Document Type : Original Article


1 Department of Technology Management, Allameh Tabatabai University, Tehran, Iran.

2 Department of Business Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.

4 Department of Industrial Engineering, Faculty of Technical Engineering, University of Tehran, Tehran, Iran.


Purpose: Value creation through technology learning at the corporation level has been remarkable in recent years because three essential reasons for paying attention to wealth-making subjects, development of technological abilities, and creating competitive advantage have been converted to the strategic topics of corporations. This research aims to define and prioritize the factors affecting technology learning levels at the Isfahan state corporation level.
Methodology: The statistical population of 104 corporations active in the industrial context examines the quintet assumptions by accumulating needed data using a questionnaire with standard components and a reliability coefficient of 0.845.
Findings: Regarding the results, it is demonstrated that four studied levels in a structural equations model have indeed explained technological learning levels at the Isfahan state corporations level, and each one has been effective straight on the mentioned process. Accordingly, the straightest effectiveness related to the average level (0.78), basic level (0.67), and global level (0.65) are respectively in the following degrees of importance, and each of these affective factors on these levels has been discussed.
Originality/Value: The study recommends that managers improve the levels of technology learning at the enterprise level and pay attention to the identified factors to understand them.


  • Aliahmadi, A., Sadeghi, M. E., Nozari, H., Jafari-Eskandari, M., & Najafi, S. E. (2015). Studying key factors to creating competitive advantage in science Park. Proceedings of the ninth international conference on management science and engineering management (pp. 977-987). Springer Berlin Heidelberg.
  • Attaran, M. (1989). The automated factory: justification and implementation. Business horizons, 32(3), 80-86.
  • Sarmad, Z. Bazargan, A., & Hejazi, E. (2005). Research methods in behavioral sciences. Agah Publishing. (In Persian).
  • Böhm, H., Goers, S., & Zauner, A. (2019). Estimating future costs of power-to-gas–a component-based approach for technological learning. International journal of hydrogen energy, 44(59), 30789-30805.
  • Bucher, P., Birkenmeier, B., Brodbeck, H., & Esche, J. P. (2003). Management principles for evaluating and introducing disruptive technologies: the case of nanotechnology in Switzerland. R&D management, 33(2), 163-149.
  • Christensen, C. M., & Rosenbloom, R. S. (1995). Explaining the attacker's advantage: technological paradigms, organizational dynamics, and the value network. Research policy, 24(2), 233-257.
  • Churchill Jr, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of marketing research, 16(1), 64-73.
  • Conca, F. J., Llopis, J., & Tarı́, J. J. (2004). Development of a measure to assess quality management in certified firms. European journal of operational research, 156(3), 683-697.
  • Cronbach, L. J. (1951). Coefficient alpha and the internal structure of test. Psychometrica, 16(3), 297-334.
  • Dabnoon, M. (2008). Development of a measurement for technology learning process (TLP)(Doctoral dissertation, Dublin City University).
  • Xu, Z., Fang, C., & Ma, T. (2020). Analysis of China’s olefin industry using a system optimization model considering technological learning and energy consumption reduction. Energy, 191, 116462.
  • Wang, W., Yu, B., Yao, X., Niu, T., & Zhang, C. (2018). Can technological learning significantly reduce industrial air pollutants intensity in China? based on a multi-factor environmental learning curve. Journal of cleaner production, 185, 137-147.
  • Viotti, E. B. (2002). National learning systems: a new approach on technological change in late industrializing economies and evidences from the cases of Brazil and South Korea. Technological forecasting and social change, 69(7), 653-680.
  • Edler, J., Meyer-Krahmer, F., & Reger, G. (2000). Changes in the strategic management of technology: results of a global benchmarking study. R&D management, 32(2), 149-164. DOI: 1111/1467-9310.00247
  • Elyasi, M., Mohammadi, M., Baharloo, M., Khosropour, H., & Taheri, Z. (2021). Developing a knowledge managements framework for identification of success factors in the product acquisition cycles-case of aviation industries organization. International journal of innovation in engineering, 1(1), 76-100.
  • Fallah, M., Sadeghi, M. E., & Nozari, H. (2021). Quantitative analysis of the applied parts of internet of things technology in Iran: an opportunity for economic leapfrogging through technological development. Science and technology policy letters, 11(4), 45-61. (In Persian).
  • Fuglsang, L., & Sundbo, J. (2005). The organizational innovation system: three modes. Journal of change management, 5(3), 329-344.
  • Guo, J., Guo, B., Zhou, J., & Wu, X. (2020). How does the ambidexterity of technological learning routine affect firm innovation performance within industrial clusters? the moderating effects of knowledge attributes. Technological forecasting and social change, 155, 119990.
  • Guridi, J. A., Pertuze, J. A., & Pfotenhauer, S. M. (2020). Natural laboratories as policy instruments for technological learning and institutional capacity building: the case of Chile's astronomy cluster. Research policy, 49(2), 103899.
