Nicholas J. Gonedes, Yuji Ijiri, Improving Subjective Probability Assessment for Planning and Control in Team-Like Organizations, Journal of Accounting Research, Vol. 12, No. 2 (Autumn, 1974), pp. 251-269
Presents an interview with Professor Herbert A. Simon of Carnegie Mellon University, Pittsburgh, Pennsylvania. Impact of information technologies on organizations; Views on the relative importance of the evolutionary and design perspective of institutional literature; Assessment of the impact of information technologies on accounting and control.
The purpose of this study is to examine some of the relative merits in applying different accounting techniques to the measurement of wealth, and to the measurement of differences in wealth, such as income. The activities of a commodity trading firm are measured using historical cost FIFO, historical cost LIFO, and current cost accounting. Ijiri and Jaedicke's [1966] reliability measure is used as the basis for comparison of the three techniques, which differ systematically because of their different treatments of the stochastic commodity price. Two basic results emerge. First, any advantage of current cost over historical cost because of the recency of the data used in the current cost measurement may be outweighed because current cost does not take full advantage of past data to average out random errors. Second, the reliability of current cost relative to historical cost can deteriorate as the measurement moves from wealth to income (the difference in wealth) and to force (the difference in income).
Presents a reply to the commentary on the study on the quadratic cost-volume relationship and timing of demand information. Types of quadratic cost functions analyzed; Alternative proposed in the commentary regarding fixed costs; Failure of the study to point out the implications of the optimization analysis according to four possibilities.
Let us now summarize the major results of our analyses. First, after discussing the useful properties of a quadratic cost curve, we showed how the timing of information might actually be evaluated relative to the flexibility of operations. We provided a model in which the timing of information alone (not its content) can affect the profit of the firm not in an all-or-nothing fashion but as a factor that may hopefully make a procrastinator more aware of his guilt. Specifically, our analysis of the effect of the timing of perfect information on the total cost of a firm showed that there is a law of increasing marginal loss with respect to information delay. This analysis also serves as an example of how the framework of information evaluation developed by accountants and information economists can be utilized to yield a specific result. We then demonstrated how the quadratic daily cost-volume relationship might be used to evaluate monthly cost performance. The cost variance analysis showed how the forecasting and information gathering ability of the sales department can he evaluated along with the "genuine" cost performance of production operations. We also analyzed the problem of the optimal balance between the timing and accuracy of information in the case where earlier information is of less accuracy. We were able to derive an optimal information timing explicitly from our imperfect information model. Finally, we pointed out that a study on cost curves may have to be more sensitive to the time period on which the cost curve is constructed. A cost curve is not homogeneous with respect to the length of time of the period. A monthly cost curve may have quite a different shape from a daily cost curve if we allow the production manager to optimize.
A new method is proposed for deriving skew distributions of business finn sizes from the assumption of Gibrat's Law. The growth of the firm is decomposed into an industry-wide component and an individual component, the latter governed by a one-period Markov process. The model is fitted to data on the recent growth of large American firms. A NUMBER of stochastic models, embodying various forms of Gibrat's law of proportionate effect, have been shown to generate skew distribution functions resembling the actual size distributions of business firms. (See [2] and references cited there.) In a previous paper [1] we presented some results of the simultation of such a model permitting serial correlations over time in the size changes of individual firms. The aim of the present paper is to carry further the analysis of autocorrelated growth, by proposing an economically meaningful scheme for its analysis, and applying the scheme to some data on large American firms. In studying business firm growth, we often encounter cases where a firm suddenly acquires an impetus for growth. Perhaps by innovating in production or marketing processes, or perhaps as an effect of new management staffs or techniques, the firm grows much more rapidly than the other firms in the industry, as measured, say, by the ratio of the current firm size to its size in the previous time period. Thus, we may observe that, while most of the firms in the industry are growing at, say, 5% a year, some firms grow 10%. Furthermore, a firm that grew 10% last year is likely to grow more rapidly than average again this year as a result of the carry-over effects of an innovation that occurred in a previous year on operations in subsequent periods. This carry-over becomes more and more likely as we shorten the length of the time period we are considering from a year to a month, week, or day. Moreover, on the average, a firm which grew rapidly in one year subsequently retains a greater share of the industry assets (or market share if sales are used as a measure of firm size) from that time on than do firms that have enjoyed only the average industry growth. Therefore, not only the growth rate over and above the average growth rate, but also the period when the extra growth took place are important factors in the individual firm's growth relative to the industry growth. In this paper, we develop a model to represent such characteristics of firms' growth, so that the process may be analysed further. In the final section we estimate the key parameter of the model for the recent growth of large American business
The article applies mathematical control theory to the analysis of a simple model of an accounting and budgeting problem. The objective is not only to make the model operational for practical applications but also to use the framework and results of mathematical control theory in order to get new insights into accounting and budgeting problems. The article shall be primarily concerned with the process of the operations of the firm looked at from an accounting standpoint. In accounting, a firm is represented by a set of quantities that indicate the stocks of assets of various types that are under the control of the firm and the firm's activities are indicated by the changes in these quantities. Using mathematical notation one can say that the firm at time t has a vector x(t) whose components indicate the physical amounts of each asset the firm has at time t. The results of the activities of the firm between t1 and t2 are measured by the vector difference x(t2)-x(t1). It is found that firm's activities at time t are indicated by &xdot;(t), which in turn affects x(t).
The reliability of accounting measurements was defined as the degree of objectivity plus a bias factor. This relationship shows that the degree of objectivity can be measured without regard for the use of the measure but reliability cannot because the bias depends on the alleged value which in turn is related to the particular use of the measure. This conceptual relationship, which depends on the variance of measurement observations from their mean and the distance of this mean from the alleged value, gives valuable insights into some of the possible steps which can be taken by the members of the accounting profession to improve the reliability of accounting measurements. First, accountants ought to cooperate to the fullest possible extent with the users of accounting data to search for and define those factors or quantities which will be useful in decision processes. These factors were designated as y's. Accountants must then undertake to develop measurements which will produce accounting measurements (x's) that will give good predictions of these decision variables (y's). Both of these research efforts should help to reduce the bias, B. Accountants should also at- tempt to educate the users of the accounting data as well as be educated in the manner described above. If users of accounting data can be more effectively educated, they might change their function for relating x and y and this change can also result in decreasing the bias and improving reliability. Hence, reliability is definitely a two-way street. It may be that the reliability of a measurement can be improved by changing the measurement system; on the other hand, the reliability of the system may also be improved by changing the manner in which the output from the system is used. Another way to improve the degree of reliability may be to sacrifice objectivity, if the bias can be made much smaller as a result of sacrificing the objectivity.