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Despite its central role in the theory of incentives, empirical evidence of a trade-off between risk and incentives remains scarce. We reexamine this trade-off in a workplace lab environment and find that, in line with theory, principals increase fixed pay while lowering performance pay when the relationship between effort and output is noisier. Unexpectedly, agents produce substantially more in the noisy environment than in the baseline despite weaker incentives. In addition, principals' earnings are significantly higher in the noisy environment. We show that these findings can be accounted for when agents maximize a non-CARA utility function or when they exhibit loss aversion. Data and the online appendix are available at https://doi.org/10.1287/mnsc.2017.2914. This paper was accepted by Uri Gneezy, behavioral economics. [ABSTRACT FROM AUTHOR]

Transaction cost economics predicts that investments in management control will enable risky interfirm transactions. Risk is rarely eliminated, because firms trade off costs of management control and expected costs of control loss (together, the 'cost of control'). The resultant solution typically comprises a mix of control investments with residual performance and residual relational risks. Transaction cost economics also predicts that the control-residual risk trade-off will vary with the cost of control. We use survey data on 287 risky information technology transactions to test whether the control-residual risk trade-off varies predictably with two partnership-specific factors that proxy for variation in the cost of control: prior ties between exchange partners and the criticality of strategic resources to the transaction. The results support the hypotheses, providing novel evidence on trade-offs that managers make when investing in management controls while also prudently accepting some risks. This paper was accepted by Mary Barth, accounting. [ABSTRACT FROM AUTHOR]

Discrete choice experiments such as choice-based conjoint and dual response are popular preference elicitation methods, yet, they can suffer from context effects, extreme response behavior, and problems with estimating consumers' willingness to pay accurately when the purchase probability is high. This study proposes two new features to avoid these limits: (a) strictly separating all forced and all free choice questions and (b) an adaptive mechanism to select fewer, but more informative, free choice questions. The use of these two features invokes two new discrete choice experiment methods: separated dual response (SDR), with just the first feature, and separated adaptive dual response (SADR), with both features. A conceptual comparison, simulation study, tests to address endogeneity concerns, and three empirical studies demonstrate the appeal of these two new features, relative to the benefits of existing discrete choice experiments, especially when estimating willingness to pay. Data, as supplemental material, are available at . This paper was accepted by Pradeep Chintagunta, marketing. [ABSTRACT FROM AUTHOR]

An important component of marketing strategy is to "position" a product in perceptual space. But to realize a perceptual position we must model the link from physical characteristics to perceptual dimensions and we must use this model to maximize profit. This paper integrates psychological theories on how consumers process information with economic models and psychometric measurement, and it develops a theory for selecting physical features and price to achieve a profit maximizing perceptual position. The theory begins with a Lancaster-like transformation from goods space to characteristics space and investigates the implications of a mapping to a third space, perceptual space. We show that all products efficient in perceptual space must be efficient in characteristics space but not conversely. But consumers vary in their preference and the way they perceive products. Thus we introduce distributional components to the theory and derive both geometric and analytic methods to incorporate this consumer heterogeneity. Next, we investigate costs and derive a perceptual expansion path along which a profit maximizing position must exist. Conjoint analysis and quantal choice models provide the measurements to implement the theory. The theory is illustrated with a hypothetical example from the analgesics market and some of the potential psychometric measurements are illustrated with an empirical application in the communications market. [ABSTRACT FROM AUTHOR]

This paper postulates and tests for a time-cost tradeoff during the establishment of manufacturing plants abroad based on U.S. technology. Data on twenty international projects are used to estimate negatively sloped tradeoff functions for which time-cost elasticities are subsequently calculated. The determinants of these elasticities are then analyzed and shown to bear some similarities with the determinants of time-cost tradeoffs in technological innovation. The elasticity measurements were higher for projects where the technology had not been previously commercialized, for large-scale projects, and for projects carried out by the larger firms. [ABSTRACT FROM AUTHOR]

Theoretical models of information asymmetry have identified a trade-off between the desire to learn and the desire to prevent an opponent from learning private information. This paper reports a laboratory experiment that investigates if actual bidders account for this trade-off, using a sequential procurement auction with private cost information and varying information revelation policies. Specifically, the Complete Information Revelation Policy, where all submitted bids are revealed between auctions, is compared to the Incomplete Information Revelation Policy, where only the winning bid is revealed. The experimental results are largely consistent with the theoretical predictions. For example, bidders pool with other types to prevent an opponent from learning significantly more often under a Complete Information Revelation Policy. Also as predicted, the procurer pays less when employing an Incomplete Information Revelation Policy only when the market is highly competitive. Bids are usually more aggressive than the risk-neutral quantitative prediction, which is broadly consistent with risk aversion. [ABSTRACT FROM AUTHOR]

