The Performance Effects of Organizational Architecture
Antonio Davila
Professor of Entrepreneurship and Accounting and Control
IESE Business School
University of Navarra
and
Mahendra Gupta
Professor
Olin School of Business
Washington University in St. Louis
and
Richard J. Palmer*
Professor
260 Dempster Hall, MS 5815
Department of Accounting
Harrison College of Business
Southeast Missouri State University
One University Plaza
Cape Girardeau, MO 63701
Phone: (573)-651-2908
E-Mail: rpalmer@semo.edu
* Please direct correspondence to Professor Palmer.
Special thanks to David Rhoads for his assistance with the analysis in this paper
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The Performance Effects of Organizational Architecture
Abstract
Balancing the three components of organizational architecture—decision rights,
performance measurement system, and reward system—is a key element in organizational
design. Top managers that fail to manage these variables are predicted to face lower
performance. Two lines of theoretical accounting research have examined the
interdependence among these three variables. Responsibility accounting has focused on the
joint design of decision rights and reward system, while measurement theory has studied the
relationship between measurement system characteristics and reward system. The concept of
organizational architecture brings these two lines of research together. However, little
empirical evidence exists on the predicted relationship between organizational architecture
and performance. This study brings initial evidence on this important empirical question. A
unique database on the implementation of corporate purchasing card programs—a new
financial tool to reduce transaction costs—allows us to compare the performance of several
organizational architectures. We find that a particular organizational architecture outperforms the rest. This architecture combines delegation, intense performance measurement,
and performance-based rewards and is consistent with an optimal design in the presence of
information asymmetry.
While the optimal organizational architecture found in this study is consistent with
extant theory, the diminution of performance by organizations with sub-optimal
organizational configurations appears to deviate from expectations. Our findings generally
indicate that each successive addition of a “high performing” component of organizational
architecture adds incrementally to elevate the performance. This “Hawthorne” type effect
may indicate that managers are loathe to ignore the organizational priorities reflected in the
organization’s investment of time and resources into either measuring performance,
establishing rewards, or formulating appropriate delegations of authority.
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The Performance Effects of Organizational Architecture
1. Introduction
A key component in defining the management process is the allocation of
decision rights associated with the resources of the firm (Jensen and Meckling 1992).
Decision rights, together with performance evaluation and reward systems, define the
concept of organizational architecture (Brickley, Smith et al. 1995; Brickley et al. 2003).
These three variables are predicted to jointly interact to affect organizational
performance. The design of the reward systems must consider the decision rights
delegated to the agent and the information captured through the measurement systems.
Similarly, the design of the measurement system depends on the allocation of decision
rights. If one of these variables changes, the other two also have to change in order to
maintain the right balance (Zimmerman 1999), otherwise organizational performance
deteriorates.
Organizations where information asymmetry among hierarchical levels is
significant are more likely to allocate decision rights to those agents with specific
knowledge (Jensen and Meckling 1992). However, the concept of organizational
architecture predicts that delegation alone is not sufficient (Jensen and Wruck 1994).
Delegation of decision rights has to be matched with a measurement system that tracks
the performance of the agent and a reward system that minimizes agency costs.
This concept of organizational architecture brings together two significant bodies
of theoretical accounting research. The first one is “measurement theory” research
(Banker and Datar 1989; Feltham and Xie 1994). This literature holds decision rights
constant to highlight the interdependence between measurement systems and the design
of incentive contracts. The second body of research is responsibility accounting
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(Melumad and Reichelstein 1987; Melumad, Mookherjee et al. 1992; Baiman and Rajan
1995). This research stresses the importance of delegation (allocation of decision rights)
to the design of the design of incentive contracts, holding constant the performance
measurement system’s characteristics. Organizational architecture highlights the need for
an adequate performance measurement system to fully characterize the interdependence
between decision rights and incentive contracts.
Empirical research has followed from these two lines of research. A sizeable body
of empirical research has focused on the relationship between performance measurement
and incentive contracts (Lambert and Larcker 1988; Bushman, Smith et al. 1996; Ittner,
Larcker et al. 1997; Rankin and Sayer 2000). The overall conclusion from these studies is
consistent with theoretical predictions associating noisiness, informativeness, and
congruency with reward system design.
In contrast, empirical research capturing allocation of decision rights is scarcer
because of the difficulty to find adequate data (Nagar 2002). Once allocation of decision
rights is included in the empirical specification, the three components of organizational
architecture become internal to organizations. The data required is typically not publicly
available and difficult to compare across firms because of differences in organizational
design. Previous empirical work more closely related to this study includes Nagar (2002)
and Widener et al.(2008). Nagar (2002) examines two of the three components of
organizational architecture consistent with the responsibility accounting literature; this is
allocation of decision rights and reward systems. Based on a professional survey of retail
banks, he finds that these two variables are simultaneously determined. Widener et al.
(2008) extend this finding to include performance measurement—the third component of
organizational architecture. Using a survey-based research design with a sample of 53
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Internet firms, they find that the three components of organizational architecture are
interdependent and that the structural relations are complementary.
This study extends the empirical literature on organizational architecture. In
particular, it examines the prediction that decision rights, performance measurement, and
reward system interact to affect performance. This research question requires a setting
where organizations are still experimenting with different organizational architectures.
This experimentation insures that certain organizational architectures are not designed
optimally and thus correlated with performance.
We capitalize on the introduction of a relatively new financial tool—corporate
purchasing cards (hereafter, p-cards)—and a proprietary database to examine this
research question. Our database includes a cross-sectional sample of organizations that
have implemented p-card programs. Financial institutions that issue these cards require a
program manager as a contact person in each organization; this requirement facilitates
cross-sectional comparison across the organizational architecture that different firms
implement. The database stems from a detailed survey from the leading market research
company in p-card technology conducted in 2010.
We use the concept of organizational architecture to explain the cross-sectional
variance in the performance of p-card programs. We find that firms are using different
organizational architectures to implement their p-card programs, consistent with
experimentation that goes with new technologies before the optimal architecture is found.
We also find that organizational architecture affects the performance of p-card programs.
In particular, the p-card programs at organizations that delegate decision rights to the pcard program manager and match this allocation of decision rights with performance
measurement and rewards perform significantly better. This finding is consistent with
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information asymmetry characterizing p-card programs and requiring delegation of
decision rights. However, this allocation of decision rights is only effective if properly
balanced with the other two components of organizational architecture.
The reminder of the paper is structured as follows. Section 2 provides a
theoretical discussion of allocation of decision rights, performance measurement, and
incentive contracts. Section 3 describes the database, while section 4 describes the
research variables, and model specification. Section 5 presents the results and section 6
concludes.
