ERP IMPACT ON NUMBER OF EMPLOYEES AND PERSONNEL
COSTS IN SMALL AND MEDIUM SIZED ENTERPRISES
A Panel Data Approach
Jose Esteves Sousa and Victor Bohorquez Lopez
IE Business School, Maria de Molina 12, 4to derecha, Madrid, Spain
Keywords: ERP impact, panel data, number of employees, personnel costs, SME.
Abstract: Enterprise Resource Planning (ERP) vendors have emphasized a positive impact of their ERP projects in
company performance and in costs reduction. Recently, some researchers have started to analyze the impact
on business performance of the organizational changes that complement IT investments. This study attempts
to analyze the impact of ERP implementations in the SMEs’ number of employees and personnel costs. We
have collected information of 168 Spanish SME during the period 1997-2005, concerning the type of
purchased ERP, implementation period, number of employees, personnel costs and some financial
indicators. We use two panel data models to compare and analyze the number of employees’ evolution and
the related personnel costs, before and after ERP implementations. Our preliminary findings suggest that as
bigger the SME as lower will be the decrease its number of employees. On the other hand, ERP impacts
positively in personnel costs. This trend to increase personnel costs can be explained in the sense that SMEs
using an ERP system need people not only with specific operative skills but also with a very holistic
approach to understand and obtain maximum benefits to the ERP system.
1 INTRODUCTION
Enterprise Resource Planning (ERP) vendors have
emphasized a positive impact of their ERP projects
in company performance and in costs reduction.
Recently, some researchers have started to analyze
the impact on business performance of the
organizational changes that complement IT
investments. However, most of the research about
ERP benefits has been in the form of individual case
studies (e.g. McAfee, 1999; Gibson et al., 1999;
Tagliavini et al., 2005), while experiences on the
field show that for the segment of Small and
Medium-sized Enterprises (SMEs), these often fail
in recognizing the economic and organizational
impacts related to their use of their implemented
ERP (Esteves and Bohórquez, 2007).
According to Sircar et al. (2000), both
Information Technology (IT) and corporate
investments have a strong positive relationship with
sales, assets, and equity, but not with net income. In
this sense, spending on IT staff and staff training is
positively correlated with firm performance, even
more than computer capital. Shang and Seddon
(2002) have categorized the expected ERP benefits
in their five-dimensional framework: operational,
managerial, strategic, IT infrastructure and
organizational dimension. This framework provides
a detailed list of benefits acquired through ERP
implementation. Nevertheless, previous works had
considered the impact in number of employees or
personnel costs in these organizations.
This research-in-progress study attempts to
investigate the impact of ERP implementation in
number of employees and personnel costs in SMEs.
A common myth around ERP implementations is
that they cause a reduction in the number of
employees, especially in IT employees previously
needed to develop and maintain the prior IT system
substituted by the new ERP system (e.g. Mabert et
al., 2001; Poston and Grabski, 2001). Additionally,
there are some contradictory results on employee
turnover. For instance, a study of 233 cases of ERP
implementations reported that the impact at
organizational level consists mainly in facilitating
organizational learning and to foment empowerment
between employees (Shang and Seddon, 2002). So
far, there has been a lack of research on this topic
(Esteves and Bohorquez, 2007).
196
Esteves Sousa J. and Bohorquez Lopez V. (2008).
ERP IMPACT ON NUMBER OF EMPLOYEES AND PERSONNEL COSTS IN SMALL AND MEDIUM SIZED ENTERPRISES - A Panel Data Approach.
In Proceedings of the Tenth International Conference on Enterpr ise Information Systems - DISI, pages 196-202
DOI: 10.5220/0001693001960202
Copyright
c
 SciTePress

This paper is structured as follows. First, we
describe the theoretical background. Next, we
present the research methodology used. Then, we
explain the preliminary findings and finally, we
draw some conclusions and further work.
2 LITERATURE REVIEW
Although ERP vendors claimed for the impact of
their ERP systems in companies’ performance, few
studies had demonstrated this impact in companies’
performance or in enterprise size. The press is plenty
of examples of not so successfully ERP
implementations and, in some cases, there is the
evidence of high expectations before the ERP
implementation.
By the late 1990’s, the research concerning the
IT impact on financial performance broadened with
a new research path that focused on the business
value of ERP systems. Once adopted within and
across organizations, ERP systems achieve the
integration of such business functions as accounting,
sales and marketing, operations and logistics, and
human resources. ERP systems are built on a single
database that enables modules to share data, thus
speeding up the information flow within
organizations.
