9,068 research outputs found
UNDERSTANDING USER PERCEPTIONS AND PREFERENCES FOR MASS-MARKET INFORMATION SYSTEMS â 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 consumerâs preferences for multiple factors combing functional, non-functional and economic aspects. The results include a âFramework for Conjoint Analysis Studies in ISâ 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.
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Les technologies cloud et mobiles ont fait Ă©merger une nouvelle catĂ©gorie de produits informatiques qui sâadressent Ă des utilisateurs hĂ©tĂ©rogĂšnes par le biais de systĂšmes d'information (SI) distribuĂ©s. Les termes âSI de masseâ sont employĂ©s pour dĂ©signer ces nouveaux systĂšmes. Une conception rĂ©ussie de ceux-ci passe par une phase essentielle de comprĂ©hension des besoins et des facteurs d'adoption des utilisateurs. Pour ce faire, la recherche en SI suggĂšre plusieurs thĂ©ories et modĂšles tels que le âIS Success Modelâ et le âTechnology Acceptance Modelâ. Bien que ces approches contribuent Ă la comprĂ©hension thĂ©orique de l'adoption et de l'utilisation des SI de masse, elles sont critiquĂ©es pour ne pas ĂȘtre en mesure de fournir des informations exploitables sur la conception de SI car elles considĂšrent l'artefact informatique comme une boĂźte noire. En dâautres termes, ces approches ne traitent pas suffisamment des caractĂ©ristiques internes du systĂšme. Nous soutenons que la recherche en SI doit adopter des techniques d'Ă©tude de marchĂ© afin de mieux intĂ©grer les exigences du client (âVoice of Customerâ) dans un scĂ©nario de marchĂ© de masse. Plus prĂ©cisĂ©ment, l'analyse conjointe (AC), issue de la recherche sur les consommateurs, peut contribuer au dĂ©veloppement de systĂšme SI Ă forte valeur d'usage. Si lâAC a gagnĂ© en popularitĂ© au sein de la recherche en SI, des recommandations quant Ă son utilisation dans ce domaine restent rares. Nous entendons soutenir la conception de SI de masse en facilitant une identification fiable des prĂ©fĂ©rences des consommateurs sur de multiples facteurs combinant des aspects fonctionnels, non-fonctionnels et Ă©conomiques. Les rĂ©sultats comprennent un âCadre de rĂ©fĂ©rence pour les Ă©tudes d'analyse conjointe en SIâ et des recommandations mĂ©thodologiques pour l'application de lâAC. Nous avons utilisĂ© ces contributions pour concevoir un SI de masse particuliĂšrement sensible au respect de la vie privĂ©e des utilisateurs et nous avons Ă©valuĂ© lâimpact de nos recherches sur l'adoption de ce systĂšme par ses utilisateurs. Ainsi, notre travail contribue tant Ă la thĂ©orie quâĂ la pratique des SI. Pour le monde universitaire, nous contribuons en proposant une conceptualisation plus nuancĂ©e de l'artefact informatique (c'est-Ă -dire du systĂšme) Ă travers le prisme des fonctionnalitĂ©s et par une approche basĂ©e sur les prĂ©fĂ©rences utilisateurs. Par ailleurs, les chercheurs peuvent Ă©galement s'appuyer sur nos directives mĂ©thodologiques pour Ă©tudier les perceptions et les prĂ©fĂ©rences des utilisateurs pour diffĂ©rentes variations de conception et Ă©tendre cela Ă l'adoption. De plus, nos Ă©tudes empiriques sur la conception dâun SI de masse sensible au respect de la vie privĂ©e des utilisateurs contribuent Ă une meilleure comprĂ©hension de lâapplication des techniques CA dans ce domaine spĂ©cifique. Nos Ă©tudes incluent notamment une Ă©valuation nuancĂ©e des prĂ©fĂ©rences des utilisateurs sur des fonctionnalitĂ©s de protection de la vie privĂ©e. Pour les praticiens, nous proposons des lignes directrices qui permettent dâintĂ©grer les exigences des clients afin de concevoir un SI rĂ©ussi
Cloud Service Selection System Approach based on QoS Model: A Systematic Review
The Internet of Things (IoT) has received a lot of interest from researchers recently. IoT is seen as a component of the Internet of Things, which will include billions of intelligent, talkative "things" in the coming decades. IoT is a diverse, multi-layer, wide-area network composed of a number of network links. The detection of services and on-demand supply are difficult in such networks, which are comprised of a variety of resource-limited devices. The growth of service computing-related fields will be aided by the development of new IoT services. Therefore, Cloud service composition provides significant services by integrating the single services. Because of the fast spread of cloud services and their different Quality of Service (QoS), identifying necessary tasks and putting together a service model that includes specific performance assurances has become a major technological problem that has caused widespread concern. Various strategies are used in the composition of services i.e., Clustering, Fuzzy, Deep Learning, Particle Swarm Optimization, Cuckoo Search Algorithm and so on. Researchers have made significant efforts in this field, and computational intelligence approaches are thought to be useful in tackling such challenges. Even though, no systematic research on this topic has been done with specific attention to computational intelligence. Therefore, this publication provides a thorough overview of QoS-aware web service composition, with QoS models and approaches to finding future aspects