Component sharing--the use of a component on multiple products within a firm's product line--is widely practiced as a means of offering high variety at low cost. Although many researchers have examined trade-offs involved in component sharing, little research has focused on the impact of component sharing on quality. In this paper, we examine how component sharing impacts one dimension of quality--reliability--defined as mean time to failure. Design considerations suggest that a component designed uniquely for a product will result in higher reliability due to the better fit of the component within the architecture of the product. On the other hand, the learning curve literature suggests that greater experience with a component can improve conformance quality, and can increase reliability via learning from end-user feedback. The engineering literature suggests that improved conformance in turn increases reliability. Sharing a component across multiple products increases experience, and hence, should increase reliability. Using data from the automotive industry, we find support for the hypothesis that higher component reliability is associated with higher cumulative experience with a component. Further, we find support for the hypothesis that higher component reliability is associated with a component that has been designed uniquely for a product. This finding suggests that the popular design strategy of component sharing can in some cases compromise product quality, via reduced reliability. [ABSTRACT FROM AUTHOR]

This paper reports the results of an experimental parameter-free elicitation and decomposition of decision weights under uncertainty. Assuming cumulative prospect theory, utility functions were elicited for gains and losses at an individual level using the tradeoff method. Subsequently, decision weights were elicited through certainty equivalents of uncertain two-outcome prospects. Furthermore, decision weights were decomposed using observable choice instead of invoking other empirical primitives, as in previous experimental studies. The choice-based elicitation of decision weights allows for a quantitative study of their characteristics, and also allows, among other things, for the examination of the sign-dependence hypothesis for observed choice under uncertainty. Our results confirm concavity of the utility function in the gain domain and bounded subadditivity of decision weights and choice-based subjective probabilities. We also find evidence for sign dependence of decision weights. [ABSTRACT FROM AUTHOR]

The fundamental question encountered in acquiring capacity to meet nonstationary demand over a multiperiod horizon is how to balance the trade-off between having insufficient capacity in some periods and excess capacity in others. In the former situation, part of the demand is subcontracted while, in the latter, capacity that has been paid for is rendered idle. Capacity and subcontracting decisions arise in many economic activities ranging from production capacity planning in semiconductor fabs to leasing communication networks, from transportation contracts to staffing of call centers. In this paper, we investigate the trade-offs between acquiring capacity, subcontracting, production, and holding inventory to satisfy nonstationary demand over a finite horizon. We present capacity acquisition models with holding and without holding inventory and identify forecast-robust properties of the models that restrict the dependence of optimal capacity decisions on the demand forecasts. We develop algorithms for numerous practical cost structures involving variable and fixed charges and prove that they all have polynomial time complexity. For models with inventory, we solve a sequence of constant capacity lot-sizing and subcontracting subproblems, which is also of independent interest. [ABSTRACT FROM AUTHOR]

Based on our previous work on the standard measure of risk, this paper presents two classes of measures for perceived risk by decomposing a lottery into its mean and standard risk. One of the classes of our risk measures presumes that there is no risk when there is no uncertainty involved, and the other allows different degenerate lotteries to be evaluated with different values of "risk." The former has more prescriptive appeal in risky decision making, but the latter may have more descriptive power for subjective risk judgments. Our risk measures can also take into account the asymmetric effects of losses and gains on perceived risk based on an appropriate choice of the standard measure of risk. The perceived risk models we propose unify a large body of empirical evidence regarding risk judgments, and provide sufficient flexibility to better capture people's perceptions of risk than previously developed risk models. In particular, our risk measures provide clear ways to accommodate financial measures of risk and psychological measures of risk, and they can be incorporated into preference models in an appealing form based on mean-risk tradeoffs. [ABSTRACT FROM AUTHOR]

Drawing on the premise that the diversification decisions are driven by antecedent factors such as a firm's existing resources (Teece 1982) and industry structural conditions, this paper develops formal hypotheses for reciprocity between the type of diversification and mode of expansion decisions. We consider the specificity of antecedent resources that affect these two decisions and conceptually demonstrate that there is a Contradictory tension in trying to optimize the decisions jointly implying that one or both diversification decisions have to be sub-optimized (i.e., there has to be a trade-off). We make a conceptual argument that this sub-optimization is likely to be in the form of subordination of the mode decision subject to constraints imposed by resources that are highly specific to the mode decision. Following this, we empirically investigate this contradictory tension by using a simultaneous equation model (SEM) on a large sample of firms between 1981 and 1989. The results suggest that one antecedent factor--internal funds--act as the key mediating influence in the joint optimization and leads to a subordination of the mode decision in the joint optimization process. However, the existence of time compression economies and market power benefits are the exceptions to this subordination and trade off process. [ABSTRACT FROM AUTHOR]