Empirical studies show little financial gains
associated with ERP implementations. For example,
Poston and Grabski (2001) examined the impact of
ERP systems implementation on firm financial
performance during an analysis period of 3 years
before and 3 years after ERP implementation. They
found no significant improvements in the financial
ratios. However, the firms obtained a significant
decrease of Cost of Goods Sold (COGS) as a
percentage of revenue, in the third year after
implementation. In another study, Bharadwaj (2000)
results indicate that firms with high IT capability
tend to outperform a control sample of firms on a
variety of profit and cost-based performance
measures. On the other hand, market and managers
perceive value in ERP announcements, and ERP
implementations, respectively (Mabert et al., 2001;
Hayes et al., 2001). Thus, the question about the
business value realization of ERP implementations
still remains unanswered. One anecdotal answer lies
in Hitt and Brynjolfsson’s (1996) suggestion that the
ERP financial gains are passed on to consumers
through lower prices. Or, it may be that ERP
financial gains are positively associated with
successful ERP implementations. Stratopoulos and
Dehning (2000) tested whether successful IT
projects lead to a higher financial performance
compared with ineffective IT projects. Their
findings reveal that the successful IT investments
entail higher financial performance for 3 or 4 years.
However, the higher financial performance is short-
lived. The quality of ERP implementations is a
variable that could have explanatory power when
looking into how ERP systems affect the financial
performance of adopters. The financial impact of
successful ERP adoptions is expected to exceed the
financial impact of less successful ERP adopters,
because the asset utilization and the business
processes efficiency are higher for the former group
of companies. Another point of view suggests that
when enterprises implement ERP systems, they need
to redesign their business processes in a way that
information flows smoothly within organizations.
Enterprises can not obtain expected returns from
ERP investments unless these changes are
effectively managed after ERP systems go live (Lee
and Lee, 2004).
Summarizing, this ERP literature review
provides ambiguity for predicting the impact of ERP
on firm size in terms of number of employees and
personnel costs. Indeed, there is a lack of studies in
this topic for SMEs (Esteves and Bohorquez, 2007).
2.1 Hypotheses Development
The European Union (EU) classifies SMEs in three
main categories:
• Micro enterprises are defined as
enterprises which employ fewer than 10
persons and whose annual turnover or
annual balance sheet total does not exceed 2
million euro
• Small enterprises are defined as enterprises
which employ fewer than 50 persons and
whose annual turnover or annual balance
sheet total does not exceed 10 million euro
• Medium enterprises are defined as
enterprises which employ fewer than 250
persons and whose annual turnover or
annual balance sheet total does not exceed
50 million euro.
Based on this categorization, our study attempts
to analyze the following research issues:
Number of Employees. ERP implementation and
usage is likely to affect the SMEs size in terms of
number of employees. In particular, SMEs vary in
their degree of dependence on skilled people to use
the system. As discussed above, SMEs are divided
ERP IMPACT ON NUMBER OF EMPLOYEES AND PERSONNEL COSTS IN SMALL AND MEDIUM SIZED
ENTERPRISES - A Panel Data Approach
197

by EU in categories. Do ERPs will impact in a
different way in each of these categories?
H1: According to the SMEs size, ERP
implementation and usage are negatively associated
with their number of employees.
Personnel Costs. ERP implementation and usage is
also likely to affect SMEs personnel costs. In
particular, SMEs could obtain important savings in
personnel costs. As discussed above, SMEs are
divided by EU in categories. Do ERPs will impact in
a different way in each of these categories?
H2: According to the SMEs size, ERP
implementation and usage are negatively associated
with their personnel costs.
3 RESEARCH METHODOLOGY
The literature review shows that ERP impact on
enterprises is intrinsically associated with the time
dimension. Thus, we have collected data from SMEs
in a period range that would include some years
previously the ERP implementation and some years
after the ERP implementation. For Data analysis, we
have used Panel Data approach.
