The Impact of Organizational Slack and Lag Time on Economic
Productivity: The Case of ERP Systems
John J. Morris*
Kent State University
Kevin E. Dow
Kent State University
Please do not quote without permission
*Corresponding Author
John J. Morris*
Kent State University
Kent, OH 44242
jjmorris@kent.edu
(330) 677-9806
1
The Impact of Organizational Slack and Lag Time on Economic
Productivity: The Case of ERP Systems
Abstract: Much like research on the productivity paradox of the 1980s, empirical research in
the 1990s has not provided a clear association between investments in ERP systems and
improved productivity, even though these systems were designed, developed, and marketed as
productivity improvement tools. This paper explores the relationship between organizational
slack, lag time, and productivity improvements resulting from investments in ERP systems.
Using data from a sample of firms that implemented these systems during the latter half of the
1990s, we provide support for the proposition that during the period of time surrounding
implementation, firms build organizational slack; and following implementation organizational
slack is absorbed and productivity and profitability increases. Thus suggesting that firms do
realize improvements, however the improvements lag behind due to behavior of the firm during
the implementation process.
1. Introduction
Enterprise resource planning (ERP) systems erupted in the 1990s as one of the significant IT
investments of the decade. While these systems were marketed as tools to improve productivity
by integrating business processes across functional areas, early research on ERP systems
generally found no association between spending on ERP systems and improved productivity.
Research on ERP systems has been similar to the productivity paradox research of the 1980s.
This paper explores this relationship further by further exploring the concept of organizational
slack and introducing the concept of lag time to the extant literature.
2
Organizational slack (Cyert et al. 1963; 1992), provides an operational model to explore the
lag effect offered by Brynjolfsson (1993) as a possible explanation for the productivity paradox.
Many ERP projects require a major commitment of both capital and human resources over a time
period that can span months or even years depending on the size of the organization and the
number of modules implemented. During that time, the behavior of the firm can change
significantly, as organizations often reengineer their business processes to meet the needs of the
new system.
This paper explores the proposition that these changes initially lead to an increase in
organizational slack rather than an increase in productivity; and that once the implementation
process is completed, this organizational slack will be absorbed, leading then to increases in
productivity. To test this proposition, data was collected for a number of firms that announced
their implementation of ERP systems during the last half of the 1990s. Both slack and economic
productivity was measured before, during, and after implementation the ERP implementation.
The results provide limited support for the proposition that during implementation, slack
increases; and that in the time period following implementation slack decreases while economic
productivity increases.
These results suggest that Brynjolfsson’s (1993) time lag explanation for the productivity
paradox of the 1980s may also apply to the ERP paradox of the 1990s. In other words, firms do
realize improved productivity and profitability as a result of investments in ERP systems.
However, the improvement lags behind due to the behavior of the firm during the
implementation process, which in turn creates organizational slack.
3
The remainder of this paper is organized as follows: Section 2 develops the theory and
hypothesis, Section 3 presents the research design and methodology, Section 4 presents the
results, and Section 5 provides a discussion of the results and final conclusions.
2. Theory and Hypothesis Development
2.1 The Productivity Paradox
The productivity paradox has motivated many empirical studies in the fields of economics,
management science, and information systems. Dedrick et al. (2003) provides a critical review
of several of these studies, beginning with the early studies in the 1980s that found no connection
between IT investment and productivity at the firm, industry or overall economic level (Loveman
1994; Roach 1987; 1989; 1991; Strassmann 1990). Later studies using more rigorous and
refined research methods, concluded that the productivity paradox does not hold, and that greater
investment in IT is associated with greater productivity growth at both the firm and country level
(Bosworth et al. 2000; Bresnahan 1999; Brynjolfsson 1993; 1996; Brynjolfsson et al. 1995;
1996; 1998; Jorgenson 2001; Jorgenson et al. 2000). Dedrick further concluded that although
several firm level studies show an association between IT investment and productivity, most
have failed to show a clear link to profitability. This was especially true for service firms where
output measurement is difficult, and where relationships between productivity and profitability
are less discernable than for manufacturing firms. It has been generally assumed that more
productive firms will enjoy higher profitability than their competitors. Dedrick et al. (2003)
further suggest that failure to document a clear link to profitability stems from the inability of
researchers to quantify and incorporate the various unobservable factors that determine a firm’s
competitive position and outcomes.