2. Theory
Top management needs to address the information asymmetry that emerges when
agents have specific knowledge relevant to the management of the firm. One alternative
is to move this knowledge up to the principal who has the decision rights. However, this
alternative may be costly and the best solution is to allocate decision rights to the agent
with private information (Melumad and Reichelstein 1987; Melumad, Mookherjee et al.
1992; Baiman and Rajan 1995). Delegation matches decision rights with the location of
specific knowledge in the organization and increases the impact of the agent’s decisions
upon the principal’s objective function. In other words, the productivity of the agent’s
effort increases with delegation. This solution is not without cost and the principal has to
bear the agency costs associated with the moral hazard problem induced. To minimize
agency costs, delegation is implemented together with an incentive contract that matches
the decision rights delegated to the agent (Melumad, Mookherjee et al. 1992). The
incentive contract is intended to motivate the manager to exert effort congruent with the
principal’s objectives. Principal-agent theory indicates that as the productivity of the
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agent’s effort increases, other things equal, the weight of performance measures on the
incentive contract increases. Nagar (2002) provides evidence consistent with this
predicted relationship between delegation and the extent of incentive compensation.
Another line of theoretical accounting research has examined the relationship
between performance measurement and incentive contracts. In contrast with the
responsibility accounting research, “measurement theory” research assumes allocation of
decision rights as given to focus on the characteristics of performance measurement
systems. This literature provides relevant insights to understand how incentive contracts
vary with the quality of the measurement system. This quality is reflected in two main
characteristics. The first one is captured by the concept of signal-to-noise ratio (Banker
and Datar 1989). In particular, the weight of a performance measure in a compensation
contract is directly proportional to its sensitivity and inversely proportional to its
noisiness. The second characteristic that affects the quality of a measurement system is
its congruency—its ability to properly capture and weight the different measures of the
agent’s performance (Holmstrom and Milgrom 1991; Feltham and Xie 1994). The
prediction derived from these models indicates a positive relationship between the quality
of the measurement system and the extent of incentive compensation. The empirical
evidence is consistent with this prediction (Lambert and Larcker 1988; Bushman, Smith
et al. 1996; Ittner, Larcker et al. 1997).
Bushman, Injejikian et al. (2000) provide a theoretical model where delegation
and performance measurement are examined within a linear-contracting framework.
These authors investigate the value of delegation as a function of the nature of the agent’s
private information in the presence of an imperfect measure of agent’s performance.
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These three key variables in accounting research have also been brought together
through the concept of organizational architecture (Brickley, Smith et al. 1995; Brickley,
Smith et al. 2003). This concept captures the interdependencies highlighted in the
previous two research literatures and indicates that the three variables need to match each
other. In particular, delegation of decision rights requires incentive contracts that
motivate the agent to exert effort commensurate with the decision rights that have been
allocated to him, as well as a performance measurement system with information
characteristics that facilitate the structure of these contracts. Failure to properly match the
three components of organizational architecture is reflected in lower performance. The
impact on performance rests on the assumption that some organizations have not selected
their best organizational architecture and, consequently, they do not behave optimally.
This assumption is not uncommon in previous empirical research. For example, a highly
debated question in the CEO compensation literature is the relationship between
incentive contract design and firm performance (Murphy 1985; Baker, Jensen et al. 1988;
Tosi and Gomez-Mejia 1989; Leonard 1990; Hubbard and Palia 1995; Hall and Liebman
1998). Such an out-of-equilibrium behavior is more likely to be relevant for new
business processes where firms are still experimenting with alternative organizational
architectures.
Information asymmetry is the variable that theoretical models identify as driving
the need to delegate. Delegating decision rights may be the best organizational structure
when the private information is costly to move through the hierarchy. Private
information may be costly because of motivational reasons—the cost to the principal of
motivating the agent to reveal it is too high (Melumad, Mookherjee et al. 1992)—or
because of its nature—the information is specific knowledge and therefore difficult to
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code and transmit (Jensen and Meckling 1992). Once delegation is found to be the
optimal organizational structure, the other two variables of organizational structure have
to match this delegation. In particular, delegation requires a reward system more
sensitive to performance because the impact of the agent’s decision and effort on the
process is larger. It also requires a more detailed measurement system where
performance measures are linked to the reward system. Such a system has better
measurement characteristics—signal to noise ratio and congruity—and thus enhances the
effectiveness of the reward system.
3. Data
Our database provides information on the various organizational architectures that
companies have adopted to implement a new payment technology known as purchasing
cards as well as on the success that companies have had in implementing the technology.
P-cards are a bank commercial card technology product designed to increase the
efficiency of the procure-to-payment business cycle by enabling employee-cardholders to
charge purchases for which the company agrees to pay in full within an agreed upon
number of days. Cardholders receive a monthly statement and are assigned a monthly
and a transactional credit limit. Unlike personal credit cards, the company is obligated to
pay one bill that summarizes monthly purchases made by all cardholders. This process
reduces or eliminates all activities and paperwork previously required to process these
purchases including approvals, requisitions, purchase orders, invoices, and payment
(Palmer et al., 1996).
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The use of p-cards remains a relatively new phenomenon in the marketplace. Pcards were developed in the late 1980’s as a means to help federal government agencies
acquire goods without subjecting their vendors to the long wait for payment associated
with government bureaucracy at that time (Herbst-Murphy, 2011). Use of purchasing
cards moved to the private sector in the mid-1990’s. Their relevance stimulated a rich
accounting practice literature with anecdotal evidence of on-going corporate
experimentation regarding the appropriate management practices to optimize and control
the purchasing card goods acquisition process that continues to this day (see, for
example, Fitzgerald 2009; Gamble 1996; Hamel 2009; Larson, 2010; Martinson 2002).
Notwithstanding on-going experimentation, key structural elements of
organizational use of p-cards are comparable across organizations. For example, card
issuers require a contact person who is responsible for the daily activities of the p-card
program. Within the card-using organization this person is commonly known as the
purchasing card administrator (PCA). The purchasing process based on p-cards is
reasonably standardized, with employees having pre-approved spending limits on their
cards, using their cards to purchase goods from suppliers, and the company paying a
consolidated monthly bill. Thus, the goods acquisition process as enabled by p-cards is
self-contained and comparable across a cross-section of firms, yet with variance in terms
of the organizational architecture and performance.
The data comes from a survey administered by a leading market research firm in bank
commercial card technology. The firm developed the survey working together with credit
card brands and participating financial institutions. An extensive design process insured that
the main issues associated with p-cards were appropriately addressed and that the questions
were both clear and concise. The final survey—a benchmark study—was designed to be
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comprehensive in order to be of the highest value to the participants. The final survey was
29 pages and the expected completion time was 2 to 4 hours. Data from the survey is
published widely and used by both financial institutions and business and governmental
entities to describe “norms” of organizational use of p-cards.
