Committee Reports: Supplement to Volume XLVII of The Accounting Review || Report of the 1969-70,...

Report of the 1969-70, 1970-71 Committee on Managerial AccountingSource: The Accounting Review, Vol. 47, Committee Reports: Supplement to Volume XLVII ofThe Accounting Review (1972), pp. 317-335Published by: American Accounting AssociationStable URL: http://www.jstor.org/stable/244889 .

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Report of the 1969-70, 1970-71 Committee

on Managerial Accounting

INFORMATION ISSUES IN MANAGERIAL ACCOUNTING

The original charge to this committee was to regard contemporary problems in developing information for management's planning and control functions and to critically appraise proposed solutions in these areas. Hopefully, a consideration of these problems and proposals would enable the committee to issue position statements where appropriate and to suggest areas where research is necessary.

At its earlier meetings, the committee at- tempted to define the relevant set of problems and proposed solutions which should be evaluated. The discussions at these meetings indicated, however, that perhaps the charge to the committee was too ambitious. Problems in accounting reduce to one of making a choice between two or more alternative sets of information which could be reported to a decision maker. The committee could issue position statements only if its members could determine, in any given problem area, which information set pos- sessed the highest utility for the decision maker(s) whose needs were assumed. Unfor- tunately, our profession has not reached the point where an unequivocal statement can be made about the relative utility of alternative sets of information. Indeed, it became obvious to us that the appropriate criteria to use in evaluating alternative information proposals constitute a separate research area beyond the charge of our committee.

Accordingly, we took the liberty of defin- ing a new charge which would be on a more

modest plane than the original one, but still consistent with its intent. Eventually we agreed on the objective of developing a taxonomy of information problems in managerial accounting which can be used as a basis for classifying and evaluating proposals which have appeared in the literature. The substance of our report centers on the classification and evaluation of the recent managerial literature. We should emphasize that our evaluation of the literature is limited to an assessment of the state of the art in the various problem areas. In particular, we have gone out of our way to avoid strong statements about the relative quality of the research we cite in this report. Our hope is that this overall taxonomy will be useful in structuring advanced courses in managerial accounting and in aiding future researchers to identify major information problems which have yet to be resolved.

As an initial step, we will attempt to outline the various points at which an information choice problem may arise. Our analysis will be based on a simple illustration involving a decision maker (say, a manager), an information evaluator an accountant in the broadest sense of the term-and a given information system. We will assume that the objective of the information evaluator is to choose an optimal information system, which can be defined as one which generates signals (i.e., information) leading to an optimal resolution of a decision maker's problems. The optimal resolution of a decision maker's problems involves an analysis of the expected benefits and costs of alternative sets of information which could be commu-

317



318 The Accounting Review

nicated. As we will imply later, the achievement of an optimal information system seems beyond our present capabilities. Nev- ertheless, such an objective may serve as a basis for structuring our thinking regarding current and future information proposals.

A SIMPLIFIED FRAMEWORK

In general, management's decision problems revolve around the optimal allocation of the resources within its control. These decisions are based on methods of evaluation ranging from intuitive or ill-defined techniques for assessing the expected payoffs from the adoption of various actions to well- structured representations of how decision variables, future events and actions interact to yield various levels of expected payoffs. A well structured representation of a decision situation is usually referred to as formal decision model.

Accountants have often assumed that if the decision maker can structure his decision problem within the context of a particular model which is then made known to the information evaluator, the problem of selecting an optimal information system is consid- erably reduced if not made trivial. However, this is an overstatement of the advantages) of knowing a decision maker's models. In the following illustration, we will try to show that knowledge of a decision maker's model does not provide all of the criteria needed by the information evaluator. Knowledge of the decision maker's model will allow an information evaluator to identify some critical variables to consider in his system. Howev- er, how these variables should be estimated and monitored during the model's implementation is not obvious from the model's structure.

Assume a firm's management is contem- plating the production and distribution of two new products xi and X2. Conditions exist such that the production of these products need only be geared to next period's

demands. This assumption allows us to ig- nore any inventory problems.'

The firm's management has obtained forecasts of the physical inputs required to produce and distribute each product. These forecasts could be based on engineering analyses or observed past relationships between input services and outputs. For the moment we are not concerned with how management, our decision maker, obtains his forecasts.

Each product passes through two production departments and a distribution department. Using the forecasted relationships between input services and outputs and making forecasts of the prices of input services, our decision maker develops a cost function for each product in each department. Other data and analyses are used by the decision maker (hereafter D.M.) to develop a linear revenue function for each product.

Both production departments have capacity constraints, but the distribution department is not constrained. The D.M.'s problem is to maximize the expected payoffs from the utilization of his two production departments given the feasible mixes of X1 and X2 which could be produced. We will assume that the D.M. uses a standard (deterministic) linear programming model to select an optimal solution to his problem.2

'As an example, we might assume that all units produced can be sold at a constant price and that the production cycle is short enough to meet demand as it aris- es.

2The problem can be structured as follows: Maximize Z = (ri - b3 - b2 - bi) X1 + (r2 - C3 - C2 - ci) X2 Subject to

ai Xi + a2 X2 < A, 01 X1 + 02 X2 < A2

X1, X2> 0 aj and flj are the technical coefficients for the two production departments; Al and A2 represent the capacities of the two departments. The rj, the bj and the cj are taken from the revenue and cost functions; which are:

(1) Total revenue = ri X1 + r2 X2 + e1 (2) Total cost-Production I = a, + b, X1 + cl X2

+ e2



Committee Reports 319

Suppose that the model yields an optimal solution, Xi* = B1 and X2* = B2, and that this information and the entire structure of the model is made known to the accountant. His initial task is to design an information system which will generate the signals needed to communicate the solution of the model and all that it entails to those responsible for model implementation. In conjunction with this task, the accountant will also have to design a reporting system which will generate signals on actual results so that any deviations from the expectations of the model can be analyzed in an optimal manner. An optimal analysis of deviations implies that the costs and benefits of re- sponding to deviations can be estimated and used to select an appropriate response.

Initially we will assume that the only formal information system available to the accountant consists of the data and the forecasts used by the D.M. to construct his model and to arrive at the optimal output decision. At this point we have the first potential information issue since the accoun- tdnt may not agree with the forecasted demands, the cost functions, or the technological coefficients of the model. For one thing, the accountant may believe that the cost functions are not linear over the entire output range. If he is correct, the structure of the model will have to be changed. Alterna- tively, the accountant may accept the linear- ity assumptions, but he may disagree with the coefficients of one or more of the cost functions. The accountant then has the choice of deferring to the D.M.'s forecasts until he has more data, or of modifying the information system in such a way that its resulting signals lead the D.M. to accept

(3) Total cost-Production II = a2 + b2 XI + c2 X2 + e3

(4) Total cost-Distribution = a3 + b3 XI + C3 X3 + e4

We have inserted error terms (ei) for each function to recognize the possibility that these functions are not deterministic even though they are assumed to be in the construction of the model.

revised forecasts. If the D.M. accepts these, he will change the forecasts of the model which presumably will require a revision of the model's solution.

