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The purpose of this paper is to identify user-generated content (UGC) features that determine web purchase decision making.

The authors embed a spike-and-slab Bayesian variable selection mechanism into a logistic regression model to identify the UGC features that are critical to web purchase intent. This enables us to make a highly reliable analysis of survey data.

The results indicate that the web purchase decision is driven by the relevance, up-to-dateness and credibility of the UGC information content.

The results show that the characteristics of UGC are seen as positive and the medium enables consumers to sort information and concentrate on aspects of the message that are similar to traditional word-of-mouth (WOM). One important implication is the relative importance of credibility which has been previously hypothesized to be lower in the electronic word-of-mouth (e-WOM) context. The results show that consumers consider credibility important as the improved technology provides more possibilities to find out about that factor. A limitation is that the data are not fully representative of the general population but our Bayesian method gives us high analytical quality.

The study shows that UGC impacts consumer online purchase intentions. Marketers should understand the wide range of media that provide UGC and they should concentrate on the relevance, up-to-dateness and credibility of product information that they provide.

The analytical quality of the spike- and- slab Bayesian method suggests a new way of understanding the impact of aspects of UGC on consumers.

The purpose of this paper is to suggest the use of reverse medical supply chain data to infer changes of a population's health status with regard to a focal disease. It includes a detailed illustration of how health status information can be obtained from drug reverse chains.

A Bayesian dynamical model linking drug reverse supply chain data to relevant health status indicators with regard to a focal disease is developed. A detailed implementation of the model on computer‐simulated data is considered. The predictive ability of the methodology is also assessed using out‐of‐sample Monte Carlo‐based predictive analysis.

The results substantiate the good fit of the model to the empirical data.

Difficulty in obtaining actual return data and in selecting appropriate health status indicators. The correspondence disease‐drug is typically not one‐to‐one. Experts' opinion is required in setting up suitable mixing weights as many drugs may inform the health status relative to a given disease and vice versa.

Reverse logistics data may contain potential information, and this is not exclusive to medical chains.

The paper's suggestions tend to reinforce the notion that supply chain data may be used in many unsuspected settings. Solutions to issues of immediate concern in public health require multidisciplinary cooperation, and this paper shows how supply chain management can contribute. It is believed that the potential of reverse chain data in the health status prospect has previously hardly ever been pointed out.

The purpose of this paper is to introduce a 3R (right time, right place, and right material) principle for characterizing failure in humanitarian/relief supply chains’ response to natural disasters, and describes a Bayesian methodology of the failure odds with regard to external factors that may affect the disaster-relief outcome, and distinctive supply chain proneness to failure.

The suggested 3Rs combine simplicity and completeness, enclosing all aspects of the 7R principle popular within business logistics. A fixed effects logistic regression model is designed, with a Bayesian approach, to relate the supply chains’ odds for success in disaster-relief to potential environmental predictors, while accounting for distinctive supply chains’ proneness to failure.

Analysis of simulated data demonstrate the model’s ability to distinguish relief supply chains with regards to their disaster-relief failure odds, taking into account pertinent external factors and supply chain idiosyncrasies.

Due to the complex nature of natural disasters and the scarcity of subsequent data, the paper employs computer-simulated data to illustrate the implementation of the proposed methodology.

The 3R principle offers a simple and familiar basis for evaluating failure in relief supply chains’ response to natural disasters. Also, it brings the issues of customer orientation within humanitarian relief and supply operations to the fore, which had only been implicit within the humanitarian and relief supply chain literature.