Information system erp

International Journal of Accounting
Information Systems 4 (2003) 165 – 184

Enterprise resource planning systems: comparing firm
performance of adopters and nonadopters
James E. Huntona,*, Barbara Lippincottb, Jacqueline L. Reckb
a
Department of Accounting, Bentley College, 175 Forest Street, Waltham, MA 02452 4705, USA
b
School of Accountancy, University of South Florida, Tampa, FL, USA

Received 15 April 2002; received in revised form 10 October 2002; accepted 18 October 2002

Abstract

The current study examined the longitudinal impact of ERP adoption on firm performance by
matching 63 firms identified by Hayes et al. [J. Inf. Syst. 15 (2001) 3] with peer firms that had not
adopted ERP systems. Results indicate that return on assets (ROA), return on investment (ROI), and
asset turnover (ATO) were significantly better over a 3-year period for adopters, as compared to
nonadopters. Interestingly, our results are consistent with Poston and Grabski [Int. J. Account. Inf.
Syst. 2 (2001) 271] who reported no pre- to post-adoption improvement in financial performance for
ERP firms. Rather, significant differences arise in the current study because the financial performance
of nonadopters decreased over time while it held steady for adopters. We also report a significant
interaction between firm size and financial health for ERP adopters with respect to ROA, ROI, and
return on sales (ROS). Specifically, we found a positive (negative) relationship between financial
health and performance for small (large) firms. Study findings shed new light on the productivity
paradox associated with ERP systems and suggest that ERP adoption helps firms gain a competitive
advantage over nonadopters.
D 2003 Elsevier Science Inc. All rights reserved.

Keywords: Enterprise resource planning, ERP; Firm performance; Productivity paradox; Longitudinal study

* Corresponding author. Tel.: +1-781-891-2422.
E-mail address: jhunton@bentley.edu (J.E. Hunton).

1467-0895/03/$ – see front matter D 2003 Elsevier Science Inc. All rights reserved.
doi:10.1016/S1467-0895(03)00008-3

166 J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184

1. Introduction

Enterprise resource planning (ERP) reflects an innovative business strategy, as ERP
adoption involves business process improvement, best practices implementation, intra-
enterprise integration and inter-enterprise coupling. ERP systems are designed to facilitate
the ERP concept by replacing disparate patchworks of legacy systems across business
organizations with synchronized suites of enterprise-wide applications. Potential benefits of
an ERP system include productivity and quality improvements in key areas, such as product
reliability, customer service, and knowledge management. As a result, ERP systems are
expected to enhance market value and firm performance through efficiency and effectiveness
gains.
Hayes et al. (2001) offered evidence that the capital market placed incremental value on
firms that adopted ERP systems, as investors reacted positively to ERP implementation
announcements. Similarly, a behavioral study by Hunton et al. (submitted for publication)
found that financial analysts significantly increased mean earnings forecast revisions when a
firm announced plans to implement an ERP system. While both studies indicated that capital
market participants believed ERP adoption would improve future firm performance, the
extent to which expected returns eventually materialize remains unknown.
To investigate this issue, Poston and Grabski (2001) examined the effect of ERP systems
on firm performance over a 3-year period. They found a significant decrease in the ratio of
employees to revenues in all 3 years, and a reduction in the ratio of cost of goods sold to
revenues in year 3. However, they reported no significant improvement in the ratio of selling,
general and administrative expenses to revenues, or residual income (net operating income
minus imputed interest). Hence, they suggested a contradiction—while ERP systems appear
to yield efficiency gains in some areas, higher offsetting cost-to-revenue increases elsewhere
leave residual income unaffected. Other researchers have also observed little or no
performance effects associated with increasing information technology (IT) expenditures, a
phenomenon that is often referred to as the productivity paradox (e.g., Grover et al., 1998;
Harris 1994; Pinsonneault, 1998).
Robertson and Gatignon (1986) and Hitt and Brynjolfsson (1996) suggested another way
to look at the productivity paradox; that is, to the extent that increased spending on IT yields
efficiency and effectiveness improvements, firms will pass on financial gains to consumers
through decreased prices in a competitive marketplace. To investigate this possibility, we
examined the longitudinal impact of ERP adoption on firms by comparing financial
performance indicators of adopters and nonadopters. To the extent that adopters realize
and transfer financial rewards, performance of adopters might not change using a pre- to post-
adoption analysis; however, the performance of nonadopters would be expected to decline by
comparison. As expected, our results indicate that ERP adopters performed significantly
better than non-ERP adopters, primarily due to declining performance of non-ERP adopters.
Further, we examined whether the financial performance of ERP-adopting firms was
affected by the interaction of firm size and financial health. Theory and research evidence
indicates that the performance of small/healthy firms will be greater than small/unhealthy
firms, and large/unhealthy firms will be greater than large/healthy firms (Hayes et al., 2001;

J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184 167

Hunton et al., submitted for publication). In accordance with the hypotheses, we found a
significant interaction between firm health and size. Support for these propositions sheds new
light on the productivity paradox by suggesting that performance gains and losses resulting
from increased IT expenditures may be masked in aggregate, but such effects could be more
precisely identified when firms are discriminated along key dimensions.
The next section provides theory and develops the study hypotheses. Section 3 presents the
sample selection technique and research method. The final two sections analyze study results
and discuss the research findings.

2. Hypotheses formulation

2.1. ERP systems and innovation

While many studies have attempted to find a positive relationship between IT investments
and firm performance, for the most part, research findings have yielded nonsignificant results
(e.g., Weill, 1992; Mahmood and Mann, 1993; Hitt and Brynjolfsson, 1996). As a result,
researchers have yet to provide compelling evidence that IT investments equate to meas-
urable, positive value for business organizations. However, Dos Santos et al. (1993)
suggested that a more refined analysis of IT investments could provide clarity in this regard.
Dos Santos et al. (1993) argued that non-innovative technologies (those that maintain the
status quo) are not likely to improve a firm’s market value or financial performance, whereas
innovative technologies (those that improve business processes) are expected to enhance
value and performance. To empirically test their proposition, they observed the market
response to IT investment announcements and found no overall effect; however, further sub-
analysis revealed that the market reacted positively to innovative investment announcements
(Dos Santos et al., 1993). Similarly, Peffers and Dos Santos (1996) reported a positive
relationship between innovative IT investments and firm performance. Hence, the lack of
market and performance effects in prior studies may be due to a failure to discriminate
between innovative and non-innovative investments.
Drucker (1988) and Huber (1990) suggested that information technologies are considered
innovative if they facilitate key business process improvements, such as

1. more accurate, comprehensive, timely, and available organizational intelligence from
internal and external information sources at greatly reduced costs,
2. greater speed and accuracy in identifying problems and opportunities,
3. fewer intermediate human nodes within the organizational information-processing network,
4. reduced number of organizational levels involved in authorizing and making decisions,
and
5. less time being consumed in the decision-making process.

