Intuition and previous research suggest that creating a compelling online environment for Web consumers will have numerous positive consequences for commercial Web providers. Online executives note...
Academic work on sales compensation plans features agency models prominently, and these models have also been used to build decision aids for managers. However, empirical support remains sketchy. We conducted three experiments to investigate three unresolved predictions involving the incentive-insurance trade-off posited in the model. First, compensation should be less incentive loaded with greater effort-output uncertainty so as to provide additional insurance to a risk-averse agent. Second, flat wages should be used for verifiable effort so as to avoid unnecessary incentives. Third, less incentive-loaded plans should be used with more risk-averse agents so as to provide additional insurance. Our design implemented explicit solutions from a specific agency model, which offers greater internal validity, compared to extant laboratory designs that either did not implement explicit solutions or excluded certain parameters. In Experiment I, data from working manager subjects supported the first prediction but only when risk-averse agents undertook nonverifiable effort. We interpret this as disclosing the model's “core” circumstance, wherein it orders the data when the incentive-insurance trade-off is relevant. Thus, when verifiable effort made incentives moot, as is the case for the second prediction, the model failed to order the data. Building on these results, we reasoned that the third prediction should find support among risk-averse agents but not among risk-neutral agents, because insurance is a moot point with the latter agents. To this end, we added risk-neutral utility functions for agents in Experiment II. Data from MBA-candidate student subjects supported the predictions, but only when risk-averse agents undertook nonverifiable effort. In those cells in which the incentive-insurance trade-off was moot (either because of risk-neutrality or else verifiability), the data did not support the predictions. We confronted several validity threats to these results. To begin, Experiment I used the standard agency solution, which equalizes an agent's expected utility from the predicted plan with his expected utility from rejecting it. Subjects might have broken these ties on such grounds as fairness. To assess whether this confounded the results, we derived new solutions in Experiment II that broke ties in favor of the predicted plan (by a 10% margin in the expected utility). Our results were robust to this change. Second, our agents' behavior in Experiments I and II was much more consistent with predictions, compared to the principals' behavior, which broughtup task comprehension as a validity threat because our principals faced a more complex experimental task than the agents. To address this threat, we used three decision rounds in Experiment III to reduce the principals' task comprehension problems. A related validity threat arose from the relatively small gap in some cells between a principal's predicted expected utility and the principal's next best choice. To address this threat, we derived new solutions with larger gaps to make the principal's choices “easier.” The results were again robust to these changes, which removes these validity threats. We also addressed two alternative explanations. Might principals be predisposed to pick salary plus commission plans regardless of the model's predictions? If so, we should find such plans chosen uniformly across different experimental conditions. Pooling the data from our three experiments, we rejected this predisposition explanation by finding variation that was more consistent with treatment differences across cells. Second, mightagents choose higher effort levels because of a demand bias? If so, we should find agents picking high effort regardless of the plan actually offered to them. Using pooled data, we rejected this explanation by finding variation that was more consistent with a utility-maximizing reaction to the plan actually offered to them. Finally, we included manipulation checks to assess whether principals and agents perceived experimental stimuli identically, as per the “common knowledge” assumption in game theory. These data showed no differences between agents' and principals' perceptions of stimuli. Our experiments move the literature from simply asking whether the model works to pinpointing the circumstances in which itorders behavior. The primary stylized fact we uncovered is the persistent and striking lack of support for the agency model outside of the circumstance in which riskaverse agents undertake nonverifiable effort. The model's failure when there is no material insurance-incentive tradeoff deserves scrutiny in future work.
