Fairness and Transparency in Crowdsourcing

International audienceDespite the success of crowdsourcing, the question of ethics has not yet been addressed in its entirety. Existing efforts have studied fairness in worker compensation and in helping requesters detect malevolent workers. In this paper, we propose fairness axioms that generalize existing work and pave the way to studying fairness for task assignment, task completion, and worker compensation. Transparency on the other hand, has been addressed with the development of plug-ins and forums to track workers' performance and rate requesters. Similarly to fairness, we define transparency axioms and advocate the need to address it in a holistic manner by providing declarative specifications. We also discuss how fairness and transparency could be enforced and evaluated in a crowdsourcing platform

An Image Is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures

The dynamics of attention in social media tend to obey power laws. Attention concentrates on a relatively small number of popular items and neglecting the vast majority of content produced by the crowd. Although popularity can be an indication of the perceived value of an item within its community, previous research has hinted to the fact that popularity is distinct from intrinsic quality. As a result, content with low visibility but high quality lurks in the tail of the popularity distribution. This phenomenon can be particularly evident in the case of photo-sharing communities, where valuable photographers who are not highly engaged in online social interactions contribute with high-quality pictures that remain unseen. We propose to use a computer vision method to surface beautiful pictures from the immense pool of near-zero-popularity items, and we test it on a large dataset of creative-commons photos on Flickr. By gathering a large crowdsourced ground truth of aesthetics scores for Flickr images, we show that our method retrieves photos whose median perceived beauty score is equal to the most popular ones, and whose average is lower by only 1.5%.Comment: ICWSM 201

Tag based Bayesian latent class models for movies : economic theory reaches out to big data science

For the past 50 years, cultural economics has developed as an independent research specialism. At its core are the creative industries and the peculiar economics associated with them, central to which is a tension that arises from the notion that creative goods need to be experienced before an assessment can be made about the utility they deliver to the consumer. In this they differ from the standard private good that forms the basis of demand theory in economic textbooks, in which utility is known ex ante. Furthermore, creative goods are typically complex in composition and subject to heterogeneous and shifting consumer preferences. In response to this, models of linear optimization, rational addiction and Bayesian learning have been applied to better understand consumer decision- making, belief formation and revision. While valuable, these approaches do not lend themselves to forming verifiable hypothesis for the critical reason that they by-pass an essential aspect of creative products: namely, that of novelty. In contrast, computer sciences, and more specifically recommender theory, embrace creative products as a study object. Being items of online transactions, users of creative products share opinions on a massive scale and in doing so generate a flow of data driven research. Not limited by the multiple assumptions made in economic theory, data analysts deal with this type of commodity in a less constrained way, incorporating the variety of item characteristics, as well as their co-use by agents. They apply statistical techniques supporting big data, such as clustering, latent class analysis or singular value decomposition. This thesis is drawn from both disciplines, comparing models, methods and data sets. Based upon movie consumption, the work contrasts bottom-up versus top-down approaches, individual versus collective data, distance measures versus the utility-based comparisons. Rooted in Bayesian latent class models, a synthesis is formed, supported by the random utility theory and recommender algorithm methods. The Bayesian approach makes explicit the experience good nature of creative goods by formulating the prior uncertainty of users towards both movie features and preferences. The latent class method, thus, infers the heterogeneous aspect of preferences, while its dynamic variant- the latent Markov model - gets around one of the main paradoxes in studying creative products: how to analyse taste dynamics when confronted with a good that is novel at each decision point. Generated by mainly movie-user-rating and movie-user-tag triplets, collected from the Movielens recommender system and made available as open data for research by the GroupLens research team, this study of preference patterns formation for creative goods is drawn from individual level data

Industrial Symbiosis Recommender Systems

For a long time, humanity has lived upon the paradigm that the amounts of natural resources are unlimited and that the environment has ample regenerative capacity. However, the notion to shift towards sustainability has resulted in a worldwide adoption of policies addressing resource efficiency and preservation of natural resources.One of the key environmental and economic sustainable operations that is currently promoted and enacted in the European Union policy is Industrial Symbiosis. In industrial symbiosis, firms aim to reduce the total material and energy footprint by circulating traditional secondary production process outputs of firms to become part of an input for the production process of other firms.This thesis directs attention to the design considerations for recommender systems in the highly dynamic domain of industrial symbiosis. Recommender systems are a promising technology that may facilitate in multiple facets of the industrial symbiosis creation as they reduce the complexity of decision making. This typical strength of recommender systems has been responsible for improved sales and a higher return of investments. That provides the prospect for industrial symbiosis recommenders to increase the number of synergistic transactions that reduce the total environmental impact of the process industry in particular

ERP implementation methodologies and frameworks: a literature review

Enterprise Resource Planning (ERP) implementation is a complex and vibrant process, one that involves a combination of technological and organizational interactions. Often an ERP implementation project is the single largest IT project that an organization has ever launched and requires a mutual fit of system and organization. Also the concept of an ERP implementation supporting business processes across many different departments is not a generic, rigid and uniform concept and depends on variety of factors. As a result, the issues addressing the ERP implementation process have been one of the major concerns in industry. Therefore ERP implementation receives attention from practitioners and scholars and both, business as well as academic literature is abundant and not always very conclusive or coherent. However, research on ERP systems so far has been mainly focused on diffusion, use and impact issues. Less attention has been given to the methods used during the configuration and the implementation of ERP systems, even though they are commonly used in practice, they still remain largely unexplored and undocumented in Information Systems research. So, the academic relevance of this research is the contribution to the existing body of scientific knowledge. An annotated brief literature review is done in order to evaluate the current state of the existing academic literature. The purpose is to present a systematic overview of relevant ERP implementation methodologies and frameworks as a desire for achieving a better taxonomy of ERP implementation methodologies. This paper is useful to researchers who are interested in ERP implementation methodologies and frameworks. Results will serve as an input for a classification of the existing ERP implementation methodologies and frameworks. Also, this paper aims also at the professional ERP community involved in the process of ERP implementation by promoting a better understanding of ERP implementation methodologies and frameworks, its variety and history

