Online social networks produce a visuality that reflects the attention economy governing this space. What is seen becomes elevated into prominence by networked publics that ‘perform’ affective expressions within platform affordances. We mapped Twitter images of refugees in two language spaces - English and Arabic. Using automated analysis and qualitative visual analysis, we found similar images circulating both spaces. However, photographs generating higher retweet counts were distinct. This highlights the impact of affective affordances of Twitter — in this case retweeting — on regimes of visibility in disparate spheres. Representations of refugees in the English language space were characterized by personalized, positive imagery, emphasizing solidarity for refugees contributing to their host country or stipulating innocence. Resonating images in the Arabic space were less personalized and depicted a more localized visuality of life in refugee camps, with an emphasis on living conditions in refugee camps and the efforts of aid organizations.
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In the era of social media, online reviews have become a crucial factor influencing the exposure of tourist destinations and the decision-making of potential tourists, exerting a profound impact on the sustainable development of these destinations. However, the influence of review valence on visit intention, especially the role of affective commitment and reputation (ability vs. responsibility), remains unclear. Drawing on emotion as a social information theory, this paper aims to elucidate the direct impact of different review valences on tourists’ visit intentions, as well as mediating mechanisms and boundary conditions. Three experiments indicate that positive (vs. negative) reviews can activate stronger affective commitment and visit intention, with affective commitment also playing a mediating role. Additionally, destination reputation significantly moderates the after-effects of review valences. More specifically, a responsibility reputation (compared with an ability reputation) weakens the effect of negative valence on affective commitment and visit intention. This study provides valuable theoretical insights into how emotional elements in online reviews influence the emotions and attitudes of potential tourists. Particularly for tourism managers, review valence and responsibility reputation hold practical significance in destination marketing.
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The past decades have shown an accelerated development of technology-enhanced or digital education. Although an important and recognized precondition for study success, still little attention has been paid to examining how an affective learning climate can be fostered in online training programs. Besides gaining insight into the dynamics of affective learning itself it is of vital importance to know what predicts trainees’ intention to transfer new knowledge and skills to other contexts. The present study investigated the influence of five affective learner characteristics from the transfer literature (learner readiness, motivation to learn, expected positive outcomes, expected negative outcomes, personal capacity) on trainees’ pre-training transfer intention. Participants were 366 adult students enrolled in an online course in information literacy in a distance learning environment. As information literacy is a generic competence, applicable in various contexts, we developed a novel multicontextual transfer perspective and investigated within one single study the influence of the abovementioned variables on pre-training transfer intention for both the students’ Study and Work contexts. The hypothesized model has been tested using structural equation modeling. The results showed that motivation to learn, expected positive personal outcomes, and learner readiness were the strongest predictors. Results also indicated the benefits of gaining pre-training insight into the specific characteristics of multiple transfer contexts, especially when education in generic competences is involved. Instructional designers might enhance study success by taking affective transfer elements and multicontextuality into account when designing digital education.
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Information and Communication Technologies (ICTs) affect the environment in various ways. Their energy consumption is growing exponentially, with and without the use of ‘green’ energy. Increasing environmental awareness within information science has led to discussions on sustainable development. ‘Green Computing’ has been introduced: the study and practice of environmentally sus- tainable computing. This can be defined as ‘designing, manufacturing, using, and disposing of com- puters, servers, and associated subsystems - such as monitors, printers, storage devices, and net- working and communications systems - efficiently and effectively with minimal or no impact on the en- vironment’. Nevertheless, the data deluge makes it not only necessary to pay attention to the hard- and software dimensions of ICTs but also to the value of the data stored. We explore the possibilities to use information and archival science to reduce the amount of stored data. In reducing this amount of stored data, it’s possible to curb unnecessary power consumption. The objectives of this paper are to develop a model (and test its viablility) to [1] increase awareness in organizations for the environ- mental aspects of data storage, [2] reduce the amount of stored data, and [3] reduce power consump- tion for data storage. This model integrates the theories of Green Computing, Information Value Chain (IVC) and Archival Retention Levels (ARLs). We call this combination ‘Green Archiving’. Our explora- tory research was a combination of desk research, qualitative interviews with information technology and information management experts, a focus group, and two exploratory case studies. This paper is the result of the first stage of a research project that is aimed at developing low power ICTs that will automatically appraise, select, preserve or permanently delete data based on their value. Such an ICT will automatically reduce storage capacity and curb power consumption used for data storage. At the same time, data disposal will reduce overload caused by storing the same data in different for- mats, it will lower costs and it reduces the potential for liability.