Manufacturers and distributors in marketing channels commonly establish prices, margins, and other trade terms through negotiations. These negotiations have significant impact on channel members' profit streams over the duration of the business relationship. We consider a situation where a manufacturer and an exclusive, independent distributor are negotiating the transfer (wholesale) price of a new product. The transfer price should lie between the manufacturer's production cost and the maximum resale price that the distributor can charge end consumers (consumers' reservation price). We assume that the negotiations occur in an incomplete and asymmetric information environment such that the manufacturer is uncertain about the consumers' reservation price, whereas the distributor knows it precisely because of proximity to the consumer. The negotiation is time-sensitive because of the threat of potential competitive entry. Both parties have identical opportunity costs of delay in reaching agreement. In this incomplete and asymmetric information environment, the negotiators must learn before they can reach agreement. However, each negotiator has an incentive to convince the other that the available surplus is smaller than it really is. Hence, a high (low) offer (counteroffer) has little credibility without opportunity costs of delay. For any given manufacturer offer, a distributor facing a low consumer reservation price has a small available surplus and therefore more incentive to delay agreement than if the price is high. Willingness to delay agreement and incur delay costs lends credibility to the price signal in an offer (counteroffer), providing a means for communicating credibly and facilitating agreement. Thus, with incomplete, asymmetric information and opportunity costs of delay, a signaling formulation with alternating offers and counteroffers captures key strategic characteristics of marketing channel negotiations. We adapt a game-theoretic model (Grossman and Perry 1986a, 1986b) to predict bargaining behavior and outcomes in this channel negotiation scenario. We derive both point predictions and directional implications from this sequential equilibrium (SE) bargaining model regarding how manufacturer uncertainty about distributor value (consumers' reservation price), opportunity cost of delay, and the actual reservation price (total surplus) should influence bargaining outcomes. The predictions are tested in two experiments. The point predictions serve as benchmarks against which we evaluate the observed bargaining outcomes, as we focus on testing the model's directional implications. We also explore the underlying bargaining process to assess the extent to which subjects conform to the SE signaling rationale in optimizing channel profits. Both experiments show that the point predictions of the SE model fall considerably short in describing bargaining behavior and outcomes. The players bargained suboptimally, took longer to agree, and could not extract the total available surplus. Nevertheless, the data are consistent with several directional predictions of the SE model. There is consistent support for the predicted directional effects of manufacturer uncertainty and consumer reservation prices. As expected, high uncertainty impeded efficient negotiation, eliciting high first offers from manufacturers and increasing bargaining duration. Also, higher reservation prices (higher surplus) lowered bargaining duration, increased bargaining efficiency, and raised profits for both parties. However, support for the predicted directional effects of opportunity cost of delay is mixed. Higher delay costs produced quicker agreements, but distributors did not benefit from their informational advantage. Although the directional results suggest that the SE model is a good representation of bargaining behavior, a closer analysis shows that the bargaining process data did not correspond to the specific signaling rationale of the SE model. Rather, these data suggest that the bargainers created simplified representations of the price negotiation and used heuristics to develop their offers and counteroffers. We observe two systematic patterns of deviations from the SE model. Some manufacturers may have used the counteroffer levels to infer the distributors' competitive stance and factored this into their responses. Thus, even though the distributor counteroffers carried signals of the consumer reservation price, the manufacturers delayed agreement because they either did not recognize the signal or thought it was unreliable. In other cases, the data are consistent with a simple, nonstrategic model (EMP) in which the manufacturer and the distributor divide the monetary payoff (surplus) equally. The results show that the effectiveness of signaling mechanisms depends not only on the economic characteristics of the bargaining situation, but also on shared individual and social contexts that influence how signals are transmitted and interpreted.
