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The current data indicate that assessment of FKBP5 mutations could provide a basis for the identification of primary NS patients more likely to be efficiently treated with steroids.
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PurposeUsing information systems via data mining and cluster analysis technologies, consumers' strategic behaviour can be measured, and their patience levels can be accurately described. This paper investigates the retailer's pricing and ordering policies when facing strategic consumers with different levels of patience and discusses the impacts of consumers' patience levels and proportions on retailers' maximum expected profits.Design/methodology/approachBy cluster analysing transaction data on the number of websites visited, browsing time and purchase decision time, consumers' patience levels can be obtained. The authors formulate a newsvendor model considering customers' different patience levels. Three scenarios are investigated: two segments of consumers with two different levels of patience (Scenario I), multiple segments of consumers with different levels of patience (Scenario II) and a continuum of consumers whose levels of patience follow a continuous distribution (Scenario III). Then, general formulas are deduced for retailers' optimal prices, ordering quantities and profits.FindingsUnder Scenario I, if the proportion of less patient consumers is greater (less) than a threshold, the retailer's optimal price is equal to the less (more) patient consumers' reserve price. Under Scenario II, once the proportion of fully strategic consumers exceeds a certain threshold, the retailers' optimal price is equal to the fully strategic consumers' reserve price regardless of consumers' patience levels and proportions. Under Scenario III, the retailer's pricing and ordering policies depend on the distribution of their patience level.Originality/valueFew studies have considered consumers' different levels of patience when making retailer pricing and ordering decisions. In this paper, strategic consumer behaviour is measured, and consumers' patience levels and proportions are obtained by cluster analysing consumer transaction data recorded by an information system. Three scenarios in which strategic consumers may be heterogeneous and have different patience levels are investigated. The results can guide retailer decision-making.