Food waste has received increasing attention over the last decade, owing to its economic, environmental, and social impacts. Much of the existing research has investigated consumers’ buying behaviour towards sub-optimal and upcycle food, but surplus meal buying behaviours are poorly understood. Thus, this study performed consumer segmentation through a modular food-related lifestyle (MFRL) instrument and determined consumers’ buying behaviour towards surplus meals in canteens employing the theory of reasoned action (TRA). A survey was conducted using a validated questionnaire from a convenient sample of 460 Danish canteen users. Four food-related lifestyle consumer segments were identified by employing k-means segmentation: Conservative (28%), Adventurous (15%), Uninvolved (12%), and Eco-moderate (45%). The Partial Least Square Structural Equation Modelling (PLS-SEM) analysis indicated that attitudes and subjective norms were significantly influencing surplus meal buying intention to further influence buying behaviour. Environmental objective knowledge was significantly influencing environmental concerns to further influence attitudes and behavioural intention. However, environmental objective knowledge had no significant influence on attitude towards surplus meals. Male consumers with higher education, those having higher food responsibility and lower food involvement, and convenience scores had higher surplus food buying behaviour. The results can be used to inform policymakers, marketers, business professionals, and practitioners to promote surplus meals in canteens or similar settings.
Randomness is a common uncertainty encountered in practical multi-objectives decision-making. But it is always a challenge for decision-makers to process randomness in multi-objective programming problems. This paper takes the decision-making objectives as fuzzy events and aims to solve numerical multi-objective programming problems under random environment. We first analyze the effects of randomness on multi-objective decision-making results. With the expectation value and the probability of fuzzy events as quantitative index of randomness, we then establish a two-stage random multi-objective programming model based on reliability (i.e., TS-MOPM). Specifically, we give several probability calculation methods of fuzzy events with common distributions, and further present the corresponding calculation procedures for solving TS-MOPM. Finally, a case study is implemented to test the proposed model TS-MOPM. Theoretical analysis and case study indicate that our model has better interpretability and operability. The research results enrich the existing random multi-objective programming methods to some extent.
The compliance issue of electric power companies is the focus of supervision of various countries' regulatory agencies. This article first puts forward a compliance evaluation index system for power companies based on the compliance practices of typical companies. Secondly, based on the Bayesian best-worst and matter-element extension method, the power enterprise compliance evaluation model is constructed. Finally, an empirical analysis is carried out by taking a certain provincial power grid company as an example. The empirical results show that the company's compliance management is at a medium level, and it is particularly necessary to improve the implementation of the compliance plans.
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