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A significant proportion of the world's population has no access to grid-based electricity and so relies on off-grid lighting solutions. Rechargeable lamp technology is gaining popularity as an alternative off-grid lighting model in developing countries. In this paper, we explore consumer behavior and the operational inefficiencies that result under this model. Specifically, we are interested in (i) measuring the impact of inconvenience (of traveling to recharge the lamp) along with the impact of liquidity constraints (because of poverty) on lamp usage and (ii) evaluating the efficacy of strategies that address these factors. We build a structural model of consumers' recharge decisions that incorporates several operational features of the low-income regions. We conducted large-scale field experiments in Rwanda in partnership with a local rechargeable lamp operator and use the resultant data to estimate and test our model. We find that the complete removal of inconvenience and liquidity constraints from the current business model results in 73% and 126% increases in both recharges and revenue, thereby suggesting that these constraints are major sources of inefficiency. By implementing simple operations-based strategies—such as starting more recharge centers, visiting consumers periodically to collect their lamps for recharge, and allowing consumers to partially recharge their lamps and pay flexibly for the recharge—more than half the benefit of completely eliminating the inefficiencies can be attained. By contrast, the price- and capacity-based strategies that vary the economic variables (i.e., the amount paid per recharge and the amount of light obtained in return) but not the operational model perform far worse than the aforementioned strategies. Overall, our analysis emphasizes the importance of managing operations effectively even in markets with cash-constrained consumers, in which firms may have a natural tendency to focus more on reducing prices. This paper was accepted by Vishal Gaur, operations management. Funding: This work was supported by the International Growth Centre; Wharton School, University of Pennsylvania; and Institut Européen d'Administration des Affaires; The Environment and Energy Partnership; Grand Challenges Canada (Stars in Global Health); INSEAD's Emerging Markets Institute; INSEAD's Randomized Controlled Trials Laboratory; the INSEAD–Wharton Alliance, and International Growth Centre. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4844. [ABSTRACT FROM AUTHOR]

Problem definition: A large proportion of the world's population has no access to electricity and so relies on noxious kerosene for their lighting needs. Solar-based solutions require a large up-front investment and are often unaffordable in this market owing to consumers' tight liquidity constraints. As an alternative, there are business models relying on rechargeable light bulbs that are sold at a subsidized price (which renders them affordable) and require regular micropayments for recharges (which eases liquidity constraints). These bulbs provide a cheaper and healthier light source than kerosene, yet their adoption is lower than expected, and some consumers continue to use kerosene. This paper explores the potential drivers of such preferences and proposes strategies to alter them. Academic/practical relevance: Unlike most of the existing operations management literature, which focuses on the problems in developed economies, our paper studies a problem specific to the poor population. Our novel modeling approach, which incorporates several operational features of the impoverished regions, could also serve as a template for other potential modeling attempts in similar settings. Methodology: We propose a stylized consumer behavior model that accounts for—in addition to the monetary cost incurred while using a particular light source—the inconvenience cost (resulting from repeated travel to the purchase center) and blackout cost (resulting from liquidity constraints) associated with that source to explain the consumer preference for kerosene and to recommend strategies that could mitigate that preference. Results: Although kerosene lighting is more expensive than bulbs, consumers who face either high inconvenience costs or high blackout costs prefer kerosene to bulbs because the former's flexibility, with regard to quantity, helps reduce whichever cost is dominating. At the firm level, there is an optimal bulb capacity and recharge price pair that maximizes the firm's revenue; furthermore, a firm can reverse the preferences for kerosene by increasing the flexibility of the bulbs (e.g., by allowing partial recharges). Although strategies—such as price discounts and mobile micropayments—which alleviate liquidity constraints are not in themselves sufficient to ensure higher adoption rates, increased bulb use becomes more likely when they are combined with inconvenience-reducing strategies. Managerial implications: Our paper sheds light on the structure of the market in which firms operate by identifying the characteristics of the market segments that prefer kerosene. It also helps the firms make better decisions by evaluating the efficacy of several strategies in terms of increasing the adoption of bulbs. The online appendix is available at https://doi.org/10.1287/msom.2017.0673. This paper has been accepted for the Manufacturing & Service Operations Management Special Issue on Value Chain Innovations in Developing Economies. [ABSTRACT FROM AUTHOR]

