SOFT SKILLS OF HIGHER EDUCATION IN INDUSTRY 4.0 ERA USING BUCKLEY’S FUZZY-AHP

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Published Apr 28, 2022
Yulius Christian Raton Jozef Richard Raco James V Krejci Johanis Ohoitimur Jeanette E.M. Soputan Tryadi Wilhelmus Tumewu Merry Jeanned’arc Korompis Frankie J.H Taroreh Ronald A. Rachmadi Stevanus Ngenget

Abstract

Industry 4.0 is characterized by the digitalization of systems and processes in service and manufacturing industries and has changed the way people live. Education plays a significant role in preparing the future workforce with the necessary technological skills and competencies required by industries and institutions. Studies have shown that soft skills improve a student’s ability to learn, increase their potential for success, and typically increase future economic benefits. This study aims to determine the dominant soft skills that University students in Manado should possess. The perceptions of twenty-four lecturers about four criteria and twelve sub-criteria were compared using both the Analytic Hierarchy Process (AHP) and Fuzzy Analytical Hierarchy Process (F-AHP) methods. From this, the researchers found teamwork to be the dominant skill (26%). Global analysis uncovered that integrity was the dominant factor overall (10.5% with AHP or 10.3% with F-AHP). The findings were provided to University leaders with recommendations to incorporate the elements of teamwork and integrity into their teaching materials, teaching methods, and curriculum. Students need to understand that these elements are essential to their future. This research proved that both the AHP and Fuzzy-AHP methods were effective tools in analyzing and determining the dominant factors of soft skills in the Industry 4.0 era. This research contributes to determining the priority factors related to soft skills needed by higher education graduates in the Industry 4.0 era using a combination of AHP and Fuzzy-AHP. The researchers recommended that other scholars conduct future studies using entrepreneurs or business practitioners as respondents.

How to Cite

Raton, Y. C., Raco, J. R. ., Krejci, J. V., Ohoitimur, J., Soputan, J. E., Tumewu, T. W. ., Korompis, M. J. ., Taroreh, F. J., Rachmadi, R. A., & Ngenget, S. . (2022). SOFT SKILLS OF HIGHER EDUCATION IN INDUSTRY 4.0 ERA USING BUCKLEY’S FUZZY-AHP. International Journal of the Analytic Hierarchy Process, 14(1). https://doi.org/10.13033/ijahp.v14i1.943

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Keywords

AHP, fuzzy AHP, Industry 4.0, soft skills, La Salle, higher education, sensitivity analysis

References
Aulbur, A., Arvind, C., & Bigghe, R. (2016). Skill development for Industry 4.0.

Azmi, A. N., Kamin, Y., Noordin, M., & Nasir, A. (2018). Towards industrial revolution 4.0: Employers’ expectations on fresh engineering graduates. International Journal of Engineering and Technology (UAE), 7(4), 267–272. Doi: https://doi.org/10.14419/ijet.v7i4.28.22593

Basak, I., & Saaty, T. (1993). Group decision making using the Analytical Hierarchy Process. Mathematics and Computer Modelling, 17(415), 101-109as.

Cunha, F., & Heckman, J. (2007). The technology of skill formation (NBER Working Paper No. 12840). NBER Working Paper Series. Massachusetts.

Dombrowski, U., Wullbrandt, J., & Fochler, S. (2019a). Center of Excellence for Lean Enterprise 4.0. Procedia Manufacturing, 31, 66–71. Doi: https://doi.org/10.1016/j.promfg.2019.03.011

Dombrowski, U., Wullbrandt, J., & Fochler, S. (2019b). Center of Excellence for Lean Enterprise 4.0. Procedia Manufacturing, 31(2019), 66–71. Doi: https://doi.org/10.1016/j.promfg.2019.03.011

Durisova, M., Kucharcikova, A., & Tokarcikova, E. (2015). Assessment of higher education teaching outcomes (Quality of Higher Education), Procedia-Social and Behavioral Sciences, 174(2015), 2497–2502. Doi: https://doi.org/10.1016/j.sbspro.2015.01.922

Fitsilis, P., Tsoutsa, P., & Gerogiannis, V. (2018). Industry 4.0 : Required personel competencies. International Scientific Journal “Industry 4.0,” 133(3), 130–133.

