan, S. A. H. S., Tan, S. C., & Yusof, K. M. (2016). MCDM for engineering education: Literature review and research issues engineering education for a smart society (pp. 204–214). Springer. https://doi.org/10.1007/978-3-319-60937-9_16
Hung, Y.-H., Chou, S.-C. T., & Tzeng, G.-H. (2006). Using a fuzzy group decision approach-knowledge management adoption. Paper presented at the APRU DLI 2006 Conference.
Iranmanesh, S. H., Tavakoli, M., Heydari, K., Bastan, M., & Yazdanparast, R. (2019). An integrated resilience engineering algorithm for performance optimisation of electricity distribution units. International Journal of Computer Applications in Technology, 60(3), 254–266. https://doi.org/10.1504/IJCAT.2019.100303
Kai, J. (2009). A critical analysis of accountability in higher education: Its relevance to evaluation of higher education. Chinese Education & Society, 42(2), 39–51. https://doi.org/10.2753/CED1061-1932420204
Kaplan, R. S., & Norton, D. P. (1995). Putting the balanced scorecard to work. Performance Measurement, Management, and Appraisal Sourcebook, 66, 17511.
Kaplan, R. S., & Norton, D. P. (2004). Strategy maps: Converting intangible assets into tangible outcomes. Harvard Business Press.
Karra, E. D., & Papadopoulos, D. L. (2008). The evaluation of an academic institution using the balanced scorecard (academic scorecard): The case of University of Macedonia, Thessaloniki, Greece. Social Science Research Network (SSRN). https://doi.org/10.2139/ssrn.492783
Lin, C.-J., & Wu, W.-W. (2008). A causal analytical method for group decision-making under fuzzy environment. Expert Systems with Applications, 34(1), 205–213. https://doi.org/10.1016/j.eswa.2006.08.012
Ling Sim, K., & Chye Koh, H. (2001). Balanced scorecard: A rising trend in strategic performance measurement. Measuring Business Excellence, 5(2), 18–27. https://doi.org/10.1108/13683040110397248
Liou, T.-S., & Wang, M.-J. J. (1992). Ranking fuzzy numbers with integral value. Fuzzy Sets and Systems, 50(3), 247–255. https://doi.org/10.1016/0165-0114(92)90223-Q
Martilla, J. A., & James, J. C. (1977). Importance-performance analysis. The Journal of Marketing, 41(1), 77–79. https://doi.org/10.1177/002224297704100112
Mourato, J., Patrício, M. T., Loures, L., & Morgado, H. (2019). Strategic priorities of Portuguese higher education institutions. Studies in Higher Education, 1–13. https://doi.org/10.1080/03075079.2019.1628202
Nazarko, J., & Šaparauskas, J. (2014). Application of DEA method in efficiency evaluation of public higher education institutions. Technological and Economic Development of Economy, 20(1), 25–44. https://doi.org/10.3846/20294913.2014.837116
Nurcahyo, R., Wardhani, R. K., Habiburrahman, M., Kristiningrum, E., & Herbanu, E. A. (2018). Strategic formulation of a higher education institution using balance scorecard. In 2018 4th International Conference on Science and Technology (ICST). IEEE. https://doi.org/10.1109/ICSTC.2018.8528294
Nuut, A. (2006). Evaluation of library performance: current developments in Estonia. Performance Measurement and Metrics, 7(3), 163–172. https://doi.org/10.1108/14678040610713129
Nuut, A., Lepik, A., & Liivamägi, T. (2002). Developing performance measurement and quality – evaluation in Estonian research libraries: Survey of current situation. In Meaningful Measurement for Emerging Realities: Proceedings of 4th Northumbria International Conference on Performance Measurement in Libraries and Information Services (pp. 159−170). Association of Research Libraries.
Opricovic, S., & Tzeng, G.-H. (2003). Defuzzification within a multicriteria decision model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(05), 635–652. https://doi.org/10.1142/S0218488503002387
Özdemir, A., & Tüysüz, F. (2017). An integrated fuzzy DEMATEL and fuzzy ANP based balanced scorecard approach: Application in Turkish Higher Education Institutions. Journal of Multiple-Valued Logic & Soft Computing, 28(2), 251–287.
