Strategic Revenue Sharing with Daily Deal Sites: A Competitive Analysis

Promoting through daily deal sites has become popular in the past few years. Groupon is perhaps the best known provider of these promotions (also known as online discount vouchers) to customers. Daily deals differ from traditional coupons on three important dimensions: (i) They are not offered to consumers directly by firms but are offered through an intermediary, (ii) the promotional depth offered by these localized promotions tends to be higher, and (iii) consumers prepay for the discount vouchers, and then the daily deals site reimburses the retailer a prespecified negotiated percentage of each paid voucher. In this study, we build a theoretical model to explore the profitability of undertaking a daily deals campaign by firms. Our analysis identifies the role of types of consumers in the market, relative bargaining power of the local firms, revenue sharing agreements, and market characteristics, such as degree of product substitutability and extent of competition, on the profitability attained through daily deal promotions. Surprisingly, when demand spillovers exist, an asymmetric outcome, with one firm offering an online discount voucher and the competing firm not offering an online discount voucher, is an equilibrium under some conditions.      

Advance Selling by a Newsvendor Retailer

Retailers often face a newsvendor problem. Advance selling helps retailers to reduce demand uncertainty. Consumers, however, may prefer not to purchase in advance unless given a discount because they are uncertain about their valuation for the product in advance. It is then unclear whether or when advance selling to pass some uncertainty risk to consumers is optimal for the retailer. This paper examines the advance selling price and inventory decisions in a two‐period setting, where the first period is the advance selling period and the second is the selling (and consumption) period. We find that an advance selling strategy is not always optimal, but is contingent on parameters of the market (e.g., market potential and uncertainty) and the consumers (e.g., valuation, risk aversion, and heterogeneity). For example, we find that retailers should sell in advance if the consumers' expected valuation exceeds consumers' expected surplus when not buying early by a certain threshold. This threshold increases with the degree of risk aversion but decreases with stock out risk. If the degree of risk aversion varies across consumers, then a retailer should sell in advance if the probability for a consumer to spot buy is less than a critical fractile.     

Repurposing Materials and Waste through Online Exchanges: Overcoming the Last Hurdle

Online material and waste exchanges (OMWEs) provide online channels to repurpose by‐products, unused materials and waste from industrial and commercial facilities. Unfortunately, OMWE's also have challenges. First, sellers may have access to other disposal options and, as a result, may not fully commit to the exchange. Second, buyers can face high uncertainty about the product exchanged and the transaction being undertaken. Overcoming these challenges is the “last hurdle” to making OMWEs successful. This study investigates the factors that reduce the buyers' uncertainty and increase the sellers' commitment to the OMWE. We analyze novel transaction‐level data from an online exchange (MNExchange.org) combined with other archival public records on county‐level repurposing and disposal statistics. First, we find that regional repurposing policies and alternatives have a complementary effect on sellers' commitment toward OMWEs, resulting in increased OMWE exchanges. However, regional disposal policies and alternatives have a substitution effect on sellers' commitment, resulting in reduced exchange success. Further, greater product and transaction information reduce the buyer's uncertainty and increase exchange success. Finally, the analysis shows that users' (buyers and sellers) heavily rely on their prior experience with OMWEs. Specifically, higher familiarity between the buyer–seller pair and familiarity with the OMWE system leads to higher likelihood of exchange success. This study lays the foundation for understanding OMWEs and has important implications for developing policies and operations to increase online transactions of by‐products, materials and wastes.     

Entry Deterrence of Capacitated Competition Using Price and Non‐Price Strategies

We analyze the role of pricing and branding in an incumbent firm's decision when facing competition from an entrant firm with limited capacity. We do so by studying two price competition models (Stackelberg and Nash), where we consider the incumbent's entry‐deterrence pricing strategy based on a potential entrant's capacity size. In an extension, we also study a branding model, where the incumbent firm, in addition to pricing, can also invest in influencing market preference for its product. With these models, we study conditions under which the incumbent firm may block the entrant (i.e., prevent entry without any market actions), deter the entrant (i.e., stop entry with suitable market actions) or accommodate the entrant (i.e., allow entry and compete), and how the entrant will allocate its limited capacity across its own and the new market, if entry occurs. We also study the timing difference between the two different dynamics of the price competition models and find that the incumbent's first‐mover advantage benefits both the incumbent and the entrant. Interestingly, the entrant firm's profits are not monotonically increasing in its capacity even when it is costless to build capacity. In the branding model, we show that in some cases, the incumbent may even increase its price and successfully deter entry by investing in consumer's preference for its product. Finally, we incorporate demand uncertainty into our model and show that the incumbent benefits from demand uncertainty while the entrant may be worse off depending on the magnitude of demand uncertainty and its capacity.     