4
Brynjolfsson (1993) offered four possible explanations for the productivity paradox: (1)
mismeasurement of outputs and inputs, (2) lags due to learning and adjustment, (3)
redistribution and dissipation of profits, and (4) mismanagement of information and technology.
Subsequent studies have focused on one or more of these explanations. For instance,
Brynjolfsson and Hitt (1996) focused on the mismeasurement explanation by using a
neoclassical production theory approach to determine the contribution of IT investments. They
found that gross marginal product for computer capital was at least as large as other types of
capital investment, indicating that computers do contribute significantly to firm-level output. In
a study focused on the redistribution explanation, Hitt and Brynjolfsson (1996) suggest that in a
competitive marketplace, firms will pass financial gains on to consumers through decreased
prices, thus resulting in higher productivity and consumer value, but lower profits. Stratopoulos
and Dehning (2000) conclude that increasing investment in IT and a high failure rate for IT
projects, which they consider to be a proxy for mismanagement, off-set the productivity
improvements realized by successful IT projects in many empirical studies. Relatively few
studies have addressed the lag issue, which Brynjolfsson (1993) suggests can take several years
to show results on the “bottom line.” He points out that because of the complexity of IT systems,
firms and individuals may require some experience before becoming proficient, resulting in a
learning curve that would logically be longer for more complex IT investments. More recently,
Brynjolfsson and Hitt (2000) have found that payoffs to IT investment occurred not just in labor
productivity increases but also in multifactor productivity (MFP) growth, and that the impact of
MFP growth is maximized after a lag of 4 to 7 years.
5
2.2 Enterprise Resource Planning (ERP) Systems
Over the past decade companies have spent significant portions of their IT budgets on ERP
systems. Annual license and maintenance revenue seems to have peaked in 2000 at an estimated
cost of $21.5 billion worldwide (Hossain et al. 2002), which does not include significant related
spending for computer hardware, infrastructure and consulting services. These systems were
designed, developed and marketed as productivity improvement investments. As such,
expectations that this type of IT spending would be associated with productivity and profitability
were commonplace. However, empirical research targeted at ERP investments has found only
limited support for any such association (Hayes et al. 2001; Hunton et al. 2003; Hunton et al.
2002; Poston et al. 2001). A number of explanations have been offered for these mixed results,
most of which follow one of Brynjolfsson’s (1993) four explanations for the productivity
paradox. For instance, Poston and Grabsky (2001) suggested that companies might be passing
cost savings on to customers in the form of lower prices, therefore profitability did not improve.
Hunton et al. (2003) found that comparing ERP implementers to non-implementers provided
evidence that non-implementer results declined vs. implementers even though implementer
results did not improve, suggesting that relative improvement may be a better measurement than
absolute change.
Another factor that may have confounded these early empirical studies was the impact of the
millennium change. This so-called “Y2K event” influenced a significant amount of IT spending
during the last half of the 1990s as firms replaced their legacy systems with Y2K compliant
systems, especially ERP. Unfortunately, many firms experienced problems with their ERP
implementation projects, including cost over-runs, time delays, and other related major
implementation failures (Davenport 1998). As the much anticipated December 31, 1999 date
6
approached, focus shifted from productivity improvement to meeting the Y2K deadline
suggesting that Brynjolfsson’s (1993) time lag explanation for the productivity paradox may be a
more appropriate explanation for the lack of productivity improvements from ERP systems.
Furthermore, during an ERP implementation project, firms tend to add employees, consultants
and temporary workers to complete tasks that may not have been anticipated, resulting in a build
up of organizational slack.
2.3 Organizational Slack
Organizational slack (Cyert et al. 1963; 1992) is the difference between resources available
to the organization and the payments required to maintain the organization. They argue that
slack absorbs variability in a firm’s environment, playing both a stabilizing and adaptive role in
that it operates to stabilize the system by absorbing excess resources in good times and providing
a pool of emergency resources in bad times. Although they provide a few examples of slack
such as wages in excess of those required to maintain labor, the concept of organizational slack
is presented as a hypothetical construct to help explain over-all organizational phenomena.