Increasingly, conjoint analysts are being asked to design and analyze studies involving large numbers of attributes and / or attribute levels. Various types of approaches, including attribute bridging, Adaptive Conjoint Analysis, and hybrid models have been proposed to deal with the problem. This paper describes recent developments in hybrid modeling. Four hybrid models are described and compared in terms of their performance in an industry-based study entailing 15 product attributes. Comparisons are made in terms of internal cross-validation, market share estimates, attribute importances clustering, and its relationship to exogenous background variables. The proposed models are also compared to selected models from the transportation science literature. The authors emphasize the point that comparative model performance may strongly depend on the ways in which the models are to be used. [ABSTRACT FROM AUTHOR]

In this paper the theory and estimation procedures for several consumer preference models are discussed. Predictive accuracy in the form of internal consistency of these models is compared an empirical application. Consumer decision situations are classified into two classes: decisions under certainty and decisions under uncertainty. For each of the two classes of decision situations, two modeling strategies have been used: statistical estimation and algebraic solution. An additive conjoint, an additive and a multiplicative measurable value, and an additive and a multiplicative utility model are considered. Our main finding is that the statistical estimation procedures outperform their algebraic counterparts on the criterion of predictive accuracy. The utility model provides better predictions for decisions under uncertainty than the widely used conjoint models. The relationship between models for decisions under certainty and decisions under uncertainty is discussed. It is shown how a conjoint or a measurable value function model can be transformed into a utility model with minimum additional information from the subjects. A concept of relative risk attitude is proposed to segment consumers by the degree of their risk aversion or risk seeking propensities. [ABSTRACT FROM AUTHOR]

The focus of this paper is on determining appropriate combination rules for idiosyncratic ordinal utility functions in conjoint measurement. An axiomatic diagnosis is used which is based on explanatory criteria rather than goodness-of-fit or predictive criteria. Experimental results are presented comparing the selection of combination rules based on axiomatic tests and empirical fitting procedures for additive (x + y + z) and distributive (x(y + z)) combinations of three factors. A number of conflicting diagnoses result from the simultaneous applications of axiomatic tests and goodness-of-fit criteria, suggesting the importance of testing certain necessary conditions for simple polynomial combination rules, such as additive and distributive rules. Because the axiomatic approach is effective in rejecting inappropriate functional forms, we argue that it should complement rather than replace the empirical fitting diagnostic approaches. [ABSTRACT FROM AUTHOR]

This paper deals with a class of project scheduling problems concerning the allocation of continuously divisible resources under conditions in which both total usage at every moment and total consumption over the period of project duration are constrained. Typical examples of such resources, called doubly constrained, are money or energy, when the constraint on power or the rate of expenditure cannot be ignored as neither, of course, can, the constraint on resource consumption. Also manpower must often be considered as a doubly constrained resource. Mathematical models of project activities in which performing speeds are continuous functions of resource amounts are considered. The objective is a schedule which minimizes project duration. Thus, the problems considered are generalizations of both the classical project scheduling problem and the time-cost trade-off problem. The properties of optimal schedules are given for strictly concave, concave and convex activity models. On the basis of these properties, methods for finding optimal schedules are described for independent and dependent activities. We also consider the minimum resource consumption ensuring minimum project duration for a given level of resource usage, and the minimum level of resource usage ensuring minimum project duration for a given level of resource consumption. Possible generalizations of the presented results are indicated. [ABSTRACT FROM AUTHOR]

The importance of material flow management for a profit-maximizing firm has been well articulated in the supply chain literature. We demonstrate in our analytical model that a firm must also actively manage information flows within the supply chain, which translates to controlling what it knows, as well as what its competitors and suppliers know. In our model of horizontal competition between an informed and an uninformed firm with a common upstream supplier, material and information flows intersect through leakage of demand (order) information to unintended recipients. As a result, the informed firm's drive to control information flows within the supply chain can trigger operational losses through material flow distortion. These losses can be so severe that the firm may prefer not to acquire information even when it is costless to do so. Our results underscore the importance of strategic information management--actively managing the supply chain's information flows, and making trade-offs with material flows where appropriate, to maximize profits. [ABSTRACT FROM AUTHOR]

Conjoint analysis is a statistical technique used to elicit partworth utilities for product attributes from consumers to aid in the evaluation of market potential for new products. The objective of the share-of-choice problem (a common approach to new product design) is to find the design that maximizes the number of respondents for whom the new product's utility exceeds a specific hurdle (reservation utility). We present an exact branch-and-bound algorithm to solve the share-of-choice problem. Our empirical results, based on several large commercial data sets and simulated data from a controlled experiment, suggest that the approach is useful for finding provably optimal solutions to realistically sized problems, including cases where partworths contain estimation error. [ABSTRACT FROM AUTHOR]

There are two sources of agency costs under moral hazard: (1) distortions in incentive contracts and (2) implementation of suboptimal decisions. In the accounting literature, the relation between conservative accounting and agency costs of type (1) has received considerable attention (cf. Watts 2002). However, little appears to be known about the effects of accounting conservatism on agency costs of type (2) or trade-offs between agency costs of types (1) and (2). The purpose of this study is to examine this void. In a principal-agent setting in which the principal motivates the agent to expend effort using accounting earnings, this study shows that accounting earnings become more useful for reducing agency costs of type (2) when measured conservatively than when measured aggressively. Combined with the result in Kwon et al. (2001) that agency costs of type (1) decrease with accounting conservatism, this analysis suggests that conservative accounting enhances the incentive value of accounting signals with respect to both types of agency costs. [ABSTRACT FROM AUTHOR]

