Designing a Competency Model for Creative Industry Leaders using Discrete Consensus Support Methods and Interpretive Structural Modeling

Document Type : Research Paper


1 Associate Prof., Department of Business Management, Faculty of Management, University of Tehran, Tehran, Iran.

2 Prof., Department of Media Management, Faculty of Management, University of Tehran, Tehran, Iran.

3 Assistant Prof., Department of Business Management, Faculty of Management, University of Tehran, Tehran, Iran.

4 Associate Prof., Department of Social Communication and Journalism, Faculty of Communication Sciences and Media Studies, Azad University, Tehran, Iran.

5 PhD, Department of Media Management, Faculty of Management, University of Tehran, Tehran, Iran.


Objective: With changes in audience behavior, the growth of information and communication technologies, globalization as well as the removal of business market constraints, creative industries and media organizations need to adapt more quickly to these changes, and effort to compatibility leads to more business dynamics. In this regard, relying on organizational assets and capitals ensures success. One of the key assets of the creative industry is the competencies of leaders. The purpose of this study is to design a competency model for creative industry leaders. This model is used to select and hire leaders, develop and train them, identify the talents of creative industries, promote and appoint them.
Methods: In this paper, both qualitative and quantitative approaches in two main steps were used. In the first step, the main competencies of creative industries leaders identified and in the second step, the conceptual model od competencies developed.
Results: To identify competencies, Systematic Literature Review (SLR) has been used and 50 competencies have been identified. Then, through Discrete Consensus Support Method (DCSM), these competencies are filtered, reduced and reached to 40 cases. Finally, through the Structural Interpretive Modeling (ISM), the conceptual model of 16 competencies has been developed. In four levels: basic, behavioral, technical and excellence.
Conclusion: According to the final model of competency, creative industry leaders should have four clusters of competencies: basic, behavioral, technical and excellence.


