Document Type : Research Paper

Authors

Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

Abstract

This study aims to identify and prioritize the effect of technology capability drivers on the supply chain performance of automotive companies. Technology capability indicators are ranked and prioritized using the fuzzy hierarchical analysis technique. The research method is applied in terms of purpose, is described as the data collection method, and is considered quantitative research. After reviewing the theoretical literature of the research, the drivers of technology capability on the organization's performance were identified for prioritization; they were weighed by a number of experts in the field of automotive companies using questionnaires and fuzzy hierarchical analysis. Indicators and sub-indices of variable technology capability were ranked and prioritized. Based on the results of this research model, it was found that of the eight indicators examined, "strategic technology capability", "product technology capability", and "supplier technology" was the most important, and of the 38 technology capability sub-indicators examined, "Technology Development" is the most important.

Keywords

Main Subjects

  • Ajripour, I., Asadpour, M., & Tabatabaie, L. (2019). A model for organization performance management applying MCDM and BSC: a case study. Journal of applied research on industrial engineering, 6(1), 52-70.‏ http://www.journal-aprie.com/article_83034.html
  • Akoğlu, N., Civelek, M. E., & Başaran, Y. (2022). The role of information technology in the effect of innovation capability on logistics service quality. İşletme araştırmaları dergisi, 14(1), 249-265.‏ https://www.ceeol.com/search/article-detail?id=1049764
  • Aliakbari Nouri, F., Khalili Esbouei, S., & Antucheviciene, J. (2015). A hybrid MCDM approach based on fuzzy ANP and fuzzy TOPSIS for technology selection. Informatica, 26(3), 369-388.‏
  • Annarelli, A., Battistella, C., & Nonino, F. (2020). Competitive advantage implication of different product service system business models: consequences of ‘not-replicable’capabilities. Journal of cleaner production, 247, 119121.‏ https://doi.org/10.1016/j.jclepro.2019.119121
  • Azizi, E., Javanshir, H., Jafari, F., & Ebrahimnejad, S. (2021). Designing a sustainable agile retail supply chain using multi-objective optimization methods (case study: SAIPA company). Scientia Iranica, 28(5), 2933-2947. DOI: 24200/sci.2019.53311.3179
  • Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European journal of operational research, 95(3), 649-655.‏
  • CORDIS (2015). Integrated health technology assessment for evaluating complex technologies (ID: 306141). https://cordis.europa.eu/project/id/306141
  • De Guimaraes, J. C. F., Severo, E. A., & de Vasconcelos, C. R. M. (2018). The influence of entrepreneurial, market, knowledge management orientations on cleaner production and the sustainable competitive advantage. Journal of cleaner production, 174, 1653-1663.‏
  • De Mori, C., Batalha, M. O., & Alfranca, O. (2016). A model for measuring technology capability in the agrifood industry companies. British food journal.‏ 188(6), 1422-1461. DOI:1108/BFJ-10-2015-0386
  • Deepu, T. S., & Ravi, V. (2021). Supply chain digitalization: an integrated MCDM approach for inter-organizational information systems selection in an electronic supply chain. International journal of information management data insights, 1(2), 100038.‏ https://doi.org/10.1016/j.jjimei.2021.100038
  • Deng, H. (1999). Multicriteria analysis with fuzzy pairwise comparison. International journal of approximate reasoning, 21(3), 215-231.‏ https://doi: 10.1109/Fuzzy.1999.793038
  • Ferreira, J., Coelho, A., & Moutinho, L. (2020). Dynamic capabilities, creativity and innovation capability and their impact on competitive advantage and firm performance: the moderating role of entrepreneurial orientation. Technovation, 92, 102061.‏ https://doi.org/10.1016/j.technovation.2018.11.004
  • Genus, A., & Iskandarova, M. (2020). Transforming the energy system? Technology and organisational legitimacy and the institutionalisation of community renewable energy. Renewable and sustainable energy reviews, 125, 109795.‏ https://doi.org/10.1016/j.rser.2020.109795
  • Halder, P. K., Karmarker, C. L., Kundu, B., & Daniel, T. (2018). Evaluation of factors affecting the productivity of RMG in Bangladesh: a fuzzy AHP approach. International journal of research in industrial engineering, 7(1), 51-60.‏ DOI: 22105/riej.2017.94987.1011
  • Hernández, C. C. P., Gómez, G. L., & Hernández, D. G. (2017). Evolution of state clusters related with technological capability in Mexico: application of a multivariate statistical analysis of cluster. Contaduría y administración, 62(2), 528-555.‏ https://doi.org/10.1016/j.cya.2017.02.003
  • Huang, C. C., & Huang, S. M. (2020). External and internal capabilities and organizational performance: does intellectual capital matter?. Asia pacific management review, 25(2), 111-120.‏ https://doi.org/10.1016/j.apmrv.2019.12.001
  • Iooss, B., & Lemaître, P. (2015). A review on global sensitivity analysis methods. Uncertainty management in simulation-optimization of complex systems: algorithms and applications, 101-122.‏ https://doi.org/10.1007/978-1-4899-7547-8_5
