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Purpose An increasing number of companies have become aware of the considerable commercial potential of firm-hosted online communities (FOC) and initiated their own platform for different purposes. However, limited research has systematically explored oppositional loyalty and customer satisfaction in the context of FOC. By applying the expectation–confirmation model (ECM), the purpose of this paper is to investigate the determinants of oppositional loyalty and satisfaction from the perspective of social capital and e-quality. Design/methodology/approach A research model was tested by applying partial least squares technique, and data were collected from a survey of members (n=512) of two popular smartphone communities in China. Findings Results revealed that satisfaction, trust and shared language are the significant antecedents of oppositional loyalty. Benefits confirmation, information quality, service quality, trust and social tie exert strong effects on the formation of satisfaction. Originality/value This study is an original empirical research guided by several theories. It contributes to the information system usage literature and provides opinions regarding how users’ oppositional loyalty and satisfaction can be developed in the FOCs. This work also widens the application of ECM and provides an alternative theoretical framework for future research on oppositional brand loyalty.

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Purpose Many users build personal projects in co-innovation community to accomplish their innovations. However, very few projects from such communities are successful and understanding of this phenomenon is limited. The purpose of this paper is to identify the factors facilitating user projects success in online co-innovation communities. Design/methodology/approach Based on the theories of persuasion and diffusion of innovation (DOI), a conceptual model is proposed to explain how project success likelihood is affected by the creator, project and user participation characteristics. Then, the model and hypotheses are tested through binary logistic regression on a secondary data set of 572 projects collected from a typical user co-innovation community, Local Motors. Findings The results show that creator characteristics (prior success rate), project characteristics (project popularity, length and duration) and user participation characteristics (participation users and degree) have significant and positive impacts on project success likelihood. The number of prior projects, which can hardly represent the creator’s credibility in open and unrestricted situations, has no significant influence on the project success likelihood. Practical implications This study offers project creators the keys to increase their projects successful possibility. Besides, this study recommends a new way to attract users and helps to identify creative and effective users for community practitioners. Originality/value This study expands the research scope in online co-innovation community by focusing on user personal projects. In addition, it combines persuasion theory and DOI theory to add the holistic understanding of user project success likelihood.

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Topological data analysis (TDA) has emerged as one of the most promising techniques to reconstruct the unknown shapes of high-dimensional spaces from observed data samples. TDA, thus, yields key shape descriptors in the form of persistent topological features that can be used for any supervised or unsupervised learning task, including multi-way classification. Sparse sampling, on the other hand, provides a highly efficient technique to reconstruct signals in the spatial-temporal domain from just a few carefully-chosen samples. Here, we present a new method, referred to as the Sparse-TDA algorithm, that combines favorable aspects of the two techniques. This combination is realized by selecting an optimal set of sparse pixel samples from the persistent features generated by a vector-based TDA algorithm. These sparse samples are selected from a low-rank matrix representation of persistent features using QR pivoting. We show that the Sparse-TDA method demonstrates promising performance on three benchmark problems related to human posture recognition and image texture classification.Index Terms-Topological data analysis, sparse sampling, multi-way classification, human posture data, image texture data. !

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