A test of basic assumptions of Affective Events Theory (AET) in call centre work

Based on data from 2091 call centre representatives working in 85 call centres in the UK, central assumptions of affective events theory (AET) are tested. AET predicts that specific features of work (e.g. autonomy) have an impact on the arousal of emotions and moods at work that, in turn, co-determine job satisfaction of employees. AET further proposes that job satisfaction is an evaluative judgement that mainly explains cognitive-based behaviour, whereas emotions and moods better predict affective-based behaviour. The results support these assumptions. A clear separation of key constructs (job satisfaction, positive and negative emotions) was possible. Moreover, correlations between several work features (e.g. supervisory support) and job satisfaction were, in part, mediated by work emotions, even when controlling for gender, age, call centre type (in-house versus outsourced centres) and call centre size. Predictions regarding consequences of satisfaction and affect were partly corroborated as continuance commitment was more strongly related to job satisfaction than to positive emotions. In addition, affective commitment and health complaints were related to both emotions and job satisfaction to the same extent. Thus, AET is a fruitful framework for explaining why and how specific management strategies used for designing work features influence important organizational attitudes and well-being of employees. © 2006 British Academy of Management.

Utilising online shopping environment attributes holistically to create competitive advantage

As more consumers shop online, it becomes crucial for marketers to know how online shopping environments (OSEs) can be used to gain competitive advantage. This dissertation aims to explain theoretically how OSE attributes work together holistically to produce desirable consumer responses, applying and extending a theory from the environmental psychology literature to the online context. Firstly, the study conceptualises OSEs as virtual environments which may be perceived and experienced both cognitively and affectively through a technology-mediated interaction with a computer screen. A multi-disciplinary approach identifies key characteristics of OSEs: they involve consumers; they are more complex than their offline counterparts; they are likely first apprehended holistically; and they can elicit high levels of emotions and cognition. Secondly, the research uses a gestalt approach and extends Kaplan and Kalan’s (1982) Preference Framework, taking account of the specific characteristics of OSEs, which one visits specifically to obtain product information. The results support the proposition that OSEs are perceived in terms of their Sense-making and Exploratory attributes. Thirdly, the research explains how OSE attributes work together to produce desirable consumer responses. As hypothesised, Exploratory potential produces both Hedonic and Utilitarian value, and both kinds of value contribute to Site commitment. An unexpected result is that Sense-making potential does not produce Utilitarian value directly, but only through the mediation of Exploratory potential. The research contributes to marketing theory by: (1) identifying ways the internet has changed the nature of the shopping experience; (2) extending Kaplan and Kaplan’s Preference Framework to explain how consumers perceive OSEs holistically; (3) identifying the distinction between page-level and site-level perceptions, and (4) distinguishing between different sources of information (marketer vs. non-marketer). Managerially, the research provides a model for marketers to conceive and design retail websites whose attributes work together to create competitive advantage.

Weakly supervised joint sentiment-topic detection from text

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.

Joint sentiment/topic model for sentiment analysis

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.

We want drama! The effect of online conflict on social capital in online communities of consumption

Online communities (OC) are an expanding social phenomenon gaining increasing interest from marketing practitioners. Community managers thus aim to increase OCs’ social capital. Diversity of individuals interacting in OCs provokes a lot of conflict. However, the influence of online conflict on OCs’ social capital is not clear as research indicates both positive and negative effects. The research aims to explain these contradictory effects by conceptualizing conflict as drama and developing a typology of online conflict. Based on netnographic investigations of a forum, four types of conflicts are thus distinguished depending on valence of emotions and the type of members involved. The research contributes to literature on OC dynamics and is of particular interest for community managers working in any company or organization.

What's new pussycat? A genealogy of animal celebrity

Animal celebrity is a human creation informing us about our socially constructed natural world. It is relational, expressive of cultural proclivities, political power plays and the quotidian everyday, as well as serious philosophical reflections on the meaning of being human. This article attempts to outline some key contours in the genealogy of animal celebrity, showing how popular culture, including fairground attractions, public relations, Hollywood movies, documentary films, zoo attractions, commercial sport and mediatised moral panics - particularly those accompanying scientific developments such as cloning - help to order, categorise and license aspects of human understanding and feelings. The nature of [animal] charisma and celebrity are explored with assistance from Jumbo the Elephant, Guy the Gorilla, Paul the clairvoyant octopus, Uggie the film star, Nénette the orang-utan and Dolly the sheep. It argues that the issue of what it is to be human lies beneath the celebritised surface or, as Donna Haraway noted, the issue 'of having to face oneself'. © 2013 Taylor & Francis.

A new appraisal-based framework underlying hope in conflict resolution

Hope is a positive emotion that plays a pivotal role in intractable conflicts and conflict resolution processes by inducing conciliatory attitudes for peace. As a catalyser for conflict resolution, it is important to further understand hope in such contexts. In this paper we present a novel framework for understanding hope in contexts of intergroup conflict. Utilizing appraisal theory of emotions and heavily relying on the implicit theories framework, we describe three targets upon which hope appraisals focus in intractable conflict - the conflict, the outgroup, and the ingroup. Next, we describe the importance of developing ways to experimentally induce hope, and utilize the appraisal-target framework to describe and classify existing and potential interventions for inducing hope in intractable conflict resolution.