Detecting Well-being in Digital Communities: An Interdisciplinary Engineering Approach for its Indicators

In this thesis, the challenges of defining, refining, and applying well-being as a progressive management indicator are addressed. This work\u27s implications and contributions are highly relevant for service research as it advances the integration of consumer well-being and the service value chain. It also provides a substantial contribution to policy and strategic management by integrating constituents\u27 values and experiences with recommendations for progressive community management

Detecting Well-being in Digital Communities: An Interdisciplinary Engineering Approach for its Indicators

In this thesis, the challenges of defining, refining, and applying well-being as a progressive management indicator are addressed. This work\u27s implications and contributions are highly relevant for service research as it advances the integration of consumer well-being and the service value chain. It also provides a substantial contribution to policy and strategic management by integrating constituents\u27 values and experiences with recommendations for progressive community management

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

Predicting judging-perceiving of Myers-Briggs Type Indicator (MBTI) in online social forum

The Myers-Briggs Type Indicator (MBTI) is a well-known personality test that assigns a personality type to a user by using four traits dichotomies. For many years, people have used MBTI as an instrument to develop self-awareness and to guide their personal decisions. Previous researches have good successes in predicting Extraversion-Introversion (E/I), Sensing-Intuition (S/N) and Thinking-Feeling (T/F) dichotomies from textual data but struggled to do so with Judging-Perceiving (J/P) dichotomy. J/P dichotomy in MBTI is a non-separable part of MBTI that have significant inference on human behavior, perception and decision towards their surroundings. It is an assessment on how someone interacts with the world when making decision. This research was set out to evaluate the performance of the individual features and classifiers for J/P dichotomy in personality computing. At the end, data leakage was found in dataset originating from the Personality Forum Café, which was used in recent researches. The results obtained from the previous research on this dataset were suggested to be overly optimistic. Using the same settings, this research managed to outperform previous researches. Five machine learning algorithms were compared, and LightGBM model was recommended for the task of predicting J/P dichotomy in MBTI personality computing

Concepts and experiments on psychoanalysis driven computing

This research investigates the effective incorporation of the human factor and user perception in text-based interactive media. In such contexts, the reliability of user texts is often compromised by behavioural and emotional dimensions. To this end, several attempts have been made in the state of the art, to introduce psychological approaches in such systems, including computational psycholinguistics, personality traits and cognitive psychology methods. In contrast, our method is fundamentally different since we employ a psychoanalysis-based approach; in particular, we use the notion of Lacanian discourse types, to capture and deeply understand real (possibly elusive) characteristics, qualities and contents of texts, and evaluate their reliability. As far as we know, this is the first time computational methods are systematically combined with psychoanalysis. We believe such psychoanalytic framework is fundamentally more effective than standard methods, since it addresses deeper, quite primitive elements of human personality, behaviour and expression which usually escape methods functioning at “higher”, conscious layers. In fact, this research is a first attempt to form a new paradigm of psychoanalysis-driven interactive technologies, with broader impact and diverse applications. To exemplify this generic approach, we apply it to the case-study of fake news detection; we first demonstrate certain limitations of the well-known Myers–Briggs Type Indicator (MBTI) personality type method, and then propose and evaluate our new method of analysing user texts and detecting fake news based on the Lacanian discourses psychoanalytic approach.This publication is part of the Spanish I+D+i project TRAINERA (ref. PID2020-118011GB-C21), funded by MCIN/AEI/10.13039/ 501100011033Peer ReviewedPostprint (published version

Do memorable restaurant experiences affect eWOM? The moderating effect of consumers' behavioural engagement on social networking sites

Many restaurants offer high-quality service to their customers, hoping to provide memorable experiences that influence their loyalty and electronic word of mouth (eWOM). However, consumers' memorable experiences do not always imply positive eWOM. This study aims to (1) verify the direct impacts of the perceived quality by consumers of casual dining restaurants on positive emotions, negative emotions and memorable experiences; (2) investigate the impacts of memorable experiences on the propensity to loyalty and eWOM; (3) test the moderating effect of consumer behavioural engagement on social networking sites (CBE-SNS) on the relationship between memorable experiences and eWOM.Design/methodology/approachThis survey included 475 university students in Brazil. Participants answered an electronic form about their experiences in casual dining restaurants. Structural equation modelling tested the hypothetical model based on the stimulus-organism-response (S-O-R) theory (Mehrabian and Russell, 1974).FindingsThe quality perceived by restaurant consumers (stimulus) positively impacts their memorable experiences and positive emotions and negatively affects their negative emotions (organism). Memorable experiences positively impact the propensity to loyalty (response). The CBE-SNS moderates the intensity of the relationship between memorable experiences (organism) and eWOM (response).Originality/valueThis study is the first that demonstrates the relationships between perceived quality, positive and negative emotions, memorable experiences, the propensity to loyalty and CBE-SNS and e-WOM in restaurants. Casual dining restaurants must offer their customers services with high perceived quality, positively impacting their emotions and their memorable experiences. Finally, restaurants must create strategies and actions to increase the CBE-SNS to encourage them to share their memorable experiences through eWOM.info:eu-repo/semantics/publishedVersio

Exploring the potential of online self-reported and routinely collected electronic healthcare record data in self-harm research

Background:Self-harm is a major public health concern and is a leading cause of death from injury. Reaching participants for self-harm research raises a number of challenges, however an opportunity exists in the use of both the internet for data collection and in the use of routinely collected healthcare data.Aims and objectives:The aim of this project was to explore the potential of both online and routinely collected healthcare data for self-harm research and the way in which these data sources can be brought together.Methods:This thesis represents a series of projects exploring the use of various data sources for self-harm research. The first was the development and piloting of an online platform (SHARE UK) for self-harm research. This website incorporated multiple functions: hosting questionnaires; sign-up for a research register; sign-up for linkage with routinely collected data and uploads to a media databank. Next a national survey was conducted to explore young people’s perspectives on the use of both online and healthcare data for self-harm research. Lastly a population level electronic health record cohort study analysing trends over time and contacts across healthcare services was conducted.Results:Participants engaged well with research online: 498 participants signed up to the SHARE UK platform; of whom 85% signed up for the research register. Sixty-two participants uploaded 95 items to the media databank. Alternative formats are discussed. Only 15% of participants consented for linkage with healthcare data. A total of 2,733 young people aged 10-24 who self-harm completed the national survey. Results demonstrated that the necessity for participants to give their address for linkage poses a significant barrier. Opinions around the use of Big Data, encompassing social media, marketing and health data are explored.A total of 937,697 individuals aged 10-24 provided 5,269,794 person years of data from 01.01.2003 to 20.09.2015 to the electronic health record cohort study. Self-harm incidence was highest in primary care. Males preferentially present to emergency departments. Male are less likely than females to be admitted following attendance. This difference persists in the youngest age groups and for self-poisoning. Analysis supports the importance of non-specialist services.Conclusions:This thesis has explored both online and routinely collected healthcare data and their utility for self-harm research, exploring participant views and issues via a national survey. An online platform for self-harm research was successfully piloted and issues identified. This series of projects explores possibilities for future self-harm research. The use of multiple data sources allows research to represent both those in the community and those presenting to healthcare settings, lowering many of the barriers to participating in self-harm research. The future utility of the SHARE UK platform through its collaboration with the Adolescent Mental Health Data Platform (ADP) is discussed. Results of this series of projects will be used to inform the development of this platform with lessons learnt from the pilot addressed and findings from both the national survey and the electronic health record cohort study informing and shaping future research