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Purpose – The purpose of this paper is to investigate risk reduction strategies in online shopping through the perspective of buyer’s trust. Design/methodology/approach – An explanatory research approach is adopted to identify cause-and-effect relationships between e-quality and customers’ loyalty, satisfaction and trust. This approach is accomplished through field research, which is based on a structured questionnaire that utilizes the E-S-QUAL tool, which is a multiple-item general scale for measuring electronic service quality. The sample is consisted of 92 e-buyers (consumers). Findings – The field research revealed that three e-quality dimensions, namely, ease of use, customization and assurance, e-scape and responsiveness, have significant positive effects on e-loyalty and e-satisfaction. Regarding e-trust, only customization and assurance exerts a significant positive effect. Research limitations/implications – The field research provides in-depth understanding of relationships among e-loyalty, e-satisfaction and e-trust. The majority of the respondents are young people living in Athens, Greece, highly educated, with a relative low monthly income. Originality/value – This study investigates how trust is affecting the consumers’ engagement to e-commerce, suggesting the appropriate security that should be taken to mitigate perceived risks. Reviewing security measures can help reduce risks of an e-company and simultaneously enforce the level of trust and customers' intentions to buy.

Finding the correct category (class) a new unclassified document belongs to is an interesting and difficult problem, with a wide range of applications. Our methodology for narrative text classification is based on two techniques: we calculate the distance (similarity) between the new unclassified document and all the pre-classified documents of each class and also calculate the similarity of the new document to the ‘average class document’ of each class. In both cases we use key phrases (text phrases or key terms) as the distinctive features of our text classification methodology and eventually the proposed text classification method is based on the automatic extraction of an authority list of key phrases that is appropriate for discriminating between different classes. In this paper, we apply this methodology in handling Greek text and we present the key concepts, the algorithms, and some critical decisions. A number of parameters of the mining algorithm are also fine tuned. The actual text classification system, the adopted (embedded) ideas and the alternative values of parameters are evaluated using two training sets (test collections).

The population of the Earth is moving towards urban areas forming smart cities (SCs). Waste management is a component of SCs. We consider a SC which contains a distribution of waste bins and a distribution of waste trucks located in the SC sectors. Bins and trucks are enabled with Internet of Things (IoT) sensors and actuators. Prior approaches focus mainly on the dynamic scheduling and routing issues emerging from IoT-enabled waste management. However, less research has been done in the area of the stochastic reassignment process during the four seasons of the year over a period of two years. In this paper we aim to stochastically reassign trucks to collect waste from bins through time. We treat this problem with a multi-agent system for stochastic analyses.