Importance, Cohesion and Structural Equivalence in the Evolving Citation Network of the International Journal of Research in Marketing

The citation network of the International Journal of Research in Marketing (IJRM) is examined from 1981 to 1995. We propose a model that contains log-linear and logmultiplicative terms to estimate simultaneously the importance, cohesion, and structural equivalence of journals in the network across time. Our findings show that the overall importance of IJRM in its network is low but growing. The importance of psychology journals in the network appears to be decreasing. Clear cohesive and structurally equivalent groups of core marketing, methodology, managerial and psychology journals with distinct functions in the network are identified. Recommendations for future citation research are offered.Citation analysis;social networks;log-multiplicative models

Importance, Cohesion and Structural Equivalence in the Evolving Citation Network of the International Journal of Research in Marketing

The citation network of the International Journal of Research in Marketing (IJRM) is examined from 1981 to 1995. We propose a model that contains log-linear and logmultiplicative terms to estimate simultaneously the importance, cohesion, and structural equivalence of journals in the network across time. Our findings show that the overall importance of IJRM in its network is low but growing. The importance of psychology journals in the network appears to be decreasing. Clear cohesive and structurally equivalent groups of core marketing, methodology, managerial and psychology journals with distinct functions in the network are identified. Recommendations for future citation research are offered.

Advice seeking network structures and the learning organization

Organizational learning can be described as a transfer of individuals’ cognitive mental models to shared mental models. Employees, seeking the same colleagues for advice, are structurally equivalent, and the aim of the paper is to study if the concept can act as a conduit for organizational learning. It is argued that the mimicking of colleagues’ advice seeking structures will induce structural equivalence and transfer the accuracy of individuals’ cognitive mental models to shared mental models. Taking a dyadic level of analysis authors revisit a classical case and present novel data analyses.The empirical results indicate that the mimicking of advice seeking structures can alter cognitive accuracy. It is discussed the findings’ implications for organization learning theory and practice, addressed the study’s limitations, and suggested avenues for future research

Networks for change: How networks influence organizational change

This paper contributes to the literature on organizational change by examining organizations as social entities embedded in inter-organizational networks. In contrast to extant research that focuses on macro environment and internal factors to explain organizational change we put forth the social network surrounding the firm as a major driver of any change process. In specific we examine organization change as driven by the organizations? positions and relations in an interorganizational network. Our conceptual framework demonstrates that inter-organizational networks are important mid-level environmental factors that complement the macro-environment and internal organizational factors for the study of organizational changes. We conclude with a discussion on normative implications for organizations and avenues for future research.organizational change, social networks

CHAPTER 10: UPPER-MIDDLE-CLASS POLITICS AND POLICY OUTCOMES: DOES CLASS IDENTITY MATTER?

This chapter in Clark and lipset\u27s book on class in American politics resulted from a multi-day workshop at the Woodrow Wilson Center for International Scholars in the summer of 1999. The piece reverses the normal causality of class politics. It does not analyze citizens in elections, but government officials creating policies. It asks why policies differ across localities (specifically public transit decisions in 42 U.S. metropolitan areas). It probes how some government officials work with an upper-middle-class citizenry in mind, while others do so less. The chapter then tests for differences across localities and finds quite distinct patterns. The chapter next elaborates specific contours of the American upper middle class, in a creative merging of themes from Thorsein Veblen and David Riesman to current work on public policy

Different approaches to community detection

A precise definition of what constitutes a community in networks has remained elusive. Consequently, network scientists have compared community detection algorithms on benchmark networks with a particular form of community structure and classified them based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different reasons for why we would want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different approaches to community detection also delineates the many lines of research and points out open directions and avenues for future research.Comment: 14 pages, 2 figures. Written as a chapter for forthcoming Advances in network clustering and blockmodeling, and based on an extended version of The many facets of community detection in complex networks, Appl. Netw. Sci. 2: 4 (2017) by the same author

Animating the development of Social Networks over time using a dynamic extension of multidimensional scaling

The animation of network visualizations poses technical and theoretical challenges. Rather stable patterns are required before the mental map enables a user to make inferences over time. In order to enhance stability, we developed an extension of stress-minimization with developments over time. This dynamic layouter is no longer based on linear interpolation between independent static visualizations, but change over time is used as a parameter in the optimization. Because of our focus on structural change versus stability the attention is shifted from the relational graph to the latent eigenvectors of matrices. The approach is illustrated with animations for the journal citation environments of Social Networks, the (co-)author networks in the carrying community of this journal, and the topical development using relations among its title words. Our results are also compared with animations based on PajekToSVGAnim and SoNIA