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This paper proposes a new approach to modeling heterogeneity in choice data that can accommodate fixed-point ratings data and text. This paper proposes a new approach to modeling heterogeneity in choice data that can accommodate fixed-point ratings data and text. Respondent choices, survey responses, and narratives are combined to form latent archetypes that provide an integrated description of respondents in terms of the objects and drivers of their wants. We propose a measure of coherence to assess the value of integrating these data elements and demonstrate the value of integrating text data into an analysis of choice and scaled response data. A conjoint data set is used to illustrate the model where we find that the text data helps clarify the origin of demand. [ABSTRACT FROM AUTHOR]

Problem definition: We consider the problem of demand learning and pricing for retailers who offer assortments of substitutable products that change frequently, for example, due to limited inventory, perishable or time-sensitive products, or the retailer's desire to frequently offer new styles. Academic/practical relevance: We are one of the first to consider the demand learning and pricing problem for retailers who offer product assortments that change frequently, and we propose and implement a learn-then-earn algorithm for use in this setting. Our algorithm prioritizes a short learning phase, an important practical characteristic that is only considered by few other algorithms. Methodology: We develop a novel demand learning and pricing algorithm that learns quickly in an environment with varying assortments and limited price changes by adapting the commonly used marketing technique of conjoint analysis to our setting. We partner with Zenrez, an e-commerce company that partners with fitness studios to sell excess capacity of fitness classes, to implement our algorithm in a controlled field experiment to evaluate its effectiveness in practice using the synthetic control method. Results: Relative to a control group, our algorithm led to an expected initial dip in revenue during the learning phase, followed by a sustained and significant increase in average daily revenue of 14%–18% throughout the earning phase, illustrating that our algorithmic contributions can make a significant impact in practice. Managerial implications: The theoretical benefit of demand learning and pricing algorithms is well understood—they allow retailers to optimally match supply and demand in the face of uncertain preseason demand. However, most existing demand learning and pricing algorithms require substantial sales volume and the ability to change prices frequently for each product. Our work provides retailers who do not have this luxury a powerful demand learning and pricing algorithm that has been proven in practice. History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1080. [ABSTRACT FROM AUTHOR]

In this paper, I examine how lower-cost production and new market opportunities influence the divestment decisions of firms. I argue that lower-cost production and new market opportunities in foreign markets can provide a better use of existing firm resources and posit that these opportunities are likely to influence firm divestment of home-country operations. The empirical results from a panel of 190 U.S. firms over a 20-year period (1981-2000) show that lower-cost production and new market opportunities influence the divestment decisions of firms. However, the results also reveal several interesting moderating influences on the hypothesized trade-offs and differences across the growth strategies of firms in low- and high-research and development intensive industries. By considering how and when investment in lower-cost production and new market opportunities impacts firm divestment decisions, this study examines divestment not only as a choice managers make when dealing with poor or struggling operations, but also as a response to better opportunities for firm resources in other markets. By focusing on the trade-offs managers make across product and geographic markets, this paper examine the role divestment can play in firm growth and expansion strategies [ABSTRACT FROM AUTHOR]

A firm contemplating improvements to its product attributes would be interested in the dollar value the market attaches to any potential product modification. In this paper, we derive a measure of market value such that the comparison of the measure against the incremental unit cost of the attribute improvement is key in deciding whether or not the attribute improvement is profitable. Competition from other brands, the potential for market expansion, and heterogeneity in customer preference structures are explicitly modeled using the multinomial logit framework. The analysis yields a closed form expression for the market's value for an attribute improvement (MVAI). A key result we obtain is that customers should be differentially weighted based on their probability of purchasing the firm's product. In particular, customers who exhibit a very high or very low probability of choosing the firm's product should receive less weight in determining MVAI. Because the probability of choice varies across products, the answer to the question of how much the market values an improvement depends on which firm is asking the question. It is shown that customers whose utilities have a greater random component should be weighted less. Furthermore, the measure developed is robust to the influence of outliers in the sample. An empirical illustration of the MVAI measure in the context of a new product development study is provided. The study illustrates the advantages of the proposed measure over currently used approaches and explores the possibility of competitive price reactions. [ABSTRACT FROM AUTHOR]

