Technology Market Transactions

Technology Market Transactions

Auctions, Intermediaries and Innovation

Frank Tietze

Frank Tietze delivers an in-depth discussion of the impact of empirical results upon transaction cost theory, and in so doing, provides the means for better understanding technology transaction processes in general, and auctions in particular. Substantiating transaction cost theory with empirical auction data, the author goes on to explore how governance structures need to be designed for effective distributed innovation processes. He concludes that the auction mechanism is a viable transaction model, and illustrates that the auction design, as currently operated by market intermediaries, requires thorough adjustments. Various options for possible improvements are subsequently prescribed.

Chapter 10: Analysis of Auctioned Technologies

Frank Tietze

Subjects: economics and finance, economics of innovation, intellectual property, innovation and technology, economics of innovation, intellectual property, technology and ict

Extract

10.1 OPERATIONALIZATION OF VARIABLES Following Aggarwal and Walden (2009), above I define technologies as systems consisting of technical sub-components, that is inventions protected by patents (see Chapter 6). Consequently, for the operationalization, I primarily used measures developed in the patent literature1 and particularly econometric studies of patent values, although this area of work is still largely in the research stage.2 According to OECD (2009: 137), the ‘evaluation and measurement are at a very crude stage compared to other areas where economic indicators are widely available (for example, exports and imports or R&D investments)’. In the following section, I refer extensively to the recent and comprehensive OECD (2009) report, which provides a summary of available and relevant measures.3 As a first step to develop the study’s measures with all the variables in the dataset, a correlation analysis (see Annex III) was carried out to identify variables that are highly correlated (Backhaus et al., 2006). In a second step, following suggestions in the literature, I computed a number of alternative measures for some variables. In order to select the most suitable version to be used in the multivariate models, bivariate regressions were applied (Hair, 2006). Within the sensitivity analyses, however, some of the alternatively measured variables were also used. Table 10.1 summarizes the 11 variables indicating the measurement scale for each variable (8 x metric, 8 x nominal), the value range (minimum and maximum values), and the source of the raw data (auction catalogue, patent database or post-auction press release)...

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