Drivers of in-group and out-of-group electronic word-of-mouth (eWOM)

Purpose – The purpose of this study is to address a recent call for additional research on electronic word-of-mouth (eWOM). In response to this call, this study draws on the social network paradigm and the uses and gratification theory (UGT) to propose and empirically test a conceptual framework of key drivers of two types of eWOM, namely in-group and out-of-group. Design/methodology/approach – The proposed model, which examines the impact of usage motivations on eWOM in-group and eWOM out-of-group, is tested in a sample of 302 internet users in Portugal. Findings – Results from the survey show that the different drivers (i.e. mood-enhancement, escapism, experiential learning and social interaction) vary in terms of their impact on the two different types of eWOM. Surprisingly, while results show a positive relationship between experiential learning and eWOM out-of-group, no relationship is found between experiential learning and eWOM in-group. Research limitations/implications – This is the first study investigating the drivers of both eWOM in-group and eWOM out-of-group. Additional research in this area will contribute to the development of a general theory of eWOM. Practical implications – By understanding the drivers of different eWOM types, this study provides guidance to marketing managers on how to allocate resources more efficiently in order to achieve the company's strategic objectives. Originality/value – No published study has investigated the determinants of these two types of eWOM. This is the first study offering empirical considerations of how the various drivers differentially impact eWOM in-group and eWOM out-of-group.

Drivers of in‐group and out‐of‐group electronic word‐of‐mouth (eWOM)

Purpose– The purpose of this study is to address a recent call for additional research on electronic word‐of‐mouth (eWOM). In response to this call, this study draws on the social network paradigm and the uses and gratification theory (UGT) to propose and empirically test a conceptual framework of key drivers of two types of eWOM, namely in‐group and out‐of‐group. Design/methodology/approach– The proposed model, which examines the impact of usage motivations on eWOM in‐group and eWOM out‐of‐group, is tested in a sample of 302 internet users in Portugal. Findings– Results from the survey show that the different drivers (i.e. mood‐enhancement, escapism, experiential learning and social interaction) vary in terms of their impact on the two different types of eWOM. Surprisingly, while results show a positive relationship between experiential learning and eWOM out‐of‐group, no relationship is found between experiential learning and eWOM in‐group. Research limitations/implications– This is the first study investigating the drivers of both eWOM in‐group and eWOM out‐of‐group. Additional research in this area will contribute to the development of a general theory of eWOM. Practical implications– By understanding the drivers of different eWOM types, this study provides guidance to marketing managers on how to allocate resources more efficiently in order to achieve the company's strategic objectives. Originality/value– No published study has investigated the determinants of these two types of eWOM. This is the first study offering empirical considerations of how the various drivers differentially impact eWOM in‐group and eWOM out‐of‐group.

Asynchronous distributed parallelization of mobile network optimization algorithms

It has been years since the introduction of the Dynamic Network Optimization (DNO) concept, yet the DNO development is still at its infant stage, largely due to a lack of breakthrough in minimizing the lengthy optimization runtime. Our previous work, a distributed parallel solution, has achieved a significant speed gain. To cater for the increased optimization complexity pressed by the uptake of smartphones and tablets, however, this paper examines the potential areas for further improvement and presents a novel asynchronous distributed parallel design that minimizes the inter-process communications. The new approach is implemented and applied to real-life projects whose results demonstrate an augmented acceleration of 7.5 times on a 16-core distributed system compared to 6.1 of our previous solution. Moreover, there is no degradation in the optimization outcome. This is a solid sprint towards the realization of DNO.