Non-deceptive counterfeiting involves producing and selling counterfeit products without misleading consumers about their authenticity. This practice intensifies price competition for genuine brands while contributing either to brand dilution or network externalities. Using a two-period game modeling approach, we analyze how genuine products should adjust their advertising and pricing strategies in response to non-deceptive counterfeiting. Our findings reveal that in the presence of brand dilution, manufacturers should reduce advertising investments and prices to remain competitive. However, when counterfeiting generates network externalities, the optimal response depends on market conditions and managerial effectiveness. In some cases, a survival strategy (reducing advertising and/or prices) may be necessary to maintain profitability. In other cases, a differentiation strategy (raising advertising and prices) can lead to profit gains. Regardless of the approach adopted, manufacturers must improve the targeting, content, and effectiveness of advertising to further differentiate their offerings and reduce positive spillover effects to counterfeit products.

Product recalls pose significant challenges across supply chains, making high product quality essential to reducing recall likelihood and mitigating associated costs. This study examines a supply chain where a manufacturer sells through a retailer while considering the risk of a quality-related recall and subsequent recovery. Using a game-theoretic model, we evaluate four collaborative contracts - Typical Cost Sharing (TCS), Cost Sharing through Bargaining (CSB), Typical Revenue Sharing (TRS), and Revenue Sharing through Bargaining (RSB) - to identify the most effective approach for improving quality and maximizing profits. Results show that all contracts outperform a non-collaborative (NC) setup, with RSB driving the highest quality, lowest recall probability, and greatest profitability for the manufacturer and the entire supply chain. While the retailer prefers TRS, the manufacturer can use profit-sharing to incentivize the adoption of the superior RSB contract.

Risk-Adjusted Revenue (RAR) is a key customer valuation metric that incorporates risk factors into financial assessments. While traditional methods have focused on structured data to estimate RAR, the potential impact of unstructured textual data has not been fully explored. This study addresses this gap by extracting information from 126,000 peer-to-peer (P2P) online loan descriptions using Topic Modelling techniques. The extracted textual data is integrated into both individual and hybrid Machine Learning (ML) models to predict and explain customers’ RAR. A key contribution of this research is the application of Artificial Intelligence by designing and implementing hybrid ML models in finance, combining both structured (hard) and unstructured (soft) features. Unlike conventional credit risk assessment models, which often overlook the value of textual data, this study demonstrates how the incorporation of loan description text can significantly enhance predictive accuracy, customer segmentation, and overall valuation. Our results show that hybrid ML models, particularly those using Latent Semantic Analysis (LSA) for topic-based customer clustering, outperform individual algorithms, achieving a predictive power of R2=97.55%. These models enable the creation of customer portfolios with distinct risk and return profiles, offering valuable insights for financial institutions seeking more accurate customer valuation and segmentation. The findings suggest that integrating textual data into RAR prediction models can provide substantial improvements in both prediction performance and decision-making in the P2P lending market.

This paper investigates the impact of a private brand (PB) introduction by an e‐commerce platform. Contrary to previous research, the platform allows competing manufacturers to sell their national brands (NBs) directly to consumers for an agency fee. Our game‐theoretic analysis allows us to derive the following key insights. The levels of competition between NBs, and NBs and the PB, as well as the agency fees manufacturers pay to the platform are critical in determining the profitability of introducing PBs. Introducing a PB may not benefit the platform, especially when the PB and the NBs are asymmetric and are competing closely. However, when the platform can profit by introducing a PB, it is at the expense of NB manufacturers as they are pressured to reduce their prices and also experience a decline in sales. Finally, introducing PBs enhances consumer welfare by reducing NB prices and expanding consumer demand in the product category.

This paper investigates the impact of decision timing for pricing and marketing efforts in a supply chain led by competing manufacturers. We develop and solve six games to consider the scenarios (games) where prices and marketing efforts (ME) are decided simultaneously, and when they are not (i.e., ME is set either before or after prices). We examine these three scenarios for the benchmark case of a bilateral monopolistic channel, then extend the analysis to a supply chain with competing manufacturers. We identify the optimal decision timing by comparing equilibrium profits and strategies across games in each supply chain setup. We find that a monopolistic manufacturer always prefers that prices and ME be decided simultaneously. However, this result does not hold when product competition is taken into account. The optimal decision timing for competing manufacturers depends on the retailer's and manufacturers' ME effectiveness levels as well as on competition intensity. Specifically, when ME are not very effective, a simultaneous decision scenario is preferred because it provides the advantage of higher profit margins or sales. However, for highly effective ME, manufacturers prefer to decouple ME and pricing decisions. The retailer's optimal scenario is either to make all decisions simultaneously or to choose prices prior to ME. This means that supply chain firms can face conflict due to the decision timing for prices and ME.