Bourgeois (1981), proposed a method to operationalize the slack concept with a model that
measured changes in slack as a function of changes in key financial indicators. Bourgeois &
Singh (1983), grouped these indicators into three categories: available slack, recoverable slack,
and potential slack. Each of these distinct categories represent a segment of a continuum along
which the ability to redeploy slack resources becomes progressively more difficult. For instance
available slack that consists of cash and near cash items could be redeployed quickly.
Recoverable slack (when measured by other working capital components and discretionary
administrative spending) may take more effort and time to redeploy. Finally, potential slack
7
(characterized by long-term capital raising ability measured by ratios such as debt/equity and
price/earnings) may not ever be redeployed in the organization. A number of researchers have
used this operational model or parts of it in empirical studies to explain organizational
phenomenon (Miller et al. 1996; Sharfman et al. 1988). More recently, Dehning et al. (2004)
introduced the concept to the information technology literature as a partial explanation for the
productivity paradox. They concluded that during the productivity paradox period, firms
experienced an increase in organizational slack rather than increased productivity.
ERP systems are extremely complex and difficult to implement, with many projects taking
several months or even years to complete. It is therefore reasonable to conclude that during this
implementation time period organizational slack, would be affected, especially available and
recoverable slack. The potential category of slack would probably not be affected during an ERP
implementation process, unless the firm experienced a major problem that resulted in a
significant financial loss that would impact their ability to raise additional capital. On the other
hand, firms implementing ERP systems may tend adjust dividend policy and defer other capital
spending projects to build up cash reserves as an insurance policy in case unanticipated problems
arise, thus having a direct impact on available slack. Recoverable slack, measured by changes in
working capital and administrative expense, could be affected in a couple of ways. First,
management of working capital components, such as accounts receivable and inventory, could be
expected to suffer during the transition period as employees learn how to use the new
applications and modify business processes to match the new system. Secondly, during the
implementation process, firms usually hire temporary employees and consultants to
accommodate the additional work load, which would have a direct effect on administrative
8
expenses. These anticipated changes in measures of slack during the implementation process
lead to the first hypothesis:
Hypothesis 1: During the time period surrounding implementation of ERP systems, firms
will experience an increase in organizational slack.
Once an ERP implementation project has been completed, firms could be expected to resume
dividend payments, make capital purchases that had been deferred, and otherwise take steps to
invest surplus cash that had been held in reserve. Furthermore, as employees are trained and
learn how to use the new ERP system, working capital ratios should improve, as more attention
is paid to using the new tools for that purpose. Also, as the project winds down, consultants,
temporary employees, and other project related expenses will be discontinued, leading to a
reduction in G&A expenses. This leads to the second hypothesis:
Hypothesis 2: In the time period following implementation of ERP systems, firms will
experience a decrease in organizational slack.
2.4 Productivity Improvements
Economists typically divide productivity into two categories: (1) labor productivity, defined
as real output per hour of work, and (2) total factor productivity, defined as real output per unit
of all inputs (Steindel et al. 2001). However, many empirical studies on ERP systems have
simply used proxies for productivity such as financial ratios, (Hunton et al. 2003; Poston et al.
2001) or stock market and analysts’ reaction to announcements (Hayes et al. 2001; Hunton et al.
9
2002). On the other hand, the National Academy of Sciences (NAS) (1979) suggest that
productivity can be measured at the individual firm level by a number of asset, cost, and
employment ratios. Table 1 outlines the primary productivity measures outlined by the NAS.
These ratios change to reflect changes in economic productivity. For instance, as productivity
increases sales per dollar of operating assets or per employee would be expected to increase, and
costs per dollar of sales would be expected to decrease, resulting in an increase in operating
profit per dollar of sales. Many of the underlying factors used to measure productivity and
profitability behave in opposite directions from the slack factors discussed earlier. Thus as slack
is absorbed by the firm these underlying measures of productivity and profitability will change,
which leads to the final hypothesis:
Hypothesis 3: In the time period following implementation of ERP systems, firms will
experience an increase in productivity and profitability.
