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AbstractPurpose -This study aims to identify the similarities and differences between the perspectives of providers and customers regarding the important dimensions and attributes of e-service quality (e-SQ). Design/methodology/approach -Ten criteria are proposed for assessment of e-SQ in both business-to-business (B2B) and business-to-consumer (B2C) transactions. Confirmatory factor analysis confirms the validity of grouping these criteria into five proposed dimensions. The e-SQ dimensions and criteria are then ranked in terms of their importance by a survey of respondents from small and medium-sized enterprises with experience in conducting e-business in Greece. The results are compared with selected surveys of customers' perceptions from the literature. Findings -The results indicate that the providers' perceptions are in agreement with customers' perceptions with regard to e-SQ dimensions, but not with regard to specific criteria (items) within those dimensions. The study also finds that providers have similar perceptions of the importance of the suggested e-SQ criteria in B2B and B2C electronic transactions.Research limitations/implications -The findings should be generalised with care if extrapolated to other socio-cultural settings and specific industries. Practical implications -Managers should recognise that there might be differences between their views of e-SQ and those of their customers. Originality/value -This is one of the few studies to have focused on the perceptions of providers in assessing e-SQ.
Purpose
This paper aims to address privacy concerns that arise from the use of mobile recommender systems when processing contextual information relating to the user. Mobile recommender systems aim to solve the information overload problem by recommending products or services to users of Web services on mobile devices, such as smartphones or tablets, at any given point in time and in any possible location. They use recommendation methods, such as collaborative filtering or content-based filtering and use a considerable amount of contextual information to provide relevant recommendations. However, because of privacy concerns, users are not willing to provide the required personal information that would allow their views to be recorded and make these systems usable.
Design/methodology/approach
This work is focused on user privacy by providing a method for context privacy-preservation and privacy protection at user interface level. Thus, a set of algorithms that are part of the method has been designed with privacy protection in mind, which is done by using realistic dummy parameter creation. To demonstrate the applicability of the method, a relevant context-aware data set has been used to run performance and usability tests.
Findings
The proposed method has been experimentally evaluated using performance and usability evaluation tests and is shown that with a small decrease in terms of performance, user privacy can be protected.
Originality/value
This is a novel research paper that proposed a method for protecting the privacy of mobile recommender systems users when context parameters are used.
Technology changes rapidly over years providing continuously more options for computer alternatives and making life easier for economic, intra-relation or any other transactions. However, the introduction of new technology "pushes" old Information and Communication Technology (ICT) products to non-use. E-waste is defined as the quantities of ICT products which are not in use and is bivariate function of the sold quantities, and the probability that specific computers quantity will be regarded as obsolete. In this paper, an e-waste generation model is presented, which is applied to the following regions: Western and Eastern Europe, Asia/Pacific, Japan/Australia/New Zealand, North and South America. Furthermore, cumulative computer sales were retrieved for selected countries of the regions so as to compute obsolete computer quantities. In order to provide robust results for the forecasted quantities, a selection of forecasting models, namely (i) Bass, (ii) Gompertz, (iii) Logistic, (iv) Trend model, (v) Level model, (vi) AutoRegressive Moving Average (ARMA), and (vii) Exponential Smoothing were applied, depicting for each country that model which would provide better results in terms of minimum error indices (Mean Absolute Error and Mean Square Error) for the in-sample estimation. As new technology does not diffuse in all the regions of the world with the same speed due to different socioeconomic factors, the lifespan distribution, which provides the probability of a certain quantity of computers to be considered as obsolete, is not adequately modeled in the literature. The time horizon for the forecasted quantities is 2014-2030, while the results show a very sharp increase in the USA and United Kingdom, due to the fact of decreasing computer lifespan and increasing sales.
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