Multiissue negotiations present opportunities for tradeoffs that create gains for one or more parties without causing any party to be worse off. The literature suggests that parties are often unable to identify and capitalize on such trades. We present a Negotiation Support System, called NEGOTIATION ASSISTANT, that enables negotiators to analyze their own preferences and provides a structured negotiation process to help parties move toward optimal trades. The underlying model is based on a multiattribute representation of preferences and communications over a computer network where offers and counteroffers are evaluated according to one's own preferences. The parties can send and receive both formal offers and informal messages. If and when agreement is reached, the computer evaluates the agreement and suggests improvements based on the criteria of Pareto-superiority. In this paper, we motivate the system, present its analytical foundations, discuss its design and development, and provide an experimental assessment of its "value-in-use." Our results strongly suggest that parties using the system in structured negotiation settings would achieve better outcomes than parties negotiating face to face or over an e-mail messaging facility, other things being equal. For example, only 4 of the 34 dyads (11.1 %) negotiating a simulated sales transaction face to face or over e-mail reached an "integrative" settlement, as compared with 29 of the 68 dyads (42.6%) using NEGOTIATION ASSISTANT. Systems such as NEGOTIATION ASSISTANT have the potential to be used in emerging "electronic markets." [ABSTRACT FROM AUTHOR]

Preference models and utility functions are often assessed value trade-offs among attributes. Prior research has shown that trade-off judgements can be biased in systematic ways: for example, the attribute which is used as response receive more relative subjective weight, i.e. the so-called scale compatibility effect (Tversky et al. 1988). This paper proposes a new procedure to elicit value trade-offs called bidimensional matching or "bi-matching", designed to alleviate this effect. Bi-matching differs from traditional trade-off judgements in that both attributes are adjusted simultaneously to reach indifference judgments. Bi-matching is compared with simple matching and choice in four experimental studies, to measure preferences for lotteries and riskless multiattribute alternatives. The main results are: (1) bi-matching produces tradeoffs intermediate between those derived from matching on the "more important" attribute and matching on the less important attribute, although closer to the former; (2) the trade-offs derived from choice reflect more relative weight on the more important dimension than those from bi-matching; (3) bi-matching appears to reduce response error compared to standard matching. These results are generally consistent with theoretical predictions. We discuss the normative question of which preference assessment method is preferable. The current results as a whole and the built-in features of the bi-matching procedure already position this elicitation method as a worthwhile alternative to traditional methods for helping decision-makers introspect and construct their value tradeoffs. [ABSTRACT FROM AUTHOR]

This paper proposes a new method, the (gamble-) tradeoff method, for eliciting utilities in decision under risk or uncertainty. The elicitation of utilities, to be used in the expected utility criterion, turns out to be possible even if probabilities are ambiguous or unknown. A disadvantage of the trade off method is that a few more questions usually must be asked to clients. Also, the lotteries that are needed are somewhat more complex than in the certainty-equivalent method or in the probability-equivalent method. The major advantage of the tradeoff method is its robustness against probability distortions and misconceptions, which constitute a major cause of violations of expected utility and generate inconsistencies in utility elicitation. Thus the tradeoff method retains full validity under prospect theory, rank-dependent utility, and the combination of the two, i.e., cumulative prospect theory. The tradeoff method is tested for monetary outcomes and for outcomes describing life-duration. We find higher risk aversion for life duration, but the tradeoff method elicits similar curvature of utility. Apparently the higher risk aversion for life duration is due to more pronounced deviations from expected utility. [ABSTRACT FROM AUTHOR]

Product design is increasingly recognized as a critical activity that has a significant impact on the performance of firms. Consequently, when firms undertake a new (existing) product design (redesign) activity, it is important to employ techniques that will generate optimal solutions. As optimal product design using conjoint analysis data is an NP-hard problem, heuristic techniques for its solution have been proposed. This research proposes the use of and evaluates the performance of Genetic Algorithms (GA), which is based on the principles of natural selection, as an alternative procedure for generating "good" (i.e., close to optimal) solutions for the product design problem. The paper focuses in (1) how GA vis-á-vis the dynamic programming (DP) heuristic (Kohli and Krishnamurti 1987, 1989) in generating solutions to the product design problems, (3) the sensitivity of the GA solutions to variations in parameter choices, and (4) generalizing the results of the dynamic programming heuristic to product designs involving attributes with varying number of levels and studying the impact of alternating attribute sequencing rules. [ABSTRACT FROM AUTHOR]