Do Markets Favor Agents able to Make Accurate Predictions?

Blume and Easley (1992) show that if agents' have the same savings rule, those who maximize the expected logarithm of next period's outcomes will eventually hold all wealth (i.e. are ‘most prosperous’). However, if no agent adopts this rule then the most prosperous are not necessarily those who make the most accurate predictions. Thus, agents who make inaccurate predictions need not be driven out of the market. In this paper, it is shown that, among agents who have the same intertemporal discount factor (and who choose savings endogenously), the most prosperous are those who make accurate predictions. Hence, convergence to rational expectations obtains because agents who make inaccurate predictions are driven out of the market.     

Using Advance Purchase Discount Contracts under Uncertain Information Acquisition Cost

We study the use of advance purchase discount (APD) contracts to incentivize a retailer to share demand information with a dual‐sourcing wholesaler. We analyze such contracts in terms of two practical considerations that are relevant in this context but have been overlooked by previous work that has largely studied the direct offer of APD to customers: the retailer's information acquisition cost and the wholesaler's limited information about that cost. The wholesaler's limited knowledge of the retailer's cost leads to a departure—from the normal “full observability” APD design—that is asymmetric and depends on the extent of unobservability; if the uncertainty is small (resp., large) then the optimal discount is higher (resp., lower) than in the case of full observability. An APD contract that ignores the retailer's cost or the wholesaler's uncertainty about it will yield fewer benefits for the wholesaler and the supply chain. We offer a numerical illustration (calibrated on real industry data) establishing that for a representative product, an APD contract can improve the wholesaler's profit margin by as much as 3.5%.     

If You're so Smart,why Aren't You Rich? Belief Selection in Complete and Incomplete Markets

This paper provides an analysis of the asymptotic properties of Pareto optimal consumption allocations in a stochastic general equilibrium model with heterogeneous consumers. In particular, we investigate the market selection hypothesis that markets favor traders with more accurate beliefs. We show that in any Pareto‐optimal allocation whether each consumer vanishes or survives is determined entirely by discount factors and beliefs. Whereas equilibrium allocations in economies with complete markets are Pareto optimal, our results characterize the limit behavior of these economies. We show that, all else equal, the market selects for consumers who use Bayesian learning with the truth in the support of their prior and selects among Bayesians according to the size of their parameter space. Finally, we show that in economies with incomplete markets, these conclusions may not hold. With incomplete markets, payoff functions can matter for long‐run survival, and the market selection hypothesis fails.     

Constrained Efficiency in the Neoclassical Growth Model With Uninsurable Idiosyncratic Shocks

We investigate the welfare properties of the one‐sector neoclassical growth model with uninsurable idiosyncratic shocks. We focus on the notion of constrained efficiency used in the general equilibrium literature. Our characterization of constrained efficiency uses the first‐order condition of a constrained planner's problem. This condition highlights the margins of relevance for whether capital is too high or too low: the factor composition of income of the (consumption‐)poor. Using three calibrations commonly considered in the literature, we illustrate that there can be either over‐ or underaccumulation of capital in steady state and that the constrained optimum may or may not be consistent with a nondegenerate long‐run distribution of wealth. For the calibration that roughly matches the income and wealth distribution, the constrained inefficiency of the market outcome is rather striking: it has much too low a steady‐state capital stock.     

Drivers and Outcomes of Long‐term Orientation in Cooperative Relationships

We argue in this paper that a buyer may deliberately develop long‐term orientation as a governance mechanism to deal with risks arising from exchange hazards, and to reduce the opportunistic behaviour of a supplier. While the exchange hazards of asset specificity pose a safeguarding problem, those of market uncertainty pose an adaptation problem. We test our model on a sample of 221 procurement partnerships. Our results show that satisfactory prior history of a supplier, asset specificity of the buyer and market uncertainty are all positively related to a buyer's long‐term orientation towards a supplier. Consistent with the idea that asset specificity and market uncertainty pose different governance problems, we find that satisfactory prior history reduces the positive relationship between asset specificity and a buyer's long‐term orientation, but enhances the positive relationship between market uncertainty and a buyer's long‐term orientation. We also find that a buyer's long‐term orientation fully mediates the relationship between satisfactory prior history and a supplier's opportunistic behaviour. Finally, implications on the theory and the practices of relationship governance are discussed.     