Many product recalls are caused by quality-related product failures. When such recalls occur, the effects may not only be limited to the firm selling the product but also extend to competing firms in the category. This paper analyzes quality and pricing strategies for competing firms facing the risk of a severe quality-related recall making the product hazardous and leading to its removal from the market. We develop a two-stage Nash game where the probability of recall depends on the firms' chosen quality investments, and either firm can experience a recall. We consider a utility-based model where consumers' sensitivity to price and quality can change following the recall. Our results indicate that the competitor should lower its price after a recall if consumers' price sensitivity changes enough and may increase or keep its price the same otherwise. Surprisingly, considering the risk of a recall does not always lead firms to enhance their product quality. If the change in consumer quality sensitivity is low enough, firms adopt an inferior product quality level than when they overlook the product recall risk, even if consumer quality sensitivity increases and/or consumer price sensitivity decreases after the recall. These results can help companies plan their pricing and quality decisions in competitive industries with potential product quality failures leading to recalls.& COPY; 2023 Elsevier B.V. All rights reserved.

A Peer-to-Peer (P2P) service is a decentralized platform that directly connects individuals, buyers (lenders) and sellers (investors) without the intermediation of a third party. In the P2P lending market, customer cash flows are undeniably linked to their financial risk of default. Thus, forecasting customers' Risk-Adjusted Revenue (RAR) value is one of the most critical issues in financial decision-making. With the emergence of big data, traditional forecasting methods cannot provide the high predictive power needed for such metrics. We propose a hybrid method by integrating the use of supervised and unsupervised Machine Learning (ML) algorithms to enhance the accuracy of predicting customer-adjusted risk metrics. Using a real P2P dataset from the Lending Club, containing over two million cases, we forecast customers' risk-adjusted revenue by applying ML algorithms for the first time. These include individual methods such as gradient boosting and decision trees, and hybrid frameworks that group customers using a clustering algorithm (k-Means or Density-Based Spatial Clustering of Applications with Noise (DBSCAN)) prior to implementing the individual methods. We compare the efficiency (processing time and accuracy) of this hybrid approach with the performance of individual regressor-based models to predict RAR. Our results indicate high predictive power for many individual ML algorithms (R2 score over 90%). Further, in most cases, hybrid models outperform the individual ones in both predictive performance and processing time. Finally, the feature importance analysis in the best predictive frameworks helps identify the most influential factors in predicting customers' RAR in the P2P lending market.

The profitability of cooperative advertising (CA) programs is analyzed in a supply chain where competing manufacturers sell their products through competing retailers. We study a two-period game-theoretic model that accounts for positive and negative long-term effects of retail advertising on consumer preferences. We obtain closed-form equilibria in two particular cases where either the stores or the products are perfectly differentiated. For the general case where both products and stores can be substitutable, we develop a numerical algorithm to find the equilibrium. We compare the equilibria obtained in games where CA is offered and where it is not. The results show that the second-period effects of first-period retail advertising and the levels of substitutability between products and between retailers all play a key role in assessing the profitability of CA programs. CA only benefits manufacturers when store and product competition are both low, or when retailers are highly differentiated. However, in most cases, retailers do not find such programs profitable except when product substitutability levels are high while store competition is low. Finally, CA can only be win-win arrangements for manufacturers and retailers when the level of store differentiation is very high, the products are moderately substitutable, and first-period retail advertising has a substantial positive impact on second-period sales. The manufacturers' cooperative advertising support rates increase with the second-period effects of retail advertising.

This study proposes new methods to formulate customers' risk-adjusted revenue (RAR) metrics applied to the financial industry. Using a customer dataset provided by a loan company, we compute RAR using benchmark approaches presented in the literature and new formulas that combine the Customer Portfolio Theory and the Multiple Sources of Revenues approaches. We validate the efficiency and originality of our formulations by implementing statistical tests to check for differences across the different RAR measures. We find that the proposed RAR models are unique and can be implemented in the industry to account for multiple sources of risk, hence providing managers with ways to improve their valuation of customers' portfolios.