The influence of perceived brand personality of social media on users' attitude motivations and behaviour

The study examines the effect of the users’ perceived brand personality (PBP) of social media (Facebook, LinkedIn and YouTube) on attitude, motivation, behavioural intent, and behaviour. The study applies Aaker’s (1997) brand personality scale (BPS) to social media brands to test the extent to which it is applicable to social media brands, to examine the underlying structure of the PBP of social media brands and tests the hypothesised model for the interrelationship between the PBP of social media and attitude, motivation, behavioural intent, and behaviour. A quantitative approach was employed, where data was collected using an online survey. A sample of 380 respondents per platform was used, giving a total of 1140 respondents for the study. Exploratory factor analyses were done to examine the underlying structure of the PBP of social media brands. Structural equation modelling was used to assess the fit of the hypothesised model and subsequent relationships between the constructs. A two-factor solution for the brand personality of social media (Facebook, LinkedIn and YouTube) was extracted. The brand personality of Facebook is represented by two traits: excitement and sincerity. The Facebook model fit indices are as follows: __2 = 1298.658; d.__ = 510; p = .000; NFI = .846; IFI = .901; TLI = .890; CFI = 900, RMSEA = 0.066 PCLOSE (.000) The brand personality of LinkedIn is represented by two traits: competence and sincerity. The LinkedIn model fit indices were attained at: at: __2 = 1124.7067; d.__ = 478; p = .000; RMSEA = 0.0601; NFI = .872; IFI = .922; TLI = .914; CFI = 922; RMSEA = 0.0601 PCLOSE (.000). The brand personality of YouTube is represented by two traits: excitement and sincerity. The YouTube model fit indices were attained at: __2 = 1133.485, d.__ = 510; p-value = .000, NFI = .844, IFI = .908, TLI = .898, CFI = .907, and RMSEA = .060 (PCLOSE = 0.000). The results of the hypothesis tests revealed that H1: which states that there is a significant positive relationship between PBP of social media and users’ attitude and H2: which indicates a significant positive relationship between PBP of social media and motivation were partially supported for the three social media platforms (Facebook, LinkedIn, and YouTube). H3: which states that there is a significant positive relationship between users’ attitude and behavioural intent was supported for Facebook and LinkedIn and was not supported for YouTube. H4: which states that there is a significant positive relationship between users’ attitude and behaviour was not supported for the three social media platforms. H5: which states that there is a significant relationship between users’ motivation and behaviour was partially supported for Facebook and was supported for LinkedIn and YouTube. H6: which states that there is a significant positive relationship between users’ motivation and behaviour was partially supported for Facebook and not supported for LinkedIn and YouTube. The last hypothesis H7: which states that there is a significant positive relationship between behavioural intent and behaviour was not supported for the three platforms.Thesis (PhD)--University of Pretoria, 2018.Marketing ManagementPhDUnrestricte

The relevance of prediction markets for corporate forecasting

Prediction markets (PMs) are virtual stock markets on which shares are traded taking advantage of the wisdom-of-crowds principle to access collective intelligence. It is claimed that the accumulation of information by groups leads to joint group decisions often better than individual participants’ approaches to solutions. A PM share represents a future event or a market condition (e.g. expected sales figures of a product for a specific month) and provides forecasts via its price which is interpreted as the probability of the event occurring. PMs can be used in competition with other forecasting tools; when applied for forecasting purposes within a company they are called corporate prediction markets (CPMs). Despite great praise in the (academic) literature for the use of PMs as an efficient instrument for bringing together scattered information and opinions, corporate usage and applications are limited. This research was directed towards an examination of this discrepancy by means of focusing on the barriers to adoption within enterprises. Literature and reality diverged and neglected the important aspect of corporate culture. Screening existing research and interviews with business executives and corporate planners revealed challenges of company hierarchy as an inhibitor to the acceptance of CPM outcomes. Findings from 55 interviews and a thematic analysis of the literature exposed that CPMs are useful but rarely used. Their lack of use arises from senior executives’ perception of the organisational hierarchy being taxed and fear of losing power as CPMs (can) include lower rungs of the corporate ladder in decision-making processes. If these challenges can be overcome the potential of CPMs can be released. It emerged – buttressed by ten additional interviews – that CPMs would be worthwhile for company forecasting, particularly supporting innovation management which would allow idea markets (as an embodiment of CPMs) to excel. A contribution of this research lies in its additions to the PM literature, explaining the lack of adoption of CPMs despite their apparent benefits and making a case for the incorporation of CPMs as a forecasting instrument to facilitate innovation management. Furthermore, a framework to understand decision-making in the adoption of strategic tools is provided. This framework permits tools to be accepted on a more rational base and curb the emotional and political influences which can act against the adoption of good and effective tools

The Palgrave Handbook of Digital Russia Studies

This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today

The Palgrave Handbook of Digital Russia Studies

This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today