Budgetary Participation and Managerial Performance: The Impact of Informationand Environmental Volatility

Leslie Kren

The Accounting Review, Vol. 67, No. 3. (Jul., 1992), pp. 511-526.

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THE ACCOUNTlNG REVIEW Vol. 67,No. 3 July 1992 pp. 511-526

Budgetary Participation and Managerial Performance:

The Impact of Information and Environmental Volatility

Leslie Kren University of Wisconsin-Milwaukee

SYNOPSIS AND INTRODUCTION: Organizational theorists have posited a positive relationship between employee performance and participation in budgeting or goal setting (Argyris 1952; Becker and Green 1962). In exam- ining this proposition, empirical research has studied the motivational and cognitive mechanisms by which participation may be related to employee performance (Locke et al. 1986; Murray 1990). Cognitive mechanisms in- clude factors such as the acquisition and use of information and compre- hension of job requirements (Locke et al. 1986).

Kren and Liao (1988) argue that empirical accounting research has generally focused on the motivational effects of participation. In general, results have been mixed (Murray 1990). While Merchant (1981) found a positive relationship between motivation and participation, Brownell and Mclnnes (1986) did not find such a relationship. The results prompted Brownell and Mclnnes to suggest that future research should examine per- formance benefits of participation that are not mediated by motivation.

Recently, accounting researchers have studied the role of cognitive factors in explaining the relationship of participation to performance. Chen- hall and Brownell (19881, for example, found that budgetary participation provided information that reduced role ambiguity that contributed to im- proved performance, and Mia (1989) found the relationship between participation and performance to be moderated by job difficulty.

The objective of this article is to examine the perceived level of job- relevant information as an intervening variable between budgetary partic- ipation and individual performance. Job-relevant information (JRI) is infor- mation that facilitates job-related decision making. The results of this

The author gratefully acknowledges helpful comments by Frank Collins, Chester A. Schriesheim, Jeffrey L. Kerr, David McLain, Mark A. Mone, and the referees on earlier drafts of this article.

Submitted July 1990. Accepted January 1992.

The Accounting Review, July 1992

study, based on a questionnaire survey of division managers in large corporations, suggest that participation affects performance, not directly, but through JRI. In addition, this positive performance effect of participa- tion persists and is more pronounced when environmental volatility is high, although the results do not provide unambiguous evidence.

Key Words: Participation, Budgeting, Environmental volatility, Cognitive effects.

Data Availability: Data are available from the author upon request.

THE remainder of this article is organized as follows. The next section discusses the role of JRI as an intervening variable between budgetary participation and individual performance. The effects of environmental volatility are also dis-

cussed. Subsequent sections contain a description of the research method, an analysis of the results, and a summary and conclusion.

I. Job-Relevant Information as an Intervening Variable

The accounting literature (Baiman 1982; Baiman and Demski 1980; Tiessen and Waterhouse 1983) has identified two primary types of information in organizations: decision influencing, which is collected about a manager's behavior for the purposes of performance evaluation; and JRI, which helps a manager to improve his or her action choice through better-informed effort. The latter provides the manager with a better un- derstanding of decision alternatives and actions needed to reach objectives (Locke et al. 1986). JRI has also been called decision-facilitating (Demski and Feltham 1976) and ex ante information (Baiman 1982; Tiessen and Waterhouse 1983). Most accounting information in the organization is of one or the other type.

In most budgetary situations, environmental factors, managerial skills, and effort jointly determine performance (Chalos and Haka 1990). JRI can improve performance because it allows more accurate predictions of environmental states and thus allows more effective selection of appropriate courses of action. Campbell and Gingrich (1986) provided evidence supporting the positive performance effects of JRI. In their experiment, some programmers actively participated in discussing new project assignments with their superiors and jointly determined their completion targets. Objectives of equal difficulty were assigned to other programmers. The participating programmers significantly outperformed the other programmers for complex but not for simple projects. Campbell and Gingrich concluded that participation in setting goals led to task discussions with another expert (the superior) that allowed program- mers to gain specific insights into more effective approaches to complex projects. When projects were simple, however, effective task procedures may have been so obvious such that discussions with superiors provided few insights and had little effect on performance.

Budgetary participation can similarly facilitate the acquisition and use of JRI. Since participation provides an opportunity to influence the budget before it is finalized, in preparing a participatory budget, a manager must generally assume a more active role. Thus, the manager becomes more involved in considering and evaluating alternative budget goals. Participation may thus increase the manager's attempts to formulate ac-

Kren-Budgetary Participation and Managerial Performance

Figure 1 The Research Model

JOB-RELEVANT INFORMATION (2,) ,..* . .

