161,075 research outputs found
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 impact of brand communication on brand equity through Facebook
Purpose: The purpose of this study is to fill the gap in the discussion of the ways in which firm-created and user-generated social media brand communication impacts consumer-based brand equity metrics through Facebook. Design/methodology/approach: We evaluated 302 data sets that were generated through a standardized online-survey to investigate the impact of firm-created and user-generated social media brand communication on brand awareness/associations, perceived quality, and brand loyalty across 60 brands within three different industries: non-alcoholic beverages, clothing, and mobile network providers. We applied structural equation modeling techniques (SEM) to investigate the effects of social media brand communication on consumers’ perception of brand equity metrics, as well as in an examination of industry-specific differences. Findings: The results of our empirical studies showed that both firm-created and user-generated social media brand communication influence brand awareness/associations; whereas, user-generated social media brand communication had a positive impact on brand loyalty and perceived brand quality. Additionally, there are significant differences between the industries being investigated. Originality/value: This article is pioneering in that it exposes the effects of two different types of social media brand communication (i.e., firm-created and user-generated social media communication) on consumer-based brand equity metrics, a topic of relevance for both marketers and scholars in the era of social media. Additionally, it differentiates the effects of social media brand communication across industries, which indicate that practitioners should implement social media strategies according to industry specifics to lever consumer-based brand equity metrics
Mesoscopic structure conditions the emergence of cooperation on social networks
We study the evolutionary Prisoner's Dilemma on two social networks obtained
from actual relational data. We find very different cooperation levels on each
of them that can not be easily understood in terms of global statistical
properties of both networks. We claim that the result can be understood at the
mesoscopic scale, by studying the community structure of the networks. We
explain the dependence of the cooperation level on the temptation parameter in
terms of the internal structure of the communities and their interconnections.
We then test our results on community-structured, specifically designed
artificial networks, finding perfect agreement with the observations in the
real networks. Our results support the conclusion that studies of evolutionary
games on model networks and their interpretation in terms of global properties
may not be sufficient to study specific, real social systems. In addition, the
community perspective may be helpful to interpret the origin and behavior of
existing networks as well as to design structures that show resilient
cooperative behavior.Comment: Largely improved version, includes an artificial network model that
fully confirms the explanation of the results in terms of inter- and
intra-community structur
The influence of social capital on risk-taking propensity. A study on Chinese immigrant entrepreneurs
This paper studies the influence of social capital on immigrant entrepreneurs’ risk-taking propensity. The paper has a particular focus on Chinese immigrants and also explores the effects of the so-called “guanxi”, a specific form of social capital for Chinese communities. The empirical research is based on a survey conducted in 2012 on Chinese immigrant entrepreneurs in Andalusia (Spain). An ordinal logistic regression specification was employed to test the hypotheses. The results show that the Chinese immigrant entrepreneurs with greater structural, relational and cognitive social capital and better “guanxi” have a higher risk-taking propensity in their business activity
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
Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency
Given a social network G and a constant k, the influence maximization problem
asks for k nodes in G that (directly and indirectly) influence the largest
number of nodes under a pre-defined diffusion model. This problem finds
important applications in viral marketing, and has been extensively studied in
the literature. Existing algorithms for influence maximization, however, either
trade approximation guarantees for practical efficiency, or vice versa. In
particular, among the algorithms that achieve constant factor approximations
under the prominent independent cascade (IC) model or linear threshold (LT)
model, none can handle a million-node graph without incurring prohibitive
overheads.
This paper presents TIM, an algorithm that aims to bridge the theory and
practice in influence maximization. On the theory side, we show that TIM runs
in O((k+\ell) (n+m) \log n / \epsilon^2) expected time and returns a
(1-1/e-\epsilon)-approximate solution with at least 1 - n^{-\ell} probability.
The time complexity of TIM is near-optimal under the IC model, as it is only a
\log n factor larger than the \Omega(m + n) lower-bound established in previous
work (for fixed k, \ell, and \epsilon). Moreover, TIM supports the triggering
model, which is a general diffusion model that includes both IC and LT as
special cases. On the practice side, TIM incorporates novel heuristics that
significantly improve its empirical efficiency without compromising its
asymptotic performance. We experimentally evaluate TIM with the largest
datasets ever tested in the literature, and show that it outperforms the
state-of-the-art solutions (with approximation guarantees) by up to four orders
of magnitude in terms of running time. In particular, when k = 50, \epsilon =
0.2, and \ell = 1, TIM requires less than one hour on a commodity machine to
process a network with 41.6 million nodes and 1.4 billion edges.Comment: Revised Sections 1, 2.3, and 5 to remove incorrect claims about
reference [3]. Updated experiments accordingly. A shorter version of the
paper will appear in SIGMOD 201
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