This paper offers a theoretical explanation for why interfirm collaborations form yet fail, and further suggests how firms might manage them for a more positive outcome. Based on a perspective of value, we explain how a more inclusive and integrative perspective, one which combines elements from transaction costs and resource-based theory, provides more robust insight into collaboration formulation, management, and instability. In doing so, we differentiate rent-yielding firm-specific assets at the core of the resource-based view from the transaction-specific assets at the core of transaction cost theory. The paper makes a crucial distinction between the potential value attainable through collaborations and its actual realization. The crux of our argument is that firms enter into collaborative relationships because these are expected to yield superior value relative to alternate organizational forms in certain situations, offering potentially synergistic combinations of complementary resources and capabilities. Yet such relationships are frequently prone to failure because the partner firms tend not to recognize ex ante the nature and extent of transaction-specific investment that is required in the collaborative relationship to attain these synergies. In our argument, critically, the relationship between organizations is seen not simply as a governance structure of a hybrid nature but, more importantly, as a productive resource for value creation and realization. In this light, transaction-specific investment in what we term relational specificity becomes imperative. In the search for value, we explain why the transaction costs incurred in the exchange to be transacted and, similarly, why the returns realized from these resources are not independent of the relationship- and transaction-specific expenditures incurred in effectively combining them and maintaining the combination. The interdependence between the two, mediated by the quality of the relationship... [ABSTRACT FROM AUTHOR]

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]

Consumers use different attribute partworths online and offline; these differences are consistent across consumers, and we propose a method for adjusting them. Despite the growth of online retail, the majority of products are still sold offline, and the "touch-and-feel" aspect of physically examining a product before purchase remains important to many consumers. In this paper, we demonstrate that large discrepancies can exist between how consumers evaluate products when examining them "live" versus based on online descriptions, even for a relatively familiar product (messenger bags) and for utilitarian features. Therefore, the use of online evaluations in market research may result in inaccurate predictions and potentially suboptimal decisions by the firm. Because eliciting preferences by conducting large-scale offline market research is costly, we propose fusing data from a large online study with data from a smaller set of participants who complete both an online and an offline study. We demonstrate our approach using conjoint studies on two sets of participants. The group who completed both online and offline studies allows us to calibrate the relationship between online and offline partworths. To obtain reliable parameter estimates, we propose two statistical methods: a hierarchical Bayesian approach and a k-nearest-neighbors approach. We demonstrate that the proposed approach achieves better out-of-sample predictive performance on individual choices (up to 25% improvement), as well as aggregate market shares (up to 33% improvement). Data are available at https://doi.org/10.1287/mksc.2018.1124. [ABSTRACT FROM AUTHOR]

Most operations management textbooks use the following simple approximation to illustrate the computation of the capacity of a process: the capacity of each resource is first calculated by examining that resource in isolation; process capacity is then defined as the smallest among the capacities of the resources, that is, bottleneck capacity. In a recent paper, Gurvich and Van Mieghem [Gurvich I, Van Mieghem JA (2015) Collaboration and multitasking in networks: Architectures, bottlenecks, and capacity. Manufacturing Service Oper. Management 17(1):16–33.] show that, in the presence of collaboration and multitasking, this "bottleneck formula" can be significantly inaccurate, and they obtain a necessary and sufficient condition under which it correctly determines process capacity. We provide further clarity on determining process capacity by showing that it is hard to compute process capacity exactly and also to approximate it to within a reasonable factor. These results are based on a novel characterization, which we establish, of process capacity that relates it to the fractional chromatic number of the associated "collaboration graph." An important implication is that it is unlikely that we can replace the bottleneck formula with a simple but close approximation of process capacity. On the positive side, we show that capacity can be efficiently computed for processes for which the collaboration graph is a perfect graph. From a practical viewpoint, our analysis for general processes results in a natural hierarchy of subclasses of policies that require an increasing amount of sophistication in implementation and management: while process capacity is the maximum long-term process rate achievable over all feasible policies, we provide a precise expression for the maximum process rate over policies in each subclass of this hierarchy, thus highlighting the trade-off between operational difficulty and the achievable process rate. The online appendix is available at https://doi.org/10.1287/msom.2017.0689. [ABSTRACT FROM AUTHOR]

We study a product-line price optimization problem with demand given by a discrete mixed multinomial logit (MMNL) model. The market is divided into a finite number of market segments, with product demand in each segment governed by the multinomial logit (MNL) model. We show that the concavity property with respect to the choice probability vector shown for the MNL model breaks down under MMNL even for entirely symmetric price sensitivities. In addition, the equal markup property identified for the MNL model no longer holds under MMNL, suggesting that heterogeneity in customer population justifies non-equal-markup pricing. In this paper, we characterize the profit function under MMNL as the sum of a set of quasiconcave functions and present efficient optimization algorithms. We demonstrate the application of our methods using data from Intel Corporation. Our results show that the optimal prices exploit segment differences through redistribution of sales and profit among customer segments. In addition, we use our methods to generate the profit-share efficient frontier, helping the firm to set prices that effectively balance these two measures. The tools developed in this research function in three important ways at Intel: (1) they provide a new alternative for market share prediction among Intel products for different customer segments, adding to the suite of independent demand forecasting tools; (2) they optimize product prices based on segment-specific customer preferences revealed through sales data, a capability that Intel's current pricing tools include only heuristically; (3) they quantify the trade-off between profit and market share, adding a new decision support capability to the company. The online appendix is available at https://doi.org/10.1287/msom.2017.0675. [ABSTRACT FROM AUTHOR]

