This document discusses behavioral research, which examines how individuals react to and make decisions based on accounting information. Behavioral research differs from capital market research by focusing on individual decision making rather than aggregate market behavior. It can manipulate accounting variables and information presented to study how different user groups like investors and auditors react. This helps anticipate reactions to new accounting rules and develop more efficient use of data. The document outlines how behavioral research is grounded in behavioral decision theory and explains the Brunswik lens model, which views decision making as relying on imperfect cues probabilistically related to an outcome.

1. REACTIONS OF INDIVIDUALS TO FINANCIAL REPORTING: AN EXAMINATION OF BEHAVIOR RESEARCH
Learning Objectives:
Upon completing this chapter readers should understand:
 How behavior research differs from capital market research
 How different accounting-related variables can be manipulated in behavior research
 How the results of behavioural research can be of relevance to corporations and the accounting profession for anticipating individual reactions to accounting disclosures
 How the results of behavioural research can form the basis for developing was to more efficiently use accounting-related data; The limitations of behavioural research
Opening issues
The accounting profession often considers introducing new regulations relating to the disclosure of new items of information, or specifically requiring information to be disclosure in a particular format. A concern that often arises is how or whether various categories of financial statement users will react to the new disclosures which are potentially going to be mandated, particularly given that new disclosure requirements typically impose costs on those entities required to make the disclosures. How can behavioural research be used to assist the concerns of accounting regulators about financial statement users’ reactions to the proposed requirements?
Introduction
In chapter 10 we considered capital market research capital market research considers the aggregate behavior of investors in the capital market. This aggregate behavior is typically observed by looking at movements in share prices around the time of particular events, such as when earnings announcements are made.
In this chapter we consider decision making at the individual level the research, which we refer to as behavioural research, involves performing studies to see how a variety of financial statement user groups (not just investors, as is the case in capital market research) react to a variety of accounting information, often presented in different forms, and in different contexts. By generating knowledge about how different categories of financial statement users (for example, investor, research analysis, auditors, bankers, load and so on) react to particular accounting disclosures, corporations and the accounting profession will be better placed to anticipate how different individuals will react to particular information
Apart from the anticipatory implications associated with behavioural research results of analysis of the decision making process of individuals can also provide the basis for developing procedures to improve future decision making
An overview of behavioral research

2. In chapter 10 we considered research that investigated the aggregate reaction of the capital market to various accounting disclosures. In this chapter we turn to a different approach to research which considers how individuals react to various accounting disclosures. Research that considers how individuals reactor behave when provided with particular items of information can be classified as behavioural research. Accounting to Libby (1981, p, 2) research that attempts to describe individual behavioural is often grounded in a branch of psychology called behavioral decision theory, which has its roots in negative psychology, economics and statistics, according to Libby (1981, p, 2):
The goal of much of this work is to describe actual decision behavior, evaluate its quality, and test theories of the underlying psychological processes which produce the behavioural, in addition, these descriptions reveal flaws in the behavioural and often suggest remedies for these deficiencies.
Behavioral research was first embraced by accounting researchers in the 1960s (maines, 1995) but become particularly popular in the 1970s when embraced by researchers such as Ashton and Libby it has been used to investigate a variety of decision making processes of load officers, the assessment of bankruptcy by bankers or auditors, and the assessment of risk by auditors.
Some of the various published behavioural research studies have been undertaken in a laboratory setting where a group of individuals are assigned a number of simple or complex tasks (which may or may not be reflective of real life decisions), while other research has been conducted in the individuals own workplace1 behavioural research can have a number of aims. Some research has been undertaken to understand underlying decision making processes, while other research has been conducted to improve decision making. Some research manipulates the amount and types of information provided to particular subjects to assess how such differences with the same information and attempts to derive a model to explain how decision by a particular category of decision maker appear to be made (for example, decision by auditors, stakeholders, bankers, or lending officer)
The Brunswik Lens Model
In explaining behavioural research, a number of researchers have found it useful to relate their work to a model developed by Brunswik, this being the Brunswik lens model (brunswik, 1952), libby (1981, p. 6) provides a simplistic representation of the lens model, see figure 11.
Libby (1981, p. 5) illustrates the application of the brunswik lens model to the decision by graduate school to admit students. As indicated in figure 11.1 the criterion event is the student’ future success, donated by $ (on the left-hand ride of the model). Given that this event will take place in the future, decision made by admissions officers within particular school much be based on a number of factor or environmental “cues” (pieces of information), which can be probabilistically related to the particular event under consideration (in this case, student success).
1 A laboratory setting would constitute a setting different to where the subjects would normally undertake their work and where the researcher is relatively mere able to control certain variable relating to the decision making task that would otherwise be possible

