Social network analysis and dyadic identification in the classroom

Autor: MACIEL, CRISTIANO DE OLIVEIRA
Jazyk: Spanish; Castilian
Rok vydání: 2018
Předmět:
Zdroj: RAM. Revista de Administração Mackenzie, Vol 19, Iss 1 (2018)
RAM. Revista de Administração Mackenzie v.19 n.1 2018
RAM. Revista de Administração Mackenzie
Universidade Presbiteriana Mackenzie (UPM)
instacron:MACKENZIE
RAM. Revista de Administração Mackenzie, Volume: 19, Issue: 1, Article number: eRAMG180051, Published: 05 APR 2018
ISSN: 1678-6971
Popis: Purpose: For this work I established as research objective the examination of antecedents (i.e., similarity of structural position, competence and sociability) and the consequences (i.e., formation of work group) of dyadic identification among students on a Business Administration course at a private University. Originality/Value: Identification has been examined anthropomorphically in the relationship between employee and organization or between employee and work team. However, organizations and teams are not human beings. I propose to investigate identification at the dyadic level, indeed among people, to transcend that barrier. Design/methodology/approach: I employed the sociometric survey as a method. Data were collected in two phases, with an interval of 12 months (t0 and t1). I tested the hypotheses by means of the LR-QAP non-parametric technique. Findings: The results allowed me to point out that structural equivalence and similarity of deferences for competence and for sociability, influence dyadic identification. In examining the work groups’ training on completion of the course, I confirmed the hypothesis on the influence of dyadic identification, but also found that such groups are mainly formed by students who share the same contacts and are very similar in sociability (fun) and slightly similar in terms of competence deferences. Structural equivalence, deferences of competence, and deference to sociability con­stitute a structural-deferential basis for social judgment and construction of social profiles and categories that enable comparison between peers and subsequent identification in a non-anthropomorphic way.
Databáze: OpenAIRE