Cooperative Advertising with Bilateral Participation

We study cooperative (co‐op) advertising strategies in a two‐tier distribution channel and extend the popular unilateral participation strategy to bilateral participations. Our research shows that a properly designed bilateral participation has several advantages over unilateral participation. Bilateral participation is capable of coordinating the distribution channel under a very general demand function. In addition, when channel members determine participation parameters endogenously, the bilateral participation improves the channel efficiency and leads to a Pareto improvement over the corresponding unilateral participation.     

An Analysis of Manufacturer‐Retailer Supply Chain Coordination in Cooperative Advertising*

In the literature of cooperative (co‐op) advertising, the focus of the research is on a relationship in which a manufacturer is the leader and retailers are followers. This relationship implies the dominance of the manufacturer over retailers. Recent market trends have shown a shift in power from manufacturers to retailers. Retailers, as a result, may now possess equal or even greater power than a manufacturer in some instances when it comes to retailing. Based on this new market phenomenon, we intend to explore the role of co‐op advertising in a manufacturer‐retailer supply chain through brand name investments, local advertising expenditures, and sharing rules of advertising expenses. Two co‐op advertising models are developed and compared. The first co‐op advertising model is based on the traditional leader‐follower relationship of a manufacturer and a retailer. The second model incorporates partnership into co‐op advertising coordination. Business examples and managerial implications of the models have been discussed. A cooperative bargaining technique is utilized to implement the partnership co‐op advertising model.     

The Impact of Manufacturers' Wholesale Prices on a Retailer's Shelf‐Space and Pricing Decisions*

This article examines shelf‐space allocation and pricing decisions in the marketing channel as the results of a static game played à la Stackelberg between two manufacturers of competing brands and one retailer. The competing manufacturers act as leaders that play a simultaneous and noncooperative game. They fix their transfer prices by taking into account the shelf‐space allocation and price‐markup decisions of their common exclusive dealer. The results indicate that the wholesale prices of brands are strongly linked to their share of the shelf. The main results of our numerical simulations may be summarized as follows: first, the lower the unit cost and/or the greater the price elasticity, the greater the shelf space allocated to that brand. Second, the higher the shelf‐space elasticity, the lower are the wholesale prices and the profits of all channel members.     

Bootstrap Testing of Hypotheses on Co‐Integration Relations in Vector Autoregressive Models

It is well known that the finite‐sample properties of tests of hypotheses on the co‐integrating vectors in vector autoregressive models can be quite poor, and that current solutions based on Bartlett‐type corrections or bootstrap based on unrestricted parameter estimators are unsatisfactory, in particular in those cases where also asymptotic χ² tests fail most severely. In this paper, we solve this inference problem by showing the novel result that a bootstrap test where the null hypothesis is imposed on the bootstrap sample is asymptotically valid. That is, not only does it have asymptotically correct size, but, in contrast to what is claimed in existing literature, it is consistent under the alternative. Compared to the theory for bootstrap tests on the co‐integration rank (Cavaliere, Rahbek, and Taylor, 2012), establishing the validity of the bootstrap in the framework of hypotheses on the co‐integrating vectors requires new theoretical developments, including the introduction of multivariate Ornstein–Uhlenbeck processes with random (reduced rank) drift parameters. Finally, as documented by Monte Carlo simulations, the bootstrap test outperforms existing methods.     

Cooperative Advertising and Pricing in a Dynamic Stochastic Supply Chain: Feedback Stackelberg Strategies

Cooperative (co‐op) advertising is an important instrument for aligning manufacturer and retailer decisions in supply chains. In this, the manufacturer announces a co‐op advertising policy, i.e., a participation rate that specifies the percentage of the retailer's advertising expenditure that it will provide. In addition, it also announces the wholesale price. In response, the retailer chooses its optimal advertising and pricing policies. We model this supply chain problem as a stochastic Stackelberg differential game whose dynamics follows Sethi's stochastic sales‐advertising model. We obtain the condition when offering co‐op advertising is optimal for the manufacturer. We provide in feedback form the optimal advertising and pricing policies for the manufacturer and the retailer. We contrast the results with the advertising and price decisions of the vertically integrated channel, and suggest a method for coordinating the channel.     

