163
Views
0
CrossRef citations to date
0
Altmetric
Articles

Predicting research projects’ output using machine learning for tailored projects management

& ORCID Icon

References

  • Abdel-Basset, M., Mohamed, M., & Kumar-Sangaiah, A. (2018). Neutrosophic AHP-Delphi Group decision making model based on trapezoidal neutrosophic numbers. Journal of Ambient Intelligence and Humanized Computing, 9(5), 1427–1443. https://doi.org/10.1007/s12652-017-0548-7
  • Abramo, G., D’Angelo, C. A., & Felici, G. (2019). Predicting publication long-term impact through a combination of early citations and journal impact factor. Journal of Informetrics, 13(1), 32–49. https://doi.org/10.1016/j.joi.2018.11.003
  • Amara, N., Landry, R., & Halilem, N. (2015). What can university administrators do to increase the publication and citation scores of their faculty members? Scientometrics, 103(2), 489–530. https://doi.org/10.1007/s11192-015-1537-2
  • Arik, S., & Pfister, T. (2021). TabNet: Attentive interpretable tabular learning. 35th AAAI conference on artificial intelligence.
  • Bai, X., Xia, F., Lee, I., Zhang, J., & Ning, Z. (2016). Identifying anomalous citations for objective evaluation of scholarly article impact. PLoS One, 11(9), e0162364.
  • Baker, N. R., & Pound, W. H. (1964). R&D project selection: Where we stand. IEEE Transactions on Engineering Management, 11(4), 124–134. https://doi.org/10.1109/TEM.1964.6446420
  • Chen, Z., Gong, C., Lin, L., Xu, S., Zhang, M., & Zhou, X. (2015). Assessing junior faculty research productivity in the IS field: Recommendations for promotion and tenure standards for Asian schools. Communications of the Association for Information Systems, 36(19), 357–336.
  • Costantino, F., Gravio, G. D., & Nonino, F. (2015). Project selection in project portfolio management: An artificial neural network model based on critical success factors. International Journal of Project Management, 33(8), 1744–1754. https://doi.org/10.1016/j.ijproman.2015.07.003
  • Dundar, H., & Lewis, D. R. (1998). Determinants of research productivity in higher education. Research in Higher Education, 39(6), 607–631. https://doi.org/10.1023/A:1018705823763
  • Edquist, C., & Mckelvey, M. (1998). The Swedish paradox: High R&D intensity without high-tech products. In K. Nielsen, & B. Johnson (Eds.), Evolution of institutions, organizations and technology, aldershot (pp. 131–149.
  • Eilat, H., Golany, B., & Shtub, A. (2008). R&D project evaluation: An integrated DEA and balanced scorecard approach. Omega, 36(5), 895–912. https://doi.org/10.1016/j.omega.2006.05.002
  • Elnasri, A., & Fox, K. J. (2015). R&D, innovation and productivity: The role of public support. KDI Journal of Economic Policy, 37(1), 73–96. https://doi.org/10.23895/kdijep.2015.37.1.73
  • Galbraith, C., DeNoble, A., Ehrlich, S., & Kline, D. (2007). Can experts really assess future technology success? A neural network and Bayesian analysis of early stage technology proposals. The Journal of High Technology Management Research, 17(2), 125–137. https://doi.org/10.1016/j.hitech.2006.11.002
  • Ghapanchi, A. H., Tavana, M., Khakbaz, M. H., & Low, G. (2012). A methodology for selecting portfolios of projects with interactions and under uncertainty. International Journal of Project Management, 30(7), 791–803. https://doi.org/10.1016/j.ijproman.2012.01.012
  • Hall, B. H. (2011). Innovation and productivity. National Bureau of Economic Research Working Paper No.17178.
  • Han, C., Thomas, S. R., Yang, M., Leromonachou, P., & Zhang, H. (2017). Evaluating R&D investment efficiency in China’s high-tech industry. The Journal of High Technology Management Research, 28(1), 93–109. https://doi.org/10.1016/j.hitech.2017.04.007
  • Heo, J., Kim, H., Cho, Y., Cho, S., & Cho, S. (2008). Developing bibliometric indicators for analysis & evaluation of national R&D programs. Journal of Korea Technology Innovation Society, 11(3), 376–399.
  • Hsu, Y. L., Lee, C.-H., & Kreng, V. B. (2010). The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection. Expert Systems with Applications, 37(1), 419–425. https://doi.org/10.1016/j.eswa.2009.05.068
  • Hwang, S. (2019). Comparison of US, China, and Japan science and technology policies and R&D trends. A report from the National Research Foundation, Daejeon, Republic of Korea.
  • Jacob, B. A., & Lefgren, L. (2011). The impact of research grant funding on scientific productivity. Journal of Public Economics, 95(9–10), 1168–1177. https://doi.org/10.1016/j.jpubeco.2011.05.005
  • Jang, H. (2019). A decision support framework for robust R&D budget allocation using machine learning and optimization. Decision Support Systems, 121, 1–12. https://doi.org/10.1016/j.dss.2019.03.010
  • Jang, P., Yoo, J., & Oh, S. H. (2020). Predictive analysis of the impact of corporate R&D support using deep learning. Journal of Korea Technology Innovation Society, 23(1), 20–41. https://doi.org/10.35978/jktis.2020.2.23.1.20
  • Jin, X. H., & Zhang, G. (2011). Modeling optimal risk allocation in PPP projects using artificial neural networks. International Journal of Project Management, 29(5), 591–603. https://doi.org/10.1016/j.ijproman.2010.07.011
