An Empirical Evaluation Of Social Influence Metrics

Predicting when an individual will adopt a new behavior is an important problem in application domains such as marketing and public health. This paper examines the perfor- mance of a wide variety of social network based measurements proposed in the literature - which have not been previously compared directly. We study the probability of an individual becoming influenced based on measurements derived from neigh- borhood (i.e. number of influencers, personal network exposure), structural diversity, locality, temporal measures, cascade mea- sures, and metadata. We also examine the ability to predict influence based on choice of classifier and how the ratio of positive to negative samples in both training and testing affect prediction results - further enabling practical use of these concepts for social influence applications.Comment: 8 pages, 5 figure

The Performance of University Spin-Offs: The Impact of Entrepreneurial Capabilities and Social Networks of Founding Teams during Start-Ups

Objectives: University spin-offs have increasingly received attention from academia, governments, and policymakers because they not only generate new innovations, productivity, and jobs the regional economies but also significantly improve university productivity and creativity (Hayter, 2013, Urbano and Guerrero, 2013). However, a lack of understanding of the contribution made by a founding team to a spin-off’s performance still remains within current studies. Employing a resource-based view theory and social networks approach, this paper addresses this gap by exploring university spin-offs in Spain. Prior work: University spin-off studies have concentrated on analysing entrepreneurial business models (Ndonzuau et al., 2002, Vohora et al., 2004b, Bower, 2003, Mets, 2010) to understand how the commercialization of research is undertaken to create a university spin-off. University spin-offs were also been analysed from the perspective of a university’s capabilities (Powers and McDougall, 2005), or capabilities and social networks of an established spin-off instead of the founding teams (Walter et al., 2006). Moreover, Vohora et al. (2004a) and Shane (2004) have suggested founders need to build capable teams, which must have entrepreneurial capabilities and qualitative social networks, to create effective university spin-offs. Both entrepreneurial capability and social network theory have been studied in prior entrepreneurship research, but have received less attention within the context of the university spin-offs (Gonzalez-Pernia et al., 2013). Approach: Utilising an internet-based survey, this paper explores entrepreneurial capabilities and social networks of founding teams in Spanish university spin-offs using quantitative data analysis. Basing upon resource-based view theory of Barney (1991) to study entrepreneurial capabilities of the founding teams, the research employ entrepreneurial technology, strategy, human capital, organizational viability, and commercial resources (see Vohora et al., 2004a). To study social networks of a founding team, we employ the conceptual model of Hoang and Antoncic (2003) that divides networks into three components: structure, governance, and content. Results and implications: The results from an examination of the sample of 181 Spanish university spin-offs empirically demonstrate that by exploiting social networks a founding team can improve its entrepreneurial capabilities, which in turn enhance its spin-off’s performance. By employing the work of Vohora et al. (2004a) and Shane (2004), this paper constructs a model in which entrepreneurial capabilities play a mediate role between social networks and spin-off’s performance. Thus, the paper has implications for universities in training and policy development to support spin-off’s activity. Value: This study addresses some fundamental questions to contribute to the theory-based understanding of university spin-offs: How do entrepreneurial capabilities of founding teams influence the performance of university spin-offs? How do social networks of founding teams contribute to the process of the university spin-offs

Broker Positions in Task-Specific Knowledge Networks

In this paper we empirically investigate various benefits and costs associated with broker characteristics of individuals who operate in the account management system of financial service providers. We narrow our focus to broker positions in two specific task-specific knowledge networks that facilitate account management. We study the effect of broker positions on the contribution of individuals to organizational performance. We measure such a contribution by measuring the perceptions of others concerning a particular individual. We also explore how certain personal costs are associated with these task-specific broker positions. More specifically, we explore how these positions affect role ambiguity and role conflict, as self-perceived by that particular individual. To test the hypothesized effects we collect data for a network consisting of 55 individuals. We conclude with stating that service specification broker positions benefit organizations, but service delivery broker positions are detrimental to an organization and that they also invoke personal costs.social networks;account management;role stress;task-specific broker positions

SOFTWARE ENTREPRENEURSHIP: KNOWLEDGE NETWORKS AND PERFORMANCE OF SOFTWARE VENTURES IN CHINA AND RUSSIA

This study examines the impact of entrepreneurs’ network structure and knowledge homogeneity/heterogeneity of their network members on product development, and revenue growth of software ventures in China and Russia. The empirical data are composed of structured interviews with 159 software entrepreneurs in Beijing and Moscow. The study found that structural holes and knowledge heterogeneity affect positively product diversity in interactive ways. The study also found that knowledge homogeneity accelerates product development. Product development speed enhances revenue growth in the long term. However, the combination of speed with dense and homogeneous networks harms revenue growth over time. The effects of structural holes and knowledge heterogeneity on product diversity and revenue growth over time are more salient in Russia due to the unique institutional, social, and cultural conditions present in the country.http://deepblue.lib.umich.edu/bitstream/2027.42/40137/3/wp751.pd

The Self-Organization of Interaction Networks for Nature-Inspired Optimization

Over the last decade, significant progress has been made in understanding complex biological systems, however there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in Evolutionary Algorithm populations; a self-organized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical Evolutionary Algorithm or in Evolutionary Algorithms with structured populations such as the Cellular Genetic Algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population. This capacity for sustained genetic diversity is not imposed on the population but instead emerges as a natural consequence of the dynamics of the system