But a modification of the information system could consume some resources of the firm, and this imposes a cost on the collection and communication of any new information. This cost must be balanced against the expected benefits of the new information. Assuming the model's original solution is sensitive to the contemplated change in information, the benefits from the new signals may be assessed by evaluating the effects of the changes on the objective function, or by comparing the opportunity losses which would be incurred if the relied upon forecasts turn out to be incorrect.

Notice that the accountant cannot evaluate the net benefits from a modification in the information system without predicting how the D.M. will react to the new signals. At the two extremes, the accountant's information could be completely accepted or completely rejected by the D.M. As a general rule, the analysis would be based on the accountant's subjective probabilities about the D.M.'s reactions to new information, which adds an additional degree of complex- ity to the problem.

We are not interested at this stage in the specific techniques which can be used to assess changes in the information system before the model is implemented. Instead we are concerned with an identification of points in the entire implementation process at which information issues may arise.

Assume now that the accountant agrees with the structure of the model and the forecasted revenue and cost functions. The structure and forecasted functions of the model are based on assumed organizational relationships between men, machines and departmental activities. In other words, the parameter values of the model are really aggregates of all the tasks and functions needed to produce Xi andX2 in the desired




ratios of inputs to outputs. In most organizations, these tasks and functions are depict- ed as sets of hierarchies of responsibilities for achieving the goals of the organization. For our simple illustration, we are assuming that the goal is the implementation of the output solution [X1* = B1 and X2* = B2]. The accountant's task is to disaggregate the parameter forecasts of costs and demand into individual responsibilities or standards. This task would be relatively simple if the accountant could accept the decision model as a perfect representation of the input and output relationships of the firm. In this case, he would merely reverse the aggregation process used to develop the functional equations of the model.

However, the forecasted relationships used to construct the model are usually ex- treme abstractions of the behavioral rela- tions in the firm. For example, the structure of the L.P. model implies a complete independence between the elements of the three cost functions and between the revenue functions of each product. But in situations involving interdepartmental transfers, the level of efficiency in one department will often influence the level of efficiency in departments which receive its output. It is very difficult to incorporate these interdepartmental effects into disaggregated departmental standards. Consequently, the standards may not be considered accurate by those responsible for their achievement. Indeed, the indi- viduals held responsible may have some in- side information regarding how the functional relationships could be linked to yield improved forecasts of the parameter values of the model. This may motivate the accountant to seek their participation in developing standards for which they will be held responsible. A decision to have subordinates participate in the setting of their own standards has far-reaching implications for both the evaluation of performances and the D.M.'s estimates of his parameter values. Hence, the design of a control system is not

completely independent of the model construction phase of our problem.

There is still another possibility which complicates the accountant's task. The functional relationships of the model may in fact represent what should occur in a normative sense if certain inputs are combined in the production and distribution processes. How- ever, those responsible for achieving outputs consistent with the model's solution may be motivated to do so only if a different set of standards is communicated to them. In fact, accountants have often assumed that a high achievement standard has positive effects on motivation even though the standard will not be achieved in all cases.' This implies that a predictable relationship would have to be established between a communicated standard, call it Sc, and performance at the desired level, Sd. In effect, the desired standard will be achieved only if the accountant communicates a standard which includes an intentional amount of error. The proper amount of error is not obvious from any- thing contained in the structure of the decision model.

Thus, knowledge of the decision maker's model does not provide the accountant with all of the criteria he needs in the design of his control system. Critical variables to monitor can be identified, but how these variables should be disaggregated to individual standards is an issue which cannot be resolved by reference to the details of the model.

We turn now to a related issue of disaggregation. The discussion above suggests that we can design a system for communi- cating standards of responsibility while ig- noring considerations of how performance statistics are to be collected and reported. Obviously, this is unrealistic since the level of disaggregation of standards places certain constraints on the reporting process. Gener- ally, we would expect performance statistics

'This is a reasonable assumption since it is basic to the use of good attainable performance standards.




to be collected for each standard developed in the control system. However, there is reason to believe that the two systems of control and evaluation may not be completely parallel in design.

Consider the overall purpose of comparing standards against performance. Model implementation takes place under conditions of imperfect information about future events. Feedback on the actual results from a run of a model can be used to assess the accuracy of the cost and revenue functions and the structure of the model as well as the efficiency of the personnel responsible for achieving performance levels. An observed deviation of performance from standard in our illustration could indicate any one (or more) of the following possibilities:

a) Random error in the standard which can be tolerated;

b) Performance error which can be cor- rected by the firm through instruction or training;

c) Coefficient errors in the cost and revenue equations;

d) Errors in the structure of the model (e.g., the technological coefficients may be incorrect or perhaps additional relationships should be incorporated in the model);

e) Changes in the processes being mod- eled or in the environmental assumptions regarding its implementation (the model may have been an accurate representation of the processes, but learn- ing, deterioration of assets, inflation, etc., have occurred over time which require changes in the model's structure or its parameter values);

f) An error in the measurement of performance (either deliberate or because it is not possible to measure performance with complete accuracy).

Ideally, the accountant should decompose a particular variance according to its various

sources since the required responses of the D.M. will vary accordingly. For example, random and measurement errors ((a) and (f)) may be estimated using probability distributions, in which case, bands of tolerable errors can be derived. If the deviation falls within these bands, no response is required. If a deviation falls outside of them, then either corrective action at the performance level (type (b) error) is required or the D.M.'s model will have to be modified (types (c), (d) and (e)). Notice that a decomposition of a deviation can be finer (i.e., more partitioned) than the responsibility standard to which it relates.4

This can be illustrated by reference to price standards for raw materials. An overall deviation from a standard price might be due to inefficient buying (db). plus a difference in quantity purchased (dq), plus a change in the specific price level of this ma- terial (d,).5 A deviation from the standard price is sufficient information to assign overall responsibility, but a decomposition of a deviation from this standard would be bene- ficial to the D.M. in determining his proper response to it.

Summary

Our objective has been to isolate some points in the decision process at which information issues may arise. This illustration will serve as a framework of analysis for the remaining sections of this report in which we evaluate recent proposals in the area of providing information for management.

In our discussion, we have shown how an information evaluator's problem may be

4The overall problem is treated more formally by J. Ronen, "Nonaggregation versus Disaggregation of Variances," mimeographed paper, University of Chica- goi December, 1970.

