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Many electronic commerce platforms and retailers have increasingly adopted free product sampling to promote products and to attract product reviews. We conjecture that consumers who receive free samples may reciprocate by giving higher ratings as a return to retailers’ beneficial action, which causes rating biases. Specifically, we are interested in understanding how free sampling promotion of a product affects the product’s rating and the roles of important contingent factors, including product pricing (i.e., list price and price discount) and product popularity. Analyzing data collected from Taobao.com , we find that, on average, engaging in free product sampling increases product rating by 1.1%. Moreover, the bias would be larger with higher original price but smaller with larger price discount and higher product popularity. Our findings suggest that retailers could conduct free sampling promotions to improve their product ratings, but consumers should be cautious about the possible biases in ratings, and platform operators or rating system designers should offer solutions to correct the biases. Free product sampling has increasingly become a popular promotional strategy and served as a new mechanism of product review generation in e-commerce. We empirically analyze how a product’s engagement in free product sampling affects the product’s review rating, and we also examine important contingent factors of product pricing and product popularity. Using a rich data set from Taobao.com and multiple identification strategies and estimation methods, we find that engaging in free product sampling increases product rating by 1.1%. We argue that it is consumers’ reciprocal behavior of giving higher ratings as a return to retailers’ beneficial actions that causes rating bias. We further find that the bias would be larger with higher original price but smaller with larger price discount and higher product popularity. Our empirical findings provide important contributions to the literature on product sampling and word-of-mouth and offer critical managerial implications to online retailers, rating system designers, and consumers. The online appendix is available at https://doi.org/10.1287/isre.2018.0801 .

By use of the so-called max-product method, in this paper we associate to the truncated linear sampling operators based on the Fejér-type kernel, nonlinear sampling operators of Kantorovich type, for which we prove convergence results in the Lp -norm, 1 ≤ p ≤ +∞, with quantitative estimates.

Many software firms offer a fully functional version of their products free of charge, for a limited trial period, to ease consumers' uncertainty about the functionalities of their products and to help the diffusion of their new software. This paper examines the trade-off between the effects of reduced uncertainty and demand cannibalization, uncovers the condition under which software firms should introduce the time-locked free trial software, and finds the optimal free trial time. As software firms have the option of providing free trial software with full functionalities but a limited trial time or limited functionalities for an unlimited trial time, we develop a unified framework to provide useful guidelines for deciding which free trial strategy is preferred in the presence of network externalities and consumer uncertainty.

In this paper, we explore the economics of free under perpetual licensing. In particular, we focus on two emerging software business models that involve a free component: feature-limited freemium ( FLF ) and uniform seeding ( S ). Under FLF , the firm offers the basic software version for free, while charging for premium features. Under S , the firm gives away for free the full product to a percentage of the addressable market uniformly across consumer types. We benchmark their performance against a conventional business model under which software is sold as a bundle (labeled as \"charge for everything\" or CE ) without free offers. In the context of consumer bounded rationality and information asymmetry, we develop a unified two-period consumer valuation learning framework that accounts for both word-of-mouth (WOM) effects and experience-based learning, and use it to compare and contrast the three business models. Under both constant and dynamic pricing, for moderate strength of WOM signals, we derive the equilibria for each model and identify optimality regions. In particular, S is optimal when consumers significantly underestimate the value of functionality and cross-module synergies are weak. When either cross-module synergies are stronger or initial priors are higher, the firm decides between CE and FLF . Furthermore, we identify nontrivial switching dynamics from one optimality region to another depending on the initial consumer beliefs about the value of the embedded functionality. For example, there are regions where, ceteris paribus, FLF is optimal when the prior on premium functionality is either relatively low or high, but not in between. We also demonstrate the robustness of our findings with respect to various parameterizations of cross-module synergies, strength of WOM effects, and number of periods. We find that stronger WOM effects or more periods lead to an expansion of the seeding optimality region in parallel with a decrease in the seeding ratio. Moreover, under CE and dynamic pricing, second period price may be decreasing in the initial consumer valuation beliefs when WOM effects are strong and the prior is relatively low. However, this is not the case under weak WOM effects. We also discuss regions where price skimming and penetration pricing are optimal. Our results provide key managerial insights that are useful to firms in their business model search and implementation.

