Cognitive absorption and behavioural intentions in virtual health communities : a focus on posters

Abstract: Purpose - This paper aims at providing a conceptual model that elucidates the role of cognitive absorption in explaining behavioural intentions in virtual health communities. Design/methodology/approach - Data was collected from 361 contributing members of virtual health communities from Gauteng, South Africa using a structured questionnaire. Structural equation modelling using AMOS software was used to analyse the data. Findings - The findings show that cognitive absorption has significant direct positive influence on members’ intentions to continue participating on virtual health community platforms. Cognitive absorption was also found to have indirect influence on behavioural intentions through its influence on members’ attitude. It was also found to play a mediating role on the influence of perceived usefulness and behavioural intention. Research limitations/implications - The study shows the value of linking the flow theory and the technology acceptance model to provide a comprehensive understanding of behavioural intentions in virtual health community forums. Practical Implications - Managers of virtual health communities need to pay attention to experiential aspects of their sites. Success in ensuring that community members are cognitively absorbed is key to the development of positive attitude and intentions towards virtual health community forums. Originality/value - Virtual health communities play a new and growing role in the way health-related information and support is offered and accessed by those in need. Despite their importance, not much research has been done to explain the role of consumer experience on member behaviour on such forums. The study contributes to this understanding by demonstrating the value of cognitive absorption..

Investigating and Supporting Sensemaking within Online Health Communities

This dissertation focuses on understanding and supporting individual and collective sensemaking within online health communities (OHCs). This major goal was achieved in three aims. In Aim 1, this dissertation contributes a rich descriptive account of collective sensemaking in OHCs forums by describing how it occurs and develops, what triggers it, what elements constitute collective construction of meaning, and what conversational moves positively contribute to this process. Further, it describes how collective sensemaking in OHCs is impacted by the interplay between informational and socio-emotional needs of OHCs members. Moreover, it examines how design of different social computing platforms influences OHCs members’ ability to meet their informational and socio-emotional needs and engage in collective sensemaking. In Aim 2, this dissertation explores the design space of tools for supporting individual sensemaking through optimized information access. Through the design and evaluation of a prototype DisVis it examines the impact of such tools on OHCs members’ ability to understand information within discussion threads. In the final Aim 3, this dissertation proposes a novel approach for meeting the three main needs identified in Aims 1 and 2: promoting individual sensemaking, while at the same time encouraging collective sensemaking, and facilitating development of social awareness and ties among community members. The design and evaluation of the novel solution for visualizing discussion threads that synergistically addresses these three needs—dSense—provides insights for future research and design of interactive solutions for supporting individual and collective sensemaking within OHCs

A importância do ambiente urbano para o bem-estar: análise em Lisboa utilizando redes sociais

O objetivo da generalidade das pessoas é ser feliz. Alcançar esse feito depende de muitos fatores, alguns relativos ao local onde vivemos. Os especialistas referem que o segredo para a “cidade feliz” está ligado às pessoas, mais precisamente ao seu bem-estar e qualidade de vida. Este estudo tem o intuito de compreender o impacte que o ambiente urbano da cidade de Lisboa tem no bem-estar dos indivíduos. A análise do bem-estar com recurso à rede social Twitter permite identificar os locais em que o mal-estar e bem-estar prevalecem. Para a sua identificação são considerados um conjunto de variáveis que caracterizam a morfologia da cidade de Lisboa: popularidade dos locais, índices de forma urbana, zonas de sol/sombra, largura da rua, estrutura verde e azul, idade média do edificado, etc. Contudo, não é uma tarefa fácil porque as pessoas recebem diferentes tipos de informação sensorial de um espaço, tornando-se desafiador determinar quais os aspetos da experiência no ambiente urbano que afetam o bem-estar. É nesse sentido que se aplica a machine learning (ML), de modo a determinar quais as variáveis que influenciam o sentimento positiva e negativamente. O ML é reconhecido por alcançar resultados de exatidão superiores aos métodos tradicionais. Estes tipos de modelos apresentam diversas vantagens, tais como, a capacidade de lidar com dados de diferentes tipos, estruturas e quantidades (i.e., big data).The goal of most people is to be happy. Achieving this feat depends on many factors, some related to where we live. Experts say that the secret to a "happy city" is linked to people, more precisely to their well-being and quality of life. This study aims to understand the impact that the urban environment of the city of Lisbon has on the well-being of individuals. The analysis of well-being using the social network Twitter allows identifying the places where malaise and well-being prevail. For its identification are considered a set of variables that characterize the morphology of the city of Lisbon: the popularity of the places, indexes of urban shape, sun/shadow areas, the width of the street, green and blue structure, the average age of the building, etc. However, it is not an easy task because people receive different types of sensory information from space, making it challenging to determine which aspects of the experience in the urban environment affect well-being. In this context, machine learning (ML) is applied to determine which variables influence positive and negative sentiment. ML is recognized for superior accuracy results to traditional methods. These types of models have advantages, such as the ability to handle data of different types, structures, and quantities (i.e., big data)