These potential sources are also discussed by D. Solomons, "The Analysis of Standard Cost Vari- ances," in his Studies in Cost Analysis, 2nd Edition (Homewood: R. D. Irwin, 1968), pp. 428-33.




viewed as a sequence of issues relating to model selection and parameter forecasts (the latter could be viewed as part of the model selection stage), and to the control and evaluation of the model's solution. Our sequence is somewhat arbitrary since the interdepen- dency of these stages implies that they form a closed circle.

This takes us back to an earlier part of the discussion. As the model is implemented, the information system will have to ex- pand to include the original data used to make forecasted values of the parameters, the parameter values themselves, the de- tailed standards, and measures of actual performance. The issues facing the accountant become much more complex. He must now decide on the frequency with which performance statistics must be collected and reported. Related to this problem is the extent to which performance statistics must be aggregated (classified) so as to permit the proper feedback information on individual performances and the overall success of the model's implementation. But each receiver of performance statistics becomes a new decision maker, and the accountant has to be able to anticipate how this new set of D.M.'s will react to various signals before he can decide on the optimal frequency and extent of reporting. Unfortunately, there is an endless set of alternative reporting schemes (i.e., potential alternative sets of signals which could be reported), and each could result in cost differentials. These cost differentials will have to be balanced against the expected benefits of each alternative so that their net benefits can be ranked. A ranking of net benefits implies some form of information model which is capable of en- compassing the whole system, including the effects of information on the original decision model. Add to this the possibility that any one D.M. faces several related problems and tries to resolve these by using different kinds of decision models. Thus a formal

analysis of the entire information problem may be unmanageable.6

This probably explains why the proposals relating to managerial information systems which we will discuss have concentrated on only small segments of the overall problem. The one advantage of a segmented approach to the problem is that we can review these proposals in the order in which we first out- lined information issues model selection, parameter selection, and control and evaluation.

THE EVALUATION OF PROPOSALS

We have now succeeded in isolating some areas of information issues which fall under the charge of this committee. The second phase of our modified charge is to classify and evaluate proposals in the literature in terms of how they relate to these areas. As we indicated earlier, a critical evaluation of proposals was considered to be beyond the scope of our new charge. In one respect, a critical evaluation of proposals would be presumptive on our part. These proposals have already passed the critical review of editorial boards, and this should be proof of at least a minimum contribution to the information issues we have discussed. Our analysis then will be limited to a review of the scope and the research methodology which underlies these proposals.

Model Selection

Generally, we assume that a D.M. has the prerogative of determining the appropriate decision models he will use. Under this assumption, our information problem would be first encountered at the stage at which the forecasts of parameter values needed to implement the D.M.'s models are to be devel-

6One such attempt to structure the problem can be found in G. Feltham and J. Demski, "The Use of Models in Information Evaluation," The Accounting Review, Vol. XLV, No. 4 (October, 1970), pp. 623-40.




oped. But earlier we observed that the D.M.'s choice of model would be influenced by the information system made available to him. Conceivably, the routine availability of certain information signals could bias his selection process. This would be a tolerable situation if the accountant could be sure that the bias is in the right direction, in the sense that it motivates a D.M. to select an optimal model. However, models are selected under conditions of imperfect knowledge about future events so that it is unlikely that an optimal model can be defined in many decision situations.

As one alternative, the accountant could collect and make available information which would enable any type of model to be implemented at equal information costs. This would probably lead to a storage of data on a firm's transactions in its rawest form (i.e., with a minimum amount of aggregation). Unfortunately, the costs and benefits of storing data in this form is a problem which has not been researched ex- tensively, if at all, so we have no basis for assessing such an approach.

It seems to us that the accountant has adopted a different approach to this problem. In general, he has relied on a priori reasoning to identify the types of models decision makers should use in various decision situations. For example, it is currently believed that an evaluation of long-term asset acquisitions should be based on cash flow information which can be processed through various discounting models. Some accounting discussions of discounting models have been even more specific, ending with a recommendation that the excess present value model should be used.' But, this

7This is probably most evident in leading text books dealing with the subject. See also the review of techniques by H. M. Weingartner, "The Excess Present Value Index-A Theoretical Basis and Critique," Jour- nal of Accounting Research, Vol. 1, No. 2 (Autumn, 1963), pp. 213-24.

model is optimal only under very restrictive conditions,8 and the accountant may not be justified in relying on it as the basis for de- fining relevant information for asset acquisition decisions. Similar doubts may be ex- pressed about recommendations for specific cost-volume-profit models, such as the use of direct costing.9

The committee can only speculate on this overall problem and point out the need to learn more about a manager's process of choosing among alternative decision models and the extent to which different information signals actually bias his choices. It may be possible to assess the significance of any bias by using sensitivity analysis to determine how decisions would vary as alternative models are employed.10 This type of analysis could be extended to an evaluation of the degree to which different models come closest to describing the reality of the decision process in actual trial runs. Persis- tent patterns of deviations may give some insight into the appropriateness of the various models. Of course, the degree of real- ism of a model is also dependent on the set of values assigned to its parameters, and this leads into a related information issue.

Forecasting Parameter Values

For any particular decision problem there are several possible model structures which might be appropriate to the problem. More- over, each model structure has competing sets of parameter values which could be used

8See R. Ball and P. Brown, "Portfolio Theory and Accounting," Journal of Accounting Research, Vol. 7, No. 2 (Autumn, 1969), pp. 300-23.

9No specific reference is needed here since practically every article on this topic follows the same approach. Basically direct costing is preferred over absorption because its measurements are more consistent with the type of information management should use in making cost-volume-profit decisions.

'0For example, see M. Sarnat and H. Levy, "The Relationship Between Rules of Thumb to the Internal Rate of Return: A Restatement and Generalization," Journal of Finance, Vol. XXIV, No. 3 (June, 1969), pp. 479-89; see also their reference in footnote 27.




to implement the model. Hence, the D.M. conceivably would first select an appropriate decision model and then choose from the set of parameter values which yields the highest net benefits--e.g., the one which yields the best ratio of expected accuracy to the costs of obtaining the values."

Our search of the literature did not produce many proposals relating to the problem of evaluating forecasts of parameter values. Recall that we initially viewed this problem as being one in which the accountant had to consider whether to report signals which would motivate the decision maker to revise his original forecasts of parameter values. Ideally, the accountant would report that set of signals which leads to the best forecasts possible under the given circumstances. This would usually involve a trade-off between the opportunity costs of using various sets of forecasts (assuming the true values of forecasted variables are not known in advance) and the costs of obtaining forecasts. But apart from this statement of the problem very little can be said of a general nature about how it should be analyzed.

It is conceivable that the accountant could evaluate in an a priori manner those forecasts used by the D.M. which are based on the financial statistics of his information system. Generally, the accountant is more familiar with the characteristics of financial information which can affect the quality of forecasts. These include such factors as the timelines of the information, the reliability of any functional relationships between cost items, and the stability of these relationships over time. An analysis of these factors may be used as a basis for deciding which signals should be communicated to the D.M. before the model is implemented.

