Product Characteristics and Price Advertising with Consumer Search

Many advertisements inform the consumer about product characteristics, while others give price information with very little product information, and some provide both types of information. We propose a framework to analyze the incentives for firms to provide various types of information. We consider the case of a single seller. There is no incentive to provide information on product characteristics only, since doing so leads to a holdup problem that the consumers would rationally expect the firm to charge such a high price that no consumer would wish to incur the prior search cost. (A more general argument applies to markets with several firms.) However, price-only and price-and-characteristics advertising can arise depending on the relative strength of product differentiation and consumer search costs. Even when it costs the firm very little to inform consumers the firm may have no incentive to advertise if consumers will sample it anyway. For low search costs the firm has a strict incentive NOT to let consumers know because the firm garners higher profit when consumers have sunk the search cost. Forced disclosure and dissemination of information improves social welfare by eliminating useless search behavior that leads to no purchases (as well as enabling consumers to buy at lower prices). Second, even when the firm must advertise to bring in consumers (i.e., for larger search costs), the firm may prefer to keep consumers in the dark about how much they like the product - this behavior again entails excessive search. Finally, even when the firm finds it optimal to inform consumers of both their match values and the price charged, the level of advertising is too small because the firm only accounts for its private benefit per consumer informed when determining how much to advertise, and not the extra benefit to consumers of making a valuable match.

An Analysis of Pricing Strategy and Price Dispersion on the Internet

Using prices obtained from shopbots, we test several hypotheses regarding the economics of information and optimal search. We find that price dispersion is positively (negatively) related to product price and the number of sellers in cross-sectional (time series) analysis. Price dispersion increases over time when the sample includes new entrants, but decreases in the absence of entry. Controlling for shipping charges and seller heterogeneity reduces, but does not eliminate, price dispersion. Finally, prices appear to be correlated across products and over time – low price sellers for one product (time period) generally charge low prices for all items (time periods).

Consumer online search with partially revealed information

Modern-day search platforms generally have two layers of information presentation. The outer layer displays the collection of search results with attributes selected by platforms, and consumers click on a product to reveal all its attributes in the inner layer. The information revealed in the outer layer affects the search costs and the probability of finding a match. To address the managerial question of optimal information layout, we create an information complexity measure of the outer layer, namely orderedness entropy, and study the consumer search process for information at the expense of time and cognitive costs. We first conduct online random experiments to show that consumers respond to and actively reduce cognitive cost for which our information complexity measure provides a representation. Then, using a unique and rich panel tracking consumer search behaviors at a large online travel agency (OTA), we specify a novel sequential search model that jointly describes the refinement search and product clicking decisions. We find that cognitive cost is a major component of search cost, while loading time cost has a much smaller share. By varying the information revealed in the outer layer, we propose information layouts that Pareto-improve both revenue and consumer welfare for our OTA.This paper was accepted by Juanjuan Zhang, marketing

Internet Shopping Search: A Decision Theoretic Perspective

Advances and widespread use of Internet shopping intermediaries have empowered consumers to collect detailed product information and discover the price dispersion for the product and its alternatives. These intermediaries provide in-depth decision support systems for consumers to search and sort out vast amount of information available on Internet. This paper examines the impact of these intermediaries upon consumer strategy and payoffs. We extend pioneering works in the area of information economics to find out optimal consumer search strategies varying level of market competition, signal qualities from intermediaries, and search costs. Our major findings are: (1) consumer payoff is continuous along the quality of signal dimension, but it may have kinks because of strategy changes, (2) cost of search decreases the incentive to search, and (3) market competition increases the incentive to search. This work reinforces the existing literature propositions on the impact of search costs in a stochastic problem setting and includes an intriguing analysis of market competition and corresponding consumer strategies. Our findings should assist firms in their design of infomediaries and precipitate in improved understanding of the impacts on market competition and rationale for consumer behavior and strategies