Customer Learning Processes,Strategy Selection,and Performance in Business‐to‐Business Service Firms*

Learning about customers takes place through relevant dialogues with those customers, also known as customer relationship management (CRM). As relationships develop, information about the customer is gathered in the firm's customer information systems (CIS): the content, processes, and assets associated with gathering and moving customer information throughout the firm. This research develops a measure of CIS management capabilities based on learning organization theory and measured by the ability to get, store, move, and use information throughout the business unit. This measure is then used to analyze customer learning processes and associated performance in the context of marketing strategic decision making. This study of 209 business services firms finds that generic marketing strategy positioning (low‐cost and differentiation) and the marketing tactics of personalization and customization are related to CIS development. Customer information systems development in turn is associated with higher levels of customer‐based performance, which in turn is associated with increased business growth. Since the strongest association with customer‐based performance is strategy selection, the long‐term benefits of the knowledge gained from the CIS may be in the ability to assist in measuring customer‐based performance, rather than in the ability to immediately contribute to performance. Finally, for these firms, customization and personalization are not directly associated with performance and thus may not be necessary to support every firm's marketing strategy.     

Opt‐Out Options in New Product Co‐development Partnerships

We study three contractual arrangements—co‐development, licensing, and co‐development with opt‐out options—for the joint development of new products between a small and financially constrained innovator firm and a large technology company, as in the case of a biotech innovator and a major pharma company. We formulate our arguments in the context of a two‐stage model, characterized by technical risk and stochastically changing cost and revenue projections. The model captures the main disadvantages of traditional co‐development and licensing arrangements: in co‐development the small firm runs a risk of running out of capital as future costs rise, while licensing for milestone and royalty (M&R) payments, which eliminates the latter risk, introduces inefficiency, as profitable projects might be abandoned. Counter to intuition we show that the biotech's payoff in a licensing contract is not monotonically increasing in the M&R terms. We also show that an option clause in a co‐development contract that gives the small firm the right but not the obligation to opt out of co‐development and into a pre‐agreed licensing arrangement avoids the problems associated with fully committed co‐development or licensing: the probability that the small firm will run out of capital is greatly reduced or completely eliminated and profitable projects are never abandoned.     

Efficiency Evaluation in Search Advertising

In this article, we use data envelopment analysis combined with principal component analysis to evaluate the efficiency of online retailers in search advertising. We examine various efficiency model specifications involving several resource and performance‐related variables in search advertising. Our analysis based on 200 retailers suggests different efficiency patterns for multichannel and Web‐only retailers. The results of our efficiency pattern analysis indicate that the performance metrics, impressions, online sales, click‐through rate, and conversion rate together reveal differences in efficiency mainly for multichannel retailers. On the other hand, ad positions in sponsored and organic links reveal differences in efficiency for Web‐only retailers. In terms of overall efficiency, we find that multichannel retailers occupy relatively most of the top positions. These results contribute to organizational level understanding of search advertising practices in online retailing and offer insights into keyword management, resource utilization, and performance metrics in search advertising campaigns.     

An Information‐Processing Approach for Evaluating In‐Store Retail Operational Design Strategies

We develop a model to evaluate retail store operational design strategies using an information‐processing perspective of organizational design. We propose that three model constructs pertaining to in‐store shopper task uncertainty—the product mix complexity, the service production complexity, and the product mix changeover—create shopper encounter information requirements (IR). These requirements can be met using specific retail service operational design choices for managing shopper encounters, namely, designing layouts for self‐service (SS) and providing employees with task empowerment (TE). The model is then operationalized using a two‐stage approach to develop new multi‐item, measurement scales. The psychometric properties and predictive validity of the scales and model are then confirmed by using structural equation modeling with survey data from 175 merchandise retail store managers. We find that our model can be generically applied across the retail industry to understand how shopper encounter IR motivate retailer store design choices and can be used to determine whether to design stores for SS or to provide store employees with TE. We then evaluate the efficacy of the studied store design choices on customer delivery satisfaction, and offer some suggestions for future research.     