An initial mailing of 2,364 surveys was made to mid-size to large organizational
purchasing card-using customers of nineteen major banks that issue MasterCard and/or Visa
products. Sixty-hundred and seventy-two responses were received (28.3%). Because
instructions indicated that the respondent could skip a question if the answer was not known,
some survey questions have responses that are 1% to 12% fewer than the sample response.
The sample includes a wide mix of organizations in terms of industry composition, size and
purpose (for profit, not-for-profit). Table 1 provides descriptive statistics on the sample and
industry breakdown.
-----------------------------Insert Table 1
------------------------------
4 Research variables
4.1 P-card performance measures
The chief objective of p-card program is to reduce transaction costs by
streamlining and improving the administrative efficiency of the purchasing process.
Transactional cost saving are derived, in part, from the impact that p-cards have on the
manpower required to conduct work activities using the traditional purchase-order driven
process to acquire and pay for goods and services (including sourcing, vendor
communications, and activities and approvals needed to process requisitions, purchase
orders, receiving documents, invoices, and checks). Some transactional cost savings
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attributable to purchasing cards may be “soft.” Soft savings occur when the level of
transaction activity shifted to p-cards reduces the administrative workload but not in a
manner sufficient to enable the elimination of personnel. By contrast, “hard” savings
occur when the administrative workload reduction, driven by shifting higher levels of
transaction activity to p-cards, supports the elimination of personnel in the Purchasing
and Accounts Payable functional areas.
Consequently, we use two measures of organizational performance of the p-card
program. The first measure of p-card program performance is the estimated percentage
of low-value (under $2,500) transactions that are paid by p-cards.1 For the second
measure, we asked respondents to provide both the number of people currently employed
in Accounts Payable and Purchasing and the additional number of people that would need
to be hired in those areas if the p-card was removed from the organization. From their
responses, we created a measure of the percentage of workforce reduction in
Purchasing/AP due to the p-card program as a correlative approach to measuring the
success of the p-card program (to wit, a surrogate for “hard savings”).
4.2 Organizational architecture
Our unit of analysis is the p-card program administrator (PCA). Accordingly, the
design of the organizational architecture converges in the decision rights, the performance
measurement, and the compensation system of this person.
Decision rights. Aside from oversight of administrative details, the primary role of
the PCA is to improve organizational efficiency by facilitating the organizational shift from
1
Low value transactions are the typical target for purchasing card payment because some controls
associated with the traditional purchasing process can be relaxed or eliminated for transactions of lower
financial risk. Further, organizations rarely pay for low-value purchases by means other than paper-based
check or card technology. Payments via ACH, EFT, or wire transfers are primarily restricted to larger
dollar purchases. Consequently, this percentage reflects a movement away from paper to electronic
payment.
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paper-based to electronic transaction processing methods. The decision rights allocated to
the PCA relate to his/her ability to distribute or influence the distribution of p-cards and
assign or influence the assignment of appropriate monthly and per transaction limits for
cardholders in light of employee spending needs and organizational borrowing constraints.
Further, absent vocal top management support, employees given p-cards may be hesitant
about or resist card use (as is common with any new technology). Research has shown that
top management support is sine qua non to the success of managerial efforts to implement
new technologies and processes within an organization (Howell and Higgins 1990; Day
1994). Hence, one must consider top management support of the PCA’s activities as an
imprimatur legitimating the decision rights assigned to the administrator.
Thus, we construct our measure of decision rights in several steps. First, we measure
the organization’s total p-card line of credit (LOC), calculated by multiplying the number of
cards in the organization by the average monthly spending limit. This construction is
multiplied by twelve to annualize the spending authority. The LOC is then multiplied by two
adjustment factors. The first adjustment factor reflects any impingement to the cardholder’s
ability to acquire goods and services due to additional “per transaction” spending limits (75%
of organization so limit card use in this manner). Practically speaking, the cardholder’s
ability to utilize the full measure of his/her monthly spending limit is diminished if the
maximum amount allowed for a transaction is low. Hence, we created a per transaction
adjustment factor by multiplying the organization’s average per transaction spending limit by
the organization’s average number of monthly transactions per card and then divided the
product by the organization’s average monthly spending limit. In this way, if a cardholder
cannot reach his/her monthly spending limit by transacting the “average” number of
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transactions with his or her p-card, the monthly spending limit would be adjusted downward
to reflect a “practical” spending authority.2
The practical spending authority is then adjusted by a simple measure to express the
degree of top management support for the p-card program (and, by implication, the activities
of the PCA). Top management support is measured by the degree of agreement (measured
on a 7-point Likert scale) with the statement, “At my organization there is a top management
sponsor who gives strong vocal support to the p-card program the through purchasing cards.”3
The numerator is then divided by the organization’s annual revenues (or budget, for
government and not-for-profits entities) to adjust for organizational size, thus creating a
measure of the allocation of spending authority expressed as a percentage of revenue (or
annual budget). In mathematical summary, the decision rights (DECRIGHT) variable is
defined as:
(Monthly Line of Credit * 12) * (Per Transaction Adjustment Factor) x (Top Mgt. Support Factor)
DECRIGHT = ----------------------------------------------------------------------------------Annual Sales Revenue (or Budget)
2
Assume, for example, an organization has an average monthly purchasing card spending limit of$10,000,
a per transaction limit of $500, and 6 average monthly transactions per card. In this case, we multiply the
monthly spending limit by 30% (($500 x 6)/$10,000) to reflect the practical limit in the delegation of
spending authority imposed by the per transaction threshold. If, on the other hand, an organization had an
average monthly purchasing card spending limit of$10,000 but a per transaction limit of $5,000, no
adjustment was made to the monthly spending limit since the cardholder could reach his monthly spending
limit in less than 6 transactions.
3
Where 1=do not agree, 7=fully agree. The factor is calculated by dividing respondents answer by 7.
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Where the per transaction adjustment factor (PTAF) is expressed as:
Respondent avg. per transaction spending limit x Respondent avg.
number of transactions per card per month
PTAF =
-----------------------------------------------------------------------------------------Respondent average monthly spending limit
where PTAF maximum ≤ 1.0.
