ERGMs

Organizations have been experimenting with intraorganizational crowdsourcing (IOC), yet the mechanisms of IOC production remain an underresearched topic. Drawing on a 2-mode ERGM, we examine structural mechanisms and individual-level factors that shape the network structure of idea generation and selection yielded by an IOC idea challenge in a global IT corporation. Results show a Matthew effect leading to 1) highly centralized employee participation around a few " superactive " employees who engage with many ideas and 2) highly centralized idea popularity with a few ideas attracting most employee attention. We find support for shared affiliations among employee-idea clusters in the first half of the participation, which is, however, less likely in the second half. We also find support for geographic homophily. The rise of crowdsourcing platforms for idea generation and selection has attracted research on their implications for organizational problem solving and innovation (Brabham, 2013). However, most studies have focused on open crowdsourcing platforms, which allow the influx of ideas beyond a clearly defined, formal organizational boundary. By contrast, relatively few studies have examined how intraor-ganizational crowdsourcing (IOC) platforms allow organizations to tap the collective wisdom within a closed system of employees for new ideas and innovation. More importantly, little attention has been

This research investigated effects of narcissism and emotional intelligence (EI) on popularity in social networks. In a longitudinal field study we examined the dynamics of popularity in 15 peer groups in two waves (N=273).We measured narcissism, ability EI, explicit and implicit self-esteem. In addition, we measured popularity at zero acquaintance and three months later. We analyzed the data using inferential network analysis (temporal exponential random graph modeling, TERGM) accounting for self-organizing network forces. People high in narcissism were popular, but increased less in popularity over time than people lower in narcissism. In contrast, emotionally intelligent people increased more in popularity over time than less emotionally intelligent people. The effects held when we controlled for explicit and implicit self-esteem. These results suggest that narcissism is rather disadvantageous and that EI is rather advantageous for long-term popularity.

In this paper, I argue that we can better understand the relationship between social structure and materiality by combining qualitative analysis of practices in shared physical space with statistical analysis. Drawing on the two-mode approach, I treat social and material structures together with the relationship between them as a two-level socio-material network. In a mixed method study, formalized ethnographic data on such networks in five European artistic collectives are subjected to multilevel exponential random graph modelling. It sheds light on how different kinds of interpersonal ties condition the engagement of individuals with similar materiality over time.

Archaeological network analysts often represent archaeological data as static networks and explore their structure.However,most networks changed through time and static network representations do not allow archaeologists to test assumptions about the dynamic processes driving this change. The study of visibility networks in archaeology is a good example of this. Archaeologists propose hypotheses of the role of lines of sight between settlements, which imply dynamic processes for the establishment of the observed visibility networks. However, commonly used methods do not allow us to evaluate these hypotheses. In this paper we introduce exponential random graph modelling (ERGM) as a method for bridging static and dynamic approaches to interpreting visibility networks. This method offers a number of advantages: (1) it explicitly addresses the assumptions inherent in visibility network creation aboutwhat relationshipsbetweennodes mean and the types of processes they allow for; (2) it allows one to investigate the range of network structures that these assumptions give rise to;and(3) itexplores thedynamic processes thatmighthave led to observed networks. This method is used to evaluate hypotheses of the role of lines of sight in facilitating visual controlandcommunication during the later IronAgein Southern Spain. This studyshows thatERGMs can be used as a reflective technique to evaluate competing hypotheses, and that ERGM results subse- quently require more contextualised evaluation. Future work on ERGMs should focus on incorporating geographical constraints to further enhance its potential for studying visibility networks.

How do social networks differ between highly collaborative and less collaborative forms of governance? Drawing on a prior study that characterized the level of collaboration for three federal hydropower relicensing processes, we develop exponential random graph models of meeting attendance and participation networks. We find that the highly collaborative relicensing process had lower overall density and a propensity for relatively fewer and stronger interactions. Reciprocity is highest in the high-collaboration process, indicating that it is characterized by mutual interactions. In the low-collaboration process, patterns of connections between any three members of the network displayed a more unidirectional structure, suggesting asymmetrical interactions between active versus passive members of the network. By linking network structure to collaborative dynamics, this study helps elaborate potential mechanisms of successful collaboration.