In addition to an a priori analysis, alternative forecasts may be evaluated against

"A formal approach to this problem is developed by R. D. Smallwood, "A Decision Analysis of Model Selection," IEEE Transactions on Systems Science and Cybernetics, Vol. S3C-4, No. 3 (September, 1968), 333-42.

past observations using such criteria as min- imizing the sum of squared or absolute errors and by performing sensitivity analyses of the significance of differences between various forecasts.12

Notice that, up to this point, we have not considered forecasting methods as constitut- ing a part of the parameter forecast problem. This aspect of the problem was ignored on the assumption that there are information issues which can be discussed which are common to all methods. To illustrate, all forecasts are conditioned by prior experience, and the past relationships which should be relied upon are not dependent on whether we use formal analyses of past relationships (e.g., regression) or merely extrap- olate from the most recent observation. However, independence between method and information issues may not be as clear- cut as we would like to believe. As one example, an analysis of past relationships using multiple regression will be reliable only if the storage of past data meets certain requirements. Some of these requirements have been discussed in the literature1 and need not be reviewed here. It should be obvious though that, whatever the nature of the requirements, methods of forecasting which rely on only a single observation of a past relationship would not be affected in the same way. Thus, an analysis of the parameter forecasting problem may have to include a consideration of how data should be collected and stored to facilitate the implementation of certain forecasting methods.

Control and Evaluation of Decision Models

Our concern here is with the numerous issues arising in the design of a recording

"2A Rappaport, "Sensitivity Analysis in Decision Making," The Accounting Review, XLII, No. 3 (July, 1967), pp. 441-56; a related paper is R. Jensen, "Sensi- tivity Analysis and Integer Linear Programming," The Accounting Review, XLIII, No. 3 (July, 1968), pp. 425-56.

"See G. Benston, "Multiple Regression Analysis of Cost Behavior," The Accounting Review, XLI, No. 4 (October, 1966), pp. 657-72.




and reporting system which communicates responsibilities for implementing decisions and which provides a basis for evaluating the extent to which results coincided with the expectations or standards on which the decisions are based.

Decision Model Evaluation

Perhaps the first information issue to arise in this area is whether to design the system for the collection and storage of data around specific decision models or around general standards of performance. The issue is really one of degree since even general standards can be broken down around individual decision centers. However, the dis- tinction we have in mind stems from more recent proposals found in the literature. To be more specific, various accountants have proposed that specific decisions concerning capital acquisitions,'4 inventories'5 and output mixes'6 serve as individual accounting entities. Each of these decisions relies on an objective function and a set of forecasts regarding relevant inputs and outputs. Thus, each could provide the basis for a separate recording and reporting system. For example, capital acquisitions would be monitored around the expectations of individual projects. The control and evaluation of inventory and output mix decisions would be based on the structure and parameter values of the specific decision models used in these two decision situations.

1 4Project accounting was first proposed by J. Cough- lan, "Industrial Accounting," The Accounting Review, Vol. XXXIV, No. 3 (July, 1959), pp. 415-28, although with a financial rather than managerial perspective in mind. A more recent proposal for project accounting is offered by A. J. Hirsch, "Accounting for Fixed Assets: A New Perspective," The Accounting Review, Vol. XXXIX, No. 4 (October, 1964), pp. 972-78.

15See M. Gordon, "Toward a Theory of Responsibil- ity Accounting Systems," NAA Bulletin (now Manage- ment Accounting) (December, 1963), pp. 3-9. In this article M. Gordon indicates the need to link inventory control to inventory models.

16See J. Demski, "An Accounting System Structured on a Linear Programming Model," The Accounting Review, Vol. XLII, No. 4.(October, 1967), pp. 701-12.

The primary difference between this approach and the more traditional approach to recording and reporting is reflected in the types of standards encompassed within each. In the traditional system the emphasis is on price and input standards for raw materials, direct labor and variable and fixed overhead. These same standards would also be present in the proposed system, but they would be supplemented by standards for such variables as the levels and selling prices of outputs and the quantities of inventory ordered and on hand.

Referring back to our illustration, we can isolate two related advantages of designing the recording and reporting systems around decision models. First, planning and control information should be consistent in such systems since the standards of performance are linked directly to the parameter forecasts used in each model. This will increase the probability that the model's solution will be implemented according to plan. Second, the significance of any observed deviation between forecasted and actual values can be assessed in terms of the structure of the model. Thus, the significance of deviations can be assessed first with reference to the confidence intervals around the various parameter values and, if significant at this level, whether they signal the need to change the original decisions generated by the model (decision significance).1' Such evaluations are more difficult in general information systems since deviations must be traced back to the individual decision models which are affected.

The design of a recording and reporting system around individual decision models necessitates additional classifications of performance statistics, thereby increasing the costs of providing information. The expected benefits from this information may not be sufficient to cover these additional costs.

17 N. Dopuch, J. Birnberg, J. Demski, "An Extension of Standard Cost Variance Analysis," The Accounting Review, XLII, No. 3 (July, 1967), pp. 526-36.




For one thing, management may not be interested in evaluating the results of specific models on a continuous basis. Instead, it may be content to evaluate overall results which can be traced back to individual models to the extent overall results are not satis- factory. More significant, perhaps, is the fact that it may not be possible to evaluate individual models if performance results overlap and cannot be disaggregated. This is particularly a problem with capital acquisition decisons. These decisions are often based on the expected incremental effect the acquisition will have on overall results of operations. However, overall results of operations are affected by many decisions, including capital acquisition decisions made in previous periods, and it is quite difficult to determine from observed results whether a particular incremental effect was realized. Since these proposals to design accounting systems around specific decisions models are of a relatively recent origin, we do not have much evidence on their feasibility in actual firms. Hence, we can only pose some potential advantages and disadvantages of such systems.

Performance Control and Evaluation

Much more research has been done in the area of performance control and evaluation. This area encompasses information issues concerned with all aspects of standard setting and variance analysis. Regarding the former we have observed issues about the proper level of standards, the degree of aggregation of standards, their proper form of measurement, and the types of variables to standardize. Variance analysis is a more restrictive topic with the primary concern placed on measures of significance of performance variances.

The proper levels of standards were first discussed within the classification of ideal, attainable good performance, and basic levels of performance. This issue was generally resolved in favor of attainable good perfor-

mance on the assumption that this performance level provided the motivation for workers to perform efficiently. Recently, some accountants have questioned the notion that this level of performance would have positive effects in all situations. Count- er proposals have appeared ranging from participation"' by workers in setting their own standards to tailoring the standards to fit the personality characteristics of individual workers.'9 Whether the resulting standards in either case would be equal to an attainable good performance standard cannot be determined by a priori analysis.