Consumer Attention Allocation and Firm Strategies

Nowadays consumers can easily access to vast amounts of product information before making a purchase. Yet, limitations on the ability to process information force consumers to make choices regarding the subjects to which they pay more or less attention. In this dissertation, I study how a consumer optimally allocates attention to various product information before making a purchase decision and how a seller should design the marketing strategies taking into account the consumer's attention allocation decision. I find that either a consumer engages in “confirmatory” search under which she searches more information that favors her prior belief or the consumer engages in “disconfirmatory” search under which she searches more information that disfavors her prior belief. In particular, the consumer conducts more disconfirmatory search when the information processing cost is low, while she conducts more confirmatory search when the cost is high. This suggests that “confirmatory bias” widely studied in psychology literature could be optimal behavior coming out of people optimizing attention to different types of information, especially when people has high information processing costs. Furthermore, a consumer's purchase likelihood may vary with her information processing cost in a non-monotonic way, depending on the consumer's prior belief and the utilities of buying a matched product and a mismatched product. Moreover, I show that when more information becomes available or credible, the consumer would increase attention to negative information when the prior utility of the product is high but she would increase attention to positive information when the prior utility is low. In terms of seller's strategies, I find that when the consumer has a low information processing cost, the seller would charge a relatively high price such that consumers always process information; but when the consumer has a high information processing cost, the seller would charge a relatively low price such that consumers purchase the product without any learning. The optimal price and profit would first decrease and then increase in consumer's information processing cost. In addition, offering the return policy induces the consumer to pay more attention to positive information and less attention to negative information, and the seller would offer the return policy except when the consumer has a very high information processing cost. Finally, when a seller can influence the information environment, he would have a lower incentive to suppress the negative information when the consumer has a lower prior belief about product fit. Moreover, a higher information processing cost for a consumer would increase or decrease a seller's incentive to suppress the negative information in the environment, depending on whether the seller can adjust the product price and whether the consumer has a high or low prior belief. Interestingly, the seller may charge a lower price when he can fully control the information environment than when he can not

The enforcement of mandatory disclosure rules

This paper examines the incentives of a firm to invest in information about the quality of its product and to disclose its findings. If the firm conceals information, it might be detected and fined. We show that optimal monitoring is determined by a trade-off. Overall, stricter enforcement reduces the incentives for selective reporting but crowds out information search. Our model implies that there are situations in which the relationship between the two monitoring instruments might be complementary. We also show that the welfare effects of mandatory disclosure depend on how it is enforced and that imperfect enforcement (in which some information remains concealed) might be optimal. In particular, the optimal fine might be smaller than the largest possible fine, even though the latter requires lower resource costs for inspections

Research on the time optimization model algorithm of Customer Collaborative Product Innovation

Purpose: To improve the efficiency of information sharing among the innovation agents of customer collaborative product innovation and shorten the product design cycle, an improved genetic annealing algorithm of the time optimization was presented. Design/methodology/approach: Based on the analysis of the objective relationship between the design tasks, the paper takes job shop problems for machining model and proposes the improved genetic algorithm to solve the problems, which is based on the niche technology and thus a better product collaborative innovation design time schedule is got to improve the efficiency. Finally, through the collaborative innovation design of a certain type of mobile phone, the proposed model and method were verified to be correct and effective. Findings and Originality/value: An algorithm with obvious advantages in terms of searching capability and optimization efficiency of customer collaborative product innovation was proposed. According to the defects of the traditional genetic annealing algorithm, the niche genetic annealing algorithm was presented. Firstly, it avoided the effective gene deletions at the early search stage and guaranteed the diversity of solution; Secondly, adaptive double point crossover and swap mutation strategy were introduced to overcome the defects of long solving process and easily converging local minimum value due to the fixed crossover and mutation probability; Thirdly, elite reserved strategy was imported that optimal solution missing was avoided effectively and evolution speed was accelerated. Originality/value: Firstly, the improved genetic simulated annealing algorithm overcomes some defects such as effective gene easily lost in early search. It is helpful to shorten the calculation process and improve the accuracy of the convergence value. Moreover, it speeds up the evolution and ensures the reliability of the optimal solution. Meanwhile, it has obvious advantages in efficiency of information sharing among the innovation agents of customer collaborative product innovation. So, the product design cycle could be shortened.Peer Reviewe

Research on the time optimization model algorithm of Customer Collaborative Product Innovation