Given the large amount of customer data available to financial companies, the use of traditional statistical approaches (e.g., regressions) to predict customers’ credit scores may not provide the best predictive performance. Machine learning (ML) algorithms have been explored in the credit scoring literature to increase predictive power. In this paper, we predict commercial customers’ credit scores using hybrid ML algorithms that combine unsupervised and supervised ML methods. We implement different approaches and compare the performance of the hybrid models to that of individual supervised ML models. We find that hybrid models outperform their individual counterparts in predicting commercial customers’ credit scores. Further, while the existing literature ignores past credit scores, we find that the hybrid models’ predictive performance is higher when these features are included.

This paper studies the profitability of a store brand introduction by an e-commerce platform offering competing manufacturers’ products. We develop a game-theoretic model and obtain equilibrium quantity and pricing solutions for the manufacturers and the platform. We compare equilibrium strategies and profits in games where the platform does and does not offer a store brand. Our main findings indicate that the platform benefits from introducing a strong store brand to the detriment of manufacturers.

This paper investigates how should manufacturers optimally allocate resources to retailer-initiated (retailer) advertising through cooperative advertising programs and own (manufacturer) advertising in a bilateral monopoly. Retailer advertising stimulates immediate sales but may also harm long-term (post-advertising) demand, whereas manufacturer advertising aims at building brand equity and stimulates both immediate and long-term sales. A game-theoretic model in which a manufacturer and a retailer set pricing and advertising decisions over a two-period planning horizon is developed to account for the differences between manufacturer and retailer advertising. We characterize equilibrium solutions for four advertising scenarios for the manufacturer, ranging from no investment in any advertising activity to undertaking own advertising and supporting retailer advertising simultaneously. Comparing the two players’ equilibrium strategies and profits across these scenarios, we find that manufacturers should avoid offering exclusively cooperative advertising programs to retailers. When retailer advertising positively influences long-term sales, manufacturers should offer cooperative advertising supports to retailers in addition to undertaking their own advertising. When retailer advertising negatively affects long-term sales, manufacturers can still undertake own advertising and offer cooperative advertising under certain conditions. However, if these conditions are not met, focusing exclusively on own advertising is their best advertising strategy. Retailers also prefer scenarios in which manufacturers advertise, but may choose not to participate in manufacturers’ cooperative advertising programs. This leads to suboptimal outcomes if cooperative advertising programs are not enhanced by additional incentives (e.g., side payments or other services).

This research studies the impact of a store brand's introduction in a supply chain where a retailer offers the national brands of competing manufacturers. The focus in this paper is to study such impact given different manufacturers' decision timing choices with regards to how they set pricing and advertising decisions. We develop a game-theoretic model that is based on consumer utility functions to represent competition between the national and store brands. We then solve six games to take into account different decision timing choices. In particular, we consider whether manufacturers decide of advertising before, after, or at the same time than pricing. Comparisons of equilibrium profits for each supply chain member before and after store brand entry under each decision timing scenario show that national brand manufacturers incur losses as a result of the retailer's store brand when they keep their decision timing unchanged. Interestingly, however, the retailer may restrain from introducing the store brand especially if the national brand manufacturers set pricing decisions before advertising, as is the case with fixed pricing contracts, and if the level of competition between the national brands is sufficiently high. Further, manufacturers can strategically change their decision timing to either benefit from, prevent or restrict losses from the retailer's store brand. These results provide new insights on how competitive interactions and contractual agreements in manufacturer-led supply chains can impact the success of store brands for retailers and mitigate or intensify their threat for national brands.