Despite various planning efforts, airspace capacity can sometimes be exceeded, typically because of disruptive events. Air traffic flow management (ATFM) is the process of managing flights in this situation. In this paper, we present an ATFM model that accounts for different rerouting options (path rerouting and diversion) and preexisting en route flights. The model proposes having a central authority to control all decisions, which is then compared with current practice. We also consider interflight and interairline fairness measures in the network. We use an exact approach to solve small- to medium-sized instances, and we propose a modified fix-and-relax heuristic to solve large-sized instances. Allowing a central authority to control all decisions increases network efficiency compared with the case where the ATFM authority and airlines control decisions independently. Our experiments show that including different rerouting options in ATFM can help reduce delays by up to 8% and cancellations by up to 23%. Moreover, ground delay cost has much more impact on network decisions than air delay cost, and network decisions are insensitive to changes in diversion cost. Furthermore, the analysis of the tradeoff between total network cost and overtaking cost shows that adding costs for overtaking can significantly improve fairness at only a small increase in total system cost. A balanced total cost per flight among airlines can be achieved at a small increase in the network cost (0.2%–3.0%) when imposing airline fairness. In conclusion, the comprehensiveness of the model makes it useful for analyzing a wide range of alternatives for efficient ATFM. [ABSTRACT FROM AUTHOR]

Existing research on choice designs focuses exclusively on compensatory models that assume that all available alternatives are considered in the choice process. In this paper, we develop a method to construct efficient designs for a two-stage, consider-then-choose model that involves a noncompensatory screening process at the first stage and a compensatory choice process at the second stage. The method applies to both conjunctive and disjunctive screening rules. Under certain conditions, the method also applies to the subset conjunctive and disjunctions of conjunctions screening rules. Based on the local design criterion, we conduct a comparative study of compensatory and conjunctive designs--the former are optimized for a compensatory model and the latter for a two-stage model that uses conjunctive screening in its first stage. We find that conjunctive designs have higher level overlap than compensatory designs. This occurs because level overlap helps pinpoint screening behavior. Higher overlap of conjunctive designs is also accompanied by lower orthogonality, less level balance, and more utility balance. We find that compensatory designs have a significant loss of design efficiency when the true model involves conjunctive screening at the consideration stage. These designs also have much less power than conjunctive designs in identifying a true consider-then-choose process with conjunctive screening. In contrast, when the true model is compensatory, the efficiency loss from using a conjunctive design is lower. Also, conjunctive designs have about the same power as compensatory designs in identifying a true compensatory choice process. Our findings make a strong case for the use of conjunctive designs when there is prior evidence to support respondent screening. [ABSTRACT FROM AUTHOR]

Random effects or mixed logit models are often used to model differences in consumer preferences. Data from choice experiments are needed to estimate the mean vector and the variances of the multivariate heterogeneity distribution involved. In this paper, an efficient algorithm is proposed to construct semi-Bayesian D-optimal mixed logit designs that take into account the uncertainty about the mean vector of the distribution. These designs are compared to locally D-optimal mixed logit designs, Bayesian and locally D-optimal designs for the multinomial logit model and to nearly orthogonal designs (Sawtooth (CBC)) for a wide range of parameter values. It is found that the semi-Bayesian mixed logit designs outperform the competing designs not only in terms of estimation efficiency but also in terms of prediction accuracy. In particular, it is shown that assuming large prior values for the variance parameters for constructing semi-Bayesian mixed logit designs is most robust against the misspecification of the prior mean vector. In addition, the semi-Bayesian mixed logit designs are compared to the fully Bayesian mixed logit designs, which take also into account the uncertainty about the variances in the heterogeneity distribution and which can be constructed only using prohibitively large computing power. The differences in estimation and prediction accuracy turn out to be rather small in most cases, which indicates that the semi-Bayesian approach is currently the most appropriate one if one needs to estimate mixed logit models. [ABSTRACT FROM AUTHOR]

The use of adaptive designs in conjoint analysis has been shown to lead to an endogeneity bias in part- worth estimates using sampling experiments. In this paper, we re-examine the endogeneity issue in light of the likelihood principle. The likelihood principle asserts that all relevant information in the data about model parameters is contained in the likelihood function. We show that, once the data are collected, adhering to the likelihood principle leads to analysis where endogeneity becomes ignorable for estimation. The likelihood principle is implicit to Bayesian analysis, and discussion is offered for detecting and dealing with endogeneity bias in marketing. [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]

Firms develop products by manipulating the attributes of offerings, and consumers derive utility from the benefits that the attributes afford. While the field of marketing has long been aware of the distinction between attributes and benefits, it has not developed methods for understanding how attributes and benefits are related. This paper develops a benefit-based model for conjoint analysis that assumes consumers satiate on attributes that are perceived to provide the same benefit. A latent-variable model is proposed that estimates the map between attributes and benefits, and is applied to data from two conjoint studies involving a durable product and a household consumable. The model is shown to fit the data better, provide improved predictions, and lead to different product design implications than the standard conjoint model. Data, as supplemental material, are available at . [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]

