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This study examines the effects of non-additivity valuations on cash flows and sales growth by using the Choquet fuzzy approach as a pooled non-additive integral. The study targets 62 parent companies with 322 subsidiaries in the Tehran stock market from 2011 to 2019. The study divides firms' assets into four categories (inventories, receivables, fixed, and long-term investments). It uses the Choquet integral's properties to determine the firms' total non-additivity values. The Choquet integral approach used in this study considers the synergy of a set's componential factors in different measurements by considering and implementing the weights and coefficients of the elements. The results indicate that while the market valuation of companies (based on non-additivity valuations) has no significant correlation with their operating cash flows, it has a positive correlation with their sales growth, which could be attributed to the synergy created by the business combination of the parent companies with their subsidiaries. Moreover, the findings show a significant correlation between sales growth and the market price-to-book ratio as a simple approach to measuring a company's performance. However, the results suggest that non-additivity valuations offer a better estimation in measuring the efficiency of companies than market price-to-book ratio-based approaches. Indeed, the valuations of companies are determined based on their abilities in using their resources compared to similar companies in the same industry when they use non-additivity valuations. These findings are expected to be very helpful for potential investors and shareholders. [ABSTRACT FROM AUTHOR]

The strategy and marketing of ski resorts in the European Alps are still characterized by growth orientation and the extension of technical snow production to ensure high snow reliability. In contrast, this study hypothesizes that besides a high‐performing ski resort, winter ski vacationists have further expectations for activities during their stay varying by their skiing skill level and personal preferences. The present study surveys 1413 Alpine ski tourists to identify their daily routines, activities, and used ski resort services. The findings uncover significant differences in the activity profiles with six clearly distinguishable guest segments. The results highlight the need for custom‐tailored product development considering the complete set of winter experiences as well as pricing strategies that specifically target the identified segments. Furthermore, the findings underline that ski resort and destination managers need to include daily changes in snow and weather conditions in their strategies considering climate and demographic change. [ABSTRACT FROM AUTHOR]

Accepted by: Konstantinos Nikolopoulos This research studies the pricing strategy of a supply chain with a manufacturer and a retailer. We analyze the customers' purchase behaviour in advance selling with deposit by using utility theory and obtain the pricing decision of the supply chain by using the Stackelberg model. We find that when consumers are not sensitive to the distribution lead time, the retailer's advance selling strategy could Pareto improve the whole supply chain. Better informed consumers and moderate advance selling distribution lead time information could expand the retailer's pre-sale possibilities by enlarging the feasible range of advance selling. [ABSTRACT FROM AUTHOR]

With growing concerns about environmental sustainability, reverse supply chains play a crucial role in recycling, remanufacturing, and reducing waste. Investigating the power structure's impact on decisions related to acquisition prices and inventory management can provide insights into the integration of sustainable practices. This study analyzes a reverse supply chain with a supplier and a remanufacturer, in which the supplier provides the remanufacturer with sorted used cores. We investigate the acquisition pricing and inventory decisions under different power structures (supplierled Stackelberg model, remanufacturer-led Stackelberg model) through game theory analysis. We first establish the centralized model to explore optimal acquisition decisions and profit of the total system. Then we evaluate the acquisition quantity, wholesale prices, the profit of supplier and remanufacturer in the supplier-led model and in the remanufacturer-led model. Finally, the impact of power structure on the performance of supplier and remanufacturer are explored by numerical analysis, as well as the supply chain system. Our significant findings show that (i) the total profit of the supplier-led and remanufacturer-led reverse supply chain are less than the centralized model due to the double marginalization; (ii) the remanufacturer-led reverse supply chain is superior to the supplier-led model reverse supply chain; (iii) the selected grades in the supplier-led model is proved the same as that in the remanufacturer-led model, which could not been affected by the wholesale price; (iv) the greater the inventory risk (the uncertainty in demand), the greater the profit share for the remanufacturer (supplier) in the supplier-led (remanufacturer-led) model. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to provide a theoretically rigorous and practically relevant summary of research findings that enables managers to drive sustainable profits improvements via pricing. It showcases multiple case studies that demonstrate how companies can achieve higher-than-average profitability by implementing intelligent pricing strategies and tactics. Design/methodology/approach: Over the past 20 years, this writer has conducted dozens of academic surveys with managers exploring the antecedents, moderators and consequences of pricing practices for existing and new products. The writer has analyzed all pricing research published in leading academic journals over the past decades. Finally, as equity partner of Hinterhuber & Partners, a pricing consultancy (www.hinterhuber.com), this writer – through collaborations with companies and workshops conducted with practicing managers – has collected data and insights on best practices in managing pricing as a strategic activity. Findings: Pricing is the most powerful driver of superior profits, yet managers view pricing as relevant only in the context of innovation. This narrow view prevents companies from realizing their full potential. Best practice examples of pricing as well as rigorous academic research suggest that pricing based on solid scientific principles helps average companies to achieve above-average results. This paper presents a review of recent research and summarizes the fundamental principles that managers must master so that pricing becomes an enabler of lasting superior performance. Research limitations/implications: Academic research in pricing surpasses managerial practice. Managers often rely on outdated concepts when it comes to pricing strategy and tactics. Practical implications: The paper presents a framework that allows managers to implement pricing strategies that improve performance. Social implications: Effective pricing strategies benefit companies, customers and other stakeholders. Originality/value: The paper provides a comprehensive overview of the latest research on pricing and thus documents that pricing based on solid, scientific principles is an enable of lasting, above-average profitability. [ABSTRACT FROM AUTHOR]

