Knowledge that Transforms

Intertemporal decision making under risk involves two dimensions: time preferences and risk preferences. This paper focuses on the impact of time on risk preferences, independent of the intertemporal trade-off of outcomes, i.e., time preferences. It reports the results of an experimental study that examines how delayed resolution and payment of risky options influence individual choice. We used a simple experimental design based on the comparison of two-outcome monetary lotteries with the same delay. Raw data clearly reveal that subjects become more risk tolerant for delayed lotteries. Assuming a prospect theory–like model under risk, we analyze the impact of time on utility and decision weights, independent of time preferences. We show that the subjective treatment of outcomes (i.e., utility) is not significantly affected by time. In fact, the impact of time is completely absorbed by the probability weighting function. The effect of time on risk preferences was found to generate probabilistic optimism resulting in a higher risk tolerance for delayed lotteries.

We consider an assemble-to-order system in which multiple products are assembled from a common component and a set of product-dedicated components. Component capacities are chosen prior to a finite-horizon selling season, and the common component is allocated to the products based on observed demands. We propose a collection of allocation mechanisms involving varying degrees of demand aggregation, ranging from a scheme under which all demands are observed prior to making the allocation decision to allocations made for each arriving demand. In this context, we explore the impact of the allocation scheme on sales, profits, and capacity decisions, including the degree of capacity imbalance. We find that the benefit from increased demand aggregation is closely linked to the degree of capacity imbalance: profit gains from delayed allocation tend to be higher in systems in which the optimal capacity portfolio is highly unbalanced when the allocation decision is made after observing all demands. We develop insights into what detailed system parameters lead to the largest gains from demand aggregation and also explore the trade-offs associated with the choice of an allocation scheme when customers exhibit impatience if the allocation scheme forces them to wait to be served.

Many markets have historically been dominated by a small number of best-selling products. The Pareto principle, also known as the 80/20 rule, describes this common pattern of sales concentration. However, information technology in general and Internet markets in particular have the potential to substantially increase the collective share of niche products, thereby creating a longer tail in the distribution of sales. This paper investigates the Internet's “long tail” phenomenon. By analyzing data collected from a multichannel retailer, it provides empirical evidence that the Internet channel exhibits a significantly less concentrated sales distribution when compared with traditional channels. Previous explanations for this result have focused on differences in product availability between channels. However, we demonstrate that the result survives even when the Internet and traditional channels share exactly the same product availability and prices. Instead, we find that consumers' usage of Internet search and discovery tools, such as recommendation engines, are associated with an increase the share of niche products. We conclude that the Internet's long tail is not solely due to the increase in product selection but may also partly reflect lower search costs on the Internet. If the relationships we uncover persist, the underlying trends in technology portend an ongoing shift in the distribution of product sales.

A fast fashion system combines quick response production capabilities with enhanced product design capabilities to both design “hot” products that capture the latest consumer trends and exploit minimal production lead times to match supply with uncertain demand. We develop a model of such a system and compare its performance to three alternative systems: quick-response-only systems, enhanced-design-only systems, and traditional systems (which lack both enhanced design and quick response capabilities). In particular, we focus on the impact of each of the four systems on “strategic” or forward-looking consumer purchasing behavior, i.e., the intentional delay in purchasing an item at the full price to obtain it during an end-of-season clearance. We find that enhanced design helps to mitigate strategic behavior by offering consumers a product they value more, making them less willing to risk waiting for a clearance sale and possibly experiencing a stockout. Quick response mitigates strategic behavior through a different mechanism: by better matching supply to demand, it reduces the chance of a clearance sale. Most importantly, we find that although it is possible for quick response and enhanced design to be either complements or substitutes, the complementarity effect tends to dominate. Hence, when both quick response and enhanced design are combined in a fast fashion system, the firm typically enjoys a greater incremental increase in profit than the sum of the increases resulting from employing either system in isolation. Furthermore, complementarity is strongest when customers are very strategic. We conclude that fast fashion systems can be of significant value, particularly when consumers exhibit strategic behavior.