According to O’Leary (2000), ERP systems are designed to support business process
improvements of this nature, thereby enhancing information quality, decision making and


firm performance. In concept, the realization of such business process improvements is
facilitated by ERP systems due to the integrated nature of enterprise-wide information via
relational databases. To the extent that such improvements are not realized, much of the
responsibility rests with inadequate planning and implementation of the ERP system, as the
technology is capable of supporting the innovative process improvements mentioned above.
Hence, while we recognize that some ERP system implementations have not coincided with
business process improvements, we nevertheless categorize an ERP system as an innovative
technology based on its potential in this regard. Accordingly, we expected to find significant
positive effects of ERP adoption on market value and firm performance.
Research evidence offered by Hayes et al. (2001) reinforces the notion that ERP systems
are perceived to be innovative IT investments, as they observed a positive reaction from
investors when firms announced that they were planning to implement an ERP system. In a
similar study by Hunton et al. (submitted for publication), financial analysts significantly
increased mean earnings forecast revisions when they learned that a firm was planning to
implement an ERP system. The positive reaction of capital market participants to ERP
adoption announcements reflects initial beliefs about the potential impact of ERP systems on
future firm performance. However, whether ERP systems positively impact performance in
the long run remains largely unanswered.

2.2. ERP systems and firm performance

A recent study by Poston and Grabski (2001) investigated the impact of ERP system
implementation on firm performance. They examined the post-implementation performance
of 50 ERP-adopting firms over a 3-year post-implementation time horizon, after controlling
for pre-implementation performance. They found no significant improvement in residual
income (net operating income less imputed interest for cost of capital) or in the ratio of
selling, general and administrative expenses to revenue throughout the 3-year window.
However, they reported a significant decrease in the ratio of employees to revenue in each of
the 3 years and a significant improvement in the ratio of cost of goods sold to revenue in year
3. Overall, they noted that ERP firms exhibited efficiency gains in some areas, but increased
costs elsewhere seemed to offset such gains.
Other researchers have also indicated little or no relation between IT investment and
financial performance, which is often referred to as the productivity paradox (Harris, 1994).
However, as suggested by Dos Santos et al. (1993), delineating between innovative and non-
innovative uses of IT could offer clarity in this regard. How then could it be that Poston and
Grabski (2001) examined the performance of companies that adopted an innovative IT
investment (ERP system), yet found no significant gain in financial performance?
While there are likely many answers to this question, one possible explanation suggested
by Hitt and Brynjolfsson (1996) is that any financial gain associated with ERP adoption is
passed through to customers in the form of lower prices. Robertson and Gatignon (1986)
offered a similar explanation when they examined the impact of competitive factors on
innovative technology diffusion. Through analytic modeling, Eliashberg and Jeuland (1986)
discussed and Eliashberg and Chatterjee (1985, 1986) demonstrated that prices drop


immediately after the adoption of innovative technologies and demand increases as a result of
price sensitivity. They further indicated that the financial performance of adopters might or
might not improve significantly, depending on a host of exogenous factors such as
competitive intensity, industry heterogeneity, demand uncertainty, and adoption rate of
competitor firms; nevertheless, the performance of nonadopters would be expected to
deteriorate by comparison in a competitive marketplace.
If we view ERP adoption through this lens, we would not necessarily expect to see pre- to
post-adoption financial gains for ERP firms. Instead, we would anticipate the financial
performance of nonadopters to decline relative to adopters. Hence, we offer the following
hypothesis (alternate form):

H1: Longitudinal financial performance of firms that have not adopted ERP systems will be
significantly lower than ERP-adopting firms.

2.3. Financial performance indicators

One way to evaluate firm performance is through financial statement analysis, which uses
traditional accounting measures that are based on relationships among financial statement
items. In the current study, we used four measures of performance. The first measure, return
on assets (ROA), is frequently used by researchers as a measure of firm performance (e.g.,
Balakrishnan et al., 1996; Barber and Lyon, 1996; Barua et al., 1995; Bharadwaj, 2000; Hitt
and Brynjolfsson, 1996; Weill, 1992). Since ROA incorporates both firm profitability and
efficiency (Skousen et al., 1998), it tends to be a useful overall performance indicator. We
purposely focused on ROA because it has been proposed that the benefits of ERP systems
include improved efficiency and profitability (Brakely, 1999; Schaeffer, 1996; Stein, 1998;
Vaughan, 1996; Wah, 2000).
The combined effects of profitability and efficiency represented by ROA can be separated
into return on sales (ROS) and asset turnover (ATO)—two secondary measures of perform-
ance used in the current study. ROS, represented as income per dollar of sales, is a measure of
the firm’s profitability or margin. ATO, represented by the sales generated per dollar of assets,
is a measure of asset efficiency.
The last performance measure used in this study, return on investment (ROI) is included as
a check on the robustness of the results using ROA, and because it has been cited as a key
performance measure in the ERP implementation literature (e.g., Mabert et al., 2000;
Stedman, 1999; Stein, 1998). We next examine the interaction of firm size and financial
health on the financial performance of firms that adopt ERP systems.

2.4. Interaction of firm size and health: large firm effect

The adoption of an ERP system by small firms is a significant undertaking, particularly
with respect to the consumption of financial resources. For instance, Mabert et al. (2000)
found that implementation costs, as a percent of revenue, range from 0.82% for very large
firms (revenues > $5000 million) to 13.65% for very small firms (revenues <$50 million).


