An integrative framework for theory construction and testing

Accounting, Organizations andSociety, Vol. 15, No. 3, pp. 179-191,

1990.

036L3682/90 13.00+.00 Pergamon Press plc

Printed in Great Britain

AN INTEGRATIVE FRAMEWORK FORTHEORY

CONSTRUCTION AND TESTING*

MARIE ADELE HUGHES and SOON-YONG University of Oklahoma

KWON

Abstract A framework that allows researchers to state and test both auxiliary measurement theory and substantive theory is presented. The integrative advantages of the framework are illustrated by a reanalysis of relationships, posited by Onsi and Merchant, concerning prepensity to create slack

The problem of attempting to reconcile inconsistent findings across studies arises hequently in accounting research, as it does in other fields of study. Such inconsistencies may arise, partially, because of difficulties in making the transition from the abstract concepts referenced in theoretical propositions to the observable/ measurable indicators of those concepts that are necessary for empirical tests. In these cases, substantive theories, such as those concerning the relationship between abstract concepts, may be confounded with auxiliary measurement theories (Blalock, 1968) that connect the theoretically defined abstract concepts to their observable indicators. As a result, when a poor fit is obtained between data and theoretical predictions, it is di&ult to determine whether the fault lies with the substantive theory, with the auxiliary measurement theory, or with both. The purpose of this paper is to present an integrative framework for theory construction and testing that may aid researchers in resolving these types of ambiguities. For pedagogical clarity, the framework is illustrated by using it to reexamine two hypotheses concerning propensity to create slack

PROBLEMS WITH CURRENT APPROACHES Measurement, the process of linking abstract concepts to empirical indicators, involves both theoretical and empirical considerations. When the relationships between concept and indicators are weak or faulty, empirical analysis involving the indicators may lead to incorrect inferences and misleading conclusions conceming the underlying concepts. Indeed, Torgerson ( 1958, p. 2) stated, The development of a theoretical science.. . would seem to be virtually impossible unless its variables can be measured adequately.

Thus, with respect to the expansion of the knowledge base in a field of study, auxiliary measurement theories specifying relationships between the theoretical concepts and empirical indicators are equally as important as substantive theories linking concepts to one another. Although exploratory factor analysis and coefficient alpha are widely used in accounting research to identify and evaluate constructs underlying a set of items (e.g. Haried, 1972; Onsi, 1973; Short, 1978; Whittington & Whittenburg,

*We wish to thank Kenneth Merchant for generously sharing his data with us and for his comments on the manuscript. We appreciate the comments and suggestions of the reviewers which greatly assisted in improving the manuscript. 179

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M. k HUGHES and SOON-YONG KWON

1980; Merchant, 1985), there are limitations associated with the use of these techniques in examining auxiliary measurement theory. Each factor from an exploratory factor analysis is defined as a weighted sum of all observed indicators in the analysis. However, these factors do not generally correspond directly to the constructs hypothesized by the researcher because, unless a one factor model is being analysed, each theoretical construct is defined by only a subset of the indicators in the study. Furthermore, while exploratory factor analysis can provide a useful preliminary analysis, it does not directly assess unidimensionality. In addition, factor scores are defined and estimated as linear combinations of observed variables and thus usually contain at least moderate amounts of error. As shown by Goldberger ( 1971), a failure to explicitly represent and control for errors in measurement in linear models (e.g. correlation, regression) can lead to biased and inconsistent estimates of causal parameters. The obtained estimates in such studies may thus confirm a relationship where none exists, mask a true underlying relationship, or significantly understate or overstate the magnitude of an actual relationship (Johnston, 1972). Problems also exist with respect to the use of coefficient alpha in the development and evaluation of measurement scales. In the computation of coefficient alpha, it is assumed that the set of observed indicators is unidimensional (Green, Lissitz & Mulaik, 1977) and that the indicators have equal and independent errors of measurement (Werts, Linn & JGreskog, 1974), conditions that may not be met or tested in practice. Finally, it is generally recognized that theoretical constructs achieve their meaning both through their relationship to observed indicators (auxiliary measurement theory) and through their relationships with other theoretical constructs (the nomological network that comprises substantive theory). While exploratory factor analysis and coefficient alpha may be used to examine auxiliary measurement theory,

they were not developed to address the latter aspect of construct validity.