Quantity Flexibility Contract in the Presence of Discount Incentive

We design a new contract, which we refer to as the QFi contract, that combines the quantity flexibility (QF) mechanism and the price‐only discount incentive. Under the QF contract, the buyer does not assume full responsibility for the forecast, yet the supplier guarantees the availability of the forecasted quantity and extra buffer inventory. In contrast, the price‐only discount contract places full inventory burden on the buyer. We show that the proposed QFi contract effectively balances the inventory risk for both the buyer and the supplier considering both the QF and discount mechanisms. We also show that the QFi contract is able to achieve supply chain coordination. More importantly, the QFi contract's coordinating price scheme does not require knowledge of demand distribution. We identify areas where the buyer and supplier may both benefit from implementing the QFi contract as opposed to the extant QF or price‐only (wholesale) discount contractual decisions in a decentralized supply chain. We also specify the conditions under which supply chain coordination can be achieved in a win‐win manner. We conclude with managerial implications and provide directions for future research.     

Role of Random Capacity Risk and the Retailer in Decentralized Supply Chains with Competing Suppliers

This research considers a supply chain under the following conditions: (i) two heterogeneous suppliers are in competition, (ii) supply capacity is random and pricing is endogenous, (iii) consumer demand, with and without an intermediate retailer, is price dependent. Specifically, we examine how uncertainty in supply capacity affects optimal ordering and pricing decisions, supplier and retailer profits, and the incentives to reduce such uncertainty. When two suppliers sell through a monopolistic retailer, supply uncertainty not only affects the retailer's diversification strategy for replenishment, but also changes the suppliers’ wholesale price competition and the incentive for reducing capacity uncertainty. In this dual‐sourcing model, we show that the benefit of reducing capacity uncertainty depends on the cost heterogeneity between the suppliers. In addition, we show that a supplier does not necessarily benefit from capacity variability reduction. We contrast this incentive misalignment with findings from the single‐supplier case and a supplier‐duopoly case where both suppliers sell directly to market without the monopolistic retailer. In the latter single‐supplier and duopoly cases, we prove that the unreliable supplier always benefits from reducing capacity variability. These results highlight the role of the retailer's diversification strategy in distorting a supplier's incentive for reducing capacity uncertainty under supplier price competition.     

Impact of Demand Uncertainty on Stability of Supplier Alliances in Assembly Models

In their recent paper, Nagarajan and Sošić study an assembly supply chain in which n suppliers sell complementary components to a downstream assembler, who faces a price‐sensitive deterministic demand. Suppliers may form alliances, and each alliance then sells a kit of components to the assembler and determines the price for that kit. The assembler buys the components (kits) from the alliances and sets the selling price of the product. Nagarajan and Sošić consider three modes of competition—supplier Stackelberg, vertical Nash (VN), and assembler Stackelberg models—which correspond to different power structures in the market, and study stable supplier alliances when the assembler faces linear and isoelastic demand. In this paper, we study the impact that demand uncertainty has on stability results obtained in Nagarajan and Sošić. We first analyze models in which all decisions are made before the uncertainty is resolved, and show that the alliance of all suppliers remains stable when demand is isoelastic, or under Stackelberg models when demand is linear. However, demand uncertainty may change stability results when both parties make decisions simultaneously (VN model) and demand is linear. We then extend our results by considering scenarios in which some decisions may be postponed and made after the actual demand is known. When the ordering quantity can be determined after observing the true demand, we show that stable outcomes correspond to those obtained in the deterministic case and uncertainty has no impact on coalition stability; if only the assembler's pricing decision is postponed, we need additional conditions for stability results to carry over in the additive demand model.     

What Happens When Wal‐Mart Comes to Town: An Empirical Analysis of the Discount Retailing Industry

In the past few decades multistore retailers, especially those with 100 or more stores, have experienced substantial growth. At the same time, there is widely reported public outcry over the impact of these chain stores on other retailers and local communities. This paper develops an empirical model to assess the impact of chain stores on other discount retailers and to quantify the size of the scale economies within a chain. The model has two key features. First, it allows for flexible competition patterns among all players. Second, for chains, it incorporates the scale economies that arise from operating multiple stores in nearby regions. In doing so, the model relaxes the commonly used assumption that entry in different markets is independent. The lattice theory is exploited to solve this complicated entry game among chains and other discount retailers in a large number of markets. It is found that the negative impact of Kmart's presence on Wal‐Mart's profit was much stronger in 1988 than in 1997, while the opposite is true for the effect of Wal‐Mart's presence on Kmart's profit. Having a chain store in a market makes roughly 50% of the discount stores unprofitable. Wal‐Mart's expansion from the late 1980s to the late 1990s explains about 40–50% of the net change in the number of small discount stores and 30–40% for all other discount stores. Scale economies were important for Wal‐Mart, but less so for Kmart, and the magnitude did not grow proportionately with the chains' sizes.     