Some small firms reported that total ERP implementation costs were nearly half of their
annual revenues. Based on this analysis, Mabert et al. (2000, p. 56) stated ‘‘. . .although
larger firms incur a greater implementation cost because of their operating size, a
comparison of revenues shows that there are economies of scale working in favor of the
larger firms. . .[T]he move to ERP by smaller firms represents a major commitment in time
and money.’’
Additionally, ERP implementations can span a considerable amount of time. For instance,
implementation periods from 1 to 2 years are common, and some implementation periods can
stretch to 4 or 5 years (Cooke and Peterson, 1998; Mabert et al., 2000). Further, considerably
large cost overruns are not unusual throughout extended ERP implementation periods.
Overall firm performance can also decrease during implementation, due to temporary
coordination and control disruptions (Koch, 1996; Wah, 2000). Thus, it can take several
years to achieve a positive payback and desired ROI (Davenport, 2000; Stedman, 1999). As a
result, a firm must have sufficient resources, or access to resources, to withstand the strain of
ERP implementation. While firm size is a key contextual factor to consider when postulating
the impact of ERP on firm performance, financial health is also important since healthiness
can affect a company’s access to additional resources from external stakeholders, such as
creditors and investors (Hayn, 1995).
In general, large firms possess higher levels of resources and hold greater ability to attract
additional resources than small firms. Thus, large firms can more easily absorb and withstand
ERP implementation costs. However, research evidence offered by Khurana and Lippincott
(2000) indicated that potential performance improvements are greater for relatively unhealthy,
as compared to healthy, large firms. Consider a large financially healthy company that is
efficient (e.g., high employee productivity and low cost producer) and effective (e.g., large
market share and high-quality products) in its industry. Expected performance gains due to
ERP adoption for this firm are less than anticipated gains of a relatively unhealthy
counterpart, as the latter has greater potential for further improvement.
Hayes et al. (2001) and Hunton et al. (submitted for publication) offered similar
arguments with respect to the reaction of capital market participants to large firm ERP
implementation announcements. Hayes et al. (2001) reported that the market reacted more
strongly to ERP implementation announcements from large/unhealthy firms (standardized
cumulative abnormal returns=0.272), as compared to large/healthy firms (standardized
cumulative abnormal returns=0.061); however, the difference was not significant due, in
part, to the low power of the small sample. Hunton et al. (submitted for publication)
indicated that the mean earnings forecast revision (for 2001) was greater for large/unhealthy
firms ($0.18) than large/healthy firms ($0.06). While they did not report the significance of
this comparison, we analyzed their data and found that the difference was significant
(t=3.01, P=.02). Based on extant theory and research evidence, for large firms, we expect a
negative relationship between firm health and performance due to ERP adoption, as posited
below (alternate form):

H2a: For relatively large ERP-adopting firms, there will be a significant negative
association between firm health and performance.


2.5. Interaction of firm size and health: small firm effect

Small firms also stand to benefit from ERP adoption, particularly if the firms use ERP
systems to facilitate business process improvements. In this manner, small/healthy firms can
become larger, and small/unhealthy firms can become healthier. With respect to relative
health, on the surface, one would expect the logic just presented to hold for small firms,
thereby suggesting that ERP-related performance gains should increase as financial health
declines. However, small firms have greater variability than large firms with respect to
liquidity and solvency (Huff et al., 1999). Such variability, which equates to increased loan
and investment risk, coupled with an economy of scale disadvantage, means that small/
unhealthy firms may run into considerable obstacles with regard to acquiring needed financial
and intellectual resources to complete full ERP integration.
This situation places small/healthy firms in a better position to have or obtain imple-
mentation resources than small/unhealthy firms. Another issue facing small/unhealthy firms is
that they might be forced into a partial implementation of the ERP system (due to cost
considerations and resource acquisition constraints), thereby foregoing the acclaimed innov-
ative benefit of using a fully integrated system.
Hayes et al. (2001) and Hunton et al. (submitted for publication) also posited that capital
market participants would react more positively to ERP announcements from small/healthy
firms, as compared to small/unhealthy firms. In support of this proposition, Hayes et al.
(2001) indicated a significantly stronger market reaction to ERP implementation announce-
ments from small/healthy firms (standardized cumulative abnormal returns=0.283) than
small/unhealthy firms (standardized cumulative abnormal returns=À0.270), and Hunton et
al. (submitted for publication) indicated that the mean earnings forecast revision (for 2001)
was significantly greater for small/healthy firms ($0.08) than small/unhealthy firms ($0.02).
Hence, we posit that firm performance will be positively related to firm health for small ERP-
adopting firms (alternate form):

H2b: For relatively small ERP-adopting firms, there will be a significant positive association
between financial health and performance.

3. Sample selection and method

3.1. Sample selection

We obtained data on ERP-adopting firms from Hayes et al. (2001)1 and information
concerning firm performance from Compustat. Since it may take several years for the benefits
of ERP adoption to accrue, we constrained our sample to include ERP announcing firms for

1
Hayes et al. (2001) included in their sample all firms announcing ERP implementation in the time period
January 1, 1990 through December 31, 1998. Announcing firms were identified through Lexis/Nexis Academic
Universe’s (News) Wire Service Reports.


which at least 3 years of financial data were available on Compustat subsequent to the ERP
adoption announcement. As a result of this constraint, the sample included no ERP adoption
announcements after 1996.2 For the inter-temporal comparisons, 3 years of pre-announce-
ment performance information and 3 years of post-announcement performance information
had to be available on Compustat. As a result of the constraints, the final sample resulted in
63 firms that announced ERP adoption. For tests of hypothesis 2 (H2a and H2b), we lost an
additional three observations due to missing information on financial health (z-score).
In our tests of the effect of ERP adoption on firm performance, we controlled for
macroeconomic conditions that could influence test results. This was accomplished by
matching ERP firms to a control sample of non-ERP firms.3 As has been done in prior
studies of firm performance (e.g., Balakrishnan et al., 1996; Barber and Lyon, 1996), we
matched firms on SIC code and firm size. The total sample of ERP and non-ERP firms was
126 for the full sample (H1) and 120 for the reduced sample (H2a and H2b).
To ensure that no ERP-adopting firms were included in the control sample, we conducted a
telephone survey to determine whether the identified firms had indeed implemented an ERP
system. A total of 21 of the 63 firms (33.33%) responded to the survey. With respect to the
nonresponding firms, we searched through Lexis-Nexis and found that none of the control
firms had a news wire disclosure concerning ERP adoption (see Hayes et al., 2001 for the
search technique). Additionally, through Lexis-Nexis, we searched the SEC database and
annual reports using the name of the firm and the following search string: ERP or enterprise
resource planning or QAD or SAP R/3 or Oracle or Peoplesoft or J. D. Edwards or SSA or
Baan or Geac or Lawson or Hyperion. With regard to foreign firms, we conducted an
additional search of Lexis-Nexis using International Company Reports.
One telephone survey respondent indicated ERP adoption. The Lexis-Nexis search resulted
in three firms that possibly had adopted ERP. As a result, we selected four new control firms.
The new control firms were subjected to the Lexis-Nexis search procedure to ensure no ERP
disclosures, and none were found.

3.2. Measuring performance

As mentioned earlier, we used four measures of performance (ROA, ROS, ATO, and ROI).
Descriptions of the performance variables, along with descriptions of the metrics we used to
determine firm size and health, are shown on Table 1.
Performance was divided into two time periods—pre-adoption and post-adoption. The
fiscal year of the ERP announcement, identified as year zero (t0), served as the baseline year
for aligning the ERP-adopting and non-adopting firms. The pre-adoption period encompassed
3 years (tÀ3 to tÀ1), and the post-adoption period covered 3 years (t+1 to t+3).

2
The sample size for firms having 4 years of post-adoption information was too small to allow for testing.
3
When industry averages were calculated for the sample firms, it was found that the industry size varied
considerably from the sample firms. In some instances, the industry average was much smaller than the sample
firm, and in other instances, the industry average was much larger than the sample firm. As a result, a matched-pair
design was used rather than an industry average control variable.


