The Age Context of Performance Evaluation Decisions

Organizational demography contends that demographic characteristics of individuals, examined at individual, dyadic, group, and organizational levels of analysis, exert significant effects on organizational processes. The purpose of this paper was to test the contextual effects created by the interaction of work group age composition and supervisor age on supervisor evaluations of subordinate performance. Two competing models of age demography were tested. The similarity model predicts that supervisors similar in age to the work group they supervise will issue generally higher performance ratings. The dissimilarity model developed in this paper predicts the opposite. Support was indicated for the dissimilarity model. Implications of the results are discussed

Context-driven progressive enhancement of mobile web applications: a multicriteria decision-making approach

Personal computing has become all about mobile and embedded devices. As a result, the adoption rate of smartphones is rapidly increasing and this trend has set a need for mobile applications to be available at anytime, anywhere and on any device. Despite the obvious advantages of such immersive mobile applications, software developers are increasingly facing the challenges related to device fragmentation. Current application development solutions are insufficiently prepared for handling the enormous variety of software platforms and hardware characteristics covering the mobile eco-system. As a result, maintaining a viable balance between development costs and market coverage has turned out to be a challenging issue when developing mobile applications. This article proposes a context-aware software platform for the development and delivery of self-adaptive mobile applications over the Web. An adaptive application composition approach is introduced, capable of autonomously bypassing context-related fragmentation issues. This goal is achieved by incorporating and validating the concept of fine-grained progressive application enhancements based on a multicriteria decision-making strategy

Group-centered framework towards a positive design of digital collaboration in global settings

Globally distributed groups require collaborative systems to support their work. Besides being able to support the teamwork, these systems also should promote well-being and maximize the human potential that leads to an engaging system and joyful experience. Designing such system is a significant challenge and requires a thorough understanding of group work. We used the field theory as a lens to view the essential aspects of group motivation and then utilized collaboration personas to analyze the elements of group work. We integrated well-being determinants as engagement factors to develop a group-centered framework for digital collaboration in a global setting. Based on the outcomes, we proposed a conceptual framework to design an engaging collaborative system and recommend system values that can be used to evaluate the system furtherComment: 6 Pages, 3 Figures, Positive computing, International Conference on Industrial Enterprise and System Engineerin

A realist process evaluation of Enhanced Triple P for Baby and Mellow Bumps, within a Trial of Healthy Relationship Initiatives for the Very Early years (THRIVE): study protocol for a randomized controlled trial

Background: THRIVE is a three-arm randomised controlled trial (RCT) that aims to evaluate whether antenatal and early postnatal interventions, Enhanced Triple B for Baby (ETPB) plus care as usual (CAU) or Mellow Bumps (MB) plus CAU (versus CAU alone), can: 1) improve the mental health and well-being of pregnant women with complex health and social care needs; 2) improve mother-infant bonding and interaction; 3) reduce child maltreatment; and 4) improve child language acquisition. This paper focuses on THRIVE’s realist process evaluation, which is carefully monitoring what is happening in the RCT. Methods: Realistic evaluation provides the theoretical rationale for the process evaluation. We question: 1) how faithfully are MB and ETPB implemented? 2) What are the mechanisms by which they work, if they do, and who do they work for and how? 3) What contextual factors are necessary for the programmes to function, or might prevent them functioning? The mixed-methods design includes quantitative measures, which are pre- and post-training/intervention questionnaires for facilitators and mothers-to-be, and post-session evaluation forms. Qualitative data collection methods include participant observation of facilitator training and the delivery of a series of antenatal sessions in selected intervention groups (n = 3 for ETPB and n = 3 for MB), semi-structured interviews with facilitators, pregnant women, partners, and referring facilitators, and telephone interviews examining the content of the postnatal components of ETPB and MB. Discussion: The findings of this process evaluation will help researchers and decision makers interpret the outcomes of THRIVE. It will provide a greater understanding of: how the interventions work (if they do); the extent and quality of their implementation; contextual factors facilitating and constraining intervention functioning; variations in response within and between subgroups of vulnerable parents; and benefits or unintended consequences of either intervention. Few studies to date have published detailed research protocols illustrating how realist process evaluation is designed and conducted as an integral part of a randomised controlled trial

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

A Human-centric Perspective on Digital Consenting: The Case of GAFAM

According to different legal frameworks such as the European General Data Protection Regulation (GDPR), an end-user's consent constitutes one of the well-known legal bases for personal data processing. However, research has indicated that the majority of end-users have difficulty in understanding what they are consenting to in the digital world. Moreover, it has been demonstrated that marginalized people are confronted with even more difficulties when dealing with their own digital privacy. In this research, we use an enactivist perspective from cognitive science to develop a basic human-centric framework for digital consenting. We argue that the action of consenting is a sociocognitive action and includes cognitive, collective, and contextual aspects. Based on the developed theoretical framework, we present our qualitative evaluation of the consent-obtaining mechanisms implemented and used by the five big tech companies, i.e. Google, Amazon, Facebook, Apple, and Microsoft (GAFAM). The evaluation shows that these companies have failed in their efforts to empower end-users by considering the human-centric aspects of the action of consenting. We use this approach to argue that their consent-obtaining mechanisms violate principles of fairness, accountability and transparency. We then suggest that our approach may raise doubts about the lawfulness of the obtained consent—particularly considering the basic requirements of lawful consent within the legal framework of the GDPR

Location-based and contextual mobile learning. A STELLAR Small-Scale Study

This study starts from several inputs that the partners have collected from previous and current running research projects and a workshop organised at the STELLAR Alpine Rendevous 2010. In the study, several steps have been taken, firstly a literature review and analysis of existing systems; secondly, mobile learning experts have been involved in a concept mapping study to identify the main challenges that can be solved via mobile learning; and thirdly, an identification of educational patterns based on these examples has been done. Out of this study the partners aim to develop an educational framework for contextual learning as a unifying approach in the field. Therefore one of our central research questions is: how can we investigate, theorise, model and support contextual learning