  • Jung, U., & Seo, D. W. (2010). An ANP approach for R&D project evaluation based on interdependencies between research objectives and evaluation criteria. Decision Support Systems, 49(3), 335–342. https://doi.org/10.1016/j.dss.2010.04.005
  • Karasakal, E., & Aker, P. (2016). A multicriteria sorting approach based on data development analysis for R&D project selection problem. Omega, 73, 79–92. https://doi.org/10.1016/j.omega.2016.12.006
  • Khoshnevis, P., & Teirlinck, P. (2018). Performance evaluation of R&D active firms. Socio-economic Planning Sciences, 61, 16–28. https://doi.org/10.1016/j.seps.2017.01.005
  • LeDell, E., & Poirier, S. (2020). H2o AutoML: Scalable automatic machine learning. 7th ICML Workshop on Automated Machine Learning (AutoML), July 2020.
  • Lee, H., & Yoo, H. (2020). The 2019 performance analysis report of national R&D program in Korea. An Internal Report from Korea Institute of Science & Technology Evaluation and Planning, Jincheon, Republic of Korea.
  • Liu, C. (2011). A study for allocating resources to research and development programs by integrated fuzzy DEA and fuzzy AHP. Scientific Research and Essays, 6(19), 3973–3978. https://doi.org/10.5897/SRE10.838
  • Liu, P., Zhu, B., & Wang, P. (2019). An ANP approach for R&D project evaluation based on interdependencies between research objectives and evaluation criteria. International Journal of Fuzzy Systems, 21(7), 2168–2191. https://doi.org/10.1007/s40815-019-00687-x
  • Ndour, B., Force, J. E., & McLaughlin, W. J. (1992). Using the Delphi method for determining criteria in agroforestry research planning in developing coutries. Agrofrestry Systems, 19(2), 119–129. https://doi.org/10.1007/BF00138502
  • Pathak, S. K., Sharma, V., Chougule, S. S., & Goel, V. (2022). Prioritization of barriers to the development of renewable energy technologies in India using integrated modified Delphi and AHP method. Sustainable Energy Technologies and Assessments, 50, 101818. https://doi.org/10.1016/j.seta.2021.101818
  • Pudovkin, A. I., & Garfield, E. (2004). Rank-Normalized impact factor: A way to compare journal performance across subject categories. Proceedings of the 67th ASIS&T Annual Meeting, 41(1), 507–515.
  • Smarandache, F., Ricardo, J. E., Caballero, G., Yelandi, M., Vazquez, L., & Hernandez, N. B. (2020). Delphi method for evaluating scientific research proposals in a neutrosophic environment. Neutrosophic Sets and Systems, 34(26), 204–213.
  • Song, G., Yoo, H., Kim, K., & Jang, H. (2015). A study on the efficiency analysis of R&D general management cost for domestic R&D agency institutes. Journal of Korean Society for Quality Management, 43(1), 85–101. https://doi.org/10.7469/JKSQM.2015.43.1.085
  • Souder, W. (1972). A scoring methodology for assessing the suitability of management science models. Management Science, 18(10), 526–543. https://doi.org/10.1287/mnsc.18.10.B526
  • Stephan, P. E., & Levin, S. G. (1992). Striking the mother lode: The importance of age, place, and time. Oxford University Press.
  • Talias, M. (2007). Optimal decision indices for R&D project evaluation in the pharmaceutical industry: Pearson index versus Gittins index. European Journal of Operational Research, 177(2), 1105–1112. https://doi.org/10.1016/j.ejor.2006.01.011
  • Tan, B., Anderson, E., Dyer, J., & Parker, G. (2010). Evaluating system dynamics models of risky projects using decision trees: Alternative energy projects as an illustrative example. System Dynamics Review, 26(1), 1–17. https://doi.org/10.1002/sdr.433
  • Um, I., & Lee, J. (2020). The efficiency evaluation and future direction of national innovation system. An Internal Report from Korea Institute of Science & Technology Evaluation and Planning, Jincheon, Republic of Korea.
  • Walworth, T., Yearworth, M., Davis, J., & Davies, P. (2013). Early estimation of project performance: The application of a system dynamics rework model. Proceedings of IEEE International Systems Conference (SysCon) (pp. 123–130).
  • Wang, Y., & Gibson, G. (2010). A study of preproject planning and project success using ANNs and regression models. Automation in Construction, 19(3), 341–346. https://doi.org/10.1016/j.autcon.2009.12.007
  • Wang, Y., Yu, C., & Chan, H. (2012). Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models. International Journal of Project Management, 30(4), 470–478. https://doi.org/10.1016/j.ijproman.2011.09.002
  • Zhang, W. Q., & Xi, Z. L. (2021). Application of Delphi method in screening of indexes for measuring soil pollution value evaluation. Environmental Science and Pollution Research, 28(6), 6561–6571. https://doi.org/10.1007/s11356-020-10919-5
  • Zhou, J., Li, J., Jiao, H., Qui, H., & Liu, Z. (2020). The more funding the better? The moderating role of knowledge stock on the effects of different government-funded research projects on firm innovation in Chinese cultural and creative industries. Technovation, 92, 102059. https://doi.org/10.1016/j.technovation.2018.11.002

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.