Meta-Analysis on the Influencing Factors of Technology Commercialization in Korea

Abstract

In this study, the various influencing factors presented in the prior studies in Korea relating to technology commercialization were classified and grouped into 8 upper group factors and 16 subgroup factors based on their theoretical background. Meta-Analysis was then conducted to figure out the average effects size of overall influencing factors and to identify the key success factors of technology commercialization. In addition, moderator effects were analyzed based on paper type, data type, and sample size. The results showed .3576 of correlation coefficient (ESr) as the summary effect size from 72 studies with 18,370 samples and six (6) factors were derived as the key success factors of technology commercialization: Marketing Capability, Strategic Planning, Manufacturing Capability, Internal Cooperation, External Cooperation, and Learning Capability. Moderator effects were found with data type and sample size but no significant results were obtained with the paper type.

Analysis of Success Factors for Technology Commercialization using Meta-Analytic Structural Equation Modeling

“…Crook et al (2011) found that human capital relates strongly to performance by using the meta-analysis method. Chung & Hyun (2018) conducted a meta-analysis in which a broad range of influencing factors were covered as variables of technology commercialization. In this study, as a further step, a meta-analysis was conducted by narrowing the focus on the firm's competency as variables of technology commercialization in Korea based on Resource-Based view (RBV).…”

“…As the research on technology commercialization became increasingly popular over the past decade, many empirical studies have been accumulated. In connection to this, some efforts have been made to synthesize the results of the precedent individual studies to present an integrated result (Song et al, 2008;Crook et al, 2011;Chung & Hyun, 2018). Across many research areas, meta-analysis (MA) has proven to be a very useful technique in synthesizing empirical studies to present an integrating result from a wide variety of settings (Landis, 2013).…”