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PurposeThis study aims to identify and describe the relationships among different consumption values, anxiety and organic food purchase behaviour considering the moderating role of sustainable consumption attitude from the viewpoint of the theory of consumption values.Design/methodology/approachData were collected using a structured questionnaire survey in first-tier cities in China. A total of 344 consumers of organic foods participated in the study. Structural equation modelling and hierarchical regression analysis were employed for data analysis.FindingsThe results indicated the significant association of functional value-price, emotional value, social value and epistemic value with purchase behaviour. Anxiety had a positively significant influence on functional (quality), functional (price), emotional, social, conditional and epistemic values. In addition, the results indicated that functional (price), emotional, social and epistemic values played mediating effects in the relationships between anxiety and purchase behaviour. Moreover, sustainable consumption attitude had a positive moderating effect on functional value-price and purchase behaviour.Practical implicationsThe research not only provides novel and original insights for understanding organic consumption but also provides a reference for organic retailers to develop sales strategies and policymakers to formulate policies to guide organic consumption that are conducive to promoting sustainable consumption.Originality/valueFor the first time, this research attempts to explore the relationships among different consumption values, anxiety and purchase behaviour. It may improve the gap of inconsistency in attitude and behaviour in organic consumption, and provide a new perspective for the study of organic consumption.
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Objectives: To develop and internally validate two clinical risk scores to detect coronavirus disease 2019 (COVID-19) during local outbreaks. Methods: Medical records were extracted for a retrospective cohort of 336 suspected patients admitted to Baodi hospital between 27 January to 20 February 2020. Multivariate logistic regression was applied to develop the risk-scoring models, which were internally validated using a 5-fold cross-validation method and Hosmer-Lemeshow (H-L) tests. Results: Fifty-six cases were diagnosed from the cohort. The first model was developed based on seven significant predictors, including age, close contact with confirmed/suspected cases, same location of exposure, temperature, leukocyte counts, radiological findings of pneumonia and bilateral involvement (the mean area under the receiver operating characteristic curve [AUC]:0.88, 95% CI: 0.84–0.93). The second model had the same predictors except leukocyte and radiological findings (AUC: 0.84, 95% CI: 0.78–0.89, Z = 2.56, p = 0.01). Both were internally validated using H-L tests and showed good calibration (both p > 0.10). Conclusion: Two clinical risk scores to detect COVID-19 in local outbreaks were developed with excellent predictive performances, using commonly measured clinical variables. Further external validations in new outbreaks are warranted.