1
CAUSALITY IN A PERFORMANCE MEASUREMENT MODEL: A CASE STUDY
IN A BRAZILIAN POWER DISTRIBUTION COMPANY
André Carlos Busanelli de Aquino
Faculdade de Economia, Administração e Contabilidade de Ribeirão Preto
Universidade de São Paulo.
R. José Curvello da Silveira Júnior, 445, ap.04, Ribeirão Preto, SP, Brasil
CEP 14026-240 - Tel. 16-36024747; 16-38772761
Email: [email protected]
Ricardo Lopes Cardoso
EBAPE – Fundação Getulio Vargas
R. Gilberto Cardoso, 260, ap.402, Leblon, Rio de Janeiro, RJ, Brasil
CEP 22430-070 - Tel. 21-25595781
Email: [email protected]
Valéria Lobo Archete Boya
FUCAPE Business School
R. Valentim P. da Rocha, 100, ap.407, Centro, Cataguases, MG, Brasil
CEP: 36770-000 - Tel. 27-40094444
Email: [email protected]
ABSTRACT
This study extends prior balanced scorecard research by incorporating the effects of
uncertainty, payment schemes and the strength of causal relations proposed in that
performance measurement model (PMM) on the budgetary dynamics. Our analysis was
restricted to two strategic business units, engineering projects and electricity distribution
service, from a Brazilian electric power concessionaire. We postulate a mediated moderation
association between uncertainty (treatment), bonus scheme (mediator), dispersion of payment
scheme and the strength of causal relations proposed in the PMM (moderators) on budgetary
slack (outcome). Additionally, we postulate that the use of accounting based measures also
mediates the effect of uncertainty on budgetary slack. We gathered monthly observations
from 102 indicators containing the target and achievement values throughout 2002-2006
periods. Managers were later asked to answer questionnaires about the possible cause-effect
relations between these indicators, then 215 causal maps of the department and corporate
indicators were drawn up. Econometric analysis find evidence that the budgetary slack
observed is directly impacted by uncertainty, and this impact is moderated by the dispersion
of payment scheme. However, we did not find any evidence that supported the mediation
process proposed between uncertainty, accounting-based measures and budgetary slack.
Partially implementation of balanced scorecard and the level of analysis adopted are possible
explanations for that.
Key-words: Budgetary slack; executive payment; balanced scorecard.
1. INTRODUCTION
In this study we tested the impact of associations between indicators within a
Performance Measurement Model (PMM), proposed by executive officers and managers, on
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budgetary slack. The purpose of this study is to assess how the combination of indicators in a
PMM impacts on the budgetary slack previously arranged in the goal-setting process,
according to the typology proposed by Malina and Selto (2004b).
PMM are used as means to promote individual behavior alignment to organization’s
strategy, to enhance the organizational learning, and are usually associated with budgetary
processes. Goals are set in the course of budgeting negotiations, and the evaluation of
individual and team performance is tied both to goal attainment and to variance analysis.
Accordingly, budgetary dynamics are employed to provide incentives to agents. As a result,
organizations frequently use the same budget dynamics for planning the resources allocation
and evaluating the performance of operations. Sprinkle (2003) suggests that this simultaneous
use, for both decision-influencing and decision-facilitating purposes, should be considered in
investigations aimed at understanding the role of budgets in organizations. Sprinkle’s
suggestion is aligned with a perspective that considers performance-evaluation and reward
systems as having both a motivational and an informational role (MERCHANT, 1998).
It has also been suggested in the literature that the nature of indicators present in a
PMM interacts with the contingencies of its use, with subsequent effects on the budgetary
process. These effects can encompass goal acceptance by personnel, individual motivation,
less information asymmetry, occurrence of budgetary slack and manipulation of accounting
measures (HARTMANN, 2000). Thus, it is expected that the cause-effect relations proposed
in a PMM impact on the perception of how the work should be done in order to achieve goals.
As a result, the perceived cause-effect relations exert influence on goal negotiation, individual
motivation and task commitment (WEBB, 2004).
Once the PMM indicators are adopted, a bargaining process begins to set goals to be
attained. The perception of a valid cause-effect relation between action, performance and
reward should motivate the individual to pursue the aspired goals, but given the prospect of
both reward and enforcement, the individual will strive to set easily attainable goals. Thus,
indicators included in the PMM that reflect valid causal connection between action and
outcome are considered more effective than lists of indicators without this characteristic.
Nevertheless, causal relations can be mistaken both for logical relations and for finality
relations. The former are established using a financial calculation and cannot be empirically
tested, and the latter are based on human desires, in which an individual outlines routes or
means which are believed to lead to the planned objectives. Finality relations, therefore, can
be part of a credible tale constructed by the organization (MALINA & SELTO, 2004b),
which, if institutionalized, might dictate behavior.
Performance measurement models that posit the existence of valid causal relations have
been criticized with regard to authenticity of these relations. Critics have centered attention
over the rhetorical arguments used by balanced scorecards advocates, a prevalent performance
measurement model in organizations (NORREKLIT, 2000, 2003). Despite the existence of
numerous studies that tested BSC effects in diverse organizational settings, empirical tests on
causality in PMMs have not resulted in statistical significance of the proposed causality
associations (MALINA & SELTO, 2004b). They tested 18 causal relationships among nine
indicators using Granger’s causality test and found only three significant relations.