Performance measurement. The existence of performance measurement of the p-card
program was measured by yes/no response to the question, “Does your organization use a
measure or measures to evaluate purchasing card program performance?” If respondents
indicated that p-card program performance was measured, a second set of questions asked about
the type and importance of the performance measurement used for the job evaluation of the pcard manager. The importance of the performance metric is measured using a 7-point scale
anchored from “very unimportant” (1) to “very important” (7), with an additional score of
zero if the measure is not used. Eight measures are included: (1) number of transaction on pcards, (2) dollar amount of p-card purchases, (3) cardholder satisfaction with p-card program,
(4) number of active cardholder accounts, (5) targeted percent of small dollar spending or
transactions charged on p-card, (6) rebate paid by the card issuer, (7) percent of targeted
vendors accepting p-cards, and (8) number of active cardholder accounts.
Incentives. The structure of the reward system is measured using three 7-point scale
items. The items asked the respondent to rate the impact of p-card program performance on
the p-card manager’s (a) pay and bonuses, (b) promotions, and (c) obtaining of preferred
future assignment(s). The responses were anchored from “no impact” (1) to “significant
impact” (7). Principal component factor analysis extracted one factor. The Cronbach alpha
reliability measure for the three variables is 0.89. We summed the scores on the three items
to create the incentive variable.
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4.3 Control variables
In addition to the hypothesized variables, we control for additional variables that may
affect the performance of the p-card program. We control for organizational size inasmuch
as larger companies may present more complexities in implementing and administering pcard programs. This variable is proxied by the natural logarithm of the number of employees
(Lnemp). We also control for the length of time since the p-card program was initiated in
years (Program age). Initiatives that have been in place longer are expected to perform
better as more managers and employees have gone down the learning curve associated with
p-card use. Three other control variables reflect the potential impact of the type of
organization, the internal controls related to card use that are employed at the organization,
and different aspects of p-card training, discussed seriatum.
Type of organization. Different types of organizations may see more or less value to
p-cards as a method for streamlining the procure-to-pay cycle. Hence we controlled for
respondent-identified organizational categorization. The organization types include (1)
public corporations, (2) private corporations, (3) universities, (4) cities and counties, (5) state
agencies, (6) federal agencies, (7) school districts, and (8) not-for-profit entities.
Internal controls. The number and nature of internal controls utilized can positively
or negatively impact employee use of the p-card and subsequent capture of low-value
transactions on p-cards. We control for internal control practices by adding affirmative
answers to eight questions related to training, including whether or not the organization: (1)
requires users to maintain a logbook of card activity, (2) has a documented policy regarding
receipt retention, (3) officially reprimands/disciplines cardholders that fail to submit receipts,
(4) evaluates the spending patterns of cardholders who tend to have a high number of
disputed transactions, (5) de-activates unused p-card accounts, (6) tracks and resolves
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disputed transactions, (7) formally audits the spending approval process, and (8) conducts
data mining of p-card transactions to identify potential misuse. We then counted the number
of controls used by each organization to create the internal control variable. The eight
questions constitute a single factor with a Cronbach alpha reliability measure of 0.52 and are
included in Appendix A.
Training. We control for training by adding affirmative answers to ten questions
related to training, including whether or not the organization (1) provides easy access to a
copy of the policies and procedures manual for p-card use to every p-card cardholder, (2)
requires initial training of cardholders, (3) requires refresher training for cardholders, (4)
provides “web-based” purchasing card training materials, (5) provides “in-person” purchasing
card training, (6) provides “self-study” purchasing card training materials, (7) provides a Web
site that answers p-card questions, (8) tracks completion of training and training updates by
employees, (9) supports p-card program administrator attendance at “p-card user conferences”
to identify new ways to use p-cards, and (10) has an ongoing method of communicating p-card
information to cardholders and managers. In addition, an above median response of agreement
to the statement “At my organization, a strong "business case" is made to employees about the
benefits of p-cards (where 1= do not agree, 7=fully agree) added 1 to the training measure. The
eleven questions constitute a single factor with a Cronbach alpha reliability measure of 0.72 and
are included in Appendix A.
4.4 Statistical specification
Theory predicts that organizations that optimally balance the three components of
organizational architecture perform better. The argument hinges on organizations not having
found the optimal equilibrium between decision rights, performance measurement, and
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compensation system. For stable business processes that have been in place for a long time,
such out-of-equilibrium argument may be too ambitious; but for p-card programs where
organizations are still experimenting with different organizational architectures, such out-ofequilibrium is likely. Theory does not provide a unique prediction concerning the best
combinations of these three variables. But if information asymmetry characterizes the p-card
process, then theory indicates that allocating decision rights to the p-card manager may be
the best organizational design. To be fully effective, delegation must come together with a
compensation system that rewards good performance and detailed performance measures that
provide the basis for an effective compensation system. This combination is expected to lead
to better performance.
To examine this hypothesis whether a particular combination of the three components
leads to better performance, we divide the sample into four different groups. Groups are
formed as follows. Observations for each component of organizational architecture—
decision rights, performance measurement, and compensation—are split into high and low
around the median. For example, firms with decision rights above the median are labeled
high-decision rights and firms below to the median are labeled low-decision rights.
Similarly, firms with performance measurement (compensation) above the median are
labeled high-performance measurement (high-compensation) and below the median are
labeled low-performance measurement (low-compensation). We identify organizations that
are above the median for all three components as TripleHi. We identify a second group of
organizations that score above the median for two out of the three components as DoubleHi.
A third group that we label DoubleLo has one component above the median and two below
the median. The fourth group, TripleLo, is companies that score below the median for all
three components of organizational architecture.
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To more closely examine whether a particular combination of the three variables
leads to better performance, we further divide the sample into eight different groups. Groups
are formed as follows. Observations for each variable are split into high and low around the
median. For example, firms with decision rights above the median are labeled high-decision
rights and firms below to the median are labeled low-decision rights. Similarly, firms with
performance measurement (compensation) above the median are labeled high-performance
measurement (high-compensation) and below the median are labeled low-performance
measurement (low-compensation). The combination of these three different groupings leads
to eight possible combinations which we identify with a dummy variable as follows:
Status of Organizational Architecture
Descriptor of
Organizational
Architecture
TripleHi
Decision Rights
High
Performance
Evaluation
High
Rewards
High
HiPerfMeasRwds
Low
High
High
HiPerfMeasDecRights
High
High
Low
HiRwdsDecRights
High
Low
High
HiPerfMeas
Low
High
Low
HiRwds
Low
Low
High
HiDecRights
High
Low
Low
TripleLo
Low
Low
Low
Hence the statistical specification is:
Perf = β0 + Σi (βi * groupi) + Σj γj * controlsj + ε
Where β0 is the condition which the group members are low on all three factors
(decision rights, performance evaluation, and incentives) and i varies from 1 to 7 for the
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seven different groups, and the control variables include the age of the program, the
logarithm of the number of employees, and measures of training and internal controls. We
expect Triple Hi to perform better than the other combinations. We run the specification
using an ordinary least squares model with performance as our dependent variable and
dummy variables for each of the different groups while controlling for heteroskedasticity and
outliers.