It is difficult to evaluate these more recent proposals since their validity may be a function of the organizational structure of the institution in which they are implemented. For example, participation in standard setting seems to be more consistent with the so- called democratic style of leadership, where- as manipulating standards to suit individual characteristics probably requires a more autocratic structure wherein standards are expected to be set by management.

Issues concerning the aggregation question can be discussed with reference to both the timing of reporting and the structure of the information reported. The timing question has not been researched to any great extent, and this is probably understandable. Text books on the subject usually imply that the timing of reports is tailored to the individual needs of each institution. However, there have been at least two studies recently which use the timing of reports as a variable in explaining the quality of the decisions reached by the subjects studied.2" Unfortu-

18 For example, see S. Becker and D. Green, Jr., "Budgeting and Employee Behavior," Journal of Busi- ness, Vol. 35, No. 4 (October, 1962), pp. 392-402.

19A. Stedry, Budget Control and Cost Behavior (Englewood Cliffs, Prentice Hall, Inc., 1960).

2'FSee W. Bruns, Jr., "The Accounting Period Con- cept and Its Effect on Management Decisions," Empir- ical Research in Accounting: Selected Studies, 1966, pp. 1-14 (Supplement to Journal of Accounting Re- search). See also D. Cook, "The Effect on Frequency of Feedback on Attitudes and Performance," Empirical Research in Accounting: Selected Studies, 1967, pp. 213-24.




nately, the results of these studies cannot be interpreted outside the context of the specific decision situations analyzed in the studies.

The question of how to structure or aggre- gate measurements has also been left largely to the prerogative of the individual organization. Again, however, two recent studies have attacked this problem in an attempt to derive some general approaches which can be used in all organizations. One of these studies relied on information theory as a basis for aggregating performance statistics across many departments or products. If variances from performance expectations have a common source there may be little benefit in reporting the variances of each department or product.2" The approach has intuitive appeal, but not enough evidence is available at this time to make any more de- finitive statements about its usefulness. As part of a recent dissertation, an experiment was conducted to demonstrate that decision makers may change their preferences for working on alternative jobs involving sequential operations, even though each job has the same probability of successful com- pletion.22 For example, it was found that Job A would be preferred over Job B if the initial or first stages of Job A had higher probabilities of success than comparable stages of Job B. This may have implications for reporting in process-type firms.

Other recent research in the area of control and evaluation has concentrated on the general problem of determining the structure or proper measurement of standards and the types of variables which might be measured. Regarding the former, we found two recent studies concerned with the question of whether standards should be based on absorption or direct costing concepts. The subjects in an experiment in one study

2' B. Lev, Accounting and Information Theory, Stud- ies in Accounting Research #2 (Evanston, American Accounting Association, 1969), Ch. III.

22J. Ronen, "Some Effects of Sequential Aggrega- tion in Accounting on Decision-Making Behavior," mimeographed paper, University of Chicago, 1970.

were given data based on both methods. An analysis was then made of whether subjects' decisions were different using the different sets of data.23 No attempt was made to determine which set of information produced better decisions. The other experiment uti- lized a simulation model, and in this case the question at issue was which costing method led to better predictions of future variances.24 The results of the simulation were not clear-cut enough to allow us to rate the overall superiority of either of the two methods.

Proposals relating to the types of variables in the system have concentrated on two acknowledged deficiencies in managerial accounting. The first concerns the measurement of the opportunity costs of capacity. In one proposal, it was suggested that the traditional volume variance be decomposed into a marketing and production variance.25 The latter would reflect the lost contribution margin from the under utilization of capacity. This particular proposal is best applied in a situation involving a single product and a single capacity constraint. Proposals to measure opportunity costs in a multi-product, multi-constraint situation have been based on the optimal decisions generated by specific allocation models.26

These proposals have intuitive appeal since the traditional volume variance is generally considered deficient as a measure of

23W. Bruns, Jr., "Inventory Valuation and Manage- ment Decisions," The Accounting Review, Vol. XL, No. 2 (April, 1965), pp. 345-57.

24J. Demski, "Predictive Ability of Alternative Per- formance Measurement Models," Journal of Account- ing Research, Vol. 7, No. 1 (Spring, 1969), pp. 96-115.

25C. T. Horngren, "A Contribution Margin Ap- proach to the Analysis of Capacity Utilization," The Accounting Review, Vol. XLII, No. 2 (April, 1967), pp. 254-64.

26J. Samuels, "Opportunity Costing: An Application of Mathematical Programming," Journal of Account- ing Research, Vol. 3, No. 2 (Autumn, 1965), pp. 182-91; see also Y. Ijiri, F. Levy, and R. C. Lyon," "A Linear Programming Model for Budgeting and Financial Planning," Journal of Accounting Research, Vol. 1, No. 2 (Autumn, 1963), pp. 198-212, and J. Dem- ski, "An Accounting System Structured on a Linear Programming Model," op. cit.




the opportunity costs of deviating from planning capacity utilization. However, the implementation of the proposals has led to a number of practical and conceptual problems which remain to be resolved.2 One problem in particular concerns the question of how management should respond to information about lost opportunities. To some extent, such variances reflect errors in the forecasts of the future levels of capacity utilization. The use of such variances as measures of efficiency or inefficiency may create the behavioral problem that managers will attempt to bias their future forecasts to avoid unfavorable variances.

The second deficiency referred to above relates to human resource accounting. At least two psychologists have criticized accountants for not measuring the quality of the work force as part of the control system of the firm.29 The argument is that certain characteristics of the work force (e.g., mor- ale and attitude) should be included in the budget of a department with managers being held responsible for maintaining or increasing these values. This proposal has developed into research projects concerned with measuring the value of the work force and at least one aspect of human resource accounting is being implemented within a specific firm.30 The experiment should eventually provide some evidence about the usefulness of such standards.

The area of variance analysis is a relatively unexplored one in accounting. Most text- books are not explicit regarding manage-

2 One main difficulty with such approaches is that they must rely on estimates of the future values of the relevant variables. If these values are not realized, the original optimal plan must be modified which changes the opportunity costs of capacities.

28In this connection, see R. Barefield, "A Model of Forecast Biasing Behavior," The Accounting Review, Vol. XLV, No. 3 (July, 1970), pp. 490-501.

29See R. Likert and S. E. Seashore. "Making Cost Control Work," Harvard Business Review, Vol. 41, No. 6 (November-December, 1963), pp. 96- 108.

.3 R. Brummet, A. Flamholtz, and W. C. Pyle, "Human Resource Measurement-A Challenge for Accountants," The Accounting Review, Vol. XLIII, No. 2 (April, 1968), pp. 217-30.

ment's proper responses to observed variances. Often the implication is that all variances are significant and require investigation. In addition, it is often assumed that investigations will discover that the sources of deviations can be traced to the quality of the performance of those responsible for implementing management's decisions.

