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Abstract. This paper presents a fuzzy multicriteria group decision making approach for evaluating and selecting information systems projects. The inherent subjectiveness and imprecision of the evaluation process is modeled by using linguistic terms characterized by triangular fuzzy numbers. A new algorithm based on the concept of the degree of dominance is developed to avoid the complex and unreliable process of comparing fuzzy numbers usually required in fuzzy multicriteria decision making. A multicriteria decision support system is proposed to facilitate the evaluation and selection process. An information systems project selection problem is presented to demonstrate the effectiveness of the approach.

The dairy incident in 2008 influenced Chinese residents' attitudes towards domestic and foreign brands in the market. This paper highlights the strong consumer perceptions existing in the Chinese dairy market towards the country of origin of dairy products. Chinese residents generally believe dairy products from foreign countries are superior than those from China. A new theoretical framework is developed to explore the driving factors of country-of-effects and its corresponding impacts. Consumers' image of different countries and national stereotypes, consumer ethnocentrism and animosity, product familiarity and experience, product involvement and some cultural value differences were found to drive country-of-origin effects. These effects directly impact on consumer's perceived quality, brand awareness, brand association and loyalty towards the related goods in the market, then influence the brand equity of products from different countries. This study provides a better understanding of country-of-origin effects on consumer behaviour, and will help relevant domestic and foreign firms improve their business strategies in China. K E Y W O R D Sbaby formula scandal, consumer behaviour, China, country-of-origin effects, dairy market brand

This paper explores the potential of machine learning algorithms for weed and crop classification from UAV images. The identification of weeds in crops is a challenging task that has been addressed through orthomosaicing of images, feature extraction and labelling of images to train machine learning algorithms. In this paper, the performances of several machine learning algorithms, random forest (RF), support vector machine (SVM) and k-nearest neighbours (KNN), are analysed to detect weeds using UAV images collected from a chilli crop field located in Australia. The evaluation metrics used in the comparison of performance were accuracy, precision, recall, false positive rate and kappa coefficient. MATLAB is used for simulating the machine learning algorithms; and the achieved weed detection accuracies are 96% using RF, 94% using SVM and 63% using KNN. Based on this study, RF and SVM algorithms are efficient and practical to use, and can be implemented easily for detecting weed from UAV images.

This study sought academic staff and students’ views of electronic exams (e-exams) system and the benefits and challenges of e-exams in general. The respondents provided useful feedback for future adoption of e-exams at an Australian university and elsewhere too. The key findings show that students and academic staff are optimistic about the future adoption of e-exams if the e-exams system is sufficiently improved. They are fully aware of the benefits the technology could offer in supporting learning and education in general and see e-exams as an innovation for learning and teaching in higher education.