I n this study, we consider a supplier's contract offerings to a buyer who may obtain improved forecasts for her demand over time. We investigate how the supplier can take advantage of the buyer's better forecasts and what kind of contracts he should offer to the buyer in order to maximize his profits. We model a natural forecast evolution where the buyer can obtain a more accurate forecast closer to the selling season. We assume there is information asymmetry between the buyer and the supplier at all times in that the buyer understands her demand better than the supplier. Three types of contracts that the supplier can offer are considered: (1) one where a contract is offered before the buyer has a chance to obtain improved forecasts, (2) one where a contract is offered after the buyer has obtained improved forecasts, and (3) a contingent (dynamic) contract which offers an initial contract to the buyer before she obtains improved forecasts, followed by a later contract (contingent on the initial contract) offered after improved forecasts have been obtained. We consider two scenarios: (1) where the supplier is certain that the buyer can obtain more accurate forecasts over time, and (2) where the supplier is uncertain about the buyer's forecasting capability (or forecasting cost). In the first scenario, we show that among the three types of contracts, the contingent contract is always the most profitable for the supplier. Furthermore, using the contingent contract, the supplier always benefits from higher accuracy of the buyer's demand forecasts. In the second scenario, we explicitly model the supplier's level of certainty about the buyer's capability of obtaining better forecasts, and explore how the supplier can design contracts to induce the buyer to obtain better forecasts when she is capable. 3 revisions. that such a contract can guarantee that the supplier always benefits from the buyer's improved demand information. Recently, we have worked with a major

T his study investigates the effects of conditional promotions (e.g., buy 2 or more, get 30% off; spend $50 or more, get $15 off) on consumer behavior and the seller's profit. When a deal is presented with a minimum purchase quantity or a minimum spending requirement, experimental studies have shown some consumers are induced to spend more in order to obtain a discount. To study this behavior, we model a market in which consumers can be heterogeneous in two dimensions: willingness to pay for the product and deal proneness to a price discount. We examine two types of conditional promotions that are widely used in practice: (i) all-unit discount, in which a price reduction applies to every unit of a purchase once the minimum requirement is met, and (ii) fixed-amount discount, in which a fixed amount of discount is awarded to the total expense that meets the requirement. We show that deal-prone consumers may be induced to overspend when offered a conditional discount. However, consumer overspending benefits the seller only when the market contains a sufficiently large proportion of highly deal-prone or high-valuation consumers. Comparing the two types of discounts, we show that the all-unit discount outperforms the fixed-amount discount when the regular price for the product is high, whereas the fixed-amount discount is more profitable than the all-unit discount when some consumers would make a purchase even without a discount. Our study suggests adopting an appropriate type of conditional discount can effectively improve the seller's profit over what would be obtained through selling at the regular price or a conventional price markdown. Furthermore, we find that conditional discounts can also improve consumer welfare, resulting in win-win situations for both retailers and consumers.

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Purpose This study aims to investigate a startup accelerator’s decisions toward exerting effort in an information acquisition process and selecting an information disclosure strategy. In particular, the authors are interested in examining which factors may cause the accelerator to report more or less accurate information, which will subsequently affect the investment decision and the outcome of the ventures. This study examines the impact of the equity share taken by the accelerator on the effort level being exerted in the information acquisition process, as well as the accelerator’s decision on the information disclosure regime. Design/methodology/approach The authors use mathematical models built upon well-established theoretical and practical concepts to analyze the research problems and derive the findings. Findings The authors show that when the accelerator takes a sufficiently large equity share from the entrepreneur in exchange for admitting the entrepreneur’s venture into the acceleration program, the accelerator is motivated to exert a significant level of effort to observe an accurate signal for the quality of the venture, and then disclose the information about the venture’s quality consistently with the observed signal (informative disclosure regime). On the other hand, if the accelerator takes a small equity share, it is optimal for her to exert no effort in the information acquisition process and simply adopt the basic disclosure regime, where the accelerator reports the quality of the venture based solely on the ex ante expected payoff of the venture, regardless of the observed signal. Practical implications The results indicate that an equity sharing scheme, which awards a sufficient amount of equity to the accelerator, can be an effective tool to help obtain accurate information about the quality of a startup venture and make a well-informed investment decision. Originality/value This research illustrates that the ownership stake of the accelerator can potentially indicate the accuracy of the information about the venture provided by the accelerator to outside investors. That is, when the stake held by the accelerator is large, the investors can conjecture that the information about the venture reported by the accelerator may be highly accurate and reliable. In contrast, if the accelerator holds a small stake, then it is likely that the information provided by the accelerator may not add any value to the publicly available information. These insights can guide investors (e.g. angle investors, venture capitalists, etc.) in making well-informed startup investment decisions.

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