Table 1
Description of variables
Variable Description
ROA Return on assets is income before extraordinary items (available for common stockholders), divided
by the average of the beginning and ending total assets. Compustat then multiplies the ratio by 100
ROS Return on sales is income before extraordinary items (available for common stockholders), divided
by net sales for the period
ATO Asset turnover is net sales for the period, divided by the average of the beginning and ending total
assets
ROI Return on investment is income before extraordinary items (available for common stockholders),
divided by the sum of total long-term debt, preferred stock, minority interest and total common
equity. Compustat then multiplies the ratio by 100
Size The natural log of total assets
Health Altman’s z-score, as represented by the Compustat variable ZSCORE

For H1, we tested for differences between pre- and post-adoption for ERP firms and non-
ERP firms. Additionally, we conducted parametric and nonparametric tests and a regression
analysis of performance differences between ERP and non-ERP-adopting firms. The
regression model allowed us to control for the firms’ pre-adoption performance. In the
regression model, we regressed performance measures on pre-adoption financial performance
and a dummy variable representing ERP versus non-ERP adoption as follows:
Financial Ratio ¼ a0 þ a1 Pre-Ratio þ a2 Non-ERP Adoption þ e ð1Þ

where, Financial Ratio=post-adoption performance, as measured by average performance
over the time period t+1 to t+3 for the ratios ROA, ROS, ATO, and ROI, Pre-Ratio=pre-
adoption performance, as measured by average performance over the time period tÀ3 to tÀ1
for the ratios ROA, ROS, ATO, and ROI, Non-ERP Adoption=1 if the firm was a non-ERP
adopter, and 0 if an ERP adopter, e=error term.
To be included in the sample, a firm must have at least one value in the time period tÀ3 to
tÀ1. For firms with values in more than 1 year (tÀ3 to tÀ1), we averaged the values and used the
average as the measure of the pre-ratio. The regression results helped to ensure that differences
between ERP and non-ERP-adopting firms were not due to an omitted variables problem.4
To test hypothesis 2 (H2a and H2b), we regressed the financial ratios for ERP firms on the
firms’ size, financial health, and the interaction of firm size and financial health, along with
the control variable reflecting pre-adoption performance:

Financial Ratio ¼ b0 þ b1 Pre-Ratio þ b2 Size þ b3 Health þ b4 ðSize Â HealthÞ þ u
ð2Þ
where, Financial Ratio=post-adoption performance, Pre-Ratio=pre-adoption performance, as
measured by performance in the time period tÀ3 to tÀ1, Size=measured as the log of total

4
Barber and Lyon (1996) indicate that in addition to controlling for industry and size, it is important that pre-
event performance be controlled in models testing for abnormal performance. The Pre-Ratio variable used in the
study represents such a lagged performance measure.


assets at time t0, Health=financial health, measured using Altman’s z-score at time t0, and
u=error term.
Since we matched firms on size during the implementation year (t0), it was selected as the
most appropriate year for measuring the size variable in H2a and H2b.5 For consistency, we
also measured financial health at time t0. However, if financial health was measured at time
tÀ1, the results presented in the next section remain qualitatively the same.6

4. Results

4.1. Descriptive statistics

The ERP sample was distributed over 23 two-digit SIC classifications (see Table 2, panel A),
indicating little clustering by industry. We matched 62 of the firms using at least a two-
digit SIC classification, with one firm matched on a one-digit SIC classification.7 As
indicated by Table 2, panel B, over 58% of ERP implementation announcements occurred
in 1996. This result is not surprising given the relatively recent development of ERP
systems.8
Table 3, panel A, indicates that, on average, ERP firms were slightly larger than non-ERP
firms when measured by assets or sales. However, we did not find a statistically significant
difference when we measured assets or sales in either the year of the announcement (t0) or the
year preceding the announcement (tÀ1), as a was greater than .10 for both years. Table 3 also
provides raw descriptive statistics for years tÀ3 through tÀ1 with respect to the performance
measures (ROA, ROS, ATO, and ROI). While the raw measures provided on Table 3 are not
used in the study tests, they do provide an indication that there was no trend in performance
measures prior to ERP adoption and that the measures were skewed due to extreme
observations. To reduce the influence of extreme performance measure observations, we
restated outliers to the 5th and 95th percentiles for the pre- and post-performance 3-year
distributions used in the subsequent tests.9

5
An analysis was also conducted using the log of sales as the size variable. The levels of significance for the
ERP firms remained unchanged using sales as the size variable. For the non-ERP firms, none of the inferences
reported using total assets were changed.
6
When using the z-score at tÀ1, the interaction term for ROI was not significant at a<.10.
7
Baber and Lyon (1996) indicate that the power of the tests is not significantly affected by the use of a four-
digit match versus a two-digit match.
8
All tests reported in this study were rerun controlling for event clustering in 1996. While results were slightly
stronger after controlling for the clustering, the reported inferences remain unchanged.
9
Restating to the 95th and 5th percentiles is a procedure followed by Balakrishnan et al. (1996). Restatement
is recommended by Barber and Lyon (1996) as a method of reducing conservatism of the parametric t statistic.
That is, resetting the outliers reduces the differences between the parametric and nonparametric statistics. Tests
were rerun using the 1st and 99th percentiles. The highly nonnormal distributions at the 1st and 99th percentiles
greatly increased the conservatism of the parametric statistics.


Table 2
Sample characteristics
Panel A: SIC classification of ERP sample firms
Two-digit SIC Full sample Reduced sample firms
firms (H1) (H2a and H2b)a
13 1 1
15 1 1
20 4 4
22 1 1
25 2 1
27 2 2
28 5 5
29 2 2
30 1 1
32 1 1
33 4 4
34 2 2
35 6 6
36 10 9
37 1 0
38 5 5
48 4 4
49 3 3
51 1 1
59 1 1
63 1 1
73 3 3
80 2 2
Total 63 60

Panel B: Year in which ERP sample firm announcements occurred
Announcement year Number of firms Number of firms for the
for the full sample reduced sample
1992 5 5
1993 2 2
1994 11 11
1995 8 7
1996 37 35
Total 63 60
a
For analysis of hypothesis 2(H2a and H2b), three observations are deleted due to missing z-scores.