A suggested price is oftentimes utilized in practice to enhance the profitability of pay what you want (PWYW). In this paper, we focus on this suggested price strategy. We conduct five experiments to explore factors that impact buyers' payments after seeing the suggested price. In Study 1, we show a relationship between the suggested price and payments. Intuitively, a higher suggested price results in increased payments. Studies 2A and 2B demonstrate that the relationship between the suggested price and payments is mediated by cost estimation, while Studies 3A and 3B display that the relationship is moderated by the presence of a charitable element. Interestingly, in the case with a charitable element, increasing the suggested price does not stimulate an increment in payments on a par with the case without a charitable element. This paper proposes that sellers who use PWYW could employ a suggested price to furnish buyers with pertinent cost information, but utilizing PWYW with a suggested price may not be suitable for products with a charitable component. [ABSTRACT FROM AUTHOR]

Considering the restriction of supply risk on optimal profit realization in advance selling, we discuss three selling strategies of a seller who produces and sells a seasonal product to a consumer under uncertain supply and demand: all advance selling, partial advance selling, and non‐advance selling. The robust newsvendor model is designed to solve the problem. Our results show that implementing an advance selling strategy is always beneficial from the demand uncertainty perspective to the seller. However, sellers should choose advance selling carefully from the standpoint of supply uncertainty: sellers will non‐advance selling under certain conditions. This condition is contingent on the market, capacity level, selling price, supply–demand correlation, consumer characteristics, and seller's pricing power. Interestingly, pricing power is the key driver to stimulate advance selling under supply uncertainty. In addition, the impact of supply and demand uncertainty and supply–demand correlation on these strategies are related to price discounts. [ABSTRACT FROM AUTHOR]

The staple food commodity that is crucial to the Indonesian society is rice. Rice often experiences fluctuations in prices. These fluctuations can be predicted using machine learning methods. This research aims to evaluate the accuracy of machine learning algorithms in predicting premium rice prices in the West Java Province, Indonesia. Two methods used in this study are Linear Regression and Random Forest. The dataset used consists of 6096 records from the Indonesian Food Commodity Management Agency. The evaluation results show that the Random Forest algorithm has an accuracy rate of 98.69%, while the Linear Regression algorithm has an accuracy rate of 95.08%. Based on these results, it is concluded that the Random Forest algorithm is more effective in predicting premium rice prices in the West Java Province compared to the Linear Regression algorithm. [ABSTRACT FROM AUTHOR]

We propose that a person's desire to consume an object or possess an attribute increases in how much others want but cannot have it. We term this motive imitative superiority-seeking and show that it generates preferences for exclusion that help explain a host of market anomalies and make novel predictions in a variety of domains. In bilateral exchange, trade becomes more zero-sum, leading to an endowment effect. People's value of consuming a good increases in its scarcity, which generates a motive for firms and organizations to engage in exclusionary policies. A monopolist producing at constant marginal cost can increase profits by randomly excluding buyers relative to the standard optimal mechanism of posting a common price. In the context of auctions, a seller can extract greater revenues by randomly barring a subset of consumers from bidding. Moreover, such non-price-based exclusion leads to higher revenues than the classic optimal sales mechanism. A series of experiments provides direct support for these predictions. In basic exchange, a person's willingness to pay for a good increases as more people are explicitly barred from the opportunity to acquire it. In auctions, randomly excluding people from the opportunity to bid substantially increases bids amongst those who retain this option. Consistent with our predictions, exclusion leads to bigger gains in expected revenue than increasing competition through inclusion. Our model of superiority-seeking generates "Veblen effects," rationalizes attitudes against redistribution and provides a novel motive for social exclusion and discrimination. [ABSTRACT FROM AUTHOR]

The multi-microgrid structure is emerging as one of the most promising concept for future distribution systems to provide resilience and independence energy operation with the energy exchange of other entities. In the distribution system, all microgrid owners and other stakeholders are benefited by sharing the locally generated energy with the adjacent microgrid entities with the help of energy trading process. The peer-to-peer (P2P) energy trading is one of the best suitable energy-trading method for multi-microgrid systems because of the absence of third-party entity. This paper proposes a hierarchical P2P energy trading model with the incorporation of an energy management scheme for multi-microgrid systems to provide efficient and effective results in the energy trading market. The proposed model is applied to a typical multi-microgrid community system to show the economic benefits of various microgrid stakeholders. The different case studies have been performed in the result analysis to show the impact of energy management scheme on the P2P energy-trading model. From the simulation results, it is evident that the implementation of energy management scheme reduces the operation cost of all microgrids together in the community area by 28.38% in scenario 1, 38.86% in scenario 2, and 39.21% in scenario 3 respectively. The overall result analysis shows that the proposed hierarchical P2P model with the energy management integration can provide the better cost reduction when compared to other scenarios, and also the total load demand is reduced by 29.51%, the renewable energy utilization is improved by 22.39% within the multi-microgrid community system. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) printing technology has opened up possibilities for product design collaborations between device providers and customers. To enable an environment of cocreation, device providers are now renting 3D printers via the 3D-as-a-Service (3DaaS) model. Although prior research has examined pricing and quality issues in the traditional manufacturing setup, these studies have not analyzed such decisions in the 3D printing supply chain setting, where end users possess the ability to customize product designs. Therefore, several important questions remain unanswered from the perspective of the 3D printing device provider. For example, what is the appropriate pricing model for providing 3DaaS? How do factors such as the extent of design customization and the complexity influence the pricing strategy of the 3DaaS firm? Our analysis shows that if the customers' impact on the product quality is relatively high or low, the pay-per-build pricing model generates a higher profit than the fixed-fee pricing model. Interestingly, we also find that if customers frequently print highly intricate product designs, the firm might choose the pay-per-build pricing model, only if the likelihood of design failure for these complex structures is low. Otherwise, the firm might opt for the fixed-fee pricing model. [ABSTRACT FROM AUTHOR]