This study extends prior research (SPRINKLE, 2003; HARTMANN, 2000) by
incorporating the effect of uncertainty, payment schemes and the strength of causal relations
proposed in the PMM on budgetary dynamics. Sprinkle (2003) questions about the impact of
incentive plans on budgetary slack in performance measurement models that contain both
financial and non-financial indicators, such as BSC. Also, Sprinkle (2003) asserts the need to
understand how evaluators weight and integrate the various performance measures to form an
overall appraisal of performance, and how this occurs in group settings. Thus, we included
team incentives in our analysis. Hartmann (2000) suggests that uncertainty impacts on the
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appropriateness of choosing accounting based measures in PMM. Then, managers should
balance the use of indicators in PMMs according to their consequence on functional and
dysfunctional behaviors (HARTMANN, 2000). We then tested the impact of accounting
based indicators as mediators of budgetary slack.
Besides that, we incorporate the effect of payment schemes on budgetary dynamics. As
a result, we postulate a mediated moderation association between uncertainty (treatment),
bonus scheme (mediator), dispersion of payment scheme and the strength of causal relations
proposed in the PMM (moderators) on budgetary slack (outcome). The effect of uncertainty
on budgetary slack is moderated both by the dispersion of payment scheme and by the
strength of causal relations proposed in the PMM. Also, this moderation is mediated by the
use of bonus schemes. Additionally, we postulate that the use of accounting based measures
(ABM) also mediates the effect of uncertainty on budgetary slack. The causal association
assumes that the effect of uncertainty on budgetary slack is mediated by the use of ABM.
Our analysis was performed in an electric power concessionaire, listed on the São Paulo
Stock Exchange (Bovespa), with around 1000 employees, and accountable to the Brazilian
Electricity Regulatory Agency (ANEEL). This study covers two strategic business units
(SBU) from the holding corporation, which are engineering projects and electricity
distribution service. Each of them pursue distinct targets and amounts of bonus, and the
holding corporation is responsible for various targets related with both SBU. In 2001, the
company started the implementation of the BSC. This process was completed in late 2006,
when the consulting firm transferred BSC coordination to internal support team. Department
teams in each SBU have payment schemes associated to financial and non financial
indicators, even so bonuses are granted only if the main target is attained. Additional
incentives, such as promotions and prerogatives of dismissal exist, but are contingent to
discretionary evaluations by superior officers.
2. THEORETICAL STRUCTURE AND HYPOTHESIS DEVELOPMENT
Budgets, as part of control systems, incorporate indicators that will be used for decision
and control purposes. Comparison of individual and team performance and an association
with a more objective or subjective reward distribution are examples of the use of budgetary
information. Comparisons between threshold indicators and actual data indicators are
periodic. Agents and team workers which performance is measured in comparison to goals
have their stress, motivation, satisfaction, and effort affected, in order to keep it closer to
target. The intensity of these effects depends on budget dynamics, for instance, on agents’
participation on goals establishment, on how difficult it is to reach the threshold, and or on
rewards (punishment) association to thresholds’ attainment (LUFT & SHIELDS, 2003).
Incentives and uncertainties related to goal’s attainment increase the relative importance
of each target and their attractively (WEBB, 2004). That may increase commitment
(HOLLENBECK & KLEIN, 1987; KLEIN, WESSON, HOLLENBECK & ALGE, 1999),
and dysfunctions such as data manipulation and budgetary slack (HOPWOOD, 1972; HIRST
& YETTON, 1984; MERCHANT, 1985a; HUGHES & KWON, 1990; LAL, DUNK &
SMITH, 1996). While data manipulation is related to ex post data of actual performance,
budgetary slack is related to ex ante data of potential performance.
Simons (1988, p.268) appraises that “budget slack is the outcome of setting easily
attainable budget goals so that individuals receive organizational rewards for performance that
is below the level that would be expected if goals were tightly set”. Dunk and Perera (1997)
present other definitions for slack, but they are all related to managers’ intentional action in
the sense to make goals easily attainable.
In both cases, information asymmetry between those that monitor performance and
those who have their performance monitored increases the probability of occurrence of
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dysfunctions (MERCHANT, 1985a), and drives some firms to adopt participative budget
(SHIELDS & YOUNG, 1993). This study emphasizes on budgetary slack as a dysfunction
because it affects information relevance for decision making process.
2.1.
Budgetary Slack, Incentives and Uncertainty
Agents are able to use private information to protect themselves from uncertainties
related to changes on organization’s environment and on their own activities technology
(HARTMANN, 2000) – that affect negatively future performance forecast and judgment.
Since agents’ reward (punishment) is related to budget’s thresholds attainment, they may seek
to keep their performance close to those thresholds, even if some dysfunction becomes
“necessary”, such as budget slack (ex ante) or actual data manipulation (ex post).
Consequently, the higher the uncertainty level, more information of actual performance is
needed, both financial and non-financial in nature. Although the higher the uncertainty about
agent’s ability to reach thresholds related to their bonus, the lower will be the incentives’
enforcement, increasing budget slack probability. Agency Theory and prior studies (VAN
DER STEDE, 2000; MERCHANT & MANZONI, 1989; MERCHANT, 1985b; SIMONS,
1988; YOUNG, 1985; CHOW et al, 1988; HOLMSTROM & MILGROM, 1991) help in
understanding that rationale.
