Setting the stage for successful electronic reverse auctions

Based on case survey data, this article provides a framework to help organizations evaluate if electronic reverse auctions (ERAs) are likely to be successful in terms of product cost reductions. The results of logistic regression and correlation analysis underline that it is vital that the product or service to be auctioned can be properly specified and that the companies must spend sufficient time and effort to carefully analyze, understand and, if possible, increase competition among suppliers participating in an ERA. Further recommendations how to instill competition are provided. Organizations should be aware of these conditions and focus purchasing measures prior to an auction accordingly.     

Forecasting technology costs via the experience curve — Myth or magic?

To further understand the effectiveness of experience curves to forecast technology costs, a statistical analysis using historical data is carried out. Three hypotheses are tested using available datasets that together shed light on the historical ability of experience curves to forecast technology costs. The results indicate that the Single Factor Experience Curve is a useful forecasting model when errors are viewed in their log format. Practitioners should note that due to the convexity of the log curve a mean overestimation of potential cost reductions can arise as values are converted into monetary units. Time is also tested as an explanatory variable, however forecasts made with endogenous learning based on cumulative capacity as used in traditional experience curves are shown to be vastly superior. Furthermore the effectiveness of increasing weights for more recent data is tested using Weighted Least Squares with exponentially increasing weights. This results in forecasts that are less biased, though have increased spread when compared to Ordinary Least Squares.     

How Do Price Perceptions of Different Brand Types Affect Shopping Value and Store Loyalty?

This research investigates the relationships among price perceptions for different brand types (national brands, standard store brands, regional store brands, organic store brands), shopping value dimensions (quality, price, social, and emotion value), and store loyalty (retention and word of mouth (WOM)). A comprehensive model depicts determinants of customer store loyalty. Using structural equation modeling, the model test includes 671 consumers intercepted during shopping trips. The data analysis yields several surprising results. In particular, low product price perceptions do not necessarily signal negative store quality evaluations. Shopping value dimensions influence store retention loyalty and WOM behavior differently. Furthermore, different brand types exert distinct effects on the value creation process. Favorable prices for national and standard store brands have comparable positive effects on store price value and emotional value creation; appealing prices of regional store brands instead reduce the emotional value of the store, and low prices for organic store brand products significantly increase social value creation.     

Meta‐Regression Models and Observational Research

Meta‐regression models were originally developed for the synthesis of experimental research where randomization ensures unbiased and consistent estimation of the effect of interest. Most economics research is, however, observational and specification searches may often result in estimates that are biased and inconsistent, for example, due to omitted‐variable biases. We show that if the authors of primary studies search for statistically significant estimates in observational research, meta‐regression models tend to make false‐positive findings of genuine empirical effects. More research is needed to better understand how meta‐regression models need to be specified to help identifying genuine empirical effects in observational research.     

FROM SMILE ASYMPTOTICS TO MARKET RISK MEASURES

The left tail of the implied volatility skew, coming from quotes on out‐of‐the‐money put options, can be thought to reflect the market's assessment of the risk of a huge drop in stock prices. We analyze how this market information can be integrated into the theoretical framework of convex monetary measures of risk. In particular, we make use of indifference pricing by dynamic convex risk measures, which are given as solutions of backward stochastic differential equations, to establish a link between these two approaches to risk measurement. We derive a characterization of the implied volatility in terms of the solution of a nonlinear partial differential equation and provide a small time‐to‐maturity expansion and numerical solutions. This procedure allows to choose convex risk measures in a conveniently parameterized class, distorted entropic dynamic risk measures, which we introduce here, such that the asymptotic volatility skew under indifference pricing can be matched with the market skew. We demonstrate this in a calibration exercise to market implied volatility data.