Linda L. Golden

The Attribution Process in Consumer Decision Making

Attribution, as a process, is related to consumer decision making by a descriptive model. Published papers that have applied attributional approaches to consumer scenarios are analyzed to highlight both existing problems and opportunities for enhanced understanding. An assessment of the potential for attribution theory in consumer research is offered.

Comparative Analysis of the Relative Effectiveness of One- and Two- Sided Communication for Contrasting Products

An apparatus for the hydroautomatic sprinkling of land areas with hydrants which rise and lower which comprises a cylinder whose piston is displaceable by the water from one pipeline through either a long stroke or a short stroke depending upon the engagement of a guide with a selected groove in the piston to open a gate permitting water from this line to pass into a selected one of a group of hydrants, to drive the sprinkler upwardly and permit sprinkling. When pressure from another line is admitted to the opposite side of the cylinder the gate is closed.

Evaluating solvency versus efficiency performance and different forms of organization and marketing in US property--liability insurance companies

Abstract Solvency is a primary concern for regulators of insurance companies, claims paying ability is a primary concern for policyholders, and return on investment is a primary concern for investors. These interests potentially conflict, and the decision-makers for the firm must trade off one concern versus another. Here we examine the efficiency of insurance companies via data envelopment analysis using solvency, claims paying ability, and return on investment as outputs and using a financial intermediary model for the insurance company. The effect of solvency on efficiency is then examined. These efficiency evaluations are further examined to study stock versus mutual form of organizational structure and agency versus direct marketing arrangements, which are examined separately and in combination.

DEA evaluations of the efficiency of organizational forms and distribution systems in the US property and liability insurance industry

The efficiency effects of different forms of ownership (stock versus mutual) and types of marketing system (agency versus direct) are studied using 1989 data for the US property-liability insurance industry. Data envelopment analysis (DEA) results are obtained from the recently developed RAM (Range Adjusted Measure) model and then extended for comparison with studies by others. Using agency theory (and like approaches) these studies assume that operations all occur only on the efficient frontier. The need for that assumption is obviated by using operations provided by DEA to project all observations onto their efficient frontiers. A use of (non-parametric) rank-order statistics then produces results which differ from these other studies.

Enterprise Risk Management Through Strategic Allocation of Capital

This article presents a conceptual framework for operationalizing strategic enterprise risk management (ERM) in a general firm. We employ a risk-constrained optimization approach to study the capital allocation decisions under ERM. Given the decision maker’s risk appetite, the problem of holistically managing enterprise-wide hazard, financial, operational, and real project risks is treated by maximizing the expected total return on capital, while trading off risks simultaneously in Value-at-Risk type of constraints. This approach explicitly quantifies the concepts of risk appetite and risk prioritization in light of the firm’s default and financial distress avoidance reflected in its target credit rating. Our framework also allows the firm to consider a multi-period planning horizon so that changing business environments can be accounted for. We illustrate the implementation of the framework through a numerical example. As an initial conceptual advancement, our formulation is capable of facilitating more general ERM modeling within a consistent strategic framework, where idiosyncratic variations of firms and different modeling assumptions can be accommodated. Managerial implications are also discussed.

Biological and Psychobehavioral Correlates of Credit Scores and Automobile Insurance Losses: Toward an Explication of Why Credit Scoring Works

The most important new development in the past two decades in the personal lines of insurance may well be the use of an individuals credit history as a classification and rating variable to predict losses. However, in spite of its obvious success as an underwriting tool, and the clear actuarial substantiation of a strong association between credit score and insured losses over multiple methods and multiple studies, the use of credit scoring is under attack because there is not an understanding of why there is an association. Through a detailed literature review concerning the biological, psychological, and behavioral attributes of risky automobile drivers and insured losses, and a similar review of the biological, psychological, and behavioral attributes of financial risk takers, we delineate that basic chemical and psychobehavioral characteristics (e.g., a sensation-seeking personality type) are common to individuals exhibiting both higher insured automobile loss costs and poorer credit scores, and thus provide a connection which can be used to understand why credit scoring works. Credit scoring can give information distinct from standard actuarial variables concerning an individuals biopsychological makeup, which then yields useful underwriting information about how they will react in creating risk of insured automobile losses.

