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Personalized services provided by Location Based Services (LBS) are becoming increasingly prevalent to the large population of semi-literate users living in emerging economies due to the low costs and ubiquity. However, usage of LBS is still threatened by location privacy threats as it keeps track of the individuals’ location. Studies typically only addressed how to mitigate location privacy apprehensions for the literate users and not the semi-literate users. To fill that gap and better understand location privacy apprehensions among different communities, this study draws upon theories of Restrict Access/Limited Control and Familiarity to identify the antecedents of location privacy apprehensions related to personalized services provided by LBS and user literacy. The proposed research model is empirically tested in a laboratory experiment. The findings show that the different types of LBS do affect the degree of location privacy apprehensions between the literate and semi-literate users. Implications for enhancing usage intentions and mitigating location privacy apprehensions for different types of mobile applications are discussed.

The reduction of crop yield losses caused by pests is a major challenge to productive and sustainable food production for preventing food insecurity and emergencies, and for alleviating world food crisis. Multiple regression (MR) and artificial neural network (ANN) are two widely adopted modelling approaches for the prediction of crop pest risks, which are based on empirical statistics and artificial intelligence, respectively. Each of the two alternative approaches has its advantages and disadvantages. This study evaluates the two models from two aspects: their performances on pest risk prediction, and their methodological advantages and disadvantages. Two pest species are modelled using the two approaches as case studies, which are the melon thrip Thrips palmi Karny (T. palmi) and the diamondback moth Plutella xylostella (L.) (P. xylostella). Results show that ANN has higher prediction accuracy for both species. However, ANN has some methodological demerits compared to MR modelling.