  • Jajja, M. S. S., Asif, M., Montabon, F., & Chatha, K. A. (2020). The indirect effect of social responsibility standards on organizational performance in apparel supply chains: a developing country perspective. Transportation research part E: logistics and transportation review, 139, 101968.‏ https://doi.org/10.1016/j.tre.2020.101968
  • Kavilal, E. G., Venkatesan, S. P., & Kumar, K. H. (2017). An integrated fuzzy approach for prioritizing supply chain complexity drivers of an Indian mining equipment manufacturer. Resources policy, 51, 204-218. https://doi.org/10.1016/j.resourpol.2016.12.008
  • Kuan, M., & Chen, Y. (2014). A hybrid MCDM framework combined with DEMATEL-based ANP to evaluate enterprise technological innovation capabilities assessment. Decision science letters, 3(4), 491-502.‏
  • Lim, S., & Trimi, S. (2014). Impact of information technology infrastructure flexibility on the competitive advantage of small and medium sized-enterprises. Journal of business & management, 3(1), 1-12.‏
  • Liu, G., Fan, S., Tu, Y., & Wang, G. (2021). Innovative supplier selection from collaboration perspective with a hybrid mcdm model: a case study based on nevs manufacturer. Symmetry, 13(1), 143.‏
  • Mikalef, P., & Pateli, A. (2017). Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: findings from PLS-SEM and fsQCA. Journal of business research, 70, 1-16.‏ https://doi.org/10.1016/j.jbusres.2016.09.004
  • Motaghed, S., Yazdani, A., Nicknam, A., & Khanzadi, M. (2018). Sobol sensitivity generalization for engineering and science applications. Journal of modeling in engineering, 16(54), 217-226. (In Persian). DOI: 22075/jme.2017.12259.1221
  • Najafi, S. E., Amiri, M., & Abdolah Zadeh, V. (2015). Ranking practicable technologies within the science and technology corridor of Isfahan, using MCDM techniques. Journal of applied research on industrial engineering, 2(3), 139-153.‏ http://www.journal-aprie.com/article_42518.html
  • Zhang, Y., Sun, J., Yang, Z., & Wang, Y. (2020). Critical success factors of green innovation: technology, organization and environment readiness. Journal of cleaner production, 264, 121701.‏ https://doi.org/10.1016/j.jclepro.2020.121701
  • Oghojafor, B. E. A.26, Kuye, O. L., Ogunkoya, O. A., & Shobayo, P. B. (2014). Competitive strategies, technological capabilities and organizational performance: evidence from Nigerian manufacturing industry. Arabian journal of business and management review (Nigerian chapter), 2(1), 11-22.‏
  • Pantha, R. P., Islam, M., Akter, N., & Islam, E. (2020). Sustainable supplier selection using integrated data envelopment analysis and differential evolution model. Journal of applied research on industrial engineering, 7(1), 25-35.‏
  • Qin, J., van der Rhee, B., Venkataraman, V., & Ahmadi, T. (2021). The impact of IT infrastructure capability on NPD performance: the roles of market knowledge and innovation process formality. Journal of business research, 133, 252-264.‏ https://doi.org/10.1016/j.jbusres.2021.04.072
  • Radfar, R., & Khamseh, A. (2016). Technilogy management (a comprehensive approach to technology, innovation and commercialization. Scientific and Cultural Publications Company. (In Persian). https://www.gisoom.com
  • Saeedi Mehrabad, M., Aazami, A., & Goli, A. (2017). A location-allocation model in the multi-level supply chain with multi-objective evolutionary approach. Journal of industrial and systems engineering, 10(3), 140-160.‏
  • Sarmad, Z., Bazargan, A., & Hejazi, A. (2022). Research methods in behavioral sciences. Agah Publishing. (In Persian). https://www.adinehbook.com/gp/product/9643290514
  • Shahzad, F., Du, J., Khan, I., Shahbaz, M., Murad, M., & Khan, M. A. S. (2020). Untangling the influence of organizational compatibility on green supply chain management efforts to boost organizational performance through information technology capabilities. Journal of cleaner production, 266, 122029.‏ https://doi.org/10.1016/j.jclepro.2020.122029
  • Srivastava, M. K., Gnyawali, D. R., & Hatfield, D. E. (2015). Behavioral implications of absorptive capacity: the role of technological effort and technological capability in leveraging alliance network technological resources. Technological forecasting and social change, 92, 346-358.‏ https://doi.org/10.1016/j.techfore.2015.01.010
  • Tripathy, S., Aich, S., Chakraborty, A., & Lee, G. M. (2016). Information technology is an enabling factor affecting supply chain performance in Indian SMEs: a structural equation modelling approach. Journal of modelling in management, 11(1), 269-287.‏
  • Tseng, S. T., & Levy, P. E. (2019). A multilevel leadership process framework of performance management. Human resource management review, 29(4), 100668.‏ https://doi.org/10.1016/j.hrmr.2018.10.001
  • Wu, Y., Gu, F., Ji, Y., Guo, J., & Fan, Y. (2020). Technological capability, eco-innovation performance, and cooperative R&D strategy in new energy vehicle industry: evidence from listed companies in China. Journal of cleaner production, 261, 121157.‏ https://doi.org/10.1016/j.jclepro.2020.121157