Abstract
Purpose The purpose of this study is to contribute to a better understanding of the antecedents and consequences of loyalty to consumer networks hosted by companies in the scope of social networking sites (SNS). These company social networks (CSNs) have traditionally been studied as online brand communities but more research is needed to understand their role for host companies. Design/methodology/approach This study identifies CSN performance dimensions (host reputation, informativeness, communication self-expressiveness, rewarding activity and consumption support) and analyzes how they influence attitudes and behavioral intentions toward CSNs (identification with the community, satisfaction and loyalty) and toward the host company (satisfaction and loyalty). A conceptual model is tested through a survey administered to members of a large grocery retailer CSN on Facebook. Findings Results show that all six identified performance dimensions significantly impact CSN loyalty. However, while self-expressiveness, communication and rewarding activity (which are closely related to social and hedonic value) are predictors of loyalty to the CSN, through the mediation of identification with the community, they neither indirectly (through the mediation of identification) nor directly impact satisfaction with the host. Conversely, informativeness, communication and host reputation are good predictors of loyalty to the CSN, through mediation of satisfaction with the CSN, and also exert an indirect positive influence on satisfaction with the host. Finally, consumption support positively influences loyalty to the CSN through the mediation of identification with the community and directly positively influences satisfaction with the host company. Originality/value These results reveal the dichotomous nature of CSNs, as communities of people with shared interests and supplementary services created by companies to add value to their core offering. While perceptions regarding the community facet are independent from attitude toward the host, perceptions regarding supplementary service are significant predictors of satisfaction with the host. These results offer implications for future research and management of companies' social media presence.

Abstract
Wireless sensor networks have been considered as an effective solution to a wide range of applications due to their prominent characteristics concerning information retrieving and distributed processing. When visual information can be also retrieved by sensor nodes, applications acquire a more comprehensive perception of monitored environments, fostering the creation of wireless visual sensor networks. As such networks are being more often considered for critical monitoring and control applications, usually related to catastrophic situation prevention, security enhancement and crises management, fault tolerance becomes a major expected service for visual sensor networks. A way to address this issue is to evaluate the system dependability through quantitative attributes (e.g., reliability and availability), which require a proper modeling strategy to describe the system behavior. That way, in this paper, we propose a methodology to model and evaluate the dependability of wireless visual sensor networks using Fault Tree Analysis and Markov Chains. The proposed modeling strategy considers hardware, battery, link and coverage failures, besides considering routing protocols on the network communication behavior. The methodology is automated by a framework developed and integrated with the SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) tool. The achieved results show that this methodology is useful to compare different network implementations and the corresponding dependability, enabling the uncovering of potentially weak points in the network behavior.

Abstract
In ubiquitous streaming data sources, such as sensor networks, clustering nodes by the data they produce gives insights on the phenomenon being monitored. However, centralized algorithms force communication and storage requirements to grow unbounded. This article presents L2GClust, an algorithm to compute local clusterings at each node as an approximation of the global clustering. L2GClust performs local clustering of the sources based on the moving average of each node's data over time: the moving average is approximated using memory-less statistics; clustering is based on the furthest-point algorithm applied to the centroids computed by the node's direct neighbors. Evaluation is performed both on synthetic and real sensor data, using a state-of-the-art sensor network simulator and measuring sensitivity to network size, number of clusters, cluster overlapping, and communication incompleteness. A high level of agreement was found between local and global clusterings, with special emphasis on separability agreement, while an overall robustness to incomplete communications emerged. Communication reduction was also theoretically shown, with communication ratios empirically evaluated for large networks. L2GClust is able to keep a good approximation of the global clustering, using less communication than a centralized alternative, supporting the recommendation to use local algorithms for distributed clustering of streaming data sources.

Abstract
While the importance of consent request in today's society is increasing, specially online as a lawful basis for the processing of personal data, no detailed analysis of current technological solutions is available. In this work, we describe the existing technological solutions to express online consent in a positive fashion, including all the properties that an online solution should hold. We conclude by offering a risk proposal based on the linear combination of the rating of each one of these properties. We observe a low agreement between observers, highlighting that it is not easy to fulfill the requirements of the GDPR and showing that these studies are important when performing a Data Protection Impact Assessment. To overcome the low agreement, we propose the median of the observers' rate.

Abstract

Abstract
This article analyzes the strategic bilateral bidding behavior in the Spanish electricity wholesale market (OMEL). The collection of data includes information regarding weekly averages of spot prices, the quantity bid in the wholesale market, the quantities purchased in the wholesale market and sold in the open market, and the behavior of conduct parameters for the period from January 2002 to April 2007 for the four largest firms of the Spanish electricity market: Endesa, Iberdrola, Unin Fenosa and Hidrocantabrico. This article employs the New Empirical Industrial Organization approach. The empirical analysis was based on the autoregressive distributed lag approach to cointegration and on the Toda-Yamamoto Granger causality tests to validate the standard version of the theoretical formulation of the standard Cournot model, and its theoretical extension, to encompass the hypothesis of the presence of bid interdependence for electricity quantities sold and bought in the Spanish electricity wholesale market. The results of cointegration and causality analysis reinforce the empirical results of the extended Cournot model with the inclusion of the two main bidding variables that solved the optimization problem of profit maximization for each of the four firms analyzed.