Open Innovation: The New Imperative for Creating and Profiting from Technology

本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

国际刊物《Scientometrics》文献计量研究

Although the literature on university–industry links has begun to uncover the reasons for, and types of, collaboration between universities and businesses, it offers relatively little explanation of ways to reduce the barriers in these collaborations. This paper seeks to unpack the nature of the obstacles to collaborations between universities and industry, exploring influence of different mechanisms in lowering barriers related to the orientation of universities and to the transactions involved in working with university partners. Drawing on a large-scale survey and public records, this paper explores the effects of collaboration experience, breadth of interaction, and inter-organizational trust on lowering different types of barriers. The analysis shows that prior experience of collaborative research lowers orientation-related barriers and that greater levels of trust reduce both types of barriers studied. It also indicates that breadth of interaction diminishes the orientation-related, but increases transaction-related barriers. The paper explores the implications of these findings for policies aimed at facilitating university–industry collaboration.

Investigating the factors that diminish the barriers to university-industry collaboration

A review of the literature on citation impact indicators

Business cycles are both less volatile and more synchronized with the world cycle in rich countries than in poor ones. We develop two alternative explanations based on the idea that comparative advantage causes rich countries to specialize in industries that use new technologies operated by skilled workers, while poor countries specialize in industries that use traditional technologies operated by unskilled workers. Since new technologies are difficult to imitate, the industries of rich countries enjoy more market power and face more inelastic product demands than those of poor countries. Since skilled workers are less likely to exit employment as a result of changes in economic conditions, industries in rich countries face more inelastic labour supplies than those of poor countries. We show that either asymmetry in industry characteristics can generate cross-country differences in business cycles that resemble those we observe in the data. We are grateful to Daron Acemoglu and Fabrizio Perri for useful comments. The views expressed here are the authors' and do not necessarily reflect those of The World Bank. Business cycles are not the same in rich and poor countries. A first difference is that fluctuations in per capita income growth are smaller in rich countries than in poor ones, in the top panel of Figure 1 , we plot the standard deviation of per capita income growth against the level of (log) per capita income for a large sample of countries. We refer to this relationship as the volatility graph and note that it slopes downwards. A second difference is that fluctuations in per capita income growth are more synchronized with the world cycle in rich countries than in poor ones. In the bottom panel of Figure 1 , we plot the correlation of per capita income growth rates with world average per capita income growth, excluding the country in question, against the level of (log) per capita income for the same set of countries. We refer to this relationship as the comovement graph and note that it slopes upwards. Table 1 , which is self-explanatory, shows that these facts apply within different sub-samples of countries and years. 1 Why are business cycles less volatile and more synchronized with the world cycle in rich countries than in poor ones? Part of the answer must be that poor countries exhibit more political and policy instability, they are less open or more distant from the geographical center, and they also have a higher share of their economy devoted to the production of agricultural products and the extraction of minerals. Table 1 shows that, in a statistical sense, these factors explain a substantial fraction of the variation in the volatility of income growth, although they do not explain much of the variation in the comovement of income growth. More important for our purposes, the strong relationship between income and the properties of business cycles reported in Table 1 is still present after we control for these variables. In short, there must be other factors behind the strong patterns depicted in Figure 1 beyond differences in political instability, remoteness and the importance of natural resources. With the exception that the comovement graph seems to be driven by differences between rich and poor countries and not within each group. Acemoglu and Zilibotti (1997) also present the volatility graph. They provide an explanation for it based on the observation that rich countries have more diversified production structures. We are unaware of any previous reference to the comovement graph. In this paper, we develop two alternative but non-competing explanations for why business cycles are less volatile and more synchronized with the world in rich countries than in poor ones. Both explanations rely on the idea that comparative advantage causes rich countries to specialize in industries that require new technologies operated by skilled workers, while poor countries specialize in industries that require traditional technologies operated by unskilled workers. This pattern of specialization opens up the possibility that cross-country differences in business cycles are the result of asymmetries between these types of industries. In particular, both of the explanations advanced here predict that industries that use traditional technologies operated by unskilled workers