4.2. Hypothesis 1

The results of the tests of H1 are reported on Table 4. The last column of Table 4 presents
the difference in pre- and post-adoption performance. As expected, no significant difference
occurred between pre- and post-performance for ERP firms. However, over the same period,
non-ERP firms experienced a significant decline in ROA (t=2.239; one-sided P=.014), ROI


Table 3
Descriptive statistics for ERP firms and non-ERP firms
Panel A: Matching ERP firms and non-ERP firms on firm size (n=63)
Item Sample Mean Median Standard t Statistic P valuea
deviation
Time=tÀ1
Total assets ERP adopter 10,861 3654 17,448
nonadopter 9195 3003 13,161 0.61 .55
Net sales ERP adopter 9877 4492 15,967
nonadopter 8308 2969 12,209 0.62 .54

Time=t0
Total assets ERP adopter 11,051 4394 16,888
nonadopter 9483 3396 13,008 0.58 .56
Net sales ERP adopter 10,111 4814 15,521
nonadopter 8540 3105 12,004 0.64 .53

Panel B: Descriptive information concerning firm performance
Time Item Sample Mean Median Standard n
deviation
tÀ3 ROA ERP adopter 5.557 4.626 7.871 55
nonadopter 5.467 4.739 6.853 49
ROI ERP adopter 9.020 7.431 11.764 57
nonadopter 9.255 7.975 11.442 51
ROS ERP adopter 0.057 0.045 0.077 57
nonadopter 0.048 0.056 0.053 52
ATO ERP adopter 1.193 1.001 0.718 51
nonadopter 1.244 1.082 0.738 48
nonadopter 5.639 4.945 6.392 56
ROI ERP adopter 11.151 8.913 18.367 62
nonadopter 9.039 7.024 11.397 61
ROS ERP adopter 0.057 0.056 0.080 63
nonadopter 0.058 0.052 0.053 61
ATO ERP adopter 1.202 1.008 0.688 57
nonadopter 1.268 1.030 0.754 51
nonadopter 5.788 4.937 5.318 63
ROI ERP adopter À2.578 9.042 92.685 63
nonadopter 8.369 7.695 7.081 63
ROS ERP adopter 0.035 0.054 0.145 63
nonadopter 0.057 0.057 0.053 63
ATO ERP adopter 1.202 1.078 0.667 63
nonadopter 1.195 1.016 0.690 63
a
A nonparametric Wilcoxon test provides P values which are larger than those reported below, indicating no
significant difference at (a<.10) between the ERP firms and non-ERP firms.


Table 4
Performance comparison between and within ERP firms and non-ERP firmsa
Panel A: Pre- and post-adoption means [medians] for ERP firms (n=63)
Financial Pre Post1 Post2 Post3 Pre vs. Post3c
ratiob
ROA 5.341 [4.637] 4.448 [5.031] 4.916d [4.715]d 4.899d [4.692]d t=0.731; P=.468
ROS 0.052 [0.050] 0.048 [0.045] 0.050 [0.053] 0.054 [0.054] t=0.351; P=.727
ATO 1.158 [1.041] 1.136 [1.105] 1.128 [1.091] 1.135 [1.051] t=1.016; P=.314
ROI 8.576 [8.614] 7.691 [8.957]d 7.533d [8.411]e 8.002e [8.395]e t=0.518; P=.606

Panel B: Pre- and post-adoption means [medians] for non-ERP firms (n=63)
Financial Pre Post1 Post2 Post3 Pre vs. Post3f
ratio
ROA 5.455 [5.280] 3.946 [3.725] 3.571 [3.151] 3.720 [3.501] t=2.239; P=.014
ROS 0.053 [0.047] 0.045 [0.043] 0.042 [0.045] 0.047 [0.045] t=1.018; P=.157
ATO 1.156 [1.018] 1.057 [0.959] 1.061 [0.936] 1.066 [0.989] t=2.976; P=.002
ROI 8.498 [7.861] 5.804 [5.462] 4.649 [5.153] 5.457 [5.731] t=2.397; P=.010
a
The Pre information represents the average for the time periods À3 through À1. The Post information
reflects performance in year +1 for Post1, the average of performance in years +1 and +2 for Post2, and the
average of performance in years +1 through +3 for Post3. Due to non-normality, means for the Pre sample and the
Post samples were winsorized at the 5th and 95th percentile.
b
For a description of the financial ratios, see Table 1.
c
P values are for two-sided tests of the mean difference between Pre performance and Post3 performance. A
two-sided test is used since we made no prediction concerning direction of performance for ERP firms.
d
Indicates that the ERP firm ratio is significantly larger than the non-ERP firm ratio using a one-sided P<.10
based on t tests of means, or Wilcoxon tests of medians.
e
Indicates that the ERP firm ratio is significantly larger than the non-ERP firm ratio using a one-sided P <.05
based on t tests of means, or Wilcoxon tests of medians.
f
P values are for one-sided tests of the mean difference between Pre performance and Post3 performance. A
one-sided test is used since we anticipated a significant decline in performance for non-ERP firms.

(t=2.397; one-sided P=.010), and ATO (t=2.976; one-sided P=.002). While non-ERP firm
ROS also experienced a noticeable decline, it was not significant at conventional levels.10 A
test (not shown) comparing pre-adoption performance to base year (t0) performance indicated
a moderately significant (t=1.680; P=.098) increase in ROS for ERP firms and a significant
(t=2.473; one-sided P=.008) decrease in ATO for non-ERP firms. No other performance
differences were significant at conventional levels. The results indicate that subsequent to
ERP adoption, ERP firms’ performance was relatively unchanged, while non-ERP firms’
performance significantly declined.
Table 4 also shows that performance of ERP firms differed little from non-ERP firms until
two years after adoption, when non-ERP firms performed significantly ( P<.10) worse than
ERP firms on ROA and ROI. 3 years after adoption, non-ERP firms also performed

10
A Wilcoxon matched-pairs test provided qualitatively similar results; however, the results were not as strong
as those provided by the t test.


Table 5
Regression results for performance variables, controlling for pre-adoption results (n=126)
Financial ratio for third year (t+3)=Intercept+Pre-Ratio+Non-ERP adoptera
Financial Intercept Pre-Ratio Non-ERP adopter Adjusted
ratio R2
ROA 2.772 (3.82) [<.001] 0.398 (4.90) [<.001] À1.225 (À1.49) [.069] .161
ROS 0.024 (3.37) [.001] 0.573 (6.58) [<.001] À0.008 (À0.99) [.162] .252
ATO 0.170 (3.70) [<.001] 0.833 (24.98) [<.001] À0.067 (À1.92) [.029] .834
ROI 5.284 (4.27) [<.001] 0.317 (3.60) [<.001] À2.521 (À1.82) [.036] .103
a
For each independent variable, the coefficient value, (t statistic) and [ P values] are provided. The P values for
the Non-ERP Adopter variable are one-sided, all others are two-sided. The Pre-ratio reflects the average of the
financial ratio from time period tÀ3 through tÀ1. Non-ERP Adopter is 1 if the firm is not an ERP adopter and 0 if the
firm is an ERP adopter. The dependent variable and the pre-financial variable are winsorized. Cook’s D indicates no
influential observations that could be unduly affecting results. Variable descriptions are provided in Table 1.