Cloud Service Provider Evaluation System using Fuzzy Rough Set Technique
Cloud Service Providers (CSPs) offer a wide variety of scalable, flexible,
and cost-efficient services to cloud users on demand and pay-per-utilization
basis. However, vast diversity in available cloud service providers leads to
numerous challenges for users to determine and select the best suitable
service. Also, sometimes users need to hire the required services from multiple
CSPs which introduce difficulties in managing interfaces, accounts, security,
supports, and Service Level Agreements (SLAs). To circumvent such problems
having a Cloud Service Broker (CSB) be aware of service offerings and users
Quality of Service (QoS) requirements will benefit both the CSPs as well as
users. In this work, we proposed a Fuzzy Rough Set based Cloud Service
Brokerage Architecture, which is responsible for ranking and selecting services
based on users QoS requirements, and finally monitor the service execution. We
have used the fuzzy rough set technique for dimension reduction. Used weighted
Euclidean distance to rank the CSPs. To prioritize user QoS request, we
intended to use user assign weights, also incorporated system assigned weights
to give the relative importance to QoS attributes. We compared the proposed
ranking technique with an existing method based on the system response time.
The case study experiment results show that the proposed approach is scalable,
resilience, and produce better results with less searching time.Comment: 12 pages, 7 figures, and 8 table
Security risk assessment in cloud computing domains
Cyber security is one of the primary concerns persistent across any computing platform. While addressing the apprehensions about security risks, an infinite amount of resources cannot be invested in mitigation measures since organizations operate under budgetary constraints. Therefore the task of performing security risk assessment is imperative to designing optimal mitigation measures, as it provides insight about the strengths and weaknesses of different assets affiliated to a computing platform.
The objective of the research presented in this dissertation is to improve upon existing risk assessment frameworks and guidelines associated to different key assets of Cloud computing domains - infrastructure, applications, and users. The dissertation presents various informal approaches of performing security risk assessment which will help to identify the security risks confronted by the aforementioned assets, and utilize the results to carry out the required cost-benefit tradeoff analyses. This will be beneficial to organizations by aiding them in better comprehending the security risks their assets are exposed to and thereafter secure them by designing cost-optimal mitigation measures --Abstract, page iv
CC-PSM: A Preference-Aware Selection Model for Cloud Service Based on Consumer Community
In order to give full consideration to the consumerâs personal preference in cloud service selection strategies and improve the credibility of service prediction, a preference-aware cloud service selection model based on consumer community (CC-PSM) is presented in this work. The objective of CC-PSM is to select a service meeting a target consumerâs demands and preference. Firstly, the correlation between cloud consumers from a bipartite network for service selection is mined to compute the preference similarity between them. Secondly, an improved hierarchical clustering algorithm is designed to discover the consumer community with similar preferences so as to form the trusted groups for service recommendation. In the clustering process, a quantization function called community degree is given to evaluate the quality of community structure. Thirdly, a prediction model based on consumer community is built to predict a consumerâs evaluation on an unknown service. The experimental results show that CC-PSM can effectively partition the consumers based on their preferences and has good effectiveness in service selection applications
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