Luận án: Kinh tế Sản xuất Tri thức Đại học & Đổi mới Thương mại tại Colorado State University

DISSERTATION THREE ESSAYS ON THE ECONOMICS OF UNIVERSITY KNOWLEDGE PRODUCTION AND COMMERCIAL INNOVATION: THE CASE OF COLORADO STATE UNIVERSITY RESEARCH AND TECHNOLOGY TRANSFER Submitted by Yoo Hwan Lee Department of Agricultural and Resource Economics In partial fulfillment of the requirements For the Degree of Doctor of Philosophy Colorado State University Fort Collins, Colorado Summer 2016 Doctoral Committee: Advisor: Gregory D. Graff Co-Advisor: Dana L. Mushinski Copyright by Yoo Hwan Lee 2016 All Rights Reserved ABSTRACT THREE ESSAYS ON THE ECONOMICS OF UNIVERSITY KNOWLEDGE PRODUCTION AND COMMERCIAL INNOVATION: THE CASE OF COLORADO STATE UNIVERSITY RESEARCH AND TECHNOLOGY TRANSFER The central aim of this study is to analyze the research production and R&D activities of Colorado State University (CSU) across its different colleges, departments and other research units, and to evaluate how those activities impact the Colorado economy’s agriculturally-related sectors. The study consists of three main chapters, to introduce the dynamics of university knowledge transfer to local agricultural economies.

Chapter 1 explores CSU research production and technology-transfer activities, using a unique panel date set for each of 54 academic departments over the period of 1989-2012. In order to estimate the empirical knowledge-production function (KPF), this chapter attempts to build a negative binomial panel regression model with a polynomial distributed lags (PDL) of research expenditures. Three categories of research outputs are modelled, including (1) published journal articles, (2) industry collaboration, and (3) technology transfer mechanisms. In the regression results, publications are clearly the most common research outputs of the university, with a more systematic relationship between research inputs and publications than the other two types of research outputs.

Moreover, it appears to exhibit decreasing returns to scale, whereas the collaborative and tech transfer research outputs appear to show increasing returns to scale. In the results of a seemingly unrelated regressions (SUR) model among the three different types of research outputs, publications and the tech transfer mediated research outputs are the primary research outputs in the university and have the maximum impact from past research expenditures. ii Furthermore, results indicate that collaboration mediated outputs are substitutional relative to the more formal tech transfer outputs. Chapter 2 explores the agency of knowledge production, viewing scientific research teams as “quasi-firms” arising as independent knowledge-creating entities within the university context.

First, the findings from the ego-centric social network showed that the participation of outside members makes it possible to increase the size of the ego-centric teams and the growth patterns of the percent share are an obvious parallel to the patterns of team size. Particularly, the growth rate of team size is opposite of the percent share of ego’s home department co-authors with upward and downward tendencies, respectively. Second, the findings from the regression results showed that the number of CSU departments per team is statistically significant in the team’s assembly mechanism for both the article teams and patent teams. Thus, it seems reasonable to conclude that cross-functional team formation is more effective and common in the university research team formation and has a positive impact on the size of research teams.

Finally, the quality of research teams’ knowledge production tells us that the group with multiple departments per article has a higher research impact than a group using a single department per article. By the same token, larger-sized teams have higher impacts than relatively smaller-sized teams, as well as field variety. The group with multiple references per patent had a higher impact than the groups with a single or no reference per patent. This result tells us that the citation mapping from backward citations to forward citations is a significant factor for testing the research teams’ impacts on the economic and social benefits with respect to knowledge spillover.

Chapter 3 has focused on CSU’s knowledge spillovers within agriculturally related fields and technologies. The findings indicated that academic knowledge spillovers are geographically bounded, but they are not strictly limited to the regional scale. Crucially, the impact of university iii spillovers on agriculturally-related industries depends upon which type of knowledge dissemination channel is utilized by university researchers. Broadly speaking this chapter evaluates four types of channel—including the publication mechanism, the industry collaboration and extension mechanism, the technology patenting/licensing mechanism, and the venture creation mechanism—each of which are variously adapted to transmitting different degrees of sticky (tacit) versus slippery (codified) knowledge.

The results showed that in both aggregate level of technology and six different technological categories, the spillover impacts of journal publications, are rarely localized within Colorado; rather, the geographic scope of these impacts are national and even global. However, the extent to which the spillover impacts of patented knowledge is localized within Colorado is open to question because it is possible to control permissions for use, but at the same time it is impossible to limit everyone’s awareness and use of it, particularly in foreign jurisdictions where patents are not taken out by the university. However, the collaboration mechanism requires closer interaction and greater geographic proximity, which usually prevents global dissemination. Thus, we observe geographic proximity is significantly important for these channels.

However, there are even distinctions within these. For example, we find industry coauthorship on articles to be less likely to be localized than privately sponsored grant awards. Nevertheless, the stickiness of these channels might depend also on the different technological categories. As mention as above, the geographic proximity is important only in aggregate level of technology, but it can be varied across the different technological categories, especially the slippery form of knowledge in animal health and nutrition health technology.

Finally, university start-ups are highly geographically bounded near universities because in the early stages start-up companies need support from their host university. iv ACKNOWLEDGEMENTS I would like to sincerely thank my advisor, Professor. Graff, for supporting and advising me during my doctoral program years. Graff showed commitment to and put his heart into my dissertation and long-term career goals.