will be more sensitive to country-specific shocks. Ceteris paribus, these industries will not only be more volatile but also less synchronized with the world cycle since the relative importance of global shocks is lower. To the extent that the business cycles of countries reflect those of their industries, differences in industrial structure could potentially explain the patterns in Figure 1 . One explanation of why industries react differently to shocks is based on the idea that firms using new technologies face more inelastic product demands than those using traditional technologies. New technologies are difficult to imitate quickly for technical reasons and also because of legal patents. This difficulty confers a cost advantage on technological leaders that shelters them from potential entrants and gives them monopoly power in world markets. Traditional technologies are easier to imitate because enough time has passed since their adoption and also because patents have expired or have been circumvented. This implies that incumbent firms face tough competition from potential entrants and enjoy little or no monopoly power in world markets. The price-elasticity of product demand affects how industries react to shocks. Consider, for instance, the effects of country-specific shocks that encourage production in all industries. In industries that use new technologies, firms have monopoly power and face inelastic demands for their products. As a result, fluctuations in supply lead to opposing changes in prices that tend to stabilize industry income. In industries that use traditional technologies, firms face stiff competition from abroad and therefore face elastic demands for their products. As a result, fluctuations in supply have little or no effect on their prices and industry income is more volatile. To the extent that this asymmetry in the degree of product-market competition is important, incomes of industries that use new technologies are likely to be less sensitive to country-specific shocks than those of industries that use traditional technologies. Another explanation for why industries react differently to shocks is based on the idea that the supply of unskilled workers is more elastic than the supply of skilled workers. A first reason for this asymmetry is that non-market activities are relatively more attractive to unskilled workers whose market wage is lower than that of skilled ones. Changes in labour demand might induce some unskilled workers to enter or abandon the labour force, but are not likely to affect the participation of skilled workers. A second reason for the asymmetry in labour supply across skill categories is the imposition of a minimum wage. Changes in labour demand might force some unskilled workers in and out of unemployment, but are not likely to affect the employment of skilled workers. The wage-elasticity of the labour supply also has implications for how industries react to shocks. Consider again the effects of country-specific shocks that encourage production in all industries and therefore raise the labour demand. Since the supply of unskilled workers is elastic, these shocks lead to large fluctuations in employment of unskilled workers. In industries that use them, fluctuations in supply are therefore magnified by increases in employment that make industry income more volatile. Since the supply of skilled workers is inelastic, the same shocks have little or no effects on the employment of skilled workers. In industries that use them, fluctuations in supply are not magnified and industry income is less volatile. To the extent that this asymmetry in the elasticity of labour supply is important, incomes of industries that use unskilled workers are likely to be more sensitive to country-specific shocks than those of industries that use skilled workers To study these hypotheses we construct a stylized world equilibrium model of the cross-section of business cycles. Inspired by the work of Davis (1995), we consider in section one a world in which differences in both factor endowments a la Heckscher-Ohlin and industry technologies a la Ricardo combine to determine a country's comparative advantage and, therefore, the patterns of specialization and trade. To generate business cycles, we subject this world economy to the sort of productivity fluctuations that have been emphasized by Kydland and Prescott (1982). 2 In section two, we characterize the cross-section of business cycles and show how asymmetries in the elasticity of product demand and/or labour supply can be used to explain the evidence in Figure 1 . Using available microeconomic estimates of the key parameters, we calibrate the model and find that: (i) The model exhibits slightly less than two-thirds and one-third of the observed cross-country variation in volatility and comovement, respectively; and (ii) The asymmetry in the elasticity of product demand seems to have a quantitatively stronger effect on the slopes of the volatility and comovement graphs, than the elasticity in the labour supply. We explore these results further in sections three and four. In section three, we extend the model to allow for monetary shocks that have real effects since firms face cash-in-advance constraints. We use the model to study how cross-country variation in monetary policy and financial development affect the cross-section of business cycles. Once these factors are considered, the calibrated version of the model exhibits roughly the same cross-country variation in volatility and about 40 percent of the variation in comovement as the data. In section four, we show th

http://link.springer.com/content/pdf/10.1007/BF01322868.pdf

Business cycles: A methodological approach

How can academic innovation performance in university–industry collaboration be improved?