Recently, a number of attempts have been made to structure this problem within the context of statistical or decision theory models. The first proposals consisted of recommendations to analyze variances using statistical control chart techniques."' The reasoning behind these proposals is simply that a certain amount of variation around standards is due to random and, therefore, noncontrollable causes. Confidence limits based on the probabilities of observing variances of a certain size can be constructed to aid in distinguishing between controllable (to be investigated) and random (not to be investigated) variances. Control chart techniques have since been extended to incorporate the costs of investigating variances and the expected monetary benefits from such investigations To our knowledge, these proposals have not been carried beyond the a priori state of evaluation.

Since standards are basically forecasts of what future performance should be under various conditions, we must expect a certain amount of random or normal variances. However, the implementation of these control models rests on the assumption that either we can describe the actual underlying probability distributions of variances, or we can appeal to sampling theorems as the basis for developing confidence limits. The

" For example, see Z. Zannetos, "Standard Costs as a First Step to Probabilistic Control: A Theoretical Justification, an Extension and Implications," The Accounting Review, Vol. XXXIX, No. 2 (April, 1964), pp. 296-304.

32 R. Kaplan, "Optimal Investigation Strategies with Imperfect Information," Journal of Accounting Re- search, Vol. 7, No. 1 (Spring, 1969), pp. 32-43, and T. Dyckman, "The Investigation of Cost Variances," Journal of Accounting Research, Vol. 7, No. 2 (Autumn, 1969), pp. 215-44.




former alternative may be impractical, and the latter may not be reasonable, particularly for the costs of overhead items. One of the assumptions in sampling theory is that the samples are drawn from a stable distribution, and yet overhead variances are usually not that well behaved. This is an empirical question which needs to be investigated. In addition, the cost-benefit models often rely on simplifying assumptions regarding the measurement of benefits from investigating variances and the costs of investigation. Generally, benefits are measured by the magnitude of the variance, which suggests that all variances reflect performance deviations which can be controlled by management. If a variance is unfavorable it will be eliminated. If it is favorable it can be incorporated within the standard to im- prove future performances. However, some variances may reflect changes inherent in the process or the environment being monitored, in which case the benefits from investigation are more difficult to specify. Simi- larly, costs of investigation are assumed to be constant when in fact they may change depending on the source of the variance being investigated. Whether these assumptions about benefits and costs are valid is an issue we cannot resolve.

A related proposal in this area uses information theory as a basis for measuring the significance of variances from standards` Information measures can be used to reflect the stability of relationships between classes of costs over time. These measures may be applied at both the firm and the departmental levels. If a comparison of departmental and firm level measures of stability indicates a greater instability at the departmental level, this may suggest significant departmental variances. Otherwise, it could be assumed that common sources affected all departments, in which case an investigation of

33H. Theil, "How to Worry About Increased Expen- ditures," The Accounting Review, Vol. XLIV, No. 1 (January, 1969), pp. 27-37; see also B. Lev, op. cit.

departmental variances would not yield ben- eficial results.

Intuitively, we cannot specify the conditions under which sources of deviations could be expected to be common across all departments of a firm. The extent of inter- dependency between departments probably varies from firm to firm. In this event the usefulness of the technique will have to be assessed by each individual firm.

CRITERIA USED TO SUPPORT PROPOSALS

Our review of the proposals in the previous section is not meant to be exhaustive. However, we believe the works cited are representative of the current thinking in these areas. In this section we discuss some of the criteria used to develop and rationalize these proposals.

A Priori Reasoning

Practically all of the proposals we have cited rest on logical reasoning from a set of assumptions about the need and usefulness of certain information. In general, a particular information system is proposed because its resulting signals will lead to improved decisions on the part of the D.M., given assumptions about his model and his behavioral reactions to the signals. This approach is reflected in such articles as those cited in references [14], [15], [17], [18], [25], [26], [3 1 ], and [32].

A priori reasoning is a legitimate method of developing cases for reporting various types of information, and we cannot be too critical of it. Indeed, an a priori framework is a necessary requisite for building any theory of information processing. But the final validation of a theory requires empirical research, and this has been especially lacking in the area of managerial information systems.

There are probably some legitimate rea- sons why empirical testing has not been a




common basis for theory testing in our field. The fundamental fact is that the information systems of firms are particularly suited to their organizational structures and to some specific characteristics of their managers.34

We have noted several times that the choice of models can be influenced by the information made available within each firm. In addition, the choice of models may also be influenced by the differences among managers in cognitive style. For example, a recent laboratory experiment was designed to test the impact of cognitive style on the implementation of recommendations by accountants and other consultants. Imple- mentation failed when a non-analytic manager was given recommendations that em- phasized the analytic procedures leading to these recommendations.35 Thus, the specification of management information may depend on the cognitive style of the opera- tional manager for whom the information is intended.

The diversity of approaches followed by firms in their attempts to resolve information problems can be appreciated from a review of the surveys taken by the National Association of Accountants and published in their research reports.36 The same conclusion applies to a fairly recent report on the decision models used by firms.37 Thus, empirical tests may have to be conducted on a

34The relationship between organizational structure and behavior is implied in the study by Don T. De- Coster and John P. Fertakis, "Budget Induced Pressure and Its Relationship to Supervisory Behavior," Journal of Accounting Research, Vol. 6, No. 2 (Autumn, 1968), pp. 237-46. Their tests related supervisory leadership styles and budget pressure based on samples from several firms.

35See Jan H. B. M. Haymans, "The Effectiveness of the Cognitive-Style Constraint in Implementing Opera- tions Research Proposals," Management Science, Vol. 17, No. 1 (September, 1970), pp. 92-104.

36See NAA Research Reports and Accounting Prac- tice Reports. Many of the latter were published as sup- plements to NAA Bulletin-now Management Ac- counting, National Association of Accountants, New York.

37W. J. Vatter, "The Use of Operations Research in American Companies," The Accounting Review, Vol. XLII, No. 4 (October, 1967), pp. 421--30.

case study basis which makes it difficult if not impossible to generalize the findings.

But the outlook may not be as hopeless as we have implied. A large firm can be viewed as a composite of smaller firms with different organizational structures and leadership styles. We would expect then that the firm may offer such a diversity of models and approaches to systems designs that a case study of the firm will permit the develop- ment of valid generalizations.38

Significance of Differences

At one step removed from a priori reasoning is the attempt to assess alternative measurements or information systems in terms of whether their differences are significant. Significance is measured either as a certain magnitude (e.g., 10%), as in the recent study of the impact of price changes on a firm's income," or as a change in the decisions of the users of the different information signals. The latter is the approach followed in the assessment of different inventory and depreciation measurement systems.