Item‐Level RFID in the Retail Supply Chain

Analyzing the proliferation of item‐level RFID, recent studies have identified the cost sharing of the technology as a gating issue. Various qualitative studies have predicted that conflict will arise, in particular in decentralized supply chains, from the fact that the benefits and the costs resulting from item‐level RFID are not symmetrically distributed among supply chain partners. To contribute to a better understanding of this situation, we consider a supply chain with one manufacturer and one retailer. Within the context of this retail supply chain, we present analytic models of the benefits of item‐level RFID to both supply chain partners. We examine both the case of a dominant manufacturer as well as the case of a dominant retailer, and we analyze the results of an introduction of item‐level RFID to such a supply chain depending on these market power characteristics. Under each scenario, we show how the cost of item‐level RFID should be allocated among supply chain partners such that supply chain profit is optimized.     

Managing Retail Shelf and Backroom Inventories When Demand Depends on the Shelf‐Stock Level

Inventory displayed on the retail sales floor not only performs the classical supply function but also plays a role in affecting consumers’ buying behavior and hence the total demand. Empirical evidence from the retail industry shows that for some types of products, higher levels of on‐shelf inventory have a demand‐increasing effect (“billboard effect”) while for some other types of products, higher levels of on‐shelf inventory have a demand‐decreasing effect (“scarcity effect”). This suggests that retailers may use the amount of shelf stock on display as a tool to influence demand and operate a store backroom to hold the inventory of items not displayed on the shelves, introducing the need for efficient management of the backroom and on‐shelf inventories. The purpose of this study is to address such an issue by considering a periodic‐review inventory system in which demand in each period is stochastic and depends on the amount of inventory displayed on the shelf. We first analyze the problem in a finite‐horizon setting and show under a general demand model that the system inventory is optimally replenished by a base‐stock policy and the shelf stock is controlled by two critical points representing the target levels to raise up/drop down the on‐shelf inventory level. In the infinite‐horizon setting, we find that the optimal policies simplify to stationary base‐stock type policies. Under the billboard effect, we further show that the optimal policy is monotone in the system states. Numerical experiments illustrate the value of smart backroom management strategy and show that significant profit gains can be obtained by jointly managing the backroom and on‐shelf inventories.     

Retailer's Response to Alternate Manufacturer's Incentives Under a Single‐Period,Price‐Dependent,Stochastic‐Demand Framework*

This article considers the joint development of the optimal pricing and ordering policies of a profit‐maximizing retailer, faced with (i) a manufacturer trade incentive in the form of a price discount for itself or a rebate directly to the end customer; (ii) a stochastic consumer demand dependent upon the magnitude of the selling price and of the trade incentive, that is contrasted with a riskless demand, which is the expected value of the stochastic demand; and (iii) a single‐period newsvendor‐type framework. Additional analysis includes the development of equal profit policies in either form of trade incentive, an assessment of the conditions under which a one‐dollar discount is more profitable than a one‐dollar rebate, and an evaluation of the impact upon the retailer‐expected profits of changes in either incentive or in the degree of demand uncertainty. A numerical example highlights the main features of the model. The analytical and numerical results clearly show that, as compared to the results for the riskless demand, dealing with uncertainty through a stochastic demand leads to (i) (lower) higher retail prices if additive (multiplicative) error, (ii) lower (higher) pass throughs if additive (multiplicative) error, (iii) higher claw backs in both error structures wherever applicable, and (iv) higher rebates to achieve equivalent profits in both error structures.     

