Existing literature, based on signaling theory, suggests that money-back guarantees (MBGs) will be utilized by high-quality firms, where high quality is defined as a low likelihood of product return. However, in today's world, MBGs are ubiquitous among major retailers, even when the likelihood of product return varies greatly between them. To understand this phenomenon, we explore a competitive environment between high- and low-quality retailers where consumers are fully informed and risk neutral, and retailers realize a salvage value for returned products. When MBGs are profitable, under continuous demand it is Nash equilibrium for both retailers to offer MBGs, and the low-quality retailer gains while the high-quality retailer loses relative to when MBGs are not offered. In contrast, if demand is lumpy, retailers can act monopolistically over their respective market segments, allowing both retailers to gain from MBGs, although the low-quality retailer still gains more. This paper was accepted by J. Miguel Villas-Boas, marketing.

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Firms use samples to increase the sales of almost all consumable goods, including food, health, and cleaning products. Despite its importance, sampling remains one of the most under-researched areas. There are no theoretical quantitative models of sampling behavior other than the pioneering work of Jain et al. (1995), who modeled sampling as an important factor in the diffusion of new products. In this paper we characterize sampling as having two effects. The first is the change in the probability of a consumer purchasing a product immediately after having sampled the product. The second is an increase in the consumer's cumulative goodwill formation, which results from sampling the product. This distinction differentiates our model from other models of goodwill, in which firm sales are only a function of the existing goodwill level. We determine the optimal dynamic sampling effort of a firm and examine the factors that affect the sampling decision. We find that although the sampling effort will decline over a product's life cycle, it may continue in mature products. Another finding is that when we have a positive change in the factors that increase sampling productivity, steady-state goodwill stock and sales will increase, but equilibrium sampling can either increase or decrease. The change in the sampling level is indeterminate because, while increased sampling productivity means that firms have incentives to increase sampling, the increase in the equilibrium goodwill level indirectly reduces the marginal productivity of sampling, thus reducing the incentives to sample. We discuss managerial implications, and how the model can be used to address various circumstances.Diffusion, Learning, Product Sampling, Goodwill, Forgetting, Experimenting

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Learning by using is introduced into an adoption model to explain why larger and more educated firms adopt earlier. Dynamic economies of scale arise in learning by using that speed up adoption. The empirical estimation of time of adoption using Tobit analysis integrates the concepts of adoption and diffusion, allowing the diffusion of the technology to be derived from the time of adoption analysis. In addition, by introducing heterogeneity among the adopters, Tobit analysis is shown to provide superior results to the traditional logit and probit analysis of the dichotomous adopt/not adopt variable.Time of adoption, learning-by-using, tobit, diffusion J.E.L. Codes: 033, C24,