A Model for Using Physiological Conditions for Proactive Tourist Recommendations

Mobile proactive tourist recommender systems can support tourists by recommending the best choice depending on different contexts related to herself and the environment. In this paper, we propose to utilize wearable sensors to gather health information about a tourist and use them for recommending tourist activities. We discuss a range of wearable devices, sensors to infer physiological conditions of the users, and exemplify the feasibility using a popular self-quantification mobile app. Our main contribution then comprises a data model to derive relations between the parameters measured by the wearable sensors, such as heart rate, body temperature, blood pressure, and use them to infer the physiological condition of a user. This model can then be used to derive classes of tourist activities that determine which items should be recommended

UNDERSTANDING USER PERCEPTIONS AND PREFERENCES FOR MASS-MARKET INFORMATION SYSTEMS – LEVERAGING MARKET RESEARCH TECHNIQUES AND EXAMPLES IN PRIVACY-AWARE DESIGN

With cloud and mobile computing, a new category of software products emerges as mass-market information systems (IS) that addresses distributed and heterogeneous end-users. Understanding user requirements and the factors that drive user adoption are crucial for successful design of such systems. IS research has suggested several theories and models to explain user adoption and intentions to use, among them the IS Success Model and the Technology Acceptance Model (TAM). Although these approaches contribute to theoretical understanding of the adoption and use of IS in mass-markets, they are criticized for not being able to drive actionable insights on IS design as they consider the IT artifact as a black-box (i.e., they do not sufficiently address the system internal characteristics). We argue that IS needs to embrace market research techniques to understand and empirically assess user preferences and perceptions in order to integrate the "voice of the customer" in a mass-market scenario. More specifically, conjoint analysis (CA), from market research, can add user preference measurements for designing high-utility IS. CA has gained popularity in IS research, however little guidance is provided for its application in the domain. We aim at supporting the design of mass-market IS by establishing a reliable understanding of consumer’s preferences for multiple factors combing functional, non-functional and economic aspects. The results include a “Framework for Conjoint Analysis Studies in IS” and methodological guidance for applying CA. We apply our findings to the privacy-aware design of mass-market IS and evaluate their implications on user adoption. We contribute to both academia and practice. For academia, we contribute to a more nuanced conceptualization of the IT artifact (i.e., system) through a feature-oriented lens and a preference-based approach. We provide methodological guidelines that support researchers in studying user perceptions and preferences for design variations and extending that to adoption. Moreover, the empirical studies for privacy- aware design contribute to a better understanding of the domain specific applications of CA for IS design and evaluation with a nuanced assessment of user preferences for privacy-preserving features. For practice, we propose guidelines for integrating the voice of the customer for successful IS design. -- Les technologies cloud et mobiles ont fait émerger une nouvelle catégorie de produits informatiques qui s’adressent à des utilisateurs hétérogènes par le biais de systèmes d'information (SI) distribués. Les termes “SI de masse” sont employés pour désigner ces nouveaux systèmes. Une conception réussie de ceux-ci passe par une phase essentielle de compréhension des besoins et des facteurs d'adoption des utilisateurs. Pour ce faire, la recherche en SI suggère plusieurs théories et modèles tels que le “IS Success Model” et le “Technology Acceptance Model”. Bien que ces approches contribuent à la compréhension théorique de l'adoption et de l'utilisation des SI de masse, elles sont critiquées pour ne pas être en mesure de fournir des informations exploitables sur la conception de SI car elles considèrent l'artefact informatique comme une boîte noire. En d’autres termes, ces approches ne traitent pas suffisamment des caractéristiques internes du système. Nous soutenons que la recherche en SI doit adopter des techniques d'étude de marché afin de mieux intégrer les exigences du client (“Voice of Customer”) dans un scénario de marché de masse. Plus précisément, l'analyse conjointe (AC), issue de la recherche sur les consommateurs, peut contribuer au développement de système SI à forte valeur d'usage. Si l’AC a gagné en popularité au sein de la recherche en SI, des recommandations quant à son utilisation dans ce domaine restent rares. Nous entendons soutenir la conception de SI de masse en facilitant une identification fiable des préférences des consommateurs sur de multiples facteurs combinant des aspects fonctionnels, non-fonctionnels et économiques. Les résultats comprennent un “Cadre de référence pour les études d'analyse conjointe en SI” et des recommandations méthodologiques pour l'application de l’AC. Nous avons utilisé ces contributions pour concevoir un SI de masse particulièrement sensible au respect de la vie privée des utilisateurs et nous avons évalué l’impact de nos recherches sur l'adoption de ce système par ses utilisateurs. Ainsi, notre travail contribue tant à la théorie qu’à la pratique des SI. Pour le monde universitaire, nous contribuons en proposant une conceptualisation plus nuancée de l'artefact informatique (c'est-à-dire du système) à travers le prisme des fonctionnalités et par une approche basée sur les préférences utilisateurs. Par ailleurs, les chercheurs peuvent également s'appuyer sur nos directives méthodologiques pour étudier les perceptions et les préférences des utilisateurs pour différentes variations de conception et étendre cela à l'adoption. De plus, nos études empiriques sur la conception d’un SI de masse sensible au respect de la vie privée des utilisateurs contribuent à une meilleure compréhension de l’application des techniques CA dans ce domaine spécifique. Nos études incluent notamment une évaluation nuancée des préférences des utilisateurs sur des fonctionnalités de protection de la vie privée. Pour les praticiens, nous proposons des lignes directrices qui permettent d’intégrer les exigences des clients afin de concevoir un SI réussi

Attitude towards branded mobile applications and reuse intention : the moderating effect of gender and prior brand involvement