If a financial asset's price movement impacts a firm's product demand, the firm can respond to the impact by adjusting its operational decisions. For example, in the automotive industry, automakers decrease the selling prices of fuel-inefficient cars when the oil price rises. Meanwhile, the firm can implement a risk-hedging strategy using the financial asset jointly with its operational decisions. Motivated by this, we develop and solve a general risk-management model integrating risk hedging into a price-setting newsvendor. The optimal hedging strategy is calculated analytically, which leads to an explicit objective function for optimizing price and "virtual production quantity" (VPQ). (The latter determines the service level—that is, the demand-fulfillment probability.) We find that hedging generally reduces the optimal price when the firm sets the target mean return as its production-only maximum expected profit. With the same condition on the target mean return, hedging also reduces the optimal VPQ when the asset price trend positively impacts product demand; meanwhile, it may increase the VPQ by a small margin when the impact is negative. We construct the return-risk efficient frontier that characterizes the optimal return-risk trade-off. Our numerical study using data from a prominent automotive manufacturer shows that the markdowns in price and reduction in VPQ are small under our model and that the hedging strategy substantially reduces risk without materially reducing operational profit. This paper was accepted by Kay Giesecke, finance. Funding: Financial support from the Hong Kong Research Grants Council [General Research Fund 17200521] is gratefully acknowledged. Supplemental Material: The data files and e-companion are available at https://doi.org/10.1287/mnsc.2023.4942. [ABSTRACT FROM AUTHOR]

Against the backdrop of COVID-19, we study how the interactions of mutual funds and dealers introduce fragility to the municipal bond market and induce lasting market impacts. During the crisis, trading surges, whereas dealers' liquidity provision plunges for mutual-fund-held bonds, leading to greater price depressions in these bonds. Importantly, the crisis reshapes the market's perceptions of mutual fund fragility risks, with the aftermath-yield spreads widening significantly more for bonds with greater mutual fund exposures. Such postcrisis pricing effects reflect dealers' continued reluctance to provide liquidity for mutual fund–held bonds, and they are stronger for bonds whose mutual fund holders are more susceptible to investor runs. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4912. [ABSTRACT FROM AUTHOR]

This study examines price data for coworking spaces in the City of New York, between September and October 2020. The data were released by an online source (Datarade 2022). The results and data can be used in future research to examine reasons behind frequent price changes at coworking spaces. [ABSTRACT FROM AUTHOR]

This study developed several machine learning models to predict defaults in the invoice-trading peer-to-business (P2B) market. Using techniques such as logistic regression, conditional inference trees, random forests, support vector machines, and neural networks, the prediction of the default rate was evaluated. The results showed that these techniques can effectively improve the detection of defaults by up to 56% while maintaining levels of specificity above 70%. Unlike other studies on the same topic, this was performed using sampling techniques to address the imbalance of classes and using different time periods for the training and test datasets to ensure intertemporal validation and realistic predictions. For the first-time, default explainability in the invoice-trading market was studied by examining the impact of macroeconomic factors and invoice characteristics. The findings highlighted that gross domestic product, exports, trade type, and trade bands are significant factors that explain defaults. Furthermore, the pricing mechanisms of P2B platforms were evaluated with the observed and implicit probabilities of the default to analyze the price risk adjustment. The results showed that price reflects a significantly higher implicit probability of default than observed default, which in turn suggests that underlying factors exist besides the borrowers' probability of default. [ABSTRACT FROM AUTHOR]

Analytics services provided by marketplace platforms have become increasingly important for sellers seeking market insights. In this paper, we examine a scenario in which an analytics service plays a vital role in enhancing sellers' understanding of market size and improving their decision-making. Using a game-theoretic model, we analyze the pricing strategies of the platform and the adoption strategies of sellers for the analytics service. Our study identifies two distinct effects of analytics services: the competition effect and the accuracy effect. Specifically, the competition effect manifests in opposing ways across different market scenarios, with a competition-intensifying effect in lowdemand markets and a competition-weakening effect in high-demand markets. Consequently, sellers using an analytics service command lower prices in low-demand markets and higher prices in highdemand markets. More interestingly, our results reveal that offering an analytics service could potentially hurt the total market demand, subsequently impacting the platform's revenue from the marketplace service and potentially leaving the platform worse off. Additionally, driven by both the accuracy and competition effects, adopting an analytics service may adversely affect seller profitability and consumer surplus without necessarily improving overall welfare. Moreover, the transaction fee for the marketplace service plays a crucial role in the interplay between the analytics and marketplace services. Specifically, in low-demand (high-demand) markets, as the transaction fee increases, platforms should consider reducing (increasing) the subscription fee to encourage more (fewer) sellers to adopt the analytics service, thereby enhancing overall market demand and increasing revenue from the marketplace service. Our findings also suggest that platforms should refrain from offering analytics services in high-demand markets when the transaction fee is relatively high. Furthermore, policymakers (sellers) should be mindful of the potential negative consequences associated with the adoption of analytics services in high-demand (low-demand) markets. [ABSTRACT FROM AUTHOR]

