Clima e comportamentos de aprendizagem grupal

Abstract

RESUMOHá um aumento do reconhecimento por parte das organizações da necessidade de reestruturar o trabalho em torno de equipes, proporcionando oportunidades para os membros aprenderem com os colegas por meio do diálogo e discussão. Este estudo teve como objetivo definir e operacionalizar o clima para a aprendizagem grupal (CAG) como um preditor dos comportamentos de aprendizagem grupal. CAG foi definido e operacionalizado como uma variável latente de segunda ordem com sete dimensões, de modo a obter validade de conteúdo, convergente e discriminante. A hipótese foi testada a partir de uma amostra de 217 trabalhadores que atuam em equipe, em uma organização de desenvolvimento e treinamento para o uso de softwares. A modelagem de equações estruturais revelou que o CAG tem forte relação com os comportamentos de aprendizagem grupal. Assim, a promoção de um CAG requer que a equipe perceba que tem: apoio da gestão; tempo suficiente para realizar o trabalho e aprender; controle sobre eventos organizacionais, possiblidade de iniciar uma ação e tomar decisões; oportunidades de aprender com os especialistas; acesso fácil a informações e orientações escritas e sentimento geral de satisfação com o local de trabalho. Palavras-chave:Clima para a aprendizagem grupal. Comportamentos de Aprendizagem grupal. Modelo de equações estruturais. Viés do método. ABSTRACTThere is increased recognition by the organizations of the need to restructure work around teams, thus providing opportunities for members to learn from colleagues through dialogue and discussion. In this study, our purpose was to define and to operationalize the group learning climate (GLC) as a predictor of the group learning behavior. We defined and operationalized GLC as a second order latent variable with seven dimensions, in order to obtain content validity, both convergent and discriminant. We tested this hypothesis with a sample of 217 workers who work as a team in an organization that develops software and provides training courses in software use. The structural equation modeling showed that the GLC has a strong correlation with the group learning behavior. Thus, the promotion of the GLC requires that the team realize that they have: management support; enough time to do the work and learn; control over organizational events; the possibility to initiate action and make decisions; opportunities to learn from experts; easy access to information and written guidelines, and a general feeling of satisfaction with the workplace.

Development of scale and model for evaluating the individual performance appraisals—Process in public management

“…The proposed objectives for the study were all met, as we measured the scale and determined the factors that influence the engagement of those involved in the IPA process using EFA (Bido et al, 2018;Hinkin, 1995;Almanasreh et al, 2019;Charmaz, 2006;Dixon-Woods, 2010). The objective of proposing and testing the hypothetical model, on the other hand, was achieved through the literature review after identifying the critical constructs of the process and applying SEM-CB to the empirical data to test whether the proposed model had validity (Hair et al, 2010;Malhotra et al, 2010;Cohen et al, 2003;Onça et al, 2018).…”

“…This form of scale is widely used within the social sciences. It can capture respondents' perceptions with scale statements and is easily operationalized statistically through multivariate regression methods such as Structural Equation Modeling-SEM (Hair et al, 2010;Malhotra et al, 2010;Cohen et al, 2003;Onça et al, 2018). These items were transformed into an electronic form using Google Forms and sent via link to potential respondents within our target research audience.…”

“…The covariance matrix analysis of the constructs (Table 3) that emerged from the EFA indicates that they do not have correlations above 0.70, pointing to the nonexistence of multicollinearity among the emerging constructs (Brown, 2006;Onça et al, 2018;Conway and Huffcutt, 2003). Cumulative analysis indicates that these eight constructs capture 55.46% of the covariance of the emergent scale items.…”

“…To test whether these constructs did not have discriminant validity, we performed the test indicated by Jöreskog (Brown, 2006;Onça et al, 2018). This test consists of restricting the correlation of the constructs you want to test discriminant validity and checking whether the change in the model fit indices is statistically significant (Brown, 2006;Onça et al, 2018).…”

“…To test whether these constructs did not have discriminant validity, we performed the test indicated by Jöreskog (Brown, 2006;Onça et al, 2018). This test consists of restricting the correlation of the constructs you want to test discriminant validity and checking whether the change in the model fit indices is statistically significant (Brown, 2006;Onça et al, 2018). For our model, when we tested the difference between the indices of the free model and the indices of the model with the restriction in the correlation, the results were statistically significant, pointing to discriminant validity between the constructs tested.…”