A potential enforcement is related to the possibility of ending an employment
relationship or loss of potential promotion, or even a cut in annual bonus. Under such
circumstances, the agent looks for means to protect himself from the risk of failing to achieve
the goals and consequently receiving an unfavourable evaluation, and one of the possible
protection modes is to manipulate goals, making them easily attainable (VAN DER STEDE,
2000). In addition to goal-achievement rewards, intermediary managers are motivated to
bargain looser goals to maintain autonomy and credibility, and are motivated also by the
feeling of being winners when they achieve the goals which may induce them to negotiate
them downward (MERCHANT & MANZONI, 1989).
Senior managers are also encouraged to keep the subordinates’ goals highly attainable
in order to increase the predictability of corporate gains, but also because easily achievable
goals reduce the risk of lack of commitment, and the risk of engaging in accounting
information manipulation. Easily attainable budgetary goals help to keep low negative
variations that reduce the need for senior managers’ analysis and interference in control.
Additionally, goal flexibility permits the firm to reward intermediary managers’ performance
and assure a competitive compensation package in order to prevent intermediary managers’
remuneration to be incompatible with the market.
2.2.
Dispersion of Pay Structure
Financial bonuses associated with teamwork tend to provoke contest that may be either
positive or negative to the organization goals. So, the effect of financial bonuses is moderated
by bonus sharing criteria. Individual commitment and individualized bonus sharing criteria
may be jointly used, in order to avoid free-riding (ALCHIAN & DEMSETZ, 1972;
BESANKO et al, 2007; LEIBOWITZ & TOLLISON, 1980), even if subjective evaluation
indicators are used (BESANKO et al, 2007). This effect increases in multi-works scenario, in
which the higher the number and complexity of activities under responsibility of each group,
the higher is the difficulty in measuring individual contribution to output.
Hence, bonus sharing moderates the strength of bonus incentives (WIDENER, 2006).
This moderation is more apparent in egalitarian payment structures. Even tough egalitarian
structures promote cooperation; on the other hand they increase the incidence of free-riding
and could provoke dissatisfaction on most qualified employees (BLOOM, 1999;
FIESSBACH et al, 2007; PFEFFER & LANGTON, 1993).
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2.3.
Causality and Measures Attractiveness
PMM models with valid cause-effect relations between indicators are deemed to be
important for organisations on account of more reliable predictions of the future effects of
present actions. They also provide insights about how actions affect results, and improve
motivation and incentives (MALINA & SELTO, 2004a), since they differ from random lists
of indicators that do not harmonize organizational objectives.
Individual’s commitment to goals depends on the belief that goals are attainable, and on
self-efficacy beliefs (WEBB, 2004). Also, the individual’s commitment is affected by the
belief that the efforts can affect performance measurements in which rewards are based
(MALINA & SELTO, 2004a; KLEIN et al, 1999).
Firms usually associate non-financial indicators to bonus scheme, often related to goals
based on customers and internal process (MALINA & SELTO, 2001; ITTNER &
LARCKER, 1998), even if these indicators have low weight (ITTNER, LARCKER &
MEYER, 2003). Webb (2004) suggests that firms tend to associate non-financial indicators as
cause of financial indicators related to bonus1. He found that managers are more committed
when there are stronger associations between non-financial indicators and financial indicators,
in comparison to when associations are weak. Thus, the perception of a valid cause-effect
relation between action, performance and reward should motivate the individual to pursue the
goals. The precedent discussion suggests the following hypothesis:
H1: the relation between uncertainty, bonus related indicators and propensity to
budgetary slack occurs by means of a mediated moderation process. Moderation occurs
through both the diffusion of payment scheme and the strength of causal relations.
2.4.
Uncertainty, Emphasis on Accounting Numbers and Budgetary Slack
Uncertainty is usually assumed as being positively related to budget variances
(LUKKA, 1988). Also, uncertainty is mediated by risks assumed by agents – based on how
performance is evaluated (HARTMANN, 2000). In the literature about reliance on accounting
performance measures (RAPM) one can find that PMM may have more or less emphasis on
accounting-based measures (ABM).
Accounting-based measures are more objective than other measures, and they alleviate
ambiguity of interpretation. So, when managers’ performance measures are based on ABM,
their probability to create budgetary slack increases (HIRST & YETTON, 1984;
MERCHANT, 1985a; HUGHES & KWON, 1990; LAL, DUNK & SMITH, 1996). In
addition, uncertainty increases the risks related to non-controllable events (HARTMANN,
2000). However, the effects of non-controllable events are mitigated by the use of
subjectivity, that leads to the usage of non-accounting-based measures (NABM)
(HOLMSTROM, 1979). Gibbs, Merchant, Van der Stede and Vargus (2004) found that
NABM included in bonus schemes are used to mitigate distortions and risks associated to
non-controllable events, particularly when difficulty targets and bonuses criteria that could
result in reputation and/or payment losses are at stake. The discussion suggests:
H2: Uncertainty, accounting-based measure and propensity to budgetary slack are
associated in a mediating process.
Figure 1 presents how variables are associated to each other.
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Webb (2004, p.931) defines causal relation strength as manager’s expectations about how much of a
change on outcome is an effect of a change on the cause.
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Figure 1: Relations among key variables in theoretical framework
[ DPS ]
[ CES ]
[ BNS ]
H1
[ SLK ]
[ UNC ]
[ ABM ]
3.
