Quarterly Publication

A Technological Learning Model in Joint R&D Projects in Petroleum Industries

Document Type : Original Article

Authors

1 Ph.D. Candidate, Faculty of Management and Accounting, Allameh Tabataba`i University, Tehran, Iran

2 Associate Professor Management of Technology and Entrepreneurship Department Allameh Tabataba`i University, Tehran, Iran

3 Associate Professor, Faculty of Management and Accounting, Allameh Tabataba`i University, Tehran, Iran

4 Professor ,Faculty of Chemical Engineering, Tarbiat Modarres University, Tehran, Iran

Abstract
Technological learning and the drive to self-sufficiency in different industries emphasize the role of companies in the knowledge acquisition from external sources. Iran's petroleum industry is also a suitable case to study in this area, given the large firms on the one hand and the long-term historical partnerships with foreign companies on the other. Some of the industry's achievements, such as sustainability under sanctions, the country's largest source of export and some recent breakthroughs, particularly in the registration of international patents and localization of various technologies, show the success of learning efforts. This study, which examines the learning processes to joint R&D (JRD) projects in the petroleum industry, analyzes the path of technological learning in this industry using a mixed method approach and multi case study method. For this purpose, four successful JRD projects in technological learning in upstream and downstream are selected and 16 interviews are conducted with project managers and experts of selected projects and using the JRD's life cycle to present a technological learning model in JRDs. The results of theme analysis of interviews show that the most important and effective component of the model is "effective factors". The most affected component is "types of learning". Also, the most effective factors and the most effective learning mechanism are "absorption capability", "cultural homogeneity" and "learning by interacting" Respectively.

