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• Nine dimensions of narcissism are proposed for citizen science. • Narcissism presents an inverted U-shaped effect for resource mobilization. • Formal researchers receive less tolerance on narcissism over amateur researchers. • Social role plays a role on narcissism in citizen science resource mobilization. Narcissism, as a form of self-awareness of self-importance or self-influence, can be characterized by a constant attention to success and the need for authority, competitiveness, and grandiose, which may be introduced to promote the attractiveness of a project. This study analyzes the effect of textual narcissism on the willingness to back citizen science projects by considering the social roles of the citizen project owners: expert, student, and amateur researchers. This paper uses social role theory to anchor the seven forms of narcissism and proposes 9 dimensions of narcissism, which are then classified into 3 categories: social role advantage, psychological superiority, and identity aspiration. Using a web crawler, 850 citizen science projects are employed as a corpus. Text mining is used to quantify the narratives, and econometric models are applied to estimate the effect of textual narcissism. This study reports that narcissism presents an inverted U-shaped effect. On social role, formal researchers (experts and students) receive lesser degrees of public tolerance to textual narcissism, over amateur researchers. By extending narcissistic influence and social role theory to citizen science, this paper can guide stakeholders and regulators such as scientific experiment platforms in generating suitable text descriptions for better research resource mobilization. [ABSTRACT FROM AUTHOR]

A dramatic global increase in the pervasiveness of chronic illness has coincided with a reduction in the availability of healthcare resources, coupled with increased costs for care, forcing a fundamental reevaluation of care processes. These trends have driven interest in the use of advanced healthcare information systems and telematics applications to improve care availability while reducing overall costs, but such measures require taking an integrated approach to a range of interrelated social, economic, political and cultural impacts and challenges. Telecare technologies allow hospitals to continuously monitor biomedical indicators, while providing patients with online services such as clinical appointment scheduling, medical consulting, remote alerts, etc. Telecare has the potential to transform the healthcare industry by reducing costs, increasing quality, and enhancing patient satisfaction. The development of a real-time monitoring healthcare service model through the integration of information and communications technologies (ICT) has emerged as a research priority. This study explores the design, value creation, development and evaluation of Telecare systems and mobile health applications for autonomous health management to ensure appropriate home-based health monitoring and treatment while improving care performance. A well-defined methodology is needed to develop artifacts due to increasing disease complexity. This study develops an Android-based self-management application based on design science research methodology. The App assists users in collecting and monitoring indicators to prompt appropriate care services. This study uses individual home self-care as the basic Telecare unit to design a service model integrating six kinds of healthcare services. Usability testing is conducted to reflect five constructs: system usefulness, ease of learning, information quality, interface quality, and overall satisfaction. Experimental results support previous research findings regarding the Chronic Care Model and enhance the effectiveness of mobile-based services. Our work provides a useful reference to researchers and practitioners interested in understanding how hospitals can better facilitate more effective mobile-based technology adoption in today’s e-health environment. [ABSTRACT FROM AUTHOR]

• Comments are classified into antecedent comments and posterior comments. • FastText is better than other algorithms for sentiment analysis for crowdfunding reviews. • FNN achieves better prediction performance than other approaches. This study focuses on the predictive power of online reviews on fundraising outcomes. Based on when the review is posted, we divide these online reviews into antecedent comments, made at the prelaunch and financing stages, and posterior comments, after the fundraising stage. Then, we conduct a sentiment analysis of these online reviews with a variety of algorithms to determine which one is best at sentiment extraction, finding that the deep learning algorithm is more effective than the shallow learning algorithms. Using fastText, we find that, as financing progresses, sentiment in the comments gradually turns negative, and sentiment strength is lower for successfully funded projects than for failed projects. The reason for this pessimistic turn is the topic discussed at various stages. FNN performs better than other approaches in the prediction of successful fundraising outcomes, with the addition of the antecedent online comments' sentiment factor. Other prediction algorithms also show some degree of improvement with the addition of sentiment extracted from antecedent online reviews, which confirms the predictive power of antecedent comments. This study enhances the identification of the economic value of antecedent online reviews with the addition of sentiment analysis and shows a path for further developing internet finance. [ABSTRACT FROM AUTHOR]