AN INTEGRATIVE

FRAMEWORK

An integrative framework would allow researchers to evaluate both the measurement properties of constructs and their meaning within the nomological network. One such framework is offered by LISRJZL’- Linear Structural Relationships - (Jiireskog, 1973; Jijreskog & S&born, 1981). That framework subsumes both confirmatory factor analysis and structural equations. The advantages of this framework is that it: ( 1) allows the researcher to explicitly define and test the hypothesized relationships that define each construct as a function of multiple indicator variables (i.e. the auxiliary measurement theory); (2) permits an explicit evaluation of unidimensionality; (3) provides assessments of reliability that rely only on the unidimensionality (i.e. single-factoredness) assumption; (4) allows the explicit modeling and estimation of errors in measurement; and (5) makes possible direct tests of substantive theory that posits relationships between theoretical constructs within the same framework used to examine auxiliary measurement theory. In the most general form of the LISREL model, the researcher posits a causal structure among a set of unobservable constructs. These constructs are represented by latent variables, which are empirical measures of the constructs. Each latent variable is measured by a set of observable indicator variables that may be assumed to be measured with error. The formal LISREL model describes the structural and measurement assumptions of the researcher and consists of two parts; the latent variable model and the measurement model. The latent variable model specifies the hypothesized causal structure among the unobserved theoretical constructs. The measurement model specifies how the latent variables are measured in terms of the ob-

‘LISREL is available directly from Scientific Software. Inc., 1369 Neitzel Road, Moorcsville, IN 46158 (3 17-83l-6336). Other such frameworks are offered by latent structure analysis(Goodman, 1974), and partial least square-s(Weld, 1980).

THEORY CONSTRUCTION AND TESTlNG

served variables and represents the correspondence rules by which observed variables are linked to the unobservable constructs. The LISREL algorithm analyses the structural equations that define the latent variable and measurement models to obtain estimates for the unknown model parameters. Tests of goodnessof-fit of the hypothesized model are based on a comparison of the covariance matrix predicted by the model with the covariance matrix of the observed variables. (For a critique of LISREL,see Fomell, 1983, and Bagozzi, 1983.)

AN ILLUSl-RATIVEIIWESTIGATION In 1952, Argyris suggested that organizational controls, particularly budgets, may have dysfunctional consequences. Following Argyris’ suggestion, a great deal of research on budgetary systems has been directed toward determining the effect of organizational controls on managerial attitudes and budgetary oriented behavior (e.g. Stedry, 1960; DeCoster & Fertakis, 1968; Swieringa & Moncur, 1972, 1975; Milani, 1975; Kenis, 1979; Brownell & McInnes, 1986). Among these studies, several have directly focused on the relationship between the budgetary systems and individual managers’ attitudes toward slack (e.g. Onsi, 1973; Merchant, 1985). However, the findings in the existing literature are often contradictory (Moriarity & Allen, 1987). Slack, which occurs when an organization controls more resources than are needed to maintain a viable coalition (Cyert & March, 1963) is an Important component of such theories as agency theory (e.g. Jensen & Meckling, 1976) management control theory (e.g. Kerr & Slocum, 198 1), X-efficiency theory (e.g. Leibenstein, 1966). and behaviorally oriented microeconomic theory (e.g. Cyert & March, 1963). Budgetary slack is defined as the excess of the amount budgeted In an area over that which Is necessary (Merchant, 1985). In a study published in 1973, Onsi used information from a review of the literature and personal Interviews with managers to identify be-

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havioral variables that appeared to influence budgetary slack build up and utilization. He developed an Instrument to operationalize items relevant to the domain of the study and then empirically Investigated the relationships between and importance of these variables, using factor analysis. Based on an analysis of significant correlations among oblique factors, he found that managers’ need to create slack was: ( 1) positively related to a control system that places high importance on the attainment of departmental budgets, and (2) negatively related to budget participation. Later, an empirical study by Merchant ( 1985) investigated how managers’ propensities to create budgetary slack are affected by the budgeting system and the technical context. Merchant took Onsi’s findings with respect to the budget as hypotheses to be tested. The findings in Merchant’s study supported the hypothesis that the propensity to create slack is negatively related to the extent of participation allowed in budget processes but did not support the relationship between managers’ propensity to create slack and the importance placed on meeting budget targets. Below, the advantages of an integrative framework for explicitly modeling and testing both auxiliary measurement theory and substantive theory are Illustrated by a reanalysis of the relationships posited by Onsi and Merchant. The data for the analysis was originally collected and analysed by Merchant ( 1985). In that study, measures for each variable were obtained by calculating an unweighted sum of the indicators, and hypotheses were tested by examining paIrwise correlations. The Appendix contains a description of the scales Merchant used to measure each variable.