An important product strategy for firms in mature markets is value-adding modifications to existing products. Marketing information that reveals consumers' preferences, buying habits, and lifestyle is critical for the identification of such product modifications. We consider two types of value-adding modifications that are often facilitated by marketing information: retention-type modifications that increase the attractiveness of a product to a firm's loyal customers, and conquesting-type modifications that allow a firm to increase the appeal of its product to a competitor's loyal customers. We examine two aspects of the markets for product modification information: (1) the manner in which retention and conquesting modifications affect competition between downstream firms, and (2) the optimal selling and pricing policies for a vendor who markets product modification information. We consider several aspects of the vendor's contracting problem, including how a vendor should package and target the information to the downstream firms and whether the vendor should limit the type of information that is sold. This research also examines when a vendor can gain by offering exclusivity to a firm. We address these issues in a model consisting of an information vendor facing two downstream firms that sell differentiated products. The model analyzes how information contracting is affected by differentiation in the downstream market and the quality of the information (in terms of how “impactful” the resulting modifications are). We analyze two possible scenarios. In the first, the information facilitates modifications that increase the appeal of products to the loyal customers of only one of the two downstream firms (i.e., one-sided information). In the second scenario, the information facilitates modifications that are attractive to the loyal consumers of both the firms (i.e., two-sided information). The effect of modifications on downstream competition depends on whether they are of the retention or the conquesting type. A retention-type modification increases the “effective” differentiation between the firms and softens price competition. Conquesting modifications, however, have benefits as well as associated costs. A conquesting modification of low impact reduces the “effective” differentiation between competing products and leads to increased price competition. However, when conquesting modifications are of sufficiently high impact, they also have the benefit of helping a firm to capture the customers of the competitor. The vendor's strategy for one-sided information always involves selling to one firm, the firm for which the modifications are of the retention type. When the identified modifications are of low impact, this result is expected because conquesting modifications are profit-reducing for downstream firms. However, even when the information identifies high-impact modifications (and positive profits are generated by selling the information as conquesting information), the vendor is strictly better off by targeting his information to the firm for which the modification is the retention type. With two-sided information, the equilibrium strategy is for the vendor to sell the complete packet of information (information on both retention and conquesting modifications) to both downstream firms. However, in equilibrium, both firms only implement retention-type modifications. The information on conquesting modifications is “passive” in the sense that it is never used by downstream firms. Yet the vendor makes strictly greater profit by including it in the packet. This obtains because the price charged for information depends critically on the situation an individual firm encounters by not buying the information. The presence of conquesting information in the packet puts a nonbuyer in a worse situation, and this underlines the “passive power of information.” The vendor gains by including the conquesting information even though it is not used in equilibrium.
In a merchandise depth test, a retail chain introduces new products at a small sample of selected stores for a short period prior to the primary selling season and uses the observed sales to forecast demand for the entire chain. We describe a method for resolving two key questions in merchandise testing: (1) which stores to use for the test and (2) how to extrapolate from test sales to create a forecast of total season demand for each product for the chain. Our method uses sales history of products sold in a prior season, similar to those to be tested, to devise a testing program that would have been optimal if it had been applied to this historical sample. Optimality is defined as minimizing the cost of conducting the test, plus the cost of over- and understocking of the products whose supply is to be guided by the test. To determine the best set of test stores, we apply a k-median model to cluster the stores of the chain based on a store similarity measure defined by sales history, and then choose one test store from each cluster. A linear programming model is used to fit a formula that is then used to predict total sales from test sales. We applied our method at a large retailer that specializes in women's apparel and at two major shoe retailers, comparing results in each case to the existing process used by the apparel retailer and to some standard statistical approaches such as forward selection and backward elimination. We also tested a version of our method in which clustering was based on a combination of several store descriptors such as location, type of store, ethnicity of the neighborhood of location, total store sales, and average temperature of the store location. We found that relative to these other methods, our approach could significantly improve forecasts and reduce markdowns that result from excessive inventory, and lost margins resulting from stockouts. At the apparel retailer the improvement was enough to increase profits by more than 100%. We believe that one reason our method outperforms the forward selection and backward elimination methods is that these methods seek to minimize squared errors, while our method optimizes the true cost of forecast errors. In addition, our approach, which is based purely on sales, outperforms descriptor variables because it is not always clear which are the best store descriptors and how best to combine them. However, the sales-based process is completely objective and directly corresponds to the retailer's objective of minimizing the understock and overstock costs of forecast error. We examined the stores within each of the clusters formed by our method to identify common factors that might explain their similar sales patterns. The main factor was the similarity in climate within a cluster. This was followed by the ethnicity of the neighborhood where the store is located, and the type of store. We also found that, contrary to popular belief, store size and location had little impact on sales patterns. In addition, this technique could also be used to determine the inventory allocation to individual stores within a cluster and to minimize lost demand resulting from inaccurate distribution across size. Finally, our method provides a logical framework for implementing micromerchandising, a practice followed by a significant number of retailers in which a unique assortment of merchandise is offered in each store (or a group of similar stores) tuned to maximize the appeal to customers of that store. Each cluster formed by our algorithm could be treated as a "virtual chain" within the larger chain, which is managed separately and in a consistent manner in terms of product mix, timing of delivery, advertising message, and store layout.