3. A number of cues can be used, for example GMAT scores, Grade point averages in prior studies, quality of the undergraduate school attended, recommendations or references from various people, whether the individual participates in extracurricular activities, and answer to particular subjective questions.2 As Libby indicates, none of these individual cues, or combinations of cues, can be expected to provide a perfect indication of the future success of the student, but some may be linked, with some degree of probability, to success, As Libby explains, in effect, perspectives about the environment (the issue in the question in this case being student success) are generated (observed) through a “lens” of imperfect cues. The relationship between these imperfect cues and the judgement about success are represented by broken line3
There would also be an expectation that some of the cues will be interrelated. For example the GMAT score might be expected to be correlated with grade point averages, as well as quality of school attended such interrelationship are represented by broken lines linking the various cues as indicated in figure 11.1 to determine the weighting (or importance) of the various cues (independent variables) to the criterion event of success (the dependent variable which could simplistically be categorised as eithter success or failure in this case), as well as the correlation between the cues, various advanced statistical modelling approaches are applied. One model might be developed that provides a linear representation of the assessors’ weightings of the various cues. This then provides a model of how the assessors actually went about their job of assessing applications. Knowledge of this model may be useful to number of parties. For example intending student would know what factors (cues) are particularly important to the assessors and hence the students may then know what factors to concentrate on. From the assessors perspective t might be interesting for them to see how as a group they appear to be making be interesting for them so see how as group they appear to be making their judgements. This might not be obvious until such a model is developed.
A model could Also be developed that looks at the relationship between the actual outcome (student success or failure) and the various items of the reverse direction from the event (the left-hand side) back to the cues (that is, not involving individuals making judgements). Obviously, such analysis could only be undertaken when a measure of actual success or failure can be obtained.
Libby (1981) Provides an insight into the general applicability of the end model to various decision making scenarios. As he states (p 6):
This structure is very general and can be applied to almost any decision-making scheme, again consider a simplified commercial lending decision in which the principal task of the loan officer is to predict load default. Load default-load default is mainly a function of the future cash flows which will be available to the costumer to service the debt. The costumer provides a number of cues, some of which are probabilistically related to future cash flows. These include indicators of
2 As we show later, in an accounting study such as the prediction. Of bankruptcy. The “cues” might he information about various accounting ratios
3 Libby (1981, p, 5) notes also that relative reliance on various cues is likely to change over time as a result of fatigue, special circumstances, learning, and so on

4. liquidity, leverage, and profitability drawn from financial statement, management evaluations resulting from interview, plant visits discussions with other knowledgeable parties, and outside credit rating. No individual cue or combination of cues is a perfect predicator of future cash flows, and there is overlap in the information (e.g. credit rating are closely associated with profitability and liquidity measures). In making this judgement, the load officer combines these cues into a prediction of future cash flows. Even if bankers judgemental policy is highly stable over time, some inconsistencies are likely to arise, which will result in a probabilistic relationship between the cues and the final judgement. At the end of the term of each load, the officer’s prediction of cash flows can be compared with the actual event, and any resulting losses can be computed to measure achievement, while this example is hight simplifield, it illustrates the generality of the framework and its importance for accountants. The models principal concern with information-processing achievements in an uncertain world coincides with accountants interest in improving the decision made by users of accounting information and their more recent attention to the quality of their own decisions.
In applying the lens model it is common for researchers to mathematically model both the left-hand and right-hand sides of the lens. For example, on the right-hand side of the model we are interested in providing a model (typically linier) of how the individual uses cues to make an alternative decision about the cue under investigation. This is often the major goal of much behavioral research. This can be undertaken by considering how each particular cue individually relates to the ultimate decision (univariate analysis), or how the entire set of cues relates to the ultimate decision or judgement (multivariate analysis). If statistical regressions is undertaken as part of the multivariate analysis, the decision market’s response might be summarized or modelled as follows:
Ys= as + BbX1 + B2X2 +… Bb X1 (equation 1)
Where:
Y1, is the model’s prediction of the Judgement (for example, That the Student success or fails) based on the individual’s Judgements or predictions:
X1,X2,… represent the set of cues (for example, the GMAT score grade point average, ets) for cue number 1 through to cue number k; B1,B2…. Represent the weighting in the model given to each of the cues, based on the responses of subjects.
If a cue contributes nothing to the prediction, it will be given a zero weighting. Because the model will need to be generated from many observations and because models such as the above assume that individual cues constribute to the decision in a linier manner, it is clear that the model will not explain or predict with total accuracy judgements made by particular individuals- but as we would appreciate, it is not expected to it is a model of individual behavior: as Libby states (1981, p. 22):
It is important to note that the algebira models resulting from these studies simply indicate the functional relationship between the cues and the judgement. These, like all models, are abstractions and do not purport to represent “real” mental processes.

5. Some researchers also model the left-hand site of the lens model (often referred to as the environmental side) which looks at the relationship between the actual phenomenon under consideration and the particular cues provided without relying on judgements provided by individual this equation can be used to predict a particular environmental even. The model can be represented as follows: