Firm‐like behavior of journals? Scaling properties of their output and impact growth dynamics

Havemann, Frank; Heinz, Michael G.; Wagner-Döbler, Roland

doi:10.1002/asi.20090

Cited by 18 publications

(12 citation statements)

References 29 publications

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“…It thus follows that to the extent that offenders commit crimes in other jurisdictions (states) than those in which they reside, they are more likely to do so in: (a) adjacent states, and/or (b) jurisdictions that are closer. To capture preferential attachment or a Matthew Effect (Yule 1925;Merton 1968;Havemann, Heinz, Wagner-Döbler 2005) we include a measure of each alter states' labs indegree that is centered around the global mean: These metrics are all lagged based on the nature and availability of the data. The length of the respective lag of each measure is noted in table 2 below, along with a description of the measure along with its source.…”

Section: Qualitative and Quantitative Data Collection And Analysismentioning

confidence: 99%

“…These distributional realizations may be the result of preferential attachment processes or a Matthew Effect (Yule 1925;Merton 1968;Havemann, Heinz, Wagner-Döbler 2005). In our context such processes imply that a lab in state i is more likely to seek counsel in difficult cases from labs in state j (but not necessarily vice versa) if that lab is more frequently contacted by other labs (m) for advice.…”

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confidence: 97%

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Social Constraints, Agency, Inter-Organizational Tie Formation and Knowledge Diffusion

et al. 2011

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Social capital is currently one of social structure's most prominent and debated manifestations. However, we have a limited understanding of how social ties as the basis of social capital form in the first place. From one perspective social capital is viewed as: -investment in social relations with expected returns in the marketplace‖ (Lin 2001, p. 19). A second perspective on social capital formation stresses contextual and environmental features beyond the control of individuals that may yield benefits Both perspectives are based on premises implicating various motives and structural constraints pertaining to relationship formation including: exchange, power, and dependency; legitimacy seeking or preferential attachment based on status or prestige; homogeneity or homophily and related selection processes; propinquity; or cultural or institutional forces. These categories of mechanisms do not, however, specify a model of how social relationships as social capital are formed in the first place.If social capital results from -investment strategies,‖ it is important to determine what these strategies are. If social capital originates from structural factors beyond individual control it is important to clarify what mechanisms lead to tie formation within social structures.The objective of this research is to specify mechanisms of social tie formation and reinforcement by peering inside the black-box of foci (Feld 1981) in which social ties are formed. We do so by focusing on the structural contexts within which individual (micro-level) -corporate actors‖ form social relationships for knowledge acquisition that results in macro-level knowledge sharing. A mixed-method analytical approach is employed to this end. Findings illustrate how the subtleties of social structure define the parameters within which social relationships are (strategically) formed.

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Section: Qualitative and Quantitative Data Collection And Analysismentioning

confidence: 99%

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confidence: 97%

Social Constraints, Agency, Inter-Organizational Tie Formation and Knowledge Diffusion

et al. 2011

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“…Researchers have developed a variety of models to simulate complex systems. Indicators commonly used to compare innovation systems have been used to confirm that many important properties exhibited by these models can be seen in empirical data (Albert and Barabasi, 2002;Amaral et al, 2001;Havemann et al, 2005). Examples of these will be discussed later.…”

Section: Complex Systemsmentioning

confidence: 99%

Indicators for complex innovation systems

Katz

2006

Research Policy

105

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Performance indicators such as national wealth (GDP per capita), R&D intensity (GERD/GDP) and scientific impact (citations/paper) are used to compare innovation systems. These indicators are derived from the ratio of primary measures such as population, GDP, GERD and papers. Frequently they are used to rank members of an innovation system and to inform decision makers. This is illustrated by the European Research Area S&T indicators scoreboard used to compare the performance of member states.A formal study of complex systems has evolved over the past few decades from common observations made by researchers from many fields. Complex systems are dynamic and many of their properties emerge from the interactions among the entities in them. They also have a propensity to exhibit power law or scaling correlations between primary measures used to characterize them.Katz [Katz, J.S., 2000. Scale independent indicators and research assessment. Science and Public Policy 27, 23-36] showed that scientific impact (citations/paper) scales with the size of the group (papers). In this paper it will be shown that two other common measures, R&D intensity and national wealth, scale with the sizes of European countries and Canadian provinces. Some of these scaling correlations are predictable. These findings illustrate that a performance indicator derived from the ratio of two measures may not be properly normalized for size. This paper argues that innovation systems are complex systems. Hence scaling correlations are expected to exist between the primary measures used to characterize them. These scaling correlations can be used to construct scale-independent (scale-adjusted) indicators and models that are truly normalized for size. Scale-independent indicators can more accurately inform decision makers how groups of different sizes contribute to an innovation system. The ranks of member groups of an innovation system by scaleindependent indicators can be subtly and profoundly different than the ranks given by conventional indicators. The differences can result in a shift in perspective about the performance of members of an innovation system that has public policy implications.

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“…An economist would call the Matthew effect increasing returns to scale. Like Havemann, Heinz, and Wagner-Doebler (2004), I borrow from the latter literature. Simon (1955) shows that, if one considers a number of firms of different size, and those firms all grow at the same rate, then the size distribution of those firms will converge to Gibrat's law.…”

Section: Introductionmentioning

confidence: 98%

The Matthew effect defined and tested for the 100 most prolific economists

Tol

2008

J. Am. Soc. Inf. Sci.

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The Matthew effect has that recognition is bestowed on researchers of already high repute. If recognition is measured by citations, this means that often-cited papers or authors are cited more often. I use the statistical theory of the growth of firms to test whether the fame of papers and authors indeed exhibits increasing returns to scale, and confirm this hypothesis for the 100 most prolific economists.

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Firm‐like behavior of journals? Scaling properties of their output and impact growth dynamics

Cited by 18 publications

References 29 publications

Social Constraints, Agency, Inter-Organizational Tie Formation and Knowledge Diffusion

Social Constraints, Agency, Inter-Organizational Tie Formation and Knowledge Diffusion

Indicators for complex innovation systems

The Matthew effect defined and tested for the 100 most prolific economists

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