Dynamic processes in the contingent valuation of an endangered mammal species

Reports experimental results involving 204 members of the public who were asked their willingness to pay for the conservation of the mahogany glider Petaurus gracilis on three occasions: prior to information being provided to them about the glider and other wildlife species; after such information was provided, and after participants had an opportunity to see live specimens of this endangered species. Variations in the mean willingness to pay are analysed. Concerns arise about whether information provision and experience reveal ‘true’ contingent valuations of public goods and about the choice of the relevant contingent valuation measure.

The effects of normalisation on the ability of business end-users to detect data anomalies: An experimental evaluation

With the proliferation of relational database programs for PC's and other platforms, many business end-users are creating, maintaining, and querying their own databases. More importantly, business end-users use the output of these queries as the basis for operational, tactical, and strategic decisions. Inaccurate data reduce the expected quality of these decisions. Implementing various input validation controls, including higher levels of normalisation, can reduce the number of data anomalies entering the databases. Even in well-maintained databases, however, data anomalies will still accumulate. To improve the quality of data, databases can be queried periodically to locate and correct anomalies. This paper reports the results of two experiments that investigated the effects of different data structures on business end-users' abilities to detect data anomalies in a relational database. The results demonstrate that both unnormalised and higher levels of normalisation lower the effectiveness and efficiency of queries relative to the first normal form. First normal form databases appear to provide the most effective and efficient data structure for business end-users formulating queries to detect data anomalies.