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This paper explores a method for deducing the affective state of runners using his/her movements. The movements are measured on the arm using a smartphone’s built-in accelerometer. Multiple features are derived from the measured data. We studied which features are most predictive for the affective state by looking at the correlations between the features and the reported affect. We found that changes in runners’ movement can be used to predict change in affective state.
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Computers create environmental problems. Their production requires electricity, raw materials, chemical materials and large amounts of water, and supplies (often toxic) waste. They poison dumping sites and pollute groundwater. In addition, the energy consumption in IT is growing exponentially, with and without the use of ‘green’ energy. Increasing environmental awareness within information science has led to discussions on sustainable development. ‘Green Computing’ has been introduced: the study and practice of environmentally sustainable computing or IT. It is necessary to pay attention to the value of the information stored. In this paper, we explored the possibilities of combining Green Computing components with two theories of archival science (Archival Retention Levels and Information Value Chain respectively) to curb unnecessary power consumption. Because in 2012 storage networks were responsible for almost 30 % of total IT energy costs, reducing the amount of stored information by the disposal of unneeded information should have a direct effect on IT energy use. Based on a theoretical analysis and qualitative interviews with an expert group, we developed a ‘Green Archiving’ model, that could be used by organizations to 1] reduce the amount of stored information, and 2] reduce IT power consumption. We used two exploratory case studies to research the viability of this model.
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Post-partum hemorrhaging is a medical emergency that occurs during childbirth and, in extreme cases, can be life-threatening. It is the number one cause of maternal mortality worldwide. High-quality training of medical staff can contribute to early diagnosis and work towards preventing escalation towards more serious cases. Healthcare education uses manikin-based simulators to train obstetricians for various childbirth scenarios before training on real patients. However, these medical simulators lack certain key features portraying important symptoms and are incapable of communicating with the trainees. The authors present a digital embodiment agent that can improve the current state of the art by providing a specification of the requirements as well as an extensive design and development approach. This digital embodiment allows educators to respond and role-play as the patient in real time and can easily be integrated with existing training procedures. This research was performed in collaboration with medical experts, making a new contribution to medical training by bringing digital humans and the representation of affective interfaces to the field of healthcare.
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Studying images in social media poses specific methodological challenges, which in turn have directed scholarly attention toward the computational interpretation of visual data. When analyzing large numbers of images, both traditional content analysis as well as cultural analytics have proven valuable. However, these techniques do not take into account the contextualization of images within a socio-technical environment. As the meaning of social media images is co-created by online publics, bound through networked practices, these visuals should be analyzed on the level of their networked contextualization. Although machine vision is increasingly adept at recognizing faces and features, its performance in grasping the meaning of social media images remains limited. Combining automated analyses of images with platform data opens up the possibility to study images in the context of their resonance within and across online discursive spaces. This article explores the capacities of hashtags and retweet counts to complement the automated assessment of social media images, doing justice to both the visual elements of an image and the contextual elements encoded through the hashtag practices of networked publics.
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Technology designed to sense behavior, often neglects to directly incorporate subjective input from (elderly) users. This paper presents experiences in deploying technology that considers the elderly user and their subjective input as a way to enrich sensor data systems and empower the user. For this purpose, the paper draws on: (1) Observations of shortcomings in terms of capturing objective data from sensors as experienced in long-term deploymentt in the homes of older adults; (2) The design and evaluation of a wide range of applications especially designed to enable older adults to give subjective input on how they are doing, including an interactive television quiz, a talking picture frame and a tangible mood board, and (3) The development and field study of one application, the ‘Mood button’ in particular, that was tested in real-world sensing settings to work with a commercial sensing system. In doing this, this work aims to contribute towards successful sensing deployments and tools that give more control to the (elderly) end-user.
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This chapter discusses educational aspects and possibilities of serious games. For researchers as well as game designers we describe key learning theories to ground their work in theoretical framework. We draw on recent metareviews to offer an exhaustive inventory of known learning and affective outcomes in serious games, and to discuss assessment methods valuable not only for research but also for efficient serious game design. The implementation and design of serious games are outlined in separated sections. Different individual characteristics that seem to be strongly affecting process of learning with serious games (learning style, gender and age) are discussed with emphasis on game development.
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