Trust and reciprocity effect on electronic word-of-mouth in online review communities

Purpose Social media developments in the last decade have led to the emergence of a new form of word of mouth (WOM) in the digital environment. Electronic word-of-mouth (eWOM) is considered by many scholars and practitioners to be the most influential informal communication mechanism between businesses and potential and actual consumers. The purpose of this paper is to extend knowledge about WOM in this new context by proposing a conceptual framework that enables a better understanding of how trust and reciprocity influence eWOM participation in ORCs. Design/methodology/approach This study applies non-probability convenience sampling technique to conduct a quantitative study of data from an online survey of 189 members of ORCs. Partial least squares (PLS) is used to analyse the correlations between individuals’ intention to seek opinion, to give their own opinion and to pass on the opinion of another within ORCs. Findings The data analysis reveals that opinion seeking within ORCs had a direct effect on opinion giving and opinion passing. Ability trust and integrity trust had a positive effect on opinion seeking, while benevolence trust had a direct positive effect on opinion passing. Reciprocity had a direct impact on opinion passing. While reciprocity did not affect opinion giving, the relationship between these two concepts was mediated by integrity trust. Research limitations/implications By studying the complexities that characterise the relationships between reciprocity, trust and eWOM, the study extends understanding of eWOM in ORCs. Originality/value To the best of the authors’ knowledge, this is one of only a few papers that have examined the complex interrelationships between reciprocity, trust and eWOM in the context of ORCs

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

A cross-disciplinary examination of the user behaviours involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data. Two analytical frameworks rooted in information retrieval and science technology studies are used to identify key similarities in practices as a first step toward developing a model describing data retrieval

Building Effective Responses: An Independent Review of Violence against Women, Domestic Abuse and Sexual Violence Services in Wales

Independent researchers from the Connect Centre for International Research on Interpersonal Violence based in the School of Social Work at the University of Central Lancashire were commissioned by the Welsh Government in 2013 to conduct research into violence against women, domestic abuse and sexual violence services in Wales. The research aimed to inform the forthcoming Ending Violence Against Women and Domestic Abuse (Wales) Bill, implementation of the legislation and future policy more generally, as well as informing future funding decisions. The remit of the review covers: Domestic abuse, including that experienced in Lesbian, Gay, Bisexual and Transgender (LGBT) relationships and elder abuse. Violence against women, including female genital mutilation (FGM), forced marriage and honour-based violence. Sexual violence including rape, sexual assault and harassment Sexual exploitation including prostitution and trafficking1 for sexual purposes. Services for women and men who are victims or perpetrators of violence against women, domestic abuse or sexual violence. The review does not encompass criminal justice services or housing services and, with the exception of prevention work, services for children and young people in Wales were also excluded from this study

Virtual Geodemographics: Repositioning Area Classification for Online and Offline Spaces

Computer mediated communication and the Internet has fundamentally changed how consumers and producers connect and interact across both real space, and has also opened up new opportunities in virtual spaces. This paper describes how technologies capable of locating and sorting networked communities of geographically disparate individuals within virtual communities present a sea change in the conception, representation and analysis of socioeconomic distributions through geodemographic analysis. We argue that through virtual communities, social networks between individuals may subsume the role of neighbourhood areas as the most appropriate units of analysis, and as such, geodemographics needs to be repositioned in order to accommodate social similarities in virtual, as well as geographical, space. We end the paper by proposing a new model for geodemographics which spans both real and virtual geographies

Mapping networks of influence: tracking Twitter conversations through time and space

The increasing use of social media around global news events, such as the London Olympics in 2012, raises questions for international broadcasters about how to engage with users via social media in order to best achieve their individual missions. Twitter is a highly diverse social network whose conversations are multi-directional involving individual users, political and cultural actors, athletes and a range of media professionals. In so doing, users form networks of influence via their interactions affecting the ways that information is shared about specific global events. This article attempts to understand how networks of influence are formed among Twitter users, and the relative influence of global news media organisations and information providers in the Twittersphere during such global news events. We build an analysis around a set of tweets collected during the 2012 London Olympics. To understand how different users influence the conversations across Twitter, we compare three types of accounts: those belonging to a number of well-known athletes, those belonging to some well-known commentators employed by the BBC, and a number of corporate accounts belonging to the BBC World Service and the official London Twitter account. We look at the data from two perspectives. First, to understand the structure of the social groupings formed among Twitter users, we use a network analysis to model social groupings in the Twittersphere across time and space. Second, to assess the influence of individual tweets, we investigate the ageing factor of tweets, which measures how long users continue to interact with a particular tweet after it is originally posted. We consider what the profile of particular tweets from corporate and athletes’ accounts can tell us about how networks of influence are forged and maintained. We use these analyses to answer the questions: How do different types of accounts help shape the social networks? and, What determines the level and type of influence of a particular account