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The focus of this paper is on the generation of food waste by small and micro restaurants, specifically on the perceptions of representatives of these restaurants. Do they perceive this generation of food waste as problematic, and do they perceive that they have enough knowledge about how to minimize this generated food waste? With data from a sample of 200 Dutch restaurants, which were collected through surveys and analyzed with regression analysis, we came to two key findings. The first key finding is that our respondents hardly perceive the food waste that is generated in their own restaurant as problematic. The second key finding is that they perceive they have limited knowledge about how to minimize the food waste that is generated in their own restaurant. The main influencing factor for both perceptions appeared to be the actual level of food waste generated in their own restaurant. This paper continues with a number of recommendations for future research, to apply other research techniques and to study other sectors as well. The paper ends with practical recommendations for the representatives of the small and micro restaurants, as the findings of this paper suggest a need for targeted educational and training programs to enhance food waste management in their restaurants, contributing to broader sustainability goals. [ABSTRACT FROM AUTHOR]

This paper deals with a factor analysis model. Usually one assumes that the model has normally distributed variables. If these variables are not normally distributed, the well-known estimators using only variances and covariances of the observed variables may lead to biased estimates of the factor loadings. This paper uses characteristic functions to derive an approximately consistent BGLS-estimator of the factor loadings in a model with both normal and non-normal variables.An example with generated data and an empirical example will evaluate the estimator. Furthermore, the estimator will be compared to well-known estimators using only second order moments, consistent estimators using third order moments and estimators using transform ations to get normal variables.It turns out that our method gives good estimates of the unknown model parameters for sample sizes larger than 50. Our estimation method also avoids the disadvantage of the above mentioned estimation methods.

We argue that many models for multivariate longitudinal and cross-sectional data analysis have a common ancestry. They all are based on the qualitative idea that if we knew the actual state of the world, the relations between the observed quantities would be truly simple. This is shown to lead directly to factor analysis, IRT, state space models, mixture densities, latent Markov chains, MIMIC, LISREL, and various other common models and techniques. We show how our approach provides a convenient framework for looking at these models. The EM algorithm can be used to estimate the unknown parameters. An additional advantage of our approach is that it can incorporate continuous as well as interval, ordinal and categorical variables .

By using information technology, local governments can develop alternative forms of citizen engagement. Civic crowdfunding campaigns supported by online platforms enable citizens to participate financially in social projects and can be matched with government funding. As such, an alternative for subsidies seems to be developing. In this paper, we assess empirically the success of civic crowdfunding campaigns in the Netherlands by using data collected during 2018 from 269 civic crowdfunding projects and local demographic data from the neighborhoods of these projects. The factors--the use of match-funding, the target amount of money, and the theme of the project, as well as the age structure, the province, and the degree of urbanization of the neighborhood of the civic crowdfunding project--turn out to be empirically related to the success of a civic crowdfunding campaign. [ABSTRACT FROM AUTHOR]

For the last two decades, scholars have tried to link entrepreneurship typologies originally constructed in the West with firm performance in Africa's Least Developed Countries (LDCs). Results were often surprising in the sense that they were different from what was found in Western economies. In this paper we take a step back and investigate the importance of small business owners' universal socio-demographics: their nationality, start-up experience, age, gender and highest completed education. Unlike theoretically more sophisticated concepts used in earlier studies, these indicators have the advantage that they are universally applicable and do not suffer from context-specific meanings and interpretations. Hence, they form a good starting point to understand firm performance in LDCs. Using new and unique primary data, this study explores these socio-demographic factors using a cross-country sample of 306 small business owners from two African LDCs. Using ordered logit models, we find that also for these socio-demographic characteristics, the link with firm performance (measured in terms of employee growth and sales growth) seems to be different in LDCs. Accordingly, one of the key lessons which can be drawn from this study is that scholars should search for more relevant, context-specific, indicators of firm performance in the extreme impoverished areas of the world. [ABSTRACT FROM AUTHOR]