BACKGROUND: Human emotion classification is traditionally achieved using multi-channel electroencephalogram (EEG) signal, which requires costly equipment and complex classification algorithms.OBJECTIVE: The experiments can be implemented in the laboratory environment equipped with high-performance computers for the online analysis; this will hinder the usability in practical applications.METHODS: Considering that other physiological signals are also associated with emotional changes, this paper proposes to use a wearable, wireless system to acquire a single-channel electroencephalogram signal, respiration, electrocardiogram (ECG) signal, and body postures to explore the relationship between these signals and the human emotions.RESULTS AND CONCLUSIONS: Compared with traditional emotion classification method, the presented method was able to extract a small number of key features associated with human emotions from multiple physiological signals, where the algorithm complexity was greatly reduced when incorporated with the support vector machine classification. The proposed method can support an embedded on-line analysis and may enhance the usability of emotion classification.
PurposeMembers' knowledge contribution behavior has positive significance for maintaining the activity of the knowledge community, as well as for improving knowledge interaction efficiency and member viscosity. With the development of the mobile Internet, knowledge communities based on social platforms have become more convenient and popular. This study aims to explore what and how factors influence members' knowledge contribution behavior in social knowledge communities from the perspective of social distance.Design/methodology/approachBased on the theory of reciprocity and on the theory of self-efficacy, hypotheses and research models are proposed. In the empirical study, WeChat learning group is selected as the research case. The empirical investigation (N = 244) collects research data through questionnaires.FindingsI-intention and we-intention both have positive influence on members' knowledge contribution behavior. Knowledge self-efficacy positively moderates the influence of we-intention and affects knowledge contribution behavior. In addition, I-intention is positively affected by expected knowledge benefit, expected emotional benefit and expected image benefit, while costs have no effect. We-intention is positively influenced by affective commitment, continuance commitment and normative commitment in relationship strength, as well as affiliation to the contributing climate.Originality/valueThis paper aims to discuss I-intention, we-intention, and their roles in members' knowledge contribution behavior. It is a beneficial development for existing research to combine the characteristics of new style communities with systematical analysis of knowledge contribution behavior. Findings may provide enlightenment to the social knowledge community on diversity development and differentiated marketing strategies.
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