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Title:Innovation capability and firm performance heterogeneity in the small and medium sided enterprise sector
Author(s):Kim, Sung Sup
Director of Research:Deltas, George
Doctoral Committee Chair(s):Deltas, George
Doctoral Committee Member(s):Bera, Anil K.; Hong, Seung-Hyun; Chalioti, Evangelia
Department / Program:Economics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Small and medium sized enterprise (SME)
Innovation Indicators
Abstract:This study attempts to find empirical evidence for the effect of firm-level innovative activity as a new source of firm performance heterogeneity. It also proposes several innovation indicators that measure a firm’s capability to innovate so as to help understand the innovation of small medium sized enterprise (SME) as a contributor to firm productivity. In this study, I propose and estimate a structural model containing the link between innovative activities and firm performance measured by labor productivity. I try to modify the basic idea and specification of the econometric model suggested by Crépon, Duguet and Mairesse (1998) in order to apply some unique features of SMEs and to use the data from the Korean Innovation Survey (KIS) administered according to OECD guidelines. The first and second chapter provides the theoretical foundation and analysis relevant to the process of innovation within SMEs to support the econometric model that contains the relations among innovation input, innovation output and productivity. The conceptual establishment of the innovation process within a firm is formalized as an input-output system where knowledge capital as an intermediate output of firm-level innovative activities is hypothesized to contribute to enhancing labor productivity through the Constant Elasticity of Substitution (CES) production function. The third chapter introduces the econometric system equations which describe the whole innovation process of a firm from the initial stage of engagement in innovative activities to the final stage of production and performance. In each stage, the system equations are estimated sequentially in a fashion to avoid the simultaneity and selection bias which arise from the fact that a dependent variable from a previous stage is incorporated as an explanatory variable at the next stage. The results of the estimation show that the probability of having each type of innovation output increases. Investments in innovation, innovation output and labor productivity are all positively related with each other in the SME sector, and those relations vary by industry, firm size, area and cohort. One interesting point in those results is that the more export-oriented and government-supported a firm is, the more likely it is to participate in innovative activities. An extended explanation of these issues requires more in-depth study related to policy measures of governments and should be the focus of future work. These empirical findings lead us to roughly conclude that innovative activities could be regarded as another important factor affecting firm-level heterogeneity in productivity. It should be noted, however, that although this result reveals some underlying connections of innovative activity to performance, it does not provide any detailed information about the innovating capability of each firm, which also plays a significant role as a determinant of productivity. Thus, it is necessary to measure firm-level innovation capability in order to predict the possibility of future growth for a firm to the extent that innovative activities contribute to another important factor determining firm-level performance. In the fourth chapter, I develop several possible innovation indicators that measure innovation capability of a firm in an econometric way. Based on the binary responses to each type of innovation and other related information provided by the Korean Innovation Survey (KIS) 2008–Manufacturing, the underlying factors that affect the inputs and outputs of the innovation process are extracted from a traditional factor analysis. They help establish two different kinds of models: the Latent Trait (Factor) Model (LTM) and the Multivariate Probit Factor Model (MVPFM), and consequently construct several innovation indicators that represent firm-level innovation capabilities across industries and sizes of firms. Innovation indictors 1 & 2, constructed by the LTM, represent the weighted scores of common factors that underlie four binary innovation outputs and two binary innovation inputs from the KIS 2008–Manufacturing. Innovation indictors 3 & 4, constructed by the MVPFM, represent the expected probabilities of being engaged in innovation inputs and outputs. Some plausibility tests for the LTM are implemented to support the fitness of the proposed model to other similar data, confirming the validity of the proposed indicators. A second plausibility test is implemented by making a comparison to the industry and size distribution of Korean innovative SMEs certified by the Korean government. The industry and size distribution of SMEs at or above the 85th percentile of the proposed innovation indicators in the KIS 2008–Manufacturing have a similar pattern to Korean innovative SMEs, implying that indicators proposed in this study could be applied as a self-diagnostic tool for the innovation capability of a firm.
Issue Date:2013-05-24
Rights Information:Copyright 2013 Sung-Sup Kim
Date Available in IDEALS:2013-05-24
Date Deposited:2013-05

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