This study explores the relationships between group-affect tone, teams’ transactive memory systems (TMSs), and teams’ incremental creativity. Data were collected from 334 team members and 70 team leaders across 70 teams. Results indicate that positive group-affect tone enhances TMS, while negative group-affect tone impedes it. TMS positively impacts team incremental creativity. Additionally, both types of group-affect tone influence incremental creativity through TMS mediation. This research advances TMS theory and group-affect tone, substantiating the affect-cognition model and deepening the understanding of TMS’s role in incremental creativity.

A hegemonic neoliberal ideology dominates all areas of work in Turkey, including healthcare. Though neoliberalism has been studied extensively from the perspective of meaning, values, and processes, managerial and leadership behavior dynamics require further research. This study analyzes the relationship between managerialism, toxic leadership, and ethical climate in an industry swept up by untamed neoliberalism, particularly in a nation where employment and human rights are ceremoniously protected. Through an analysis of medical doctors working in 207 public and private university hospitals in Turkey, we explored the role of managerialism and four distinct ethical climate types, resulting in the emergence of toxic leadership behaviors during the global pandemic. We theorize the extent to which toxic leaders emerge from managerialism. We further explain why the hegemonic Turkish leadership culture thrives in toxic behaviors such as paternalism, fealty, ingratiation, nepotism, and cronyism in the context of neoliberal expansion.

The germination percentage (GP) is commonly employed to estimate the viability of a seed population. Statistical methods such as analysis of variance (ANOVA) and logistic regression are frequently used to analyse GP data. While ANOVA has a long history of usage, logistic regression is considered more suitable for GP data due to its binomial nature. However, both methods have inherent issues that require attention. In this study, we address previously unexplored challenges associated with these methods and propose the utilization of a likelihood ratio test as a solution. We demonstrate the advantages of employing the likelihood ratio test for GP data analysis through simulations and real data analysis.

We use attribution theory to show that firms that make more internal attributions to positive performance outcomes engage slack resources more freely for corporate entrepreneurship (CE) than firms that make fewer of such attributions. In addition, we show that the way in which companies make external attributions to performance outcomes moderates this relationship. To examine this empirically, we explore how top management teams discuss the factors that contribute to firm performance. Specifically, we look at attributions provided in the Management's Discussion and Analysis section of the annual reports of 144 pharmaceutical firms over a 2-year period. In line with our predictions, we find that greater internal attribution to positive performance outcomes leads to increased use of slack resources for CE. Furthermore, we find that this effect is stronger when firms make more external attributions to negative performance outcomes than positive performance outcomes.

A classical approach in precision agriculture consists in validating within field zones defined from high spatial resolution observations by agronomic information (AI). Zones validation generally involves a two-step process. First, AI are obtained on a regular grid or following a target sampling strategy inside the field. Then, a statistical test, most often an ANOVA, is used to determine if the management zones created with the high spatial resolution auxiliary data explain differences in the AI values. Unfortunately, in precision agriculture, many of the works using such an approach omit a necessary condition for the implementation of the aforementioned ANOVA test, i.e. the observations need to be independent from each other. This condition is unfortunately seldom satisfied since AI are often spatially auto-correlated. In order to highlight this problem, simulated datasets with different and known AI spatial autocorrelation were used. Results show that as AI are more and more spatially auto-correlated, ANOVA tests almost always conclude that the management zones obtained with auxiliary data are significant whatever the zoning, i.e. even a completely random one. To overcome this problem, the paper introduces two methods directly inspired from published works in the field of ecology. Two cases were considered: the first one applies when large AI dataset (n>40) is available and the other one applies for small AI dataset (n<40). Both methods are implemented on a real precision viticulture example.

The interaction between wheat and perennial ryegrass seed density and seedlings of different ages was studied under controlled conditions. Root length of perennial ryegrass, after sowing, was suppressed by wheat and was dependent on the density of wheat seeds. Shoot growth of perennial ryegrass, however, was unaffected by the presence of wheat. Perennial ryegrass density had no effect on the first 2 wk of wheat seedling growth. The age of wheat seedlings had no appreciable influence on either root or shoot growth of perennial ryegrass. The present study highlights the need for an unbiased two-way experimental design to identify the dominant competitor.

Statistical analysis is increasingly used in seed germination/viability studies across different disciplines. The objective of this opinion piece is to assess current trends in statistical analysis of such data, and draw attention of readers to the limitations of the usual inferential statistics in controlling error rates. The assessments are based on a survey of 429 papers published in 139 peer-reviewed journals in the past 11 years. My intention is to identify areas of concern across a wide range of studies. Accordingly, the areas of greatest concern found in the analysis of percentage seed germination and viability data were: (1) pseudoreplication and/or use of a few replicates; (2) ignoring assumptions of ANOVA and non-parametric tests (NPARTs); (3) uncritical data transformation; (4) arbitrary choice of multiple comparison tests; and (5) lack of emphasis on effect sizes. Given the prevalence of these problems, in my opinion we would be building a body of knowledge on a shaky ground. The discussions that follow will: (1) describe situations where germination data violate assumptions of ANOVA and NPARTs; (2) highlight the implications of the various problems to Type I and Type II error rates; and (3) indicate remedial measures based on the recent statistical literature.

Stillbirth rates among single and multiple births show markedly decreasing temporal trends. In addition, several studies have demonstrated that the stillbirth rates are dependent on maternal age, in general, showing a U- or J-shaped association with maternal age. In this study, the temporal trends in and the effect of maternal age on the stillbirth rate were considered simultaneously. Our goal was to split the variation into temporal trends and maternal age effects. We applied two-dimensional analysis of variance because no linear association between maternal age and stillbirth rate can be assumed. The temporal trends of stillbirth rates also were not clearly linear. However, the possibility of applying regression analyses based on linear time trends was also considered. Our study is mainly based on official data from England and Wales for the period between 1927 and 2004. These results were compared with registered birth data from Finland between 1937 and 1997. The best fit was obtained when the models were built for the logarithm of the stillbirth rate. Our interpretation of this result is that an association exists between the effects of the factors and the mean stillbirth rate, and consequently, a multiplicative model was applied. Relatively high stillbirth rates were observed among twin births of young mothers and among all births of older mothers.