Search results

Prior studies have mainly attributed customer incivility to dispositional characteristics, whereas little attention has been paid to exploring service employees' role in triggering or reducing customer incivility. The purpose of the present study is to propose and test a model in which service employees' emotional labor strategies affect customer incivility via influencing customers' self-esteem threat, as well as examine the moderating role of customer's perception of service climate.

Based on a matched sample consisting of 317 employee-customer dyads in China, multiple regression analysis and indirect effect tests were employed to test our model.

The study shows that employee surface acting is positively related to customer incivility, whereas deep acting is negatively associated with customer incivility. Moreover, customer self-esteem threat mediates the relationship between both types of emotional labor and customer incivility. Customer perception of service climate moderates the relationship between deep acting and customer self-esteem threat.

The current research broadens the antecedents of customer incivility from the employee perspective and sheds more light on the role of customer self-esteem in the interactions between employees and customers. It also demonstrates a complementary relationship between service climate and individual employees' emotional labor strategies, thereby expanding the existing understanding of the management of employees' emotional labor.

The purpose of this paper is to present a Big Data solution as a methodological approach to the automated collection, cleaning, collation, and mapping of multimodal, longitudinal data sets from social media. The paper constructs social information landscapes (SIL).

The research presented here adopts a Big Data methodological approach for mapping user-generated contents in social media. The methodology and algorithms presented are generic, and can be applied to diverse types of social media or user-generated contents involving user interactions, such as within blogs, comments in product pages, and other forms of media, so long as a formal data structure proposed here can be constructed.

The limited presentation of the sequential nature of content listings within social media and Web 2.0 pages, as viewed on web browsers or on mobile devices, do not necessarily reveal nor make obvious an unknown nature of the medium; that every participant, from content producers, to consumers, to followers and subscribers, including the contents they produce or subscribed to, are intrinsically connected in a hidden but massive network. Such networks when mapped, could be quantitatively analysed using social network analysis (e.g. centralities), and the semantics and sentiments could equally reveal valuable information with appropriate analytics. Yet that which is difficult is the traditional approach of collecting, cleaning, collating, and mapping such data sets into a sufficiently large sample of data that could yield important insights into the community structure and the directional, and polarity of interaction on diverse topics. This research solves this particular strand of problem.

The automated mapping of extremely large networks involving hundreds of thousands to millions of nodes, encapsulating high resolution and contextual information, over a long period of time could possibly assist in the proving or even disproving of theories. The goal of this paper is to demonstrate the feasibility of using automated approaches for acquiring massive, connected data sets for academic inquiry in the social sciences.

The methods presented in this paper, together with the Big Data architecture can assist individuals and institutions with a limited budget, with practical approaches in constructing SIL. The software-hardware integrated architecture uses open source software, furthermore, the SIL mapping algorithms are easy to implement.

The majority of research in the literature uses traditional approaches for collecting social networks data. Traditional approaches can be slow and tedious; they do not yield adequate sample size to be of significant value for research. Whilst traditional approaches collect only a small percentage of data, the original methods presented here are able to collect and collate entire data sets in social media due to the automated and scalable mapping techniques.