William W. Cooper

Bio: William W. Cooper is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Data envelopment analysis & Linear programming. The author has an hindex of 79, co-authored 254 publications receiving 76641 citations. Previous affiliations of William W. Cooper include Harvard University & Carnegie Mellon University.

How Has the Sarbanes-Oxley Act Affected CPA Firm Fee Productivity?

Abstract: In this paper, we estimate a production function for CPA firms to examine the effect of the Sarbanes-Oxley (SOX) Act on CPA firm fee productivity. Using annual survey data for 57 large CPA firms in the U.S. for the period from 2000 to 2005, we find that the fee productivity of CPA firms in transforming human resources into service revenues increases in the post-Act period. Consistent with our expectations and with the first year deadline for compliance of Section 404 of SOX, this increase in the fee productivity of CPA firms was particularly strong in 2004. The current evidence seems to suggest that SOX is value-enhancing for CPA firms.

Communications to the Editor---Models of Fact and Laws of Behavior

Abstract: The querulous and exaggerated tones which seem to attend so many contemporary activities have now been permitted an entrance to the pages of Management Science where they are amply represented in the article published by A. S. C. Ehrenberg “Models of Fact: Examples from Marketing,” Management Science Vol 16, No. 7 March 1970, pp. 435--445.. The tone of Ehrenberg's article provides a warning that should naturally make anyone reluctant to attempt to reply to any of his criticisms or to question any of his claims. Some possibility for a productive response may be available, however, if one avoids a direct reply to Ehrenberg's claims and criticisms in order to respond by indicating possibilities and considerations other than those which Ehrenberg allows for in modeling, at least in management science.

Application Driven Theory: Rigorously Combining Applied and Basic Research Relevant to Accounting and Marketing

Abstract: Many business and social science research programs have come under attack as not being relevant or worthwhile [1,2] expenditures of money in a research university. In times of financial difficulty, business school research has been viewed as either not impacting practice sufficiently, as insufficient in preparing students for business careers, or as too vocational (which some question as a goal of higher education). Pfeffer and Fong [2] gave an analysis of the criticisms and the facts related to these impressions, and the reality continues that the general perception of many is that much research in business is not relevant or cannot be generalized into practice. In this editorial we argue that accounting and marketing research can be both practically relevant and theoretically well-grounded, if a paradigm of application driven theory is adopted and rigorously applied.We start with a brief description of the nature of research, simplifying it into two different dichotomies: 1) basic research and non-basic research on one continuum, and 2) applied research and pure research on the other. We then give illustrations from science, accounting and marketing to demonstrate each of the four possible combinations of research: pure-basic, pure-non basic, applied-basic and applied non basic when these terms are dichotomized. We discuss the lasting impact of basic research and argue for the importance of basic research as being both applied and practical when the theoretical constructs developed arise from a specific application and are then generalized. We call this research approach “Application Driven Theory” and give historical examples of impactful application driven theory in accounting and marketing. An application driven theory approach to research development insures that theories created are relevant to practice [3].Basic research is research that is foundational in that it either opens new lines of research or cuts off existing lines of research. Basic research allows generalizations that can impact areas outside the original area for which it was invented. Examples of this from the history of science include the invention of probability theory by Pascal and Fermat, the development of the Arabic numeral system (taken from the Hindus) for recording numbers, and Pasteur’s germ theory of disease. That which is not basic is deemed non-basic.The dichotomy (or, conceptualized another way, possible continuum) between pure and applied research is more difficult, and comes from the motivation for the research rather than the impact of the research . Pure research is undertaken for greater understanding and applied research is undertaken to solve a particular problem or to be used in a specific practice. Another differentiating factor is that pure research is more generally disseminated (usually via publications) for the purpose of increasing the body of knowledge and application areas, whereas applied research often stops with the particular motivating application with little effort given to generalizations beyond the problem which gave rise to the investigation (e.g., industry consulting situations). We give illustrations of each combination in Table 1.Consulting and general problem solving reflects non-basic applied research. In accounting, a specific audit, tax preparation or preparation of annual statements falls into this category. In marketing, consulting, designing company specific marketing or advertising plans and product placements are non-basic and applied.Application driven theory resides in the top right quadrant of Table 1. An example of this is Fibonacci’s [4] book Liber Abaci, which introduced Arabic numbers to Europe in 1202 to solve the problems in calculation of mathematics.Had he stopped there, it would have been of interest to mathematicians but little or no other disciplines. Instead he showed how to apply these results to construct a theory of bookkeeping, of doing interest calculations and other computations of importance to merchants. According to David [5], Paccioli [6] (who is sometimes referred to as the father of accounting) copied Fibonacci’s [4] book almost in its entirety and popularized the applications (such as double entry bookkeeping), showing further its uses, and creating another example of application driven theory. Thus, Fibonacci’s [4] book had its origination in an applied problem, but after solving this applied problem created generalizations, as motivated by Paccioli’s [6] work, outside the specific application that initiated the research.A similar development (the invention of probability theory in 1654 by Pierre de Fermat and Blaise Pascal) started as a solution to a particular gambling problem posed by an avid gambler (the Chevalier de Mere) trying to explain why he kept losing on a gambling bet that seemed to him to be in his favor. This began the widely known area of “probability theory”. Probability theory has since spread to affect all areas of scientific thought, including accounting (e.g., how to take audit samples) and marketing (e.g., consumer decision-making modeling).Another example from marketing research is illustrated by the development of the Golden Numerical Scale for questionnaire design [7]. A very practical applied problem (a consumer attitude survey for a restaurant) motivated the problem: The researchers needed many repeated scales for multiple different restaurants in a more concise, but still accurate, presentation format. The practical problem of creating survey instruments with repeated scales across multiple restaurants by existing methods would require numerous questionnaire pages. The solution was to create a new response scale format, which was done to evaluate multiple restaurants and characteristics on a single continuous graphical scale. This scale was then generalized to multi-object and multi-attribute questionnaires in general, and published with the statistical and measurement properties of the scale investigated for