This paper discusses the history and interrelations of three central ideas in preference theory: the independence condition in decision under risk, the sure-thing principle in decision under uncertainty, and conjoint independence for multiattribute decisions and consumer theory. Independence was recognized as an important component of decision under risk in the late 1940s by Jacob Marschak, John Nash, Herman Rubin, and Norman Dalkey, and first appeared in publication in Marschak (1950) and Nash (1950). The sure-thing principle can be credited to Savage (1953, 1954). Conjoint independence for consumer theory was introduced by Sono (1943) and Leontief (1947a, b); a form of it can also be recognized in Samuelson (1947), presented earlier in Samuelson (1940). Independence and the sure-thing principle are equivalent for decision under risk, but in a less elementary way than has sometimes been thought. The sure-thing principle for decision under uncertainty and conjoint independence are identical in a mathematical sense. The mathematics underlying our three preference conditions has an older history. The independence condition for decision under risk can be recognized in the characterization of "associative means," and conjoint independence for multiattribute decisions in solutions to the "generalized associativity functional equation." [ABSTRACT FROM AUTHOR]

Many practical product line design problems have large numbers of attributes and levels. In this case, if most attribute level combinations define feasible products, constructing product lines directly from part-worths data is necessary. For three typical formulations of this important problem, Kobli and Sukumar (1990) present state-of-the-art heuristics to find good solutions. In this paper, we develop improved heuristics based on a beam search approach for solving these problems. In our computations for 435 simulated problems, significant improvements occur in five important performance measures used. Our heuristic solutions are closer to the optimal, have smaller standard deviation over replicates, take less computation time, obtain optimal solutions more often and identify a number of "good" product lines explicitly. Computation times for these problems are no more than 22 seconds on a PC, small enough for adequate sensitivity analysis. We also apply the heuristics to a real data set and clarify computational steps by giving a detailed example. [ABSTRACT FROM AUTHOR]

Designing and pricing a product-line is the very essence of every business. In recent years quantitative methods to assist managers in this task have been gaining in popularity. Conjoint analysis is already widely used to measure preferences for different product profiles, and build market simulation models. In the last few years several papers have been published that suggest how to optimally choose a product-line based on such data. We formalize this problem as a mathematical program where the objective of the firm is either profit or total welfare. Unlike alternative published approaches, we introduce fixed and variable costs for each product profile. The number of products to be introduced is endogenously determined on the basis of their desirability, fixed and variable costs, and in the case of profits, their cannibalization effect on other products. While the problem is difficult (NP-complete), we show that the maximum welfare problem is equivalent to the uncapacitated plant location problem, which can be solved very efficiently using the greedy interchange heuristic. Based on past published experience with this problem, and on simulations we perform, we show that optimal or near optimal solutions are obtained in seconds for large problems. We develop a new greedy heuristic for the profit problem, and its application to simulated problems shows that it too runs quickly, and with better performance than various alternatives and previously published heuristics. We also show how the methodology can be applied, taking existing products of both the firm and the competition into account. [ABSTRACT FROM AUTHOR]

A dynamic-programming heuristic is described to find approximate solutions to the problem of identifying a new, multi-attribute product profile associated with the highest share-of-choices in a competitive market. The input data consist of idiosyncratic multi-attribute preference functions estimated using conjoint or hybrid-conjoint analysts. An individual is assumed to choose a new product profile if he/she associates a higher utility with it than with a status-quo alternative. Importance weights are assigned to individuals to account for differences in their purchase and/or usage rates and the performance of a new product profile is evaluated after taking into account its cannibalization of a seller's existing brands. In a simulation with real-sized problems, the proposed heuristic strictly dominates an alternative lagrangian-relaxation heuristic in terms of both computational time and approximation of the optimal solution. Across 192 simulated problems, the dynamic-programming heuristic identifies product profiles whose share-of-choices, on average, are 98.2% of the share-of-choices of the optimal product profile, suggesting that it closely approximates the optimal solution. [ABSTRACT FROM AUTHOR]

Firms are increasingly selling through both offline and online channels, allowing customers to experience the touch and feel of product attributes before purchasing those products. Consequently, the selection of products offered offline affects the demand in both channels. We address how firms should select an optimal offline assortment to maximize profits across both channels; we call this the showcase decision problem. We incorporate the impact of physical evaluation on preferences into the consumer demand model. Under this model, we show that the decision problem is NP-hard. Analytically, we derive optimal results for special cases and near-optimal approximations for general cases. Empirically, we use conjoint analysis to identify changes in consumer preferences resulting from physically evaluating products. For this application, we demonstrate gains in expected revenue of up to 40% due to accounting for the impact of offline assortment on the online sales. [ABSTRACT FROM AUTHOR]