This paper studies the impact of retailers' loyalty programs (LPs) in a supply chain context. We consider a supply chain formed by one manufacturer selling its product through two competing retailers and focus on frequency loyalty programs that reward consumers with a free product after several purchases. Two different games are modeled: (1) a Stackelberg game, where the manufacturer is the leader and decides about the wholesale price, and the retailers are followers, and (2) a Nash game between the retailers, which decide about their price, whether to offer an LP and reward timing if they offer an LP. To do so, we formulate all players' demand/profit using a multinomial logit choice model where consumers' purchasing decisions depend on the utility they get from the different retailers' products and rewards. To include different consumer mentalities, we consider two parameters: sensitivity to reward value and sensitivity to time distance. Using an iterative algorithm, we numerically solve the games for the market's equilibrium prices and LP policies under serving consumers with different mentalities. We also study the market conditions under which retailers follow pre-set LP policies. Our main findings indicate that LPs can inflate prices at both levels of the supply chain, which can aggravate vertical externalities related to double marginalization. We show that supply chain managers should consider a profit-sharing agreement when retailers do not benefit from LPs. Further, we show that retailers should only offer LPs when consumers either have a low discount rate or/and they value rewards highly.

This research investigates whether it is beneficial for competing firms offering loyalty programs (LPs) to restrict the reward redemption time. We develop a game-theoretic model where competing firms decide an LP’s redemption policy and pricing and identify firms’ restriction levels. The results show that, at equilibrium, firms implement a restrictive policy when customers value rewards more than time, while an unrestricted policy is implemented when customers value time. Each firm should increase prices in response to its competitor’s restrictive policy.

We investigate the profitability of multichannel retailing for competing offline retailers. Each firm can sell a product both offline and online in a position of a first mover or follower in the online market. We find that, depending on the online market size and price competition levels across channels and retailers, the adoption of multichannel retailing may or may not enhance an offline retailer's profits in the first-mover position. If one retailer profitably expands online, the second can also improve its profit by introducing another online channel to the detriment of the pioneer. However, when offline retailers are given the possibility of selecting an equilibrium channel mix, multichannel retailing could be adopted strategically by the two retailers to maintain their market shares, resulting in a prisoner's dilemma situation. In such a context, it drives down retail prices, increases sales, and reduces the profits of the two competing retailers.

This research investigates the effectiveness of consumer rebates offered by competing manufacturers in a distribution channel. We consider a two-manufacturer, two-retailer channel and develop a two-period model where consumers' preferences are distributed along a Hotelling line. The rebate consists in a price discount that can be redeemed on the second period. We solve three Stackelberg games: a benchmark where no rebate is offered, a symmetric game where both manufacturers offer rebates, and an asymmetric game where only one manufacturer provides a rebate. Comparisons of equilibrium solutions show that while manufacturers should not offer rebates, they could do so due to a prisoner dilemma situation when their wholesale prices are high.

This paper studies pricing strategies of competing retailers offering substitutable products in multiple product categories. For such retailers, in addition to accounting for within-category pricing effects, cross-category effects can also influence consumers' purchase decisions and thereby impact the retailers' optimal pricing strategies. We model consumers' utility for purchasing substitutable products from the same category and from other categories as well. We then solve a game-theoretic model to identify the retailers' optimal prices and profits. Our results show that accounting for cross-category effects largely influences the retailers' pricing and profitability. In particular, cross-category effects influence the sensitivity of prices to within-category substitution levels. While cross-category effects have an impact on retailers' equilibrium strategies, this effect is only relevant when within-category substitution effects are present. We also find that intentionally ignoring cross-category effects leads to lower prices for categories that are either substitutable or highly complementary, and to higher prices otherwise. When not accounting for the fact that the two categories are demand-related implies lower prices than when this information is accounted for, then at least one retailer chooses to disregard the cross-category effect at equilibrium. Finally, we find evidence for prisoner's dilemma situations where both retailers ignore cross-category effect at equilibrium while accounting for it would yield them higher profits.

This paper investigates how the use of an accrual constraint in a cooperative advertising program affects channel members' profits in a bilateral monopoly, as well as their pricing and advertising decisions. The main findings indicate that, compared to unconstrained cooperative advertising programs, when an accrual constraint is used and the manufacturer's contribution to the retailer's advertising costs exceeds the accrued cooperative advertising budget, the retailer reduces both her retail price and advertising efforts to the level where cooperative advertising is not offered; while the manufacturer also reduces his wholesale price and advertising efforts, but this time, the wholesale price remains higher than when there is no cooperative advertising. These strategic moves translate to less (more) profits for the manufacturer (retailer). The use of an accrual constraint is counterproductive for the manufacturer as the retailer uses the accrued advertising fund as a side payment rather than a direct incentive to invest more in advertising. The manufacturer and retailer are better off when unconstrained cooperative advertising programs are supplemented with other incentives, including side payments and advertising support services.