RESUMEN
Change is ubiquitous in the study of organizations. Organizational change is characterized by multiple perspectives, both conceptually and methodologically. Computational modeling efforts are not the exception. In this work, we aim to provide an analysis of computational modeling approaches to organizational change. For that, we first review published works that directly connect to developing knowledge in organizational change from a computational lens. Second, we offer an account of unexplored topics in computational organizational change. Last, we highlight the potentialities of computer simulation models based on agent interactions in regard to how they could contribute to the understanding of central issues in this organizational research subfield.
Asunto(s)
Conocimiento , Simulación por Computador , HumanosRESUMEN
A considerable body of research has documented the negative effects of job burnout on employees and their organizations, emphasizing the importance of the identification of early signs of the phenomenon for the purposes of prevention and intervention. However, such timely identification is difficult due to the time and cost of assessing the burnout levels of all employees in an organization using established scales. In this paper, we propose an innovative way to identify employees at risk of job burnout by analyzing their e-mail communication patterns. Building on the Job Demands-Resources model, we theorize about the relationship between e-mail communication patterns and levels of employee exhaustion and disengagement (two dimensions of burnout). We analyzed 52,190 e-mails exchanged between 57 employees of a medium sized R&D company over a five-month period. We then related these employees' communication patterns to their levels of burnout, collected using an established scale (the OLBI-Oldenburg Burnout Inventory). Our results provide support for the overall proposition of the paper, that e-mail communications can be used to identify individuals at risk of job burnout. Our models explain up to 34% of the variance of burnout and up to 37% and 19% respectively of the variance of exhaustion and disengagement. They also successfully distinguish between employees with a higher risk of burnout and those with lower levels of risk (F1 score of 84% with recall of 100% and 73% precision). We discuss the implications of our results and present suggestions for future research.
Asunto(s)
Agotamiento Profesional/psicología , Correo Electrónico , Investigadores/psicología , Compromiso Laboral , Adulto , Área Bajo la Curva , Agotamiento Profesional/diagnóstico , Comunicación , Diagnóstico Precoz , Correo Electrónico/estadística & datos numéricos , Fatiga/psicología , Femenino , Humanos , Italia , Modelos Lineales , Masculino , Modelos Teóricos , Medición de Riesgo/economía , Medición de Riesgo/métodos , Conducta Social , Encuestas y CuestionariosRESUMEN
Increasingly diversity researchers call for further studies of group micro-processes and dynamics to understand the paradoxical effects of diversity on group performance. In this study, based on analyses of in-group, networked, homophilous interactions, we aim to explain further the effects of diversity on group performance in a parallel problem-solving task, both experimentally and computationally. We developed a 'whodunit' problem-solving experiment with 116 participants assigned to different-sized groups. Experimental results show that low diversity and high homophily levels are associated with lower performance while the effects of group size are not significant. To investigate this further, we developed an agent-based computational model (ABM), through which we inspected (a) the effect of different homophily and diversity strengths on performance, and (b) the robustness of such effects across group size variations. Overall, modeling results were consistent with our experimental findings, and revealed that the strength of homophily can drive diversity towards a positive or negative impact on performance. We also observed that increasing group size has a very marginal effect. Our work contributes to a better understanding of the implications of diversity in-group problem-solving by providing an integration of both experimental and computational perspectives in the analysis of group processes.