Accommodating the effects of brand unfamiliarity in the multidimensional scaling of preference data

This paper presents a multidimensional scaling (MDS) methodology (vector model) for the spatial analysis of preference data that explicitly models the effects of unfamiliarity on evoked preferences. Our objective is to derive a joint space map of brand locations and consumer preference vectors that is free from potential distortion resulting from the analysis of preference data confounded with the effects of consumer-specific brand unfamiliarity. An application based on preference and familiarity ratings for ten luxury car models collected from 240 consumers who intended to buy a luxury car within a designated time frame is presented. The results are compared with those obtained from MDPREF, a popular metric vector MDS model used for the scaling of preference data. In particular, we find that the consumer preference vectors obtained from the proposed methodology are substantially different in orientation from those estimated by the MDPREF model. The implications of the methodology are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47094/1/11002_2004_Article_BF00994083.pd

Advanced Magnetics for Power and Energy Development - A Multidisciplinary Consortium between the University of Pittsburgh, Carnegie Mellon University, and North Carolina State University

Emerging societal trends drive the need for advanced magnetic materials and power magnetic components including the electrification of domestic and military transportation; the emergence of solid state transformers as a practical and viable alternative to conventional transformers; increased penetration of renewables and other distributed energy resources which require power electronics converters and novel electric machines for grid integration. A historical gap in research and development funding for advanced power magnetics has created a severe shortfall in the necessary workforce required to support these quickly emerging areas within both nascent and established industries in the electric power sector. Since January 2020, faculty members in the engineering school having been establishing a consortium called Advanced Magnetics for Power and Energy Development (AMPED). The consortium will focus on magnetic materials development, manufacturing techniques, and their utilization in power electronic systems at medium frequency and medium power levels. Other AMPED university partners include North Carolina State University and Carnegie Mellon University. Faculty from the School of Engineering will lead this proposal effort with support from the Katz School of Business and School of Computing and Information Science. Faculty from the Katz School of Business will offer expertise in technology-to-market planning and competitive analysis, and faculty from the School of Computing and Information will aid in the development of novel algorithms for optimizing magnetics and power electronics technology like transformers, inductors, and electric motors given cost, weight, performance and volume constraints. The faculty received $60,000 to establish synergies through facilitated team collaborations, supporting graduate student stipends, and investing into laboratory space at the Energy GRID Institute. The first goal will be to submit concept papers followed by full proposals for attracting federal dollars from the DoE, DoD, or NSF. The second goal will be to attract$100,000 in company investment for AMPED through the membership model

R&D, Marketing Innovation, and New Product Performance: A Mixed Methods Study

This paper investigates the relationship between investments in marketing innovation, that is, the way in which technologically unchanged products are designed, priced, distributed, and/or promoted, and a firm's new product performance. Marketing innovation, such as calorie‐based packaging or unusual distribution channels, may lead to new products. However, it is unclear whether they pay off, particularly when the firm follows a dual strategy, that is, investing in both innovative marketing and R&D at the same time. We draw from theory on competence development as well as diffusion of innovation and argue that pursuing a dual strategy lowers performance, an effect that we attribute to the role of complexity in innovation. Based on a mixed methods study that integrates a data set of 866 firms from a representative set of industries in Germany and extensive interview evidence, we find empirical support for our hypotheses. Our research contributes to the emerging stream of literature that seeks to better understand the role of marketing in firms' innovation processes

Deriving ultrametric tree structures from proximity data confounded by differential stimulus familiarity

This paper presents a new procedure called TREEFAM for estimating ultrametric tree structures from proximity data confounded by differential stimulus familiarity. The objective of the proposed TREEFAM procedure is to quantitatively “filter out” the effects of stimulus unfamiliarity in the estimation of an ultrametric tree. A conditional, alternating maximum likelihood procedure is formulated to simultaneously estimate an ultrametric tree, under the unobserved condition of complete stimulus familiarity, and subject-specific parameters capturing the adjustments due to differential unfamiliarity. We demonstrate the performance of the TREEFAM procedure under a variety of alternative conditions via a modest Monte Carlo experimental study. An empirical application provides evidence that the TREEFAM outperforms traditional models that ignore the effects of unfamiliarity in terms of superior tree recovery and overall goodness-of-fit.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45752/1/11336_2005_Article_BF02294391.pd

THE INNOVATION DIFFUSION PROCESS IN A HETEROGENEOUS POPULATION: AN ANALYTICAL MODEL BASED ON AN INDIVIDUAL LEVEL APPROACH (FORECASTING, INFORMATION INTEGRATION, BAYESIAN LEARNING, ADOPTION)

The pattern of market penetration of an innovation and the factors underlying the diffusion process have been an important subject of study in marketing. This dissertation develops an analytical model of the innovation diffusion process in a heterogeneous population, applicable for high-involvement innovations that are durable in nature. The model employs a Bayesian decision theoretic framework to predict timing of adoption at the individual level, incorporating a potential adopter\u27s preference structure and the dynamics of uncertain perceptions about the innovation. Individual level predictions are then aggregated to yield the penetration curve. In particular, the analytical development of the model considers heterogeneity with respect to initial perceptions, attitude toward risk, susceptibility to information, and the trade-off between price and performance. The impact of variability in information about the innovation\u27s performance is captured by modeling an individual\u27s path to adoption as a stochastic process. We develop a parsimonious basis for classifying potential adopters in terms of their relative timing of adoption. The aggregate-level diffusion model is very flexible in its ability to accommodate a wide variety of possible patterns of diffusion. The model is employed to investigate the effects of heterogeneity in the population, the true performance of the innovation, and the pattern of information on the rate of diffusion and the shape of the diffusion curve. A pilot study is conducted to illustrate an approach to calibrating the model and to provide a preliminary test of its key implications. The results provide tentative support for the predictive performance of the model, and encourage a full-scale application. The model is next employed to derive normative implications for promotional policy. It is shown that, in general, promotional expenditures should decline over time. The model also provides guidelines for selective targetting of promotional effort. The extended model (considering several non-price attributes) provides a basis for determining the attribute(s) to be emphasized in the firm\u27s communication program. Potential managerial applications of the model include (a) pre-launch prediction of the penetration curve, (b) segmentation of the potential adopter population, and (c) planning communication strategy