This paper presents a profit optimization model for substitute products in a competitive, time-sensitive market with scarcity and shifting user preferences. The model maximizes profit, considering production costs and inventory maintenance. It uses a discrete choice model to represent demand, sensitivity to price, availability, and changing preferences. A two-phase PSO-type metaheuristic solution tackles the nonlinear, recursive model, efficiently managing inventories and evolving consumer preferences. The model integrates production decisions, inventories, and sales prices, considering scarcity conditions and user preferences. It uses a multinomial logit for the consumers' demand function with soft exogenous constraints, which influence utility and change consumption preferences and choices. This research offers a tool for companies to manage stock, production, and pricing in a context where goods are substitutes, providing a new perspective on business strategy. [ABSTRACT FROM AUTHOR]

Blockchain, a promising technology, has been extensively applied in numerous fields, such as network security, finance, and medical care. However, due to the low power consumption and weak computing power of the mobile environment, the application of blockchain in this environment still faces many challenges. Therefore, edge computing has been introduced to improve the computing power of mobile devices and encourage more mobile edge devices to join the blockchain network. In this paper, we propose a double auction model to address the issue of edge computing resource allocation in blockchain networks. Based on this auction model, we first propose a truthful double auction mechanism based on breakeven (TDAMB) to determine matched pairs of edge computing service providers (ECSPs) and miners. Furthermore, to improve the system efficiency, we propose a double auction mechanism based on a critical value (DAMCV). We also theoretically analyze the individual rationality, budget balance and truthfulness of the proposed mechanisms. Extensive experiments show that TDAMB and DAMCV have good effects on edge computing resource allocation in blockchain networks. [ABSTRACT FROM AUTHOR]

Background: In Jordan, medicines are priced by the Jordan Food and Drug Administration (JFDA), which mandates pharmacists to sell pharmaceutical products without any discounts or rebates. However, discounting drug prices in community pharmacies is commonplace. The present study aims to understand the motives and perceptions of pharmacists regarding drug price discounts in community pharmacies in Jordan. Methods: Qualitative interview sessions were conducted with a convenient sample of 20 community pharmacists. A total of 25 participants were interviewed, anonymized, and audio recorded. To ensure maximum comfort, respondents were given the option to choose the interview location or participate through audio-visual communication tools. The sessions followed a pre-designed interview guide focusing on discounts offered on prescription drugs in community pharmacies, pharmacists' experiences, and their attitudes toward these practices. Interview recordings were transcribed and analyzed thematically. Results: Three main themes emerged: patient-related factors, pharmacist-related factors, and rules and regulationsrelated factors, each with several subthemes. This study identified several barriers that hinder pharmacists’ adherence to medicine pricing policies in community pharmacies in Jordan, including psychosocial and economic factors, customer attraction and profit increase strategies, sales tactics, market share expansion, avoidance of medicine accumulation and expiration, and issues related to regulations and law enforcement. Conclusion: The findings demonstrate that adherence to medicine pricing policies in community pharmacies in Jordan varies depending on different factors. Pharmacists may exhibit weak adherence under specific circumstances while demonstrating stronger adherence under others. This study provides insights that could inform the revision of regulations and laws governing pharmacy practices and adherence to pricing policies. [ABSTRACT FROM AUTHOR]

Digital products companies are increasingly adopting subscription pricing, but consumers who pay the subscription service may not be able to access all digital products sold on the sales platform. This paper explores the question of why some digital products are not in the subscription service of the sales platform. To address this problem, we develop an analytical model to examine two strategies of the firm. One is that the firm does not add the new product into the subscription service, the other is that the firm adds the new product into the subscription service. By comparing the profits under two sets, we find the condition under which the firm should add the new digital product into the subscription service. The results show that if the percentage of existing subscribers is below a certain threshold, and the subscription price is over a certain threshold, it is better for the firm to add the new product into the subscription service. We also analyze how the main variables affect the firm's profit and the piracy rate. Our research provides useful insights for firms in choosing pricing schemes for the newly released product and offers advice for policymakers on controlling the piracy rate of digital products industry. [ABSTRACT FROM AUTHOR]

The on-demand food delivery platform assigns orders from customers to independent couriers, known as agents. An agent's decision to provide services depends on the wage and the order assignment. The platform can adjust the actual supply and demand through two-sided pricing, such that an equilibrium between customers and agents that maximizes the platform's profit can be formed. We develop a stylized model to investigate the optimal price and wage of a platform facing delay-sensitive customers and income-sensitive agents. We find three possibilities for participatory equilibrium in the platform as the pair of wage and price changes. Meanwhile, all participatory equilibrium regions have two asymptotically stable equilibria, one of which is a nonparticipatory equilibrium. The platform can form a participatory equilibrium only when it attracts enough customers and agents in the initial phase. Our analysis shows that the optimal pricing strategy and the maximum revenue for a platform depend on the valuation and delay sensitivity of the target customers. Furthermore, contradicting the intuition, our results show that the optimal wage is non-increasing in the total demand rate and that the optimal price is non-decreasing in the service capacity of the platform. [ABSTRACT FROM AUTHOR]

