PROGRAMMED DECISION STRUCTURES - FUNCTIONAL APPROACH TO QUANTITATIVE UNDERGRADUATE CORE

Administrators and professionals must make social decisions within the environment of the computer revolution. This means that precise skills of logical problem solving and quantitative methods may assume an increasingly prominent place in college preparation for business and social sciences. At the same time, colleges are under pressure not to “select out” persons who are disadvantaged in quantitative areas. With a widening “quantitative gap,” major surgery of existing math and stat courses is unlikely to meet the challenge of this new social environment. Programmed Decisions Structures (PDS) is experimental coursework in a quantitative undergraduate core for business and social sciences. Particular features of PDS were developed to emphasize (1) motivation of the student, (2) credible simulation of realistic business and social environment, (3) awareness of logically developed structures, and (4) principles of applied scientific method. Constructed around the above four points, it is hoped that PDS will close the “quantitative gap” more effectively than the 20 quarter hours of conventional math, stat and computer courses which it replaces.     

AN INDEX FOR THE MEASUREMENT OF CONSUMER LOYALTY*

The concept of consumer “loyalty” (i.e., preference for a particular store or brand) is intriguing to marketing scholars and practitioners. Research in this area would be enhanced by the acceptance of an operational measure of consumer loyalty. Loyalty appears to be a tri-dimensional concept; per cent of budget, allocated to the store or brand, amount of switching, and number of alternatives explored have been used to measure loyalty. Consequently, the authors propose a “loyalty index” which combines these three measures. An empirical test of the proposed index showed that the index did discriminate among degrees of consumer loyalty, and was related to consumer psychological characteristics in a manner similar to that of the generally used single measures of consumer loyalty.     

APPLICATIONS OF SPECTRAL ANALYSIS

The basic concepts and application of spectral analysis are explained. Stationary time series and autocorrelation are first defined. Autocorrelation is related to the familiar concepts of variance and covariance. The use of autocorrelation analysis is explained in estimating the interdependent relationship of a time series over discrete time lags. In order to measure the behavior of the time series using autocorrelation, it would be necessary to examine a very large number of autocorrelation lags. Alternatively, the technique of Fourier analysis can be used to transform the autocorrelation function of the time series into a continuous function, termed a spectrum. The spectrum has a one to one correspondence to the autocorrelation for the time series and has the advantage of representing all possible autocorrelations over the discrete time lags. The spectrum can then be examined as a measure of the behavior of the time series. Spectral analysis indicates the reliability of the analysis of autocorrelated variables when familiar statistical techniques such as sample means and variances are used. The application of spectral analysis to management science problems in three general areas is illustrated: (1) inventory demand, (2) transportation simulation, and (3) stock market price behavior. Spectral analysis was used to detect cycles and trends in the data. Analyses were focused on the spectrum which provides a measure of the relative contribution of cycles in a band of frequencies to the total variance of the data.     

USING DUMMY VARIABLES TO CHECK FOR ROUNDING ERROR IN COMPUTERIZED REGRESSION PROGRAMS*

After a brief review of the role of dummy variables in regression analysis and the current state-of-the art in rounding/truncation error detection in computerized least squares programs, this paper presents a theorem that can be used to detect this type of error whenever an analyst is running a regression program that has one (or more) dummy variables as independent variables.     

EXPLORING MARKET PRICING STRATEGIES VIA DYNAMIC PROGRAMMING

Price determinants as well as strategies can be studies by use of simulation, particularly if cost and price relationships can be related to market activity [1] [9] [11]. But, through the use of dynamic programming, given the market conditions, one can extend the analysis to include an optimal strategy. This paper describes a dynamic programming approach to studying price strategy. A model is developed to show that in a market characterized by cost/volume and price/volume relationships, profitability can be extended beyond that resulting from a dominant market strategy to an optimal maximizing strategy. Extension of the model is suggested for studying (a) sensitivity of a strategy (solution) to price level and cost changes, (b) optimal timing of withdrawal, and (c) present value analysis.     

DETERMINING OPTIMAL SHIFT SCHEDULES FOR TELEPHONE TRAFFIC EXCHANGE OPERATORS

A branch and bound algorithm is presented for determining the minimum number of telephone operators, and their shift schedules, required to meet demand that varies over a 24 hour operating period. An integer linear programming formulation is used, and the algorithm is described in terms of its separation, relaxation, fathoming, and branching procedures. Computational results are provided, using actual operating data. The results indicate that practical sized problems can be solved by the algorithm, involving as many as 100 different shift type variables and demand profiles typical of those encountered in many telephone traffic exchanges.     

You Wouldn't Like Me When I'm Angry: List Experiment Misreporting*

Objective. List experiment respondents may misreport the number of list items that they associate with in order to associate themselves with a socially desirable test item or to disassociate themselves from a socially undesirable test item. Tests for such misreporting were conducted. Methods. List experiments from the 1991 National Race and Politics Survey, the 2006 Cooperative Congressional Election Study, and the 2008 Cooperative Campaign Analysis Project were analyzed or reviewed. Results. Evidence suggested that some respondents deflated their report more than necessary to avoid association with a socially undesirable test item. Conclusions. List experiments may provide inaccurate estimates of the percentage of the population to which the test item applies, but the direction of bias is predictable.     

The Effects of Judicial Strength and Rule of Law on Portfolio Investment in the Developing World*

Objective. Applying existing theories relating to investment risk, this article examines the effects of judicial strength and adherence to the rule of law on portfolio investment in the developing world. We aim to demonstrate that countries with higher levels of judicial strength and rule of law are more able to attract portfolio investment because they provide greater protection of property rights and a better risk environment for investors. Methods. Using time‐series data for 79 developing countries for the period 1996–2005, we employ panel‐corrected standard errors multivariate regressions to demonstrate that higher levels of judicial strength and rule of law are associated with higher levels of portfolio investment. Results. We find that a one standard deviation increase in overall judicial strength and rule of law results in more than a 50 percent increase in portfolio investment. In separate analyses, we show that a one standard deviation increase in specific measures of judicial independence, impartial courts, and protection of property rights leads to increases in portfolio investment ranging from 27 percent to 184 percent. Conclusion. Judicial strength and adherence to the rule of law are important determinants of portfolio investment in the developing world.