Identifying and prioritizing the components of Data driven R&D Management in artificial Intelligence companies

Document Type : Research Article (Original Article)


1 PhD candidate of Technology Management, Faculty of Management and Economics, Science and Research Unit, Islamic Azad University, Tehran, Iran

2 Professor, Faculty of Management and Economics, Islamic Azad University Science and Research Branch, Tehran, Iran

3 Associate Professor, Department of Industrial Management, Karaj Branch, Islamic Azad University, Karaj, Iran


Artificial Intelligence is an emerging technology that simulate human intelligence in machines and systems for their application business. Development of artificial intelligence requires extensive R&D activities. Management of R&D in field of AI needs to deploy novel knowledge to formulate and implementation of strategy, assign resources, organize and use of special tools. This paper aims, to identify and prioritize the components of data driven management of R&D in artificial intelligence technology. A hybrid technique was employed to perform the research. In Qualitative part, the literature of topic is reviewed, and 12 experts are interreviewed. Their opinions are analyzed based on grounded theory and 8 axial components were identified. In Quantitative part, by a questionnaire, the opinions of 85 experts of R&D and artificial intelligence were gathered through a questionaire and analyized based on structural equations model. The relvance and validity of the components were confirmed. The found components were weighted and proiritized through SWARA method as: systematic management, resources supplying, capability of big data analytics, supportive policies, infrastructures, data science development, organizational factors and business advantages.