Optimal Pricing of Two Successive‐Generation Products with Trade‐in Options under Uncertainty

To entice consumers to purchase both current and next generation products, many manufacturers and retailers offer trade‐in programs that allow buyers of the first generation product to trade‐in the product and purchase the new generation product at a lower price. By considering the interactions between “forward‐looking” consumers and a firm when a trade‐in program is offered, we analyze a two‐period dynamic game to determine the optimal prices of two successive‐generation products in equilibrium, and examine the conditions under which trade‐in programs are beneficial to the firm. Our model incorporates market heterogeneity (valuation of the first generation product varies among the consumer population), product uncertainty (the incremental value of the new product is uncertain before its introduction), and consumers' forward‐looking behavior (consumers take future product valuation and prices into consideration when making purchasing decisions). With the trade‐in option, we show that consumers are willing to pay a price that is higher than their valuations of the current product. Furthermore, trade‐in programs are more beneficial to the firm when: (i) the durability of the current product is high; (ii) the market heterogeneity is low; or (iii) the uncertainty level (or the expected incremental value) of the new product is high. Finally, when the incremental value of the new product is more uncertain, consumers are more willing to purchase the current product because of the “option” value of the trade‐in programs and thus trade‐in programs can be more beneficial to the firm in this case.     

Bootstrap Determination of the Co‐Integration Rank in Vector Autoregressive Models

This paper discusses a consistent bootstrap implementation of the likelihood ratio (LR) co‐integration rank test and associated sequential rank determination procedure of Johansen (1996). The bootstrap samples are constructed using the restricted parameter estimates of the underlying vector autoregressive (VAR) model that obtain under the reduced rank null hypothesis. A full asymptotic theory is provided that shows that, unlike the bootstrap procedure in Swensen (2006) where a combination of unrestricted and restricted estimates from the VAR model is used, the resulting bootstrap data are I(1) and satisfy the null co‐integration rank, regardless of the true rank. This ensures that the bootstrap LR test is asymptotically correctly sized and that the probability that the bootstrap sequential procedure selects a rank smaller than the true rank converges to zero. Monte Carlo evidence suggests that our bootstrap procedures work very well in practice.     

Optimal Production and Admission Policies in Make‐to‐Stock/Make‐to‐Order Manufacturing Systems

In this article, we study optimal production and admission control policies in manufacturing systems that produce two types of products: one type consists of identical items that are produced to stock, while the other has varying features and is produced to order. The model is motivated by applications from various industries, in particular, the automobile industry, where a part supplier receives orders from both an original equipment manufacturer and the aftermarket. The product for the original equipment manufacturer is produced to stock, it has higher priority, and its demands are fully accepted. The aftermarket product is produced to order, and its demands can be either accepted or rejected. We characterize the optimal production and admission policies with a partial‐linear structure, and using computational analysis, we provide insights into the benefits of the new policies. We also investigate the impact of production capacity, cost structure, and demand structure on system performance.     

Balancing Inventory and Stockout Risk in Retail Supply Chains Using Fast‐Ship

In this study, we consider a two‐retailer, one‐supplier supply chain in which retailers satisfy excess demand by offering to directly ship out‐of‐stock items on an expedited basis at no extra cost to customers. This practice is referred to as the fast‐ship option. We consider two scenarios along with the fast‐ship option. In the first scenario, retailers transship when out of stock, whereas in the second scenario, they do not. If they do not transship, some customers may perform the search on their own. In each scenario, the wholesale prices are either exogenous, or chosen endogenously by the supplier. For both cases, we determine the supplier's and the retailers’ optimal decisions. The key research question we ask and answer is the following: which of the two scenarios is preferred by either player when all decisions are made optimally? We show that when fewer customers are willing to search on their own and wholesale prices are exogenous, both the supplier and the retailers prefer to transship. In addition, the decision maker in a centralized chain will have the exact same preference as that of players in a decentralized setting when the retailers’ and the supplier's preferences coincide and wholesale prices are exogenous. This preference concordance does not hold if wholesale prices are endogenous.     