, , ..0

pzl =0.397 , 0 , .. p3,=0.452 ,, ,, ..A

PARTICIPATION (ZI ) ,

- - -- - - - - - - - - - - - - - - - PERFORMANCE (23) p31= 0.034

Note: The subscripts 1,2, and 3 refer to the variables of participation, JRI, and performance, respectively. The path coefficients (p,,) indicate the effect of variable j in explaining the variation in variable i.

curate forecasts of environmental states and can focus the manager's attention on deci- sions and behaviors needed in future periods. It may also increase the time spent thinking about budgetary objectives and alternative means-end approaches (Earley et al. 1987; Lawrence and Lorsch 1967; Locke et al. 1986). As a consequence, budgetary participation can create an environment that encourages the acquisition and use of JRI. Results of field research provide supporting evidence (Lowe and Shaw 1968; Simons 1987) as does the research on budget-related behaviors (Merchant 1984). Simons' (1987) field study of the Johnson & Johnson Company provides detailed descriptions of how budgetary participation promotes extensive JRI search activities by managers, and these activities appear to occur primarily because the budgetary process is participa- tory rather than imposed.

Several sources of JRI are available to the manager, including the external environ- ment (i.e., environmental scanning [see Bourgeois 19851) and peers, subordinates, and superiors (Hopwood 1976). Earlier research suggests that a manager's superior is an effective information source in most organizations, particularly when superiors have extensive company-specific experience, as in the case of companies that routinely promote from within (Simons 1987).

Two recent studies have examined the effects of cognitive factors associated with JRI. Mia (1989) proposed that perceptions of job difficulty moderate the relationship be- tween budgetary participation and performance because participation provides valu- able information for difficult jobs. Surveying middle-level managers, Mia found a positive relationship between participation and performance only when job difficulty was high.

Chenhall and Brownell (1988) have suggested that role ambiguity links budgetary participation and performance. In a survey study, they found that participation re- duced role ambiguity, which improved performance. JRI and role ambiguity are similar constructs in that the latter reflects the extent to which managers understand their duties and responsibilities, while the former is a measure of the information available to managers to accomplish job-related tasks.

The schematic structure in figure 1suggests that participation has an indirect effect on performance through JRI. Participation is expected to increase JRI initially, which

514 The Accounting Review, July 1992

would in turn improve performance. Thus, JRI serves as the link between participation and individual performance, suggesting the following hypothesis:

HI: The relationship between budgetary participation and managerial perfor- mance will be explained by an indirect effect whereby participation increases job-relevant information, and job-relevant information is positively associated with performance.

The Effects of Environmental Volatility

In describing the organization's external environment, organizational theorists (Downey and Slocum 1975; Duncan 1972; Tosi et al. 1973) have generally included two components: (1)diversity, the range of environmental factors faced by an organization and (2) volatility, the change or variability among these factors. Leblebici and Salancik (1981) argued that diversity is more predictable because it can be evaluated and antici- pated. Thus, it can be managed using institutionally formalized procedures (i.e., by "disentangling the pieces," as in market segmentation [p. 5831). Volatility, however, is stochastic in nature and cannot be easily anticipated; managers make inferences about effects of probabilistic environmental factors on cause and effect relationships. JRI can be used to improve such predictions. Thus, one would expect that volatility would affect the information-gathering activities of managers (Bourgeois 1985; Hopwood 1976). Leblebici and Salancik (1981), for example, found that bank loan officers sought more information when making loan decisions when the environment is volatile.

In a budgeting setting, volatility is expected to positively affect: (1)the level of budgetary participation, (2) the link between participation and JRI, and (3) the link between JRI and performance. When volatility is low, few exceptions occur and rules and procedures are adequate to specify behaviors. Managers can also rely more readily on insights gained from previous experience. As volatility increases, however, numerous exceptions can overwhelm the information system unless decisions are made at lower hierarchical levels in the organization (Galbraith 1973; Simons 1987). Consequently, greater participation in decision making by lower-level managers is required. Govindarajan (1986) and Hopwood (1976) extended this reasoning about par- ticipation in decision making in general to participation in budgeting, suggesting that greater budgetary participation should be found in organizations facing greater volatility.