Forbes, J., Hebb, A., & Mu, E. (2018). Ethical decision making in action: Evaluation hositpal attendance approaches International Journal of Analytic Hierarchy Process, 10(3), 313–347. Doi: https://doi.org/10.13033/ijahp.v10i3.592

Gallon, R., & Gales, N. (2017). Hybrid communication for Industry 4.0: Nemetic models. France: The Transformation Society.

Glas, A. H., & Kleemann, P. F. C. (2016). The impact of Industry 4.0 on procurement and supply management: A conceptual and qualitative analysis. International Journal of Business and Management Invention.

Hanushek, E. A., Schwerdt, G., Wiederhold, S., & Woessmann, L. (2015). Returns to skills around the world : Evidence from PIAAC. European Economic Review, 73, 103–130. Doi: https://doi.org/10.1016/j.euroecorev.2014.10.006

Hanushek, E. A., & Woessmann, L. (2008). The role of cognitive skills in economic development. Journal of Economic Literature, 46(3), 607–668.

Heckman, J. J., & Kautz, T. D. (2012). Hard evidence of soft skills. Cambridge, MA: National Bureau of Economics Research.

Hussin, A. A. (2018). Education 4.0 made simple : Ideas for reaching. International Journal of Education & Literacy Studies, 6(3), 92–98.

Ishizaka, A., & Labib, A. (2009). Analytic Hierarchy Process and Expert Choice : Benefits and limitations. OR Insight, 22(4), 201–220. Doi: https://doi.org/10.1057/ori.2009.10

Javanbarg, M.B., Scawthorn, C., Kiyono, J., & Shahbodaghkhan, B. (2012). Expert systems with applications Fuzzy AHP-based multicriteria decision making systems using particle swarm optimization. Expert Systems With Applications, 39(1), 960–966. Doi: https://doi.org/10.1016/j.eswa.2011.07.095

Lavic, Z., Vucijak, B., Pasic, M., & Dukic, N. (2018). Consistency check of fuzzy pairwise comparison matrices of dimesions larger than 3X3. 29th DAAM International Symposium on Intelligent Manufacturing and Automation. Doi: https://doi.org/10.2507/29th.daaam.proceedings.102

Lazarević, I. (2019). Using the Electre MLO Multi-Criteria Decision Making method in stepwise benchmarking - Application in higher education. Operational Research in Engineering Sciences: Theory and Applications, 2(3), 77–91. Doi: https://doi.org/10.31181/oresta1903077l

Lohan, A., Ganguly, A., & Kumar, C. (2020). “What’s foreign is better”: A Fuzzy-AHP analysis to evaluate factors that influence foreign product choice among Indian consumers. International Journal of Analytic Hierarchy Process, 12(3), 460–487. Doi: https://doi.org/10.13033/ijahp.v12i3.743

Lu, M., & Zhu, K. (2018). Improved Analytic Hierarchy Process based on Triangular Fuzzy Number. Advances in Social Science, Education and Humanities Research, 182, 314–317.

Meindl, B., Ayala, N. F., Mendonça, J., & Frank, A. G. (2021). The four smarts of Industry 4.0 : Evolution of ten years of research and future perspectives. Technological Forecasting & Social Change, 168(2020), 1–13. Doi: https://doi.org/10.1016

Miranda, J., Navarrete, C., Noguez, J., Molina-Espinosa, J., Ramirez-Montoya, M., Navarro-Tuch, S., … Molina, A. (2021). The core components of education 4.0 in higher education: Three case studies in engineering education. Computers and Electrical Engineering, 93(2020), 1–13. Doi: https://doi.org/10.1016/j.compeleceng.2021.107278

Mondal, U. (2015). Integrity, competency and ethical behaviour. IJMT, 3(4), 188-196.

Mu, E., & Pereyra-Rojas, M. (2018). Practical decision making using Super Decisions v3. An introduction to the Analytic Hierarchy Process. Pittsburgh: Springer.