Competing on Time: An Integrated Framework to Optimize Dynamic Time‐to‐Market and Production Decisions

This study develops a comprehensive framework to optimize new product introduction timing and subsequent production decisions faced by a component supplier. Prior to market entry, the supplier performs process design activities, which improve manufacturing yield and the chances of getting qualified for the customer's product. However, a long delay in market entry allows competitors to enter the market and pass the customer's qualification process before the supplier, reducing the supplier's share of the customer's business. After entering the market and if qualified, the supplier also needs to decide how much to produce for a finite planning horizon by considering several factors such as manufacturing yield and stochastic demand, both of which depend on the earlier time‐to‐market decision. To capture this dependency, we develop a sequential, nested, two‐stage decision framework to optimize the time‐to‐market and production decisions in relation to each other. We show that the supplier's optimal market entry and qualification timing decision need to be revised in real time based on the number of qualified competitors at the time of market‐entry decision. We establish the optimality of a threshold policy. Following this policy, at the beginning of each decision epoch, the supplier should optimally stop preparing for qualification and decide whether to enter the market if her order among qualified competitors exceeds a predetermined threshold. We also prove that the supplier's optimal production policy is a state‐dependent, base‐stock policy, which depends on the time‐to‐market and qualification decisions. The proposed framework also enables a firm to quantify how market conditions (such as price and competitor entry behavior) and operating conditions (such as the rate of learning and inventory/production‐related costs) affect time‐to‐market strategy and post‐entry production decisions.     

Fooled by Local Robustness

One would have expected the considerable public debate created by Nassim Taleb’s two best selling books on uncertainty, Fooled by Randomness and The Black Swan, to inspire greater caution to the fundamental difficulties posed by severe uncertainty. Yet, methodologies exhibiting an incautious approach to uncertainty have been proposed recently in a range of publications. So, the objective of this short note is to call attention to a prime example of an incautious approach to severe uncertainty that is manifested in the proposition to use the concept radius of stability as a measure of robustness against severe uncertainty. The central proposition of this approach, which is exemplified in info‐gap decision theory, is this: use a simple radius of stability model to analyze and manage a severe uncertainty that is characterized by a vast uncertainty space, a poor point estimate, and a likelihood‐free quantification of uncertainty. This short discussion serves then as a reminder that the generic radius of stability model is a model of local robustness. It is, therefore, utterly unsuitable for the treatment of severe uncertainty when the latter is characterized by a poor estimate of the parameter of interest, a vast uncertainty space, and a likelihood‐free quantification of uncertainty.     

Economic Evaluation of Scale Dependent Technology Investments

We study the effect of financial risk on the economic evaluation of a project with capacity decisions. Capacity decisions have an important effect on the project̂s value through the up‐front investment, the associated operating cost, and constraints on output. However, increased scale also affects the financial risk of the project through its effect on the operating leverage of the investment. Although it has long been recognized in the finance literature that operating leverage affects project risk, this result has not been incorporated in the operations management literature when evaluating projects. We study the decision problem of a firm that must choose project scale. Future cash flow uncertainty is introduced by uncertain future market prices. The firm's capacity decision affects the firm's potential sales, its expected price for output, and its costs. We study the firm's profit maximizing scale decision using the CAPM model for risk adjustment. Our results include that project risk, as measured by the required rate of return, is related to the inverse of the expected profit per unit sold. We also show that project risk is related to the scale choice. In contrast, in traditional discounted cash flow analysis (DCF), a fixed prescribed rate is used to evaluate the project and choose its scale. When a fixed rate is used with DCF, a manager will ignore the effect of scale on risk and choose suboptimal capacity that reduces project value. S/he will also misestimate project value. Use of DCF for choosing scale is studied for two special cases. It is shown that if the manager is directed to use a prescribed discount rate that induces the optimal scale decision, then the manager will greatly undervalue the project. In contrast, if the discount rate is set to the risk of the optimally‐scaled project, the manager will undersize the project by a small amount, and slightly undervalue the project with the economic impact of the error being small. These results underline the importance of understanding the source of financial risk in projects where risk is endogenous to the project design.     