  • Handayani, K., Krozer, Y., & Filatova, T. (2019). From fossil fuels to renewables: an analysis of long-term scenarios considering technological learning. Energy policy, 127, 134-146.
  • Hooman, H. (2014). Statistical inference in social sciences. Samt Organization. (In Persian).
  • Huang, P., & Bi, K. X. (2012). Research on catch-up mode of low-carbon technology in China. 2012 international conference on management science & engineering 19th annual conference proceedings (pp. 1624-1630). IEEE. DOI: 1109/ICMSE.2012.6414390
  • Hult, G. T. M., & Ferrell, O. C. (1997). A global learning organization structure and market information processing. Journal of business research, 40(2), 155-166.
  • Iansiti, M. (2000). How the incumbent can win: managing technological transitions in the semiconductor industry. Management science, 46(2), 169-185.
  • Jensen, M. B., Johnson, B., Lorenz, E., & Lundvall, B. Å. (2007). Forms of knowledge and modes of innovation. Research policy, 36(5), 680-693.
  • Jöreskog, K. G., & Sörbom, D. (1989). LISREL 7: a guide to the program and applications. Spss.
  • Kocoglu, I., Imamoglu, S. Z., Ince, H., & Keskin, H. (2012). Learning, R&D and manufacturing capabilities as determinants of technological learning: enhancing innovation and firm performance. Procedia-social and behavioral sciences, 58, 842-852.
  • Lee, K., & Lim, C. (2001). Technological regimes, catching-up and leapfrogging: findings from the Korean industries. Research policy, 30(3), 459-483.
  • Lee, J. J., & Yoon, H. (2015). A comparative study of technological learning and organizational capability development in complex products systems: distinctive paths of three latecomers in military aircraft industry. Research policy, 44(7), 1296-1313.
  • Lichtenthaler, E. (2004). Coordination of technology intelligence processes: a study in technology intensive multinationals. Technology analysis & strategic management, 16(2), 197-221.
  • Bell, M. (1984). ‘Learning’and the accumulation of industrial technological capacity in developing countries. In Technological capability in the third world (pp. 187-209). Palgrave Macmillan, London.
  • Marcelle, G. M. (2004). Technological learning: a strategic imperative for firms in the developing world. Edward Elgar Publishing.
  • Mousakhani, M., Saghafi, F., Hasanzade, M., & Sadeghi, M. E. (2020). Presenting a policy framework for high technologies, using identification of factors affecting the development of a technological innovation system with meta-synthesis. Journal of decisions and operations research, 5(1), 13-27. (In Persian). DOI: 22105/dmor.2020.221888.1138
  • Mousakhani, M., Saghafi, F., Hasanzadeh, M., & Sadeghi, M. E. (2020). Proposing dynamic model of functional interactions of iot technological innovation system by using system dynamics and fuzzy DEMATEL. Journal of operational research and its applications, 17(4), 1-21. (In Persian).
  • Nozari, H., Sadeghi, M. E., Eskandari, J., & Ghorbani, E. (2012). Using integrated fuzzy AHP and fuzzy TOPSIS methods to explore the impact of knowledge management tools in staff empowerment (case study in knowledge-based companies located on science and technology parks in Iran). International journal of information, business and management, 4(2), 75-92.
  • Nunnally, J. C. (1978). Psychometric theory (2nd Ed). Mcgraw Hill Book Company.
  • Peterson, R. A. (1994). A meta-analysis of Cronbach's coefficient alpha. Journal of consumer research, 21(2), 381-391.
  • Reger, G. (2001). Technology foresight in companies: from an indicator to a network and process perspective. Technology analysis & strategic management, 13(4), 533-553.
  • Sadeghi, M. E., Sadabadi, A. A., Mirzamohammadi, S., & Mahdavi Mazdeh, M. (2014). Determining the priorities in science parks by using fuzzy dematel case study of Sheikh-Bahai science and technology park. Roshd-e-fanavari, 11(41), 43-51. (In Persian).
  • Sadeghi, M. E., & Sadabadi, A. A. (2015). Evaluating science parks capacity to create competitive advantages: comparison of Pardis technology park and Sheikh Bahaei science and technology park in Iran. International journal of innovation and technology management, 12(6), 1550031. DOI:1142/S0219877015500315
  • Sadeghi, M. E., Nozari, H., Dezfoli, H. K., & Khajezadeh, M. (2021). Ranking of different of investment risk in high-tech projects using TOPSIS method in fuzzy environment based on linguistic variables. Journal of fuzzy extension and applications, 2(3), 226-238.
  • Sarookhani, B. (2003). Research method in social sciences. Research Organization of Human Sciences & Cultural Studies . (In Persian).
  • Shittu, E., Kamdem, B. G., & Weigelt, C. (2019). Heterogeneities in energy technological learning: evidence from the U.S. electricity industry. Energy policy, 132, 1034-1049.
  • Tang, T. (2018). Explaining technological change in the US wind industry: energy policies, technological learning, and collaboration. Energy policy, 120, 197-212.
  • Tidd, J., & Bessant, J. R. (2020). Managing innovation: integrating technological, market and organizational change. John Wiley & Sons.
  • Tschirky, H. P. (1994). The role of technology forecasting and assessment in technology management. R&D management, 24(2), 121-129.
  • Van de ven, A., & Ferry, D. (1979). Measuring and assessing organizations. John Wiley, New York.