This research investigated effects of narcissism and emotional intelligence (EI) on popularity in social networks. In a longitudinal field study, we examined the dynamics of popularity in 15 peer groups in two waves (N = 273). We measured narcissism, ability EI, and explicit and implicit self-esteem. In addition, we measured popularity at zero acquaintance and 3 months later. We analyzed the data using inferential network analysis (temporal exponential random graph modeling, TERGM) accounting for self-organizing network forces. People high in narcissism were popular, but increased less in popularity over time than people lower in narcissism. In contrast, emotionally intelligent people increased more in popularity over time than less emotionally intelligent people. The effects held when we controlled for explicit and implicit self-esteem. These results suggest that narcissism is rather disadvantageous and that EI is rather advantageous for long-term popularity.

This research investigated effects of narcissism and emotional intelligence (EI) on popularity in social networks. In a longitudinal field study, we examined the dynamics of popularity in 15 peer groups in two waves (N = 273). We measured narcissism, ability EI, and explicit and implicit self-esteem. In addition, we measured popularity at zero acquaintance and 3 months later. We analyzed the data using inferential network analysis (temporal exponential random graph modeling, TERGM) accounting for self-organizing network forces. People high in narcissism were popular, but increased less in popularity over time than people lower in narcissism. In contrast, emotionally intelligent people increased more in popularity over time than less emotionally intelligent people. The effects held when we controlled for explicit and implicit self-esteem. These results suggest that narcissism is rather disadvantageous and that EI is rather advantageous for long-term popularity.

Exponential random models have been widely adopted as a general probabilistic framework for complex networks and recently extended to embrace broader statistical settings such as dynamic networks, valued networks or two-mode networks. Our aim is to provide a further step into the generalization of this class of models by considering sample spaces which involve both families of networks and nodal properties verifying combinatorial constraints. We propose a class of probabilistic models for the joint distribution of nodal properties (demographic and behavioral characteristics) and network structures (friendship and professional partnership). It results in a general and flexible modeling framework to account for homophily in social structures. We present a Bayesian estimation method based on the full characterization of their sample spaces by systems of linear constraints. This provides an exact simulation scheme to sample from the likelihood, based on linear programming techniques. After a detailed analysis of the proposed statistical methodology, we illustrate our approach with an empirical analysis of co-authorship of journal articles in the field of neuroscience between 2009 and 2013.

Exponential random graph models (ERGM) have been widely applied in the social sciences in the past ten years. However, diagnostics for ERGM have lagged behind their use. Collinearity-type problems can emerge without detection when fitting ERGM, yielding inconsistent model estimates and problematizing inference from parameters. This article provides a method to detect multicollinearity in ERGM. It outlines the problem and provides a method to calculate the variance inflation factor from ERGM parameters. It then evaluates the method with a Monte Carlo simulation, fitting 216,000 ERGMs and calculating the variance inflation factors for each model. The distribution of variance inflation factors is analyzed using multilevel regression to determine what network characteristics lend themselves to collinearity-type problems. The relationship between variance inflation factors and unstable standard errors (a standard sign of collinearity) is also examined. The method is shown to effectively detect multicollinearity and guidelines for interpretation are discussed.

Guided by the community ecology perspective and resource dependence theory, this study examines how organizational forms and resources co-influence the financial flow among three types of players: angel investors, venture capital firms and startups. Data
from two comparable areas were collected from an online equity funding platform AngelList.com: the greater Los Angeles, and Silicon Valley. This study applies network analysis to demonstrate that the online investment networks in both markets were driven by both commensalism and symbiosis, and they both favored larger ventures. However, distinct patterns were revealed. The Los Angeles market had a higher
tendency of the financial flow from angel investors to VC firms, and between angel investors; while the financial flow in the Silicon Valley market was more from angel
investors and VC firms to startups. Furthermore, the resource signals had different effects on both markets with the investors in Silicon Valley viewing online resources as negative predictors of funding flow. Implications on what value funders bring to
ventures and whether online platform offers an alternative for venture investment are provided.

The most common explanation for the current surge of Vaccine Hesitancy is that the Internet helps vaccine science deniers reach a wide audience. This explanation is challenged by the growing success of critics who present themselves as different from antivaccinationists, using phrases such as “I’m not antivaccine but” and promoting so-called “alternative vaccination schedules”. This could mean that antivaccinationists strive to appear more moderate than they are. But it could also mean that vaccines are being taken on by less radical activists. We compare these two explanations using the contents of a large sample of French-speaking websites. We qualitatively coded the contents of these websites, analysed their citation patterns and assessed their presence in mainstream media. We found that this milieu is fragmented. The most prominent activists only criticize some vaccines and disassociate themselves from the tightly-knit community of radical antivaccinationists. We also found that some appear as moderate while being deeply embedded in the community of radicals, indicating that their “moderate” stance is a communication strategy. These results suggest both explanations are simultaneously true. They apply to different activists, highlighting the importance of exploring the diversity of contemporary vaccine criticism to understand vaccine hesitancy.