In addition, the link between participation and JRI is expected to become stronger when volatility is high because managers are expected to take greater advantage of par- ticipation to acquire JRI for effective decision making (Tung 1979). The link between JRI and performance is also expected to become stronger when volatility is high because volatile environments require managers to make maximum use of all available information to deal with more difficult decision settings (Leblebici and Salancik 1981; Tung 1979). When volatility is low, in contrast, more decisions are routine and some JRI may not be needed to carry out job functions because rules and procedures may be adequate for effective job performance, which is the second hypothesis:

HZ: When volatility is high, three effects on the model proposed in hypothesis H I (fig. 1)will result:

H2a: the level of participation will increase,

Kren-Budgetary Participation and Managerial Performance

H2b: the link between participation and JRI will be stronger, and H2c: the link between JRI and performance will be stronger.

11. Research Method

Sample

Data for this study were collected by using a questionnaire survey of profit center managers from Fortune 500 manufacturing firms. The listing for each Fortune 500 firm in the Dun and Bradstreet Reference Book of Corporate Managements was examined, and executive-level profit center managers at the hierarchical level immediately below the CEO were identified. A sample of profit center managers was contacted to ensure that they have budgetary responsibility.

A maximum of two managers in a firm were randomly selected for inclusion in the sample to allow for more company representation. Companies listed in the directory were excluded if profit center managers could not be clearly identified from their job titles. This selection process yielded 192 managers from 96 companies. A cover letter and a questionnaire were mailed to each manager. A follow-up letter and another copy of the questionnaire were sent after approximately three weeks. After several addi- tional weeks, all remaining nonrespondents were contacted by telephone.

Follow-ups of the original 192 managers revealed that 38 had retired, left the company, or had changed to new positions. Of the remaining 154 potential respon- dents, 80 usable responses were received (a response rate of 51.9 percent) from 63 different companies. Responses were obtained from two managers in each of 17 companies. Respondents were promised anonymity, but organizational affiliation was tracked to allow measurement of environmental volatility. Respondents' average tenure in their positions was 4.8 years (sad. =3.3). Appendix A provides a listing by industry (as defined by four-digit SIC code) of the final sample.

Measures

Appendix B contains an abbreviated copy of the research questionnaire used to measure the self-reported variables in this study.

Performance. Because of the promise of respondent anonymity, a self-rated measure of performance was used. Managers were asked to rate themselves on eight dimensions of performance identified by Mahoney et al. (1965): planning, investigating, coordinating, evaluating, supervising, staffing, negotiating, and representing. An overall item was also included (item 9).

The Mahoney et al. measure has been used effectively in previous studies of budget- ary participation (e.g., Brownell and McInnes 1986; Govindarajan 1986). Although the potential for bias (higher mean values in comparison with objective performance measures) exists with a self-rating of performance, it may not be consequential since there is no reason to suspect a systematic relationship with the independent variables.

According to Brownell and McInnes (1986), this performance measure should meet two criteria: (1)reasonable assessment of independent dimensions of performance, and (2) the majority of the variation in the overall rating (item 9) should be explained by the eight items. Regressing the overall rating as the dependent variable on the ratings of each of the eight individual dimensions resulted in 75.6 percent explained variation, suggesting that the second criterion was met. To meet the first criterion of indepen-

516 The Accounting Review, July 1992

dence, Brownell and McInnes assert that pair-wise correlations between the eight vari- ables should be less than the correlation of each variable with the overall rating. Only four of the 28 comparisons violated this criterion. Thus, the measure appears to encom- pass reasonably independent dimensions of performance that are correlated with the overall measure. To capture a uni-dimension of performance, the eight sub-scale items were summed up to construct a composite performance scale. The combined scale was significantly correlated (r=0.840; p< 0.01) with the overall rating (item 9).

Budgetary Participation. This variable was defined as the manager's degree of in- fluence on the budget. A three-item version of a participation measure employed by Milani (1975) was used. (This measure is similar to the scale of participatory decision making used by Abdel-halim and Rowland [I9761 and is based on Vroom [I9601 and Vroom and Mann [1960].)

A factor analysis of the scale revealed only one factor with an eigenvalue greater than 3, which explained 76.9 percent of the total variance indicating that only one construct was being measured. The reliability coefficient computed for the scale in this study was 0.85. An overall measure of budgetary participation was constructed by summing up responses to the three individual items.

Job-Relevant Information (JRI). The objective of this measure is to assess the extent to which managers perceived information availability for effective job-related deci- sions. Managers with adequate JRI are expected to perceive and report that they have adequate information to accomplish their job-related objectives and to evaluate impor- tant decision alternatives.