Features involving the taste, smell, touch, and sight of products, as well as attributes such as safety and confidence, are not easily measured in product research without respondents actually experiencing them. Moreover, product researchers often evaluate a large number of these attributes (e.g., >50) in applied studies, making standard valuation techniques such as conjoint analysis difficult to implement. Product researchers instead rely on ratings data to assess features for which the respondent has had actual experience. In this paper we develop a method of monetizing rating data to standardize product evaluations among respondents. The adjusted data are shown to increase the accuracy of purchase predictions by about 20% relative to existing methods of scale adjustment, leading to better inference in models using ratings data. We demonstrate our method using data from a large scale product use study by a packaged goods manufacturer. Data, as supplemental material, are available at . [ABSTRACT FROM AUTHOR]

As technology advances, new products (e.g., digital cameras, computer tablets, etc.) have become increasingly more complex. Researchers often face considerable challenges in understanding consumers' preferences for such products. This paper proposes an adaptive decompositional framework to elicit consumers' preferences for complex products. The proposed method starts with collaborative-filtered initial part-worths, followed by an adaptive question selection process that uses a fuzzy support vector machine active learning algorithm to adaptively refine the individual-specific preference estimate after each question. Our empirical and synthetic studies suggest that the proposed method performs well for product categories equipped with as many as 70 to 100 attribute levels, which is typically considered prohibitive for decompositional preference elicitation methods. In addition, we demonstrate that the proposed method provides a natural remedy for a long-standing challenge in adaptive question design by gauging the possibility of response errors on the fly and incorporating the results into the survey design. This research also explores in a live setting how responses from previous respondents may be used to facilitate active learning of the focal respondent's product preferences. Overall, the proposed approach offers new capabilities that complement existing preference elicitation methods, particularly in the context of complex products. Data, as supplemental material, are available at . [ABSTRACT FROM AUTHOR]

Extant research on choice designs in marketing focuses on the construction of efficient homogeneous designs where all respondents get the same design. Recently marketing scholars proposed the construction of efficient heterogeneous designs where different respondents or groups of respondents get different subdesigns, and demonstrated substantial efficiency gain when such heterogeneous designs are employed. A significant hurdle in the widespread adoption of heterogeneous designs is the high computation cost, even when the number of subdesigns contained in the heterogeneous design is restricted to be small. In this paper we propose a new approach for the construction of efficient heterogeneous choice designs. In contrast to extant approaches that are based on an exact design framework where it is computationally prohibitive to do an exhaustive search to find a globally optimal design, our proposed approach is based on the continuous design framework where well-established mathematical theories can be leveraged for quick identification of a globally optimal design. The proposed approach makes it feasible to generate a highly efficient choice design that is completely heterogeneous--a unique subdesign for each individual respondent in the choice experiment. The proposed approach is the first in the marketing literature to find a completely heterogeneous choice design with assured high global design efficiency using the continuous design framework. Results from simulation and empirical studies demonstrate superior performance of the proposed approach over extant approaches in constructing efficient heterogeneous choice designs. [ABSTRACT FROM AUTHOR]

A Searcher seeks to find a stationary Hider located at some point H (not necessarily a node) on a given network Q. The Searcher can move along the network from a given starting point at unit speed, but to actually find the Hider she must pass it while moving at a fixed slower speed (which may depend on the arc). In this "bimodal search game," the payoff is the first time the Searcher passes the Hider while moving at her slow speed. This game models the search for a small or well hidden object (e.g., a contact lens, improvised explosive device, predator search for camouflaged prey). We define a bimodal Chinese postman tour as a tour of minimum time δ which traverses every point of every arc at least once in the slow mode. For trees and weakly Eulerian networks (networks containing a number of disjoint Eulerian cycles connected in a tree-like fashion) the value of the bimodal search game is δ/2. For trees, the optimal Hider strategy has full support on the network. This differs from traditional search games, where it is optimal for him to hide only at leaf nodes. We then consider the notion of a lucky Searcher who can also detect the Hider with a positive probability q even when passing him at her fast speed. This paper has particular importance for demining problems. [ABSTRACT FROM AUTHOR]