3. Research Design and Method
3.1 Model Specification
Because the financial ratios that are used to measure both slack and productivity tend to vary
from one industry to another, it is necessary to measure the relative change in ratios. Also, to
model the impact of slack and productivity on a firm during and after an ERP implementation
project, one must begin with a baseline from which changes can be measured. A diagram of the
expected relationships between slack and productivity; before, during and after an ERP
implementation project is provided in figure 1.
[Insert Figure 1 Here]
10
As indicated in Figure 1, baseline measures of organizational slack are expected to increase
during the implementation time period, and decrease afterwards. By contrast, the baseline
productivity measures are only expected to increase after the ERP implementation is complete,
as both available and recoverable slack decrease.
3.2 Measures of Slack
Table 1, panels A and B, provides details of the following six ratios developed by Bourgeois
& Singh (1983), which were used to measure available and recoverable slack: (1) changes in
retained earnings to sales, (2) dividend payout, (3) cash & equivalents to sales, (4) accounts
receivable to sales, (5) inventory to sales, and (6) general and administrative expense to sales. A
confirmatory factor analysis provided strong support that these six factors measure the latent
variable; organizational slack.
3.3 Measures of Productivity
Table 1, panel C provides details of the following four ratios that were used to measure
productivity: (1) operating profit to sales, (2) operating profit per employee, (3) sales per
employee, and (4) sales to operating assets. These ratios were selected from the ratios suggested
by NAS, (1979) because they are all widely accepted measures of productivity and profitability
used in academic research and because the data is readily available. Furthermore, these ratios
address four key aspects of the relationship between productivity and profitability. The
operating profit to sales ratio is an indication of relative profitability. The sales-per-employee
ratio represents the more traditional labor based measure of productivity. The operating profit
11
per employee ratio combines the concept of profitability and labor productivity. Finally, the
ratio of sales to operating assets measures overall productivity, not just labor productivity.
[Insert Table 1 Here]
3.4 Sample Selection
The sample selection process began with the list of ERP announcements used by Hayes et al.
(2001), which consisted of 91 announcements between 1992 and 1998. From this list, twelve
firms were eliminated that had either merged with other firms or were no longer listed on the
stock exchange during the study period. 17 firms were eliminated because their announcement
date was prior to 1997, which was the first year for this study. This process yielded 62 firms. A
search using LexisNexis found an additional 63 firm announcements, mostly in 1999 and 2000,
which yielded a total of 125 firms that made ERP implementation announcements. Data for each
of the measures of slack and productivity were extracted from the Standard & Poor’s Research
Insight Compustat database. Another 15 firms had to be eliminated due to missing Compustat
data, leaving a final sample size of 110 firms and is summarized in Table 2.
[Insert Table 2 Here]
4. Results
4.1 Total Sample Set
Table 3 presents descriptive statistics for the mean averages of each of the factors used in the
analysis grouped by three, three year time periods: (1) prior to implementation, (2) during the
implementation period, and (3) after implementation. The implementation time period includes
the year before the announcement, the year of the announcement, and the year after the
announcement. This approach was used due to the limitations associated with the determination
of the exact timing of the ERP implementation. The three years prior to this implementation
12
time period was used to establish a baseline, and up to three years following implementation was
used as the post implementation time period. Since Compustat data is available only through
2003, only two years of post implementation data for announcements made in 2000 was
collected.
For the complete dataset (panel A), four of the six measures of slack reflect the expected
change in mean values, two each from the available and recoverable categories. Retained
earnings increased and dividend payout decreased, reflecting an increase in available slack
during the implementation period, reversing after implementation reflecting a decrease in
available slack. With respect to recoverable slack, the ratio of accounts receivable and
inventories to sales increased during implementation, and decreased after implementation
reflecting the expected pattern. Two of the slack factors did not change as expected. The ratio
of cash and equivalent and general and administrative expenses to sales both increased during
and decreased after implementation.
Three of the four measures of productivity/profitability also followed the expected pattern,
with operating profit to sales, sales per employee, and sales to operating assets all increasing
from the baseline to the post implementation period. Operating profit per employee however
decreased, reflecting a trend similar to prior research where the relationship between productivity
and profitability has not always been consistent.
