Fundraising campaigns draw support from a wide pool of contributors. Some contributors are interested in private rewards offered in exchange for contributions (buyers), whereas others are publicly-minded and value success (donors). Buyers face a coordination problem because of the positive externalities of campaign success. A leadership donor who strategically times contributions can promote coordination by dynamically signaling his valuation. The ability to signal increases the probability of success and benefits all participants relative to the donor valuation being known. We validate our modeling assumptions and theoretical predictions using Kickstarter data.
We study reward-based crowdfunding, a new class of dynamic contribution games where a private good is produced only if the funding goal is reached by a deadline. Buyers face a problem of coordination rather than free-riding. A long-lived donor may alleviate this coordination risk, signaling his wealth through dynamic contributions. We characterize platform-, donor-, and buyer-optimal equilibrium outcomes, attained by Markov equilibria with simple donation strategies. We test the model’s predictions using high-frequency data collected from the largest crowdfunding platform, Kickstarter. The model ﬁts the data well, especially for predictions concerning comparative statistics, donation dynamics, and properties of successful campaigns.
The propensity of consumers to engage in word-of-mouth (WOM) can diﬀer after good versus bad experiences, resulting in positive or negative selection of user-generated reviews. We study how the propensity to engage in WOM depends on information available to customers through diﬀerent marketing channels. We develop a model of WOM in which a target customer makes a purchase decision based on his private brand association, public product-speciﬁc information (e.g. from advertising or past reviews) and WOM content, and an early adopter of the new product engages in WOM only if her information is instrumental to the target customer’s purchase decision. We deﬁne brand image to be the distribution of the customers’ brand associations, and strength of the brand image to be the precision of this distribution. We show that if the brand image is strong, then in equilibrium only negative WOM can arise. In contrast, with a weak brand image, positive WOM must occur. Moreover, holding product quality ﬁxed, a positive advertising signal realization leads to a more positive WOM selection. We use restaurant review data from Yelp.com to motivate our model assumptions and validate the predictions. For example, a textual analysis of reviews is consistent with prevalence of an instrumental motive for WOM. Further, a review rating for national established chain restaurant locations, where the brand image is strong, is almost 1-star lower (on a 5-star scale) than a review rating for a comparable independent restaurant, controlling for reviewer and restaurant characteristics.
The propensity of consumers to engage in word-of-mouth (WOM) can diﬀer after good versus bad experiences. This can result in positive or negative selection of user-generated reviews. We show how the strength of brand image - determined by the dispersion of consumer beliefs about quality - and the informativeness of good and bad experiences impact the selection of WOM in equilibrium. Our premise is that WOM is costly: Early adopters talk only if their information is instrumental for the receiver’s purchase decision. If the brand image is strong, i.e., consumers have close to homogeneous beliefs about quality, then only negative WOM can arise. With a weak brand image, positive WOM can occur if positive experiences are suﬀiciently informative. We show that our theoretical predictions are consistent with restaurant review data from Yelp.com. A review rating for a national established chain restaurant is almost 1-star lower (on a 5-star scale) than a review rating for a comparable independent restaurant, controlling for various reviewer and restaurant characteristics. Further, negative chain restaurant reviews have more instances of expectation words, indicating agreement over beliefs about the quality, whereas positive reviews of independent restaurants feature disproportionately many novelty words.
We study reward-based crowdfunding campaigns, a new class of dynamic contribution games where consumption is exclusive. Two types of backers participate: buyers want to consume the product while donors just want the campaign to succeed. The key tension is one of coordination between buyers, instead of free-riding. Donors can alleviate this coordination risk. We analyze a dynamic model of crowdfunding and demonstrate that its predictions are consistent with high-frequency data collected from Kickstarter. We compare the Kickstarter mechanism to alternative platform designs and evaluate the value of dynamically arriving information. We extend the model to incorporate social learning about quality.
We study a canonical model of reputation between a long-run player and a sequence of short-run opponents, in which the long-run player is privately informed about an uncertain state that determines the monitoring structure in the reputation game. The long-run player plays a stage-game repeatedly against a sequence of short-run opponents. We present necessary and suﬀicient conditions (on the monitoring structure and the type space) to obtain reputation building in this setting. Speciﬁcally, in contrast to the previous literature, with only stationary commitment types, reputation building is generally not possible and highly sensitive to the inclusion of other commitment types. However, with the inclusion of appropriate dynamic commitment types, reputation building can again be sustained while maintaining robustness to the inclusion of other arbitrary types.
We study optimal contracting in a setting where a ﬁrm repeatedly interacts with multiple workers, and can compensate them based on publicly available performance signals as well as privately reported peer evaluations. If the evaluation and the eﬀort provision are done by diﬀerent workers (as in a supervisor/agent hierarchy), we show that, using both the private and public signals, the ﬁrst best can be achieved even in a static setting. However, if each worker is required to both exert eﬀort and report on his co-worker’s performance (as in a team setting), the worker’s eﬀort incentives cannot be decoupled from his truth-telling incentives. This makes the optimal static contract ineﬀicient and relational contracts based on the public signals increase eﬀiciency. In the optimal contract, it may be optimal to ignore signals that are informative of the worker’s eﬀort.