significantly worse than ERP firms on ROA ( P<.10) and ROI ( P<.05). Although ROS
appeared to remain stable for ERP firms while decreasing for non-ERP firms, the difference
was not significant. Additionally, there was no significant difference in ATO between the
ERP and non-ERP firms, although ATO did decline between the pre- and post-adoption
periods for non-ERP firms relative to ERP firms. The results reported on Table 4 suggest that
performance benefits accruing from ERP adoption may take several years to realize.
Therefore, the remainder of the tests focused on firm performance 3 years after adoption.
The results on Table 4 lend partial support to H1, as non-ERP firms experienced a
significant decline in performance; however, when compared to ERP firms, the declines were
only significantly lower for ROA and ROI. As an additional test of H1, we regressed
performance in the third year after adoption on ERP firms versus non-ERP firms, controlling
for pre-adoption performance.11 For ROA and ROI, the results were essentially unchanged
after controlling for pre-adoption performance (see Table 5), as ROA (t=À1.49; P=.069) and
ROI (t=À1.82; P=.036) remained significantly lower for non-ERP-adopting firms. In addition,
after controlling for pre-adoption performance, ATO was significantly lower (t=À1.92;
P=.029) for non-ERP firms than ERP firms, indicating that there may be efficiency benefits
associated with ERP adoption.12 Overall, test results partially supported the first hypothesis.13

4.3. Hypothesis 2

We also hypothesized that ERP firm performance may be influenced by the interaction of
firm size and financial health. Test results (Table 6) indicated a generally positive association

11
Regression allows for control of pre-adoption performance, which Barber and Lyon (1996) indicate is
necessary to minimize misspecification of test statistics.
12
The large adjusted R2 for the ATO model was related to the high correlation (.90) between the ATO variable
and the Pre-Ratio. An analysis indicated that the Pre-Ratio explained .79 of the variance associated with ATO.
13
Regression results for performance in year +1 and year +2 (not shown) substantially support the results
found on Table 4.


Table 6
Within ERP adopters regressions related to the effect of firm size and health on financial performance (n=60)a
Financial Intercept Pre-Ratio Size Health Size*health Adjusted
ratio R2
ROA À10.962 (À3.18) 0.315 (2.86) 1.476 (3.68) 1.485 (3.16) À0.130 (À2.24) .497
[.002] [.006] [<.001] [.003] [.029]
ROS À0.130 (À3.67) 0.378 (3.38) 0.017 (4.19) 0.018 (3.77) À0.002 (À2.74) .522
[<.001] [.001] [<.001] [<.001] [.008]
ATO 0.117 (0.63) 0.837 (19.06) 0.010 (0.50) 0.004 (0.18) À0.002 (À0.57) .877
[.528] [<.001] [.619] [.860] [.573]
ROI À19.447 (À3.20) 0.243 (2.04) 2.700 (3.83) 2.577 (3.14) À0.240 (À2.31) .384
[.002] [.046] [<.001] [.003] [.025]
a
For each independent variable the coefficient value, (t statistic) and [two-sided P values] are provided. Pre-
Ratio is the average of the financial ratio from the time periods À3 through À1. The Financial Ratio is the average
of the ratio from time periods +1 through +3. Variable definitions are provided in Table 1. The dependent variables
and Pre-Ratio variables have been winsorized.

between ERP firm performance and pre-ratio (control variable), firm size, and financial
health. Firm size significantly and positively affected performance as measured by ROA
(t=3.68; P<.001), ROI (t=3.83; P<.001), and ROS (t=4.19; P<.001). Additionally, firm health
significantly and positively influenced ROA (t=3.16; P=.003), ROI (t=3.14; P=.003), and
ROS (t=3.77; P<.001). Neither size nor health significantly affected the ATO of ERP
adopters.
As expected, there was a significant interaction between firm size and financial health
relative to ROA (t=À2.24; P=.029), ROI (t=À2.31; P=.025), and ROS (t=À2.74; P=.008) for
ERP adopters. Moderated regression analysis (Sharma et al., 1981) indicated that the presence
of the interaction term significantly (a=.05) increased the explanatory power of the ROA,
ROI, and ROS regressions. Interpretation of the interaction term indicates that as firm size
and financial health increased, there was a decrease in firm performance. However, to better
illustrate the precise nature of the interaction, we performed a subgroup analysis on those
models from Table 6 with significant interactions (Sharma et al., 1981).
To conduct the analysis, we first divided the sample at the mean into large and small
firms.14 Then, we regressed performance measures on the control variable and financial
health (see Table 7). Panel A of Table 7 indicates that for larger firms, financial health
significantly and negatively affected ROI (t=À1.67; one-sided P=.059). While financial
health also negatively affected ROA and ROS, the results were not significant at conventional
levels. The subgroup analysis for large firms lends partial support to the overall interaction
found on Table 6 and H2a. Panel B of Table 7 indicates that for small firms, financial health

14
Since we had no basis for determining how to define relative firm size, selection of the mean was arbitrary.
However, analyses were also conducted using the median of total assets and by dividing the sample into one-third
large and two-thirds small firms. As the analysis moved from the mean (about 1/4 of the sample reflects large
firms) to the median, the association between health and performance for large firms became less negative and
increasingly insignificant. Small firm analysis was unchanged.


Table 7
An analysis of firm sizea and financial healthb for ERP adopters
Panel A: large ERP adopters (n=16)c
Financial Intercept Pre-Ratio (À)d Health Adjusted
ratio R2
ROA 2.204 (3.63) [.003] 0.933 (4.15) [.001] À0.377 (À0.99) [.171] .801
ROS 0.010 (1.22) [.243] 1.114 (9.25) [<.001] À0.003 (À0.96) [.177] .901
ROI 4.124 (4.26) [.001] 0.946 (5.12) [.000] À0.824 (À1.67) [.059] .786

Panel B: Small ERP adopters (n=44)c
Financial Intercept Pre-Ratio (+)d Health Adjusted
ratio R2
ROA 0.978 (1.01) [.320] 0.279 (1.98) [.055] 0.425 (2.72) [.005] .374
ROS À0.003 (À0.29) [.777] 0.428 (2.86) [.007] 0.005 (2.89) [.003] .419
ROI 2.523 (1.45) [.154] 0.176 (1.17) [.247] 0.646 (2.50) [.008] .227
a
Firms are divided into small and large based on the mean of total assets.
b
Variable descriptions are provided in Table 1.
c
Mean (standard deviation) [ P value].
d
Hypothesized direction of results.

significantly and positively impacted ROA (t=2.72; one-sided P=.005), ROI (t=2.50; one-
sided P=.008) and ROS (t=2.89; one-sided P=.003). The result supports H2b, indicating that
for smaller ERP firms, as financial health improved, so did financial performance.

