Sha Yang

Crowdsourcing contests are contests by which organizations tap into the wisdom of crowds by outsourcing tasks to large groups of people on the Internet. In an online environment often characterized by anonymity and lack of trust, there are inherent uncertainties for participants of such contests. This study focuses on crowdsourcing contests with winner-take-all prizes. During these contests, submissions are made sequentially and contest hosts can provide public in-process feedback to the submissions as soon as they are received. Drawing on the uncertainty literature and contest theory, we examine how the use of prize guarantees (guaranteeing that a winner will be picked and paid) and in-process feedback (numeric ratings to individual designs and public textual comments during the contest) can help reduce the various uncertainties faced by the contestant, thereby attracting more submissions. We find that guaranteeing the prize increases submissions. The volume of in-process feedback (both numeric reviews and textual comments) has a positive effect on the number of submissions, and such an effect is bigger in contests without prize guarantees. In addition, providing highly positive or extremely negative feedback discourage overall future submissions, and the negative effect of highly positive feedback is mitigated in guaranteed contests.

It is quite common that a coupon can be applied to one of several vertically differentiated products sold at different prices within the same product line of a brand. With such a product-line coupon, consumers need to decide what specific product to buy, resulting in different levels of consumer spending. One field study and four lab experiments demonstrate that the relationship between coupon face value and consumer spending level may not always be intuitively positive, and instead it could take an inverted U-shape under certain circumstances. The authors show that the inverted U-shaped effect of coupon face value on consumer spending level occurs when the price level of products is high, when consumers have a strong saving orientation, when they experience low information load from processing a small number of products, when they are inclined to engage in thorough product comparison, or when they have a weak pre-existing preference for a specific level of product benefit.

We study the contagious switching behavior related to a consumer’s choice of wireless carriers, that is, that a consumer is more likely to switch wireless carriers if more of their contacts from the same carrier have switched. Contagious switching (or a positive network effect) can be driven by information-based social learning, as well as other mechanisms related to network size. While previous marketing literature has documented the social-learning effect, most of the applications studied involve products in which consumers usually do not enjoy any direct benefits from a large network other than from information-based social learning. We explore the importance of the social-learning effect relative to other mechanisms that may also lead to the network effect. We propose a dynamic structural model with interpersonal interactions. To model the social-learning effect, a consumer uses feedback from his or her contacts who have switched from a focal carrier to update his or her quality expectations of alternative carriers. Our model further accounts for two unique aspects of consumer strategic learning: (i) the individual’s perception on the signal of alternative carriers from contacts who switch is systematically different based on whether the signal comes from a loyal contact; and (ii) that the perceived noisiness of the signal on alternative carriers from a contact who has switched depends on the strength of the relationship between the individual and the contact. The remaining network effect not captured through social learning is modeled as a function of the size of the network. We solve the model with a two-step dynamic programming algorithm, with the assumption that a consumer is forward-looking and decides whether to stay with the same service carrier in each period by maximizing the total utility received from that day onward. We apply the proposed model to the dataset of a mobile network operator in a European country. We find that churning/switching behavior is contagious in the network context and that one-third of general network effects can be attributed to social learning. We also detect strategic learning by consumers from their contacts in two ways: the experience signal on alternative carriers from a more loyal contact who has switched from the focal carrier is perceived to be more positive than that from a less loyal contact, and the social-learning effect is stronger from an individual’s closest contacts. The simulation analysis demonstrates the value of our model in helping a company prioritize its CRM effort.

As Internet advertising infomediaries nowadays provide rich competition information, sponsored search advertisers are becoming more strategic when selecting keywords. This paper empirically examines the spillover effects in advertisers’ keyword market entry decisions, that is, how an advertiser’s likelihood of using a keyword is affected by competitors’ keyword entry decisions. We develop a structural model to characterize advertisers’ keyword market entry decisions. We apply the model to a panel dataset of 1,252 laptop-related keywords mainly used by 28 manufacturers, retailers, and comparison websites that advertise on Google. Our analysis leads to several interesting findings. First, an advertiser’s expected position affects the nature of the competition. In particular, the spillover effect from below-ranked competitors is always positive, while the spillover effect from above-ranked competitors is either positive or negative. Second, the spillover effect from above-ranked ads is directionally affected by firms’ product-line characteristics: the effect among firms offering homogenous products (e.g. comparison sites) is negative, whereas the effect among firms with more differentiated products (e.g. manufacturers and retailers) is positive. Third, the spillover effect from above-ranked ads is directionally affected by firms’ positions in a distribution channel: the effect from upstream (downstream) on downstream (upstream) firms tends to be negative (positive). Finally, a downstream firm is more likely to learn new keywords from an upstream firm but not vice versa. Our counterfactual simulations demonstrate that the keyword-specific competition information provided by infomediaries can improve the search engine’s revenue by about 5.7%.