How should a firm decide whether or not to enter an untested market when a competing firm is vying for the same market? Should a firm always speed to the market in an effort to capitalize on pioneering advantages? We address those questions by developing a simple game-theoretical model that captures the most essential factors in a firm's market entry decision, such as market uncertainty, firm heterogeneity, competition, cannibalization, and order-of-entry effects. Our analysis shows that in a competitive context, both pioneering advantages and laggard's disadvantages can motivate a firm to speed to an untested market. Therefore, pioneering advantages alone are not an adequate guide for a firm to formulate its market entry strategy. The optimal decision may call for a firm to be a prudent laggard when pioneering advantages to the firm are substantial, or to become a market pioneer when facing pioneering disadvantages. We characterize different patterns of market entry as equilibrium outcomes for different configurations of the market reward structure and offer a conceptual framework for formulating market entry strategies that go beyond the conventional dichotomy: speed or wait. We show that the paradoxical phenomenon of “disadvantaged pioneers” can arise in a competitive context as the outcome of rational firms making rational choices. To show that pioneering advantages alone are not the right litmus test for market entry decisions, we apply our general framework to a concrete case where consumer preference or the premium that consumers are willing to pay for the pioneering brand gives rise to pioneering advantages and laggard's disadvantages. We conclude that the firm with a larger pioneering premium may choose to wait, while a firm with a smaller pioneering premium speeds to the market. Our analysis also sheds light on empirical research on pioneering advantages. Because firms may race into a market solely to avoid laggard's disadvantages rather than to capture pioneering advantages, pioneers are not necessarily the firms best positioned to establish, exploit, and maintain pioneering advantages. Therefore, it is not surprising that a significant percentage of pioneers fail, as documented by recent empirical research. Our normative investigation further suggests that this predicament in empirical research will not disappear even if we have complete data, use the right measurements, and employ perfect statistical techniques. Therefore, it is perhaps more fruitful to redirect our research effort in the search for pioneering advantages. Finally, we extend our analysis to incorporate the effect of cannibalization on an incumbent firm's market entry strategy. We conclude that cannibalization can motivate an incumbent firm to wait, as the conventional wisdom suggests, but it can also be an impetus for a firm to become a market pioneer. We offer supporting evidence for our analysis and discuss managerial implications of our conclusions.