The studies referred to in [40] are limited since they used student subjects rather than actual decision makers. Nevertheless, the results may still be useful if they indicate that two information systems may yield information signals which have little impact on users' models.

The more difficult question, however, is what we can conclude if the two systems do in fact alter the decisions of users. Ideally, that system which yields optimal decisions

381n a recent dissertation, A. Hopwood used a sample of supervisors from a single firm. This sample clearly indicated the presence of rather diverse leadership styles within the same overall organizational structure. See his "An Accounting System and Managerial Be- havior," unpublished dissertation University of Chica- go 1971.

9P. Rosenfield, "Accounting for Inflation---A Field Test," Journal of Accountancy, Vol. 127, No. 6 (June, 1969), pp. 45-50.

40W. J. Bruns, "Inventory Valuation . op. cit., and T. Dyckman, "The Effects of Alternative Account- ing Techniques on Certain Management Decisions," Journal of Accounting Research, Vol. 2, No. 1 (Spring, 1964), pp. 91-107.




by users should be adopted, but the significance tests referred to above do not encompass this issue. In order to test for optimal decisions we would need to know the decision maker's objective function and determine which set of signals yield the highest value for that function. This may be possible only if we know the decision maker's complete model.

Predictive Ability

As a way of circumventing this problem some researchers have proposed predictive ability as an alternative measure of usefulness.4" Briefly, the test requires a specification of some event which a decision maker needs to predict in order to implement his particular model. The details of the model need not be known, although some a priori reasoning is necessary to justify the relationship between the event and the general form of a decision maker's model. For example, in one study, it was assumed that a critical event for a creditor's model is the probability that the firm or individual requesting a loan will experience financial failure during the period of the loan.42

In a managerial setting (see [24]), the assumption was made that a decision maker wishes to predict the variances from the parameter values of his model before he implements the model. These predictions could then serve as a basis for ranking the critical variables to be monitored in the control and evaluation system.

Predictive ability may be useful in determining whether one set of information signals is more useful than any other for predicting event A. However, it does not resolve the total question of whether the

4The method is described by W. Beaver, J. Kennel- ly, and W. Voss, "Predictive Ability as a Criterion for the Evaluation of Accounting Data," The Accounting Review, Vol. XLIII, No. 4 (October, 1968), pp. 675-83.

42 W. Beaver, "Financial Ratios as Predictions of Failure," Empirical Research in Accounting: Selected Studies, 1966, Supplement to Journal of Accounting Research, Vol. 5, pp. 71-1 1 1.

rejected information signals may be more useful for predicting alternative events. Moreover, the relative costs of the alternative systems are omitted from the analysis. What is required is a more complete theory which can encompass the entire set of relevant events, their interactions, and their relative costs.

An Information Model Approach

Dissatisfaction with the fragmented approaches to the information choice problem has motivated some researchers to attempt to develop a formal information model for evaluating alternative information systems. An information model combines decision theory and information economics to generate rules for assessing expected benefits and costs of alternative sets of signals.

As in our illustration, we can assume that a D.M. has been provided with a present information system which generates signals about the past events affecting his model and which will generate a similar set of signals about future events as they occur. These signals may be perfect representations of relevant events or they may be probabilistic estimates about what has and what will occur. The latter is the more likely situation since many financial events cannot be measured directly (e.g., depreciation, cost relationships, the allocation of "costs" of services consumed, etc.). The D.M. has a model which incorporates the present set of signals he receives and on the basis of these, generates an optimal solution to his decision problems.

An alternative set of signals could be assessed in terms of its expected incremental benefits and incremental costs, given assumptions about how these signals will be processed by the decision maker.43 Exam- ples of how the entire process may be mod-

43T. Mock, "Comparative Values of Information," Empirical Research in Accounting: Selected Studies, 1969, Supplement to Journal of Accounting Research, Vol. 7, pp. 124-81.




eled are given in references [6] and [11] and in [43] so we will not go into any details.

The difficulty in implementing such an approach is the same one we encounter in trying to provide information which can be used to implement any D.M.'s model (the accountant now becomes a D.M. with his own set of problems). Nevertheless, a con- ceptualization of these issues in a formal model context may provide a better basis for empirically assessing alternative information systems. Presently, we lack a theory of managerial accounting which extends much beyond its reliance on financial accounting theory. In fact, managerial accounting's reliance on financial accounting theory has tended to restrict our attention to problems in control and evaluation at the exclusion of any consideration of the information issues relating to model selection and parameter forecasts.

This relationship to financial accounting provides us with the motivation for evaluating the guidelines for selecting information signals which have grown out of the work of previous committees of our association. As we will note, some of the guidelines which were originally developed within a financial accounting framework have since been extended to managerial accounting.

A MORE GENERAL VIEW OF THE PROBLEM OF EVALUATING

INFORMATION PROPOSALS

Throughout this report we have considered the problem of choosing between information alternatives strictly within the context of managerial accounting. However, it is obvious that accountants are faced with the same problem in financial accounting as well. It seems reasonable to assume, therefore, that a consideration of some of the criteria and methodology employed to resolve issues there may provide us with some additional insights.

Traditionally, accountants have tended to rely on broad standards in the assessment of information proposals relating to financial accounting. This is most apparent in the advancement of a theoretical income model as the basis for financial accounting theory.44 The income approach can be character- ized as an attempt to develop an a priori case for a particular income model-e.g., historical or replacement costs, exit values, or discounted cash receipts models-on the assumption that the information provided by the model is needed by various classes of users. Alternative information proposals are then assessed in terms of which is more consistent with the chosen model.

Considerable controversy has developed around this approach primarily because accountants have not been able to agree on which model should serve as the basis for their theory. The nature of this controversy need not concern us here. What is important is that the frustration encountered in trying to follow the income approach has reached the point where less ambitious bases for assessing information are now being proposed.

One of these is the predictive ability criterion which we defined earlier. This criterion is considered easier to apply since it does not rely on a specification of what information users should use. The application of this criterion implies a more open-ended financial accounting system than we would be led to expect under the income model approach. For example, a theory based on an income model suggests that historical costs and exit values are mutually exclusive alternatives for placing valuations on assets. However, the predictive ability criterion could support both assuming each is useful in forming predictions about the occurrence of some future relevant event.

" Gordon once stated that for his paper "accounting theory will for brevity be used to refer to the theory underlying the measurement of income and wealth"; see M. Gordon, "Scope and Method of Theory and Research in the Measurement of Income and Wealth," The Accounting Review, Vol. XXXV, No. 4 (October, 1960), pp. 603.




Previous Committee Approaches

Another open-ended approach to assessing information has been proposed recently by the committee which prepared A State- ment of Basic Accounting Theory.45 Instead of trying to specify which types of accounting measurements should be communicated, the committee concentrated on the criteria which might be used to assess various alternative accounting measures. The committee proposed four criteria: relevance, verifiabili- ty, quantifiability, and unbiasedness. Rely- ing on these criteria, the committee then argued for the communication of both historical costs and current costs in accounting reports.