H2
[SLK]
[UNC]
[BNS]
[ABM]
[DPS]
[CES]
- Budgetary slack
- Uncertanty
- Bonus
- Accounting-based measures
- Diffusion pay scheme
- Cause-Effect strenght
Positive, linear, additive relations
Negative, linear, additive relations
Positive moderator effect
COMPANY BACKGROUND
The company discussed in this case study is a holding corporation with shares traded on
the São Paulo Stock Exchange (Bovespa). The holding company is controlled by members of
a Brazilian family. It has nine strategic business units (SBU), but this study focuses only on
two of them (departmental level) besides the holding company itself (corporate level). The
holding corporation controls both the SBU with 100% of their total contributed capital. Their
operational activities are related to: (1) hydroelectric plant engineering, maintenance and
operation; and (2) electric power distribution. The entire company has a decentralized
structure. The corporate level has six executive boards: financial; administrative; technical;
regulatory and strategic affairs; commercial and distribution; and energy market. The strategic
business units’ officers’ report directly to the holding corporation’s CEO. Currently (20072010), three of the six executive officers were selected among employees. The CEO, CFO
and the head of investor relations are members of the family that controls the holding
company, as are the chairman and vice-chairman of the board of directors. That family holds
62% of both SBUs common shares (with voting rights). The holding company does not
publicly disclose any information related to executive compensation, besides minimum and
maximum annual amounts allowed.
Implementation of the current PMM was started by the holding company in 2001.
Before that, management indicators were focused on strategic planning indices belonging to a
15 year forecast and were not linked to rewards. In 2001, the company began the process to
implement the BSC, which was done by external consultants with the help of internal support
staff. The process was started in the holding corporation and then spread to every SBU. This
process was completed in late 2006, when the consulting firm transferred BSC coordination to
the internal support team, but kept a helpdesk person available for inquiries.
The PMM incorporates financial and operational performance indicators, distributed in
five BSC dimensions, applicable to all levels of the organization, both firm level and business
unit level. The process to implant the PMM was spread throughout the company, but the
scope of implementation differs among the executive boards. Although it began in 2001, the
process gained impetus only since 2003, when the past series of these indicators can be
consulted. Even so, some indicators that already existed before implementing the PMM have
continued, and therefore have a longer history. Besides those indicators, each SBU suggests,
adopts and measures specific indicator sets in order to get attained to its strategic goals.
The SBU indicator thresholds are established within each SBU. Corporate targets are
established through a negotiation process, and ongoing performance is measured against these
targets. Variance reports, disseminated throughout the business units and areas of the
company, through charts and the corporate intranet, are used to highlight deviations from
targets. The evaluation process, based on BSC measures, is systemized and ongoing, at
monthly meetings attended by officers, managers, advisors and key employees. Results are
compared against targets and preventive and corrective actions are proposed and/or submitted
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for discussion. Explanations are required if targets not met along with proposals for
corrections, through total quality control (TCQ) routines. Justifications of events outside the
control of the area responsible for the indicator (i.e., possible influences of other areas or
unforeseen contingencies) were mentioned as a common practice in interviews.
The company is now developing a positions and salaries plan, with performance
measurements by competencies. For now, however the performance incentives of managers
and officers consist of sporadic promotions and compensation variations, without clear
assessment criteria. The bonus system has existed since 2002. As other indicators have been
implemented, the basis for the bonuses has been altered.
The personnel are divided into three categories: A, B and C. Level A personnel consist
of operational staff, at a lower level in the hierarchy. Level B employees also consist of
operational staff, at both low and intermediate positions, but distinguished by their basic
salaries. Level C personnel consist of operational and executive staff at the intermediate and
top positions in the hierarchy. Annual remuneration uniformly rewards A and B levels staff.
In other words, the employees in the A and B groups receive the same fixed amount as profit
sharing, independently of a particular individual’s salary or position, while C-level staff
members receive a share that varies according to each employee’s responsibilities.
The distribution of bonuses is tied to meeting the targets under the responsibility of each
area. The profit sharing depends on the each SBU operational income and on some financial
and non-financial indicators; however, greater weight is given to financial metrics. Under this
scheme, if the indicators are achieved but the profits do not reach the target, no profit sharing
is distributed. If it is exceeded, the amount to be distributed is increased proportionally to how
much the target was surpassed. This target is established by upper management and approved
by the board. Each area, within each SBU, receives a specific amount of profit sharing bonus,
based on the attainment of its respective goals (50%), while the other half independents on
area’s indicators, but on SBU performance.
Individual bonus and associated incentives vary according to its level in firm’s
hierarchy, as described above (categories A, B, and C). People at the A and B levels have a
reduced incentive, because the fixed amount does not vary with their individual salary, and
thus equates various levels of intellectual and operational contributions. Neuropsychological
studies have shown that satisfaction with compensation is a result of comparison of an
individual’s own pay package with that of his or her peers (FIESSBACH et al, 2007), so that
providing the same bonuses to employees at different pay levels and positions in the company
can reduce the satisfaction of those with higher salaries and positions within classes A and B,
reducing even more the impact of the bonus incentive on individual behavior in a team.
Hence, the greater the salary spread among employees at levels A and B in a particular area,
the greater the loss of incentive power and the stronger the propensity for free-rider behavior.
In contrast, for executives and personnel at level C the individual bonus is proportional
to the salary and varies according to position, seniority and duties. Bonus received by an
individual at level C is always higher than that of a subordinate at the same level and area.
Regarding vertical mobility within the company, employees at levels A and B can
expect to be promoted to the next higher classification, while those at level C can expect to be
promoted to executive positions. From the current department managers, only two did not
start their careers within the company and all have worked for it for more than ten years.
Starting in 2000, the company began an expansion process and gained new markets. This
resulted in the promotion and transfer of staff, including managers and executives.