PROPENSITY TO CREATE SLACK MEASUREMENT MODEL Propensity to create slack was measured by a four item scale developed by Onsi (1973). Figure 1 Is a path diagram of the auxiliary measurement theory for that construct. This model is a

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conftrmatory factor analysis model which posits that the latent variable, Slack (0, is a unidhnensional construct defined by the observed indicators, SlS4. The A, parameters are analogous to factor loadings2 The random variables 6, - 84 are called errors in measurement; their variance is analogous to the unique variance of variable in a common factor analysis in that it is not associated with variation in the latent variable for which that variable is an indicator. Estimates of the variance of latent variables and of errors in measurement of indicator variables can be used to estimate each indicator’s reliability, i.e. the proportion of the observed indicator’s variation that is free from random error. The reliability of an indicator is the squared multiple correlation between that indicator and the latent variable; it is calculated as one minus the ratio of error variance to observed variance. Reliabilities can provide direction for the futher development and purification of a scale. Table 1 contains the reliability measures for the indicators in the model in Fig. 1. Only 24% of the first indicator’s (Sl ) variance is shared with the latent variable, suggesting that the auxiliary measurement theory could be strengthened by revising, deleting or replacing that indicator. The composite reliability measure (.70 for the variable Slack) is analogous to coefficient alpha in that it estimates the internal consistency of a

TABLE 1. Reliability estimates for slack and importance of meeting budget Indicator reliability

Slack

Propensity to create slack (SLACK) Sl s2 s3

s4

Composite retiability .70

.24 .43

.39 .40

Importance of meeting budget Required explanations of variances (EXP) EXl .59 EX2 .G2

Ex3 Ex4

.84

.50 .59

Reactions to expected budget overruns (REACT) RTl .63 RT2 .63 .24 RT3

.74

Link with extrinsic rewards (REWRD) RD1 .52 RD2 .33 RD3 .17

.80

RD4 RD5 an6 RD7

.53 .40 .52 .16

latent variable. However, as previously mentioned, this reliability measure is not based on the stringent measurement assumptions required for coefficient alpha. Although no rule-ofthumb has been explicitly established for LISREL’s composite reliability measure, most authors accept Nu~ally’s (NuM~Y, 1978, p. 245) suggestion that .70 is acceptably high reliability.

IMPORTANCE

OF MEETING BUDGET

Preliminary analysis of measurement model

Fig

1. Auxiliary measurement theory for propensity to create slack.

It is hypothesized that the concept, Importance Placed on Meeting Budget Targets, comprises three constructs: Required Explanations of Variances, measured by a four item scale

‘It should be noted, however, that the A, parameters are not, in general. correlations. They “are regression coefficients and, as such. can exceed the value one” (Joreskog & S&born, 1981, p. 111.18) even if both the observed and latent variables are standardbed.

THEORY CONSTRUCTION AND TESTING

(Merchant, 1981); Reactions to Expected Budget Overruns, measured by a three item scale (Merchant, 198 1); and Link with Extrinsic Rewards, measured by a seven item scale (Hackman & Porter, 1968; Dermer, 1975; Merchant, 1981). Figure 2 depicts the confirmatory factor analy sis model used in the first phase of analysis of the auxiliary measurement model for the construct Importance Placed on Meeting Budget Targets

(IMP). This model posits that the three latent variables, [&, are unidimensional constructs defined by the observed variables EXl-EX4, RTl-RT3, and RDl-RD7, respectively. The model in Fig. 2 incorporates two structural hypotheses: ( 1) that factor loadings of indicators across common factors, e.g. the loading of the indicator, EX1, on the latent variable t2 (REACT), are zero; and (2) that the intercorrelations among the errors in measurement (i.e. the Z&s), are zero. As previously mentioned, exploratory factor analysis does not allow the researcher to specify and test such hypotheses concerning structural aspects of the auxiliary measurement theory. Table 1 contains the reliabilities for each of the indicators and latent variables in this model. As previously mentioned, indicators with low reliabilities (e.g. RD7 with a reliability of .16)

Fig 2. Oblique conhmatory

183

should be investigated further in an attempt to strengthen the auxiliary measurement theories. Composite reliabilities are, however, acceptably high. Since a hypothesis of the auxiliary measurement theory is that all three latent variables are related to one concept, the correlations among them(i.e. ~12,~,~and+23)should befairlyhigh. However, although the constructs, Required Explanations of Variances (EXP) and Link with Extrinsic Rewards (REWRD), have a moderate correlations ($I~~= .4 1). the correlation of each of these with the construct, Reactions to Expected Budget Overruns (REACT), is weak (4 ,2 = .29, +a3 = .24). analysis The explicit auxiliary measurement theory is modeled by the second-order confirmatory fattor analysis model shown in Fig. 3. Estimates of the relevant parameters are given in Table 2. As shown in Fig 3, first-order latent variables (EXP, REACT and REWRD), which are measured by observable indicators, are modeled as indicators of a second order factor (IMP). The parameters y,-ys are analogous to the loadings that would be generated by a Eactor analysis of the “factor scores” of the three first-order latent variables. The random variables [,-c3 represent

Further

factor analysis model for importance

of meeting budget.