Product sampling is a popular product promotional strategy that emphasizes providing free product trials to new customers. This study aims to investigate the impact of different product sampling campaign characteristics (stimulus, i.e., free sample quantity, free sample diversity, and advertising information quality) on consumer cognitive and affective reactions (organism, i.e., perceived diagnosticity and perceived reciprocity) and consumer loyalty (response, i.e., product purchase intention and product rating). By collaborating with a leading Chinese beauty and care product sampling platform, we distributed the questionnaires to platform users who were actual free sample receivers and collected campaign information. Both subjective and objective data were collected to empirically test the research model. Our major findings suggest that perceived reciprocity has a positive and significant effect on product rating, but it does not affect consumer purchase intention. In contrast, perceived diagnosticity positively affects consumer purchase intention while it does not affect product rating. Research findings are discussed and are expected to enrich the product sampling-related literature and contribute to both academia and practice.

The issue of versioning of information goods has resurfaced, in part as a result of the recent popularity of downloadable contents (DLC) among video game manufacturers. The central idea behind the DLC strategy, zero-day DLCs in particular, is that consumers who find the base version of a game to be sufficiently close to their tastes would want more of its capabilities and would pay a premium to upgrade by purchasing a DLC. To better understand the implications of such a product-line strategy, in this work, we combine the literature on versioning with that on consumer learning. In doing so, we uncover an interesting economic phenomenon that, for an experience good, a manufacturer's desire to vertically differentiate could actually stem from its inability to otherwise elicit unobserved heterogeneity in consumers' perceived fit. In other words, we generalize versioning to accommodate both vertical and horizontal heterogeneity.

Short video excerpts from TV shows are a tool that producers/broadcasters use to promote their programs. This study examines how video highlights that are presented online for free viewing, which can be analogous to product samples for entertainment goods, affect TV audience ratings. We investigate whether a displacement effect exists, i.e., the substitution of goods due to the availability of other similar goods. We find that positive viewer response, measured by the number of likes and views generated for the highlights, positively affects ratings, and the square of the number of likes negatively affects ratings. Our findings suggest that if viewers are overly satisfied with the highlights, some may be satisfied with merely viewing them and refrain from watching the actual show; such a response may potentially decrease TV viewership. This is the first study to examine the role of online video highlights as a promotional tool for TV shows.

Using an agent-based model to study the success of product-sampling campaigns that rely on information about social networks, this paper investigates the essential decisions of which consumers and how many of them to target with free product samples. With an unweighted and a weighted real-world personal communication network, we show that the decision of which consumers to target is more important than that of how many consumers to target. Use of social network information increases profits by at least 32 percent. Companies should use a high-degree targeting heuristic to identify the most influential consumers. Use of social network information increases profit for single-purchase products mainly because it supports targeting more influential consumers and therefore speeds up diffusion throughout the network. For repeat-purchase products, social network information decreases the optimal number of samples and thus the cost of the campaign.

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.

Product sampling is an important marketing strategy for experience goods like music, but on-line digital music sampling has not been explored in the literature. Based on an experimental study, this paper investigates some regularities of digital music sampling and identifies the factors that govern its effectiveness. Four digital music sampling strategies are modeled: segment sampling, low-quality sampling, stingy sampling, and double-sampling. Stingy sampling, now widely used in the music industry, is the least effective of the four. Segment sampling or low-quality sampling would be the best choice for music vendors under certain conditions. Double-sampling, proposed for the first time in this study, holds a great deal of promise as search costs get lower. The analysis confirms the importance of an effective digital music sampling strategy and sheds light on digital rights management (DRM) in the music industry.