Therefore, the SBUs are related to teams, composed of employees at levels A and B,
who receive less varied salaries and equal bonuses. Because of this more egalitarian situation,
they are more susceptible to incentives related to promotion to level C, or the disincentive of
being let go. The corporate level indicators are related to upper level managers and
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executives, who receive more differentiated salaries, besides bonuses tied to their salaries and
thus less egalitarian.
4. DATA COLLECTION AND METHOD
Data collection involved many stages. The first stage involved visits to all departments
making up the executive boards, where we interviewed each of the ten department managers
to get an idea of the activities undertaken by them. Also, we wanted to know how the
departments were structured, the number of employees, hierarchy, rules, procedures and
control systems. We checked the level of formalized activities, the existence of rules and
regulations, as well as reward systems. This was useful to find the organization’s degree of
decentralisation. We also gathered information about each department’s performance
indicators and the implementation status of the PMM. After the interviews, the head of the
supporting team assigned to initiate the organisation’s PMM provided more comprehensive
information. After the first stage, we gathered the past series of indicators containing the
target and achievement values of each department’s staff. In addition, the executives also
provided corporate indicators. We grouped the indicators according to their relation to the
reward system, whether financial or not, and with regard to other issues such as the
calculation formula, first year of use and the direction of the result desired by the company for
the indicator (the bigger the better or the smaller the better), as well as indicators’ frequency .
Data were provided in soft copies and the authenticity of the series could be seen by
comparing indicators that were controlled at both corporate and SBU levels at the same time.
Some financial indicators were not provided for each SBU.
Overall, past series of 102 indicators were collected for the 2002-2006 period, with
monthly observations. However, the areas differ with regard to the PMM operational status,
and consequently this reflected on the creation and inclusion of indicators, resulting in very
short historic series and incomplete data for some indicators. Thus, the number of valid
indicators for analysis was reduced to 55 between corporate and SBU indicators.
Managers were later asked to indicate in questionnaires, according to their opinions, the
possible cause-effect relations between indicators. They indicated the relations existing
between the department’s own indicators and the relation between these and the corporate
indicators. Then, 215 causal maps of the department and corporate indicators were drawn up.
Table 1 presents a summary of descriptive statistics comparing the sample means and the
company’s average.
Table 1: Characteristics of BSC measures for sample and total company indicators
Number of indicators
BSC Dimension: Financial
BSC Dimension: Customers
BSC Dimension: Internal process
BSC Dimension: Employees
BSC Dimension: Improvement
Non bonus-related
Bonus-related
Total
Sample
88
48%
27%
20%
3%
1%
63%
38%
55
47%
33%
18%
2%
56%
44%
Non formule-based
Formule-based
Coporate level
SBU level
Non accounting-based measure
Accounting based-measure
Objective/Quantitative measure
Subjective measure
Total
Sample
56%
44%
49%
51%
58%
42%
100%
-
60%
40%
47%
53%
51%
49%
100%
-
Eighty percent of the indicators are financial and customer based. From a financial
perspective, the majority are accounting based, unlike what occurs in the customer category,
as shown on Table 2. From both perspectives, indicators are equally associated with bonuses.
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Table 2: Comparison of BSC measure characteristics
BSC DIMENSION NABM
Financial
13%
Customers
32%
Internal process
4%
Employees
2%
Improvement
Total
51%
ABM
ABM
35%
14%
49%
Total
48%
32%
18%
2%
100%
BONUS associated?
No
Yes
Total
25%
22%
47%
16%
17%
33%
13%
5%
18%
2%
2%
56%
44%
100%
Formule-based?
No
Yes
Total
27%
20%
47%
27%
5%
33%
4%
15%
18%
2%
2%
60%
40%
100%
ABM - Accounting-based measure; NABM - Non accounting-based measure
n = 55 indicators
4.1.Dependent Variable: Budgetary Slack
To approximate budgetary slack (SLK), we used a time series of target’s attainment.
The index r = target value / attained value, in a given period, represents the degree of
achievement of a given target. The series was treated in order to result only in positive values
and to obtain periodic values. For “the higher the better” indicators, r > 1 indicates a not fully
achieved target, and r ≤ 1 indicates a fully achieved target. Recurrent results of r ≤ 1 are
associated with loose indicators, which have a lower incentive power. Recurrent results of r >
1 suggest that the target is being ignored by management or is impossible to be achieved.
Slack is associated with either r > 1 or r < 1, depending on whether the indicator is the higher
the better or the lower the better. The proxy for SLK was the sum of modulus of all
occurrences of r > 1 (for “the higher the better” indicators) or r < 1 (for “the lower the better”
indicators) in the series, divided by the series size.
Indicator’s sample size was controlled because time series differ in range, from one
indicator to another. Walker & Johnson (1999) use the proxy frequency, or the sum of all r
occurrences in the series, to capture the estimation bias in sales targets in the negotiation
between sales representatives and their managers.
4.2.Independent Variables: Bonus, ABM and Uncertainty
The presence of an indicator in the bonus-calculation formula, as well as the link of
attainment of this indicator’s target to the area’s right to share the bonus, was captured by the
dummy BNS, as follows: 1 if indicator is associated with the bonus and 0 if not associated.
Uncertainty hinders probability distribution of indicator’s future values’ forecast, and
hinders ex ante adjustments, resulting in more experience and effort needed to consider
contingences. On the other hand, it encourages setting more modest goals, creating a
propensity to more slack. Uncertainty (UNC) in this case was approximated by the variability
in the attainment value, measured by a standard/average deviation of that value on the series.
