Abstract-Crowdsourcing is an emerging business model where tasks are accomplished by the general public; the crowd. Crowdsourcing has been used in a variety of disciplines, including information systems development, marketing and operationalization. It has been shown to be a successful model in recommendation systems, multimedia design and evaluation, database design, and search engine evaluation. Despite the increasing academic and industrial interest in crowdsourcing, there is still a high degree of diversity in the interpretation and the application of the concept. This paper analyses the literature and deduces a taxonomy of crowdsourcing. The taxonomy is meant to represent the different configurations of crowdsourcing in its main four pillars: the crowdsourcer, the crowd, the crowdsourced task and the crowdsourcing platform. Our outcome will help researchers and developers as a reference model to concretely and precisely state their particular interpretation and configuration of crowdsourcing.

Traditionally, researchers have used either o-the-shelf models such as COCOMO, or developed local models using statistical techniques such as stepwise regression, to obtain software eort estimates. More recently, attention has turned to a variety of machine learning methods such as arti®cial neural networks (ANNs), case-based reasoning (CBR) and rule induction (RI). This paper outlines some comparative research into the use of these three machine learning methods to build software eort prediction systems. We brie¯y describe each method and then apply the techniques to a dataset of 81 software projects derived from a Canadian software house in the late 1980s. We compare the prediction systems in terms of three factors: accuracy, explanatory value and con®gurability. We show that ANN methods have superior accuracy and that RI methods are least accurate. However, this view is somewhat counteracted by problems with explanatory value and con®gurability. For example, we found that considerable eort was required to con®gure the ANN and that this compared very unfavourably with the other techniques, particularly CBR and least squares regression (LSR). We suggest that further work be carried out, both to further explore interaction between the enduser and the prediction system, and also to facilitate con®guration, particularly of ANNs. Ó

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Abstract. [Context & motivation]Digital Addiction, e.g. to social networks sites and games, is becoming a public interest issue which has a variety of socio-economic effects. Recent studies have shown correlation between Digital Addiction and certain negative consequences such as depression, reduced creativity and productivity, lack of sleep and disconnection from reality. Other research showed that Digital Addiction has withdrawal symptoms similar to those found in drug, tobacco, and alcohol addiction. [Question/problem] While industries like tobacco and alcohol are required by certain laws to have a label to raise awareness of the potential consequences of the use, we still do not have the same for addictive software. [Principal ideas/results] In this study, we advocate the need for Digital Addiction labels as an emerging ethical and professional requirement. We investigate the design of such labels from a user's perspective through an empirical study, following a mixed-methods approach, and report on the results. [Contribution] Our ultimate goal is to introduce the need for labelling to both researchers and developers and provide a checklist of questions to consider when handling this non-functional requirement.

Abstract. Gamification is an emerging technique which utilises the "fun theory" mainly to motivate people to change their perception and attitude towards certain subjects. Within enterprises, gamification is used to motivate employees to do their tasks more efficiently and perhaps more enjoyably and sometimes to increase their feeling of being members of the enterprise as a community. While the literature has often emphasised the positive side of gamification, mainly from economic and business perspectives, little emphasis has been paid to the ethical use of gamification within enterprises. In this paper we report an empirical research to explore the ethical aspects of using gamification. We follow a mixed methods approach involving participants who are gamification experts, employees and managers. Our findings show that, for gamification, there is a fine line between being a positive tool to motivate employees and being a source of tension and pressure which could then affect the social and mental well-being within the workplace. This paper will evaluate that dual effect and clarify that fine line.