Static and Dynamic Pricing of Excess Capacity in a Make‐to‐Order Environment

We consider a make‐to‐order manufacturer that serves two customer classes: core customers who pay a fixed negotiated price, and “fill‐in” customers who make submittal decisions based on the current price set by the firm. Using a Markovian queueing model, we determine how much the firm can gain by explicitly accounting for the status of its production facility in making pricing decisions. Specifically, we examine three pricing policies: (1) static, state‐independent pricing, (2) constant pricing up to a cutoff state, and (3) general state‐dependent pricing. We determine properties of each policy, and illustrate numerically the financial gains that the firm can achieve by following each policy as compared with simpler policies. Our main result is that constant pricing up to a cutoff state can dramatically outperform a state‐independent policy, while at the same time achieving most of the increase in revenue achievable from general state‐dependent pricing. Thus, we find that constant pricing up to a cutoff state presents an attractive tradeoff between ease of implementation and revenue gain. When the costs of policy design and implementation are taken into account, this simple heuristic may actually out‐perform general state‐dependent pricing in some settings.     

Fixed‐Effects Dynamic Panel Models,a Factor Analytical Method

We consider the estimation of dynamic panel data models in the presence of incidental parameters in both dimensions: individual fixed‐effects and time fixed‐effects, as well as incidental parameters in the variances. We adopt the factor analytical approach by estimating the sample variance of individual effects rather than the effects themselves. In the presence of cross‐sectional heteroskedasticity, the factor method estimates the average of the cross‐sectional variances instead of the individual variances. The method thereby eliminates the incidental‐parameter problem in the means and in the variances over the cross‐sectional dimension. We further show that estimating the time effects and heteroskedasticities in the time dimension does not lead to the incidental‐parameter bias even when T and N are comparable. Moreover, efficient and robust estimation is obtained by jointly estimating heteroskedasticities.     

Dynamic Pricing in e‐Services under Demand Uncertainty

High volatility of the e‐services market, due to increasing competition, low life cycle of products, and easy availability of information about competing service offerings to customers, makes the demand for service offerings quite uncertain. Revenue management in such markets calls for real‐time techniques to learn the demand and its dependence on both the price and the service level associated with the service offering. We assume firms reply on exploratory approaches for demand estimation, in which firms experiment with different service offerings in order to simultaneously learn the demand while doing business. Such exploration and learning process can be costly without supervision. As reported by Rothschild (Journal of Economic Theory, 9 185‐202, 1974), traditional Bayesian dynamic control approaches may conclude with suboptimal offerings. We propose a novel demand learning approach that is guaranteed to converge to the optimal offering. The approach combines simulated annealing algorithm with Bayesian learning. We further present intelligent techniques that adaptively reduce the effort of exploration on suboptimal service offerings so as to improve the long‐run average profit.     

Integrating a Top‐Down and a Bottom‐Up Approach: Formal and Informal Risk‐Handling Strategies in a Utility Company

In this study, we bring together a top‐down and a bottom‐up approach of risk handling. We do so by conceptualizing and qualitatively and quantitatively measuring formal and informal risk‐handling strategies in a Dutch utility company. We conceive of formal risk handling as regulating, training, and educating safety and enforcing rule compliance, while we distinguish three different informal risk‐handling strategies: discretionary specialization, tacit knowledge, and taking personal responsibility. We show that the formal risk‐handling strategy and the three informal risk‐handling strategies can be measured separately. Hence, we have validated the measurement of all four strategies derived from two different risk‐handling approaches. Moreover, we have demonstrated that the perceived use of the four strategies has different effects on unsafe behavior: formal risk handling and tacit knowledge decrease it, discretion increases it, and taking personal responsibility has no effect on unsafe behavior.