From the various constructs presented for RAPM (see HARTMANN, 2000), we
adopted the nature of the accounting-financial metric as the proxy, as in Langhfield-Smith
(1997), rather than just the quantitative nature of the indicator, which could exist even for
non-financial indicators. This accounting-financial nature is associated with rigidity, formality
and objectivity (LANGHFIELD-SMITH, 1997). So, the accounting-based nature of an
indicator is captured by the dummy variable ABM, where: 1 for ABM and 0 for non-ABM.
4.3.Moderate Variables: Dispersion of Pay Scheme and Cause-Effect Pairs
The dispersion in the bonus payment scheme (DPS) was proxy by the hierarchical level
at which the indicator was generated and managed, as well as to which team bonus was
associated. Thus, the dummy takes on 0 for indicators at the SBU level, meaning low
diffusion, thus neutralizing the effect of the bonus on generating slack, and 1 for indicators at
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the corporate level, which means high diffusion, heightening the effect of the bonus on the
generation of slack. In general, for other incentives, such as promotions, delimitation of
responsibilities and risk of firing, the expected behavior is similar to the distribution of the
bonus. Egalitarian structures generally are associated with larger teams, with less capacity to
hold individuals responsible, smaller efficiency salaries, less specific job positions with more
similar opportunities in job market, and more chance for horizontal promotion. Hierarchical
structures are generally associated with smaller teams, greater ease of assessing individual
responsibility, upper management positions, high vertical promotion incentives (in or outside
the firm). Hence, we believe this proxy controls for other present incentives.
Regarding the strength of the causal pairs involved, we used the typology of Malina and
Selto (2004b) to group the constructs that characterize the subjectivity involved in these
relations, and thus the strength of the relation to make the indicator attractive. According to
Malina and Selto (2004a), relation type affects indicators’ attractive. They suggest that
performance indicators’ moderates incentives’ scheme’s effect, because in the absence of
trustful cause-effect relations employees believe that reward is random-walk or based on
exogenous variables – both lead to dispersion. They divided the measures relations into three
types: i) cause-effect relations, ii) finality relations, and iii) logical relations. In the causeeffect relations, the occurrence of event X naturally implies another event Y. These relations
need to be empirically observed to be proven and rejected. Finality relations are articulations
of one of the possible means to achieve a desired objective. When the individual wishes to
achieve an objective, he or she follows the routes presumably leading to the intended
objectives, and thereby finality relations are created artificially through individuals’ wishes.
Lastly, logical relations are not empirically testable, are part of a mechanical concept and are
established by an accounting or financial calculation. Considering this logic, there is no
cause-effect relation between turnover of assets and return on investment, since the ROI
calculation contains in its formula turnover of assets and is therefore a logical relation. This
relation cannot be empirically verified but it needs to be assessed based on financial logic or a
mathematical concept.
We used the cause-effect strength as proxy of its relation nature, as suggested by Webb
(2004). Hence, relations based on logic and on mathematical concepts are strong - deductive
nature (type iii, from MALINA & SELTO, 2004b). Therefore, indicators attractive degree
based on type iii relations are higher than attractive degree based on type i and ii relations.
Second, based on others studies, Sprinkle (2003) point that the number of performance
measures may be inversely related to an evaluator’s ability to form accurate assessments of
performance, since the agent’s bounded rationality. Furthermore, the optimal amount of
performance data that should be supplied to evaluators is unclear, and may be related to the
combinations and types of financial and non-financial measures employed. The higher the
number of existing causal relations for one indicator the more scattered the dependence on
their performance, and therefore more effort to be in control of it. Similarly, more room for
the teams to find excuses, especially if one of the causal relations is related to other
responsibility centers in the company, without being able to clearly deduce the responsibility
for the poor performance.
We identified 215 cause-effect relations, that we clustered according to the presence of
“cause” measure on “effect” measurement formula – herein denoted as deductive relation
(type iii from MALINA & SELTO, 2004b), and according to ABM-NABM nature. Type iii
relations take place on the jointly analyses of two quantitative measures; by the presence of
cause metrics on effect’s formula. It happened 6 times, according to Table 3, none of them
were ABM-ABM structure.
11
Table 3: Frequency of BSC relations
Type
iii
i or ii
Total
ABM←ABM ABM←NABM NABM←ABM NABM←NABM
0
5
0
1
45
88
15
61
45
93
15
62
Total
6
209
215
215 relations; 55 indicators
ABM - Accounting-based measure
NABM - Non accounting-based measure
A significant portion of cause-effect relations analyzed in our sample of 55 indicators
are from type ii or iii. Because of accounting matters, ABM-ABM relations are indirect
related to indicators. For example, “INSOLVENCY INDEX” indicator which formula
contains Net Revenue and Cash Receipts, and has “EXTRA HOUR WORKFORCE INDEX”,
“SHORT INSOLVENCY INDEX”, and “POTENCIAL PROVISION” considered as causes –
all of then are ABM nature, does not have its cause indicators related to the effect indicators’
formula. Finally, strength relation of non-deductive relations (type i or ii) that are not
associated to ABM-ABM nature depends on how managers identify their causality, even if
that causality does not actually exist. Experience in indicators’ usage and manager’s rhetoric
affect causality identification.
For each effect indicator, one or more relations were listed by the respondents. The
variable strength of the relation (CES) was constructed by dividing the sum of the number of
type iii relations and the number of ABM-ABM relations by the total number of relations
associated with each indicator. This methodology aims to identify relations’ strength
dispersion on each 55 indicators analyzed. A variables’ summary is presented in Table 4.