Structural equation models are widely used in marketing and psychometric literature to model relationships between unobserved constructs and manifest variables and to control for measurement error. Most applications of structural equation models assume that data come from a homogeneous population. This assumption may be unrealistic, as individuals are likely to be heterogeneous in their perceptions and evaluations of unobserved constructs. In addition, individuals may exhibitdifferent measurement reliabilities. It is well-known in statistical literature that failure to account for unobserved sources of individual differences can resultin misleading inferences and incorrect conclusions. We develop a hierarchical Bayesian framework for modeling general forms of heterogeneity in partially recursive structural equation models. Our framework elucidates the motivations for accommodating heterogeneity and illustrates theoretically the types of misleading inferences that can result when unobserved heterogeneity is ignored. We describe in detail the choices that researchers can make in incorporating different forms of measurement and structural heterogeneity. Current random-coefficient models in psychometric literature can accommodate heterogeneity solely in mean structures. We extend these models by allowing for heterogeneity both in mean and covariance structures. Specifically, in addition to heterogeneity in measurement intercepts and factor means, we account for heterogeneity in factor covariance structure, measurement error, and structural parameters. Models such as random-coefficient factor analysis, random-coefficientsecond-order factor analysis, and random-coefficient, partially recursive simultaneous equation models are special cases of our proposed framework. We also develop Markov Chain Monte Carlo (MCMC) procedures to perform Bayesian inference in partially recursive, random-coefficient structural equation models. These procedures provide individual-specific estimates of the factor scores, structural coefficients, and other model parameters. We illustrate our approach using two applications. The first application illustrates our methods on synthetic data, whereas the second application uses consumer satisfaction data involving measurements on satisfaction, expectation disconfirmation, and performance variables obtained from a panel of subjects. Our results from the synthetic data application show that our Bayesian procedures perform well in recovering the true parameters. More importantly, we find that models that ignore heterogeneity can yield a severely distorted picture of the nature of associations among variables and can therefore generate misleading inferences. Specifically, we find that ignoring heterogeneity can result in inflated estimates of measurement reliability, wrong signs of factor covariances, and can yield attenuated model fit and standard errors. The results from the consumer satisfaction study show that individuals vary both in means and covariances and indicate that conventional psychometric methods are not appropriate for our data. In addition, we find that heterogeneous models outperform the standard structural equation model in predictive ability. Managerially, we show how one can use the individual-level factor scores and structural parameter estimates from the Bayesian approach to perform quadrantanalysis and refine marketing policy (e.g., develop a one-on-one marketing policy). The framework introduced in this paper and the inference procedures we describe should be of interest to researchers in a wide range of disciplines in which measurement error and unobserved heterogeneity are problematic. In particular, our approach is suitable for studies in which panel data or multiple observations are available for a given set of respondents or objects (e.g., firms, organizations, markets). At a practical level, our procedures can be used by managers and other policymakers to customize marketing activities or policies. Future research should extend our procedures to deal with the general nonrecursive structural equation model and to handle binary and ordinal data situations.
This research examines the ability of six popular Web search engines, individually and collectively, to locate Web pages containing common marketing/management phrases. We propose and validate a model for search engine performance that is able to represent key patterns of coverage and overlap among the engines. The model enables us to estimate the typical additional benefit of using multiple search engines, depending on the particular set of engines being considered. It also provides an estimate of the number of relevant Web pages not found by any of the engines. For a typical marketing/management phrase we estimate that the “best” search engine locates about 50% of the pages, and all six engines together find about 90% of the total. The model is also used to examine how properties of a Web page and characteristics of a phrase affect the probability that a given search engine will find a given page. For example, we find that the number of Web page links increases the prospect that each of the six search engines will find it. Finally, we summarize the relationship between major structural characteristics of a search engine and its performance in locating relevant Web pages.