The integration of systems for pricing and revenue management must trade off potential revenue gains against significant practical and technical challenges. This dilemma motivates us to investigate the value of coordinating decisions on prices and capacity allocation in a stylized setting. We propose two pairs of sequential policies for making static decisions—on pricing and revenue management—that differ in their degree of integration (hierarchical versus coordinated) and their pricing inputs (deterministic versus stochastic). For a large class of stochastic, price-dependent demand models, we prove that these four heuristics admit tractable solutions satisfying intuitive sensitivity properties. We further evaluate numerically the performance of these policies relative to a fully coordinated model, which is generally intractable. We find it interesting that near-optimal performance is usually achieved by a simple hierarchical policy that sets prices first, based on a nonnested stochastic model, and then uses these prices to optimize nested capacity allocation. This tractable policy largely outperforms its counterpart based on a deterministic pricing model. Jointly optimizing price and allocation decisions for the high-end segment improves performance, but the largest revenue benefits stem from adjusting prices to account for demand risk. [ABSTRACT FROM AUTHOR]

In a repeat business context, past experiences with a service provider affect customers' decisions to renew their contract. How should a strategic firm manage customized service over time to maximize the long-term value from each customer relationship? We propose a dynamic model that relies on behavioral theories and empirical evidence to capture the effect of past service experiences on service quality expectations, customer satisfaction, and retention. Although firms can benefit from managing service expectations at the beginning of a relationship, we find that varying service in the long run is not optimal. Behavioral regularities explain the structure of optimal service policies and limit the value of responsive service. Loss aversion expands the range of optimal constant policies; however, if satisfying experiences are more salient, then firms should constantly vary service levels. Loyal or high-margin customers need not warrant better service; those who anchor less on past service experiences do-provided that retention is improved by better past experiences. The effect of customer memory on service levels is determined by whether habituation or rather goodwill drives defection decisions. [ABSTRACT FROM AUTHOR]

We characterize the effect of anticipated regret on consumer decisions and on firm profits and policies in an advance selling context where buyers have uncertain valuations. Advance purchases trigger action regret if valuations turn out to be lower than the price paid, whereas delaying purchase may cause inaction regret from missing a discount or facing a stockout. Consumers whom we describe as emotionally rational act strategically in response to the firm's policies and in anticipation of regret. In this context, regret explains two types of behavioral patterns: inertia (delayed purchase) and frenzies (buying early at negative surplus). We show how firms should optimally respond to consumer regret and also characterize a normative regret threshold above which they should not advance sell. Action regret reduces profits as well as the value of advance selling and booking limit policies for price-setting firms; inaction regret has the opposite effects. These effects are diminished by capacity constraints and are reversed for firms facing price pressure in the advance period (owing, e.g., to competition or market heterogeneity). Regret heterogeneity explains premium advance selling for the capacity constrained firm, which may benefit from larger shares of regretful buyers. Finally, we show how the negative effects of regret on profits can be mitigated by regret-priming marketing campaigns and by offering refunds or options or allowing resales. Our results highlight the importance of assessing the relative strength of regret within and across market segments and of accounting for these factors in pricing and marketing policies. [ABSTRACT FROM AUTHOR]