4.2 Manufacturing Firm Sub-sample
The sample was divided into two sub-sets, one for firms in the manufacturing sector (SIC
Codes 2000 – 3999), and one for all other firms. Table 3, panel B presents descriptive statistics
for the mean averages of each of the factors for the manufacturing firms. These results were
13
similar to the overall sample results, with only the ratio of accounts receivable to sales not
reflecting the expected increase during the implementation period, although only by a small
amount, and it did decrease after implementation, reflecting the expected pattern. All three of
the same productivity/profitability ratios follow a similar pattern to the overall sample.
4.1 Non-Manufacturing Firm Sub-sample
The more interesting results came from the sub-sample of non-manufacturing firms. Table 3,
panel C presents descriptive statistics for the mean averages of each of the factors for these
firms. The pattern of change in slack factors shifted somewhat, with the retained earnings factor
not reflecting the expected change during the implementation period, and the ratio of inventory
to sales factor not following the expected pattern during either time period. Both of these results
may reflect more of the difference in firm structure than anything else, especially the inventory
factor, because inventory does not play a significant role in a lot of non-manufacturing
businesses. The most interesting result however, is in the two factors that did not follow the
expected pattern in the manufacturing sub-sample and the overall sample. The ratio of cash and
equivalents to sales and the ratio of general and administrative expense to sales both increased
during the implementation period, and then decreased afterwards, which is more supportive of
the first two hypothesis. The other interesting result is in the productivity/profitability ratios.
The non-manufacturing firms reflect an increase in operating profit per employee, which was not
present in the data for manufacturing firms. Two of the other factors; operating profit to sales
and sales to operating assets both decreased, once again providing mixed results.
14
4. Conclusion and Discussion of Results
This paper contributes to the research stream that follows the productivity paradox by
addressing a specific aspect of IT investment that was designed developed and marketed as a
productivity improvement tool, ERP systems. More specifically this paper focused on the
relationship between organizational slack and productivity/profitability as a refinement of
Brynjolfsson’s (1993) lag concept in explaining the productivity paradox as it applies to ERP
systems. This is an important contribution to both theory and practice given the high level of
spending that has taken place for ERP systems over the past decade and the mixed results that
have been reported in both the popular and academic press.
These results generally support the idea that during the period of time that firms are
implementing ERP systems, they tend to build up levels of organizational slack, which are then
absorbed following implementation, leading to improvements in productivity and profitability.
Although the results are somewhat mixed a clear pattern does exist, which is stronger in the nonmanufacturing sector. One explanation may be due to the fact that ERP systems originated in the
manufacturing sector, having evolved from manufacturing resource planning (MRP) systems.
As such, these systems were initially a better fit from a business process perspective for
manufacturing firms than for non-manufacturing firms. Therefore the lag time and the resulting
buildup of organizational slack may be greater for non-manufacturing firms as they change
business processes and/or make adjustments and modifications to the ERP software to better fit
their business processes.
Overall, these results suggest that Brynjolfsson’s (1993) lag concept combined with the
organizational slack theory of Cyert & March (1963; 1992) could explain why firms continued to
invest in expensive ERP systems with little or no direct evidence of improved productivity or
15
profitability. This is especially true given the volume of mixed press that ERP systems received
during the late 1990s, when in spite of the bad press, investments continued at record levels.
One limitation of this study is the lack of specific information on the timing of the actual
implementation process each firm undertook. Given the nature of the available data, it was
necessary to estimate the implementation time period based on public announcement dates. It
may be useful for future researchers to gather more precise data on timing and better delineate
the period of time during which implementation takes place. Also, as more data becomes
available, it would be useful to extend the timeline to explore even longer lag times. Since most
of the ERP systems were implemented during the last four years of the 1990s, data is just now
becoming available that would make longer lag time analysis possible.
Another limitation of this study is the potential that other confounding factors may have
impacted the results. Although the use of scaled data and relative change helps to mitigate this
issue, the fact remains that other external events may be exerting undue influence on the data.
One approach to addressing this limitation would be for future research to use a matched pair
concept similar to the approach used by Hunton et al. (2003) to compare implementers with nonimplementers during the same time period.