Papenhausen, C., & Einstein, W. (2006). Implementing the Balanced Scorecard at a college of business. Measuring Business Excellence, 10(3), 15–22. https://doi.org/10.1108/13683040610685757
Rezaie, K., Dalfard, V. M., Hatami-Shirkouhi, L., & Nazari-Shirkouhi, S. (2013). Efficiency appraisal and ranking of decision-making units using data envelopment analysis in fuzzy environment: A case study of Tehran stock exchange. Neural Computing and Applications, 23(1), 1–17. https://doi.org/10.1007/s00521-012-1209-6
Ramasamy, N., Rajesh, R., Pugazhendhi, S., & Ganesh, K. (2016). Development of a hybrid BSC-AHP model for institutions in higher education. International Journal of Enterprise Network Management, 7(1), 13–26. https://doi.org/10.1504/IJENM.2016.075174
Saaty, T. L. (1988). What is the analytic hierarchy process? Mathematical models for decision support (pp. 109–121). Springer. https://doi.org/10.1007/978-3-642-83555-1_5
Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process (Vol. 4922). RWS publications Pittsburgh.
Sarrico, C. S., Rosa, M. J., Teixeira, P. N., & Cardoso, M. F. (2010). Assessing quality and evaluating performance in higher education: Worlds apart or complementary views? Minerva, 48(1), 35–54. https://doi.org/10.1007/s11024-010-9142-2
Slizyte, A., & Bakanauskiene, I. (2007). Designing performance measurement system in organization. In Organizacijų vadyba: sisteminiai tyrimai (T. 43, pp. 135–148). Vytauto Didžiojo universiteto leidykla.
Sohrabvandi, S., Gitinavard, H., & Ebrahimnezhad, S. (2017). A new extended analytical hierarchy process technique with incomplete interval-valued information for risk assessment in IT outsourcing. International Journal of Engineering, 30(5), 739–748.
Solgi, E., Husseini, S. M. M., Ahmadi, A., & Gitinavard, H. (2019). A hybrid hierarchical soft computing approach for the technology selection problem in brick industry considering environmental competencies: A case study. Journal of Environmental Management, 248, 109219. https://doi.org/10.1016/j.jenvman.2019.06.120
Sun, C.-C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37(12), 7745–7754. https://doi.org/10.1016/j.eswa.2010.04.066
Tesfamariam, S., & Sadiq, R. (2006). Risk-based environmental decision-making using fuzzy analytic hierarchy process (F-AHP). Stochastic Environmental Research and Risk Assessment, 21(1), 35–50. https://doi.org/10.1007/s00477-006-0042-9
Tippins, M. J., & Sohi, R. S. (2003). IT competency and firm performance: is organizational learning a missing link? Strategic Management Journal, 24(8), 745–761. https://doi.org/10.1002/smj.337
Tseng, M. L. (2010). Implementation and performance evaluation using the fuzzy network balanced scorecard. Computers & Education, 55(1), 188–201. https://doi.org/10.1016/j.compedu.2010.01.004
Tzeng, G.-H., Chiang, C.-H., & Li, C.-W. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, 32(4), 1028–1044. https://doi.org/10.1016/j.eswa.2006.02.004
Vroon, R. (2010). How to effectively manage performance in a university. In 12th Twente Student Conference on IT. University of Twente. Twente, The Netherlands.
Wu, S.-I., & Hung, J.-M. (2007). The performance measurement of cause-related marketing by balance scorecard. Total Quality Management & Business Excellence, 18(7), 771–791. https://doi.org/10.1080/14783360701349831
Wu, W.-W., & Lee, Y.-T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499–507. https://doi.org/10.1016/j.eswa.2005.12.005
Wu, H. Y., Lin, Y. K., & Chang, C. H. (2011). Performance evaluation of extension education centers in universities based on the balanced scorecard. Evaluation and Program Planning, 34(1), 37–50. https://doi.org/10.1016/j.evalprogplan.2010.06.001
Yao, W., Xu, X., & Chen, W. (2014) Towards evaluating the performance of higher education: From a non-financial perspective. In Z. Huang, C. Liu, J. He, & G. Huang (Eds.), Web Information Systems Engineering – WISE 2013 Workshops. WISE 2013: Lecture Notes in Computer Science, Vol. 8182. (pp. 109–119). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54370-8_10
Yüksel, İ., & Dağdeviren, M. (2010). Using the fuzzy analytic network process (ANP) for Balanced Scorecard (BSC): A case study for a manufacturing firm. Expert Systems with Applications, 37(2), 1270–1278. https://doi.org/10.1016/j.eswa.2009.06.002
Zolfani, S. H., & Ghadikolaei, A. S. (2013). Performance evaluation of private universities based on balanced scorecard: Empirical study based on Iran. Journal of Business Economics and Management, 14(4), 696–714. https://doi.org/10.3846/16111699.2012.665383