The authors develop and estimate a conceptual model of how different aspects of customers’ relationships with the brand community influence their intentions and behaviors. The authors describe how identification with the brand community leads to positive consequences, such as greater community engagement, and negative consequences, such as normative community pressure and (ultimately) reactance. They examine the moderating effects of customers’ brand knowledge and the brand community's size and test their hypotheses by estimating a structural equation model with survey data from a sample of European car club members.

Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms’ computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm’s predicting power and the effective computing time.

Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms' computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm's predicting power and the effective computing time.Relationships between constituents of complex systems (be it in nature, society, or technological applications) can be represented in terms of networks. In this portrayal, the elements composing the system are described as nodes and their interactions as links. At the global level, the topology of these interactions -far from being trivial -is in itself of complex nature 1,2 . Importantly, these networks further display some level of organisation at an intermediate scale. At this mesoscopic level, it is possible to identify groups of nodes that are heavily connected among themselves, but sparsely connected to the rest of the network. These interconnected groups are often characterised as communities, or in other contexts modules, and occur in a wide variety of networked systems 3,4 .Detecting communities has grown into a fundamental, and highly relevant problem in network science with multiple applications. First, it allows to unveil the existence of a non-trivial internal network organisation at coarse grain level. This allows further to infer special relationships between the nodes that may not be easily accessible from direct empirical tests 5 . Second, it helps to better understand the properties of dynamic processes taking place in a network. As paradigmatic examples, spreading processes of epidemics and innovation are considerably affected by the community structure of the graph 6 .Taking into account its importance, it is not surprising that many community detection methods have been developed, using tools and techniques from variegated disciplines such as statistical physics, biology, applied mathematics, computer science, and socio...

Firm-hosted virtual peer-to-peer problem solving (P3) communities offer a low-cost, credible, and effective means of delivering education and ongoing assistance services to customers of complex, frequently evolving products. Building upon the social constructivist view on learning and drawing from literature on the firm-customer relationship in services marketing, we distinguish between functional and social benefits received by P3 community participants and study the central role of learning in influencing these benefit perceptions. The proposed model is tested on data gathered from 2,299 active members of a P3 community hosted by a global online auction firm, and the framework's generalizability is demonstrated using a sample of 204 members of a global B2B software firm's P3 community. Based on the results, specific recommendations are provided to marketers interested in implementing service support programs via customer communities, and future research opportunities are explored.

This longitudinal study explores the stability and change of values in childhood. Children's values were measured in Poland three times (with one-year intervals) using the Picture Based Values Survey (PBVS-C; D€ oring, Blauensteiner, Aryus, Dr€ ogekamp, & Bilsky, 2010), developed to measure values differentiated according to the circular model of Schwartz (1992). 801 children (divided into 5 cohorts aged 7, 8, 9, 10, and 11 years at the first measurement occasion) completed the PBVS-C three times on a yearly basis. Separate analyses were performed for each cohort using the data of the three measurement occasions. Multidimensional scaling revealed that, in children, Schwartz's (1992) circular structure of values is stable and does not change over time. Although priorities of values displayed moderate stability over time, the means changed between the ages of 7 and 11 years. Specifically, latent growth curve modeling revealed changes in children's values hierarchy as indicated by the decrease in the mean level of conservation values and the increase in the mean level of openness to change values. Self-transcendence and self-enhancement also changed in different directions. As indicated by mean levels over time, self-transcendence first increased in importance, slightly decreased, and finally increased again. In contrast, self-enhancement first decreased in importance, then increased, and finally began to decrease again.