M. A HUGHES and SOON-YONG KWON

184

n 53

IMP

Fig 3. Auxiliary measurement theory for importance of meeting budget.

errors in the structural equations linking the Importance Placed on Meeting construct, Budget Targets (IMP), to each of its indicators; the variances of [,-& are parameters of the model. It is a matter of logical and empirical necessity that the set of measures that operationalize a theoretical construct be unidimensional. A unidimensional (i.e. homogeneous) construct is one in which all systematic variance in the construct’s indicators is due to one common factor. One possible explanation for the high error in equation variance (.83) associated with Reactions to Expected Budget Overruns (Table 2) is that the indicators of that construct share another source of systematic variance. The analysis of the models in Figs 2 and 3 thus suggests that the three constructs hypothesized to measure Importance Placed on Meeting

Budget Targets do not converge single unidimensional construct.

Required explanations ofvariances(ExP) Reactions to expected budget overruns (REACT) Link with extrinsic rewards(REwRD) l

Standardized estimates.

a

A conceptual ?-e-examination

An examination of the survey questions (see Appendix) suggests that Importance Placed on Meeting Budget Targets may not be a one-dimensional construct. The questions used to operationalize Required Explanations of Variances and Link with Extrinsic Rewards em“administrative related importance”, phasize whereas the questions associated with Reactions to Expected Budget Overruns emphasize “task related importance”. Administrative related importance is related to reporting and evaluating budgetary performance for administrative purposes. These budgetary performance measures allow upper management to evaluate individual managers’ efforts

TABLE 2. Selected estimates from model in Fig.

Latent variable

to measure

Coe5cients Parameter Estimate*

3

Errors in equations Estimate’ Parameter

YI

.70

CI

.51

Yz

.41

52

.83

Y3

.58

53

.66

THEORY CONSTRUCTION AND TESTING

and performance with consideration given to uncontrollable events within the work environment. In contrast, task related importance is related to the extent to which individual managers’ actions are reviewed for punitive purposes. These budgetary performance measures are related to actions which individual managers take to correct unfavorable variances, such as reduc ing actual expenditures or moving monies around. The conceptual ditference posited above between administrative and task related importance is supported by the work of DeCoster & Fertakis ( 1968). They suggested that “pressure &om procedures in budget administration” and “pressure caused by the need to correct budget deviations” are different sources of role expectations. Empirical results also support the bi-dimensionality of the Importance construct. In an orthogonal factor analysis of 171~items from a questionnaire measuring managers’ budgetoriented behavior (Swieringa & Moncur, 1972), items used to operationalize Required Explanations of Variances and Link with Extrinsic Rewards loaded on a different factor than those used to operationalize Reactions to Expected Budget Overruns. The hypothesis of bi-dimensionahty of the Importance construct is further supported by a consideration of the relationship that each dimension may have with managers’ propensities to create budgetary slack One of the administrative purposes of budgetary performance evaluations is to provide formal organizational controls to inhibit individual managers’ opportunities to act in their own self-interest as opposed to the interests of the firm. As administrative desire for control increases, it is likely that control systems will become more structured. When, as a result of this increase in structure, individual items of budget related activity are more clearly and carefully defined, the individual managers may perceive a greater degree of control over their budgetary performance which decreases their perceived probability of being able to create slack. Thus, it is hypothesized that perceived administrative related importance is negatively related to managers’ motivation to create slack.

185

On the other hand, task related importance concerns the corrective and punitive nature of controls. Increasing demands from upper management on meeting the budget may create stress in managers faced with possible budget overruns withii their unique task environments. This increased stress, related to perceived lessening of a manager’s control over budgetary performance, is hypothesized to be positively related to managers’ motivation to create slack It should be noted that the theoretical analysis above has not depended upon LISREL for its development. The correspondence between theoretically defined concepts, which are mental abstracts and thus inherently unobservable, and empirical indicants, which are directly observable, is not one that can be established by any strictly logical or empirical argument. It must, instead, be established by common agreement or u priori assumption (Northrop, 1947, pp. 120-121). However, careful attention to measurement problems and ambiguities, using either this framework or other techniques, can assist us in clarifying our theoretical definitions. Tests of substantive theoy Figure 4 depicts the complete model for the hypotheses involving Propensity to Create Slack and Importance Placed on Meeting Budget Targets. Depiction of the auxiliary measurement theory has been abbreviated to simplify the diagram. Based on the previous discussion, the two substantive hypotheses are: HI. The propensity to create budgetaryslackispositively related to reaction to task related importance. H2. The propensity to create budgetary slack is nega tivcly related to perceived administrdtive related htportame.