5. Results
5.1 Descriptive statistics
Table 2 provides the descriptive statistics for the variables used in the research.
Panel A provides background statistics on the organizations in the sample as well as pcard related statistics including monthly spending. The mean number of transactions per
card per month is 10.13. The mean number of years that p-cards program has been in
place is 6.7 years; 25% of the organizations have had the program for less than 3.5 years.
This statistic reflects the fact that p-cards are still relatively new financial tools for some
organizations.
Panel A also shows that about half of PCAs indicate that p-card program
performance will have little or no impact on pay and bonuses, promotions, or the
obtaining of preferred future assignment (with median response reflecting an answer of -or “no impact”--to all three questions). Further, though not shown on Table 2, 49% of
respondents indicated that their performance was evaluated against a performance metric.
We will exploit the bifurcated nature of these two measures (they either have some
reward or none, they are evaluated against some performance metric or not) to categorize
our respondents into particular organizational architectures. Henceforth, high rewards
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will be dummy coded with 1 if the PCA will receive some measure of reward if the pcard program does well and high performance measurement will be dummy coded with 1
if the PCA is evaluated against a performance measure.
Panel B describes the performance of p-card programs. On average, 42.81% of under
$2,500 transactions are paid with purchasing cards. Further, because the purchasing card
eliminates work activities in both the Purchasing and AP functions, respondents (on average)
report a 15.44% reduction in their AP/Purchasing headcount due to the use of purchasing
cards.
In terms of organizational architecture, Panel C reflects the discussion above, to wit:
49% of p-card program managers are evaluated against a performance metric and 41%
indicate that their performance will have something other than “no impact” on pay and
bonuses, promotions, or the obtaining of preferred future assignment. The calculation of the
raw decision rights variable is discussed in section 4.2 above; the decision rights variable in
Table C reflects a dummy coding of 1 to all respondents with a raw decision rights at or
above the median. D reports descriptive statistics for the additional control variables.
-----------------------------Insert Table 2
-----------------------------Table 3 reports the Pearson correlation coefficients for the dependent and
independent variables under examination. Consistent with others who have examined
organizational architecture and performance (e.g., Widener et al. 2008) the three elements of
organizational architecture are significantly correlated. Further, both dependent variables
(capture of under $2,500 transaction and headcount reduction) are correlated with each leg of
architecture and with program age, internal control, and training.
21
-----------------------------Insert Table 3
-----------------------------Table 4 reports univariate statistics on performance for the four groups. The table
shows a pattern of incrementally improved performance (for both dependent variables)
whenever a “leg” or additional leg of positive organizational architectural is added.
-----------------------------Insert Table 4
------------------------------
5.2 Main Results
Table 5 presents the main results of the paper. The coefficient for the TripleHi group
is positive and significant for both under $2,500 transaction capture and percentage
headcount reduction, suggesting that organizations with the three components of
organizational architecture aligned perform better than alternative organizational
architectures. In the case of under $2,500 transaction capture on p-cards, the coefficients for
both DoubleLo and DoubleHi groups are significant as well. The age of the program is
positive and significant as expected. Size, as proxied by the natural logarithm of the number
of employees, is negative for both dependent variables. As expected, training related to pcard use is positive and significant for both dependent variables. Neither internal control or
the type of organization contribute significantly to explaining variability in the performance
measures.
-----------------------------Insert Table 5
------------------------------
22
Table 6 presents the F-statistics for the constraint of equal coefficients across pairs of
groups. For both dependent variables, the coefficient for TripleHi is significantly different
from the coefficient of the rest of the groups. Though less remarkable, for under $2,500
capture of transactions both DoubleHi and DoubleLo are significantly different from
TripleLo and DoubleHi is significantly different than DoubleLo. For headcount reduction,
DoubleHi is significantly different from both DoubleLo and TripleLo but DoubleLo is not
significantly different than TripleLo.
-----------------------------Insert Table 6
-----------------------------5.3 Extension
As an alternative specification, we group the various organizations in eight rather
than four groups. Table 7 reports univariate statistics on performance for the eight groups.
As with the four groups, the table generally shows a pattern of incrementally improved
performance (for both dependent variables) whenever a “leg” or additional leg of positive
organizational architectural is added. However, not all legs of positive organizational
architecture appear to have equal impact on performance. Organizations with high decision
rights (HiDecRights) report a greater difference in under $2,500 capture and headcount
reduction over TripleLo than organizations with rewards (HiRwds) or performance
measurement (HiPerfMeas) only. Further, in all cases and across both dependent variables,
organizations with two positive legs of organizational architecture report notably higher
performance than organizations with any one positive leg. Finally, in all cases and across
both dependent variables, organizations with all three positive legs of organizational
architecture report notably higher performance than organizations with any other
combination.
23
-----------------------------Insert Table 7
-----------------------------Again, each group is identified with a dummy variable and we include the same
controls as in our main specification. Table 8 presents the results.
-----------------------------Insert Table 8
-----------------------------TripleHi is significant with respect to the capture of under $2,500 transactions on
the p-card and the percentage reduction in AP/Purchasing headcount. In addition,
organizations with high decision rights (HiDecRights) and a combination of high decision
rights and rewards (HiPerfMeasRwds) have a positive coefficient for under $2,500
transaction capture.
Table 9 presents the F-statistics for the constraint of equal coefficients across pairs of
groups. The performance of TripleHi organizations is significantly different than any other
organizational architecture (at p<.10) for both dependent variables. Further, the performance
of “double high” organizations (in whatever combination) is always significantly different
than TripleLo (at p<.10%) for both dependent variables. However, the performance of
“double high” organizations is higher than “single high” organizations (whether in relation to
decision rights, performance measurement, or rewards) in only five of eighteen comparisons.
Finally, the performance of organizations with high decision rights (HiDecRights) is
significantly different than TripleLo, while organizations with rewards (HiRwds) and
performance measurement (HiPerfMeas) are not.
-----------------------------Insert Table 9
------------------------------
24
Finally, Table 10 provides covariate adjusted means. The conclusions are
consistent with our previous results.
-----------------------------Insert Table 10
-----------------------------6. Discussion
Theoretical accounting research has identified allocation of decision rights,
performance measurement systems, and rewards systems as interdependent. These three
variables have been brought together under the concept of organizational architecture.