Stakeholders often control vital resources for decision makers, and this can lead decision makers to take stakeholder opinions into account when making important decisions. This process can be complicated by a number of factors. First, many important decisions involve risk and uncertainty. When the outcome is uncertain, how does a decision maker take the views of stakeholders into account? Second, many decision makers are accountable to multiple different stakeholder groups with different preferences. How do these heterogeneous stakeholder groups affect the process of decision making? More generally, do these stakeholder considerations lead to decisions that are not socially optimal? We explore these and related questions by focusing on a specific type of high-stakes decision making in a context featuring significant risk and heterogeneous stakeholders--the decision to evacuate a community during the threat of a hurricane hitting land. There is research on weather forecasting techniques and individual evacuation behavior; however, there is no research on the behavior of local officials in making hurricane evacuation decisions. These decisions provide an excellent context for an exploration into the specific processes by which stakeholder considerations may affect the process of decision making. This study offers a simple model of the process underlying evacuation decision making. The model focuses on how the evacuation threshold is set based on the anticipated costs of Type I versus Type II errors. We then use the model--supplemented with rich qualitative and quantitative data--to offer a series of propositions about the conditions under which nonsocially optimal evacuation decisions may be made. This paper contributes to the literature on decision making by offering a simple model that integrates decision making and stakeholder considerations and by offering specific and novel propositions about this integration. [ABSTRACT FROM AUTHOR]

In recent years, many algorithms have been proposed to extract process models from process execution logs. The process models describe the ordering relationships between tasks in a process in terms of standard constructs like sequence, parallel, choice, and loop. Most algorithms assume that each trace in a log represents a correct execution sequence based on a model. In practice, logs are often noisy, and algorithms designed for correct logs are not able to handle noisy logs. In this paper we share our key insights from a study of noise in process logs both real and synthetic. We found that all process logs can be explained by a block-structured model with two special self-loop and optional structures, making it trivial to build a fully accurate process model for any given log, even one with inaccurate data or noise present in it. However, such a model suffers from low quality. By controlling the use of self-loop and optional structures of tasks and blocks of tasks, we can balance the quality and accuracy trade-off to derive high-quality process models that explain a given percentage of traces in the log. Finally, new quality metrics and a novel quality-based algorithm for model extraction from noisy logs are described. The results of the experiments with the algorithm on real and synthetic data are reported and analyzed at length. [ABSTRACT FROM AUTHOR]

Quantity discount pricing is a common practice used by business-to-business and business-to-consumer companies. A key characteristic of quantity discount pricing is that the marginal price declines with higher purchase quantities. In this paper, we propose a choice-based conjoint model for estimating consumer-level willingness to pay (WTP) for varying quantities of a product and for designing optimal quantity discount pricing schemes. Our model can handle large quantity values and produces WTP estimates that are positive and increasing in quantity at a diminishing rate. In particular, we propose a tractable WTP function that depends on both product attributes and product quantity and that captures diminishing marginal WTP. We show how such a function embeds standard WTP functions in the quantity discount literature as special cases. We also demonstrate how to use the model to estimate the consumer value potential, which is the product of the premium a consumer is willing to pay and her volume potential. Finally, we propose a parsimonious experimental design approach for implementation. We illustrate the model using data from a conjoint study of online movie rental services. The empirical results show that the proposed model has good fit and predictive validity. In addition, we find that marginal WTP in this category decays rapidly with quantity. We also find that the standard choice-based conjoint model results in anomalous WTP distributions with negative WTP values and nondiminishing marginal willingness-to-pay curves. Finally, we identify four segments of consumers that differ in terms of magnitude of WTP and volume potential, and we derive optimal quantity discount schemes for a monopolist and a new entrant in a competitive market. [ABSTRACT FROM AUTHOR]

Extant research considers the IT governance choice to be a trade-off between the cost-efficiency of centralization and the responsiveness provided by local information processing. This view predicts that firms tend to decentralize IT governance in more uncertain environments. We investigate this issue by studying the relationship between environmental uncertainty and IT infrastructure governance in a sample of business units from Fortune 1000 companies. The key proposition in this paper is that the relationship between environmental uncertainty and decentralization in IT infrastructure governance is best characterized as a curvilinear relationship. That is, when environmental uncertainty increases from low to high, firms tend to first decentralize their IT infrastructure decisions to the business units to enhance their responsiveness; and then centralize their IT infrastructure decisions to the headquarters as uncertainty increases further, to achieve the benefits of coordination and to mitigate the potential agency problem in uncertain environments. Moreover, the study proposes that business unrelatedness between business units and their headquarters moderates the curvilinear relationship between environmental uncertainty and IT infrastructure governance. We find that both the propositions are supported by the data. [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]

Choice-based conjoint analysis is a popular marketing research technique to learn about consumers' preferences and to make market share forecasts under various scenarios for product offerings. Managers expect these forecasts to be "realistic" in terms of being able to replicate market shares at some prespecified or "basecase" scenario. Frequently, there is a discrepancy between the recovered and base-case market share. This paper presents a Bayesian decision theoretic approach to incorporating base-case market shares into conjoint analysis via the loss function. Because defining the base-case scenario typically involves a variety of management decisions, we treat the market shares as constraints on what are acceptable answers, as opposed to informative prior information. Our approach seeks to minimize the adjustment of parameters by using additive factors from a normal distribution centered at 0, with a variance as small as possible, but such that the market share constraints are satisfied. We specify an appropriate loss function, and all estimates are formally derived via minimizing the posterior expected loss. We detail algorithms that provide posterior distributions of constrained and unconstrained parameters and quantities of interest. The methods are demonstrated using discrete choice models with simulated data and data from a commercial market research study. These studies indicate that the method recovers base-case market shares without systematically distorting the preference structure from the conjoint experiment. [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]

