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The establishment of empathy is the premise and foundation for diverse innovative proposals and problem solutions. Virtual reality has provided a full range of depth and breadth for the establishment of empathy in many different types of fields due to its immersive, interactive, and imaginative characteristics. In this study, bibliometric analysis and VOSviewer software are used to cluster and visualize relevant 190 articles from the Web of Science core collection. The essay proposes a positioning of how to apply virtual reality on empathy based on two dimensions, from internal world to external world, and from business innovation to social innovation, by integrating each two of them, four application methods are summarized, which are meaning shaping, value creation, individual satisfaction, and self-realization. What's more, using the bibliometric analysis result as a basis, the application landscape of virtual reality technology for establishing empathy has been constructed, including individual level, society level, and nature level, which reveals the existing and coming possibilities of using VR technology on building empathy in different fields. Last but not least, the paper has discussed the impact of virtual reality for empathy-building from five aspects, economy, politics, culture, society, and ecology. The efforts of this study reveal the VR tendency and have important reference significance for promoting the application of virtual reality technology in creating empathy and innovation in different fields.
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Social responsibility is at the core of modern Information Systems (IS) education due to increased attention by society on the ethics, human factors, and social consequences of emerging technologies. With the acknowledgement that most IS education falls short along these areas, this paper sheds light on the application of Social Learning and Social Innovation-based Learning in socially responsible IS Education. The connectivism principles were used to develop a learning model based on social innovation that was then tested by the example of an upper-division course (Systems Analysis) at a state university. The case study results suggested that the proposed learning model can help students to not only see information systems as social systems but also consider themselves as catalysts for positive change enabled by these systems. The findings also confirmed the positive impact of the proposed intervention on students' social skills. This study contributes to the future of IS education by proposing social innovation-based learning as a practical education paradigm for the digital economy.
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Multiplex social network relationships are quite strong in most occurrences, especially within a strong peer network (a cluster of near engaging friends). Moreover, hate speech is found on most online social media platforms. Hence, this study aims to identify hate speech discussions among peer networks. This paper discusses a novel model to recommend a peer under the context of multiplex social networks to minimize the hate speech engagements; Facebook, Twitter, and YouTube social media networks (SMN) were used in this experiment. Collaborative filtering defines an interest-based recommendation model. Under the context of user engagements, some topics become of more user interest. Hence, some social media posts drastically spread over multiplex layers rapidly, initiating a high social impact on a specific topic. The research gap is identifying the peer network that reduces hate speech in multiplex social networks. Hence, this study provides a social innovation platform for peer recommendations to avoid social splits. First, this research contributes by proposing a novel methodology for identifying user engagements on online social networks by mining interactive social network graphs. Secondly, it provides an algorithm for recommending a multi-dimensional recommendation model by using collaborative filtering. Upon the proposed algorithm, a system that recommends engagements in any given online social network to minimize hate speech was implemented. Accordingly, the novel algorithm evaluates by using recommendation precision. The results show that the novel algorithm is highly applicable for peer recommendation in multiplex social networks to avoid hate speech discussions.
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Cet article est une réponse prospective aux besoins de l'Internet des Objets en termes de simplicité d'utilisation, de gestion de la sécurité et de préservation de la vie privée. Nous proposons de satisfaire ces besoins à travers une plateforme d'intelligence collective utilisant des cartographies sémantiques en réalité augmentées pour récolter les interactions des utilisateurs avec des objets connectés. L'association de l'intelligence collective et des cartographies sémantiques permet d'envisager un design de connaissances où les capacités d'action des objets connectés sont facilement compréhensibles et modifiables par les utilisateurs. Dans ce dispositif, les technologies de blockchain sont utilisées pour partager en sécurité l'expression des utilisateurs et ainsi augmenter la confiance dans l'Internet des Objets et par la même contribuer au développement d'une réflexivité collective sur les usages de ces écosystèmes sociotechniques.
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- Digital (1)
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- Économie (1)
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- Empathie (1)
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- Réservé UdeM (3)
- social (1)
- VR (1)