A influência da diferenciação das relações líder-membro no comprometimento com a unidade de trabalho: o papel mediador do clima de apoio

We investigated whether the influence of LMX differentiation (the extent to which the quality of the leader-member relationships within a work unit varies) on work unit commitment is mediated by support climate. We tested this mediated relationship in a sample composed of 30 health care units. The results obtained showed that LMX differentiation measured at Time 1 was negatively related to work unit support climate measured one year later (Time 2), which in turn was positively related to work unit commitment measured at Time 2. The negative indirect effect of LMX differentiation on work unit commitment through support climate was statistically significant. Our study contributes to having a better understanding of the role of LMX differentiation in work unit functioning.Investigámos se a influência da diferenciação de LMX (o quanto a qualidade das relações de líder-membro dentro de uma unidade de trabalho varia) no comprometimento com a unidade de trabalho é mediada pelo clima de apoio. Testámos essa relação mediada em uma amostra composta por 30 unidades de saúde. Os resultados obtidos mostraram que a diferenciação da LMX medida no Tempo 1 foi negativamente relacionada ao clima de suporte da unidade de trabalho medido um ano depois (Tempo 2), que por sua vez foi positivamente relacionado ao comprometimento com a unidade de trabalho medido no Tempo 2. O efeito indireto negativo da diferenciação da LMX no comprometimento com a unidade de trabalho por meio do clima de apoio foi estatisticamente significativo. O nosso estudo contribui para uma melhor compreensão do papel da diferenciação do LMX no funcionamento da unidade de trabalho

We Trust You! A Multilevel-Multireferent Model Based on Organizational Trust to Explain Performance

This study tests organizational trust as the psychosocial mechanism that explains how healthy organizational practices and team resources predict multilevel performance in organizations and teams, respectively. In our methodology, we collect data in a sample of 890 employees from 177 teams and their immediate supervisors from 31 Spanish companies. Our results from the multilevel analysis show two independent processes predicting organizational performance (return on assets, ROA) and performance ratings by immediate supervisors, operating at the organizational and team levels, respectively. We have found evidence for a theoretical and functional quasiisomorphism. First, based on social exchange theory, we found evidence for our prediction that when organizations implement healthy practices and teams provide resources, employees trust their top managers (vertical trust) and coworkers (horizontal trust) and try to reciprocate these benefits by improving their performance. Second, (relationships among) constructs are similar at different levels of analysis, which may inform HRM officers and managers about which type of practices and resources can help to enhance trust and improve performance in organizations. The present study contributes to the scarce research on the role of trust at collective (i.e., organizational and team) levels as a psychological mechanism that explains how organizational practices and team resources are linked to organizational performance

Employers have a Duty of Beneficence to Design for Meaningful Work:A General Argument and Logistics Warehouses as a Case Study

Artificial intelligence-driven technology increasingly shapes work practices and, accordingly, employees’ opportunities for meaningful work (MW). In our paper, we identify five dimensions of MW: pursuing a purpose, social relationships, exercising skills and self-development, autonomy, self-esteem and recognition. Because MW is an important good, lacking opportunities for MW is a serious disadvantage. Therefore, we need to know to what extent employers have a duty to provide this good to their employees. We hold that employers have a duty of beneficence to design for opportunities for MW when implementing AI-technology in the workplace. We argue that this duty of beneficence is supported by the three major ethical theories, namely, Kantian ethics, consequentialism, and virtue ethics. We defend this duty against two objections, including the view that it is incompatible with the shareholder theory of the firm. We then employ the five dimensions of MW as our analytical lens to investigate how AI-based technological innovation in logistic warehouses has an impact, both positively and negatively, on MW, and illustrate that design for MW is feasible. We further support this practical feasibility with the help of insights from organizational psychology. We end by discussing how AI-based technology has an impact both on meaningful work (often seen as an aspirational goal) and decent work (generally seen as a matter of justice). Accordingly, ethical reflection on meaningful and decent work should become more integrated to do justice to how AI-technology inevitably shapes both simultaneously.</p

Job characteristics and experience as predictors of occupational turnover intention and occupational turnover in the European nursing sector

The present study aims to unravel the relationships between job demands and resources, occupational turnover intention, and occupational turnover. To do so, we tested a model wherein associations between nurses' age, tenure in profession and tenure with present employer (experience in the profession), job demands (emotional demands, work-home interference), and job resources (influence at work and opportunities for development) predicted occupational turnover intention, and, subsequently, occupational turnover. A longitudinal survey was conducted among a sample of 753 nurses working in European health care institutions (hospitals, nursing homes, and community/home care). The results supported the hypotheses that job demands are positively related with occupational turnover intention while job resources and experience in the nursing profession are negatively related with occupational turnover intention. We did not find evidence supporting the hypothesis that occupational turnover intention mediates the relationship between job demands and job resources on the one hand, and occupational turnover on the other hand. Experience in the nursing profession had a direct effect on occupational turnover, in addition to the direct effect of occupational turnover intention. The implications of our findings for understanding the process through which health care organizations can affect occupational turnover intention and actual exit behavior, are discussed

BLAST: Correlations in the Cosmic Far-Infrared Background at 250, 350, and 500 microns Reveal Clustering of Star-Forming Galaxies

We detect correlations in the cosmic far-infrared background due to the clustering of star-forming galaxies in observations made with the Balloon-borne Large Aperture Submillimeter Telescope, BLAST, at 250, 350, and 500 microns. We perform jackknife and other tests to confirm the reality of the signal. The measured correlations are well fit by a power law over scales of 5-25 arcminutes, with Delta I/I = 15.1 +/- 1.7%. We adopt a specific model for submillimeter sources in which the contribution to clustering comes from sources in the redshift ranges 1.3 <= z <= 2.2, 1.5 <= z <= 2.7, and 1.7 <= z <= 3.2, at 250, 350, and 500 microns, respectively. With these distributions, our measurement of the power spectrum, P(k_theta), corresponds to linear bias parameters, b = 3.8 +/- 0.6, 3.9 +/- 0.6 and 4.4 +/- 0.7, respectively. We further interpret the results in terms of the halo model, and find that at the smaller scales, the simplest halo model fails to fit our results. One way to improve the fit is to increase the radius at which dark matter halos are artificially truncated in the model, which is equivalent to having some star-forming galaxies at z >= 1 located in the outskirts of groups and clusters. In the context of this model we find a minimum halo mass required to host a galaxy is log (M_min / M_sun) = 11.5 (+0.4/-0.1), and we derive effective biases $b_eff = 2.2 +/- 0.2, 2.4 +/- 0.2, and 2.6 +/- 0.2, and effective masses log (M_eff / M_sun) = 12.9 +/- 0.3, 12.8 +/- 0.2, and 12.7 +/- 0.2, at 250, 350, and 500 microns, corresponding to spatial correlation lengths of r_0 = 4.9, 5.0, and 5.2 +/- 0.7 h^-1 Mpc, respectively. Finally, we discuss implications for clustering measurement strategies with Herschel and Planck.Comment: Accepted for publication in the Astrophysical Journal. Maps and other results available at http://blastexperiment.info