According to the theory that has been developed around this concept, there is an optimal
architecture. Organizations that deviate from the optimal are predicted to have lower
performance. The results in this paper provide the first empirical evidence on this important
research question. Based on a unique database, we find that organizations with higher
delegation, a more developed measurement system, and a reward system linked to
performance perform better. The superiority of this arrangement is consistent with a setting
characterized by information asymmetry where delegation is superior to centralized decision
making. Furthermore, it is consistent with the need to balance the three components of
organizational architecture: delegation is effective only if a detailed measurement system and
an appropriate reward system are in place.
While the optimal organizational architecture found in this study is consistent with
extant theory, the diminution of performance by organizations with sub-optimal
organizational configurations appears to deviate from expectations. In particular, we
expected that there would be no significant difference in the performance of organizations
with sub-optimal organizational architectures (i.e., if any leg was removed from the stool, the
25
stool would fall). However, our findings generally indicate that each successive addition of a
“high performing” component of organizational architecture (e.g., high decision rights)
added incrementally to elevate the organizational performance outcome. Whether this result
is an “Hawthorne effect” (Landsberger, 1958) artifact or some other phenomenon is a matter
for future research and investigation. Arguably, organizational investment of time and
resources into either measuring performance, establishing rewards, or formulating
appropriate delegations of authority communicate organizational priorities which managers
may be loathe to ignore.
The study is subject to important caveats. First, the complexity of simultaneously
capturing various organizational dimensions requires us to rely on perceptual data. The
complexity of the relationship studied and the fact that respondents did not choose a certain
organizational architecture over others suggest that perceptual bias does not drive the results;
although this explanation cannot be ruled out. Second, the relationship between
organizational architecture and performance relies on an out-of-equilibrium argument. This
assumption has been maintained in several prior empirical studies and the significance of the
results—consistent with theoretical predictions—suggests that the assumption may be valid.
However, it is possible that organizations are selecting their organizational architecture and
that the difference in performance is related to an omitted variable that affects the selection
of a particular architecture and performance. Third, the study relies on a single work process.
This research design feature facilitates the selection of a well-defined unit of analysis, but
limits its generalizability. Fourth, missing observations reduced the number of data points
significantly with larger organizations failing to complete relevant parts of the survey. This
fact further reduces the ability to generalize the conclusions.
26
Despite these limitations, the study provides the first empirical evidence on the
relevance of organizational architecture as a concept to explain organizational performance.
This concept brings together two important theoretical accounting research literatures and the
current study provides evidence that emphasizes their importance from an empirical
perspective.
27
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30
Table 1
Descriptive statistics
Type of Organization
Public corporation
Private corporation
University
School district
City or county
State or Federal agency
Other Not-for-Profit entity
Total
158
149
90
63
113
47
52
672
* Note: 611 answered questions related to headcount reduction and 640 responded about purchasing card capture of
under $2,500 transactions on the card. The 672 represents respondents who answered one or both questions.
Industry of Corporate Respondents
Agricultural, Mining, and Construction
Finance, Insurance, Banking, and Real Estate
Manufacturing
Other Services
Professional, Scientific, and Technical Services
Software and IT
Telecom, Media, and Entertainment
Transportation, Warehousing, and Delivery
Utilities
Wholesale and Retail
Total
37
25
103
12
22
11
20
19
17
41
307
31
Table 2
Descriptive statistics
Panel A: Organization and program statistics
Variable
N
Mean
25%
percentile
Median
75%
percentile
Standard
deviation
Sales (in $ millions)
672
2,657
122
375
1,500
8,192
Number of employees
672
7,280
575
1,721
6,000
17,189
Monthly spending total (in
$ thousands)
672
1,268
113
350
1,125
3,337
Number of p-cards
672
757
75
204
600
1,927
Age of the p-card program
671
6.74
3.50
5.50
8.50
4.27
Monthly trans. per card
672
10.13
3.75
5.92
10.01
17.07
Monthly spend per card ($)
672
5,811
748
1,403
2,941
22,981
Reward system (unadjusted
sum of three questions)
667
5.81
3.00
3.00
8.00
4.34
75%
percentile
Standard
deviation
Panel B: Program performance
Variable
N
Mean
25%
percentile
Median
Percentage of under
$2,500 transactions paid
by p-cards
664
42.81
15.00
35.00
70.00
32.02
Percentage reduction in
AP/Purchasing
Headcount
611
15.44
0.00
8.51
25.00
18.71
Panel C: Organizational architecture
Variable
N
Mean
25%
percentile
Median
75%
percentile
Standard
deviation
Decision rights
672
0.50
0.0
0.50
1.0
0.50
Performance measure
672
0.49
0.0
0.0
1.0
.50
Rewards (0/1 variable)
672
0.41
0.0
0.0
1.0
.49
The decision rights variable is constructed as per description in text. High (median or above) decision
rights are dummy coded with 1, all others assigned 0. Performance measure is the mean score of the nine
measurement-related questions (7-point scale). Reward system is the mean score of the three compensationrelated questions (7-point scale). The Rewards variable is created by assigning 0 to all answers of 3 and 1
to any answer above 3 (indicating some measure of reward to PCA based on program performance).
32
Panel D: Organizational dynamics
Variable
N
Mean
25%
percentile
Median
75%
percentile
Standard
deviation
Training
670
6.55
5.00
7.00
9.00
2.58
Internal control
656
.66
.50
.63
.88
.21
Training is the sum of affirmative answers to ten training-related questions plus 1 for an above median
response to a Likert-scaled response to a question related to employee training. Internal control is the
percentage of affirmative answers to eight internal control-related questions
33
Table 3
Pearson correlation coefficients for dependent and independent variables
Under
$2,500
Capture
Headcount
Reduction
DecRights
Under $2,500 Capture
.23***
.23***
Headcount Reduction
-----
DecRights
Rewards
Training
Internal
Control
Program
Age
.14***
.11**
.24***
.16***
.21***
.16***
.12**
.16***
.22***
.04
.16***
-----
.10**
.17***
.22***
.05
.20***
-----
0.21***
0.41***
.25***
.15***
-----
0.29***
.10**
.09*
-----
.38***
.31***
-----
.20***
PerfMeas
Rewards
Training
Internal Control
Program Age
PerfMeas
-----
*, **, *** indicate significance at 5%, 1%, and .1% respectively.