In Fig. 4, Task Related Importance (TSKIMP) is measured by the three-item scale for Reactions to Expected Budget Overruns. Administrative Related Importance (ADMINP) is modeled as a second-order factor whose indicators are the tit-order constructs, Required Explanations of Variances (EXP) and Link with Extrinsic Rewards (REWRD). The modeling ofADMINP illustrates another advantage of a framework that

M. k HUGHES and SOON-YONG KWON

186

Fig 4. Model of hypothesized

relationship

between

importance

incorporates confirmatory factor analysis. In some cases, a single unidimensional scale may represent a content domain that is too restrictive to incorporate the more broadly defined construct that is the subject of the researcher’s hypothesis. The framework discussed here allows the researcher to embed two or more unidimensional scales, as indicators themselves, within a higher order structure. The resulting second-order construct allows a single direct test of a hypothesis, as contrasted with the multiple tests (and the possibility of conflicting results) associated with other approaches. Standardized estimates of the structural parameters are shown in Fig. 4. Parameter y4 estimates the relationship in hypothesis 1, and ys estimates the relationship in hypothesis 2. The structural coefficients representing causal links between the construct Propensity to Create Slack and the two Importance constructs are both statistically significant with signs in the hypothesized directions. A linear combination of the Importance constructs (5, and S2) accounts for 3 1% of the variance in the Slack construct. It should be noted that, although the develop ment of the final model for the Importance con-

of meeting budget and propensity

to create slack.

cept was guided by a consideration of previous literature, the process was also data-driven; that is, empirical relationships manifest in the sample data were used to suggest model modifications. The process was, therefore, exploratory and should not be viewed as confirming the hypotheses stated above. Evidence to confirm or disprove the hypotheses must be obtained by subjecting an independent set of data to a confirmatory analysis.

BUDGET PARTICIPATION

Measurement model The concept, Budget

Participation, was hypothesized to comprise two subconstructs: Influence on Budget Plans, measured by a twoitem scale (Merchant, 1981>; and Personal Involvement in Budgeting, measured by a three item scale (Merchant, 1981). The auxuliary measurement model representing that hypothesis is shown in Fig. 5. The system of equations describing the model in Fig. 5 is not identified because there is not a unique set of parameter values consistent with this data, i.e. not enough information exists in

THEORY CONSTRUCTION AND TESIING

187

and Budget Participation. The substantive hypothesis is: H3. The propensity to create budgetary slack is negatively related to the extent of participation allowed in budgeting processes.

4?+ A

AS

4

a1 Fig

5.

Although the auxiliary measurement model for Budget Participation (Fig. 5) was not identified when considered by itself, the model in Fig 6 is identified and thus can be estimated. The values specified for the structural parameters in Fig. 6 are standardized estimates. Structural parameter yj of the model estimates the relationship in hypothesis 3; it is statistically signiticant (t = -2.766) and its sign is in the hypothesized direction. The Budget Participation construct accounts for 16% of the variance in the Slack construct.

Auxiliary

measurement participation.

Pl

P2

P3

s

e4

4

theory

for

budget

the variance/covariance matrix of the observed variables to uniquely estimate the unknowns. Thus, separate and unique estimates cannot be obtained for the variance of the latent variable 5, (Budget Participation) and its relationships (7, and y2) with the other two constructs. However, we can obtain some insight by estimating the relationship between the two hypothesized subconstructs using a first-order oblique confirmatory factor analysis model, as was done in the first phase of the analysis of the Importance Placed on Meeting Budget Targets construct. Table 3 contains selected estimates obtained 6rom an analysis of that model. As can be seen from the reliability measures, the auxiliary measurement theory for these two constructs is not as strong as those previously considered. However, the correlation between the two latent variables(& = S4)supports the hypothesis that they are related. Furthermore, the goodness-of-fit of the model to the data is acceptable (x2 statistic, p-value = .l 24).3 Test of substantive theory Figure 6 depicts the complete model for the hypotheses involving Propensity to Create Slack

