Quality in crowdsourced experience-based evaluations : handling subjective responses

Experience-based evaluations (XBEs) are appraisals based on what someone has understood or learned about a topic by experience. Although XBEs can be highly subjective, imprecise, and diverse, information extracted from them can result in significant benefits for companies and organizations. However, handling XBEs can entail several challenges especially when potential data quality issues, such as a lack of reliability on XBEs provided by a large and heterogeneous group of (anonymous) sources, need to be handled. In this dissertation, challenges connected with the characterization, processing and quality of XBEs have been handled. Thereby, it is studied if and how existing and novel concepts and methods in the area of computational intelligence can be used to characterize and process XBEs in such a way that one can adequately handle data quality issues on subjective data provided by a large and heterogeneous group of respondents. It has been shown that existing and novel concepts and methods connected to fuzzy set theory, which aims to find approximate, achievable and robust solutions, can be used to address these challenges. Among the novel proposed concepts, augmented appraisal degrees and augmented (Atanassov) intuitionistic fuzzy sets are deemed to be the most important contributions of this dissertation

The Impact of Cultural Dimensions on Sales Force Compensation

Financial compensation has long been held as the primary motivator of salespeople. Motivation however may be achieved differently in various countries, as the large disparities in pay schemes across countries seem to indicate. In this paper, the authors explore the impact of cultural dimensions on sales force compensation structures. Using data collected from financial companies of three European countries, they (1) assess transnational cultural profiles of managers (i.e., market, group-centric and hybrid), (2) confirm discrepancies in terms of managerial preferences for compensation structures and (3) uncover associated rationales such as rejection of incentive compensation due to its perceived immorality. The results indicate that cultural dimensions explain managers choice for (1) the use of incentive pay in the compensation package (i.e., fixed versus variable compensation) as well as (2) the basis for its allocation (i.e., individual versus group). The authors conclude by discussing the implications of their research for designing compensation plans in the global market place.sales force compensation; cross-cultural research

No-reference point cloud quality assessment based on subjective and objective scores

Recently, thanks to the increasing capability of 3D acquisition devices, point cloud has emerged as the most popular format for immersive media. In practice, a variety of distortions could be involved and affect human perception. Developing point cloud quality assessment can help to understand the distortions and carry out the quality optimization for distorted point clouds. Therefore in this work we are investigating the quality assessment of point clouds and what are the main factors effecting the quality of the point clouds. And presented as well the existing subjective and objective methods for evaluation of the quality of point clouds and applying these methods to evaluate the point clouds quality. After that, we made different experiments to apply the subjective and objective methods to estimate the quality of different data sets we have, And as well using the results of previous experiments made for farther investigations . However, applying these methods for estimating the quality is quite challenging approach as we will see later in this study. Therefore, we created various solutions to overcome the challenges that we faced during this research . At the end, we are investigating the using of the Neural Networks in the quality assessment of point clouds and how this can simplify the measurement of point clouds quality

A gamified approach to promoting empathy in children

Purpose Gamification has gained popularity in social marketing research; however, its application remains limited to a few contexts, and relatively little is known about how innovative gamification technologies such as augmented reality can be applied to social marketing programme design. This paper aims to demonstrate the application of gamification to a social marketing pilot programme designed to increase children’s empathy and empathic behaviour. Design/methodology/approach Informed by social cognitive theory (SCT), a mixed-method research design was adopted using pre- and post-programme surveys (n = 364) to assess effectiveness using paired samples t-test. Qualitative data included observations, participant’s questions and a feedback activity at the end of the programme. A thematic analysis was undertaken to examine the data and detect meaningful insights. Findings Children’s affective empathy and empathic behaviour outcomes were improved following the pilot programme. However, no effects were observed for cognitive empathy and social norms. Thematic analysis revealed three themes to further improve the game: developmentally appropriate design, user experience and game design. Research limitations/implications Findings demonstrated challenges with the application of SCT outlining a disconnect between the design of the gamified programme and theory application. Practical implications This study provides initial evidence for the application of innovative gamification technologies to increase empathy in children. Originality/value To the best of the authors’ knowledge, this paper is the first to examine how a gamified social marketing programme can increase empathy in children

Measuring Social Well Being in The Big Data Era: Asking or Listening?

The literature on well being measurement seems to suggest that "asking" for a self-evaluation is the only way to estimate a complete and reliable measure of well being. At the same time "not asking" is the only way to avoid biased evaluations due to self-reporting. Here we propose a method for estimating the welfare perception of a community simply "listening" to the conversations on Social Network Sites. The Social Well Being Index (SWBI) and its components are proposed through to an innovative technique of supervised sentiment analysis called iSA which scales to any language and big data. As main methodological advantages, this approach can estimate several aspects of social well being directly from self-declared perceptions, instead of approximating it through objective (but partial) quantitative variables like GDP; moreover self-perceptions of welfare are spontaneous and not obtained as answers to explicit questions that are proved to bias the result. As an application we evaluate the SWBI in Italy through the period 2012-2015 through the analysis of more than 143 millions of tweets.Comment: 40 pages, 2 figures. arXiv admin note: text overlap with arXiv:1512.0156