Based on O'Reilly's (1980) information overload index, a scale was developed for use in this study, with the wording modified to fit the context of this study (Roberts and O'Reilly 1974). Several colleagues, working managers, and executive MBA students with relevant work experience were asked to comment on the scale and concurred on its face validity.

Factor analysis confirmed the single-factor structure of the scale. Only one factor was present with an eigenvalue greater than 1 explaining 64.3 percent of the total variation.. The reliability coefficient calculated for the scale was 0.72. For subsequent analysis, the items were summed up.

Environmental Volatility. For this study, volatility is defined as change or vari- ability in the organization's external environment (Tung 1979). Previous studies of en- vironmental volatility have focused on variability of accounting variables (e.g., sales or income) at the industry level. Tosi et al. (1973) argue that more stable patterns in such measures across time indicate more stable environments and thus are easier to predict (Bourgeois 1985).

Tosi et al. operationalized volatility by using the following three variables: (I) market volatility, the coefficient of variation of net sales; (2)technological volatility, the coefficient of variation of the sum of research and development and capital expendi- tures divided by total assets; and (3) income volatility, the coefficient of variation of profits before taxes (used as a composite measure to capture other sources of volatility). The measures were further verified by Snyder and Glueck (1982). The coefficient of variation (the variance is standardized by the magnitude) is used because it allows comparisons across industries of different sizes.

In a later study, Bourgeois (1985) suggested using first differences of the Tosi et al. measure. Bourgeois argued that a high but constant, and thus predictable, rate of

517 Kren-Budgetary Participation and Managerial Performance

change could produce a high coefficient of variation. However, it is not only the rate of change that creates volatility, but also the unpredictability of the change (Downey and Slocum 1975; Milliken 1987). Bourgeois argued that the coefficient of variation of first differences provides a better measure of discontinuities. Thus, for this study, market, technological and income volatility were measured by using first differences as follows:

CCV(X,I, volatility (Xi) =

j=1 ,

n

where Xi =market, technological, or income variable and n =number of companies in the industry (not including the sample company) and,

where Zk=(Xirk and Xi,k =market, technological, or income variable in year k. For each firm in the sample, industry-level statistics were calculated by using all

other companies listed on Standard and Poor's COMPUSTAT data file with the same two-digit SIC code. In conformance with previous research, industry-level measures were used because they seem most relevant to the key dimensions of a company's exter- nal environment (Bourgeois 1985; Tosi et al. 1973; Tung 1979). The industry-level measure used in the analysis to be shortly presented was also significantly correlated with a correspondingly calculated company-level measure (r=0.321; p <0.05).

Factor analysis of the three resulting variables revealed only one factor with an eigenvalue greater than 1 (explaining 68.1 percent of the total variance), which indi- cates that only one construct was measured. Thus, the three variables were summed up to provide an overall measure of volatility.

Of the 63 companies in the sample, 1 7 were significantly diversified conglomerates or obtained more than 20 percent of their total revenues from unrelated operations as indicated by segment disclosures in annual reports. Because it was difficult to identify an appropriate industry reference group for these diversified companies, the analysis of volatility effects excluded these 17 companies (representing 21 respondents). Thus, for subsequent analysis of volatility effects, the sample size was reduced to 59.

111. Analysis and Results

Table 1 provides descriptive statistics for the measured variables in the study. Although the participation measure is skewed (the mean is near the top of the variable's range), the managers in this sample were at the highest level of the corporate hierarchy, suggesting that participation might be high (Searfoss and Monczka 1973). Intercorrela- tions between the measured variables are shown in table 2. The significant, zero-order correlation between participation and JRI ( r=0.397;p <0.01) is consistent with hypoth- esis HI, as is the correlation between JRI and performance ( r = 0.466; p <0.01). To inter- pret the latter coefficient in the context of hypothesis HI, however, a path-analytic approach is used. The zero-order correlation between budgetary participation and per-

- - -

The Accounting Review, July 1992

Table 1 Descriptive Statistics for Measured Variables

(n=80)

Hypothetical Actual Standard Variable Range Range Mean Deviation

Participation 3-21 8-2 1 18.2 3.0

Information 3-21 9-2 1 15.9 3.0

Volatility na 8.9-23.2 15.7 3.6 (n=59)

Performance 8-56 34-56 45.0 4.7

Table 2

Correlation Matrix for Measured Variables (n=80)

Variables 2 3 4

1. Volatility -0.044 -0.122 0.032 (n=59)

2 . Participation -

3. Information

4 . Performance -

* Significant at p<0 .01 (two-tailed test).

formance ( r =0.214; p <0.07) is weakly significant, which will be explained more clearly by participation's effects on JRI.