Comparison with tradition analysis Many hypotheses in the social sciences involve multidimensional concepts. Furthermore, the various dimensions may have different underlying theoretical bases. In these cases, a single index (e.g. the sum of items Tom multiple scales) may confound the dimensions, and estimates of structural parameters for models containing the index may be biased. On the other hand, if each dimension is represented bya separate index, a direct test of the theory is not possible in traditional linear models (e.g. regression, correlation) because there is no way to “inteTABLE 3. Selected estimates from oblique conlirmatory tor analysis for budget participation Indicator reliability

Variable Influence on budget plans (INFL) I1 I2 Personal involvement PI P2 P3

fac-

Composite reliability .M

.48 .30

in budgeting (PEW) .69 .22 .21

.62

.?‘hep-value associated with the x1 statistic provided by the LLSRELalgorithm ls the probability ofohtalning a x’ value larger than the value actually observed, under the hypotheses that the specific structural model is a true model of population relationships. Largep-values indicate that the hypothesized structure ls confirmed by the sample data.

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M. A HUGHES and SOON-YONG KWON

Fig 6. Model of’hypothesized

relationship

between

grate” the resulting estimates for each separate index into an estimate of the effect of the construct itself. Consequently when estimates do not uniformly support or fail to support the hypotheses, conclusions are either characterized as equivocal or are reached by arguments about the preponderance of evidence. Furthermore, in multiple regression models the multicollinearity among the dimensions may result in unstable parameter estimates. The hypotheses that Merchant examined involved such multidimensional concepts. A sumTABLE 4.

Dependent variable Propensity to create slack Independent variables Importance of meeting budget Required explanations of variances Reactions to expected budget overruns Link with extrinsic rewards Budget participation Influence on budget plans Personal involvement in budgeting One tailed signitlcance (n = 170). (a)p c: 0.01 (c)p (b)p C 0.05

budget participation

and propensity

to create slack.

mary of Merchant’ analysis is shown in Table 4. It consisted of an examination of pairwise correlations between the dependent variable (Propensity to Create Slack) and an index for each of the hypothesized dimensions of each construct. Merchant also performed a stepwise regression analysis (“because of the high correlations between the independent variables”, p. 206) that involved other constructs than those examined in this paper. In contrast, the integrative framework presented earlier in this paper allowed estimation of

Summary of original analysis (Merchant, 1985)

No of indicators

Alpha reliability

4

.70

Hypothesized relationships

Pairwise correlations

+ -O.lSb

4

.84

3

.72

7

.79

-0.12b

2

.52

-o.12c

3

60

c 0.10.

0.1 lC

0.21’

THEORY CONSTRUCTION AND TESTING

the theoretical construct involved in each hypothesis, and direct estimation of both the effects of those constructs on the dependent variable (e.g. y3 and y4 in Fig. 4, and y3 in Fig. 6) and the relationship between multidimensional constructs (e.g. the correlation between ADMIMP and TSKIMP in Fig 4). With respect to specitic empirical results, our analysis leads to ditferent estimates of strength of the relationship than did the analysis with pairwise correlations. In Merchant’s analysis, correlations of - .12 and - .2 1 between the Slack construct and the two dimensions of the Budget Participation construct, indicated a lower explanatory power than the 16% shared variance estimate obtained in our analysis. Similar attenuation of the pairwise correlation coefftcients due to measurement error is seen in a comparison of the results for the Importance concept. An alternative approach would have been to obtain a Edctor score rather than an additive score for each measure. However, as previously mentioned, one drawback to this approach is that factor scores horn an exploratory factor analysis involve aII variables used in the analysis rather than the subset of the indicators hypothesized by the researcher to define each construct. Furthermore, our analysis provided a systematic, unified method for examination of underlying auxiliary measurement theory. Analysis of auxiliary measurement models provided empirical support for the reconceptualization of Importance of Meeting Budget as a bidimensional concept comprising Administrative Related Importance and Task Related Importance. It also indicated where the auxiliary measurement theory might be strengthened by revising, deleting or replacing specitic indicators.

DISCUSSION As accounting schokus, we attempt to address general questions, the answers to which wiIl ex-