4.4. Post hoc analyses

While Hypotheses 2a and 2b focus on how firm size and financial health affect the
performance of ERP firms, no hypotheses concerning size and health were provided for non-
ERP firms. However, we did conduct non-ERP adopter tests (not reported) to ensure that the
results obtained for ERP adopters were associated with ERP adoption and not a spurious
correlation. We found no significant positive association between performance and firm size,
firm health, or the interaction of firm health and firm size. The lack of significant associations
in the non-ERP firms lends some support to the arguments made in hypotheses H2a and H2b.
While Altman’s z-score has been used in prior studies (e.g., Barron et al., 1999; Hayes et
al., 2001; Miller and Skinner, 1998) as a proxy for financial health, there is no theoretical
basis for defining financial health. Therefore, to test the robustness of our results using z-
scores as a proxy of financial health, a second financial health variable was tested. In the
second set of tests, we defined financial health as the difference between ‘income before
extraordinary items at year zero (the year of implementation)’ minus ‘the average of income
before extraordinary items in the 3 years prior to year zero (tÀ3 through tÀ1).’ This second
variable yielded levels of significance comparable to those using z-scores for the health and
interaction variables. However, using this alternative metric for financial health, there was no
longer a significant (a<.10) main effect for size in the models (ROA, ROI, ROS) reported on


Table 6. Since we did not hypothesize a main effect for size, the results of our analyses were
not affected.
Because small samples can be sensitive to departures from regression assumptions, we
conducted several tests to ensure the robustness of the reported results. To test for
heteroscedasticity, we used White’s (1980) test. Results indicated that heteroscedasticity
was a problem for two of the regression equations examined on Table 6. White’s correction 15
yielded essentially the same levels of significance as those reported earlier; therefore, the
uncorrected statistics were reported for all regression equations.
We checked to determine if the ERP vendor was significantly associated with firm
financial performance. Vendors were divided into two groups—SAP and Peoplesoft versus all
other vendors (in accordance with Hayes et al., 2001). Including a vendor control variable
slightly increased the test statistics reported for the interaction terms in Table 6; however, the
overall inferences remained unchanged.
Cook’s D test revealed two potentially influential observations related to the ERP firm
regressions reported on Table 6, panel A (Neter et al., 1990). Eliminating the two influential
observations did not influence the explanatory power of the regressions, and the results were
essentially the same. Finally, neither variance inflation factors (VIF) (Neter et al., 1990) nor
multicollinearity diagnostics (Belsley et al., 1980) revealed that multicollinearity was
significant among the pre-ratio, size, and health variables.

5. Discussion

In this study, we investigated the longitudinal impact of ERP adoption on firm perform-
ance. To accomplish this objective, we compared the financial performance of 63 ERP
adopters to 63 nonadopters in a matched-pair design. We examined the following perform-
ance metrics: ROA, ROS, ATO, and ROI.
We predicted that firm performance would be greater for adopters than nonadopters,
primarily because the financial performance of nonadopters would decline by comparison.
Overall, research findings suggest that three (ROA, ATO, and ROI) of the four performance
metrics support this hypothesis. More specifically, study results indicate that ROA, ROI, and
ATO were significantly lower for nonadopters than adopters, the third year after ERP
implementation. Additionally, the average 3-year ROA and ROI were significantly greater
for adopters when compared to nonadopters. Subanalysis revealed that the performance
metrics for adopters did not change significantly from pre- to post-adoption, but the metrics
declined for nonadopters over the same time period.
In addition, we investigated the interactive effect of firm size and financial health on the
performance of ERP adopters. We found a significant interaction between size and health for
three of the financial measures (ROA, ROI, and ROS). Our results suggest that large/
unhealthy adopters experience better ROI than large/healthy adopters. We also found that

15
White’s correction adjusts the t statistic to account for misspecification due to heteroscedasticity. For
additional information on the correction, see White (1980).


small/healthy firms that adopt ERP systems demonstrate better performance (ROA, ROI, and
ROS) than small/unhealthy firms. Accordingly, the second hypothesis (H2a and H2b) was
partially supported.
Our significant findings are consistently related to two measures of firm performance—
ROA and ROI. We also examined two components of ROA (ROS and ATO) that are often
described as measures of profitability (ROS) and efficiency (ATO). We found that ATO
decreased significantly for non-ERP adopters, and there was some indication that ROS
decreased for nonadopters; however, the difference was not significant. Since ROA is
computed by multiplying these two measures, study results indicate that improvements in
firm performance are the result of the combined efficiency and profitability gains.
With respect to pre- to post-ERP adoption gains, our results are similar to Poston and
Grabski (2001), in that both studies found limited evidence of efficiency gains, but little
evidence of gains elsewhere. However, a comparison of ERP adopters and nonadopters sheds
light on the productivity paradox, suggesting that financial gains arising from ERP adoption
may be passed on to customers in the form of lower prices; hence, the performance of
nonadopters declines by comparison. We recognize that we do not directly test whether ERP
gains are being transferred to customers, as micro-level archival data of this nature is not
available. Thus, future researchers might test this theory via survey or case data, as well as
examine competing reasons that might explain why the relative performance of nonadopters
declines when compared to ERP adopters.
Our findings are also consistent with Hayes et al. (2001) and Hunton et al. (submitted for
publication) in that potential ERP adopters should be aware of performance improvement
limitations that may result from a combination of size and health. That is, large/unhealthy
firms can expect greater performance gains than their large/healthy counterparts, as large/
unhealthy firms have more room for potential efficiency and effectiveness gains. Addition-
ally, small/healthy firms can anticipate greater future benefit from ERP adoption than small/
unhealthy firms, primarily because small/healthy firms can likely acquire needed resources to
complete full integration and thereby become larger players in the marketplace. Unfortu-
nately, small/unhealthy firms might suboptimize the potential impact of ERP systems, as they
may be forced into partial implementations due to resource constraints. As a result, business
process innovation may not occur to the extent desired which can hamper further improve-
ment in financial health.
However, the disadvantage of small/unhealthy firms may dissipate in the near future, as
ERP vendors are now making it more affordable for medium- and small-size firms to
implement ERP systems by scaling back the complexity of their systems and assisting their
clients in implementation activities. One relatively recent method of increasing affordability
is the use of application service providers (ASPs), where an ASP firm offers to host and
maintain the ERP system, and the service recipient pays a fee to use the system. This strategy
minimizes the technical and financial burden on medium- and small-size firms, while offering
to them the full range of ERP functionality.
The spread of ERP systems across business organizations has implications for accounting
researchers and professionals, since the systems automate a wide array of business processes
and decrease the need for many accounting functions currently being performed by


accountants. As a result, new internal control and auditing questions arise regarding the
unique risks inherent with ERP systems, as compared to legacy accounting systems.
Accountants and auditors must also deal with ways to report financial information in a more
timely manner than in the past (e.g., near-continuous financial reporting) and attest to the
reliability of such information. Additionally, eliminating some of the currently performed
accounting functions will eliminate many lower level accounting positions, while raising the
sophistication of tasks performed by upper level accountants. For instance, degreed
accountants can be expected to perform deeper financial analyses, provide more value-added
advice regarding business process improvements, develop internal controls commensurate
with ERP risks, and make tactical and strategic decisions aimed at improving firm
performance. These issues, and more, provide the backdrop for investigating a host of
interesting and relevant issues related to ERP adoption.