Participation, JRI, and Performance

Hypothesis H I posits that JRIwill act as an intervening variable to explain the relationship between budgetary participation and managerial performance. This hy- pothesis is tested by using the path analysis model summarized in figure 1.'

Each path coefficient, pi,, indicates the impact of variable j in explaining the variance in variable i . The values of the path coefficients can be interpreted in units of standard deviation. For example, the path coefficient of p,, =0.397 in figure 1 indicates that for every standard deviation increase in the participation measure, the data predict a 0.397 standard deviation increase in JRI.

' Path analysis is an application of regression and correlation appropriate for estimating a series of interre- lated parameters (Wonnacott and Wonnacott 1981). It allows statistical analysis of the direct contribution of participation to performance and its indirect contribution through JRI. This is accomplished by estimating the values of the path coefficients designated p,, in figure 1.

519 Kren-Budgetary Participation and Managerial Performance

A series of regressions are used to estimate the path coefficients, according to the following,

Z2=p21Z1, (1)

Z3=p31Z1+p32Z2, (2)

where, Z1 is the measure of participation, Z2 is JRI, and Z3 is performance. Each variable is standardized to a mean of zero and a standard deviation of 1.

The path coefficients also can be used to decompose the total relationship between two variables (i.e., performance and participation) into direct and indirect effects (i.e., through JRI). The total relationship can be measured with the zero-order correlation coefficient, r,. Thus,

r23=p32+p31r12; (4)

r13=p3]+P32r12. (5)

The subscripts 1,2, and 3 refer to the variables of participation, JRI, and perfor- mance, respectively (see fig. 1).The first term on the right-hand side is an estimate of the direct effect (the path coefficient), or the effects through unobserved intervening variables. The second term is an estimate of the indirect effect or, in the absence of pre- dicted indirect effects, the second term provides an estimate of spurious effects. Thus, for this study, equation (4) allows decomposition of the total relationship between JRI and performance (r2,) into a direct effect ( P ~ ~ ) Theand a spurious effect (p3]r12). spurious effect results from participation, which is a common antecedent of both JRI and performance. Equation (5) allows decomposition of the total relationship between participation and performance (I-,,) into a direct effect ( ~ 3 1 )and the indirect effect through JRI (p32r12). Hypothesis HI posits that the indirect effects of participation through JRI, will predominate.

The results of estimating equations (1)and (2) for the total sample are shown in table 3 and figure 1.The direct path between participation and performance (p,,) was not, as expected, statistically significant. However, the path coefficients between participation and JRI (p,,) and between JRI and performance Up3,) were significant at conventional levels. Both regression models were significant, also at conventional levels.

The decomposition of the linkages in the model (eqs. [3], [4], and [5]) is shown in table 4. The prediction of hypothesis H1 was that most of the effect of participation on performance would be indirect (through JRI) with little direct effect. The results shown in table 4 (eq. [5]) support this prediction. The direct effect of participation on perfor- mance (0.034) is small relative to the indirect effect through JRI (0.180). Thus, for every standard deviation increase in participation (eq. [3]), JRI increases by 0.397 standard deviation, and for every standard deviation increase in JRI (eq. [4]), performance increases by 0.466 standard deviation. In total, for every standard deviation increase in participation, performance increases by 0.180 standard deviation. In addition, only a small portion of the relationship between information and performance (eq. [4]) is spurious (0.014) relative to the direct effect (0.452). Overall, the results are consistent with hypothesis HI.

The Accounting Review, July 1992

Table 3 Results of Path Analysis

Total Sample High Volatility Low Volatility Dependent Path (n=80) (n=30) (n=29) Variable1 Coefficient Link to (Fig. 1) Estimate t-statistic Estimate t-statistic Estimate t-statistic

Equation ( 1):

Information1 Participation P21 0.397 3.77** 0.442 2.61* 0.252 1.30

Equation (2):

Performancel Information P32 0.452 4.06** 0.606 3.60** 0.607 3.50**

Performancel Participation P31 0.034 0.31 0.029 0.17 -0.121 -0.71

Note: For equation (1)(Information): total sample, R-square=0.158, F = 14.24**; high volatility, R-square= 0.195, F = 6.80*; low volatility, R-square =0.063, F = 1.69.

For equation (2) (Performance): total sample, R-square=0.218, F = 10.44"; high volatility, R-square =0.384, F=8.41**; low volatility, R-square=0.339, F = 6.15* *.

* Significant at p<0.05 (two-tailed test). * * Significant at p<0.01 (two-tailed test].