Generation and approximation of reach and distribution of frequencies.

Abstract: : Media planning has heretofore been restricted, by and large, only to the consideration of average frequency. A recently developed mathematical model explicitly considers both cumulative duplicating audiences over a variety of time periods and the simultaneous selection and scheduling of media. This model thus required the development of methods for handling such nonlinear concave aspects of audience characteristics within a linear programming model and for generating distribution of frequency data within the computational processes of the model. This is done by means of logarithmic transformations and, for the discrete distributions of frequencies, by log-normal approximating devices. Each of these two techniques is important in its own right as a type of method which may be employed in a wide variety of modelling or computational situations. Thus, because neither of these techniques is restricted to the area of media planning per se it seemed worthwhile to present them on their own terms--as in the present paper--instead of treating them only as a detail in developing and interpreting the LPII model.

Communication to the Editor---The Cluster Plan for Desegregating Public Schools, or a Little Less Weltanschauung, Please, for Some More Adequate Descriptions

Abstract: It may not be possible to be wholly objective, as Messrs. Stimson and Thompson among others assert see [Stimson, D. H., R. P. Thompson. 1975. The importance of 'Weltanschauung' in Operations Research: The case of the school busing problem. Management Sci.21 10, June 1123--1132.]. Nevertheless, one should be as objective as possible in OR, or any other science, and this carries with it the concomitant duty of accurate and adequate descriptions---including descriptions of work by others to the extent that it may be pertinent.

Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis

Abstract: In management contexts, mathematical programming is usually used to evaluate a collection of possible alternative courses of action en route to selecting one which is best. In this capacity, mathematical programming serves as a planning aid to management. Data Envelopment Analysis reverses this role and employs mathematical programming to obtain ex post facto evaluations of the relative efficiency of management accomplishments, however they may have been planned or executed. Mathematical programming is thereby extended for use as a tool for control and evaluation of past accomplishments as well as a tool to aid in planning future activities. The CCR ratio form introduced by Charnes, Cooper and Rhodes, as part of their Data Envelopment Analysis approach, comprehends both technical and scale inefficiencies via the optimal value of the ratio form, as obtained directly from the data without requiring a priori specification of weights and/or explicit delineation of assumed functional forms of relations between inputs and outputs. A separation into technical and scale efficiencies is accomplished by the methods developed in this paper without altering the latter conditions for use of DEA directly on observational data. Technical inefficiencies are identified with failures to achieve best possible output levels and/or usage of excessive amounts of inputs. Methods for identifying and correcting the magnitudes of these inefficiencies, as supplied in prior work, are illustrated. In the present paper, a new separate variable is introduced which makes it possible to determine whether operations were conducted in regions of increasing, constant or decreasing returns to scale in multiple input and multiple output situations. The results are discussed and related not only to classical single output economics but also to more modern versions of economics which are identified with "contestable market theories."

Fuzzy Set Theory - and Its Applications

Abstract: Fuzzy Set Theory - And Its Applications, Third Edition is a textbook for courses in fuzzy set theory. It can also be used as an introduction to the subject. The character of a textbook is balanced with the dynamic nature of the research in the field by including many useful references to develop a deeper understanding among interested readers. The book updates the research agenda (which has witnessed profound and startling advances since its inception some 30 years ago) with chapters on possibility theory, fuzzy logic and approximate reasoning, expert systems, fuzzy control, fuzzy data analysis, decision making and fuzzy set models in operations research. All chapters have been updated. Exercises are included.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software

Abstract: List of Tables. List of Figures. Preface. 1. General Discussion. 2. The Basic CCR Model. 3. The CCR Model and Production Correspondence. 4. Alternative DEA Models. 5. Returns to Scale. 6. Models with Restricted Multipliers. 7. Discretionary, Non-Discretionary and Categorical Variables. 8. Allocation Models. 9. Data Variations. Appendices. Index.