16
Figure 1 – Changes in Slack and Productivity Measures
Before Implementation
Baseline
Organizational
Slack Measures
During Implementation
Organizational
Slack Measures
(+) Increase
Baseline
Productivity
Measures
After Implementation
Organizational
Slack Measures
(-) Decrease
Productivity
Measures
(+) Increase
Table 1 – Details of Variable Calculations
Panel A: Measures of Available Slack
Variable
Description
SLK_RE
Retained Earnings to Sales
SLK_DP
Dividends to Net Worth
SLK_CE
Cash & Equivalents to Sales
Calculation (Compustat Variables)
(NI – DV) / SALE
DV / SEQ
CHE / SALE
Panel B: Measures of Recoverable Slack
Variable
Description
SLK_AR
Accounts Receivable to Sales
SLK_IN
Inventory to Sales
SLK_GA
Selling, General & Admin to Sales
Calculation (Compustat Variables)
RECT / SALE
INVT / SALE
XSGA / SALE
Panel C: Measures of Productivity
Variable
Description
PRO_OS
Operating Profit to Sales
PRO_OE
Operating Profit per Employee
PRO_SE
Sales per Employee
PRO_SA
Sales to Operating Assets
Calculation (Compustat Variables)
(SALE – COGS – XSGA) / SALE
(SALE – COGS – XSGA) / EMP
SALE / EMP
SALE / NOA
Table 2 – Summary of Sample Selection Process
Initial ERP announcements form Hayes et al. (2001) from 1992 to 1998
Less: Mergers, acquisitions, and de-listings
Announcements remaining from Hayes et al. (2001)
Additional ERP announcements collected for this study (mostly 1999 & 2000)
Total valid ERP announcements collected
Less: ERP announcements outside of study range (1997-2000)
Initial ERP announcements available for study
Less: Firms with no data available in Compustat during study period
Net ERP announcements used in study
17
91
- 12
79
89
168
- 43
125
- 15
110
Table 3 – Mean Values of Slack and Productivity Measures
3 Years Prior to
Implementation
3 Years During
Implementation
Change
Expected N
Mean
N
Mean
Panel A: All Firms
SLK_RE
288
-0.070
SLK_DP
293
0.038
SLK_CE
286
0.279
SLK_AR
266
0.226
SLK_IN
280
0.126
SLK_GA
241
0.357
PRO_OS
241
0.036
PRO_OE
222
40.770
PRO_SE
281
254.408
PRO_SA
286
2.636
Panel B: Manufacturing Firms
SLK_RE
186
-0.128
SLK_DP
190
0.032
SLK_CE
184
0.332
SLK_AR
176
0.184
SLK_IN
180
0.152
SLK_GA
172
0.398
PRO_OS
172
-0.027
PRO_OE
160
39.495
PRO_SE
180
251.957
PRO_SA
191
2.151
Panel C: Non-Manufacturing Firms
SLK_RE
102
0.035
SLK_DP
103
0.050
SLK_CE
102
0.184
SLK_AR
90
0.309
SLK_IN
100
0.078
SLK_GA
69
0.256
PRO_OS
69
0.192
PRO_OE
62
44.061
PRO_SE
101
258.775
PRO_SA
95
3.609
3 Years After
Implementation
Change
Expected
N
Mean
+
+
+
+
+
291
311
299
271
284
246
-0.006
0.007
0.141
0.232
0.127
0.282
+
+
+
+
+
220
241
228
207
225
185
185
176
232
236
-0.170
0.171
0.288
0.195
0.115
0.305
0.091
38.090
272.313
2.672
+
+
+
+
+
188
203
190
174
180
171
-0.117
0.025
0.099
0.180
0.167
0.257
+
+
+
+
+
135
148
138
127
135
126
126
123
146
149
-0.248
0.033
0.343
0.162
0.151
0.291
0.063
19.809
264.382
2.326
+
+
+
+
+
103
108
109
97
104
75
0.014
-0.027
0.213
0.324
0.057
0.341
+
+
+
+
+
85
93
90
80
90
59
59
53
86
87
-0.045
0.391
0.203
0.248
0.062
0.335
0.150
80.514
285.776
3.265
18
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