121,096 research outputs found

    Intangible trust requirements - how to fill the requirements trust "gap"?

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    Previous research efforts have been expended in terms of the capture and subsequent instantiation of "soft" trust requirements that relate to HCI usability concerns or in relation to "hard" tangible security requirements that primarily relate to security a ssurance and security protocols. Little direct focus has been paid to managing intangible trust related requirements per se. This 'gap' is perhaps most evident in the public B2C (Business to Consumer) E- Systems we all use on a daily basis. Some speculative suggestions are made as to how to fill the 'gap'. Visual card sorting is suggested as a suitable evaluative tool; whilst deontic logic trust norms and UML extended notation are the suggested (methodologically invariant) means by which software development teams can perhaps more fully capture hence visualize intangible trust requirements

    Affective feedback: an investigation into the role of emotions in the information seeking process

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    User feedback is considered to be a critical element in the information seeking process, especially in relation to relevance assessment. Current feedback techniques determine content relevance with respect to the cognitive and situational levels of interaction that occurs between the user and the retrieval system. However, apart from real-life problems and information objects, users interact with intentions, motivations and feelings, which can be seen as critical aspects of cognition and decision-making. The study presented in this paper serves as a starting point to the exploration of the role of emotions in the information seeking process. Results show that the latter not only interweave with different physiological, psychological and cognitive processes, but also form distinctive patterns, according to specific task, and according to specific user

    Critical review of the e-loyalty literature: a purchase-centred framework

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    Over the last few years, the concept of online loyalty has been examined extensively in the literature, and it remains a topic of constant inquiry for both academics and marketing managers. The tremendous development of the Internet for both marketing and e-commerce settings, in conjunction with the growing desire of consumers to purchase online, has promoted two main outcomes: (a) increasing numbers of Business-to-Customer companies running businesses online and (b) the development of a variety of different e-loyalty research models. However, current research lacks a systematic review of the literature that provides a general conceptual framework on e-loyalty, which would help managers to understand their customers better, to take advantage of industry-related factors, and to improve their service quality. The present study is an attempt to critically synthesize results from multiple empirical studies on e-loyalty. Our findings illustrate that 62 instruments for measuring e-loyalty are currently in use, influenced predominantly by Zeithaml et al. (J Marketing. 1996;60(2):31-46) and Oliver (1997; Satisfaction: a behavioral perspective on the consumer. New York: McGraw Hill). Additionally, we propose a new general conceptual framework, which leads to antecedents dividing e-loyalty on the basis of the action of purchase into pre-purchase, during-purchase and after-purchase factors. To conclude, a number of managerial implementations are suggested in order to help marketing managers increase their customers’ e-loyalty by making crucial changes in each purchase stage

    An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection

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    The biological immune system (BIS) is characterized by networks of cells, tissues, and organs communicating and working in synchronization. It also has the ability to learn, recognize, and remember, thus providing the solid foundation for the development of Artificial Immune System (AIS). Since the emergence of AIS, it has proved itself as an area of computational intelligence. Real-Valued Negative Selection Algorithm with Variable-Sized Detectors (V-Detectors) is an offspring of AIS and demonstrated its potentials in the field of anomaly detection. The V-Detectors algorithm depends greatly on the random detectors generated in monitoring the status of a system. These randomly generated detectors suffer from not been able to adequately cover the non-self space, which diminishes the detection performance of the V-Detectors algorithm. This research therefore proposed CSDE-V-Detectors which entail the use of the hybridization of Cuckoo Search (CS) and Differential Evolution (DE) in optimizing the random detectors of the V-Detectors. The DE is integrated with CS at the population initialization by distributing the population linearly. This linear distribution gives the population a unique, stable, and progressive distribution process. Thus, each individual detector is characteristically different from the other detectors. CSDE capabilities of global search, and use of LŽevy flight facilitates the effectiveness of the detector set in the search space. In comparison with V-Detectors, cuckoo search, differential evolution, support vector machine, artificial neural network, našıve bayes, and k-NN, experimental results demonstrates that CSDE-V-Detectors outperforms other algorithms with an average detection rate of 95:30% on all the datasets. This signifies that CSDE-V-Detectors can efficiently attain highest detection rates and lowest false alarm rates for anomaly detection. Thus, the optimization of the randomly detectors of V-Detectors algorithm with CSDE is proficient and suitable for anomaly detection tasks

    Electronic Publishing: Research Issues for Academic Librarians and Users

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    Review on learning orientations

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    The need has arises towards the consideration of individual difference to let learners engage in and responsible for their own learning, retain information longer, apply the knowledge more effectively, have positive attitudes towards the subject, have more interest in learning materials, score higher and have high intrinsic motivation level. As regard to the importance of individual differences, Martinez (2000) has grounded a new theory, which is Intentional Learning Theory that covered individual aspects of cognitive, intention, social and emotion. This theory hypothesizes that the fundamental of understanding how individual learns, interact with an environment, performs, engages in learning, experiences learning, and assimilate and accommodate the new knowledge is by understanding individual’s fundamental emotions and intentions about how to use learning, why it is important, when the suitable time, and how it can accomplish personal goals and change. The intent of this theory is to focus on emotions and intentions of an individual regarding why, when and how learning goals are organized, processed, and achieved. In conclusion, Learning Orientations introduced by this theory describes the disposition of an individual in approaching, managing and achieving their learning intentionally and differently from others

    Ambiance Factors, Emotions, and Web User Behaviour:a Model Integrating and Affective and Symbolical Approach

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    The present paper addresses the efficiency of manipulating music in a merchant website, and it:- proposes a review of the literature on ambiance factors in advertising & shopping behaviour, capitalizing on it to:- propose a theoretical framework that enhances our understanding of the web-user behaviour in specific ambiance factors such as music, with a specific attention devoted to his loyalty, & affiliation behaviour;- a model of is proposed.Ambiance Factors; Emotions; Fit & Symbolism; On-line Behaviour
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