The Effects of Environmental Volatility

Hypothesis HZ posits that when volatility is high, (1)the level of participation will be higher, (2) the link between participation and JRI (pz,) will be stronger, and (3) the link between JRI and performance (pJ2) will be stronger. As described above, volatility effects were examined with a reduced sample size of 59 to eliminate diversified com- panies from the sample.

To examine hypothesis H2a, the zero-order correlation between volatility and par- ticipation was calculated. As shown in table 2, the correlation was not significant. Thus, the data do not support hypothesis H2a; managers did not report greater participation as volatility increased. The implications of this finding become clearer as the two remaining predictions are examined below.

To examine the effects of volatility on the link between participation and JRI and between JRI and performance, the sample was split at the median of the volatility measure and the path coefficients in figure 1 were recalculated for the two sub- samples. The results of estimating equations (1) and (2) at high and low levels of volatility are shown in table 3 and in figure 2. As with the total sample, the direct path linkage between participation and performance (p31) is not statistically significant at either high or low levels of volatility. In fact, the point estimate is negative at the low level of volatility.

Consistent with hypothesis H2b, the path coefficient between participation and JRI (p,,) is statistically significant at the high, but not at the low, level of volatility. Com- bined with the finding that managers did not participate more as volatility increased,

Table 4 Decomposition of Path Analysis Model Relationships

Total Sample (n=80)

High Volatility (n=30)

Low Volatility (n=29)

Dependent Variable1 Link to

Total Effect

rc,

Direct Effect

P 11

Indirect1 Spurious

Effect

Total Effect

Direct Effect

P 8 ,

Indirect1 Spurious

Effect

Total Effect

r,,

Direct Effect

P 8 ,

Indirect1 Spurious

Effect

Equation (3):

Information1 Participation 0.397 0.397 -

Equation (4):

Performancel Information 0.466 0.452 0.014" 0.619 0.606 0.013"

Equation (5):

Performancel Participation 0.214 0.034 0.180' 0.297 0.029 0.268' 0.030 -0.121 0.151'

" Spurious effect. Indirect effect.

The Accounting Review, July 1992

Figure 2

Research Model at High and Low Levels of Environmental Volatility

Panel A. High Volutility: I I

IOB-RELEVANT

. Y

PERFORMANCE ( Z , )

Panel B. Low Volatility:

IOB-RELEVANT

PARTICIPATION (2,) - - - - - - - - - - - - - - - - - -+ PERFORMANCE [ z3] p3, = -0.121

this is consistent with the proposition that participation was used more effectively to acquire JRI when volatility was high. However, a general linear test (Neter and Wasserman 1974) indicates that the path coefficients (p2,) at the two levels of volatility are not significantly different from each other (F2,55= 1.12), SO the support for hypoth- esis H2b is ambiguous.

The results do not support hypothesis H2c that the link between JRI and perfor- mance would be stronger at the high level of volatility. The path coefficients between JRI and performance (p, , ) are highly significant at both levels of volatility and are not significantly different from each other, which indicates that, contrary to expectations, the relationship between JRI and performance is unaffected by the level of volatility.

The decomposition of the model linkages (eqs. 131, [4], and [5]) for high and low levels of volatility is shown in table 4. At both levels, the indirect effect of participation on performance through information is greater than the direct effect (eq. [5]). When volatility is high, the indirect effect of participation (0.268) is greater than the corre- sponding indirect effect when volatility was low (0.151). This is consistent with expec- tations that participation would be more strongly associated with performance when it is more useful in providing JRI, as when volatility is high. Once again, however, strong evidence of volatility effects is not present because estimates of the regression model in equation (2) are not significantly different across levels of volatility (F3,53 =0.70).

523 Kren-Budgetary Participation and Managerial Performance

IV. Summary and Conclusions

The objective of this study was to examine JRI as an intervening variable in explain- ing the relationship between budgetary participation and individual performance. The results are consistent with the proposition that budgetary participation facilitates JRI acquisition by managers, and that JRI, in turn, is associated with improved perfor- mance. This study and other recent research support the view that the cognitive effects of participatory budgeting may be more consistent determinants of performance than the motivational effects (Brownell and McInnes 1986; Chenhall and Brownell 1988; Mia 1989). An important component of these cognitive effects appears to be related to JRI acquisition and use. In that regard, this study provides a link to the results reported by Mia (1989) and Chenhall and Brownell (1988). In both studies, the information effects of participation played an important conceptual role, as it did in this study. Fur- thermore, Brownell and McInnes (1986, 590) recognized explicitly that participation provided information to clarify the relationship between formal rewards and budget goals, which suggests another important information role for participation. Additional research is needed in this area, with consideration of job difficulty, role ambiguity, and reward system.