Previous research has characterized knowledge networks by diffuse connectivity and/or clusters and the absence of centrality. In contrast, exponential random graph models used in this article demonstrate that the uncertainty and centralized influence typical of an emerging area of research leads to the creation of a densely interconnecting core that acts to cohere the network. Moreover, eclecticism and innovativeness, also characteristic of a developing area, lead to a diffusely connected structure. The data, comprising 2200 authors and 76 papers have been manually coded from articles on the feminization of the labor force in Asia.

Multistakeholder issue networks have been characterized as power-free, egalitarian forms of corporate–civil society engagement. Using a communication-centered conceptualization of power, our study finds that potential sources of power subtly manifest through communication and interaction patterns in multistakeholder issue networks. Our results indicate that organizations’ institutional status and resources are significant predictors of network power.

Who is popular among one's peers, and who is unpopular? This question is of major theoretical and practical importance , given that social inclusion is a basic human need and a major predictor of an array of adjustment outcomes (Baumeister & Leary, 1995). Nevertheless, the dispositional antecedents of popularity are not fully understood yet. The investigation of the links between personality and popularity in naturally occurring groups is complicated by the fact that group phenomena, such as popularity, are not only influenced by exogenous factors (e.g., personality traits and skills of group members) but also by endogenous or self-organizing factors, such as, for example, group members' tendencies to form mutual friendships or to dislike members who are disliked by a friend. The goal of the current research was to solve this problem by using a sophisticated statistical procedure that is able to account for such self-organizing forces. We employed an inferential network-analytic method called temporal exponential random graph model (TERGM) to investigate the effects of narcissism and emotional intelligence (EI) on popularity. We tested whether the well-documented finding from earlier research that narcissists have an initial, but no long-term advantage in popularity also emerges when self-organizing network forces are taken into account. Furthermore, we investigated the main effect of EI and its interactive effects with narcissism in predicting popularity. Narcissism and Popularity Grandiose narcissism is a personality trait characterized by excessively positive undeserved self-regard and a constant desire for external self-affirmation. Persons high in narcis-sism use other people instrumentally to construct and maintain their desired self-concepts. Several theories, such as the chocolate cake model of narcissism (Campbell, 2005), and the contextual reinforcement model of narcissism (Campbell & Campbell, 2009), predict that when interactions with nar-cissists are considered, excellent first impressions are followed by disappointment. In their effort to maintain a 666265P SPXXX10. Abstract This research investigated effects of narcissism and emotional intelligence (EI) on popularity in social networks. In a longitudinal field study, we examined the dynamics of popularity in 15 peer groups in two waves (N = 273). We measured narcissism, ability EI, and explicit and implicit self-esteem. In addition, we measured popularity at zero acquaintance and 3 months later. We analyzed the data using inferential network analysis (temporal exponential random graph modeling, TERGM) accounting for self-organizing network forces. People high in narcissism were popular, but increased less in popularity over time than people lower in narcissism. In contrast, emotionally intelligent people increased more in popularity over time than less emotionally intelligent people. The effects held when we controlled for explicit and implicit self-esteem. These results suggest that narcissism is rather disadvantageous and that EI is rather advantageous for long-term popularity.

Much research on higher education has discussed the positional competition induced by global rankings and the complementary concept of “world-class” universities. This paper investigates the network of social media communication between globally ranked universities. Specifically, it examines whether universities seek to preserve and reproduce status by selectively forming associations with highly ranked institutions. It uses social network analysis and exponential random graph models to investigate data on interactions through the popular social media website Twitter.com. Findings show that social media communications are significantly related to global rankings, but that the size of this effect is quite small. Instead, structural relationships within the network and geographical location appear to have more influence on network structure. These results suggest a need to critically reassess the category of “world-class” universities and the role of global rankings in global higher education.

Prior scholarship on Wikipedia's collaboration processes has examined the properties of either editors or articles, but not the interactions between both. We analyze the coauthorship network of Wikipedia articles about breaking news demanding intense coordination and compare the properties of these articles and the editors who contribute to them to articles about contemporary and historical events. Using p*/ERGM methods to test a multi-level, multi-theoretical model, we identify how editors' attributes and editing patterns interact with articles' attributes and authorship history. Editors' attributes like prior experience have a stronger influence on collaboration patterns, but article attributes also play significant roles. Finally, we discuss the implications our findings and methods have for understanding the socio-material duality of collective intelligence systems beyond Wikipedia.