مراکز تحقیقاتی مشارکتی صنعت و دانشگاه
ترجمه نشده

مراکز تحقیقاتی مشارکتی صنعت و دانشگاه

عنوان فارسی مقاله: ارزیابی مراکز تحقیقاتی مشارکتی صنعت و دانشگاه
عنوان انگلیسی مقاله: Evaluating university industry collaborative research centers
مجله/کنفرانس: پیش بینی فناورانه و تغییرات اجتماعی – Technological Forecasting and Social Change
رشته های تحصیلی مرتبط: مدیریت
گرایش های تحصیلی مرتبط: سیاست های تحقیق و توسعه، مدیریت دانش، مدیریت صنعتی
کلمات کلیدی فارسی: مدیریت تحقیق و توسعه، مدلسازی تصمیم سلسله مراتبی، مشارکت صنعت و دانشگاه، بنیاد ملی علوم
کلمات کلیدی انگلیسی: Research and Development Management، Hierarchical Decision Modeling، Industry University Collaboration، National Science Foundation
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.techfore.2019.05.014
دانشگاه: University of Colorado, Boulder, CO, USA
صفحات مقاله انگلیسی: 22
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.8552 در سال 2018
شاخص H_index: 93 در سال 2019
شاخص SJR: 1.422 در سال 2018
شناسه ISSN: 0040-1625
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: دارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E13321
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1. Introduction

2. Literature review

3. Methodology

4. Model development

5. Case study application

6. Conclusions

Acknowledgements

Appendix 1. Expert background

Appendix 2. Desirability curves

Appendix 3. Additional center analyses

References

بخشی از مقاله (انگلیسی)

Abstract

This research provides performance metrics for cooperative research centers that enhance translational research through partnerships formed by government, industry and academia. Centers are part of complex ecosystems and vary greatly in the type of science conducted, organizational structures and expected outcomes. The ability to realize their objectives depends on transparent measurement systems to assist in decision making in research translation. We introduce a hierarchical decision model that uses both quantitative and qualitative metrics. A generalizable model is developed based upon program goals. The results are validated through consultation with experts. The method is illustrated using data from the National Science Foundation’s industry/university cooperative research center (IUCRC) program. The methodology provides a basis for a generalizable model and measurement system to compares performance of university science and engineering focused research centers supported by industry and government.

Introduction

Industry-university collaborations conducting multi-disciplinary research are required to solve increasingly complex social problems (Boardman and Gray, 2010). Increased U.S. public policy support for initiatives that enhance translational research has resulted in the evolution of many different forms of technology transfer mechanisms (Boardman and Bozeman, 2015). Today, university-based research centers “are prevalent as both policy mechanisms and industry strategies” [(Boardman and Ponomariov, 2011) pg 76]. Cooperative research centers (CRCs) that involve partnership agreements with actors from three different sectors of government, academia and industry are the most sustainable business models (Lee, 2000). However, supporting these “triple-helix” (Etzkowitz and Leydesdorff, 2000a) or governmentuniversity-industry (GUI) (Carayannis et al., 2014a) collaborations is expensive, driving policy makers to shift their attention towards performance evaluation. Academia, policy makers (Perkmann et al., 2011a) and CRC managers are all invested in understanding the performance and impact of these centers (Bozeman et al., 2013a). A wealth of literature examines program evaluation through primarily qualitative case-based methods or quantitative methods based on traditional indicators such as patents and publications. Despite the effort and many excellent studies, researchers are cautioning that traditional measures are inadequate (Gray et al., 2014a), placing a call-to-arms for further research. A multidimensional-holistic study with a flexible approach that can evaluate both quantitative and qualitative output indicators is needed. This research begins to fill this gap by presenting a generalizable model for CRC performance evaluation.