Collaboration networks in economic science

When preparing a research article, Economists receive feedback from other academics, present on conference and give talks in seminars. This form of collaboration is termed informal because informal collaborators have, unlike authors, no formal property rights associated with their contribution. However, informal collaboration is so widespread that it appears to be part of the academic production function. Yet, it has received little attention in academia, least in Economics where patterns of informal collaboration differ from that of natural sciences. Social informal collaboration, the provision of direct feedback, gives rise to a social network. This thesis examines this network. The analysis focuses on the role of individual scientists in the network, which is estimated by different network centralities. Data originate from about 6000 published research articles from six Financial Economics journals between 1997 and 2011. A theoretical model describes how network centrality proxies the effort informal collaborators exert informally in a project, and how this improves the citation count of the research paper. We then investigate how observable characteristics of authors determine this and other centrality measures and find that common metrics such as productivity and number of citations correlate little with network centrality. As information transmission is an important aspect of social networks we study how network centrality of Economists relates to placement outcomes of their students in the academic job market. These findings suggest that even informal networks matter in the production of academic research; that these networks contain information above currently used measures of scholarly influence in the profession; and that these networks are used to decrease information asymmetry in the academic labor market

Social Network Measures for Nosduocentered Networks, their Predictive Power on Performance

Our purpose in this article is to define a network structure which is based on two egos instead of the egocentered (one ego) or the complete network (n egos). We describe the characteristics and properties for this kind of network which we call “nosduocentered network”, comparing it with complete and egocentered networks. The key point for this kind of network is that relations exist between the two main egos and all alters, but relations among others are not observed. After that, we use new social network measures adapted to the nosduocentered network, some of which are based on measures for complete networks such as degree, betweenness, closeness centrality or density, while some others are tailor-made for nosduocentered networks. We specify three regression models to predict research performance of PhD students based on these social network measures for different networks such as advice, collaboration, emotional support and trust. Data used are from Slovenian PhD students and their supervisors. The results show that performance for PhD students depends mostly of the emotional network, because it is significant for all three models. Trust and collaboration networks are significant for two models and advice is not significant for any model. As regards network measures, classic and tailor-made measures are about equally good. Measures related to the total intensity of contacts (e.g., density, degree centralization and size) seem to work best to predict performance.nosduocentered network; academic achievement; performance; network measures

The relationship between collaboration, productivity and publications: an empirical analysis in field of family business

Purpose: It is widely accepted that coauthorship and collaboration promotes intellectual partnerships and improves the quality of publications. This paper examines the relationship between collaboration, productivity and publications in the field of family business. Design/methodology/approach: The authors identify the most prolific authors, affiliations and countries and focus on the evolution of research in the field of family business. In doing so, the authors employ social network analysis to discover the structure of the networks and the ways in which authors, institutions and countries interact. Findings: The empirical results show that collaboration is positively related to productivity, and there is significant evidence that the shaped networks exhibit small-world characteristics, a condition in which collaboration within authors becomes integrated in conjunction with time. Practical implications: The findings highlight the mechanics of collaborative research production and can be useful to understand the importance of collaboration patterns to be followed in the field of family business. Originality/value: The contributions are as follows: (a) application of social network analysis to model the coauthorship patterns among individuals, institutions and countries in family business; (b) distinguishing the most degree-central authors in the social network of collaborating academics; (c) investigation of the academic collaborations in family business that have the characteristics of a small-world social network and (d) suggesting a unique connection, through published keywords, between the research priorities of the most central or prolific authors with the research trends in the family business literature. The authors demonstrate that authors\u27 collaboration becomes integrated in conjunction with time

Interdisciplinary Collaborative Research for Professional Academic Development in Higher Education

Although faculties are more diverse, decentralized, and increasingly isolated in technology-supported modern universities, effective technology use can also foster faculty professional academic development and collegiality. This scoping literature review applied Cooper’s systemic review model and a categorical content analysis technique targeting decentralized collaborative research teams in higher education. Findings indicate technology supports formal and informal university and nonuniversity networks, as well as various collaborative research structures; all contributing to professional academic development. Shared attributes of successful collaborative online teams include a sense of social presence, accountability, institutional and team leadership. Collaborative teams are integral to research and allow more faculty members to contribute and benefit from professional academic development through scholarship. Collaborative team research should be investigated further to understand and promote cross-discipline and cultural collaboration potential for research and professional academic development possibilities with special attention given to opportunities for women, online, and adjunct facult

Do good things and talk about them: A Theory of Academics Usage of Enterprise Social Networks for Impression Management Tactics

Enterprise social networks provide benefits especially for knowledge-intensive work as they enable communication, collaboration and knowledge exchange. These platforms should therefore lead to increased adoption and use by knowledge-intensive workers such as consultants or indeed researchers. Our interest is in ascertaining whether scientific researchers use enterprise social networks as part of their work practices. This focus is motivated by an apparent schism between a need for researchers to exchange knowledge and profile themselves, and the aversion to sharing breakthrough ideas and joining in an ever-increasing publishing and marketing game. We draw on research on academic work practices and impression management to develop a model of academics’ ESN usage for impression management tactics. We describe important constructs of our model, offer strategies for their operationalization and give an outlook to our ongoing empirical study of the use of an ESN platform by 20 schools across six faculties at an Australian university

Academic vs. biological age in research on academic careers: a large-scale study with implications for scientifically developing systems

