Feedback is an important component of the design process, but gaining access to high-quality critique outside a classroom or firm is challenging. We present CrowdCrit, a webbased system that allows designers to receive design critiques from non-expert crowd workers. We evaluated CrowdCrit in three studies focusing on the designer's experience and benefits of the critiques. In the first study, we compared crowd and expert critiques and found evidence that aggregated crowd critique approaches expert critique. In a second study, we found that designers who got crowd feedback perceived that it improved their design process. The third study showed that designers were enthusiastic about crowd critiques and used them to change their designs. We conclude with implications for the design of crowd feedback services.

PurposeCrowdworkers' sustained participation is critical to the success and sustainability of the online crowdsourcing community. However, this issue has not received adequate attention in the information systems research community. This study seeks to understand the formation of crowdworker sustained participation in the online crowdsourcing community.Design/methodology/approachThe research model was empirically tested using online survey data from 212 crowdworkers in a leading online crowdsourcing community in China.FindingsThe empirical results provide several key findings. First, there are two different types of sustained participation: continuous participation intention (CPI) and increased participation intention (IPI). Second, extrinsic motivation and intrinsic motivation positively influence crowdworker CPI and IPI. Third, community commitment negatively moderates the effects of extrinsic motivation on CPI and IPI, while it positively moderates the effects of intrinsic motivation on CPI and IPI.Originality/valueThis study has significant implications for research on online crowdsourcing community and provides practical guidance for formulating persuasive measures to promote crowdworker sustained participation in the community.

Background A conformational epitope (CE) in an antigentic protein is composed of amino acid residues that are spatially near each other on the antigen's surface but are separated in sequence; CEs bind their complementary paratopes in B-cell receptors and/or antibodies. CE predication is used during vaccine design and in immuno-biological experiments. Here, we develop a novel system, CE-KEG, which predicts CEs based on knowledge-based energy and geometrical neighboring residue contents. The workflow applied grid-based mathematical morphological algorithms to efficiently detect the surface atoms of the antigens. After extracting surface residues, we ranked CE candidate residues first according to their local average energy distributions. Then, the frequencies at which geometrically related neighboring residue combinations in the potential CEs occurred were incorporated into our workflow, and the weighted combinations of the average energies and neighboring residue frequencies were used to assess the sensitivity, accuracy, and efficiency of our prediction workflow. Results We prepared a database containing 247 antigen structures and a second database containing the 163 non-redundant antigen structures in the first database to test our workflow. Our predictive workflow performed better than did algorithms found in the literature in terms of accuracy and efficiency. For the non-redundant dataset tested, our workflow achieved an average of 47.8% sensitivity, 84.3% specificity, and 80.7% accuracy according to a 10-fold cross-validation mechanism, and the performance was evaluated under providing top three predicted CE candidates for each antigen. Conclusions Our method combines an energy profile for surface residues with the frequency that each geometrically related amino acid residue pair occurs to identify possible CEs in antigens. This combination of these features facilitates improved identification for immuno-biological studies and synthetic vaccine design. CE-KEG is available at http://cekeg.cs.ntou.edu.tw.

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People who create visual designs often struggle to find high-quality critique outside a firm or classroom, and current online feedback solutions are limited. We created a system called CrowdCrit which leverages paid crowdsourcing to generate and visualize high-quality visual design critique. Our work extends prior crowd feedback research by focusing on scaffolding the process and language of studio critique for crowds.