In their purchase decisions, online customers seek to improve decision quality while limiting search efforts. In practice, many merchants have understood the importance of helping customers in the decision-making process and provide online decision aids to their visitors. In this paper, we show how preference models which are common in conjoint analysis can be leveraged to design a questionnaire-based decision aid that elicits customers' preferences based on simple demographics, product usage, and self-reported preference questions. Such a system can offer relevant recommendations quickly and with minimal customer input. We compare three algorithms--cluster classification, Bayesian treed regression, and stepwise componential regression--to develop an optimal sequence of questions and predict online visitors' preferences. In an empirical study, stepwise componential regression, relying on many fewer and easier-to-answer questions, achieved predictive accuracy equivalent to a traditional conjoint approach. [ABSTRACT FROM AUTHOR]

Polyhedral methods for choice-based conjoint analysis provide a means to adapt choice-based questions at the individual-respondent level and provide an alternative means to estimate partworths when there are relatively few questions per respondent, as in a Web-based questionnaire. However, these methods are deterministic and are susceptible to the propagation of response errors. They also assume, implicitly, a uniform prior on the partworths. In this paper we provide a probabilistic interpretation of polyhedral methods and propose improvements that incorporate response error and/or informative priors into individual-level question selection and estimation. Monte Carlo simulations suggest that response-error modeling and informative priors improve polyhedral question-selection methods in the domains where they were previously weak. A field experiment with over 2,200 leading-edge wine consumers in the United States, Australia, and New Zealand suggests that the new question-selection methods show promise relative to existing methods. [ABSTRACT FROM AUTHOR]

Tourism Holdings Limited (THL), with 14 locations in Australia and New Zealand, operates a fleet of approximately 4,000 recreational rental vehicles of many types. It allocates vehicles to bookings centrally. If demand for a particular vehicle type at a location exceeds supply, THL may substitute vehicles of similar types or relocate vehicles from other locations to the location that needs the vehicles. The static problem that THL faces daily is to determine a vehicle schedule that minimizes the tangible and intangible costs of such substitutions and relocations. The dynamic problem is to determine--sometimes as the customer waits--whether a vehicle will be available to cover a potential booking and to incorporate that booking into the schedule. The Commonwealth Scientific and Industrial Research Organisation (CSIRO) has researched, developed, and supplied software, VASS and D-VASS, to solve the static and dynamic aspects of THL's schedule creation and maintenance. This paper describes the THL problem, the systems that CSIRO implemented, and how THL embedded these systems into its operations. [ABSTRACT FROM AUTHOR]

The authors propose two variants of lexicographic preference rules. They obtain the necessary and sufficient conditions under which a linear utility function represents a standard lexicographic rule, and each of the proposed variants, over a set of discrete attributes. They then: (i) characterize tile measurement properties of the parameters in the representations; (ii) propose a nonmetric procedure for inferring each lexicographic rule from pairwise comparisons of multiattribute alternatives; (iii) describe a method for distinguishing among different lexicographic rules, and between lexicographic and linear preference models; and (iv) suggest how individual lexicographic rules can be combined to describe hierarchical market structures. The authors illustrate each of these aspects using data on personal-computer preferences. They find that two-thirds of the subjects in the sample use some kind of lexicographic rule. In contrast, only one in five subjects use a standard lexicographic rule. This suggests that lexicographic rules are more widely used by consumers than one might have thought in the absence of the lexicographic variants described in the paper. The authors report a simulation assessing the ability of the proposed inference procedure to distinguish among alternative lexicographic models, and between linear-compensatory and lexicographic models. [ABSTRACT FROM AUTHOR]

Consumer choice in surveys and in the marketplace reflects a complex process of screening and evaluating choice alternatives. Behavioral and economic models of choice processes are difficult to estimate when using stated and revealed preferences because the underlying process is latent. This paper introduces Bayesian methods for estimating two behavioral models that eliminate alternatives using specific attribute levels. The elimination by aspects theory postulates a sequential elimination of alternatives by attribute levels until a single one, the chosen alternative, remains. In the economic screening rule model, respondents screen out alternatives with certain attribute levels and then choose from the remaining alternatives, using a compensatory function of all the attributes. The economic screening rule model gives an economic justification as to why certain attributes are used to screen alternatives. A commercial conjoint study is used to illustrate the methods and assess their performance. In this data set, the economic screening rule model outperforms the EBA and other standard choice models and provides comparable results to an equivalent conjunctive screening rule model. [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]