Disagreement abounds on what exactly constitutes an 'abuse' within article 102 TFEU, EU competition law's prohibition of an abuse of a dominant position. This situation is highly undesirable, given the important role this prohibition is expected to play in alleviating concerns about substantial market power and its use in important sectors, typified by actions against 'Big Tech'. This article responds to this problem by analysing and synthesising the jurisprudence of the Court of Justice of the European Union and its evolution to establish the constituent elements of an exclusionary 'abuse'. The article corrects crucial legal misconceptions, including the putative dichotomy between violations 'by object' and 'by effect'; the assumed disparate legal tests for pricing and non-pricing conduct; and the lost distinction between the 'as efficient competitor test' and the 'as efficient competitor standard'. This critical inquiry allows one to draw concrete predictions on the future development of this challenging, yet vital, area of law. [ABSTRACT FROM AUTHOR]

Hedonic pricing (HP) models are widely used to capture the price heterogeneity of hotel attributes and complementary services. This study uses a broad-ranging database with information about 11 hotels, located in the same Majorcan (Spain) coastal town, to identify the main attributes, complementary services and guest characteristics that affect hotel pricing, hotel revenue (RevPAR) and repeat stays. The results found that, aside from the main sources of price variability, such as accommodation type, location and season, other hotel attributes/services were found to influence these dependent variables: refurbishments, room type, occupancy level, number of stars, clients' online ratings and the specific hotel. Attributes/services did not have a homogeneous effect on the three types of models estimated, as some attributes increased prices without increasing RevPAR or number of repeat stays. [ABSTRACT FROM AUTHOR]

Pay as you wish (PAYW) pricing offers a radical shift from posted pricing schemes. Modeling consumer behavior under PAYW pricing promises insights into conditions under which PAYW is profitable. Firstly, this paper extends an established model that builds on inequity-averse consumers and models their behavior in PAYW as well as the seller's profits. The paper uses a comprehensive approach to describe consumers with low fairness concerns and points to a new segment of consumers who were not considered in previous PAYW models. They are characterized by a decision not to buy a good under a PAYW pricing policy, even if they can get it for free, and are not strongly averse to advantageous inequity. Secondly, the paper discusses the profitability of PAYW with a suggested price when the seller's ability to suggest high prices is limited. Thirdly, the paper incorporates the effect of disadvantageous inequity aversion on PAYW with a minimum price. Finally, the paper offers guidelines on how a seller should choose the optimal pricing policy. [ABSTRACT FROM AUTHOR]

Risk-neutral fuzzy probability measures play a crucial role in evaluating American fuzzy put option and fuzzy call option pricing models that depend on the amount of increase or decrease of the equity prices. Volatility and risk-free interest rate are the two significant parameters that are affecting the financial market frequently. In 2008, Muzzioli et al [9] and Xcaojian Yu [17] used non-overlapping triangular/trapezoidal and non-overlapping fuzzy trapezoidal numbers respectively to describe the same to study American put option model. In this study, we recommend fuzzy measures involving non-overlapping fuzzy octagonal numbers using which we demonstrate American Put Fuzzy Option Model (APFOM) by incorporating the uncertain values to real in the volatile series. [ABSTRACT FROM AUTHOR]

As pricing in hospitality and tourism evolves due to the opportunities presented by artificial intelligence (AI), so do the concerns that come with technological advancements. There is a fine line between what is possible, what is profitable, and what is ethical, sustainable and responsible in the use of AI for pricing. This viewpoint article draws attention to the dark side of pricing, and presents a framework towards sustainable pricing, against the backdrop of the current European Union legal framework. The framework includes ethical guidelines, self-regulation, self-protection and technological regulation, which should be considered as a whole. [ABSTRACT FROM AUTHOR]

This article describes a data-driven case study for teaching and assessing students' skills in marketing analytics, specifically in pricing. This case study combines teaching econometrics to analyze data and substantive marketing to derive managerial insights. The econometric challenge requires students to set up and implement a regression analysis to derive the demand function, detect multicollinearity, and select appropriate data visualizations. The substantive challenge requires deriving optimal pricing decisions and understanding how the parameters of the demand function impact optimal prices and the associated profit. We test the case study in a marketing analytics exam and discuss the performance of 134 students. Beyond assessing student performance in an exam, the case study facilitates teaching through in-class group work or assignments. Free of charge, under a liberal CC BY license, we encourage other educators to use the case study in their teaching. We provide the necessary data and a sample solution using the statistical programming language R. [ABSTRACT FROM AUTHOR]

The article offers information on alternative strategies to delta hedging for pricing options. Topics include the Hedge Strategy Model (HSM), which demonstrates that the Black-Scholes-Merton (BSM) hedge strategy yields an expected dealer profit and loss of zero, an excellent article by Carr, Jarrow, and Myneni (CJM) discussing a family of alternative hedging strategies for European options, and the "all or nothing" (AoN) hedge strategy.