189

pand our total knowledge base. We strive to formulate reasonably general propositions, which contain concepts that are appropriate to a wide variety of circumstances, and which at the same time are sufficiently precise to be rejectable (BlaIock, 1982, p. 24). In the quest for general ity, interest is directed toward, and hypotheses are usuaIly stated in terms of, abstract concepts rather than specific empirical variables. This is at once an advantage and a disadvantage. On one hand, the “vagueness, complexity and suggestiveness of [ abstract] concepts . . . allow them to be empirically referenced with varying degrees of success at different times and places” (ZeIIer & Carmines, 1980, p. 3). On the other hand, because abstract concepts are not directly observable, hypothesis testing cannot be accomplished by direct examination of relationships between them. Further, the complexity of theoretical meaning assigned to many concepts used in accounting research requires that they be represented by multiple indicators, the set of which as a whole adequately captures the theoretical meaning attributed to the concept. Strategies such as repetition of the hypothesis test using each of a set of indicators or the construction of an index formed horn some combination of two or more observable indicator variables have been used in research studies in auditing (see e.g. Senatra, 1980), managerial accounting (see e.g. Kenis, 1979; Brownell & McInnes, 1986), financial accounting (see e.g. Ingram & Frazier, 1980) and taxation (see e.g. Song & Yarbrough, 1978). However, as mentioned previously, neither of these strategies addresses the problem of measurement error. Multiple indicator, latent variables models offer an integrating paradigm for addressing these concerns. These methods of analysis provide researchers with a single framework for both estimating structuraI relationships among unobservable constructs and assesshig the adequacy with which those constructs have been measured.

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M. A HUGHES and SOON-YONG

KWON

BIBLIOGRAPHY Argyris. C., The Impact of Budgets on People (The School of Business and Public Administration, Cornell University, 1952. Bagoui, R P., Issues in the Application of Covariance Structure Analysis: A Further Comment.]oumul o/ Consumer-Research(March 1983) pp. 449-450. Blaiock, H. M., The Measurement Problem: A Gap Between the languages of Theory and Research, in Blalock, H. M. & Blalock, A. B.. (eds) Metbodo!ogy in Social Research (New York: McGraw-Hill, 1968). Blaiock, H. M., Conceptualization and Measurement in tbe Social Sciences (London: Sage, 1982). Browneii, P. & MCIMCS, M., Budgetary Participation, Motivation, and Managerial Performance, Tbe Accountfng Rev&w(October 1986) pp. 587a. Cyert, R. M. & March, J. G., A Behvforiul Theory of the Firm (New Jersey: Prentice-Hail, 1963). DeCoster. D. T. & Fertakls, J. P., Budget-Induced Pressure and Its Relationship to Supervisory Behavior, journal of Accounting Research (Autumn 1968) pp. 237-246. Dermer, J. D., The Interrelationship of Intrinsic and Extrinsic Motivation, Academy of Management journal(March 1975)~~. 125-129. Fomeli, C., Issues in the Application of Covariance Structure Analysis: A Comment,Journaf of Consumer Research (March 1983) pp. 443-448. Goldberger, A. S., Econometrics and Psychometrics: A Survey of Communalities, Psycbometricku (June 1971)~~. 83107. Goodman, L A., Exploratory Latent Structure Analysis Using Both Identitlable and Unidentillable Models, Biometrika (August 1974) pp. 2 15-23 1. Green, S. R, tissitz, R. W.. & Mulaik, A., Limitations of CoefBcient Aipha as an Index of Test Unidimensionaiity, Educational and Psychological Measurement (Winter 1977) pp. 827-838. Hackman, J. R. & Porter, L W., Expectancy Theory Predictions of Work Effectiveness, Organizational Behavior and Human Performance (November 1968) pp. 4 17-426. Haried, A. A., The Semantic Dimensions of Financial StatementsJournal o/AccountingReseufcb (Autumn 1972) pp. 376-39 1. Ingram, R. W., & Frazer. K B., Environmental Performance and Corporate Disclosure, Journal of Accounting Research(Autumn 1980) pp. 614-622. Jensen, M. C. & Meckling, W. H., Theory of the Fl: Managerial Behavior, Agency Costs and Ownership ?4rWNre,jOumd of Financial Economics (October 1976) pp. 305-360. Johnston, J., EconometricMethods, 2nd edn (New York: McGraw-Hill, 1972). Jiireskog. IC G., A General Method for Estimating a Linear Structural Equation System, in Goldberger, S. A. and Duncan, 0. D. (eds),StmcturalEquationModels in tbeSociufSciences(Seminar, 1973)~~. 85-l 12. Jiireskog, K G. & S&born, D., LISREL-User’s Guide (University of Uppsala, 1981). Kenis, I.. Effects of Budgetary Goal Characteristics on Managerial Attitudes and Performance, The Accounting Review (October 1979) pp. 707-72 1. Kerr, S. & Slocum, J. W. Jr, Controlling the Performance of People in Organizations, in Starburck, W. & Nystrom, P. (eds), Handbook of Organizutional Design, Vol. 2, pp. 116134 (Oxford University Press, 1981). Liebenstein, H., Ailocative Efficiency vs X-Efficiency, American Economic Review (June 1966) pp. 392415. Merchant, K A., The Design of the Corporate Budgeting System: Influences on Managerial Behavior and Performance, TheAccounting Review (October 1981) pp. 813-829. Merchant, K A., Budgeting and the Propensity to Create Budgetary Slack,Accounting 0rguniTation.r and Sociev( 1985) pp. 201-210. Milani. K, The Relationship of Participation in Budget-Setting to Industrial Supervisor Performance and Attitudes: A Field Study, TheAccounting Review (April 1975) pp. 274-284. Morlariry, S. & Allen. C. P., Cost Accounting 2nd edn (New York: Harper and Row, 1987). p. 320. Northrop, F. S. C., The Logic of the Sciences and the Humanities (New York: Macmillan. 1947). NUM~Y, J. C., Psychmet& T&wry, 2nd edn (New York: McGraw-Hiii, 1978). Onsi, M., Factor Analysis of Behavioral Variables Alfecting Budgetary Slack, TheAccounting Revfew (July 1973) pp. 535-548. Senatra, P. T., Role Contlict, Role Ambiguity, and Organizational Climate in a Public Accounting Firm, The Accounting Review (October 1980) pp. 594-603. Song, Y. & Yarbrough, T. E., Tax Ethics and Taxpayer Attitudes: A Survey, Public Administration Review

