1,721,200 research outputs found
Consideration-set heuristics
No full textConsumers often choose products by first forming a consideration set and then choosing from among considered products. When there are many products to screen (or many features to evaluate), it is rational for consumers to use consider-then-choose decision processes and to do so with heuristic decision rules. Managerial decisions (product development, marketing communications, etc.) depend upon the ability to identify and react to consumers' heuristic consideration-set rules. We provide managerial examples and review the state-of-the-art in the theory and measurement of consumers' heuristic consideration-set rules. Advances in greedoid methods, Bayesian inference, machine-learning, incentive alignment, measurement formats, and unstructured direct elicitation make it feasible and cost-effective to understand, quantify, and simulate “what-if” scenarios for a variety of heuristics. These methods now apply to a broad set of managerial problems including applications in complex product categories with large numbers of product features and feature-levels
Phenomena, theory, application, data, and methods all have impact
No full textIn his provocative essay on impactful research in this issue, my colleague and friend Gerry Tellis postulates that good papers are interesting and challenge common beliefs. He postulates further that such papers are based on ideas that are simple once proposed, although not always so obvious before being proposed. He recommends that impactful papers be focused and brief and begin with a study of the basic phenomena. Great advice
A marketing science perspective on recognition-based heuristics (and the fast-and-frugal paradigm)
Full text linkMarketing science seeks to prescribe better marketing strategies (advertising, product development, pricing, etc.). To do so we rely on models of consumer decisions grounded in empirical observations. Field experience suggests that recognition-based heuristics help consumers to choose which brands to consider and purchase in frequently-purchased categories, but other heuristics are more relevant in durable-goods categories. Screening with recognition is a rational screening rule when advertising is a signal of product quality, when observing other consumers makes it easy to learn decision rules, and when firms react to engineering-design constraints by offering brands such that a high-level on one product feature implies a low level on another product feature. Experience with applications and field experiments suggests four fruitful research topics: deciding how to decide (endogeneity), learning decision rules by self-reflection, risk reduction, and the difference between utility functions and decision rules. These challenges also pose methodological cautions.Sloan School of Managemen
Self-Reflection and Articulated Consumer Preferences
No full textAccurate measurement of consumer preferences reduces development costs and leads to successful products. Some product-development teams use quantitative methods such as conjoint analysis or structured methods such as Casemap. Other product-development teams rely on unstructured methods such as direct conversations with consumers, focus groups, or qualitative interviews. All methods assume that measured consumer preferences endure and are relevant for consumers' marketplace decisions. This article suggests that if consumers are not first given tasks to encourage preference self-reflection, unstructured methods may not measure accurate and enduring preferences. This paper provides evidence that consumers learn their preferences as they make realistic decisions. Sufficiently challenging decision tasks encourage preference self-reflection which, in turn, leads to more accurate and enduring measures. Evidence suggests further that if consumers are asked to articulate preferences before self-reflection, then that articulation interferes with consumers' abilities to articulate preferences even after they have a chance to self-reflect. The evidence that self-reflection enhances accuracy is based on experiments in the automotive and mobile phone markets. Consumers completed three rotated incentive-aligned preference measurement methods (revealed-preference measures [as in conjoint analysis], a structured method [Casemap], and an unstructured preference-articulation method). The stimuli were designed to be managerially relevant and realistic (53 aspects in automobiles, 22 aspects for mobile phones) so that consumers' decisions approximated in vivo decisions. One to three weeks later, consumers were asked which automobiles (or mobile phones) they would consider. Qualitative comments and response times are consistent with the implications of the measures of predictive ability
An Efficient Minimum-Time Trajectory Generation Strategy for Two-Track Car Vehicles
No full textIn this paper, we propose a novel approach to compute minimum-time trajectories for a two-track car model, including tires and (quasi-static) longitudinal and lateral load transfer. Given the car model and a planar track, including lane boundaries, our goal is to find a trajectory of the car minimizing the traveling time subject to steering and tire limits. Moreover, we enforce normal force constraints to avoid wheel liftoff. Based on a projection operator nonlinear optimal control technique, we propose a minimum-time trajectory generation strategy to compute the fastest car trajectory. Numerical computations are presented on two testing scenarios, a 90° turn and a real testing track. The computations allow us to both demonstrate the efficiency and accuracy of the proposed approach and highlight important features of the minimum-time trajectories. Finally, we integrate our strategy into a commercial vehicle dynamics software, thus computing minimum-time trajectories for a complex multibody vehicle model. The matching between the predicted trajectory and the one of the commercial toolbox further highlights the effectiveness of the proposed methodology
An Efficient Minimum-Time Trajectory Generation Strategy for Two-Track Car Vehicles
No full textIn this paper, we propose a novel approach to compute minimum-time trajectories for a two-track car model, including tires and (quasi-static) longitudinal and lateral load transfer. Given the car model and a planar track, including lane boundaries, our goal is to find a trajectory of the car minimizing the traveling time subject to steering and tire limits. Moreover, we enforce normal force constraints to avoid wheel liftoff. Based on a projection operator nonlinear optimal control technique, we propose a minimum-time trajectory generation strategy to compute the fastest car trajectory. Numerical computations are presented on two testing scenarios, a 90° turn and a real testing track. The computations allow us to both demonstrate the efficiency and accuracy of the proposed approach and highlight important features of the minimum-time trajectories. Finally, we integrate our strategy into a commercial vehicle dynamics software, thus computing minimum-time trajectories for a complex multibody vehicle model. The matching between the predicted trajectory and the one of the commercial toolbox further highlights the effectiveness of the proposed methodology
Optimal control of steer-braking systems: Non-existence of minimizing trajectories
No full textIn this paper, we investigate an optimal control problem in which the objective is to decelerate a simplified vehicle model, subject to input constraints, from a given initial velocity down to zero by minimizing a quadratic cost functional. The problem is of interest because, although it involves apparently simple drift‐less dynamics, a minimizing trajectory does not exist over the admissible input trajectories. This problem is motivated by a minimum‐time problem for a fairly complex car vehicle model on a race track. Numerical computations run on the car trajectory optimization problem provide evidence of convergence issues and of an apparently unmotivated ripple in the steer angle. Characterizing this ripple behavior is important to fully understand and exploit minimizing vehicle trajectories. We are able to isolate the key features of this chattering behavior in a very simple dynamics/objective setting. We show that the cost functional has an infimum, but an admissible minimizing input trajectory does not exist. We also show that the infimum can be arbitrarily approximated by bang‐bang inputs with a sufficiently large number of switches. We reproduce this phenomenon in numerical computations and characterize it by means of non‐existence of admissible minimizing trajectories
Comment: New developments in product-line optimization
No full textProduct development is key to profitability. Without well-designed products that meet the needs of customers at a reasonable cost, the firm has no sales. And without sales, the firm has no profit. But designing profitable products is hard. Eppinger, Whitney, Smith, and Gebala (1994) estimate that for a moderately complex electro-mechanical product, close to a million decisions must be made before the product is brought to market. Many of these decisions are routine, but many are not. The two product-line-optimization papers in this journal address hard decisions
- …