Which, if any, of a firm's inventions should it patent? Should it patent at all? Many companies engaged in an innovation race seek a patenting strategy that balances protection of their intellectual property against the knowledge spillovers resulting from disclosure requirements. Not much is known about factors that determine the patenting strategy best able to resolve this trade-off. Although scholars in various management, economics, and engineering disciplines have researched patents and patenting regimes, little work has addressed the normative issues that pertain to forming an appropriate firm-level patenting strategy. We develop an inventory of real-life patenting strategies and integrate them into a coherent framework. Our simulation model characterizes the optimal patenting choices for different environmental and firm-level contingencies while capturing the dynamics between competing firms.We identify the firm's research and development strategy as the most salient determinant of its optimal patenting strategy. Our research contributes to establishing a contingency theory of patenting strategies. [ABSTRACT FROM AUTHOR]

Modeling consumer heterogeneity helps practitioners understand market structures and devise effective marketing strategies. In this research we study finite mixture specifications for modeling consumer heterogeneity where each regression coefficient has its own finite mixture--that is, an attribute finite mixture model. An important challenge of such an approach to modeling heterogeneity lies in its estimation. A proposed Bayesian estimation approach, based on recent advances in reversible-jump Markov chain Monte Carlo methods, can estimate parameters for the attribute-based finite mixture model, assuming that the number of components for each finite mixture is a discrete random variable. An attribute specification has several advantages over traditional, vector-based, finite mixture specifications; specifically, the attribute mixture model offers a more appropriate aggregation of information than does the vector specification facilitating estimation. In an extensive simulation study and an empirical application, we show that the attribute model can recover complex heterogeneity structures, making it dominant over traditional (vector) finite mixture regression models and a strong contender compared to mixture-of-normals models for modeling heterogeneity. [ABSTRACT FROM AUTHOR]

Competitive positioning is a central, yet understudied, topic in strategy. Understanding positioning requires understanding two distinct mappings: how underlying policies are transformed into positions, and how positions are transformed into market performance. A complete treatment of positioning requires incorporating organizational design in the presence of policy interdependence; consumer choice in the presence of trade-offs among multiple product attributes; and competitive interactions among firms. We develop a model that integrates these elements. We show that in a multiattribute setting, trade-offs have critical, nonmonotonic effects on a range of strategy questions including the relationship between positions that are operationally efficient and those that remain viable in the face of competition as well as the concentration of market share in the industry. Of particular interest are implications for firm heterogeneity. We show that increases in business policy interdependence can decrease positioning heterogeneity among firms in an industry, depending on the nature of trade-offs. We also show that the relationship between strategy heterogeneity and positioning heterogeneity is moderated by the extent of policy interdependence. [ABSTRACT FROM AUTHOR]

Product line design decisions are determinant for a firm's market success and, simultaneously, very costly to implement and change. To evaluate the profitability of a design, a number of mathematical programming approaches have been proposed in the last three decades--typically nonlinear integer optimization problems that can only be tackled heuristically for real-world instances. Recently, Chen and Hausman (2000) efficiently exploited the fractional programming properties of a stylized model for product line and price selection with the objective to maximize contribution given homogeneous customer behavior by an aggregate multinomial logit choice model. We extend the approach of Chen and Hausman (Chen, K. D., W. H. Hausman. 2000. Technical note: Mathematical properties of the optimal product line selection problem using choice-based conjoint analysis. Management Sci. 46(2) 327--332) to determine an optimal product line under a personalized or group pricing strategy in markets with multiple heterogeneous consumers such that total profit (including fixed costs) is maximized. Personalized/group pricing is a growing discrimination strategy that more and more businesses are realizing is not only very profitable, but also often implementable in the era of e-business. Our problem formulation allows to incorporate commonly applied attraction choice models including the multinomial logit, the Bradley--Terry--Luce, and approximately the first choice model. Though the problem is generally nonlinear, we show that the fractional substructure resulting from attraction choice models can still be exploited to globally solve real-world instances in reasonable time. [ABSTRACT FROM AUTHOR]

Discrete choice models estimated using hypothetical choices made in a survey setting (i.e., choice experiments) are widely used to estimate the importance of product attributes in order to make product design and marketing mix decisions. Choice experiments allow the researcher to estimate preferences for product features that do not yet exist in the market. However, parameters estimated from experimental data often show marked inconsistencies with those inferred from the market, reducing their usefulness in forecasting and decision making. We propose an approach for combining choice-based conjoint data with individual-level purchase data to produce estimates that are more consistent with the market. Unlike prior approaches for calibrating conjoint models so that they correctly predict aggregate market shares for a "baseline" market, the proposed approach is designed to produce parameters that are more consistent with those that can be inferred from individual-level market data. The proposed method relies on a new general framework for combining two or more sources of individual-level choice data to estimate a hierarchical discrete choice model. Past approaches to combining choice data assume that the population mean for the parameters is the same across both data sets and require that data sets are sampled from the same population. In contrast, we incorporate in the model individual characteristic variables, and assert only that the mapping between individuals' characteristics and their preferences is the same across the data sets. This allows the model to be applied even if the sample of individuals observed in each data set is not representative of the population as a whole, so long as appropriate product-use variables are collected that can explain the systematic deviations between them. The framework also explicitly incorporates a model for the individual characteristics, which allows us to use Bayesian missing-data techniques to handle the situation where each data set contains different demographic variables. This makes the method useful in practice for a wide range of existing market and conjoint data sets. We apply the method to a set of conjoint and market data for minivan choice and find that the proposed method predicts holdout market choices better than a model estimated from conjoint data alone or a model that does not include demographic variables. [ABSTRACT FROM AUTHOR]