This article introduces a class of generative models based on the (G)ARCH-like continuous-time framework to unify econometric and diffusion-based methods for pricing European options. The authors formulate a partial differential equation (PDE) for the option price when the volatility of the underlying asset is described by a broad class of discrete-time GARCH models. The GARCH-PDE framework combines discrete data from the physical market with a latent stochastic volatility process, and provides flexibility in the pricing process to accommodate any underlying realized volatility dynamics. The authors reduce solving a two-dimensional degenerate PDE to finding the solutions of a system of one-dimensional PDEs, and then obtain closed-form analytical formulas for option pricing under stochastic volatility, which is typically achieved through Monte Carlo simulations. Convergence and error analysis of the analytical formula attest to the option pricing accuracy of the proposed framework, using available discrete implied volatility samples without compromising its computational accuracy. Several sets of numerical tests are presented to illustrate the authors' approach and demonstrate its superiority over other empirically well-tested pricing methods. [ABSTRACT FROM AUTHOR]

The adoption of SOFR introduces valuation and hedging challenges for derivatives due to its backward-looking settlement style. Despite the availability of analytical pricing formulae for vanilla SOFR derivatives, the early-stage SOFR swaptions market impedes model implementation and empirical validation due to insufficient liquidity and lack of historical data. This article develops a mechanism to convert volatility quotes from actively traded LIBOR swaptions to emerging SOFR swaptions. The proposed mechanism facilitates the transfer of price information embedded in LIBOR-based swaptions to SOFR-based swaptions, contributing to the establishment of a crucial SOFR swaption market for trading volatilities associated with the new benchmark. [ABSTRACT FROM AUTHOR]

The author presents a new statistical solution for pricing derivatives of compounded rates. Since the replacement of LIBOR, the compounded overnight rate has become the new standard for floating-rate loans. Many contracts contain a zero-based floor. The compounded rate is a time average of a sequence of benchmark rates. Floors and caps on compounded rates are thus Asian options. Under very weak and quite general assumptions, without explicit probability distributions, the author proves that even if the benchmark rate process is non-Gaussian, the simple Gaussian model is asymptotically correct for pricing due to Lyapunov's central limit theorem. The approximation's maximum mispricing is bounded by the Berry-Esseen inequality. [ABSTRACT FROM AUTHOR]

Currently, the hospitality industry is experiencing an increase in the adoption of subscription-based business models among restaurants. Pricing is a critical factor to consider when deploying the subscription models. However, only a few studies in the literature talk of pricing the new subscriptions and even in these studies no algorithm is given for setting the prices. Consequently, this study aims to derive an optimal pricing strategy for subscription services in the restaurant industry through a two-step implementable framework. In the first step, we try to understand the preferences of the consumers and accordingly curate different subscription packages for them. In the second step, we propose a linear programming-based optimization model to price these packages in an optimal manner. The linear programming model is solved by CPLEX 12.7 solver software. Finally, the authors discuss the theoretical and managerial implications of their findings. [ABSTRACT FROM AUTHOR]

Using novel data from the pharmaceutical industry, we study product prices and innovation around mergers. Exploiting within‐deal variation in product market consolidation, we show that prices increase more for drugs in consolidating markets than for matched control drugs. Estimates indicate a 2% average price effect that persists for about one year. Price increases expand with acquirer‐target product similarity and are more pronounced within less competitive product markets with fewer players and no generic competition. Examination of trade‐offs reveals these deals generate significant shareholder value. They also spur labeling and other manufacturing‐related innovation, but not the development of new drugs. [ABSTRACT FROM AUTHOR]

We propose a novel factor model for option returns. Option exposures are estimated nonparametrically, and factor risk premia can vary nonlinearly with states. The model is estimated using regressions with minimal assumptions on factor and option return dynamics. We estimate the model using index options to characterize the conditional risk premia for factors of interest, such as the market return, market variance, tail and intermediary risk factors, higher moments, and the VIX term structure slope. Together, market return and variance explain more than 90% of option return variation. Unconditionally, the magnitude of the variance risk premium is plausible. It displays pronounced time variation, spikes during crises, and always has the expected sign. [ABSTRACT FROM AUTHOR]

This paper establishes a supply chain under two different structures. In the first structure, one manufacturer produces a product and sells it to customers online through the e-tail channel by an e-tailer as well as traditionally through the retail channel by multiple retailers. In this setting, the manufacturer assigns responsibility of the e-tail channel to the e-tailer. In the second structure, the manufacturer establishes the e-tail channel by paying a setup cost and sells products online himself and via the retail channel by multiple retailers. First, game theory is applied to make decisions under various competitive-cooperative interactions among the members. Then, under different situations, it is investigated which structure is more beneficial from the manufacturer's view. Finally, decisions are compared and a sensitivity analysis is performed to investigate the effects of parametric changes on decisions. The results indicate that it is preferable for the manufacturer to open the online channel himself, if the setup cost of this channel is less than a threshold. From the whole system's perspective, it is beneficial to assign the responsibility of the e-tail channel to an e-tailer. Furthermore, the larger the number of the retailers in the retail channel, the lower profits for all members. [ABSTRACT FROM AUTHOR]

Using the differences in user attribution and profitability among different business models employed by technology‐trading platforms, a two‐stage dynamic game pricing model was constructed considering three pricing strategies. The research found that (1) in the information‐intermediary business model, technology‐trading platforms should adopt a two‐stage pricing model that first emphasizes transaction fees followed by mixed system charges. (2) In the one‐stop service business model, technology‐trading platforms can achieve higher returns by comprehensively using big data analysis to tailor mixed fees. (3) The traditional information‐intermediary platform can adopt a pricing model with mixed system charges to transition to a one‐stop service platform. [ABSTRACT FROM AUTHOR]