Almost universally, research and practice suggest that a brand that increases its product assortment, or variety, should benefit through increased market share. In this paper, we show this is not always the case. We introduce the construct "assortment type" and demonstrate that the effect of assortment size on brand share is systematically moderated by assortment type. We define an "alignable" assortment as a set of brand variants that differ along a single, compensatory dimension such that choosing from that assortment only requires within-attribute trade-offs. In contrast, we define a "nonalignable" assortment as a set of brand variants that simultaneously vary along multiple, noncompensatory dimensions, demanding between-attribute tradeoffs. In turn, we argue that an alignable assortment can efficiently meet the diverse tastes of consumers, thereby increasing brand share, but that a nonalignable assortment increases both the cognitive effort and the potential for regret faced by a consumer, thereby decreasing brand share. We term this effect "overchoice." Across three studies, we provide evidence of overchoice and tie the effect to the effort and regret brought about by nonalignability. In the process, we demonstrate that simplification of information presentation, reversibility of choice, and a reduction in underlying nonalignability serve to reduce or eliminate this effect. [ABSTRACT FROM AUTHOR]

Many theories of consumer behavior involve thresholds and discontinuities. In this paper, we investigate consumers' use of screening rules as part of a discrete-choice model. Alternatives that pass the screen are evaluated in a manner consistent with random utility theory; alternatives that do not pass the screen have a zero probability of being chosen. The proposed model accommodates conjunctive, disjunctive, and compensatory screening rules. We estimate a model that effects a discontinuous decision process by employing the Bayesian technique of data augmentation and using Markov-chain Monte Carlo methods to integrate over the parameter space. The approach has minimal information requirements and can handle a large number of choice alternatives. The method is illustrated using a conjoint study of cameras. The results indicate that 92% of respondents screen alternatives on one or more attributes. [ABSTRACT FROM AUTHOR]

This paper develops a model for capturing continuous heterogeneity in the joint distribution of reservation prices for products and bundles. Our model is derived from utility theory and captures both within- and among-subject variability. Furthermore, it provides dollarmetric reservation prices and individual-level estimates that allow the firm to target customers and develop customized and nonlinear pricing policies. Our experiments show that, regardless of whether the products are durables or nondurables, the model captures heterogeneity and predicts well. Models that assume homogeneity perform poorly, especially in predicting choice of the bundle. Furthermore, the methodology is robust even when respondents evaluate few profiles. Self-stated reservation prices do not have any informational content beyond that contained in the basic model. The direct elicitation method appears to understate (overstate) the variation in reservation prices across consumers for low-priced (high-priced) products and bundles. Hence this method yields biased demand estimates and leads to suboptimal product-line pricing policy. The optimization results show that the product-line pricing policy depends on the degree of heterogeneity in the reservation prices of the individual products and the bundle. A uniformly high-price strategy for all products and bundles is optimal when heterogeneity is high. Otherwise, a hybrid strategy is optimal. [ABSTRACT FROM AUTHOR]

Value trade-offs define how much must be gained in the achievement of one objective to compensate for a lesser achievement on a different objective. Value trade-offs that adequately express a decision maker's values are essential both for good decision making in multiple-objective contexts and for insightful analyses of multiple-objective decisions. This paper identifies and illustrates 12 important mistakes frequently made that limit one 's ability to determine useful value trade-offs. It then suggests how to avoid making these mistakes. The intent is to provide practical advice for making good value trade-offs, and hence, better decisions. [ABSTRACT FROM AUTHOR]

In today's turbulent business environment innovation is the result of the interplay between two distinct but related factors: endogenous R&D efforts and (quasi) external acquisition of technology and know-how. Given the increasing importance of innovation, it is vital to understand more about the alternative mechanisms--such as alliances and acquisitions--that can be used to enhance the innovative performance of companies. Most of the literature has dealt with these alternatives as isolated issues. Companies, however, are constantly challenged to choose between acquisitions and strategic alliances, given the limited resources that can be spent on research and development. This paper contributes to the literature because it focuses on the choice between innovation-related alliances and acquisitions. We focus on the question of how the trade-off between strategic alliances and acquisitions is influenced by previous direct and indirect ties between firms in an industry network of interfirm alliances. We formulate hypotheses pertaining to the number of direct ties between two companies, their proximity in the overall alliance network, and their centrality in that network. In so doing, we distinguish between ties that connect firms from the same and from different industry segments, and those that connect firms from the same or from different world regions. These hypotheses are tested on a sample of strategic alliances and acquisitions in the application specific integrated circuits (ASIC) industry. The findings show that a series of strategic alliances between two partners increases the probability that one will ultimately acquire the other. Whereas previous direct contacts tend to lead to an acquisition, this is not true of previous indirect contacts, which increase the probability that a link between the companies, once it is forged, takes the form of a strategic alliance. In the case of acquisitions, firms that are more centrally located in the network of... [ABSTRACT FROM AUTHOR]