The Identification of Target Firms and Functional Areas for Strategic Benchmarking

Abstract In recent years there has been an increased emphasis on quality, Total Quality Management (TQM), and re-engineering. The practice of benchmarking is inherent to the success of any of these. Despite its obvious strategic implications, benchmarking has received little theoretical or analytical attention in the literature. Moreover, studies show that many firms are unsure about how to implement the benchmarking process. A critical aspect of benchmarking is the identification of who to benchmark against and what functional areas to benchmark. In this paper we show that Data Envelopment Analysis (DEA) can provide a structured methodology which, when used in conjunction with expert/managerial insight, can be a useful toot for providing necessary analytic support for the managerial practice of benchmarking. DEA uses multiple input and multiple output measures to assess efficiency. In this paper we first use DEA analysis to identify a subset of efficient firms which could be targeted for benchmarking. In a second stage, a constrained least absolute value (goal programming) method is used to ascertain input elasticities for the designated efficient firms in order to pinpoint important specific areas for benchmarking comparisons. We use macro-level data from the computer industry to illustrate this application of DEA and constrained least absolute value regression to benchmarking.

Psychological foundations of economic behavior

Foreword by Tibor Scitovsky Introduction by Paul J. Albanese Novelty, Comfort, and Pleasure: Inside the Utility Function Black Box by Shlomo Maital How the Opponent-Process Theory of Acquired Motivation Came Into Economics by Richard L. Solomon A Model of Consumption and Demand Based on Psychological Opponent Processes by Lester D. Taylor The Intimate Relations of the Consistent Consumer: Psychoanalytic Object Relations Theory Applied to Economics by Paul J. Albanese Personality, Culture, and Organization by Manfred F.R. Kets de Vries and Danny Miller Decisions, Coalitions, and the Economy of the Self by Abraham Zaleznik The Entrepreneur and Society by Joshua Ronen Toward a New Paradigm by Amitai Etzioni

Flexible purchase frequency modeling

The authors present a general framework for purchase frequency modeling that enables flexible fitting and convenient computation. Their easily described purchase frequency distributions subsume many previous models and provide a connection between mixed Poisson marketing models and the conceptually distinct compound Poisson models. These distributions provide simple parametric equations for individual-level prediction of second-period purchase frequency based on observed first-period purchase frequencies. The results are applied to four marketing panel data sets.

Assessing Consumer Fraud Risk in Insurance Claims: An Unsupervised Learning Technique Using Discrete and Continuous Predictor Variables

Abstract We present an unsupervised learning method for classifying consumer insurance claims according to their suspiciousness of fraud versus nonfraud. The predictor variables contained within a claim file that are used in this analysis can be binary, ordinal categorical, or continuous variates. They are constructed such that the ordinal position of the response to the predictor variable bears a monotonic relationship with the fraud suspicion of the claim. Thus, although no individual variable is of itself assumed to be determinative of fraud, each of the individual variables gives a “hint” or indication as to the suspiciousness of fraud for the overall claim file. The presented method statistically concatenates the totality of these “hints” to make an overall assessment of the ranking of fraud risk for the claim files without using any a priori fraud-classified or -labeled subset of data. We first present a scoring method for the predictor variables that puts all the variables (whether binary “red flag indicators,” ordinal categorical variables with different categories of possible response values, or continuous variables) onto a common –1 to 1 scale for comparison and further use. This allows us to aggregate variables with disparate numbers of potential values. We next show how to concatenate the individual variables and obtain a measure of variable worth for fraud detection, and then how to obtain an overall holistic claim file suspicion value capable of being used to rank the claim files for determining which claims to pay and the order in which to investigate claims further for fraud. The proposed method provides three useful outputs not usually available with other unsupervised methods: (1) an ordinal measure of overall claim file fraud suspicion level, (2) a measure of the importance of each individual predictor variable in determining the overall suspicion levels of claims, and (3) a classification function capable of being applied to existing claims as well as new incoming claims. The overall claim file score is also available to be correlated with exogenous variables such as claimant demographics or highvolume physician or lawyer involvement. We illustrate that the incorporation of continuous variables in their continuous form helps classification and that the method has internal and external validity via empirical analysis of real data sets. A detailed application to automobile bodily injury fraud detection is presented.