Live streaming e-commerce has been booming in recent years. This paper investigates the pricing and live streaming adoption strategies of competitive e-commerce firms. We consider the differences in product quality and consumer heterogeneity and establish a duopoly competitive Hotelling model. Taking into account consumers' uncertain perception of product quality and live streaming costs, the equilibrium outcomes are examined. Our findings indicate that (1) e-commerce firms selling high-quality products should consider adopting live streaming, while others should avoid it if consumers hold moderate product quality expectations. (2) E-commerce firms can be more profitable without live streaming when the market expectation or perceived quality of the products is high. (3) If only the e-commerce firm selling high-quality products utilizes live streaming, the optimal price would be lower, even when the product quality is relatively high, provided that the perceived quality is very high. [ABSTRACT FROM AUTHOR]

This study considers a manufacturer–retailer–streamer supply chain, in which the retailer first purchases products from a manufacturer and then sells them to consumers through a streamer. In the live streaming context, the retailer usually cooperates with the streamer by providing three different contracts: only a commission of the sale (OC), only a fixed fee (OF), and a commission of the sale and fixed fee (CF). Therefore, this study develops a theoretical model to investigate the effects of these three contracts on supply chain members' optimal decisions and profits. The following results were obtained: (1) the retailer prefers an OC contract with a high-ability streamer, and the manufacturer benefits from this contract. Additionally, the manufacturer, retailer, and high-ability streamer can achieve a win–win–win outcome in certain cases. Furthermore, the retailer is willing to sign an OC contract with a low-ability streamer when the fixed fee of the OF contract is high. (2) The retailer prefers to cooperate with a low-ability streamer through an OF contract when the fixed fee is low. (3) The CF contract is the most profitable alternative for the retailer when the total commission rate is low and the fixed fee is medium. [ABSTRACT FROM AUTHOR]

We study a courier dispatching problem in an on-demand delivery system in which customers are sensitive to delay. Specifically, we evaluate the effect of temporal pooling by comparing systems using the dedicated strategy, with which only one order is delivered per trip, versus the pooling strategy, with which a batch of consecutive orders is delivered on each trip. We capture the courier delivery system's spatial dimension by assuming that, following a Poisson process, demand arises at a uniformly generated point within a service region. With the same objective of revenue maximization, we find that the dispatching strategy depends critically on customers' patience level, the size of the service region, and whether the firm can endogenize the demand. We obtain concise but informative results with a single courier and assuming that customers' underlying arrival rate is large enough, meaning a crowded market, such as rush hour delivery. In particular, when the firm has a growth target and needs to achieve an exogenously given demand rate, using the pooling strategy is optimal if the service area is large enough to fully exploit the pooling efficiency in delivery. Otherwise, using the dedicated strategy is optimal. In contrast, if the firm can endogenize the demand rate by varying the delivery fee, using the dedicated strategy is optimal for a large service area. The reason is that it is optimal for the firm to sustain a relatively low demand rate by charging a high fee for a large service radius: within this large area, the pooling strategy leads to a long wait because it takes a long time for multiple orders to accumulate. Moreover, with an exogenous demand rate to meet, customers' patience level has no impact on the dispatch strategy. However, when the demand rate can be endogenized, the dedicated strategy is preferable if customers are impatient. Furthermore, we extend our model to account for social welfare maximization, a hybrid contingent delivery policy, a general arrival rate that does not have to be large, a nonuniform distribution of orders in the service region, and multiple couriers. We also conduct numerical analysis and simulations to complement our main results and find that most insights in our base model still hold in these extensions and numerical studies. This paper was accepted by Jeannette Song, operations management. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757 and RGPIN-2021-04295]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4858. [ABSTRACT FROM AUTHOR]

Summary: Background: The high price of HPV vaccines remains a significant barrier to vaccine accessibility in China, hindering the country’s efforts toward cervical cancer elimination and exacerbating health inequity. We aimed to inform HPV vaccine price negotiations by identifying threshold prices that ensure that a government-funded HPV vaccination programme is cost-effective or cost-saving. Methods: We used a previously validated transmission model to estimate the health and economic impact of HPV vaccination over a 100-year time horizon from a healthcare payer perspective. Threshold analysis was conducted considering different settings (national, rural, and urban), cervical cancer screening scenarios (cytology-based or HPV DNA-based, with different paces of scale-up), vaccine types (four types available in China), vaccine schedules (two-dose or one-dose), mode of vaccination (routine vaccination with or without later switching to high-valency vaccines), willingness-to-pay thresholds, and decision-making criteria (cost-effective or cost-saving). Furthermore, we examined the budget impact of introducing nationwide vaccination at the identified threshold prices. Findings: Using the current market price, national routine HPV vaccination with any currently available vaccine is unlikely cost-effective. Under a two-dose schedule, the prices of the four available HPV vaccine types cannot exceed $26–$36 per dose (44.1%–80.2% reduction from current market prices) depending on vaccine type to ensure the cost-effectiveness of the national programme. Adopting vaccination at threshold prices would require an annual increase of 72.18%–96.95% of the total annual National Immunization Programme (NIP) budget in China. A cost-saving routine vaccination programme requires vaccine prices of $5–$10 per dose (depending on vaccine type), producing a 21.38%–34.23% increase in the annual NIP budget. Adding the second dose is unlikely to be cost-effective compared to a one-dose schedule, with the threshold price approaching or even falling below zero. Rural pilot vaccination programmes require lower threshold prices compared with a national programme. Interpretation: Our study could inform vaccine price negotiation and thus facilitate nationwide scale-up of current HPV vaccination pilot programmes in China. The evidence may potentially be valuable to other countries facing HPV introduction barriers due to high costs. This approach may also be adapted for other contexts that involve the introduction of a pricy vaccine. Funding: CAMS Innovation Fund for Medical Sciences (CIFMS); Bill & Melinda Gates Foundation.