He always encouraged me to improve the most important skills to me as an economist and to grow as a creative and independent researcher. Particularly, I really love his academic leadership and economic intuitions, especially regarding technological innovation and the economics of entrepreneurship. Moreover, his mentorship is paramount in advising his students, especially his international students. Graff deeply and truly understands his students’ problems and concerns, and he always endeavors to solve their problems.

Whenever I suffered a hard time in my academic journey, he always cheered me up wholeheartedly and advised me how to resolve the problem. I would like to thank him again and to say that I could never have achieved my doctoral degree without his advice and encouragement. I would also like to thank my co-advisor, Professor. Dana Hoag, for his advice and insightful suggestions on my dissertation and for his letter of recommendation for my job applications.

He is the person I met first at Colorado State University, and he hooded me at the commencement ceremonies on behalf of Dr. Moreover, I am also grateful to my other dissertation committee members at Colorado State University, Professor. David Mushinski and Stephen Koontz, for their time and valuable feedback on a preliminary version of my dissertation, especially relating to their econometric knowledge. I would like to thank the members of the Department of Agricultural and Resource Economics at Colorado State University, including the professors (Dr.

Gregory Perry and Dr. Dawn Thilmany), the administrative staffs (Ms. Denise Davis and Donna Sosna), and my colleagues, especially v Ghulam Samad, Annabelle Berklund, Neama Lariel, Chuba Suntharlingam, and Jakrapun Suksawat (Ton), for their love and support. I would like to thank the members of CSU Ventures, Dr.

Todd Headley and Ms. Sarah Belford, for sharing their valuable dataset and our insightful discussions. I am also grateful to the members of the Office of the Vice President for Research at Colorado State University, Dr. Rudolph and Dr.

Pam Harrington, for sharing their valuable dataset, discussions, and suggestions. Finally, I thank my beloved family, my parents in Gumi, parents-in-law in Suji, my sister (Yoo Jin), my nephew (Jun Hyeok), my sister-in-law (Hye Won), for their endless love and commitment. I especially thank my lovely wife (Hye Jun) for her patience, love, and faith. I also thank my little princess: my daughter (Yerin_Manna).

In addition, I would like to thank my spiritual family: Pastor Paul J. Gim and Ms. Youngin Lee in the Korean Presbyterian Church of Lawrence for their prayer and love, and the other church family members: Pastor Gi Hyun Park and the other members of the First Korean Church of Fort Collins for also their prayer, love and support. vi DEDICATION I dedicate this dissertation to my Lord and Savior Jesus Christ for the unfailing love and eternal life.

“Surely God is my salvation; I will trust and not be afraid. The LORD, the LORD, is my strength and my song; he has become my salvation.", (Isaiah 12:2, NIV) vii TABLE OF CONTENTS Abstract. vii Overall Introduction. Empircal Evidence of the University Knowledge Production Function and Knowledge Dissemination Activities: The Case of Colorado State University.

The Knowledge Production Function: Theory and Empirical Analysis. Different Types of Knowledge Dissemination Channels. Empirical Model Framework. Functional Forms for Modelling Knowledge Production.

Count Data as Research Output. Polynomial Distributed Lags (PDL) Model. Effective Labor in Knowledge Production Function. Research Input Data.

Research Output Data. Outputs Disseminated via the Public Domain Mechanism. Outputs Disseminated via the Collaboration Mechanism. Outputs Disseminated via Technology Transfer Mechanism.

Identifying research inputs on the knowledge production function. Model specification on the lags of research expenditures. Research productivity and long-run effect. Independent Regression Model Results.

Estimating Output of Published Journal Articles. Estimating Output of Doctoral Degree Awards. Estimating Output captured in the Collaboration Index. Estimating Output of Combined Tech Transfer Metrics.

Returns to scale and long-run productivity. System of Equation Model Results. Knowledge Production Based on the Theory of Classical Production. Knowledge Production in a Single Institutional Data versus an Aggregation Data.

Scientific Teams as Quasi-firms: A New Framework for Understanding the Agency of Knowledge Production within the University Context. Social network analysis and research teams. Academic research teams operate like quasi-firms. Trend of Academic Research Teams within Colorado State University.

Conceptual Frameworks of Academic Research Teams. Empirical Approaches to Analyzing Academic Research Teams. Social network analysis. The structure of ego-centric research teams.

The dynamics of ego-centric research teams. Hypotheses for academic research teams and its formations. University research teams generating published journal articles. Relative proportion of non-CSU affiliations.

University research teams producing patented inventions. 127 2) Independent and control variables. Impacts of Team Research. Impact of published journal articles as research output.

The impact of participated private companies per article. The impact of the participation of CSU departments per article. The impact of team size per article. The impact of field categories per article.

Research impact of the patents. The impact of the corporate co-assignments per patent. The impact of the participation of researchers from multiple CSU departments. The impact of the cited references per patent.

The impact of the DWPI patent family per patent. The impact of team size on patent citations. The impact of time lag between application and publication per patent. University Knowledge Spillovers to the Agricultural Economy: The Impact of Agricultural Research at Colorado State University on the Colorado Economy, and Beyond.

Technology change and interaction between university and industry. Geographic proximity and localization. The Regional Agricultural Economy and Value Chains. Empirical Study of University Knowledge Spillovers in Agriculture.

Geographic footprint of university knowledge spillovers. Public domain mechanism of knowledge dissemination. Collaboration mechanism of knowledge dissemination. 163 1) Grants and contracts awarded from private sector sponsors.

164 2) Industry co-authorship on academic journal articles. Patenting/licensing mechanism of knowledge dissemination. Venture creation mechanism of knowledge dissemination .