Biological age is an important sociodemographic factor in studies on academic careers (research productivity, scholarly impact, and collaboration patterns). It is assumed that the academic age, or the time elapsed from the first publication, is a good proxy for biological age. In this study, we analyze the limitations of the proxy in academic career studies, using as an example the entire population of Polish academic scientists and scholars visible in the last decade in global science and holding at least a PhD (N = 20,569). The proxy works well for science, technology, engineering, mathematics, and medicine (STEMM) disciplines; however, for non-STEMM disciplines (particularly for humanities and social sciences), it has a dramatically worse performance. This negative conclusion is particularly important for systems that have only recently visible in global academic journals. The micro-level data suggest a delayed participation of social scientists and humanists in global science networks, with practical implications for predicting biological age from academic age. We calculate correlation coefficients, present contingency analysis of academic career stages with academic positions and age groups, and create a linear multivariate regression model. Our research suggests that in scientifically developing countries, academic age as a proxy for biological age should be used more cautiously than in advanced countries: ideally, it should be used only for STEMM disciplines.127Scientometric

Identifying collaboration dynamics of bipartite author-topic networks with the influences of interest changes

Knowing driving factors and understanding researcher behaviors from the dynamics of collaborations over time offer some insights, i.e. help funding agencies in designing research grant policies. We present longitudinal network analysis on the observed collaborations through co-authorship over 15 years. Since co-authors possibly influence researchers to have interest changes, by focusing on researchers who could become the influencer, we propose a stochastic actor-oriented model of bipartite (two-mode) author-topic networks from article metadata. Information of scientific fields or topics of article contents, which could represent the interests of researchers, are often unavailable in the metadata. Topic absence issue differentiates this work with other studies on collaboration dynamics from article metadata of title-abstract and author properties. Therefore, our works also include procedures to extract and map clustered keywords as topic substitution of research interests. Then, the next step is to generate panel-waves of co-author networks and bipartite author-topic networks for the longitudinal analysis. The proposed model is used to find the driving factors of co-authoring collaboration with the focus on researcher behaviors in interest changes. This paper investigates the dynamics in an academic social network setting using selected metadata of publicly-available crawled articles in interrelated domains of "natural language processing" and "information extraction". Based on the evidence of network evolution, researchers have a conformed tendency to co-author behaviors in publishing articles and exploring topics. Our results indicate the processes of selection and influence in forming co-author ties contribute some levels of social pressure to researchers. Our findings also discussed on how the co-author pressure accelerates the changes of interests and behaviors of the researchers

The influence of relationship networks on academic performance in higher education: a comparative study between students of a creative and a non-creative discipline

In recent years, the literature has highlighted the importance of relational aspects on student attainment in higher education. Much of this previous work agrees with the idea that students' connectedness has beneficial effects on their performance. However, this literature has generally overlooked the influence that the discipline of study may have on this relationship, especially when creative contexts are addressed. In this sense and with the aim of looking deeper into this topic, this paper attempts to analyze by means of social network analysis techniques the relationship between social ties and academic performance in two bachelor's degrees with divergent contents and competence profiles in terms of creativity. Our findings suggest that in non-creative disciplines, the closeness of the students to the core of relationships of their network may help them to perform better academically. 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Visualising the intellectual and social structures of digital humanities using an invisible college model

This thesis explores the intellectual and social structures of an emerging field, Digital Humanities (DH). After around 70 years of development, DH claims to differentiate itself from the traditional Humanities for its inclusiveness, diversity, and collaboration. However, the ‘big tent’ concept not only limits our understandings of its research structure, but also results in a lack of empirical review and sustainable support. Under this umbrella, whether there are merely fragmented topics, or a consolidated knowledge system is still unknown. This study seeks to answer three research questions: a) Subject: What research topics is the DH subject composed of? b) Scholar: Who has contributed to the development of DH? c) Environment: How diverse are the backgrounds of DH scholars? The Invisible College research model is refined and applied as the methodological framework that produces four visualised networks. As the results show, DH currently contributes more towards the general historical literacy and information science, while longitudinally, it was heavily involved in computational linguistics. Humanistic topics are more popular and central, while technical topics are relatively peripheral and have stronger connections with non-Anglophone communities. DH social networks are at the early stages of development, and the formation is heavily influenced by non-academic and non-intellectual factors, e.g., language, working country, and informal relationships. Although male scholars have dominated the field, female scholars have encouraged more communication and built more collaborations. Despite the growing appeals for more diversity, the level of international collaboration in DH is more extensive than in many other disciplines. These findings can help us gain new understandings on the central and critical questions about DH. To the best of the candidate’s knowledge, this study is the first to investigate the formal and informal structures in DH with a well-grounded research model

Gender Disparities in Science? Dropout, Productivity, Collaborations and Success of Male and Female Computer Scientists

Scientific collaborations shape ideas as well as innovations and are both the substrate for, and the outcome of, academic careers. Recent studies show that gender inequality is still present in many scientific practices ranging from hiring to peer-review processes and grant applications. In this work, we investigate gender-specific differences in collaboration patterns of more than one million computer scientists over the course of 47 years. We explore how these patterns change over years and career ages and how they impact scientific success. Our results highlight that successful male and female scientists reveal the same collaboration patterns: compared to scientists in the same career age, they tend to collaborate with more colleagues than other scientists, seek innovations as brokers and establish longer-lasting and more repetitive collaborations. However, women are on average less likely to adapt the collaboration patterns that are related with success, more likely to embed into ego networks devoid of structural holes, and they exhibit stronger gender homophily as well as a consistently higher dropout rate than men in all career ages