Anbanandam, R., Banwet, D. K., Shankar, R. (2010).  Modeling the barriers of supply chain collaboration. Journal of Modelling in Management, 5(2), 176–193.
Antonakis, J. (2004). On why "emotional intelligence" will not predict leadership effectiveness beyond IQ or the "big five": An extension and rejoinder. Organizational Analysis, 12(2), 171-182.
Asam, M., Wakrim, M. (2018). Towards a competency model: A review Of the literature and the competency standards. Education and Information Technologies, 23, 225-236.
Bahrami, K., Karimi, M. H. (2019). Developing a Model for Agility in Overhaul through Interpretive Structural Modeling (Case Study: Defensive Overhaul Center). Journal of Industrial Management, 11(2), 255-272. (in Persian)
Becut, A.G. (2016). Dynamics of creative industries in a post-communist society. Thedevelopment of creative sector in Romanian cities. City, Culture and Society, 7(2), 63-68.
Ben-Arieh, D., & Chen, Z. (2006). Linguistic aggregation and consensus measure for autocratic decision-making using group recommendations. IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans, 36(3), 558 – 568.
Bronson, D. (2011). Finding and evaluating evidence: Systematic reviews and evidence based practice. Oxford University Press.
Brown, A., Forde, C., Spencer, D., & Charlwood, A. (2008). Changes in HRM and job satisfaction, 1998-2004: evidence from the Workplace Employment Relations Survey. Human Resouce Management Journal, 18(3), 237-256.
Burhan N. A. S.,  RosliMohamad M.,  Kurniawan Y.,  Sidek A. H. (2014). National intelligence, basic human needs, and their effect on economic growth. Intelligence, 44, 103-111.
Cerchione, E., Esposito, E. (2016). A systematic review of supply chain knowledge management research: State of the art and research opportunities. International Journal of Production Economics, 182, 276–292.
Chen, M.CH., Yang, T., & Li, H.C. (2007). Evaluating the supply chain performance of IT-based inter-enterprise collaboration. Information and Management, 44(6), 524–534.
Danaee Fard, H., Alwani, S. M., & Azar, A. (2007). Quantitative Research in Management: A Comprehensive Approach. Tehran, Saffar Publishing. (in Persian)
Ding, X., Li, Q., Zhang, H., Sheng, Z., & Wang, Z. (2017). Linking transformational leadership and work outcomes in temporary organizations: A social identity approach. International Journal of Project Management, 35(4), 543-556.
Ericksen, P. (2016). Top ten rules for selecting a good manager. Industry Week. Retrieved from: selecting good manager Essays, UK.
Huang, J. J., Tzeng, G. H., and  Ong, C. S. (2005). Multidimensional data in multidimensional scaling  using the analytic network process. Pattern Recognition Letters, 26(6), 755–767.
Imad Shah, S., Shahjehan, A., Afsar, B., Afridi, S. A., Saeed, B.B. (2020). The dynamics of leader technical competence, subordinate learning, and innovative work behaviors in high-tech, knowledge-based industry. Economic Research-Ekonomska Istraživanja, 33(1), 623-638.
Jaswal, J. (2012). The Construction Of employee competency developmental plans a private residential youth care facility. MBA Dissertation, University of Northern British Columbia.
Jesson, J., Matheson, L., & Lacey, F.M. (2011). Doing your literature review: Traditional and systematic techniques. Sage Publication.
 Ling, Z.Zengrui, T.Metawa, N. (2019). Data mining-based competency model of innovation and entrepreneurship. Journal of Intelligent & Fuzzy Systems, 37(1), 35-43.
Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F. (2017). Supply chain performance measurement systems: A systematic review and research agenda. International Journal of Production Economics Elsevier, 183, 299–315.
Maryunani, S. R., Mirzanti, I. R. (2015). The development of entrepreneurship in creative industries with reference to Bandung as a creative city. Social and Behavioral Sciences, 169, 387 – 394.
Mirzaeirabor, F. (2020). Designing and Exploration of Competency Model of Creative Industries Leaders through Media Reputation Approach Case Study: Chief Editors of Broadcating th e News of the Islamic Republic of Iran. PhD Thesis, Faculty of Management, University of Tehran. (in Persian)
Mohaghar, A., Ansari, M., Sadeghi Moghaddam, M.R., Mirkazemi Mood, M. (2018). A Meta Synthesis of the Modeling Methods of Complex Socio-technical Systems with a Multi Paradigm-multi Methodology Approach. Journal of Industrial Management, 10(2), 247-278. (in Persian)
Palomares, I., Estrella, F. J., Martinez, L., Herrera, F. (2014). Consensus under a fuzzy context: Taxonomy, analysis framework AFRYCA and experimental case of study. Information Fusion, 20(1), 252–271.
Purbasari R., Rasmini M. (2019). Entrepreneurial Behavior Model Based on Entrepreneur Competencies Using Generic Entrepreneur Competencies for Fashion Creative Industries in Soreang, West Java, Indonesia (Study on Moslem Clothing Entrepreneurs). Review of Integrative Business and Economics Research, 8(1), 117-125.
Safari, H.Jafarzadeh, A.H.Fathi, M.R. (2020). Evaluation of the branches of Iran Insurance Corporation based on data envelopment analysis-free disposal hull in the presence of weight restrictions, International Journal of Mathematics in Operational Research, 16(2), 202–216.
Safari, H.Razghandi, E.Fathi, M.R.Cruz-Machado, V.Cabrita, M.R. (2020). The effectiveness of quality awards on the company's performance – the case of Iran's national quality awards, Benchmarking, 27(4), pp. 1319–1340.
Salas, E., Prince, C., Baker, D., & Shrestha, L. (1995). Situation awareness in team performance: Implications for measurement and training. Human Factors, 37 (1), 123-136.
Sharifi, S. M., Haj Mohammadi, A., Ansari, N. (2018). Human Resources Management in Creative Industries (First Edition), Tehran, Industrial Management Organization Publishing. (in Persian)
Shum C., Gatling A., Shoemaker S. (2018). A model Of hospitality Leadership Competency for frontline and director level managers: Which competencies matter more? International Journal of Hospitality Management, 74, 57-66.
Simonton, D. (2006). Presidential IQ, openness, intelligent modeling with agent-based fuzzy cognitive map. Political Psychology, 27(4), 511-526.
Spencer, L., & Spencer, P. (2008). Competence at Work models for superior performance. John Wiley & Sons.
Spina  G., Caniato F., Luzzini D., Ronchi S. (2013).  Past, present and future trends of purchasing and supply management: An extensive literature review. Industrial Marketing Management, 42(8), 1202–1212.
Swanson E., Kim S., Lee S., Yang J.,  Lee Y.. (2020). The effect of leader competencies on knowledge sharing and job performance: Social capital theory. Journal of Hospitality and Tourism Management, 42 (March), 88-96.
Tranfield D., Denyer D., Smart P. (2003). Towards a Methodology for Developing Evidence Informed Management Knowledge by Means of Systematic Review. British Journal of Management, 14, 207-222.
Vinodh, S., Ramesh, K., Arun, C. S. (2016). Application of interpretive structural modelling for analysing the factors influencing integrated lean sustainable system. Clean Technologies and Environmental Policy, 18(2), 413-428.
Wang, J., & Zhang, J. (2015). A win-win team formation problem based on the negotiation. Engineering Applications of Artificial Intelligence, 44, 137-152.
Wirda F.HerriElfindriRivai H. A., Herizon. (2019). Competitive Advantage: Mediation Effect Between Entrepreneurial Competency and Business Performance Creative Industries in West Sumatera-Indoneseia. Academy of Entrepreneurship Journal25(1), 1-11.
Xu, J., Wu, Z. (2011). A Discrete Consensus Support Model for Multiple Attribute Grop Decision Making. Knowledge Based Systems, 24 (8), 1196 – 1202.
Xuو Z. (2009). An Automatic Approach to Reaching consensus in multiple attribute group decision making. Computers & Industrial Engineering, 56(4), 1369-1374.
Zhang, L., & Zhang, X. (2013). Multi- objective team formation optimization for new product. Computer & Industrial Engineering, 64(3), 804-811.