We develop a branch-and-price algorithm for constructing an optimal product line using partworth estimates from choice-based conjoint analysis. The algorithm determines the specific attribute levels for each multiattribute product in a set of products to maximize the resulting product line's share of choice, i.e., the number of respondents for whom at least one new product's utility exceeds the respondent's reservation utility. Computational results using large commercial and simulated data sets demonstrate that the algorithm can identify provably optimal, robust solutions to realistically sized problems. [ABSTRACT FROM AUTHOR]

We propose a new alternative preference measurement method, barter conjoint, to contrast with traditional choice-based conjoint (CBC) approaches. Barter conjoint collects a substantially larger amount of data compared to CBC and allows for information diffusion among respondents. We conducted two empirical studies that compare CBC (with and without incentive alignment) and barter conjoint. The studies employed a total of three product categories, each with two validation tasks (one follows immediately and one conducted two weeks later). Our results confirmed prior research that incentive alignment, in general, substantially improves out-of-sample predictive performance of CBC. Furthermore, we found barter conjoint performs substantially better than the incentive-aligned CBC. However, in the spirit of "no free lunch," barter conjoint is more taxing (in various ways) than CBC, suggesting a potential trade-off between consumer resource allocation (at the time of the task) and (managerial) predictive accuracy downstream. Given that this is the first study on barter conjoint, we discuss various limitations of the current implementation and fruitful directions for future research. [ABSTRACT FROM AUTHOR]

Horizontal scope--the set of products and services offered--is an important dimension of firm strategy and a potentially significant source of competitive advantage. On one hand, the ability to build close buyer-supplier relationships over multiple transactions can give an advantage to broad firms that offer buyers "one-stop shopping." On the other hand, the existence of organizational tradeoffs can give an advantage to firms that specialize in a narrower range of products or services. We develop a biform game that incorporates this tension and show how the use of three generic scope strategies--specialist, generalist, and hybrid--depends on organizational tradeoffs, client-specific scope economies, barriers to entry, heterogeneity in buyer task requirements, and the bargaining power of suppliers relative to buyers. We then use the model to study a variety of issues in supply chain management, including the gains to coordinating suppliers, the optimal level of buyer power, and the desirability of subsidizing suppliers. One of our objectives is to show how biform games, which introduce unstructured negotiations into game theory analysis, can be used to develop applied theory relevant to strategy. Generalizing from our stylized model, we identify a class of biform games involving buyers and suppliers that is useful for strategy analysis. Games in this class have the attractive property of each supplier's share of industry total surplus being the product of its added value and its relative bargaining power. [ABSTRACT FROM AUTHOR]

Given the difficulties people experience in making trade-offs, what are the consequences of using simple models that avoid trade-offs? We examine choices by such models in environments where "true" preferences are linear and attributes are characterized by binary attributes. A deterministic elimination-by-aspects (DEBA) model is highly effective over a range of conditions. When preferences are quite compensatory, however, a modified equal weighting (EW) model that uses DEBA to resolve ties is more effective. We explore the sensitivity of results to errors in using DEBA, to different distributions of alternatives, and to error in "true" preferences. Under the conditions examined here, the outcomes of these "boundedly rational" models are highly consistent with "rational" models that explicitly confront trade-offs. We emphasize the importance of binary attributes in reaching these conclusions. [ABSTRACT FROM AUTHOR]

Consumer reservation price is a key concept in marketing and economics. Theoretically, this concept has been instrumental in studying consumer purchase decisions, competitive pricing strategies, and welfare economics. Managerially, knowledge of consumer reservation prices is critical for implementing many pricing tactics such as bundling, target promotions, nonlinear pricing, and one-to-one pricing, and for assessing the impact of marketing strategy on demand. Despite the practical and theoretical importance of this concept, its measurement at the individual level in a practical setting proves elusive. We propose a conjoint-based approach to estimate consumer-level reservation prices. This approach integrates the preference estimation of traditional conjoint with the economic theory of consumer choice. This integration augments the capability of traditional conjoint such that consumers' reservation prices for a product can be derived directly from the individual-level estimates of conjoint coefficients. With this augmentation, we can model a consumer's decision of not only which product to buy, but also whether to buy at all in a category. Thus, we can simulate simultaneously three effects that a change in price or the introduction of a new product may generate in a market: the customer switching effect, the cannibalization effect, and the market expansion effect. We show in a pilot application how this approach can aid product and pricing decisions. We also demonstrate the predictive validity of our approach using data from a commercial study of automobile batteries. [ABSTRACT FROM AUTHOR]