34
Table 4
Descriptive statistics on performance
Mean
25% percentile
Median
75% percentile
% of Under $2,500
Transactions Paid
by P- Card
TripleHi
0.57
0.30
0.60
0.80
DoubleHi
0.45
0.20
0.40
0.70
DoubleLo
0.40
0.10
0.30
0.60
TripleLo
0.31
0.10
0.20
0.45
TripleHi
0.24
0.07
0.20
0.33
DoubleHi
0.17
0.00
0.13
0.28
DoubleLo
0.13
0.00
0.05
0.22
TripleLo
0.11
0.00
0.00
0.17
% Reduction in
AP/Purchasing
Headcount
TripleHi is a dummy variable that takes value of one for companies with all three systems of organizational
architecture are at or above their respective sample medians. DoubleHi is a dummy variable that takes
value of one for companies with two of the three systems of organizational architecture above their
respective sample medians. DoubleLo is a dummy variable that takes value of one for companies with two
of the three systems of organizational architecture below their respective sample medians. TripleLo is a
dummy variable that takes value of one for companies with all three systems of organizational architecture
below their respective sample medians.
35
Table 5
Regression results
Variable
Performance
% of Under $2,500
Transactions Paid by
% Reduction in
P- Card
AP/Purchasing Headcount
(t-statistic)
(t-statistic)
Organizational structure
TripleHi
DoubleHi
DoubleLo
.172
(3.82)
.086
(2.25)
***
**
.095
***
(3.53)
0.033
(1.47)
.070
(2.00)
**
0.016
(0.81)
0.011
(3.41)
0.005
(0.89)
-0.018
(-2.14)
0.015
(2.56)
0.088
(1.35)
0.236
(3.26)
***
-0.157
(-3.16)
-0.027
(-0.63)
-0.157
(-3.16)
0.033
(2.19)
-0.003
(-0.72)
0.174
(3.80)
Control variables
Age of program
Organizational type
Ln(employees)
Training
Internal control
Intercept
Adjusted R2 (%)
**
**
***
10.50
8.68
F-statistic
9.01
6.94
N
622
592
***
***
**
***
*, **, *** indicate significance at 10%, 5%, and 1% respectively.
36
Table 6
Comparison of effects of alternative organizational structures
TripleHi
TripleHi
DoubleHi
9.82
(0.00)
DoubleHi
7.58
(0.00)
DoubleLo
21.17
(0.00)
3.90
(0.05)
TripleLo
27.08
(0.00)
8.44
(0.00)
DoubleLo
TripleLo
20.73
(0.00)
38.84
(0.00)
2.71
(0.10)
14.89
(0.00)
6.27
(0.01)
1.45
(0.23)
The upper triangle reports the F-statistic and significance of constraining the coefficients
to be equal for capture of under $2,500 transactions on purchasing cards. The lower
triangle reports the same statistic for the percentage of headcount reduction experienced
in AP and Purchasing functions due to use of the purchasing card.
37
Table 7
Descriptive statistics on performance
Mean
25% percentile
Median
75% percentile
% of Under $2,500
Transactions Paid by
P- Card
TripleHi
0.57
0.30
0.60
0.80
HiPerfMeasRwds
0.41
0.15
0.30
0.60
HiPerfMeasDecRights
0.49
0.22
0.50
0.75
HiRwdsDecRights
0.46
0.20
0.50
0.65
HiPerfMeas
0.38
0.10
0.30
0.60
HiRwds
0.35
0.08
0.25
0.56
HiDecRights
0.45
0.20
0.40
0.60
TripleLo
0.31
0.10
0.20
0.45
TripleHi
0.24
0.07
0.20
0.33
HiPerfMeasRwds
0.18
0.04
0.13
0.23
HiPerfMeasDecRights
0.16
0.00
0.12
0.31
HiRwdsDecRights
0.17
0.00
0.14
0.28
HiPerfMeas
0.11
0.00
0.00
0.17
HiRwds
0.12
0.00
0.05
0.21
HiDecRights
0.15
0.00
0.08
0.25
TripleLo
0.11
0.00
0.00
0.17
% Reduction in
AP/Purchasing
Headcount
TripleHi is a dummy variable that takes value of one for companies with all three systems of organizational
architecture are at or above their respective sample medians. DoubleHi is a dummy variable that takes
value of one for companies with two of the three systems of organizational architecture above their
respective sample medians. DoubleLo is a dummy variable that takes value of one for companies with two
of the three systems of organizational architecture below their respective sample medians. TripleLo is a
dummy variable that takes value of one for companies with all three systems of organizational architecture
below their respective sample medians.
38
Table 8
Regression results
Performance
Variable
% of Under $2,500
Transactions Paid by
P- Card
(t-statistic)
% Reduction in
AP/Purchasing Headcount
(t-statistic)
Organizational structure
0.167
(3.70)
0.044
(.85)
0.106
(2.21)
0.092
(1.77)
0.038
(0.83)
0.030
(0.57)
0.113
(2.67)
***
0.010
(2.97)
0.004
(0.73)
-0.013
(-1.50)
***
0.016
(2.61)
0.103
(1.57)
0.201
(2.68)
***
TripleHi
HiPerfMeasRwds
HiPerfMeasDecRights
HiRwdsDecRights
HiPerfMeas
HiRwds
HiDecRights
**
**
0.093
(3.45)
0.044
(1.34)
0.021
(0.72)
0.037
(1.20)
-0.000
(-0.01)
0.022
(0.67)
0.026
(1.07)
***
0.005
(2.69)
-0.005
(-1.45)
***
-0.013
(-2.44)
0.012
(3.32)
-0.059
(-1.48)
0.168
(3.57)
**
Control variables
Age of program
Organizational type
Ln(employees)
Training
Internal control
Intercept
***
Adjusted R2 (%)
11.1
8.90
F-statistic
6.38
4.73
N
622
592
***
***
Control variables include the same ones as in Table 5.
