A Multifaceted Explanation of the Predisposition to Buy Organic Food.

This study explores whether implicit and explicit attitudes toward organic products explain consumers' predisposition to buy organic food, considering the hedonic and utilitarian dimensions of attitudes. The data are from an online survey, which included a section on implicit attitudes (measured using an Implicit Association Test) and a section on explicit attitudes. Two products were analyzed using 557 responses from a panel of consumers: chocolate (hedonic-oriented food) and milk (a utilitarian-oriented food). Confirmatory factor analysis and multigroup structural equations were applied to assess the proposed model. Three findings may be highlighted. First, in the model with the lowest entropy, the hedonic and utilitarian dimensions are considered to be independent. Second, different types of attitudes play different roles depending on the product. Finally, implicit attitudes influence the predisposition to buy organic food in the case of pleasure-seeking food but not utilitarian-oriented food. Thus, there is convergence between implicit and explicit attitudes for hedonic-oriented foods and divergence between such attitudes for utilitarian-oriented foods. This study's value lies in the novel use of implicit attitudes, which have generally been neglected in attitudinal models in the organic food domain.

HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario.

Nowadays, Physical Web together with the increase in the use of mobile devices, Global Positioning System (GPS), and Social Networking Sites (SNS) have caused users to share enriched information on the Web such as their tourist experiences. Therefore, an area that has been significantly improved by using the contextual information provided by these technologies is tourism. In this way, the main goals of this work are to propose and develop an algorithm focused on the recommendation of Smart Point of Interaction (Smart POI) for a specific user according to his/her preferences and the Smart POIs' context. Hence, a novel Hybrid Recommendation Algorithm (HyRA) is presented by incorporating an aggregation operator into the user-based Collaborative Filtering (CF) algorithm as well as including the Smart POIs' categories and geographical information. For the experimental phase, two real-world datasets have been collected and preprocessed. In addition, one Smart POIs' categories dataset was built. As a result, a dataset composed of 16 Smart POIs, another constituted by the explicit preferences of 200 respondents, and the last dataset integrated by 13 Smart POIs' categories are provided. The experimental results show that the recommendations suggested by HyRA are promising.