References

Balakrishnan R, Linsmeier TJ, Venkatachalam M. Financial benefits from JIT adoption: effects of customer
concentration and cost structure. Account Rev 1996;1(2):183 – 205.
Barber BM, Lyon JD. Detecting abnormal operating performance: the empirical power and specification of test
statistics. J Financ Econ 1996;41(3):359 – 99.
Barron OE, Kile CO, O’Keefe TB. MD&A quality as measured by the SEC and analysts earnings forecasts.
Contemp Account Res 1999;16(1):75 – 109.
Barua A, Kriebel CH, Mukhopadhyay T. Information technologies and business value: an analytic and empirical
investigation. Inf Syst Res 1995;6(1):3 – 23.
Belsley DA, Kuh E, Welsch RE. Regression diagnostics: identifying influential data and sources of collinearity.
New York: Wiley; 1980.
Bharadwaj AS. A resource-based perspective on information technology capability and firm performance: an
empirical investigation. MIS Quart 2000;24(1):169 – 96.
Brakely HH. What makes ERP effective? Manuf Syst 1999;17(3):120+.
Cooke DP, Peterson WJ. SAP implementation: strategies and results. New York (NY): The Conference Board;
1998.
Davenport T. Putting the enterprise into the enterprise system. Harv Bus Rev 2000;76(4):121 – 31.
Dos Santos B, Peffers K, Mauer DC. The impact of information technology investment announcements on the
market. Inf Syst Res 1993;4:1 – 23.
Drucker PF. The coming of the new organization. Harv Bus Rev 1988;66(1):45 – 53.
Eliashberg J, Chatterjee R. Analytical models of competition with implications for marketing issues, findings, and
outlook. J Mark Res 1985;22(August):283 – 96.
Eliashberg J, Chatterjee R. Stochastic issues in modeling the innovation diffusion process. In: Mahajan V, Wind
Y, editors. Innovation diffusion models of new product acceptance. Cambridge (MA): Ballinger Press; 1986.
p. 151 – 99.
Eliashberg J, Jeuland AP. The impact of competitive entry in a developing market upon dynamic pricing strategies.
Mark Sci 1986;5(1):20 – 37.
Grover V, Teng J, Segars AH, Fielder K. The influence of information technology diffusion and business process
change on perceived productivity: the IS executive’s perspective. Inf Manage 1998;34(3):141 – 59.
Harris DH. Organizational linkages: understanding the productivity paradox. Washington (DC): National Acad-
emy Press; 1994.
Hayes DC, Hunton JE, Reck JL. Market reaction to ERP implementation announcements. J Inf Syst 2001;
15(1):3 – 18.


Hayn C. The information content of losses. J Account Econ 1995;20:125 – 53.
Hitt LM, Brynjolfsson E. Productivity, business profitability, and consumer surplus: three different measures of
information technology value. MIS Q 1996;20(2):121 – 42.
Huber GP. A theory of the effects of advanced information technologies on organizational design, intelligence, and
decision making. Acad Manage Rev 1990;15(1):47 – 71.
Huff PL, Harper Jr RM, Eikner AE. Are there differences in liquidity and solvency measures based on company
size? Am Bus Rev 1999;17(2):96 – 106.
Hunton JE, McEwen RA, Wier B. Analysts’ reactions to ERP announcements. J Inf Syst 2003. [submitted for
publication].
Khurana I, Lippincott B. Restructuring and firm value: the effects of firm profitability and restructuring purpose.
J Bus Finance Account 2000;27(9):1107 – 31.
Koch C. The integration nightmare: sounding the alarm. CIO Mag 1996;(November 15):6 – 10.
Mabert VA, Soni A, Venketaramanan MA. Enterprise resource planning survey of U.S. manufacturing firms. Prod
Inventory Manage J 2000;52 – 58 [second quarter].
Mahmood MA, Mann GJ. Measuring the organizational impact of information technology investment: an ex-
ploratory study. J Manage Inf Syst 1993;10(1):97 – 122.
Miller GS, Skinner DJ. Determinants of the valuation allowance for deferred tax assets under SFAS No. 109.
Account Rev 1998;73(2):213 – 33.
Neter J, Wasserman W, Kutner MH. Applied linear statistical models. Homewood (IL): Irwin; 1990.
O’Leary D. Enterprise resource planning systems: systems, life cycle, electronic commerce, and risk. Cambridge
(MA): Cambridge Univ Press; 2000.
Peffers K, Dos Santos BL. Performance effects of innovative IT applications over time. IEEE Trans Eng Manage
1996;43(4):381 – 92.
Pinsonneault A. Information technology and the nature of managerial work: from productivity paradox to the
Icarus Paradox. MIS Q 1998;22(3):287 – 11.
Poston R, Grabski S. Financial impacts of enterprise resource planning implementations. Int J Account Inf Syst
2001;2:271 – 94.
Robertson TS, Gatignon H. Competitive effects on technology diffusion. J Mark 1986;50(3):1 – 12.
Schaeffer C. Performance measurement. IIE Solut 1996;28(3):20 – 7.
Sharma S, Durand RM, Gur-Arie O. Identification and analysis of moderator variables. J Mark Res 1981;
18(August):291 – 300.
Skousen KF, Stice EK, Stice JD. Intermediate Accounting. 13th ed. Cincinnati (OH): South-Western College
Publishing; 1998.
Stedman C. Survey: ERP costs more than measurable ROI. Computerworld 1999;33(14):6.
Stein T. Drinking in enterprise resource returns. Informationweek 1998;679(Apr 27):72 – 5.
Vaughan J. Enterprise applications. Software Mag 1996;16(5):67 – 72.
Wah L. Give ERP a chance. Manage Rev Mar 2000;20 – 4.
Weill P. The relationship between investment in information technology and firm performance: a study of the
valve manufacturing sector. Inf Syst Res 1992;3(4):307 – 33.
White H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity.
Econometrica 1980;48:817 – 38.

Information system erp

More Related Content

What's hot

Viewers also liked

Similar to Information system erp

More from Ikram KASSOU

Recently uploaded

Information system erp