Purpose: This research was based on the "happy, productive worker" hypothesis. The objective was to analyze the predictive relationships, through a multilevel approach, between the variables well-being at work, organizational justice, organizational support, and the dependent variable individual job performance. Originality/value: The multilevel study of individual job performance and its relations with well-being and organizational variables are still a current gap in the literature, as well as the possibility of testing whether well-being at work can be considered a collective phenomenon. The presence of organizational support in the model, operationalized at the team level, represents an important contribution to the development of theories focused on teams' roles in organizations, especially their impact on organizational variables. Design/methodology/approach: Considering the proposed analysis at two different levels, a multilevel design model was adopted. The final sample consisted of 730 individuals and 32 units. The data were collected through a questionnaire composed of four previously validated scales. Data analysis followed the six steps proposed by Hox, Moerbeek, and Schoot (2017) for multilevel models for each of the samples. Findings: The explanatory model presented a predictive relationship between achievement (well-being at work factor), operationalized as an individual-level variable, and individual job performance; a predictive relationship between interactional justice, also operationalized as an individual-level variable, and individual job performance, and a predictive relationship between collective perceptions of organizational support, operationalized as a team-level variable, and individual job performance.
This paper aims to discuss explanatory models in Work and Organizational Psychology by analyzing their metatheoretical underpinnings. In particular, the paper analyzes the concepts of causes and reasons, both of which perform a central role in the constitution of scientific models. We demonstrate that experimental, correlational, and case study explanatory models use causes and reasons as strategies of knowledge building, and as a means of linking phenomenon, data, and theory. We also discuss the methodological implications of such use of causes and reasons by scientific models. The paper concludes by discussing theoretical and methodological issues involved in the attempts at building complex models in WOP -showing that, in order to be complex, a model needs to include in its formulation stable-dynamic, cause-reason, and contextual distance-proximity analyses, as well as the theory-phenomenon-data triad. Modelos explicativos de fenômenos em POT: Aspectos epistemológicos, teóricos e metodológicosResumo Este artigo tem como objetivo discutir modelos explicativos em Psicologia Organizacional e do Trabalho tendo, como pano de fundo, uma análise das bases metateóricas que os sustentam epistemologicamente. Em particular, analisam-se os conceitos de causas e razões, ambos centrais na composição desses modelos. Demonstramos que os modelos explicativos experimentais, correlacionais e os estudos de caso utilizam-se distintamente de causas e razões em suas estratégias de construção do conhecimento e de articulação entre dados, fenômeno e teoria, e que essa utilização tem implicações no nível metodológico. Finaliza-se problematizando aspectos teórico-metodológicos envolvidos na construção de modelos mais complexos em POT, que levem em conta a análise das dimensões estável-dinâmico, causa-razão e distanciamento-proximidade do contexto, e a tríade teoria-fenômeno-dado. Modelos explicativos de fenómenos en POT: Aspectos epistemológicos, teóricos y metodológicosResumen Este artículo tiene como objetivo discutir modelos explicativos en Psicología Organizacional al y del Trabajo, teniendo como trasfondo un análisis de las bases metateóricas que los sostienen epistemológicamente. En concreto, se analizan los conceptos de causas y razones, ambos centrales en la formación de dichos modelos. Demostramos que tanto los modelos explicativos experimentales, como los correlacionales y los estudios de caso utilizan, indistintamente, causas y razones en sus estrategias de construcción del conocimiento y de articulación de datos, fenómenos y teoría, y que tal utilización tiene implicaciones en el nivel metodológico. Finalmente, se problematizan aspectos teórico-metodológicos implicados en la construcción de modelos más complejos en POT, que tengan en cuenta el análisis de las dimensiones estable-dinámico, causa-razón, y distanciamiento-proximidad del contexto, así como la tríada teoría-fenómeno-dato.
This article aims to analyse the process of internationalisation of scientific output in Brazilian psychology and its subfields and compare them with other countries from 2011 to 2020. Two other objectives were taken into consideration: to describe the collaboration networks formed by national psychology researchers and their influence on the impact of scientific output while analysing the relationships in the North–South and South-South axes and to reflect on the advance of the quality of the scientific output over time, considering the indicators of its scientific impact. We used SciVal, based on the Scopus database. The main result is that cooperation on the South-North axis was dominant compared to cooperation on the South-South and South-East axes. The paper also discusses the importance of public funding agencies and the growth of graduate programs in Brazil, enabling the increase in output and the internationalisation of national psychology. The final part addresses the limitations of the Scopus database and some guidelines for the future of the internationalisation of Brazilian psychology.
The global outbreak of coronavirus SARS-CoV-2 (COVID-19) disease is affecting every part of human lives. Several researchers investigated to understand how temperature, humidity and air pollution had an influence on COVID-19 transmission. Transmission of COVID-19 due to temperature and humidity is a pertinent question. There is a lack of study of Covid-19 in tropical climate countries. This study aims to analyze the correlation between weather and Covid-19 pandemic in Brasília and Manaus, two states of Brazil. The research topic is important to know how the climate affects or predisposes the spread of COVID-19. This knowledge will provide elements to decision-makers regarding health and public health standards and decisions. This study employed a secondary data analysis of surveillance data of Covid-19 from the Ministry of Health of Brazil and weather from the National Institute of Meteorology of Brazil. These are Brazilian public organizations that, on a daily basis, record this information on a systematic basis of dates. They are central federal organizations, responsible for data analysis and public policy planning to combat Covid-19. The data are reliables and obtained from reliable government sources. We systematically record all information for 51 days, during a period of high disease growth in the country. The components of weather include low temperature (°C), high temperature (°C), temperature average (°C), humidity (%), and amount of rainfall (mm). Pearson-rank correlation test showed that high temperature (r=.643; p<.001), low temperature (r=.640; p<.001) and humidity (r=.248; p<.005) were significantly correlated with deaths caused by Covid-19 pandemic used for data analysis. Social isolation rate (β = -.254; p<.001) and daily record of new cases (β = .332; p<.001), with adjusted R-squared of .623, were the predictors of deaths acummuled by Covid-19. The finding serves as an input to reduce the incidence rate of Covid-19 in Brazil. Statistical results show evidence of the relationship between climate elements and COVID-19 indicators, such as the number of deaths, spread of contamination and social isolation rate. The study of dimensions of climate as a seasonal pattern and its relationship to COVID-19 benefits epidemiological surveillance. The more geographic spaces are known, more will help to understand the differences in disease behavior in different places. The results of this research showed that environmental conditions influence the contagion and speed of transmission of Covid-19. Policies that contribute to benefits to health and sustainability need to be planned. The contribution of climate and other factors, such as air pollution, for example, require additional studies. Environmental changes, such as climate change and biodiversity, must also be investigated for their impact on human health. Acting in prevention, including the promotion of socially acceptable behaviors on the part of the population, seems to be the best way to deal with Covid-19.
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