Highlights•Strategic consumer behavior has been extensively studied in the Management Science and Operations Management community.•We survey recent developments in the literature and review possible ...operational strategies and decisions to counteract the adverse impact of strategic consumer behavior.•Specifically, we broadly characterize these decisions into three classes – Pricing, Inventory, and Information – and further discuss the influence of strategic consumer behavior on these decisions and their underlying mechanisms on counteracting consumers’ strategic waiting behavior.
Strategic consumer behavior has been extensively studied in the Management Science and Operations Management community. We survey recent developments in the literature and review possible operational strategies and decisions to counteract the adverse impact of strategic consumer behavior. Specifically, we broadly characterize these decisions into three classes – Pricing, Inventory, and Information – and further discuss the influence of strategic consumer behavior on these decisions and their underlying mechanisms on counteracting consumers’ strategic waiting behavior.
This paper studies the potential benefits of responsive pricing and demand learning to sellers of seasonal fashion goods. As typical in such markets, demand uncertainty is high at the beginning of a ...season, but there is a potential opportunity to learn about demand via early sales observations. Additionally, although the consumers have general preference for purchasing a fashion product earlier rather than later in the season, they may exhibit strategic behavior—contemplating the benefits of postponing their purchase in anticipation of end-of-season discounts. Our results demonstrate that the benefits of responsive pricing, in comparison with a benchmark case of a fixed-price policy, depend sharply on the nature of the consumers’ behavior. Interestingly, in stark contrast to markets of myopic consumers, when the consumers are all strategic, the benefits of responsive pricing tend to worsen when there is a higher potential for learning. We explain this counterintuitive outcome by pointing to two phenomena: the spread effect and information shaping. For example, sellers of fashion products that consider upgrading their pricing systems to incorporate “
accurate response
” strategies (i.e., integrating learning and responsive pricing) should be aware of the possibility that such action might lead them to a new and potentially worse equilibrium, particularly when there is a higher opportunity to learn. Despite the fact that price commitment completely eliminates the seller’s ability to learn, it appears to increasingly dominate responsive pricing as the portion of strategic consumers in the market increases. But, although performing better than responsive pricing, a price-commitment policy is typically limited in performing effective discrimination. Finally, we studied the potential benefits of quick response strategies—ones that embed both dynamic pricing and quick inventory replenishment during the sales season—and found that they are particularly significant under strategic consumer behavior. We explain this result by arguing that quick response provides the seller with a real option that serves as an effective implicit threat to the consumers: encouraging them to buy earlier at premium prices rather than wait for discounts at the end of the season.
The online appendix is available at
https://doi.org/10.1287/mnsc.2018.3114
.
This paper was accepted by Martin Lariviere, operations management.
Motivated by emerging industry practices, this paper studies the effectiveness of a new advance selling strategy in counteracting strategic consumer behavior: the preorder contingent production (PCP) ...strategy, where the seller's production decision is contingent on an advance selling target. We find that compared to other advance selling strategies, such as the traditional advance selling strategy and the capacity rationing strategy, the PCP strategy is effective in mitigating strategic waiting behavior and thus can significantly improve the seller's profit performance, especially when consumers’ discount factor is at a medium or high level, the production cost is not too high, or the market size has an unbalanced probability distribution. Moreover, when the market size is deterministic, we show that the PCP strategy can completely eliminate strategic waiting behavior and attain the seller's profit performance under myopic consumer behavior. Finally, we demonstrate that the benefits of the PCP strategy are robust under other model considerations.
In this paper, we study how provision of product information and/or market information affects buyers' and sellers' behavior and the resultant sales in an online marketplace. We first identify the ...Pareto‐dominant equilibrium for the sellers' pricing decisions. Then, we study the impact of market parameters on the sales of the platform in equilibrium, under various information structures. We find that the platform's sales increase with the size of potential buyers but change nonmonotonically with the size of potential sellers. Next, we analytically characterize the platform's optimal information strategy as a function of the underlying market parameters. We find that while it is always optimal for the platform to reveal some information, it should be strategic about which information to reveal when faced with different supply and demand conditions. In particular, in a seller's market (high ratio of potential buyers to sellers), the platform should provide both product and market information to the sellers and buyers. However, in a buyer's market (low ratio of potential buyers to sellers), it is optimal for the platform to only provide the market information—providing both the product and market information would backfire on the platform by jeopardizing its sales.