Table 4: Descriptive statistics and correlation matrix
1
2
3
4
5
6
7
8
9
10
SLK
UNC
BNS
ABM
DPS
CES
UNC*DPS
UNC*CES
BNS*DPS
BNS*CES
Mean Std. Dev.
9.53 26.96
0.72
0.73
0.43
0.50
0.49
0.50
0.47
0.50
0.28
0.33
0.48
0.76
0.24
0.58
0.18
0.38
0.12
0.26
Min
0
0
0
0
0
0
0
0
0
0
Max
1
2
3
4
136.62
1
3.53 0.381
1
1.00 0.372 0.005
1
1.00 -0.313 -0.168 0.016
1
1.00 -0.131 0.382 -0.098 0.017
1.00 -0.047 0.167 -0.014 -0.186
3.53 -0.032 0.826 -0.177 -0.071
3.53 0.064 0.687 -0.075 -0.217
1.00 0.078 0.041 0.535 -0.085
1.00 0.142 -0.009 0.541 -0.139
5
1
0.173
0.670
0.296
0.497
0.079
6
7
8
9
1
0.255
1
0.617 0.723
1
0.125 0.190 0.067
1
0.537 0.005 0.217 0.463
10
1
5. RESULTS
To test H1 we estimate coefficients from four regressions (BARON & KENNY, 1986;
MULLER, JUDD & YZERBYT, 2005). Moderation of the effect of UNC on SLK was
estimated through equation 1, and the effects of UNC on BNS and ABM through equations
(2) and (3).
SLK = β11 + β12UNC + β13BNS + β14DPS + β15CES + β16UNC*DPS + β17UNC*CES + ε1
BNS = β21 + β22UNC + ε2
(2)
ABM = β31 + β32UNC + ε3
(3)
(1)
Finally, the moderated effects of both BNS and ABM in SLK were estimated through
12
equation (4), which also shows that both the residual direct effect of UNC on SLK (β42) and
the mediator (BNS) partial effect on SLK (β43) are moderated, as seen in
β47, β48, β49, and β410, respectively.
SLK = β41 + β42UNC + β43BNS + β44ABM + β45DPS + β46CES + β47UNC*DPS +
β48UNC*CES + β49BNS*DPS +β410BNS*CES + ε4 (4)
To corroborate H1 we need:
H1A: β16 ≠ 0, β22 ≠ 0, β49 ≠ 0. If β47 = 0, we have full mediated moderation.
H1B: β17 ≠ 0, β22 ≠ 0, β410 ≠ 0. If β48 = 0, we have full mediated moderation.
With mediated moderation, there is overall moderation of the treatment effect that is β16
≠ 0. There must be mediation and one or both of the indirect paths from the treatment (UNC)
to the outcome (SLK) must be moderated (MULLER et al, 2005). That is β16 ≠ 0, β22 ≠ 0 and
β49 ≠ 0, and/or β17 ≠ 0, β22 ≠ 0 and β410 ≠ 0. Table 5 presents expected values for slope
parameters and their interpretations.
To test H2, we should use equations (5), (6) and (7) as follows (MULLER et al, 2005):
SLK = β51 + β52UNC + ε5 (5)
ABM = β61 + β62UNC + ε6 (6)
SLK = β71 + β72UNC + β73ABM + ε7 (7)
To corroborate H2 we need: β52 ≠ 0, β62 ≠ 0, and β44 ≠ 0. In addition, |β72| < |β52|
However, to avoid problems with omitted variables, we use β12, β32, β42 and β44 as
surrogates for β52, β62, β72 and β73, respectively. Table 6 presents the regression models that
estimate Equations 1 through 4 with these variables.
Table 5: Expected values and interpretation for slope parameters for H1 and H2
Slope
parameters’
expected values
β16 < 0
β22 < 0
β47 = 0
β49 < 0
β17 < 0
β22 < 0
β48 = 0
β410 < 0
β52 > 0
β62 < 0
β44 > 0
β72 < β52
Interpretation
The effect of UNC on SLK decreases as DPS increases
UNC is negative associated with BNS
The effect of UNC in SLK is independent of DPS
The effect of BNS in SLK decreases as DPS increases
The effect of UNC on SLK decreases as CES increases
UNC is negative associated with BNS
The effect of UNC in SLK is independent of CES
The effect of BNS in SLK decreases as CES increases
UNC is negative associated with SLK
UNC is negative associated with ABM
ABM is positive associated with SLK
Mediate effect of ABM on SLK
13
Table 6: Results for mediated moderation a, b, c
Test
Equation
OLS
OLS
OLS
(1)
(2)
(3)
SLK
BNS
ABM
1.69*** (2.74)
.004(0.04)
-0.12(-1.24)
UNC
------ABM
0.37(1.51)
----BNS
0.36(1.56)
----DPS
0.03(0.07)
----CES
----UNC*DPS -1.45**(-2.34)
-0.14(-0.28)
----UNC*CES
------BNS*DPS
------BNS*CES
0.63***(3.41) 0.43***(4.53) 0.57***(6.03)
Constant
1.91*
0.02
1.54
F
0.61
0.00
0.03
Adj. R2
3.59
VIF
55
55
55
N
a
Robust standard errors (White)
b
* p-value < .1, ** p-value < .05 and *** p-value < .01
c
t statistics in ( )
OLS
(4)
SLK
1.77*** (2.77)
-0.36*(1.72)
0.15(0.35)
0.25(0.86)
0.10(0.20)
-1.51**(-2.29)
-0.12(-0.23)
0.26(0.51)
0.27(0.24)
-0.58***(-3.03)
1.91*
0.61
4.07
55
Stepwise
SLK
1.51***(6.57)
-0.39**(2.12)
0.40**(2.12)
-----1.22***(-5.47)
--------0.490***(5.47)
18.20***
0.56
2.33
55
Table 7 presents a comparison of expected and estimated values for slope parameters.