A computationally attractive model for the analysis of conjoint choice experiments is the mixed multinomial logit model, a multinomial logit model in which it is assumed that the coefficients follow a (normal) distribution across subjects. This model offers the advantage over the standard multinomial logit model of accommodating heterogeneity in the coefficients of the choice model across subjects, a topic that has received considerable interest recently in the marketing literature. With the advent of such powerful models, the conjoint choice design deserves increased attention as well. Unfortunately, if one wants to apply the mixed logit model to the analysis of conjoint choice experiments, the problem arises that nothing is known about the efficiency of designs based on the standard logit for parameters of the mixed logit. The development of designs that are optimal for mixed logit models or other random effects models has not been previously addressed and is the topic of this paper. The development of efficient designs requires the evaluation of the information matrix of the mixed multinomial logit model. We derive an expression for the information matrix for that purpose. The information matrix of the mixed logit model does not have closed form, since it involves integration over the distribution of the random coefficients. In evaluating it we approximate the integrals through repeated samples from the multivariate normal distribution of the coefficients. Since the information matrix is not a scalar we use the determinant scaled by its dimension as a measure of design efficiency. This enables us to apply heuristic search algorithms to explore the design space for highly efficient designs. We build on previously published heuristics based on relabeling, swapping, and cycling of the attribute levels in the design. Designs with a base alternative are commonly used and considered to be important in conjoint choice analysis, since they provide a way to compare the utilities of profiles in different choice sets. A base alternative is a product profile that is included in all choice sets of a design. There are several types of base alternatives, examples being a so-called outside alternative or an alternative constructed from the attribute levels in the design itself. We extend our design construction procedures for mixed logit models to include designs with a base alternative and investigate and compare four design classes: designs with two alternatives, with two alternatives plus a base alternative, and designs with three and with four alternatives. Our study provides compelling evidence that each of these mixed logit designs provide more efficient parameter estimates for the mixed logit model than their standard logit counterparts and yield higher predictive validity. As compared to designs with two alternatives, designs that include a base alternative are more robust to deviations from the parameter values assumed in the designs, while that robustness is even higher for designs with three and four alternatives, even if those have 33% and 50% less choice sets, respectively. Those designs yield higher efficiency and better predictive validity at lower burden to the respondent. It is noteworthy that our "best" choice designs, the 3- and 4-alternative designs, resulted not only in a substantial improvement in efficiency over the standard logit design but also in an expected predictive validity that is over 50% higher in most cases, a number that pales the increases in predictive validity achieved by refined model specifications. [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]

This paper presents and optimizes two cases of an approximate analytic model of distribution from a single origin to many destinations via transshipment terminals. Linehaul vehicles carry items from the origin to terminals and local vehicles make deliveries on peddling routes from the terminals. It has been shown that transshipments are not desirable when local vehicles can be as large as linehaul vehicles. This paper limits the size of local vehicles and explores conditions under which transshipments are desirable. This paper focuses on situations in which local vehicles operate on multi-stop tours. The tradeoffs between the transportation, inventory and terminal cost components determine optimal values for the number of terminals, the number of stops per vehicle route and the distribution cost. The optimal type of distribution system is shown to depend on the ratio of the linehaul to local vehicle load size. Examples are presented that show how distribution via terminals can reduce the total cost. [ABSTRACT FROM AUTHOR]

This paper develops a new model for studying requirements planning in multistage production-inventory systems. We first characterize how most industrial planning systems work, and we then develop a mathematical model to capture some of the key dynamics in the planning process. Our approach is to use a model for a single production stage as a building block for modeling a network of stages. We show how to analyze the single-stage model to determine the production smoothness and stability for a production stage and the inventory requirements. We also show how to optimize the tradeoff between production capacity and inventory for a single stage. We then can model the multistage supply chain using the single stage as a building block. We illustrate the multistage model with an industrial application, and we conclude with some thoughts on a research agenda. [ABSTRACT FROM AUTHOR]

This paper describes a four-month study performed for the Dutch Minister of Transport, Public Works, and Water Management that examined the consequences of alternative policies for providing flood protection to the nontidal branches of the Rijn and Maas Rivers in The Netherlands. The paper focuses on estimating the flood damage that would occur under alternative safety standards, estimating the financial costs of alternative dike improvement strategies, and estimating the damage that would be inflicted on the landscape, natural, and cultural values along the rivers under each of these strategies. The primary objective of the study was to identify policies that would provide a high level of safety, would not cost too much, and would preserve as much as possible of the existing landscape, natural, and cultural values along the rivers. Less than six months after the completion of the study, the Dutch Parliament approved a new river dike policy that was based on the study's results. [ABSTRACT FROM AUTHOR]

This paper considers tradeoffs between cost and perturbations of the weights in a single facility, minisum location problem over a finite set of feasible points. Specifically, the paper characterizes a tradeoff curve of variability in the weights versus the degree of optimality of a solution. The general theoretical framework includes problems of location on a tree network and location using block norms; special results are given for these cases. [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]