Keywords

Subjects

Arce, M. E., Saavedra, Á., M, J. L., and Granada, E. The use of grey-based methods in multi-criteria 
decision analysis to evaluate sustainable energy systems: a review. Renewable and Sustainable 
Energy Reviews, Vol. 47, P. 924–932, 2015.
Arranz, N., Arroyabe, M. F., and Fdez. De Arroyabe, J. C. The architecture of R&D joint projects: the 
social network analysis approach. Technology Analysis and Strategic Management, Vol. 31, No. 
8, P. 902–914. https://doi.org/10.1080/09537325.2019.1573982, 2019.
Arranz, N., Arroyabe, M. F., and Fernandez de Arroyabe, J. C. Network Embeddedness in Exploration 
and Exploitation of Joint R&D Projects: A Structural Approach. British Journal of Management, 
Vol.31, No. 2, P. 421–437. https://doi.org/10.1111/1467-8551.12338, 2020.
Arranz, N., and Fdez De Arroyabe, J. C. Joint R&D projects: Experiences in the context of European
technology policy. IEEE International Engineering Management Conference, II, P. 680–684. 
https://doi.org/10.1109/IEMC.2005.1559235, 2005.
Bäck, I., and Kohtamäki, M. Joint Learning in Innovative R&D Collaboration. Industry and Innovation, 
Vol. 23, No. 1, P. 62–86. https://doi.org/10.1080/13662716.2015.1123613, 2016.
Becker, W., and Dietz, J. R&D cooperation and innovation activities of firms - Evidence for the German 
manufacturing industry. Research Policy, Vol. 33, No. 2, P. 209–223, 
https://doi.org/10.1016/j.respol.2003.07.003, 2004.
Bell, M. Time and technological learning in industrializing countries: how long does it take? How fast 
is it moving (if at all)? International Journal of Technology Management, Vol. 36, No. 1–3, P. 
25–39, 2006.
Boddy, C. R. Sample size for qualitative research. Qualitative Market Research: An International 
Journal, 2016.
Corsaro, D., Cantù, C., and Tunisini, A. Actors’ Heterogeneity in Innovation Networks. Industrial 
Marketing Management, Vol. 41, No. 5, P. 780–789, 
https://doi.org/10.1016/j.indmarman.2012.06.005, 2012.
Creswell, john w., and Clark, V. L. P. Designing and Conducting Mixed Methods Research (3rd ed.). 
SAGE Publications Sage CA: Los Angeles, CA, 2017.
Duso, T., and Röller, L. H. Collusion through Joint R&D: An Empirical Assessment. Tinbergen 
Institute Discussion, Vol. 96(May), P. 349–370, 
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1706161%5Cnpapers2://publication/uuid/4F
C11E73-435F-4183-999E-32129A8F1B55, 2010.
Dworkin, S. L. Sample size policy for qualitative studies using in-depth interviews. In Archives of 
sexual behavior, Vol. 41, No. 6, P. 1319–1320, Springer, 2012.
16 Petroleum Business Review, Vol. 7 (2023), No. 3
Faccin, K., Balestrin, A., and Bortolaso, I. The joint R&D project: The case of the first Brazilian 
microcontroller chip. Revista de Administração, Vol. 51, No. 1, P. 087–102. 
https://doi.org/10.5700/rausp1225, 2016.
Fang, S. R., Fang, S. C., Chou, C. H., Yang, S. M., and Tsai, F. S. Relationship learning and innovation: 
The role of relationship-specific memory. Industrial Marketing Management, Vol. 40, No. 5, 
P.743–753. https://doi.org/10.1016/j.indmarman.2011.02.005, 2011.
Figueiredo, P. N. The Role of Dual Embeddedness in the Innovative Performance of MNE Subsidiaries: 
Evidence from Brazil. Journal of Management Studies, Vol. 48, No. 2, P. 417–440, 
https://doi.org/10.1111/j.1467-6486.2010.00965.x, 2011.
Figueiredo, P. N., and Piana, J. Innovative capability building and learning linkages in knowledgeintensive service SMEs in Brazil’s mining industry. Resources Policy, Vol. 58, P. 21–33, 2018.
Fusch, P. I., and Ness, L. R. Are we there yet? Data saturation in qualitative research. The Qualitative 
Report, Vol. 20, No. 9, P. 1408, 2015.
Gaugler K, and Siebert R. Market power versus efficiency effects of mergers and research joint venture
EBSCOhost. The Review of Economics and Statistics, 89(November), P.645–659.
http://web.a.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=1&sid=98f77ed7-4731-4c14-929ac8e7ac6f4fa1%40sessionmgr4007, 2007.
Ghazinoory, S., and Mohajery, A. Technological Learning and Its Promotion Policies. Science and 
Technology Policy, Vol. 11, No. 2, P. 439–454, 2019.
Hagedoorn, J. The Rationale of Strategic Understanding Partnering: Technology Modes of Cooperation 
and Sectoral. Management, Vol. 14, No. 5, P. 371–385, 1993.