Table 7: Expected vs. estimated values for slope parameters
Expected values
β16 < 0
β22 < 0
β47 = 0
β49 < 0
β17 < 0
β32 < 0
Slope parameters
Estimated values
Expected values
β16 ≠ 0
β48 = 0
β22 = 0
β410 < 0
β47 ≠ 0
β62 = β32 < 0
β49 = 0
β52 = β12 > 0
β17 = 0
β72 = β42 > 0
β32 = 0
β44 > 0
β 72 < β52
Estimated values
β48 = 0
β410 = 0
β32 = 0
β12 > 0
β42 > 0
β44 < 0
β 42 > β12
Overall, the results are inconsistent with both expectations for H1. However, the high
degree of multicolinearity between the multiplicative terms and the individual constructs
precluded a meaningful interpretation of the coefficients.
Uncertainty directly affects propensity to budgetary slack in a positive way (β12 > 0; β42
> 0), tough not mediated by the presence of indicator on bonus scheme (β22 = 0). The effect of
uncertainty on budgetary slack is moderated by bonus dilution (β42 > 0). Accordingly, H1A
was not corroborated. Uncertainty is not related to the presence of indicator on bonus scheme
(β22 = 0), and bonus dilution does not moderate the effect of bonus on the propensity to
budgetary slack. Also, association of indicators to bonus is not related to propensity to
budgetary slack. These results may indicate that there are other (and stronger) incentives
present at the organization.
In recent years, one of those SBU received annual bonus that roughly amounted for one
month of managerial level salary. At the operational level, in which the salaries are lower, the
bonus was greater than one month of salary. On the other hand, middle level workers got
bonus on the same amount as of operational level workers, which are their subordinates. In
2005 there was no bonus payment at the other SBU. This situation had changed as a new
executive officer has been assigned. The entrance of a new executive officer may have
affected workers’ stress related to the threatening of being dismissed. Another explanation is
14
that the effect of uncertainty on budgetary slack does not depend on its association to bonus
because there must be other indicators, associated to promotion (dismissal) beliefs, with
stronger effects on budgetary slack. Results point to no moderation of the direct effect of
uncertainty on slack. It seems that, in spite of the informational content of cause-effect
relations, managers keep protecting themselves from uncertainty through budgetary slack.
Our finds does not confirm those presented by Webb (2004).
With respect to H2, although β12 and β42 are both significant, regression of ABM on
UNC is not, meaning that uncertainty impacts directly on the propensity to budgetary slack,
irrespective of the nature of the indicator. Thus, results are inconsistent with H2 also.
In order to retreat multicolinearity we estimate regression parameters with a stepwise
procedure, aiming at understand how the most significant factors affect the dependent variable
slack. Table 7 presents the results from the stepwise regression between SLK and all other
independent variables. The effect of uncertainty on slack is again positive and moderated by
bonus dispersion. The effect of bonus on slack increased in significance. However, the effect
of accounting-based indicators on slack is also negative, contrary to the theoretical
propositions. The effect of uncertainty on the indicators might be attenuated by the presence
of subjective assessment, goal commitment or another dysfunctional behavior such as ex post
information management (HARTMANN, 2000). Results indicate that uncertainty impacts on
slack irrespective of the nature of performance indicators.
6. FINAL COMMENTS
Firms increasingly use financial indicators jointly with non-financial indicators to
enhance decision making and controllability. Currently, effort is still needed to understand the
impacts resulted from different mixes of indicators. Dysfunctional behaviours have been
suggested to result from the choice of indicators. These behaviours can include budgetary
slack and manipulation of accounting information. From an economic perspective, managers
want to set motivating goals. On the other hand, they want to alleviate uncertainty and other
risks associated with measurement, which could cause a reduction of the agents’ efforts.
In this work we have emphasized the effect of uncertainty on the choice of performance
indicators and its subsequent effect on budgetary slack. Results were inconsistent neither with
the hypothesis of mediated moderation process between uncertainty, bonus, dispersion of
payment scheme and strength of causality with budgetary slack, nor with the mediation
process proposed between uncertainty, accounting-based measures and budgetary slack. The
budgetary slack observed in the indicators series are directly impacted by uncertainty, and this
impact is moderated by the dispersion of payment scheme. The strength of causal relations
does not moderate the impact of uncertainty on slack, as proposed here and empirically
observed by Webb (2004).
The findings are subject to a number of limitations. The company is accountable to the
Brazilian regulatory electric power agency (ANEEL) and has recently concluded the start up
of balanced scorecard, with rough definitions of both a positions and salaries plan and
incentive plans – all this factors can affect budgetary slack incentives and were not controlled
on our study. Also, the analysis was conducted at the business unit level, and some variables
associated with budgetary slack operate at the individual level, such as motivation,
satisfaction, and performance. The BSC is not fully operational, and this might have
consequences on the managers’ behaviours and on shorter indicators series. Finally, sample
size and the use of multiplicative terms impacted on the robustness of econometric analysis.
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