after an MBA in International Business. Professor Dias has over 24 years of teaching experience. He has had several visiting positions in different countries and institutions including Brazil, Angola, Spain, Poland and Finland. He regularly teaches in English, Portuguese, and Spanish at undergraduate, master and doctorate levels, as well as in executive programs. Professor Dias has produced extensive research in the field of Tourism and Management, including books, book chapters, papers in scientific journals and conference proceedings, case studies, and working papers.

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Model II regression (i.e. minimizing residuals obliquely) is the adequate alternative to Model I regression by Ordinary Least Squares (i.e. minimizing residuals vertically) given the absence of well-established dependence relationships or x measured with error. Yet, it has no perfect solution. Determining the true slope from errors-in-the-variables models requires the errors in x and y estimated from higher order moments. However, their accurate estimation requires enormous data sets and thus they are not applicable to most ecological problems. The alternative Reduced Major Axis (RMA) is dependent on a strict set of assumptions, hardly met with real data, making it prone to bias, whereas Principal Components Analysis (PCA) becomes less reliable with decreasing correlations while x and y presenting approximate variances. We used artificial data (allowing for the determination of the true slope) to demonstrate when RMA or PCA should be preferred. Consequently, we propose using PCA whenever r 2 +s 2 x /s 2 y is higher than 1.5. Otherwise, we suggest generating artificial data manipulated to match the structure of the original, and to test which method provides closer estimates to the input true slope. We provide a user-friendly script to perform this task. We tested the use of RMA and PCA with real data about intraspecific and interspecific biomass-density relations in algae and seagrass, algae frond growth, crustacean and bird morphometry, sardine fisheries and social sciences data, commonly finding widely divergent slope estimates leading to severely biased parameter estimations and model applications. Their analyses support the suggested approach for method selection summarized above.

Purpose The purpose of this paper is to identify the factors that individuals consider necessary to be happy in their job. Based on these factors, a measure of job design happiness (JDH) is proposed. Design/methodology/approach Two methods were applied: a qualitative study with content analyses (n=969) to develop an exploratory questionnaire; and exploratory and confirmatory factor analysis by applying structural equations models. In this second study the questionnaire was sent to a second sample (n=1,079). Findings Five first-order factors were identified: self-fulfillment; group working, attaining goals; leadership; and sustainability and job/family balance. These factors are explained by a second order factor: JDH. Research limitations/implications Further research is needed to determine how the identified “job design happiness” components may interact with one another. Testing the measure of different industries and national cultures is also suggested. Practical implications Managers and human resources practitioners can improve job and organizational performance by applying the scale in several moments in time measuring the job happiness “pulse,” monitoring their decisions. Social implications The adoption of this measure for decision making in organizational and job design can contribute to the improvement of living standards and firm sustainability. Originality/value Research on organizational happiness has been increasing but instruments to measure JDH, considering organizational factors, are limited.

Well-being, equity, and inclusion are central aspects of happy schools. In this context, we aim to provide information to identify the characteristics of happy schools and Portuguese children’s level of happiness at school. In total, one thousand three hundred and ninety-nine parents of children from five to twenty years old participated in this study by answering a mostly open-ended questionnaire. They indicated their perceptions of how happy their children were at school, the moments they associated with individual happiness and unhappiness, and the characteristics of schools that they found conducive to promoting happiness. The findings show that parents value the relationships their children establish at school, teachers’ personal and professional skills, learning strategies, and the fact that students can be creative while learning valuable content. From the perspective of parents, unhappy schools are characterized by teachers with negative attitudes and attributes, bullying, an excessive workload, and consequent stress. Differences in terms of gender and age were not significant in this study. We found that levels of happiness at school decrease as students’ ages increase.