This dataset contains database pricing and agreements received through public records requests made to members of the Association of Research Libraries (ARL) and a few non-ARL research libraries. Pricing from the years 2018 to 2024, depending on the institution, is included for three premium academic databases: SciFinder from the American Chemical Society’s Chemical Abstract Service, Web of Science from Clarivate, and Scopus from Elsevier. The pricing in this dataset is difficult to acquire and of significant interest to libraries that license these products and those wishing to investigate pricing strategies and approaches in the library database market.

The present research demonstrates how consumer responses to negative and positive offers are influenced by whether the administering marketing agent is an artificial intelligence (AI) or a human. In the case of a product or service offer that is worse than expected, consumers respond better when dealing with an AI agent in the form of increased purchase likelihood and satisfaction. In contrast, for an offer that is better than expected, consumers respond more positively to a human agent. The authors demonstrate that AI agents, compared with human agents, are perceived to have weaker intentions when administering offers, which accounts for this effect. That is, consumers infer that AI agents lack selfish intentions in the case of an offer that favors the agent and lack benevolent intentions in the case of an offer that favors the customer, thereby dampening the extremity of consumer responses. Moreover, the authors demonstrate a moderating effect, such that marketers may anthropomorphize AI agents to strengthen perceived intentions, providing an avenue to receive due credit from consumers when the agent provides a better offer and mitigate blame when it provides a worse offer. Potential ethical concerns with the use of AI to bypass consumer resistance to negative offers are discussed. [ABSTRACT FROM AUTHOR]

One of the prominent pricing decisions a retailer can make is its choice of pricing strategy. Previous research investigating consumers' responses to stores with frequent, shallow price advantages relative to competitors (a frequency strategy) versus stores with infrequent, deep price advantages (a depth strategy) was all conducted by allowing people to simultaneously view prices from multiple stores, a setting that emphasized across-store comparisons. The present research finds that when a store's prices are evaluated separately, as opposed to simultaneously across stores, many of the prominent findings of previous research are reversed. The authors demonstrate that without simultaneous comparisons across stores, consumers shift from using across-store prices as reference points to using within-category reference prices. As a result of this shift, deep price advantages are easier to evaluate than frequent price advantages, and therefore more influential on consumers' formation of price image. When stores are evaluated separately, the result is most often a depth advantage, where stores with a HILO pricing strategy are evaluated as having a lower price image than EDLP stores, even when the average prices are the same. These results cannot be explained by prior work related to frequency and depth pricing strategies that relied on across-store comparisons. [ABSTRACT FROM AUTHOR]

Pandemics, natural disasters (e.g., hurricanes, droughts, fires), strikes, piracy, and other events can unexpectedly disrupt supply or spike demand, creating shortages. Unlike ordinary stockouts caused by store-specific inventory policies, shortages involve the entire supply chain. One tool for managing shortages is imposing purchase limits. Purchase limits restrict the quantity each shopper can purchase of the scarce product (e.g., gasoline, toilet paper, sanitizers, meat, batteries), possibly increasing availability to other shoppers. Although altruistic stores might use purchase limits for egalitarian goals (e.g., reducing hoarding, waste, panic buying, arbitrage, unfair distribution), the authors find that profit-maximizing stores can use purchase limits to increase profits during shortages. These findings suggest that stores' price-and-limit strategies depend on shortage severity, store size, competition, and seasonality. For moderate shortages, large multiproduct stores, where average shopping basket sizes are large, should maintain low prices and impose limits, whereas small stores should increase prices and not impose limits. For severe shortages, by contrast, large stores should keep low prices but not impose limits, whereas small stores should increase prices and impose limits. Generally, large stores benefit from increased future store traffic when they impose limits. Interestingly, purchase limits can improve both store profits and, with lower prices, consumer surplus. [ABSTRACT FROM AUTHOR]

A prominent hallmark of competitive interaction is the desire to differentiate from rivals. In this article, the authors examine under what conditions firms will differentiate through product quality versus advertising intensity. Firms select quality in a first stage, advertising in a second stage, and price in the last stage. The probability that a consumer is informed of a firm's offering depends on its advertising expenditure. The authors find that when advertising is not cost effective, both firms choose a light ad spending. This allows them to minimally differentiate in quality without concern of intense price competition, as each firm has a segment of "captive" consumers. When advertising is moderately cost effective, one firm spends heavily on advertising. The rival advertises lightly, while choosing the same maximal quality level. This strategy softens price competition by inducing the heavy advertiser to price high more often to capitalize on its large captive segment. When advertising is very cost effective, both firms advertise heavily and differentiate in quality. Three extensions are examined: upfront fixed quality costs, continuous advertising levels, and simultaneous advertising and pricing decisions. This research reveals that letting market awareness be determined endogenously suggests less product differentiation than previously suspected and regions of advertising differentiation. [ABSTRACT FROM AUTHOR]

We assemble homeowner insurance claims from 28 independently operated country subsidiaries of a multinational insurance firm. We propose a new insurance model, in which consumers can make invalid claims and firms can deny valid claims, as is common in the data. In the model, trust and honesty shape equilibrium insurance contracts, disputes, and claim payments, especially when disputes are too small for courts. We test the model by investigating claim incidence, dispute, rejection, and payment, as well as insurance costs and pricing across countries. The evidence is consistent with the centrality of trust for insurance markets, as our model predicts. Authors have furnished data/code, which are available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]