Overall, the effect of participation on performance, through JRI, persisted across the levels of environmental volatility and was somewhat more pronounced when envi- ronmental volatility was high, although more conclusive evidence is needed to confirm predictions about volatility effects. A larger sample may be needed, or a longitudinal study of organizations in transition in response to volatility changes.

Hypothesis H2a, which posited a positive relationship between volatility and par- ticipation, was not supported. This may indicate that organizations fail to recognize budgetary participation as an efficient means to increase the JRI available to managers. Alternate organizational mechanisms, such as enhanced management information systems, performance or process feedback (Early et al. 1990), or expert support staff may be more efficient responses to volatility. Also, organizations may sometimes fail to evaluate the level of volatility accurately which may contribute to inadequate responses to information needs (Bourgeois 1985).

Volatility had only marginal effects on managers' responses within the model (hyp. H2b and H2c). This may be a consequence of perceptual differences between managers in the high and low volatility groups. The former group may make greater efforts to acquire and use JRI, but since they face greater information processing demands, their perception of the level of JRI may not be greater than managers with more limited infor- mation needs. In addition, organizational budgetary systems may differ as volatility differs (Merchant 1984). Volatility could affect budgetary procedures through its effects on organizational factors and thereby affect JRI acquisition opportunities.

Several limitations of this study should be identified. Common-method bias from self-reported data may lead to overestimates of model relationships, particularly between the information and participation measures. Nonresponse bias may also have had unknown effects on the results. Another limitation, common to correlational studies, is that the results are open to alternate interpretations. Cross-sectional analysis does not provide clear-cut evidence to confirm predictions of causal relationships. For example, an alternate interpretation of the relationship between participation, JRI, and performance could be that well-informed managers perform better and are conse-

524 The Accounting Review, July 1992

quently allowed to participate more in budgeting. However, such an interpretation is inconsistent with conclusions of previous accounting research (Chenhall and Brownell 1988; Mia 1989) and studies in other disciplines [Campbell and Gingrich 1986). Campbell and Gingrich's results are based on a field experiment which provided better control over confounding variables and so give stronger support for predictions of causality.

Appendix A List of Industries Included in the Sample

-- - - - ---

Number of Number of Industry Companies Respondents

Food and kindred products 3 4 Tobacco products 1 2 Lumber and wood products 1 1 Furniture and fixtures 1 1 Paper and allied products 8 11 Pr in t~ng and publishing 1 2 Chemicals 13 17 Petroleum refining 1 1 Rubber and plastic products 2 4 Stone and concrete production 2 2 Primary metal industries 2 2 Metal fabrication 4 5 Commercial machinery 9 11 Electrical equipment 7 8 Transportation equipment 6 6 Photography equipment 2

-3 -

Totals 63 80

Appendix B Abbreviated Research Questionnaire

Participation

Q1. I am involved in setting all portions of my budget. (Response anchors: 1 =strongly disagree, 7=strongIy agree.)

4 2 . My budget is not final until I am satisfied with it. (Response anchors: 1 =strongly disagree, 7=strongly agree.)

43 . My opinion is an important factor in setting my budget. (Response anchors: l=strongly disagree, 7 =strongly agree.)

Job-Relevant Information

Q1. I am always clear about what is necessary to perform well on my job. (Response anchors: 1 =strongly disagree, 7 =strongly agree.)

42. I have adequate information to make optimal decisions to accomplish my performance objectives. (Response anchors: 1=strongly disagree, 7 =strongly agree.)

4 3 . I am able to obtain the strategic information necessary to evaluate important decision alternatives. (Response anchors: 1=strongly disagree, 7 =strongly agree.)

Kren-Budgetary Participation and Managerial Performance

Appendix B--Continued Abbreviated Research Questionnaire

Performance

Rate your performance as a manager on the following tasks. (Response anchors: 1=below average perfor- mance, 7 =above average performance.]

