Optimal Structure of the Ginning Industry in the Southern High Plains of Texas

This study addresses the optimal structure of the cotton ginning industry in the Southern High Plains of Texas, assuming a monopolistically competitive market structure. Employing survey, regression, simulation, and mathematical programming techniques, the study determines the optimal structure of the ginning industry. Results indicate that a restructuring of the ginning industry in the Southern High Plains of Texas could save the industry about $15 million per season.     

Understanding Firm, Physician and Consumer Choice Behavior in the Pharmaceutical Industry

This paper argues that the pharmaceutical industry represents an exciting opportunity to carry out academic research. The nature of the industry allows researchers to answer new questions, develop new methodologies for answering these questions as well as to apply existing methodology to new data. The paper opens with some industry background, then provides a brief overview of some important research areas and discusses the open questions in each area. Issues of data type and availability are also discussed. This paper is based on a session (with the same title and participants) that was part of the Sixth Invitational Choice Symposium hosted by the University of Colorado and held at Estes Park, Colorado during June 4–8, 2004.     

ILLEGAL INSIDER TRADING: IS IT RAMPANT BEFORE CORPORATE TAKEOVERS?

This paper investigates the role of insider trading as an explanation for the observed pre-announcement price run-ups for takeover targets. We hypothesize that if insider trading is a significant contributor to such price run-ups, then observed run-ups should be smaller for takeovers occurring after May 1986 (the beginning of the “insider trading scandal”) relative to those occurring prior to May 1986. The evidence suggests that insider trading is not, on average, a significant contributor to pre-announcement price run-ups.     

Crop-Yield Distributions Revisited

This article revisits the issue of crop-yield distributions using improved model specifications, estimation, and testing procedures that address the concerns raised in recent literature, which could have invalidated previous findings of yield nonnormality. It concludes that some aggregate and farm-level yield distributions are nonnormal, kurtotic, and right or left skewed, depending on the circumstances. The advantages of utilizing nonnormal versus normal probability distribution function models, and the consequences of incorrectly assuming crop-yield normality are explored.     

Robust firm pricing with panel data

Firms often have imperfect information about demand for their products. We develop an integrated econometric and theoretical framework to model firm demand assessment and subsequent pricing decisions with limited information. We introduce a panel data discrete choice model whose realistic assumptions about consumer behavior deliver partially identified preferences and thus generate ambiguity in the firm pricing problem. We use the minimax-regret criterion as a decision-making rule for firms facing this ambiguity. We illustrate the framework’s benefits relative to the most common discrete choice analysis approach through simulations and empirical examples with field data.     

Exploiting Mobile Technology for Achieving Supply Chain Integration in Indian Retail

For decades supply chain coordination has been subject to research interest, and technology has been seen as an agent that accelerates this process. In developing countries, with far-flung markets and unorganized distribution networks, using technology for improving supply chain performances and accessing information is not an easy task. The research was directed to understand if mobile technology is being used by downstream supply chain partners for information sharing and thus improving supply chain performances. Findings suggest that supply chain integration with suppliers and customers is done through extensive use of mobile networks.     

Tracking technology trajectory through regression modelling: a retrospective techno-analysis

A significant number of studies have been conducted to forecast the expanding market and evaluate new generation smartphone technologies. However, no such study has been witnessed so far that could forecast the release time of these technologies. The purpose of the paper is to test the forecasting capabilities of stepwise regression in forecasting the smartphones commercialisation time. This technique predicts the release time of smartphones released in 2006 (belonging to the second generation of smartphones) and 2007 (belonging to the third generation of smartphones). The stepwise regression approach based on 12 year data set from 1994 to 2005 determines whether it provides a superior fitting and forecasting performance. The validation approach applied for the first- and second-generation smartphones will benefit future researchers and practitioners in understanding that a regression model developed on the basis of one generation may not give accurate results for the next generation, owing to the fact that technological developments are multi-folded.