Research Collaboration Influence Analysis Using Dynamic Co-authorship and Citation Networks

Collaborative research is increasing in terms of publications, skills, and formal interactions, which certainly makes it the hotspot in both academia and the industrial sector. Knowing the factors and behavior of dynamic collaboration network provides insights that helps in improving the researcher’s profile and coordinator’s productivity of research. Despite rapid developments in the research collaboration process with various outcomes, its validity is still difficult to address. Existing approaches have used bibliometric network analysis with different aspects to understand collaboration patterns that measure the quality of their corresponding relationships. At this point in time, we would like to investigate an efficient method to outline the credibility of findings in publication—author relations. In this research, we propose a new collaboration method to analyze the structure of research articles using four types of graphs for discerning authors’ influence. We apply different combinations of network relationships and bibliometric analysis on the G-index parameter to disclose their interrelated differences. Our model is designed to find the dynamic indicators of co-authored collaboration with an influence on the author’s behavior in terms of change in research area/interest. In the research we investigate the dynamic relations in an academic field using metadata of openly available articles and collaborating international authors in interrelated areas/domains. Based on filtered evidence of relationship networks and their statistical results, the research shows an increment in productivity and better influence over time

Who is collaborating with whom? Part I. Mathematical model and methods for empirical testing

There are two versions in the literature of counting co-author pairs. Whereas the first version leads to a two-dimensional (2-D) power function distribution; the other version shows three-dimensional (3-D) graphs, totally rotatable around and their shapes are visible in space from all possible points of view. As a result, these new 3-D computer graphs, called “Social Gestalts” deliver more comprehensive information about social network structures than simple 2-D power function distributions. The mathematical model of Social Gestalts and the corresponding methods for the 3-D visualization and animation of collaboration networks are presented in Part I of this paper. Fundamental findings in psychology/sociology and physics are used as a basis for the development of this model. The application of these new methods to male and to female networks is shown in Part II. After regression analysis the visualized Social Gestalts are rather identical with the corresponding empirical distributions (R2 > 0.99). The structures of female co-authorship networks differ markedly from the structures of the male co-authorship networks. For female co-author pairs’ networks, accentuation of productivity dissimilarities of the pairs is becoming visible but on the contrary, for male co-author pairs’ networks, accentuation of productivity similarities of the pairs is expressed

The impact of emotional intelligence on the work and carrer performance of early career academics at the University of Fort Hare

Orientation – Early Career Academics (ECAs) in their quest to make significant strides in their academic career are faced with a changing academic context, limited support from employers and circumstances emanating from globalization. All these may arouse emotions, fears, anxieties and uncertainties. Many authorities have exhibited emotional intelligence (EI) to be important in terms of self-management, coping and adaptation. In this regard EI may be influential in the career performance of ECAs. Research Purpose – The main aim of this study was to investigate the effect of EI on the career and work performance of ECAs at the University of Fort Hare. Motivation of the Study – EI is criticized for not being easily measurable and also for not being a sufficient predicting factor to individual performance. Little research exists on EI as a predictor to ECAs career progression. Research Approach, Design and Method – The study utilized quantitative data measurement scales. The Schutte Emotional Intelligence Scale was used to measure EI whilst career performance was measured using the Perceived Career Success Scale. The principles of structural equation modelling were applied in formulating the research hypotheses and in data analysis. Main Findings - Exploratory factor analysis extracted four factors from the SEIS, which were named expression of emotion, perception of emotion, use of emotion and regulation of emotion. Expression, perception and regulation of emotions were found to significantly influence job success, interpersonal success, non-organisational success and hierarchical success. The overall EI was not significant to explain change in ECAs career performance. The researcher also found no significant differences in the EI scores on the basis of ECAs age, gender and work experience. Practical/Managerial Implications - The findings to this study may be useful for career counselling and personal development such that an individual may be able to maximize performance and achievement of career goals. The study recommends that EI may be incorporated in learning programs so that ECAs and other professionals may improve their EI. Future research is encouraged on both ECAs and their senior counterparts in the same context of EI. Contribution or value-add – The study contributes to the debate on the predictive power of EI which is criticised by some authorities in the field