Nick Deschacht

Cross-cultural differences in self-promotion: A study of self-citations in management journals

We study cross-cultural differences in self-promotion by comparing the self-citation behaviour of scholarly authors originating from individualist and collectivist cultures, using original data on 1,346 journal articles published between 2009 and 2014 in the fields of Management and Business. Our main finding is that articles by authors from individualist cultures are about twice as likely to contain many self-citations. Our results confirm the presence of a gender gap in self-citations, but we show that this effect is smaller than the cultural effect and that the effect appears to be stable across cultures. These findings show that the structure of rewards and costs associated with particular self-promotion tactics differ from culture to culture. Implications of cultural variations in self-promotion are discussed. Practitioner points We develop theory and provide empirical evidence about cultural and gender differences in self-promoting behaviour. As the workforce diversifies, a broader awareness of these differences might affect the actions of both employees and HR departments.

Limited Dependent Variable Models and Probabilistic Prediction in Informetrics

This chapter explores the potential for informetric applications of limited dependent variable models, i.e., binary, ordinal, and count data regression models. In bibliometrics and scientometrics such models can be used in the analysis of all kinds of categorical and count data, such as assessments scores, career transitions, citation counts, editorial decisions, or funding decisions. The chapter reviews the use of these models in the informetrics literature and introduces the models, their underlying assumptions and their potential for predictive purposes. The main advantage of limited dependent variable models is that they allow us to identify the main explanatory variables in a multivariate framework and to estimate the size of their (marginal) effects. The models are illustrated using an example data set to analyze the determinants of citations. The chapter also shows how these models can be estimated using the statistical software Stata.