Part of this committee's report has implications for us since a section of ASOBA T was devoted to the application of these criteria for assessing information alternatives in managerial accounting. In fact, the 1967-68 managerial committee was asked to use one or all of these criteria to develop a report on information needed to implement management's decision models. More specifically, that committee ". . . decided to concentrate on how accounting systems may best identify, measure, record and report the relevant data needed to implement decision models (of management) and then report the results of decisions that are made."46 As a result of its work, the committee added eco- nomic feasibility as a fifth criterion for evaluating information proposals.

The 1968-69 managerial committee followed a similar approach and studied ". . . the types of data currently being provided to facilitate the control function and those provided for the planning model in order to explore ways of maximizing consistency of

45 A Statement of Basic Accounting Theory (Evanston: American Accounting Association, 1966).

46Report of the Committee on Managerial Decision Models, Supplement to The Accounting Review, Vol. XLIV, 1969, p. 43.

the measurement in reports of these two functions."47

It seems, therefore, that accountants in both financial and managerial accounting now believe that information proposals may be evaluated by appealing to some accept- able set of criteria, such as relevance, veri- fiability, consistency, etc. But are these criteria as useful as they seem at first glance?

Certainly, no one would disagree with the notion that information should be relevant to users. This criterion has been relied on by accountants for years, as evidenced by the use of special purpose reports in both areas of accounting. However, the criterion of relevance may be too broad to be of much guidance in the selection of a preferred information alternative, except in those in- stances where we can clearly distinguish between information which is relevant and that which is irrelevant to some decision.48 To illustrate, from our previous discussion, we may know that the forecasted values of some parameters are relevant to the implementation of a certain decision model, and that forecasts of other parameters are not. But this criterion does not help us in decid-

47Report of the Committee on Managerial Account- ing, Supplement to The Accounting Review, Vol. XLV, 1970, p. 1.

48The 1961 Managerial Accounting Committee actually considered adopting relevance as an underlying concept, but later discarded it for much the same reason as we give. Specifically, the committee stated:

There is a temptation to dispense with the whole question concerning concepts underlying internal reports by saying that the only concept of general appli- cability in this area is the concept of relevance. That is, that what is good or bad accounting is decided fun- damentally by the usefulness of the result in meeting specific management problems. While this is true of internal reporting, such a general statement hardly justifies stature as an underlying concept. It does not provide guidance to accountants regarding methods to follow nor does it assist users in the interpretation of internal reports. When used with this meaning, the term relevance is more a statement of the problem rather than a solution to it.

Report of the 1961 Managerial Accounting Committee, The Accounting Review, Vol. XXXVII, No. 3 (July, 1962), p. 536.




ing which of several sets of alternative forecasts of the same parameters is optimal. A similar type of problem may exist with the application of the other broad criteria.

In fact, the problem may become com- pounded if a given information alternative does not rank ahead of others across the entire set of criteria. For example, information alternative A may be more relevant, but less verifiable than information alternative B, and so on. What we need in such cases is a criterion or a set of criteria which could then be used to assign weights reflecting the relative importance of the first set of criteria.49 Thus, the application of broad standards for information evaluation is not as straightfor- ward as we would like to believe.

"Efficient Market" Approach

The ambiguity of the standards approach to information evaluation has led some financial accountants to embark on still another approach to the problem. Recently, empirical evidence has been gathered which supports the efficient market hypothesis, at least as it relates to stock markets.50 The relationship of this hypothesis to the information evaluation problem is quite direct. Briefly, an efficient market implies that information bearing on stock values is quickly processed by the market with the result that security prices of stocks reflect an impound- ing of all information relevant and available to the market. If this hypothesis is valid, then accountants may rely on it, as some have,51 to measure whether there is information content in accounting reports.

49The committee which prepared ASOBAT stated that relevance was the overriding standard. However, it is possible that a relevant measurement may be subse- quently classified as being irrelevant if it completely lacks one of the other standards.

"For a more rigorous definition and a review of the evidence, see Eugene F. Fama, "Efficient Capital Mar- kets: A Review of Theory and Empirical Work," Jour- nal of Finance, Vol. XXV (May, 1970), pp. 383-417.

51 For example, see Ray Ball and Philip Brown, "An Empirical Evaluation of Accounting Income Num- bers," Journal of Accounting Research, Vol. 6, No. 2 (Autumn, 1968), pp. 159-78 and William H. Beaver, "The Information Content of Annual Earnings An-

The assumption is that the market has a number of sources of information, and, over time, the value of alternative sources of information can be assessed by traders in the market. Accounting information can be evaluated by measuring various relationships between the appearance of the reports and stock price movements. The details of how these relationships can be measured need not concern us. What is significant about this general approach is the willing- ness of its followers to rely on how information is being used as a measure of the utility of the information. For many years accountants have assumed that users of their information are too ignorant (for whatever reason) about the intricacies of accounting measurements to make sound investment decisions.52 However, this view is now being discarded in favor of an "enlightened" market hypothesis regarding the usefulness of accounting information.

Perhaps, this shift in view-point is also appropriate in managerial accounting. Rath- er than being content merely to specify what information should be used by decision makers, as we have in the past, we should make a concerted effort to determine and rationalize the information actual decision makers use in various decision contexts. Our assumption would be that internal decision makers also have various sources of information available to them and that, over time, they too have been able to select and rely on those sources which have proved useful. We could then test which types of information formal accounting systems may best supply in relation to other sources.

Such an approach to information evaluation will be difficult to implement. We cannot even begin to outline the steps needed in

nouncements," Empirical Research in Accounting: Se- lected Studies, 1968, supplement to Journal of Ac- counting Research, Vol. 6, pp. 67-100.

52A recent expression of this kind of thinking is by R. R. Sterling; see his "On Theory Construction and Verification," The Accounting Review, Vol. XLV, No. 3 (July, 1970), especially pp. 452-54.




its implementation. Indeed, we are not even sure it is the right approach to follow. Our discussion of information issues has led us to believe that there is no single strategy which should be followed in designing tests or in developing criteria which can be used to assess information proposals. Evaluating information proposals constitutes the entire area of research in accounting.

Conclusion

As a committee we felt we should restrict ourselves to describing the general state of the art in managerial accounting. The care-

ful reader can read between the lines and ascertain our biases regarding areas which seem to be lacking in research and our preferences for the various methodologies which might be employed in evaluating different information proposals.

But we hesitate to go beyond this by being specific about the research strategies which should now be followed. The overall problem is complex, and we should encourage a diversity of approaches to its resolution. At this point, we have no relative advantage over anyone who has traced our thinking throughout this report.



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