Congestion derivatives for a traffic bottleneck Yao, Tao; Friesz, Terry L.; Wei, Mike Mingcheng ...
Transportation research. Part B: methodological,
12/2010, Letnik:
44, Številka:
10
Journal Article
Recenzirano
Historically, congestion pricing is considered to be an efficient mechanism used to decrease total social cost by charging users’ true costs including congestion externalities. Congestion pricing ...under uncertainty has been relatively little studied. In this paper, we review the literature on deterministic congestion pricing and introduce possible sources of uncertainty for a simple bottleneck. We show that, when prices involve exogenous uncertainty that is independent of the central authority and of individual drivers, total social cost may be expressed in closed form as a function of departure time and uncertainty. We also show that there is a class of financial derivatives based on congestion that have the potential to reduce total social cost. In particular, such derivatives are shown to have the potential to alter drivers’ departure behavior and reduce drivers’ risks of high variance in trip costs, including congestion tolls. Finally, numerical formulations and examples are given to justify the robustness of our results with respect to more general congestion uncertainty.
Supplier default is common in emerging markets. Suppliers under the threat of default have different objectives from profit‐seeking companies. This paper analytically tests how profit‐seeking or ...survival‐seeking behavior, single‐period or two‐period consideration, and buyer's subsidy influence the supplier's and buyer's final utilities. The results show that under single‐period consideration, the supplier's survival‐seeking strategy in fact drives more start‐ups or small suppliers out of business when the competition becomes severe; under two‐period consideration, no matter which strategy (profit‐seeking or survival‐seeking) the supplier selects, the second‐period price and profit are always higher than those of the first period. Furthermore, we find that providing subsidy is an effective way for buyer to keep suppliers’ competition at a certain level on the behalf of buyer's interest. By numerically estimating the benefits associated with the cost of subsidy, we provide a basis for understanding the cost–benefit analysis of buyer's subsidy strategy.
► We model a single bottleneck with pricing uncertainty and heterogeneous commuters. ► Pricing uncertainty may tremendously increase the total social cost. ► Financial derivatives have the potential ...to reduce the total social cost. ► A central planner may always induce socially optimal departure behavior. ► A market-based mechanism induces socially optimal departure behavior in specific instances.
Deterministic congestion pricing has attracted most attentions in the literature. But little attention has been given to pricing under uncertainty, especially for heterogeneous commuters. In this paper, we investigate congestion externalities by considering commuters’ risk preferences and heterogeneity. In particular, when price involves exogenous uncertainty which is independent of both central authority and individual commuters, we are able to express commuters’ departure equilibria and the total social cost in closed-form in terms of the departure time and uncertainty. Moreover, we find that uncertainty will lead heterogeneous risk-averse commuters not only to avoid traveling at the time when uncertainty level is high, but also to deviate from their optimal departure sequence. Hence, we are able to show that uncertainty can tremendously increase the total social cost. Furthermore, we also prove that both the central planner and the market-base mechanism have the potential to reduce the total social cost and alter commuters’ departure behavior. Specifically, we find out that the central planner can always find a class of financial derivatives to induce the socially optimal departure behavior, while the market-based mechanism may do so at specific cases. Finally, numerical formulation and experiments are given to assess the robustness of our results for more general forms of uncertainties and derivatives.
We propose a minimax concave penalized multi-armed bandit algorithm under
generalized linear model (G-MCP-Bandit) for a decision-maker facing
high-dimensional data in an online learning and ...decision-making process. We
demonstrate that the G-MCP-Bandit algorithm asymptotically achieves the optimal
cumulative regret in the sample size dimension T , O(log T), and further
attains a tight bound in the covariate dimension d, O(log d). In addition, we
develop a linear approximation method, the 2-step weighted Lasso procedure, to
identify the MCP estimator for the G-MCP-Bandit algorithm under non-iid
samples. Under this procedure, the MCP estimator matches the oracle estimator
with high probability and converges to the true parameters with the optimal
convergence rate. Finally, through experiments based on synthetic data and two
real datasets (warfarin dosing dataset and Tencent search advertising dataset),
we show that the G-MCP-Bandit algorithm outperforms other benchmark algorithms,
especially when there is a high level of data sparsity or the decision set is
large.
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