Issues in the estimation and application of latent structure models of choice

Our paper provides a brief review and summary of issues and advances in the use of latent structure and other finite mixture models in the analysis of choice data. Focus is directed to three primary areas: (1) estimation and computational issues, (2) specification and interpretation issues, and (3) future research issues. We comment on what latent structure models have promised, what has been, to date, delivered, and what we should look forward to in the future.     

Organizational performance in a regulated environment: The role of strategic orientation

This study explores the influence of governmental regulation on organizational strategies and performance outcomes. Drawing on literature in strategic management, we provide a longitudinal empirical analysis of viable strategies in the U.S. domestic airline industry during a 6-year period (1965-70) when it was regulated by the Civil Aeronautics Board. Results indicate that air carriers were indeed able to articulate coherent strategic postures despite regulatory constraints, and were thus able to influence profitability.     

What they know vs. what they do: how acquirers leverage technology acquisitions

Existing research suggests that in acquisitions of small technology‐based firms by large established firms post‐merger integration both enables and hinders acquirers' efforts to leverage the technology of acquired firms. This apparent paradox can be resolved once we account for the qualitatively distinct ways in which acquirers leverage technology acquisitions. Integration helps acquirers use the acquired firm's existing knowledge as an input to their own innovation processes (leveraging what they know), but hinders their reliance on the acquired firm as an independent source of ongoing innovation (leveraging what they do). We also show that experienced acquirers are better able to mitigate the disruptive consequences of the loss of autonomy entailed by integration, though we find no evidence that they achieve greater coordination benefits from integration. Copyright © 2007 John Wiley & Sons, Ltd.     

An adjustment procedure for predicting systematic risk

This paper looks at the currently available beta adjustment techniques and suggests a multiple root-linear model to adjust for the regression tendency of betas. Our empirical investigate on indicates that cross-sectional betas are not normally distributed, but their distribution tends to normal after a square-root transformation. The evidence from the Box-Cox regression model and the multivariate normality observed among betas after the transformation, make the functional form of our model correct. Also, we observe that the disturbance term of the multiple root-linear model is well behaved. These findings make the ordinary least squares estimates unbiased and efficient. Finally, the mean square and extreme errors are found to be lower when our adjustment procedure is used vis-à-vis the existing procedures.     

A spatial interaction model for deriving joint space maps of bundle compositions and market segments from pick-any/J data: An application to new product options

We propose an approach for deriving joint space maps of bundle compositions and market segments from three-way (e.g., consumers x product options/benefits/features x usage situations/scenarios/time periods) pick-any/J data. The proposed latent structure multidimensional scaling procedure simultaneously extracts market segment and product option positions in a joint space map such that the closer a product option is to a particlar segment, the higher the likelihood of its being chosen by that segment. A segment-level threshold parameter is estimated that spatially delineates the bundle of product options that are predicted to be chosen by each segment. Estimates of the probability of each consumer belonging to the derived segments are simultaneously obtained. Explicit treatment of product and consumer characteristics are allowed via optional model reparameterizations of the product option locations and segment memberships. We illustrate the use of the proposed approach using an actual commercial application involving pick-any/J data gathered by a major hi-tech firm for some 23 advanced technological options for new automobiles.     

Theory-Driven Choice Models

We explore issues in theory-driven choice modeling by focusing on partial-equilibrium models of dynamic structural demand with forward-looking decision-makers, full equilibrium models that integrate the supply side, integration of bounded rationality in dynamic structural models of choice and public policy implications of these models.     

Role of Forgetting in Memory-Based Choice Decisions: A Structural Model

We propose a structural model to investigate the impact of forgetting on consumers' brand choice decisions in frequently purchased products. Forgetting results in consumers imperfectly recalling their prior brand evaluations when making a purchase decision in the category. We conceptualize the imperfect recall by positing that consumers recall their prior evaluations with noise. Based on prior research in the behavioral area, we characterize the extent of forgetting as an increasing and concave function of time. Our framework generates analytical results on the impact of forgetting on consumers' brand evaluations and their consequent purchase behavior. We calibrate our model using scanner panel data for liquid detergents. Furthermore, we obtain insights into the consumers' extent of forgetting in the category, extent of learning, predicted price elasticities and implications on state dependence and habit persistence. Our results underscore the importance of modeling consumers' ability to recall only imperfectly.     

Modeling Methods for Discrete Choice Analysis

This paper introduces new forms, sampling and estimation approaches fordiscrete choice models. The new models include behavioral specifications oflatent class choice models, multinomial probit, hybrid logit, andnon-parametric methods. Recent contributions also include new specializedchoice based sample designs that permit greater efficiency in datacollection. Finally, the paper describes recent developments in the use ofsimulation methods for model estimation. These developments are designed toallow the applications of discrete choice models to a wider variety ofdiscrete choice problems.     

Trends in Agricultural Incomes: An Analysis at the Select Crop and State Levels in India

India's agricultural sector is at a crossroads, facing challenges of stagnation in crop yields, non‐remunerative prices, falling crop incomes and tardy responses from public service systems. There are reports of peasant suicides due to non‐profitability of farming. However, scant empirical evidence is available on changes in real income and wages in the Indian agricultural sector. The present study uses data from the National Accounts Statistics and Cost of Cultivation Surveys to analyse the changes in real income and discusses the underlying reasons. The study reveals that the purchasing power of farmers has remained low and has worsened over recent years. The value of crop output has increased, but a disproportionate rise in input costs has resulted in a fall in crop incomes in several states, with the agriculturally developed Punjab being an exception. Interestingly, real wage rates for agricultural labour have shown an increasing trend, indicating improvement in the welfare of labour.