Product variety is an important strategic tool that firms can use to attract customers and respond to competition. This study focuses on the retail industry and investigates how stores manage their product variety, contingent on the presence of competition and their actual distance from rivals. Using a unique data set that contains all Best Buy and Circuit City stores in the United States, the authors find that a store's product variety (i.e., number of stock-keeping units) increases if a rival store exists in its market but, in the presence of such competition, decreases when the rival store is collocated (within one mile of the focal store). Moreover, collocated rival stores tend to differentiate themselves by overlapping less in product range than do noncollocated rivals. This smaller and more differentiated product variety may be because of coordinated interactions between collocated stores. In summary, this paper presents evidence of both coordination and competition in retailers' use of product variety.

We analyze the competitive capacity investment timing decisions of both established firms and start-ups entering new markets, which have a high degree of demand uncertainty. Firms may invest in capacity early (when uncertainty is high) or late (when uncertainty has been resolved), possibly at different costs. Established firms choose an investment timing and capacity level to maximize expected profits, whereas start-ups make those choices to maximize the probability of survival. When a start-up competes against an established firm, we find that when demand uncertainty is high and costs do not decline too severely over time, the start-up takes a leadership role and invests first in capacity, whereas the established firm follows; by contrast, when two established firms compete in an otherwise identical game, both firms invest late. We conclude that the threat of firm failure significantly impacts the dynamics of competition involving start-ups.

We investigate how auctioneers set reserve prices in auctions. A well-established theoretical result, assuming risk neutrality of the seller, is that the optimal reserve price should not depend on the number of participating bidders. In a set of controlled laboratory experiments, we find that seller behavior often deviates from the theoretical benchmarks. We extend the existing theory to explore three alternative explanations for our results: risk aversion, anticipated regret, and probability weighting. After fitting our data to each of these models through parameter estimation techniques on both an aggregate and individual level, we find that all three models are consistent with some of the characteristics of our data, but that the regret model provides a slightly more favorable fit overall.

Although relative performance schemes are pervasive in organizations, reliable empirical data on induced sabotage behavior are almost nonexistent. We study sabotage in repeated tournaments in a controlled laboratory experiment and observe that effort and sabotage are higher for higher wage spreads. Additionally, we find that also in the presence of tournament incentives, agents react reciprocally to higher wages by exerting higher effort. Destructive activities are reduced by explicitly calling them by their name “sabotage.” Communication among principal and agents can curb sabotage when they agree on flat prize structures and increased output. If sabotage is not possible, the principals choose tournament incentives more often.

In recent years, vendor liability for software security vulnerabilities has been the center of an important debate in the software community and a topic gaining government attention in legislative committees and hearings. The importance of this question surrounding vendor security liability is amplified when one considers the increasing emergence of zero-day attacks where hackers take advantage of vulnerabilities before the software vendor has a chance to release protective patches. In this paper, we compare the effectiveness of three software liability policies: vendor liability for damages, vendor liability for patching costs, and government imposed security standards. We find that vendor liability for losses is not effective in improving social welfare in the short run, while liability for patching costs can be effective if either patching costs are large and the likelihood of a zero-day attack is low, or patching costs are small and zero-day likelihood is high. In the long run, when the vendor can invest in reducing the likelihood of security vulnerabilities, loss liability is still ineffective when the zero-day attack probability is high but can increase both vendor investment in security and social welfare when zero-day attack likelihood is sufficiently low. When the zero-day attack probability is high, patch liability is ineffective if user patching costs are large, but partial patch liability can boost vendor investment and improve welfare when patching costs are small. In contrast, in an environment with low zero-day attack probability, full vendor patch liability can be optimal. Finally, comparing the effectiveness of the three liability policies under study, we find that government imposed standards on software security investment can be preferable to both patching and loss liability on the vendor, if zero-day attack likelihood is sufficiently low. However, if zero-day attacks are a common occurrence and patching costs are not too high, partial patch liability is the most effective policy.

This paper analyzes the interactions between competitive (wholesale) spot, retail, and forward markets and vertical integration in electricity markets. We develop an equilibrium model with producers, retailers, and traders to study and quantify the impact of forward markets and vertical integration on prices, risk premia, and retail market shares. We point out that forward hedging and vertical integration are two separate mechanisms for demand and spot price risk diversification that both reduce the retail price and increase retail market shares. We show that they differ in their impact on prices and firms' utility because of the asymmetry between production and retail segments. Vertical integration restores the symmetry between producers' and retailers' exposure to demand risk, whereas linear forward contracts do not. Vertical integration is superior to forward hedging when retailers are highly risk averse. We illustrate our analysis with data from the French electricity market.