اصغری، مریم؛ خمسه، عباس و پیله‌وری، نازنین؛ مدل ارتقای توانایی‌های تحقیق و توسعه با رویکرد کیفی در صنایع ساخت تجهیزات نیروگاهی و تامین انرژی؛ فصلنامه مدیریت نوآوری در سازمان‌های دفاعی، 1399
علیزاده، پریسا؛ منطقی، منوچهر؛ سیاست‌های حمایت از تحقیق و توسعه در بخش کسب و کار، فصلنامه سیاست علم و فناوری، 1398
علیزاده، سوده؛ نوربخش، سید کامران؛ قاسمی، بهروز؛ طراحی مدل عوامل موثر بر استراتژی های تحقیق وتوسعه در شرکت‌های خودرویی با تاکید بر رویکرد ساختاری –تفسیری (ISM)، فصلنامه بهبود مدیریت، 1401
میرزازاده، ابوالفضل؛ زراعتکار، محمد؛ ارائه مدلی برای فاکتورهای کلیدی موفقیت در فرآیندهای طراحی و توسعه محصول جدید صنعت خودرو با رویکرد DFX، فصلنامه توسعه تکنولوژی صنعتی، 1401
خمسه، عباس و عصاری، محمد حسن، مدیریت تحقیق و توسعه، انتشارات سرافراز، کرج، 1398
ساروخانی، باقر. روش‌های تحقیق در علوم اجتماعی، پژوهشگاه علوم انسانی و مطالعات فرهنگی، تهران 1382
Amsden Alice H., F. Ted Tschang (2003). A new approach to assessing the technological complexity of different categories of R&D (with examples from Singapore), Research policy.
Agrafioti, Foteini (2018), How to Set Up an AI R&D Lab, RBC.
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., GilLopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82-115.
Barr, Arron and Feigenbaum, Edward A. (1981), the Handbook of Artificial Intelligence, Volume 1, DOI:
Baird, A., & Maruping, L. M. (2021). The Next Generation of Research on IS Use: A Theoretical Framework of Delegation to and from Agentic IS Artifacts. MIS Quarterly, 45(1), 315-341. 10.25300/MISQ/2021/15882
Berkhout, A. J.; Hartmann, Dap; Van Der Duin, Patrick; Ortt, Roland (2006): Innovating the innovation process. In International Journal of Technology Management 34 (3-4), pp. 390–404. DOI: 10.1504/IJTM.2006.009466
Blackburn Michael, Alexander Jeffrey, J. Legan David & Klabjan Diego (2017) Big Data and the Future of R&D Management, Research-Technology Management, 60:5, 43-51, DOI: 10.1080/08956308.2017.1348135
Botha, A. (2016), Future Thinking in R&D Management, R&D Management Conference 2016 “From Science to Society: Innovation and Value Creation” 3-6 July 2016, Cambridge, UK
Berente N., Gu B, Recker J., Santanam R. (2021). Managing Artificial Intelligence, Journal of MIS quarterly. Vol 45, No 3, 2021, doi: 10.25300/MISQ/2021/16274 
Brynjolfsson, E., & Mitchell, T. (2017). What Can Machine Learning Do? Workforce Implications. Science, 358(6370), 1530-1534.
Bughin, Jacques, Hazan, Eric, Ramaswamy, Sree, Chui, Michael, Allas, Tera, Dahlström, Peter, Henke, Nicholaus, and Trench, Monica (2017), “Artificial Intelligence: The Next Digital Frontier?” (McKinsey Global Institute, June 2017).
Bullinaria, John A. (2005). The Roots, Goals and Sub-fields of AI, School of Computer Science,
University of Birmingham
Castelvecchi, D. (2016). Can we open the black box of AI? Nature, 538(7623), 20-23.
Chen, H., Chiang, R., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impacts. MIS Quarterly, 36(4), 1165-1188.
Chiesa, V. (2001) R&D strategy and Organization, London (UK), Imperial college press.
David Silver, Huang Aja, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529 (2016): 484-89.
Eggers, W.  Mendelson, T. Chew, B.  Kishnani, P. K. K. (2021). Crafting an AI strategy for government leaders, Deloitte insight
Glikson, E., & Woolley, A. W. (2020). Human Trust in Artificial Intelligence: Review of Empirical Research. Academy of Management Annals, 14(2), 627-660.
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G.-Z. (2019). XAI—Explainable Artificial Intelligence. Science Robotics, 4(37), eaay7120.
Heston, Roxanne and Zwetsloot, Remco (2021), Mapping U.S. Multinationals’ Global AI R&D Activity, CEST.
Howe, B. 2015. A confluence of big data skills in academic and industry R&D. Presentation given at the IRI Annual Meeting, Seattle, Washington, April. Available on Slideshare as “Big Data Talent in Industry and R&D,” http://
IBM Research | Tokyo, (2020). What is next in AI? IBM.
Kensen, Alex K.; Pretorius, Jan-Harm; Petorius, Leon (2014): Towards the sixth generation of R&D management: an exploratory study. In IAMOT (Ed.): Proceedings of the International Conference for the International Association of Management of Technology. Washington, May 22st to 26st.
Kelnar, David (2016), The fourth industrial revolution: a primer on Artificial Intelligence (AI),
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at Work: The New Contested Terrain of Control. Academy of Management Annals, 14(1), 366-410.
Kitchin, R. (2014). The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences. Sage
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436.
Lyytinen, K., Nickerson, J. V., & King, J. L. (2021). Metahuman Systems = Humans + Machines That Learn. Journal of Information Technology, forthcoming.
Martin, K. (2019b). Ethical Implications and Accountability of Algorithms. Journal of Business Ethics, 160(4), 835-850.
Metcalf, L., Askay, D. A., & Rosenberg, L. B. (2019). Keeping Humans in the Loop: Pooling Knowledge through Artificial Swarm Intelligence to Improve Business Decision Making. California Management Review, 61(4), 84-109.
Mowery, David C. (2009): Plus ca change: Industrial R&D in the "third industrial revolution". In Industrial and Corporate Change 18 (1), pp. 1–50.
Nobelios D. (2003). Toward the six generation of R&D management, Journal of Project Management
NSTC (2016), Preparing for the Future of Artificial Intelligence. National science and Technology Council.
OECD (2013): supporting investment in knowledge capital growth and innovation, OECD publishing,
OECD (2015): Frascati manual 2015. Guidelines for collecting and reporting data on research and experimental development. Paris: OECD (The measurement of scientific, technological and innovation activities).
Otto, Boris; Jürjens, Jan; Schon, Jochen; Auer, Sören; Menz, Nadja; Wenzel, Sven; Cirullies, Jan (2016): Industrial Data Space. Digitale Souveränität über Daten. With assistance of Jan Cirullies. Edited by Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. München. Available online at
President’s Council of Advisors on Science and Technology (2020), Recommendations for Strengthening American Leadership in Industries of the Future (Washington, DC: Office of Science and Technology Policy) /media/_/pdf/about/pcast/202006/PCAST_June_2020_Report.pdf?la=en&hash=019 A4F17C79FDEE5005C51D3D6CAC81FB31E3ABC
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., Jennings, N. R., Kamar, E., Kloumann, I. M., Larochelle, H., Lazer, D., McElreath, R., Mislove, A., Parkes, D. C., Pentland, A., Roberts, M. E., Shariff, A., Tenenbaum, J. B., & Wellman, M. P. (2019). Machine Behaviour. Nature, 568, 477486.
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational Decision-Making Structures in the Age of Artificial Intelligence. California Management Review, 61(4), 66-83.
Shead (2020), “Facebook Plans To Double Size of AI Research”, Forbes
Stanford University “One Hundred Year Study on Artificial Intelligence (AI100),”, accessed August 1, 2016,
Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press
Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159–205
UNESCO. (1982). Guide for Collecting Statistics Relating to Science and Technology Activities. Report No.  [5] For Collecting and Reporting Data on Research and Experimental Development.
Van Duin, Stefan and Bakhshi, Naser (2018), Artificial Intelligence, Deloitte
Verstehen, W; Gestalten, Z (2018); Impulse für die Zukunft der Innovation. Fraunhofer-Verbund Innovations forschung (Ed.); Available online at:
Wetzels, M., Odekerken-Schorder, G., & Van Oppen, C. (2009) Using PLS Path Modeling for Assessing Hierarchical Construct Models: Guidelines and Empirical Illustration, MIS Quarterly, 33, (33), 177-1
Wohlfart, L.; Moll, K.; Wilke, J. (2011): Karriere- und Anreizsysteme für die Forschung und Entwicklung. Aktuelle Erkenntnisse und zukunftsweisende Konzepte aus Wissenschaft und betrieblicher Praxis. Stuttgart: Fraunhofer-Verl
Wu, L., & Lou, B. (2021). AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-scale Examination of Bio-pharma Firms. MIS Quarterly, 45, forthcoming.
Yagnik, Jay, (2019). Google Research India: an AI lab in Bangalore,
Yang, Elvina, “Microsoft R&D Center in Taiwan Starts Recruiting for AI Research.”