Hitchcock, J. H., and Onwuegbuzie, A. J. The Routledge Handbook for Advancing Integration in Mixed 
Methods Research. Taylor and Francis, 2022.
Huikkola, T., Ylimäki, J., and Kohtamäki, M. Joint learning in R&D collaborations and the facilitating 
relational practices. Industrial Marketing Management, Vol. 42, No. 7, P. 1167–1180. 
https://doi.org/10.1016/j.indmarman.2013.07.002, 2013.
Ignatius, J., Leen, J. Y. A., Ramayah, T., Hin, C. K., and Jantan, M. The impact of technological learning 
on NPD outcomes: The moderating effect of project complexity. Technovation, Vol. 32, No. 7–
8, 452–463, 2012.
Jaoua, O., and others. The impact of knowledge dissemination practices on innovativeness in SME 
technology and engineering consultancies, 2017.
Jeon, S., Min, D., Kim, S., and Sohn, K. Joint learning of semantic alignment and object landmark 
detection. Proceedings of the IEEE/CVF International Conference on Computer Vision, P. 7294–
7303, 2019.
Johnson, J. L., Sohi, R. S., and Grewal, R. The Role of Relational Knowledge Stores in Interfirm 
Partnering. Journal of Marketing, Vol. 68, No. 3, P. 21–36. 
https://doi.org/10.1509/jmkg.68.3.21.34765, 2004.
Kahouli-Brahmi, S. Technological learning in energy--environment--economy modeling: A survey. 
Energy Policy, Vol. 36, No. 1, P. 138–162, 2008.
Katila, R., Rosenberger, J. D., and Eisenhardt, K. M. Swimming with Sharks: Technology Ventures, 
Administrative Science Quarterly, Vol. 53, No. 2, P. 295–332, 2008.
Ayoubi, M. et al. / A Technological Learning Model in … 17
Kazimieras Zavadskas, E., Antucheviciene, J., Adeli, H., and Turskis, Z. Hybrid multiple criteria 
decision-making methods: A review of applications in engineering. Scientia Iranica, Vol. 23, No. 
1, P. 1–20, 2016
Kim, D., Chiou, J. S., Calantone, R., and others. Strategic orientations, joint learning, and innovation 
generation in international customer-supplier relationships. International Business Review, Vol. 
27, No. 4, P. 838–851, 2018.
Kohtamäki, M., Vesalainen, J., Henneberg, S., Naudé, P., and Ventresca, M. J. Enabling relationship 
structures and relationship performance improvement: The moderating role of relational capital. 
Industrial Marketing Management, Vol. 41, No. 8, P. 1298–1309, 
https://doi.org/10.1016/j.indmarman.2012.08.001, 2012.
Lee, T. J. Technological learning by national R&D: the case of Korea in CANDUtype nuclear fuel. 
Technovation, Vol. 24, No. 4, P. 287–297, 2004.
Li, B., and Zhu, X. Grey relational decision-making model of three-parameter interval grey number 
based on AHP and DEA. Grey Systems: Theory and Application, 2019.
Lin, B. Technology Transfer as Technological Learning: A Source of Competitive Advantage for Firms 
Technology transfer as technological learning: a source of competitive advantage for firms with 
limited R&D resources. December, P. 327–341. https://doi.org/10.1111/1467-9310.00301, 2014.
Lin, C., Wu, Y. J., Chang, C., Wang, W., and Lee, C. Y. The alliance innovation performance of R&D 
alliances - The absorptive capacity perspective. Technovation, Vol. 32, No. 5, P. 282–292. 
https://doi.org/10.1016/j.technovation.2012.01.004, 2012.
Liu, D., Bao, Y., and Wang, G. Unpacking the relationship between formal contracts and alliance 
innovation performance: the role of relationship learning and guanxi. Journal of Business and 
Industrial Marketing, 2021.
Lundvall, B.-Å. The learning economy and the economics of hope. Anthem Press.
Malerba, F. (1992). Learning by firms and incremental technical change. The Economic Journal, Vol. 
102, No. 413, P. 845–859, 2016.
March. march1991.pdf. Organization Science, Vol. 2, P. 71–87, 1991.
Mubarik, M. S., Kazmi, S. H. A., and Zaman, S. I. Application of gray DEMATEL-ANP in greenstrategic sourcing. Technology in Society, Vol. 64, p. 101524, 2021.
Nielsen, J. A., Mathiassen, L., and Hansen, A. M. Exploration and exploitation in organizational 
learning: a critical application of the 4I model. British Journal of Management, Vol. 29, No. 4, P. 
835–850, 2018.
Pandey, N., de Coninck, H., and Sagar, A. D. Beyond technology transfer: Innovation cooperation to 
advance sustainable development in developing countries. Wiley Interdisciplinary Reviews: 
Energy and Environment, Vol. 11, No. 2, p. e422, 2022.
