Exponential random graph models for management research: A case study of executive recruitment. Issue 3 (June 2017)

• Record Type: Journal Article
• Authors: Arrieta-Paredes, Mary-Paz; Cronin, Bruce
• Is Part Of: European management journal. Volume 35:Issue 3(2017:Jun.)
• Page Start: 373
• Page End: 382
• Publication Date: 2017-06
• Subjects: Executive recruitment -- Industry origin -- Managerial performance -- Shareholder value -- Social network analysis -- Exponential random graph models; ERGM exponential random graph model -- GoF goodness of fit -- NACE statistical classification of economic activities in the European Community (Nomenclature statistique des activités économiques dans la Communauté européenne); G32 -- J24 -- L25; Management -- Periodicals; Management -- Europe -- Periodicals; Gestion -- Périodiques; Gestion -- Europe -- Périodiques; Management; Europe; Periodicals; Electronic journals; 658.0094
• DOI: 10.1016/j.emj.2016.08.002
• Languages: English
• ISSNs: 0263-2373

Abstract

Abstract: We introduce a recent development in the statistical analysis of relational data that offers rigorous discrimination of a variety of structural and behavioural effects of interest to management research. Exponential random graph models account for the highly interdependent nature of network data that are problematic for the predominant inferential statistical analysis used in management research. We illustrate the value of the approach with an application focused on executive recruitment by large UK firms, modelling migrations of managers among firms as a network of relationships. We find rigorous statistical support for the influences of industry origin in executive recruitment, particularly in relation to legal and accounting activities. The flexibility and sophisticated relational variables available in the models offer considerable analytical power of value to a wide range of management applications. Highlights: Exponential random graph models (ERGMs) are used as a potentially fruitful analytical method for management research. Conventional regression models have limitations in the analysis of highly interdependent data such as network data. Case study of an application of ERGMs in explaining drivers of executive recruitment by large UK firms is presented.