An important problem in the product design and development process is to use the part-worths preferences of potential customers to design a new product such that market share is maximized. The authors present a new optimization framework for this problem, the nested partitions (NP) method. This method is globally convergent and may utilize existing heuristic methods to speed its convergence. We incorporate several known heuristics into this framework and demonstrate through numerical experiments that using the NP method results in superior product designs. Our numerical results suggest that the new framework is particularly useful for designing complex products with many attributes. [ABSTRACT FROM AUTHOR]

Selecting and pricing product lines is an essential activity in many businesses. In recent years, quantitative approaches for such tasks have been gaining in popularity. One often-employed method is to use data from traditional rankings/ratings-based conjoint analysis and attack the product line selection problem with enumeration or heuristics. In this note, we employ a relatively new methodology known as choice-based conjoint analysis (to model customer preferences) and investigate its mathematical properties when used to model the product line selection problem. Despite some inherent limitations resulting from its aggregated formulation, we show that this more parsimonious conjoint approach has some special mathematical properties that lead to an efficient optimal algorithm to tackle the product line/price selection problem. As a result, problems of realistic size can be solved efficiently using standard, commercially available mathematical programming codes. [ABSTRACT FROM AUTHOR]

This study investigates whether VCs' assessment policies of new venture survival are consistent with those arising from the strategy literature (using two established strategy perspectives). Strategy scholars suggest the nature of the markets, competition, and decisions made by the management team affect a new venture's survival chances. The findings demonstrate that VCs' assessment policies are predominantly consistent with those proposed by strategy scholars--providing insight into why VCs consider certain criteria in their assessment of new venture survival as well as why some criteria are more important in their assessment than others. Through this increased understanding of venture capitalists' decision making, entrepreneurs seeking capital may be better able to address their requests for funding to those criteria venture capitalists find most critical to the survival of a new venture. Venture capitalists may use these findings to better understand their own decision making process, which, in turn, provides the opportunity to increase evaluation efficiency. [ABSTRACT FROM AUTHOR]

Regulators of utilities that operate as local monopolies would like to set prices or allow rates of return based upon the quality of a utility's service. However, quality is highly multi-dimensional. Traditionally, regulators have collected measures of quality on many separate dimensions, and compared performance on these dimensions to explicit pass-fail standards. They have lacked a method of combining complex patterns of substandard and superstandard performance on these many dimensions to arrive at an overall evaluation of the quality of service. To redress this problem, we first describe the information processing problems regulators encounter when they attempt to integrate this complex array of information intuitively, and the problems that arise when the link between service quality (as reflected in patterns of passed and failed standards) and regulatory incentives is not made explicit to utility management. We develop a bootstrapped method for formalizing each expert regulator's evaluation policy using hierarchical conjoint analysis, and apply this method to the evaluation of local telephone companies by the Florida Public Service Commission (FPSC). We show that experts within the FPSC, the regulated utilities, and a large telephone customer exhibit very high agreement about how the various dimensions of quality should be differentially weighted. We derive a consensus measure of overall quality, Q, and identify a score associated with meeting all standards exactly, 0*. Utilities can then be rewarded based upon whether or not they exceed Q*, rather than on the basis of how many standards are met. Compared with a pattern in which each standard is met exactly, there exist patterns of mixed substandard and superstandard performance on the individual dimensions of quality that are less costly to produce and higher in overall quality. We compare utility incentives when rewards are based upon Q to those under the current pass-fail regime. [ABSTRACT FROM AUTHOR]

This research develops and implements a dyad-level procedure for testing whether the outcomes of buyer-seller negotiations correspond to settlements prescribed by Nash's theory of cooperative games. The procedure entails a multivariate statistical test in which some parameters are estimated by simulation, while others are provided directly from the assessment of negotiator utility functions by means of conjoint analysis. The procedure is applied to an experiment in which subjects participated in a realistic role-playing exercise that replicated the purchase of television advertising time. Results indicate that at the 0.01 significance level, 57.8% of the dyads can be rejected as not having achieved Nash solutions. These results, and their implications for marketing practitioners as well as future research, are discussed. [ABSTRACT FROM AUTHOR]

A conceptually new first-cut decision-aid for optimal delegation is introduced. It takes a subjective approach to capital budgeting, from the delegator's perspective. A model captures the tradeoffs between the cost of centralized (duplicated) review, and the opportunity cost risk of having (otherwise) "unacceptable" projects, Two explicit limits to optimal decentralization are identified to help headquarters (HQ) to (a) Approved and executed by divisions without HQ review. (b) Rejected by divisions without HQ review. (c) Submitted to HQ for a centralized review. A numerical example is presented to demonstrate the process. [ABSTRACT FROM AUTHOR]