3.1 Panel Data Approach
According to Shu and Strassmann (2005), our data
contains both cross-sectional and time series data
ranging. Therefore, we have used a panel data model
because a simple Ordinary Least Squares (OLS)
regression suffers from inefficiency,
multicollinearity and correlation between the
explanatory variables and the error terms with the
estimation being biased. Panel data models have
become increasingly popular among applied
researchers due to their heightened capacity for
capturing the complexity of human behavior, as
compared to cross-sectional or time series data
models, when used separately. The main motivation
for using a panel data approach is to be able to
combine the time-series analysis with the cross-
sectional approach, taking advantage of a larger
number of observations (Hsiao, 2003). Other reasons
to use panel data approximation are: controlling for
individual heterogeneity; giving more informative
data, more variability, less collinearity among the
variables, more degrees of freedom and more
efficiency (Baltagi, 2005).
3.2 Theoretical Model
As previously explained, most of the research
studies have used different methods and models to
assess the ERP impact in enterprises with some
contradictory results. In our research, we have used
a translog production function because it allows
better exploration between input variables and it is a
more flexible functional form (Evans et al., 2000).
We complement this procedure with an appropriate
analysis using panel data approach to extent the
results and because they will be statistically
unbiased and consistent (Shu and Strassmann,
2005).
The explanation of used variables is shown below:
Emp: The number of employees to the SMEs.
Logarithmic transformation was performed to
eliminate the asymmetries caused by the differences
in size among the observational units, which could
bias the results by giving too much weight to the
observations of the big SMEs.
EmpCosts: Natural logarithm of personnel cost.
Inc: Incomes in thousands of euros. Logarithmic
transformation was also performed for the same
reason as for employees.
Assets: Assets in thousands of euros. As in previous
variables, the dissimilarities in the sizes of the SMEs
called for logarithmic transformation.
With_ERP: A dummy variable to indicate every
year that enterprise use an ERP system. If a
company implemented an ERP in 1999, this variable
has a value equal to 0 for years 1997 and 1998; but
has a value equal to 1 for years 1999 until 2005.
Micro_Emp: A dummy variable to indicate if
enterprise is micro (value equal to 1) according it
number of employees or not (value equal to 0).
Small_Emp: A dummy variable to indicate if
enterprise is small (value equal to 1) according it
number of employees or not (value equal to 0).
Medium_Emp: A dummy variable to indicate if
enterprise is medium (value equal to 1) according it
number of employees or not (value equal to 0).
Micro_Inc: A dummy variable to indicate if
enterprise is micro (value equal to 1) according it
incomes or not (value equal to 0).
Small_Inc: A dummy variable to indicate if
enterprise is small (value equal to 1) according it
incomes or not (value equal to 0).
Medium_Inc: A dummy variable to indicate if
enterprise is medium (value equal to 1) according it
incomes or not (value equal to 0).
We use two different panels data to validate our
hypotheses. For hypothesis H1 the panel data is
organized as follows:
ICEIS 2008 - International Conference on Enterprise Information Systems
198

Ln(Emp
it
) = αi + β
1
Ln(EmpCosts
it
) +
β
2
With_ERP
it
+ β
3
Micro_Inc
it
+
β
4
Small_Inc
it
+ β
5
Medium_Inc
it
+ ε
i
t
(1)
In this case, the SMEs are the observational units (i
= 1, 2, ..., 168) for the cross-sectional part of the
model. Time series of the natural logarithm of the
number of employees (Emp) were used as a
dependent variable. The natural logarithm of
personnel costs (EmpCosts) and a set of dummy
variables to indicate every year that enterprise use an
ERP system (With_ERP) and the size of each
enterprise according its incomes (micro, small or
medium) were used as independent variables.
For hypothesis H2 the panel data is organized as
follows:
Ln(EmpCosts
it
) = αi + β
1
Ln(Inc
it
) +
β
2
Ln(Assets
it
) + β
3
With_ERP
it
+
β
4
Micro_Emp
it
+ β
5
Small_Emp
it
+ β
6
Medium_Emp
i
t
+ ε
i
t
(2)
In this case, the SMEs are the observational units (i
= 1, 2, ..., 168) for the cross-sectional part of the
model. Time series of the natural logarithm of the
personnel costs (EmpCosts) were used as a
dependent variable. The natural logarithm of
incomes (Inc), the natural logarithm of assets
(Assets) and a set of dummy variables to indicate
every year that enterprise use an ERP system
(With_ERP) and the size of each enterprise
according its number of employees (micro, small or
medium) were used as independent variables.