We study the dynamic pricing implications of a new, behaviorally motivated reference price mechanism based on the peak-end memory mode. This model suggests that consumers anchor on a reference price that is a weighted average of the lowest and most recent prices. Loss-averse consumers are more sensitive to perceived losses than gains relative to this reference price. We find that a range of constant pricing policies is optimal for the corresponding dynamic pricing problem. This range is wider the more consumers anchor on lowest prices, and it persists when buyers are loss neutral, in contrast with previous literature. In a transient regime, the optimal pricing policy is monotone and converges to a steady-state price, which is lower the more extreme and salient the low-price anchor is. Our results suggest that behavioral regularities, such as peak-end anchoring and loss aversion, limit the benefits of varying prices, and caution that the adverse effects of deep discounts on the firm's optimal prices and profits might be more enduring than previous models predict. [ABSTRACT FROM AUTHOR]

We introduce a measure of elasticity of stochastic demand, called the elasticity of the lost-sales rate, which offers a unifying perspective on the well-known newsvendor with pricing problem. This new concept provides a framework to characterize structural results for coordinated and uncoordinated pricing and inventory strategies. Concavity and submodularity of the profit function, as well as sensitivity properties of the optimal inventory and price policies, are characterized by monotonicity conditions, or bounds, on the elasticity of the lost-sales rate. These elasticity conditions are satisfied by most relevant demand models in the marketing and operations literature. Our results unify and complement previous work on price-setting newsvendor models and provide a new tool for researchers modeling stochastic price-sensitive demand in other contexts. [ABSTRACT FROM AUTHOR]

An important challenge faced by media broadcasting companies is how to allocate limited advertising space between upfront (forward) contracts and the spot market (referred to in advertising as the scatter market) to maximize profits and meet contractual commitments. We develop stylized optimization models of airtime capacity planning and allocation across multiple clients under audience uncertainty. In a short-term profit maximizing setting, our results provide insight for capacity planning decisions upfront and during the broadcasting season. Our results suggest that broadcasting companies should prioritize upfront clients according to marginal revenue per audience unit. We find that accepted upfront market contracts can be aggregated across clients and served in proportion to the audience demanded. Closed-form solutions are obtained in a static setting. These results remain valid in a dynamic setting, when considering the opportunity to increase allocation by airing make-goods during the broadcasting season. Our structural results characterize the impact of contracting parameters, time, and audience uncertainty on profits and capacity decisions. The results hold under general audience and spot market profit models. Overall, we find that ignoring audience uncertainty can have a significant cost for media capacity planning and allocation. [ABSTRACT FROM AUTHOR]

We consider the dynamic pricing problem of a monopolist firm in a market with repeated interactions, where demand is sensitive to the firm's pricing history. Consumers have memory and are prone to human decision-making biases and cognitive limitations. As the firm manipulates prices, consumers form a reference price that adjusts as an anchoring standard based on price perceptions. Purchase decisions are made by assessing prices as discounts or surcharges relative to the reference price in the spirit of prospect theory. We prove that optimal pricing policies induce a perception of monotonic prices, whereby consumers always perceive a discount, respectively surcharge, relative to their expectations. The effect is that of a skimming or penetration strategy. The firm's optimal pricing path is monotonic on the long run, but not necessarily at the introductory stage. If consumers are loss averse, we show that optimal prices converge to a constant steady-state price, characterized by a simple implicit equation; otherwise, the optimal policy cycles. The range of steady states is wider the more loss averse consumers are. Steady-state prices decrease with the strength of the reference effect and with customers' memory, all else equal. Offering lower prices to frequent customers may be suboptimal, however, if these are less sensitive to price changes than occasional buyers. If managers ignore such long-term implications of their pricing strategy, the model indicates that they will systematically price too low and lose revenue. Our results hold under very general reference dependent demand models. [ABSTRACT FROM AUTHOR]

Firms can increase profitability by appropriately motivating managers. We investigate drivers of managerial motivation, and propose how firms can use performance pay to alter motivational patterns. We focus on the agent's optimal effort decision in trading off compensation utility with effort cost in a static and dynamic setting. Surprisingly, we find that lower risk aversion or increased pay are not necessarily motivating factors, and identify the relevant effort drivers underlying the agent's utility and compensation plan. We characterize properties of agents' preferences for output lotteries (risk aversion, aggressiveness, prudence) that trigger systematic motivational patterns with respect to a variety of factors, such as the agent's productivity and past performance, time to evaluation, the firm's capabilities, and market factors. Our insights are robust, holding under very general modeling assumptions on preferences, rewards, and the stochastic effort-output function. [ABSTRACT FROM AUTHOR]

