Entre as tramas da indústria da moda: argumentos sobre o trabalho escravo contemporâneo na sociedade de consumo

Even after its abolition, the slave labor still exists in the world. In a new socio-historic context, the shackles and slave quarters have been left behind, nowadays the workers are tempted, subjected to degrading conditions and have their rights retrenched. The contemporary slave labor has been emerging as subject of research in the Organizational Studies since the early 2000s, calling attention to many gaps to be filled about the way organizations all around the world use this practice. Contemporary slave labor is found in many and various economic activities, since coal to textile industries or even stores. In this dissertation, we have incorporated the consumption dimension to the field of Organizational Studies, discussing the modern slavery, aiming to understand the consumers’ point of view about this topic, that is, we have researched the consumers’ interpretations concerning the slave labor in the fashion industry. Our objective is to analyze consumer’s argumentative construction in the decision of buying or not products made by industries from the fashion field that were denounced because of slave labor usage. We have adopted fashion industry as research focus because it obscures the reflection of the consumers that feel like in a new world while shopping, a world of beauty and fantasy, seeking their own satisfaction. Furthermore, the Brazilian fashion industry is one of the biggest of the world (ABIT, 2015), with a huge symbolic strength in the country. We have realized a qualitative research using semi-structured interviews with 35 consumers to identify their arguments according to the criteria defined by Liakopoulos (2002): data, propositions, guarantees, supports and refutations. The data are the statements used by the interviewees categorically, that is, those which are clear in the interviews. The propositions are what qualifies and justifies the used data. The guarantees are related to the nature of the data, they are what gives the sense to the data and are introduced implicitly in the interviewee speech. The supports are universal premises introduced in order to legitimate the arguments. The refutations, when present, counter the used arguments. As results, we’ve found consumers who developed arguments pro-consumption and anti-consumption and who have defended ideas about the responsibility of different actors for the existence of this practice and for the fight against it. From these two categories: (1) pro-consumption – consume despite the complaints and (2) anti-consumption – don’t consume, because of the accusations; we have identified the following argumentative lines: skepticism, faultfinding and moral engagement. By the end, we have presented the interviewees’ argumentative construction and the obtained results.

Uma abordagem baseada em filtragem colaborativa integrada a mapas de saliência para a recomendação de imagens

Nowadays, the amount of customers using sites for shopping is greatly increasing, mainly due to the easiness and rapidity of this way of consumption. The sites, differently from physical stores, can make anything available to customers. In this context, Recommender Systems (RS) have become indispensable to help consumers to find products that may possibly pleasant or be useful to them. These systems often use techniques of Collaborating Filtering (CF), whose main underlying idea is that products are recommended to a given user based on purchase information and evaluations of past, by a group of users similar to the user who is requesting recommendation. One of the main challenges faced by such a technique is the need of the user to provide some information about her preferences on products in order to get further recommendations from the system. When there are items that do not have ratings or that possess quite few ratings available, the recommender system performs poorly. This problem is known as new item cold-start. In this paper, we propose to investigate in what extent information on visual attention can help to produce more accurate recommendation models. We present a new CF strategy, called IKB-MS, that uses visual attention to characterize images and alleviate the new item cold-start problem. In order to validate this strategy, we created a clothing image database and we use three algorithms well known for the extraction of visual attention these images. An extensive set of experiments shows that our approach is efficient and outperforms state-of-the-art CF RS.