THEORY CONSTRUCTION

AND TESTING

(September/October 1978) pp. 442-452. Stedry, A. 0.. &uQet Control and Cosl Eebu~~ior(New Jersey: Prentice-Hall, 1960). SwIerInga, R J. & Moncur, R H., The Relationship Between Managers’ Budget-Oriented Behavior and Selected Attitude, Position, Size, and Performance Measures, Journal of Accounting Research (Supplement 1972)~~. 194-214. Swieringa, R. J. & Moncur, R H., Some Effects of Participa~iw Budgeting on Managerial Bebauior (New York: National Association of Accountants, 1975). Torgerson. W. S., Tbeofy and Metboa% of Scaling (New York: Wiley, 1958). Werts. C. E., L.enn, R L & Jiireskog, K G., Intraclass Reliability Estimates: Testing Structural Assumptions, Educational andPsycbologica1 Mensunsmen~ (Spring 1974) pp. 25-33. Whittington, R. & Whittenburg, G., Judicial Classification of Debt Versus Equity -An Empirical Study, The Accounting Review (July 1980) pp. 40-25. Wold, H., Model Construction By Partill Least Squares, in J. Ramsay, & H. Kmenta (eds), Ezxzluation of Econometric Models (Boston, MA: American Press, 1980) pp. 47-74. Zeller, R A. & Carmines, E. G., Measurement In The Social Sciences The Link BeUeen Theory and Data (Cambridge: Cambridge University Press, 1980).

APPENDIX: INSTRUMENT Dependent variable Propensity to create slack (SLACK) Sl. To be “safe”. a manager usually sets two levels of standards; one between himself and his boss, and another between himselfand hi subordinates. S2. Slack in the budget is good to do things that cannot be officially approved. S3. To protect himself, a manager submits budgets that can be safely attained. S4. In good business times, my supervisor is willing to accept a reasonable level of slack in my budget. Independent variables Required expkmations of hriances (Exp) EXI. I am required to submit an explanation in writing about causes of large budget variances. EX2. I am required to report actions I take to correct causes of budget variances. EX3. I am required to prepare reports comparing aCNd results with budget. W4. I am required to trace the cause of budget variances to groups or individuals within my unit. Reactions to expected budget ovewuns (REACT) RTl. I find it necessary to charge some activities to other accounts when budgeted funds for these activities havebeenusedup. RT2. I have to shii figures relating to operations to reduce budget variances. RT3. I find it necessary to stop some activities in my unit when budgeted funds are used up. Link with extrinsic rewards (REWRD) RDl. Budget performance Is an important factor in advancing my career. RD2. Meeting the budget goals consistently wIlI improve a manager’s job security. RD3. Exceeding budget performance wIlI lead to more responsibility. RD4. Good budget performance is a prerequisite to advancement. RDS. Pay increases are closely tied into budget performance. RD6. My talents will be better recognized if my department attains the budget. RD7. I wiII have better relations with my supervisor if my department performs welI in relation to the budget. Influence on budgelplans (INFL) Il. The budget Is not finalized until I am satisfied with it. 12. New budgets include changes I have suggested. Personul inwl wmaent in budgeting (PUW’ PI. I investigate favorable as weU as unt3vorable budget wiances for my unit. P2. Preparing the budget for my unit requires my attention to a great number of detaiIs. P3. I personally investigate budget variances in my udt.

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