1. Planning 2. Investigating 3. Coordinating 4. Evaluating 5. Supervising 6. Staffing 7. Negotiating 8. Representing 9. Rate your overall performance

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Budgetary Participation and Managerial Performance: The Impact of Information andEnvironmental VolatilityLeslie KrenThe Accounting Review, Vol. 67, No. 3. (Jul., 1992), pp. 511-526.Stable URL:

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References

Economically Optimal Performance Evaluation and Control SystemsStanley Baiman; Joel S. DemskiJournal of Accounting Research, Vol. 18, Studies on Economic Consequences of Financial andManagerial Accounting: Effects on Corporate Incentives and Decisions. (1980), pp. 184-220.Stable URL:

http://links.jstor.org/sici?sici=0021-8456%281980%2918%3C184%3AEOPEAC%3E2.0.CO%3B2-U

Budgeting and Employee BehaviorSelwyn Becker; David Green, Jr.The Journal of Business, Vol. 35, No. 4. (Oct., 1962), pp. 392-402.Stable URL:

http://links.jstor.org/sici?sici=0021-9398%28196210%2935%3A4%3C392%3ABAEB%3E2.0.CO%3B2-2

Strategic Goals, Perceived Uncertainty, and Economic Performance in Volatile EnvironmentsL. J. Bourgeois, IIIThe Academy of Management Journal, Vol. 28, No. 3. (Sep., 1985), pp. 548-573.Stable URL:

http://links.jstor.org/sici?sici=0001-4273%28198509%2928%3A3%3C548%3ASGPUAE%3E2.0.CO%3B2-W

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Uncertainty: Measures, Research, and Sources of VariationH. Kirk Downey; John W. SlocumThe Academy of Management Journal, Vol. 18, No. 3. (Sep., 1975), pp. 562-578.Stable URL:

http://links.jstor.org/sici?sici=0001-4273%28197509%2918%3A3%3C562%3AUMRASO%3E2.0.CO%3B2-Q

Characteristics of Organizational Environments and Perceived Environmental UncertaintyRobert B. DuncanAdministrative Science Quarterly, Vol. 17, No. 3. (Sep., 1972), pp. 313-327.Stable URL:

http://links.jstor.org/sici?sici=0001-8392%28197209%2917%3A3%3C313%3ACOOEAP%3E2.0.CO%3B2-F

Effects of Environmental Uncertainty on Information and Decision Processes in BanksHuseyin Leblebici; Gerald R. SalancikAdministrative Science Quarterly, Vol. 26, No. 4. (Dec., 1981), pp. 578-596.Stable URL:

http://links.jstor.org/sici?sici=0001-8392%28198112%2926%3A4%3C578%3AEOEUOI%3E2.0.CO%3B2-D

Three Types of Perceived Uncertainty about the Environment: State, Effect, and ResponseUncertaintyFrances J. MillikenThe Academy of Management Review, Vol. 12, No. 1. (Jan., 1987), pp. 133-143.Stable URL:

http://links.jstor.org/sici?sici=0363-7425%28198701%2912%3A1%3C133%3ATTOPUA%3E2.0.CO%3B2-M

Individuals and Information Overload in Organizations: Is More Necessarily Better?Charles A. O'Reilly, IIIThe Academy of Management Journal, Vol. 23, No. 4. (Dec., 1980), pp. 684-696.Stable URL:

http://links.jstor.org/sici?sici=0001-4273%28198012%2923%3A4%3C684%3AIAIOIO%3E2.0.CO%3B2-3

Perceived Participation in the Budget Process and Motivation to Achieve the BudgetD. Gerald Searfoss; Robert M. MonczkaThe Academy of Management Journal, Vol. 16, No. 4. (Dec., 1973), pp. 541-554.Stable URL:

http://links.jstor.org/sici?sici=0001-4273%28197312%2916%3A4%3C541%3APPITBP%3E2.0.CO%3B2-M

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Can Environmental Volatility Be Measured Objectively?Neil H. Snyder; William F. GlueckThe Academy of Management Journal, Vol. 25, No. 1. (Mar., 1982), pp. 185-192.Stable URL:

http://links.jstor.org/sici?sici=0001-4273%28198203%2925%3A1%3C185%3ACEVBMO%3E2.0.CO%3B2-G

On the Measurement of the Environment: An Assessment of the Lawrence and LorschEnvironmental Uncertainty SubscaleHenry Tosi; Ramon Aldag; Ronald StoreyAdministrative Science Quarterly, Vol. 18, No. 1. (Mar., 1973), pp. 27-36.Stable URL:

http://links.jstor.org/sici?sici=0001-8392%28197303%2918%3A1%3C27%3AOTMOTE%3E2.0.CO%3B2-8

Dimensions of Organizational Environments: An Exploratory Study of Their Impact onOrganization StructureRosalie L. TungThe Academy of Management Journal, Vol. 22, No. 4. (Dec., 1979), pp. 672-693.Stable URL:

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