Mobile applications (apps) have created a significant interest among advertisers and marketers, mostly because of their positive impact on user´s attitude and high level of engagement towards the brand. This study analyses the impact of gender-specific tendencies in the assessment of utilitarian and hedonic oriented branded mobile apps, highlighting that utilitarian content is favoured when intending to reuse mobile applications. The thesis strives to understand customer’s attitude towards branded mobile applications via pre-test and post-test survey focusing on four mobile branded applications- Google Map, Snapchat, Uber and Tinder. Pearson correlation and linear regression are used to understand the positive relationship between attitude and reuse intention and one-way Anova is used to understand gender preferences towards utilitarian and hedonic content of branded mobile apps. Results indicate that individual’s attitudes are strongly positively related with their reuse intention. Likewise, for Gender, men & women both have a higher tendency to reuse utilitarian apps, as contrary to the developed hypothesis that women mostly prefer hedonic app rather than utilitarian app. Additionally, the results conclude that the level of prior brand involvement may not necessarily moderate individual’s immediate response towards branded mobile application and reuse intention. Finally, implications associated with the findings are discussed with respect to clarifying possible outcomes obtained during result analysis and initiate solutions to improve further studies in this area.Este estudo analisa o impacto de tendências específicas de cada género na avaliação das marcas de aplicações móveis utilitaristas e hedónicas, realçando que a informação de conteúdo utilitarista é preferida no que toca à reutilização de aplicações. A tese procura compreender a atitude do cliente perante as marcas das aplicações através de questionário pré e pós-teste, focando-se em quatro aplicações: Google Map, Snapchat, Uber e Tinder. A correlação de Pearson e a regressão linear são usadas para entender a relação entre atitude e intenção de reutilização. Análise de variância é usada para perceber a preferência entre conteúdos das aplicações utilitaristas e hedónicas. Os resultados indicam que as atitudes individuais estão muito positivamente relacionadas com a intenção de reutilização. De igual modo, para género, homens e mulheres têm ambos uma grande tendência para reutilizar aplicações utilitaristas, contrariamente com a hipótese desenvolvida que as mulheres preferem aplicações hedónicas. Além disso, os resultados concluem que o nível de envolvimento anterior da marca pode não necessariamente moderar a resposta imediata do indivíduo em relação à aplicação móvel e à intenção de reutilização da marca. Finalmente, as implicações associadas com as conclusões são discutidas de forma a clarificar resultados possivelmente obtidos durante a análise dos dados e iniciar soluções para melhorar estudo adicionais nesta área

Self-monitoring Practices, Attitudes, and Needs of Individuals with Bipolar Disorder: Implications for the Design of Technologies to Manage Mental Health

Objective To understand self-monitoring strategies used independently of clinical treatment by individuals with bipolar disorder (BD), in order to recommend technology design principles to support mental health management. Materials and Methods Participants with BD (N = 552) were recruited through the Depression and Bipolar Support Alliance, the International Bipolar Foundation, and WeSearchTogether.org to complete a survey of closed- and open-ended questions. In this study, we focus on descriptive results and qualitative analyses. Results Individuals reported primarily self-monitoring items related to their bipolar disorder (mood, sleep, finances, exercise, and social interactions), with an increasing trend towards the use of digital tracking methods observed. Most participants reported having positive experiences with technology-based tracking because it enables self-reflection and agency regarding health management and also enhances lines of communication with treatment teams. Reported challenges stem from poor usability or difficulty interpreting self-tracked data. Discussion Two major implications for technology-based self-monitoring emerged from our results. First, technologies can be designed to be more condition-oriented, intuitive, and proactive. Second, more automated forms of digital symptom tracking and intervention are desired, and our results suggest the feasibility of detecting and predicting emotional states from patterns of technology usage. However, we also uncovered tension points, namely that technology designed to support mental health can also be a disruptor. Conclusion This study provides increased understanding of self-monitoring practices, attitudes, and needs of individuals with bipolar disorder. This knowledge bears implications for clinical researchers and practitioners seeking insight into how individuals independently self-manage their condition as well as for researchers designing monitoring technologies to support mental health management

Perspective: a conceptual framework for adaptive personalized nutrition advice systems (APNASs)

Nearly all approaches to personalized nutrition (PN) use information such as the gene variants of individuals to deliver advice that is more beneficial than a generic one-size-fits-all recommendation. Despite great enthusiasm and the increased availability of commercial services, thus far, scientific studies have only revealed small to negligible effects on the efficacy and effectiveness of personalized dietary recommendations, even when using genetic or other individual information. In addition, from a public health perspective, scholars are critical of PN because it primarily targets socially privileged groups rather than the general population, thereby potentially widening health inequality. Therefore, in this perspective, we propose to extend current PN approaches by creating adaptive personalized nutrition advice systems (APNASs) that are tailored to the type and timing of personalized advice for individual needs, capacities, and receptivity in real-life food environments. These systems encompass a broadening of current PN goals (i.e., what should be achieved) to incorporate individual goal preferences beyond currently advocated biomedical targets (e.g., making sustainable food choices). Moreover, they cover the personalization processes of behavior change by providing in situ, just-in-time information in real-life environments (how and when to change), which accounts for individual capacities and constraints (e.g., economic resources). Finally, they are concerned with a participatory dialogue between individuals and experts (e.g., actual or virtual dieticians, nutritionists, and advisors), when setting goals and deriving measures of adaption. Within this framework, emerging digital nutrition ecosystems enable continuous, real-time monitoring, advice, and support in food environments from exposure to consumption. We present this vision of a novel PN framework along with scenarios and arguments that describe its potential to efficiently address individual and population needs and target groups that would benefit most from its implementation