网络购物平台最优价格促销策略：价格折扣还是现金券？

平台价格促销已成为平台营销中的重要问题，不同于传统模式下商家决定商品的价格促销，平台价格促销是以平台作为价格促销的决策者。本文建立了平台价格折扣和现金券两种由平台作为决策者的价格促销模型，探讨平台的最优价格促销策略，并在原有模型的基础上考虑商家广告投资决策并构建了扩展模型。研究结果表明，(1)只有当商品的基本需求、消费者价格敏感度和商品价格满足一定条件时，平台价格促销才能提升平台利润。(2)在两种价格促销都能提升平台利润情况下，平台的交易费率较高且商品价格较低则平台实施现金券策略的利润高于价格折扣策略；其他情况下平台实施价格折扣策略的利润高于现金券策略。(3)在平台交易费率较低的情况下，价格折扣策略下消费者能得到更多优惠；否则现金券策略下消费者得到更多优惠。(4)增加商家广告投资能够提高平台促销活动的利润，但是平台的价格优惠降低，且平台实施价格促销活动的条件区间减小。     

Incentive Contracts in Projects with Unforeseeable Uncertainty

Designing incentive contracts that constructively guide employee efforts is a particularly difficult challenge in novel innovation initiatives, where unforeseen events may occur. Empirical studies have observed a variety of incentive structures in innovation settings: “time and material contracts” (compensation for executing orders), “downside protection” (target‐driven incentives with protection from unexpected risks), and “upside rewards” (additional remuneration for pursuing opportunities). This paper develops a model of incentives in presence of unforeseen events and offers a theoretical prediction of which of the empirically observed incentive structures should be used under which circumstances. The combination of three key influences drives the shape of the best incentive contract. First, the presence of unforeseeable uncertainty, or the occurrence of events that cannot possibly be foreseen at the outset. These may force a change in the project's plan, making pure target setting insufficient. Second, fairness concerns dictate that the employee's expected compensation cannot be shifted downward by unforeseen events, because it would cause demotivation, hostility, and defection. Third, management may not be able to observe the detailed actions of the employee (moral hazard) nor whether a positive or negative unforeseen event has occurred (asymmetric information).     

Dynamic Preference for Flexibility

We consider a decision maker who faces dynamic decision situations that involve intertemporal trade‐offs, as in consumption–savings problems, and who experiences taste shocks that are transient contingent on the state of the world. We axiomatize a recursive representation of choice over state contingent infinite horizon consumption problems, where uncertainty about consumption utilities depends on the observable state and the state follows a subjective Markov process. The parameters of the representation are the subjective process that governs the evolution of beliefs over consumption utilities and the discount factor; they are uniquely identified from behavior. We characterize a natural notion of greater preference for flexibility in terms of a dilation of beliefs. An important special case of our representation is a recursive version of the Anscombe–Aumann model with parameters that include a subjective Markov process over states and state‐dependent utilities, all of which are uniquely identified.     

Responsive Pricing Newsvendor Networks with Discretionary Commonality

Discretionary commonality is a form of operational flexibility used in multi‐product manufacturing environments. Consider a case where a firm produces and sells two products. Without discretionary commonality, each product is made through a unique combination of input and production capacity. With discretionary commonality, one of the inputs could be used for producing both products, and one of the production capacities could be used to process different inputs for producing one of the products. In the latter case, the manager can decide, upon the realization of uncertainty, not only the quantities for different products (outputs) but also the means of transforming inputs into outputs. The objective of this study is to understand how the firm's value, its inventory levels for inputs and capacity levels for resources are affected by the demand characteristics and market conditions. In pursuing this research, we extend Van Mieghem and Rudi ( 2002 )'s newsvendor network model to allow for the modeling of product interdependence, demand functions, random shocks, and firm's ex post pricing decision. Applying the general framework to the network with discretionary commonality, we discover that inventory and capacity management can be quite different compared to a network where commonality is non‐discretionary. Among other results, we find that as the degree of product substitution increases, the relative need for discretionary commonality increases; as the market correlation increases, while the firm's value may increase for complementary products, the discretionary common input decreases but the dedicated input increases. Numerical study shows that discretionary flexibility and responsive pricing are strategic substitutes.