We provide a method for deriving robust solutions to certain stochastic optimization problems, based on mean-covariance information about the distributions underlying the uncertain vector of returns. We prove that for a general class of objective functions, the robust solutions amount to solving a certain deterministic parametric quadratic program. We first prove a general projection property for multivariate distributions with given means and covariances, which reduces our problem to optimizing a univariate mean-variance robust objective. This allows us to use known univariate results in the multidimensional setting, and to add new results in this direction. In particular, we characterize a general class of objective functions (the so-called one- or two-point support functions), for which the robust objective is reduced to a deterministic optimization problem in one variable. Finally, we adapt a result from Geoffrion (1967a) to reduce the main problem to a parametric quadratic program. In particular, our results are true for increasing concave utilities with convex or concave-convex derivatives. Closed-form solutions are obtained for special discontinuous criteria, motivated by bonus- and commission-based incentive schemes for portfolio management. We also investigate a multiproduct pricing application, which motivates extensions of our results for the case of nonnegative and decision-dependent returns. [ABSTRACT FROM AUTHOR]

We provide an optimization framework for computing optimal upper and lower bounds on functional expectations of distributions with special properties, given moment constraints. Bertsimas and Popescu (Optimal inequalities in probability theory: a convex optimization approach. SIAM J. Optim. 2004. Forthcoming) have already shown how to obtain optimal moment inequalities for arbitrary distributions via semidefinite programming. These bounds are not sharp if the underlying distributions possess additional structural properties, including symmetry, unimodality, convexity, or smoothness. For convex distribution classes that are in some sense generated by an appropriate parametric family, we use conic duality to show how optimal moment bounds can be efficiently computed as semidefinite programs. In particular, we obtain generalizations of Chebyshev's inequality for symmetric and unimodal distributions and provide numerical calculations to compare these bounds, given higher-order moments. We also extend these results for multivariate distributions. [ABSTRACT FROM AUTHOR]

We investigate dynamic policies for allocating scarce inventory to stochastic demand for multiple fare classes, in a network environment so as to maximize total expected revenues. Typical applications include sequential reservations for an airline network, hotel, or car rental service. We propose and analyze a new algorithm based on approximate dynamic programming, both theoretically and computationally. This algorithm uses adaptive, nonadditive bid prices from a linear programming relaxation. We provide computational results that give insight into the performance of the new algorithm and the widely used bid-price control, for several networks and demand scenarios. We extend the proposed algorithm to handle cancellations and no-shows by incorporating oversales decisions in the underlying linear programming formulation. We report encouraging computational results that show that the new algorithm leads to higher revenues and more robust performance than bid-price control. [ABSTRACT FROM AUTHOR]

The idea of investigating the relation of option and stock prices based just on the no-arbitrage assumption, but without assuming any model for underlying price dynamics, has a long history in the financial economics literature. We introduce convex and, in particular semidefinite optimization methods, duality and complexity theory to shed new light on this relation. For the single stock problem, given moments of the prices of the underlying assets, we show that we can find best-possible bounds on option prices with general payoff functions efficiently, either algorithmically (solving a semidefinite optimization problem) or in closed form. Conversely, given observable option prices, we provide best-possible bounds on moments of the prices of the underlying assets, as well as on the prices of other options on the same asset by solving linear optimization problems. For options that are affected by multiple stocks either directly (the payoff of the option depends on multiple stocks) or indirectly (we have information on correlations between stock prices), we find nonoptimal bounds using convex optimization methods. However, we show that it is NP-hard to find best possible bounds in multiple dimensions. We extend our results to incorporate transaction costs. [ABSTRACT FROM AUTHOR]