39
Table 9
Comparison of effects of alternative organizational structures
TripleHi
TripleHi
HiPerfMeas
Rwds
10.97
(0.00)
HiPerfMeas
DecRights
HiRwdsDec
Rights
HiPerfMeas
HiRwds
2.98
(0.09)
5.12
(0.02)
17.32
(0.00)
18.64
(0.00)
7.83
(0.00)
38.84
(0.00)
2.35
(0.13)
0.88
(0.35)
0.16
(0.69)
.96
(0.33)
.76
(0.38)
3.11
(0.08)
0.33
(0.57)
4.40
(0.04)
6.23
(0.01)
.65
(0.42)
13.76
(0.00)
1.96
(0.16)
3.58
(0.06)
0.02
(0.87)
8.02
(0.00)
0.45
(0.50)
2.06
(0.15)
0.32
(0.57)
3.73
(0.05)
2.28
(0.13)
HiPerfMeasRwds
2.80
(0.09)
HiPerfMeasDecRights
5.95
(0.01)
.20
(0.65)
HiRwdsDecRights
3.49
(0.06)
001
(0.90)
.10
(0.74)
HiPerfMeas
17.23
(0.00)
3.33
(0.07)
2.43
(0.12)
3.01
(0.08)
HiRwds
11.13
(0.00)
2.31
(0.13)
1.48
(0.22)
1.97
(0.16)
0.05
(0.82)
HiDecRights
9.29
(0.00)
0.60
(0.44)
0.13
(0.71)
0.44
(0.51)
1.57
(0.21)
0.76
(0.39)
TripleLo
27.08
(0.00)
5.27
(0.02)
4.15
(0.04)
4.89
(0.02)
0.21
(0.64)
0.06
(0.80)
HiDecRights
TripleLo
10.09
(0.00)
3.04
(0.08)
The upper triangle reports the F-statistic and significance of constraining the coefficients to be equal for capture of under $2,500
transactions on purchasing cards. The lower triangle reports the same statistic for the percentage of headcount reduction experienced
in AP and Purchasing functions due to use of the purchasing card
40
Table 10
Relative performance under alternative organizational structures
Covariate adjusted means
Performance
Variable
% of Under $2,500
Transactions Paid by
P- Card
(t-statistic)
% Reduction in
AP/Purchasing Headcount
(t-statistic)
TripleHi
0.518(1)
0.217(1)
DoubleHi
0.432
0.154
DoubleLo
0.429
0.144
TripleLo
0.360
0.130
TripleHi
0.518(2)
0.217(3)
HiPerfMeasRwds
0.394
0.164
HiPerfMeasDecRights
0.454
0.141
HiRwdsDecRights
0.444
0.161
HiPerfMeas
0.393
0.124
HiRwds
0.395
0.152
HiDecRights
0.477
0.157
TripleLo
0.360
0.130
(1) Significantly different (1% or less) from DoubleHi, DoubleLo, TripleLo.
(2) Significantly different (1% or less) from HiPerfMeasRwds HiPerfMeas, HiRwds, HiDecRights,
TripleLo; significantly different (5% of less) from HwRwdsDecRights; significantly different (10% or
less) from HiPerfMeasDecRights.
(3) Significantly different (1% or less) from TripleLo, HiPerfMeas, HiRwds, HiDecRights; significantly
different (5% of less) from HiPerfMeasDecRights; significantly different (10% or less) from
HiPerfMeasRwds, HwRwdsDecRights.
41
Appendix A
DEPENDENT VARIABLES
CAPTURE OF LOW VALUE TRANSACTIONS ON PURCHASING CARD
Please estimate the current percent of ALL transactions paid by payment mode based on transaction
amount.
Payment Mode
Percent of All
Transactions of $2,500
or Less,
by Payment Mode
Percent of All Transactions
Over $2,500, But Less Than
$10,000,
by Payment Mode
Plastic and traditional ghost p-card accounts
__ __ __ %
__ __ __ %
“Electronic payables” p-card accounts
__ __ __ %
__ __ __ %
Paper check
__ __ __ %
__ __ __ %
Automated Clearing House (ACH) transfers
__ __ __ %
__ __ __ %
Wire transfer
__ __ __ %
__ __ __ %
Other (describe):_________
__ __ __ %
__ __ __ %
100%
100%
TOTAL (answers should sum to 100%)
HEADCOUNT REDUCTION
Please report answers to items (a) and (b) below on a “full time equivalent”(FTE) basis here,
for example, two half-time workers equal one FTE worker or three half-time employees
equals 1.5 FTE.)
(a) The total current number of employees working in:
(b)

Accounts Payable
__,__ __ __.___ FTE

Purchasing
__,__ __ __.___ FTE
The approximate number of additional personnel in Accounts
Payable and/or Purchasing that your organization would need to
hire if it completely eliminated its p-card program

Accounts Payable
__,__ __ __.___ FTE

Purchasing
__,__ __ __.___ FTE
42
INDEPENDENT VARIABLES
PERFORMANCE MEASUREMENT
Does your organization use a measure or measures to evaluate purchasing
card program performance (e.g., amount of spending on card)?
 Yes
 No
REWARDS
What impact will p-card program performance have on
the Purchasing Card Administrator’s…
No
Impact
Significant
Impact
pay and bonuses?
promotions?
1
2
3
4
5
6
7
1
2
3
4
5
6
7
obtaining of preferred future assignment(s)?
1
2
3
4
5
6
7
TRAINING
Identify the basic training requirements for cardholders and supervisors who approve p-card
spending.
DOES YOUR ORGANIZATION:
provide easy access to a copy of the policies and procedures manual for
p-card use to every p-card cardholder?
require initial purchase card training for cardholder?
Require refresher training periodically for cardholder?
provide “web-based” purchasing card training materials?
provide “in-person” purchasing card training?
provide “self-study” purchasing card training materials?
track completion of training and training updates by employees?
support p-card program administrator attendance at “p-card user
conferences” to identify new ways to use p-cards?
have an ongoing method of communicating p-card information (e.g., live or
video information sessions, bulletin boards, newsletters) to cardholders and
managers?
have a Web site that answers p-card questions?
Yes
No




















Please rate support of the p-card program at your organization.
AT MY ORGANIZATION….
a strong "business case" is made to employees about the benefits
of p-cards.
Do Not
Agree
1
2
Fully
Agree
3
4
5 6
43
7
INTERNAL CONTROL
DOES YOUR ORGANIZATION:
require cardholders to maintain a logbook of card activity?
 Yes
 No
have a documented policy regarding receipt retention for p-card spending?
 Yes
 No
officially reprimand or discipline cardholders who fail to submit receipts in
a timely manner?
 Yes
 No
evaluate the spending patterns of cardholders with a high number of
disputed transactions
 Yes
 No
de-activate p-card accounts that are unused for an extended period?
 Yes
 No
track and resolve disputed transactions?
 Yes
 No
formally audit and review the p-card spending approval process?
 Yes
 No
conduct data mining of p-card transactions to identify potential policy
violations or p-card misuse?
 Yes
 No
OTHER (DECISION RIGHTS RELATED)
Please identify the most common per transaction spending limit placed on plastic purchasing
cards at your organization:






$1-$500
$501-$1,000
$1,001-$2,500
$2,501-$5,000
$5,001-$10,000
More than $10,000
Please identify the most common monthly spending limit placed on purchasing cards at
your organization:







$1 to $1,000
$1,001 to $3,000
$3,001 to $5,000
$5,001 to $10,000
$10,001 to $20,000
$20,001 to $50,000
Greater than $50,000
Please rate support of the p-card program at your organization.
AT MY ORGANIZATION….
there is a top management “sponsor” who gives strong vocal
support to the p-card program.
Do Not
Fully
Agree
Agree
1
2
3
4
5 6
7
44
45