Sharan Jagpal

Willingness to Pay: Measurement and Managerial Implications

Accurately measuring consumers’ willingness to pay (WTP) is central to any pricing decision. This chapter attempts to synthesize the theoretical and empirical literatures on WTP. We fi rst present the various conceptual defi nitions of WTP. Then, we evaluate the advantages and disadvantages of alternative methods that have been proposed for measuring it. In this analysis, we distinguish between methods based on purchase data and those based on survey/experimental data (e.g. self-stated WTP, contingent valuation, conjoint analysis and experimental auctions). Finally, using numerical examples, we illustrate how managers can use WTP measures to make key strategic decisions involving bundling, nonlinear pricing and product line pricing.

Do high prices signal high quality? A theoretical model and empirical results

This paper has three objectives. First, we develop an equilibrium pricing model in which consumers have incomplete information about both product qualities and prices. Specifically, manufacturers can use high prices to signal high quality to uninformed consumers. Furthermore, prices of any given brand can vary geographically across retail outlets. We show that previous models are special cases of our model. Specifically, the hedonic regression model assumes that consumers have full information about all product qualities and prices. Second, we propose a methodology for testing price‐signaling models. Third, we test our model using data from consumer reports for several consumer durable and nondurable products. The results show that firms use prices to signal quality, regardless of whether they market durable or nondurable products. The results do not support the popular theory that markets for experience goods are more efficient than those for search goods. Finally, our model outperforms the standard hedonic regression model for four of the five product categories analyzed.

Duopoly pricing under risk aversion and parameter uncertainty

Most pricing studies assume that firms have complete information about demand. In practice, managers must make decisions, given incomplete information about the demand for their own products as well as those of their rivals. This paper develops a duopoly pricing model in which firms market differentiated products in a world of uncertainty. Results show that the predictions of standard strategic pricing models may not hold when firms face parameter uncertainty and are risk‐averse. Under well‐defined conditions, there may be a “first‐mover” disadvantage to the firm that attempts to be the Stackelberg price leader in the market, especially in a market where demand is highly uncertain. Interestingly, if parameter uncertainty is sufficiently high, it may even be necessary for the price leader to share market information with its rival. When firms are risk‐averse, uncertainty generally decreases equilibrium prices and the variabilities of profits.

Do payday loans help the working poor

Purpose – The purpose of this paper is twofold: to examine the plight of a particular segment of the poor – the working poor – in the USA relative to their exclusion from traditional financial markets and their patronage of the payday loan market; and to propose a framework that offers guidance to law makers in making laws/crafting policies that help the working poor gain better access to credit. Design/methodology/approach – This is a conceptual paper that reviews the literature on the payday loan market and uses its findings to propose a strategy to ameliorate the plight of the working poor. Findings – The study integrates the findings of studies on the payday loan market with theories of corporate social responsibility. Using these findings the authors develop a framework that can guide policy makers in making policies that address the exclusion of the working poor from financial markets. Research limitations/implications – As a conceptual paper based on theories, the study does not provide empirical v...

A Flexible Method for Protecting Marketing Data: An Application to Point-of-Sale Data

We develop a flexible methodology to protect marketing data in the context of a business ecosystem in which data providers seek to meet the information needs of data users, but wish to deter invalid use of the data by potential intruders. In this context we propose a Bayesian probability model that produces protected synthetic data. A key feature of our proposed method is that the data provider can balance the trade-off between information loss resulting from data protection and risk of disclosure to intruders. We apply our methodology to the problem facing a vendor of retail point-of-sale data whose customers use the data to estimate price elasticities and promotion effects. At the same time, the data provider wishes to protect the identities of sample stores from possible intrusion. We define metrics to measure the average and maximum loss of protection implied by a data protection method. We show that, by enabling the data provider to choose the degree of protection to infuse into the synthetic data, o...