Dynamic pricing of durables in duopoly : the effect of buyer expectations

http://deepblue.lib.umich.edu/bitstream/2027.42/35494/2/b1586087.0001.001.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/35494/1/b1586087.0001.001.tx

THE INNOVATION DIFFUSION PROCESS IN A HETEROGENEOUS POPULATION: AN ANALYTICAL MODEL BASED ON AN INDIVIDUAL LEVEL APPROACH (FORECASTING, INFORMATION INTEGRATION, BAYESIAN LEARNING, ADOPTION)

The pattern of market penetration of an innovation and the factors underlying the diffusion process have been an important subject of study in marketing. This dissertation develops an analytical model of the innovation diffusion process in a heterogeneous population, applicable for high-involvement innovations that are durable in nature. The model employs a Bayesian decision theoretic framework to predict timing of adoption at the individual level, incorporating a potential adopter\u27s preference structure and the dynamics of uncertain perceptions about the innovation. Individual level predictions are then aggregated to yield the penetration curve. In particular, the analytical development of the model considers heterogeneity with respect to initial perceptions, attitude toward risk, susceptibility to information, and the trade-off between price and performance. The impact of variability in information about the innovation\u27s performance is captured by modeling an individual\u27s path to adoption as a stochastic process. We develop a parsimonious basis for classifying potential adopters in terms of their relative timing of adoption. The aggregate-level diffusion model is very flexible in its ability to accommodate a wide variety of possible patterns of diffusion. The model is employed to investigate the effects of heterogeneity in the population, the true performance of the innovation, and the pattern of information on the rate of diffusion and the shape of the diffusion curve. A pilot study is conducted to illustrate an approach to calibrating the model and to provide a preliminary test of its key implications. The results provide tentative support for the predictive performance of the model, and encourage a full-scale application. The model is next employed to derive normative implications for promotional policy. It is shown that, in general, promotional expenditures should decline over time. The model also provides guidelines for selective targetting of promotional effort. The extended model (considering several non-price attributes) provides a basis for determining the attribute(s) to be emphasized in the firm\u27s communication program. Potential managerial applications of the model include (a) pre-launch prediction of the penetration curve, (b) segmentation of the potential adopter population, and (c) planning communication strategy

Optimal Monopolist Pricing Under Demand Uncertainty in Dynamic Markets

We examine pricing policy for a monopolist facing uncertain demand in a market characterized by dynamics on the demand side (such as diffusion or saturation effects) and/or on the cost side (experience curve effects). Our model explicitly incorporates the impact of demand uncertainty, and thus allows us to analyze the implications of uncertainty on the optimal price path, by contrasting the stochastic policy with the corresponding deterministic policy. We begin with an analysis of the general model and then focus on several special cases based on well known demand specifications to gain more specific insights and to suggest directional guidelines for dynamic pricing decisions in an uncertain environment. In general, the interaction among uncertainty, demand and/or cost dynamics, and firm's discount rate. Thus, farsighted firms operating under dynamic market conditions with high demand uncertainty, such as high tech companies with innovative products for consumer or industrial markets, should attach particular importance to the formal consideration of uncertainty in their long term pricing decisions.dynamic pricing, optimal control, stochastic models, marketing

A typology of brand alliances and consumer awareness of brand alliance integration

Brand alliances, which involve intentionally presenting two or more brands together, appear in many different forms. For example, Subway stores placed within Wal-Mart, Airbus A380 airplanes with Rolls-Royce Trent engines, and Nike + iPod co-developed personal trainers are among the more well-known manifestations of this strategy. Our study contributes to the literature on brand alliances by conceptualizing and measuring a typology of brand alliance types based on their degree of integration. We also empirically test and find that consumers are sensitive to varying degrees of brand alliance integration. We then link these findings to the managerial decision of how and with whom a brand should form an alliance. We use extensive examples, conversations with managers, and survey-based experiments to show that brand alliance integration is relevant and impactful to both managers and consumers.https://link.springer.com/journal/110022019-09-01hj2018Marketing Managemen

The Name-Your-Own-Price Channel in the Travel Industry: An Analytical Exploration

Name-your-own-price (NYOP) retailers, such as Priceline, offer an alternative distribution channel for service providers in the travel industry such as airlines, hotels, and car rental companies. Our research employs an analytical model to identify and understand key trade-offs driving the decision by a service provider to employ an NYOP channel, assuming that such a channel is available. This decision requires the existence of forces that counteract the adverse consequences of cannibalization of sales through traditional posted-price channels. Our analysis provides some insight into these forces. Contracting with the NYOP retailer facilitates market segmentation and price discrimination, and allows for disposal of excess capacity after meeting business travel demand. However, the cost of this flexibility is that the service provider can no longer credibly precommit to maintaining high prices when there is unsold capacity. Also, when contracting with an independent retailer, the service provider is unable to extract the entire revenue generated from NYOP consumers. A key insight from our model is that the rationale for contracting with an NYOP retailer is driven by the uncertainty in business travel demand, not the expectation of excess capacity. Indeed, all else equal, the larger the capacity, the less likely it is that contracting with an NYOP retailer is the right decision on the part of the service provider.pricing, name-your-own-price channel, bidding, Bayesian Nash equilibrium, e-commerce