The objective of this paper is to investigate the firm's optimal advertising and pricing strategies when introducing a new product. We extend the existing signaling literature on advertising spending and price by constructing a model in which advertising is used both to raise awareness about the product and to signal its quality. By comparing the complete information game and the incomplete information game, we find that the high-quality firm will reduce advertising spending and increase price from their respective complete information levels. In the separating equilibrium, the high-quality firm will actually spend less on advertising than the low-quality firm, resulting in a negative correlation between product quality and advertising spending. What sets our analysis apart from previous studies is that we consider advertising spending not only as a signaling device but also as an informational device. When advertising spending is just a signaling device, it is purely a dissipative expense. It can be an effective signal of quality because only the high-quality firm can afford it; thus, consumers can infer the product's quality by its advertising spending. In this case, advertising spending and product quality are positively correlated. However, when advertising also serves the purpose of raising awareness, it endogenizes the size of the market for the firm, so it is not just a dissipative expense any more. Consider the low-quality firm's mimicking strategy in this case. When the low-quality firm is believed to be a highquality one, it can charge a much higher price than if its true quality were known. Given that its marginal cost is lower than the high-quality firm's, its profit margin will be much larger in mimicry than in revealing its true quality. Indeed, its profit margin will be even greater than the high-quality firm's. Therefore, the low-quality firm in mimicry has a strong incentive to increase its advertising spending from its optimal level when its true quality is known. To deter the low-quality firm's mimicking tendency, the high-quality firm should decrease its advertising spending so that mimicry is not as appealing to the low-quality firm as revealing its true quality. Indeed, the high-quality firm should reduce its advertising spending so much that it advertises less than the low-quality firm in equilibrium Many have interpreted signaling as “burning money” or “throwing money down the drain.” In the case of advertising, the claim is that its purpose is simply to show consumers that the firm can afford to squander money on advertising to signal its quality. Hence, the advertising content need not be informative. However, our results show that simply “burning money” is not enough to signal quality. How the money is burned is also important. When advertising raises awareness as well as signals quality, “saving money” rather than “burning money” is the correct signaling approach, although ultimately the high-quality firm will sacrifice some profit by reducing its market size. The intuition behind this result is that when information is incomplete, the high-quality firm cannot fully exploit its advantages. Whenever its advantages in quality and/or marginal costs are lessened, a firm will want to spend less on advertising.
We examine the problem of parallel imports: unauthorized flows of products across countries, which compete with authorized distribution channels. The traditional economics model of a discriminating monopolist that has different prices for the same good in different markets requires the markets to be separated in some way, usually geographically. The profits from price discrimination can be threatened by parallel imports that allow consumers in the high-priced region some access to the low-priced marketplace. However, as this article shows, there is a very real possibility that parallel imports may actually increase profits. The basic intuition is that parallel importation becomes another channel for the authentic goods and creates a new product version that allows the manufacturer to price discriminate. We propose a two-country, three-stage model to quantitatively study the effects and strategies. In the third stage, and in the higher priced country where parallel imports have entered, we characterize the resulting market segmentation. One segment of consumers stays with the authorized version as they place more value on the warranty and services that come with the authorized version. Another segment switches to parallel imports because a lower price is offered due to lack of country-specific features or warranties. Parallel imports also generate a third and new segment that would not have bought this product before. Unlike counterfeits that are fabricated by imitators, all parallel imports are genuine and sourced from the manufacturer in the lower-priced country through authorized dealers. Therefore, the manufacturer's global sales quantity should increase, but profit may rise or fall depending on the relative sizes and profitability of the segments. A profit-maximizing parallel importer should set price and quantity in the second stage after observing the manufacturer's prices in both countries. There will be a threshold of across-country price gap above which parallel imports would occur. In the first stage, the manufacturer can anticipate the possible occurrence of a parallel import, its price and quantity, and its effect on authorized sales in each country to make a coordinated pricing decision to maximize the global supply chain profit. Under some circumstances the manufacturer should allow parallel imports and under others should prevent them. Through a Stackelberg game we solve for the optimal pricing strategy in each scenario. We then find in one extension that when the number of parallel importers increases, the optimal authorized price gap should narrow, but the prices and quantities of parallel imports may rise or fall. In another extension, we .nd that when the manufacturer has other means—such as monitoring dealers, differentiating designs, and unbundling warranties—to contain parallel imports, the authorized price gap can widen as a function of the effectiveness of nonpricing controls. In summary, parallel imports may help the manufacturer to extend the global reach of its product and even boost its global profit. If the manufacturer offers a discount version through its authorized dealers, it is running a high risk of confusing customers and tarnishing brand images. Parallel imports may cause similar concerns for the manufacturer, but unauthorized dealers are perceived as further removed from the manufacturer. Therefore, there is less risk of confusing consumers when parallel imports are channeled through unauthorized dealers. Furthermore, they are more nimble in diverting the product whenever their transshipment and marketing costs are small enough not to offset the authorized price gap and the valuation discount. This may explain why some manufacturers fiercely fight parallel imports, while others knowingly use this alternative channel.