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. https://doi.org/10.1016/j.inffus.2019.12.012
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. https://doi.org/ 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
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
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G.-Z. (2019). XAI—Explainable Artificial Intelligence. Science Robotics, 4(37), eaay7120.
https://doi.org/10.1126/scirobotics.aay7120
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://
www.slideshare.net/billhoweuw/iri-meeting
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.
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. https://doi.org/10.5465/annals.2018.0174
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. https://doi.org/10.1177/0268396220915917
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. https://doi.org/10.1177/0008125619862256
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 (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 http://www.industrialdataspace.org/publications/ids-whitepaper/
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) https://science.osti.gov/- /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. https://doi.org/10.1038/s41586-019-1138-y
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. https://doi.org/10.1177/0008125619862257
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,
https://ai100.stanford.edu.
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
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.