@article{82297775338b4205b6e7e628880c2819,
title = "Evolutionary learning methodology: a case study of R&D strategy development",
abstract = "This article concerns the notion of methodology in strategic management of R&D/technology. Though development of new tools and methods has received much attention during the recent decades, attention to understanding methodologies has remained disproportionally low. In this study we distinguish two methodologies that are used in strategic management of R&D/technology: planning methodology and evolutionary learning methodology. We mainly focus on defining and describing the origins, nature, and characteristics of the latter. We propose a framework for methodology selection by investigating context, content, and process factors. Using this framework, we provide supportive evidence for appropriateness of evolutionary learning methodology to develop a robust R&D strategy for Iran's power industry. We then describe the details of operationalizing the methodology for the Iranian power industry. This study is particularly focused on delineating how evolutionary learning methodology can be applied as an effective framework to improve the formation method and content of R&D strategy. We conclude that methodological knowledge can provide a powerful lens with which to understand performance of methods, and we suggest that evolutionary learning methodology is particularly appropriate for the following situations: when the environment is uncertain or fast changing, when there exist many stakeholders with conflicting interests, and when a method needs to be applied in a context other than the one for which it was initially developed.",
keywords = "Evolutionary epistemology, Expert judgment, Learning, Methodology, R&D strategy, Roadmapping",
author = "Soheil Hooshangi and Arasti, {Mohammad R.} and Hounshell, {David A.} and Sarah Sahebzamani",
note = "Funding Information: Iran's power industry consists of both governmental and private sectors. By the end of 2011, 79% of total installed electricity generation capacity of the country was government-owned. A privatization plan is ongoing in the industry, but the pace of its advancement drastically differs across segments. The transmission segment has remained completely government-owned. In the distribution segment, many operations have been contracted out regionally, but its management and a minor share of operations have remained governmental. Government-owned corporations used to have a dominant role in production and procurement of equipment and other goods and services, but this has changed radically during the last two decades. Currently almost all production, procurement, and professional service activities are performed by private corporations. Management of governmental operations and development of industry-wide policies are done mainly by Iran Generation, Transmission, and Distribution Management Corporation (TAVANIR), which is a subdivision of Iran's Ministry of Energy. TAVANIR administers 16 Regional Electric Companies (REC) that together cover all parts of the country. Each REC manages all governmental operations in its assigned geographical area. R&D activities of the industry have also been divided between governmental and private sectors. Niroo Research Institute (NRI) is by far the largest research institution of the industry and is government-owned. Niroo Technology Center (MATN) and Abbaspour University are two other research intitutions. The former has been privatized recently, and the latter is a state university established and supported by the Ministry of Energy. In addition, TAVANIR and each REC have their own R&D departments, which usually perform research projects related to regional or organizational operations [47] . In our case study, NRI is employed by TAVANIR (as the project employer) to develop the R&D strategy of Iran's power industry. Funding Information: It was not possible for the authors to conduct this study in general, and the case study in particular, if not for the unsparing support of the managers, help from researchers, and contribution of the engineers from Niroo Research Institute. Among them the authors would like to specifically thank Neda Mandegaran, Naser Bagheri Moghaddam, Maliheh Khanjari, Farrokh Amini, Alireza Mehri, Mohammad Jafari Anari, Masoud Narenji, Maryam Mohammadi, and Behshad Azodi. In addition, the lead author was supported by Funda{\c c}{\~a}o para a Ci{\^e}ncia e a Tecnologia (Portuguese Foundation for Science and Technology) through the Carnegie Mellon Portugal Program under Grant SFRH/BD/51159/2010 to prepare, revise, and finalize this article. Copyright: Copyright 2013 Elsevier B.V., All rights reserved.",
year = "2013",
month = jun,
doi = "10.1016/j.techfore.2012.08.017",
language = "English",
volume = "80",
pages = "956--976",
journal = "Technological Forecasting and Social Change",
issn = "0040-1625",
publisher = "Elsevier Inc.",
number = "5",
}