This paper uses bibliographic coupling analysis to plot out a patent citation map. It explores the current research and development in the high-tech electronic companies in Taiwan, and the relationship between companies and industries. Fifty-eight high-tech electronic companies under this study, between 1998 and 2000, obtained 4162 patents from U.S., and cited 24,852 patents during these years. Through the data from bibliographic coupling analysis, the paper categorizes these companies into 6 major groups: semiconductor, peripheral, scanners, notebook / monitor, system, IC design / packaging. This research also uses multidimensional scaling to plot out a patent citation map, graphically displaying the association among the groups. The result shows a higher similarity among companies in semiconductor sector, whereas the distinction between industries grows more and more ambivalent, even overlapping in some cases.

/pdf/constructing-a-patent-citation-map-using-bibliographic-1k7hh3qp4u.pdf

Constructing a patent citation map using bibliographic coupling: A study of Taiwan's high-tech companies

The rapid development of technology and knowledge-based economies has drawn attention to the linkage between academic institutions and private industries. Universities are a major source of knowledge creation; different industries are increasingly recognizing the importance of scientific knowledge creation and seeking alliances with universities to not only enhance their knowledge base but also gain a competitive advantage. To facilitate university–industry collaborations (UICs), financial support from governments and industries is necessary for resource allocation. This study investigates the effects of UIC funding on universities’ technology innovation performance in Taiwan. The Taiwanese government has implemented a variety of policies and programs to enhance the research innovation capability of universities and bridge the gap between academic research and industrial application. Three fundamental factors of UIC environments within universities—namely, management mechanism, innovation climate, and reward system—are identified as critical antecedents of UIC funding and universities’ technology innovation performance. The results reveal that UIC funding is directly instrumental to universities’ technology innovation. The UIC management mechanism and innovation climate within universities support diverse UIC funding. In addition, mechanism incentives affect directly and moderately university researchers’ involvement in and contribution to technology innovation.

Factors of university–industry collaboration affecting university innovation performance

The counting of papers and citations is fundamental to the assessment of research productivity and impact. In an age of increasing scientific collaboration across national borders, the counting of papers produced by collaboration between multiple countries, and citations of such papers, raises concerns in country-level research evaluation. In this study, we compared the number counts and country ranks resulting from five different counting methods. We also observed inflation depending on the method used. Using the 1989 to 2008 physics papers indexed in ISI's Web of Science as our sample, we analyzed the counting results in terms of paper count (research productivity) as well as citation count and citation–paper ratio (CP ratio) based evaluation (research impact). The results show that at the country-level assessment, the selection of counting method had only minor influence on the number counts and country rankings in each assessment. However, the influences of counting methods varied between paper count, citation count, and CP ratio based evaluation. The findings also suggest that the popular counting method (whole counting) that gives each collaborating country one full credit may not be the best counting method. Straight counting that accredits only the first or the corresponding author or fractional counting that accredits each collaborator with partial and weighted credit might be the better choices. © 2011 Wiley Periodicals, Inc.

Counting methods, country rank changes, and counting inflation in the assessment of national research productivity and impact

http://iaid.me.ntu.edu.tw/data/data_result/_a/1_69.pdf

Dar-Zen Chen

Papers

How can academic innovation performance in university–industry collaboration be improved?

Constructing a patent citation map using bibliographic coupling: A study of Taiwan's high-tech companies

Factors of university–industry collaboration affecting university innovation performance

Counting methods, country rank changes, and counting inflation in the assessment of national research productivity and impact

The influences of counting methods on university rankings based on paper count and citation count