Purpose: To improve the efficiency of information sharing among the innovation agents of customer collaborative product innovation and shorten the product design cycle, an improved genetic annealing algorithm of the time optimization was presented. Design/methodology/approach: Based on the analysis of the objective relationship between the design tasks, the paper takes job shop problems for machining model and proposes the improved genetic algorithm to solve the problems, which is based on the niche technology and thus a better product collaborative innovation design time schedule is got to improve the efficiency. Finally, through the collaborative innovation design of a certain type of mobile phone, the proposed model and method were verified to be correct and effective. Findings and Originality/value: An algorithm with obvious advantages in terms of searching capability and optimization efficiency of customer collaborative product innovation was proposed. According to the defects of the traditional genetic annealing algorithm, the niche genetic annealing algorithm was presented. Firstly, it avoided the effective gene deletions at the early search stage and guaranteed the diversity of solution; Secondly, adaptive double point crossover and swap mutation strategy were introduced to overcome the defects of long solving process and easily converging local minimum value due to the fixed crossover and mutation probability; Thirdly, elite reserved strategy was imported that optimal solution missing was avoided effectively and evolution speed was accelerated. Originality/value: Firstly, the improved genetic simulated annealing algorithm overcomes some defects such as effective gene easily lost in early search. It is helpful to shorten the calculation process and improve the accuracy of the convergence value. Moreover, it speeds up the evolution and ensures the reliability of the optimal solution. Meanwhile, it has obvious advantages in efficiency of information sharing among the innovation agents of customer collaborative product innovation. So, the product design cycle could be shortened.Peer Reviewe

Essays in platform economics

In this thesis we present three papers which investigate informative content generated by consumers, aiming to improve the usefulness for matching high quality products at lower prices. Following a general perspective, we explore platform product listing, searchable through a decision making mechanism. In a more specialized perspective, we take into account a dropping price modality service, differentiating the consumer benefit in the case of high or low quality product matching. Chapter 1 Product quality on platform markets. Abstract Many studies have questioned the meaning of \u201cproduct quality\u201d, hanging between a characteristic interpretation of a product for improving consumer satisfaction, and scientific approach to measure its benefits. Starting from the historical quality setting as mirror image of the price, we investigate the adoption of new signals, developed over the years to adjust the original relationship. Recently, bootstrapping by emperor of e-commerce platforms, the rating system has emerged as a reference contribute for product quality informativeness. We study this tendency, to show its failure in the presence of low price market and new brands. For this purpose, we collect User Generated Contents from a well-known online retailing platform. We capture and distill meaningful features in order to adjust the rating assigned by reviewers, and propose a novel quality formula able to increase the accuracy of the information provided to the consumer. We suggest that our formula better captures product quality, and, when adopted by a platform for sorting the products, it increases the products variety and, consequently the satisfaction of the consumer. Our proposal suggests a way to facilitate the consumer search (as we will show in the second chapter). Moreover, it can be used as a measure of market efficiency in the case of voluntary opacity of the platform in exposing product quality signals.Chapter 2 Optimizing Product Quality in Online Search Abstract Exploiting an original definition of product quality, based on the information we can get from the User Generated Content, and driven by a statistical learning algorithm, we propose a new ordering mechanism for product search on platforms. This product quality formula is imported in a decision making mechanism which adopts an optimal Stopping Rule, in order to set the optimal time to terminate the search process and choose a good to purchase. We show how the consumer can benefit from the implementation of such a mechanism, demonstrating an improvement in terms of consumer utility at different levels of price, with respect to other sorting traditionally adopted by platforms. We propose a utility function fitted to a Gumbel distribution, and we demonstrate a stochastic dominance of our model. Experimental evidences on the camera market category put in relevance the efficiency of our quality index for ranking the effective quality compared to the more traditional rating system. This is particularly true for the low-price accessory market segment of products, in which we show higher utility dominance and slightly higher elasticity of demand.Chapter 3 Price Matching and Platform Pricing Abstract In this study we investigate the effects of Price Matching Guarantees (PMG) commercial policies on U.S. online consumer electronics daily prices. By applying a Diff-in-Diff identification strategy we find evidence in favor of price reductions occurring after the PMG policy is repealed. We further investigate if such effect is heterogeneous according to products characteristics, by exploiting User Generated Contents (products popularity and quality) and online search visibility measures (Google Search Rank). Estimates suggest that for high quality (visibility) products PMG policies harms competition by keeping prices high, while for low quality (visibility) products, prices decrease during the policy validity period