Nyemba, W., Carter, K., Mbohwa, C., & Chinguwa, S. (2019). A systems thinking approach to collaborations for capacity building and sustainability in engineering education and sustainability in engineering education. Procedia Manufacturing, 33, 732–739. Doi: https://doi.org/10.1016/j.promfg.2019.04.092

Ohoitimur, J., Krejci, J., Raco, J. R., Raton, Y., & Taroreh, F. (2019). Pstoral strategic planning priorities for the church: Case study of the Vicarate Episcopal of Tonsea of the Diocese of Manado. International Journal of the Analytic Hierarchy Process, 11(3), 415–434. Doi: https://doi.org/10.13033

Paravizo, E., Chaim, O. C., Braatz, D., Muschard, B., & Rozenveld, H. (2018). Exploring gamification to support manufacturing education on industry 4.0 as an enabler for innovation and sustainability. Procedia Manufacturing, 21, 438–445. Doi: https://doi.org/10.1016/j.promfg.2018.02.142

Pillay, P. (2014). Integrity leadership. African Journal of Public Affairs, 7(2), 177–179.

Praščević, N., & Praščević, Ž. (2016). Application of fuzzy AHP method based on eigenvalues for decision making in construction industry. Tehnički Vjesnik, 23(1), 57–64. Doi: https://doi.org/10.17559/TV-20140212113942

Ra, S., Shrestha, U., Khatiwada, S., & Yoon, S.W. (2019). The rise of technology and impact on skills. International Journal of Training Research, 17(1), 26–40. Doi: https://doi.org/10.1080/14480220.2019.1629727

Raco, J. ., Krejci, J. V, Ohoitimur, J., Adrian, A. ., Raton, Y., Rottie, R., … Sumakud, E. (2021). Priority sector of small and medium enterprises using AHP: A case study of Yamaru enterprise. International Journal of Analytic Hierarchy Process, 13(2), 220–239. Doi: https://doi.org/10.13033/ijahp.v13i2.862

Raco, J., Ohoitimur, J., Krejci, J. V, Raton, Y., Rottie, R., Paseru, D., … Rachmadi, R. (2020). The dominant factor of lecturers' research productivity using the AHP: Case study of Catholic University of De La Salle Manado-Indonesia. International Journal of Analytic Hierarchy Process, 12(3), 546–564. Doi: https://doi.org/10.13033/ijahp.v12i3.819

Raco, J. R., & Tanod, H. (2014). The phenomenological method in entrepreneurship. International Journal of Entrepreneurship and Small Business, 22(3), 276–285.

Rauch, E., Linder, C., & Dallasega, P. (2019). Anthropocentric perspective of production before and within Industry 4 . 0. Computers & Industrial Engineering, 139(2020), 1–15. Doi: https://doi.org/10.1016/j.cie.2019.01.018

Saaty, R. . (1987). The Analytic Hierarchy Process-What and how it is used. Math Modelling, 9(3), 161–176.

Saaty, T. L. (2008). Relative measurement and its generalization in decision making. Why pairwise comparisons are central in mathematics for the measurement of intangible factors. The Analytic Hierarchy/Network Process. RACSAM, 102(2), 251–318.

Saaty, T. L. (2013). On the measurement of ingangibles. A principal eigenvector approach to relative measurement derived from paired comparisons. Notices of the American Mathematical Society, 60(2), 192–208.

Schottl, L. (2015). The concept of moral integrity and its implications for business. KICG-Forschungspapiere (Vol. 9).

Umeda, Y., Ota, J., Kojima, F., Saito, M., Matsuzawa, H., Sukekawa, T., … Shirafuji, S. (2019). Development of an education program for digital manufacturing system engineers based on ‘ Digital Triplet ’ concept. Procedia Manufacturing, 31, 363–369. Doi: https://doi.org/10.1016/j.promfg.2019.03.057

Vrana, S, A. (2008). Evaluating the knowledge, relevance and experience of expert decision makers utilizing the Fuzzy-AHP. Agricultural Economics, 54(11), 529–535. Doi: https://doi.org/10.17221/264-AGRICECON

Wang, T., & Chen, Y. (n.d.). Some issues on consistency of fuzzy Analytic Hierarchy Process.

Zavadskas, E., Turskis, Z., Stević, Ž., & Mardani, A. (2020). Modelling procedure for the selection of steel pipe supplier by applying the fuzzy AHP method. Operational Research in Engineering Sciences: Theory and Applications, 3(2), 39–53. Doi: https://doi.org//10.31181/oresta2003034z

Zhang, X., Ma, W., & Chen, L. (2014). New similarity of Triangular Fuzzy Number and its application. The Scientific World Journal, (215047), 1–7. Doi: https://doi.org/10.1155/2014/215047


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