Peng, X., Zheng, S., Collinson, S., Wu, X., and Wu, D. Sustained upgrading of technological capability 
through ambidextrous learning for latecomer firms. Asian Journal of Technology Innovation, Vol. 
30, No. 1, P. 1–22, 2022.
Reilly, M., and Sharkey Scott, P. Subsidiary driven innovation within shifting MNC structures: 
Identifying new challenges and research directions. Technovation, Vol. 34, No. 3, P. 190–202. 
https://doi.org/10.1016/j.technovation.2013.11.004, 2014.
18 Petroleum Business Review, Vol. 7 (2023), No. 3
Robertsonm, T. S., and Gatignon, H. (2016). Technology Development Mode : A Transaction Cost 
Conceptualization Author ( s ): Thomas S . Robertson and Hubert Gatignon Published by : Wiley 
Stable URL : http://www.jstor.org/stable/3094044 References Linked references are available on 
JSTOR for this ar. 19(6), 515–531.
Saad, M. (2000). Development through technology transfer: creating new organizational and cultural 
understanding. Intellect Books.
Saaty, T. L. (2004). Decision making—the analytic hierarchy and network processes (AHP/ANP). 
Journal of Systems Science and Systems Engineering, 13(1), 1–35.
Saenz, J., and Pérez-Bouvier, A. (2014). Interaction with external agents, innovation networks, and 
innovation capability: The case of Uruguayan software firms. Journal of Knowledge 
Management, 18(2), 447–468. https://doi.org/10.1108/JKM-04-2013-0150
Saldaña, J. (2021). The coding manual for qualitative researchers. Sage.
Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., and Jinks, C. 
(2018). Saturation in qualitative research: exploring its conceptualization and operationalization. 
Quality and Quantity, 52(4), 1893–1907.
Selnes, F., and Sallis, J. (2003). Promoting Relationship Learning. Journal of Marketing, 67(3), 80–95. 
https://doi.org/10.1509/jmkg.67.3.80.18656
Shih, C., Hsu, Y., Yeh, J., and Lee, P. Grey number prediction using the grey modification model with 
progression technique. Applied Mathematical Modelling, Vol. 35, No. 3, P. 1314–1321, 
https://doi.org/10.1016/j.apm.2010.09.008, 2011.
Si, S., You, X., Liu, H., and Zhang, P. (2018). DEMATEL Technique : A Systematic Review of the 
State-of-the-art Literature on Methodologies and Applications. Mathematical Problems in 
Engineering, 2018(1).
Spanos, Y. E., Vonortas, N. S., and Voudouris, I. Antecedents of innovation impacts in publicly funded 
collaborative R&D projects. Technovation, Vol. 36, P. 53–64. 
https://doi.org/10.1016/j.technovation.2014.07.010, 2015.
Tahmasebi, S., Fartookzadeh, H., Bushehri, A., Tabaian, K., and Khelejani, J. G. The Stages of 
Formation and Development of Technological Capabilities; Case Study: A Marine Industry 
Organization. Journal Of, Vol. 8, No. 4, 2017.
Tang, T. Explaining technological change in the US wind industry: Energy policies, technological 
learning, and collaboration. Energy Policy, Vol. 120, P. 197–212, 2018.
Von Hippel, E., and Tyre, M. J. How learning by doing is done: problem identification in novel process 
equipment. Research Policy, Vol. 24, No. 1, P. 1–12, 1995.
Wagner, S. M., and Hoegl, M. Involving suppliers in product development: Insights from R&D 
directors and project managers. Industrial Marketing Management, Vol. 35, No. 8, P. 936–943. 
https://doi.org/10.1016/j.indmarman.2005.10.009, 2006.
Weick, K. E., Sutcliffe, K. M., and Obstfeld, D. Organizing and the Process of Sensemaking. 
Organization Science, Vol. 16, No. 4, P. 409–421. https://doi.org/10.1287/orsc.1050.0133, 2005.
Yazdi, M., Khan, F., Abbassi, R., and Rusli, R. Improved DEMATEL methodology for effective safety 
management decision-making. Safety Science, Vol. 127, p. 104705, 2020.
Ayoubi, M. et al. / A Technological Learning Model in … 19
Zadykowicz, A., Chmielewski, K. J., and Siemieniako, D. Proactive customer orientation and joint 
learning capabilities in collaborative machine to machine innovation technology development: 
the case study of automotive equipment manufacturer. Oeconomia Copernicana, Vol. 11, No. 3, 
P. 531–547, 2020.
Zhang, D., Han, J., Yang, L., and Xu, D. SPFTN: A joint learning framework for localizing and 
segmenting objects in weakly labeled videos. IEEE Transactions on Pattern Analysis and Machine 
Intelligence, Vol. 42, No. 2, P. 475–489, 2018.

  • Receive Date 26 June 2022
  • Revise Date 12 October 2022
  • Accept Date 20 October 2022