According to Arellano and Bover (1990), the
supposition whether effects are fixed or random is
not an intrinsic quality of the specification. In fact,
the individual effects may be considered always
random without loss of generality. Treating the
effects as fixed or randomized makes no difference
when T is large, because both Least Square Dummy
Variable (LSDV) estimator and the generalized
least-squares estimator becomes the same estimator.
In fact, when T is finite and N is large, whether to
treat the effects as fixed or random is not an easy
question to answer (Hsiao, 2003).
Statistically, fixed effects models always give
consistent results, but they may not be the most
efficient model to estimate. Random effects will
give you more accurate p-values as they are a more
efficient estimator, so you should run random effects
if it is statistically justifiable to do so. In our panel
data models, we have some variables that are
constant over time but vary between cases (like
dummy variables related with Enterprise size), and
others are fixed between cases but vary over time
(like incomes and number of employees); hence, we
should include both types by using random effects.
3.3 Data Collection
During the data collection process, the first step
consisted in the analysis of the type of ERP system
implemented. After looking for information on the
main ERP vendors and implementations in Spain,
we selected the top ERP vendor in Spain because its
ERP system had a huge penetration in SMEs from
the very beginning; hence, there are many
enterprises that have used it for a long time. This
characteristic allowed us to obtain more years to
analyze the impact of the ERP system in SMEs
number of employees and personnel costs.
Furthermore, this ERP vendor agreed to provide us
its database of SME customers.
We have collected an original sample of 310
Spanish enterprises which have implemented this
ERP system since 1997 till 2005. Using SABI
database (a database that contains legal reports on
nearly 900,000 Spanish enterprises, many including
detailed historical annual accounts, financial ratios,
ownership and subsidiaries); we obtained public data
between year 1997 and year 2005 about these
enterprises, like number of employees, personnel
costs, operation revenues and so on. Then, we have
selected a sub-sample of SMEs that we obtained all
data required during the sample period defined
because neither their ERP implementations have
happened at the same time, nor their reports of
public data available were equally complete. With
these criteria, the number of SMEs was reduced to
168 enterprises (see Table 1).
Table 1: Statistics of the sample used.
Year ERP
used
by
year
ERP
accum.
by
year
Years
of
imple
m.
Ln
(Emp)
Aver.
Ln
(Emp
Costs)
Aver.
1996 1 1 9
1997 7 8 8 4.27 7.33
1998 14 22 7 4.27 7.49
1999 21 43 6 4.35 7.63
2000 27 70 5 4.42 7.77
2001 21 91 4 4.53 7.87
2002 23 114 3 4.61 7.97
2003 33 147 2 4.61 8.07
2004 21 168 1 4.65 8.12
2005 0 168 0 4.65 8.16
ERP IMPACT ON NUMBER OF EMPLOYEES AND PERSONNEL COSTS IN SMALL AND MEDIUM SIZED
ENTERPRISES - A Panel Data Approach
199

We can see that number of employees and personnel
costs increase each year rather than diminish. For
this reason, we divided our sample according to
enterprises’ size to verify whether the behavior is
identical in each case and to avoid the mistakes
which arise when SMEs of different sizes are
evaluated together. In this sense, we used the EU
classification of SMEs to organize and classify our
sample. According to this classification we show our
sample composition in Table 2.
Table 2: Companies by Number of Employees.
(1b) (2b) (3b) >50
M€
Total %
(1a) 4 1 0 0 5 3
(2a) 3 18 14 0 35 21
(3a) 0 12 81 11 104 62
>=
251
emp.
0 0 8 16 24 14
Total 7 31 103 27 168 100
% 4 19 61 16 100
(1a): Micro Enterprises between 1 and 10 employees
(2a): Small Enterprises between 11 and 50 employees
(3a): Medium Enterprises between 51 and 250 employees
(1b): Micro Enterprises with incomes until 2 M€
(2b): Small Enterprises with incomes between 2 and 10 M€
(3b): Medium Enterprises with incomes between 10 and 50
M€
4 PRELIMINARY FINDINGS
4.1 Employee Analysis
In this section, we analyse the evolution of the
number of employees and personnel costs in our
SME sample. If we consider the results per year, we
cannot isolate the effect that has each ERP
implementation because all the SMEs did not
implement and use an ERP in the same year.
To better understand our sample and explain the
behavior of SMEs according to their size, we
analyzed SMEs categorized by the EU ranking
classification.
Figure 1 represents the employees’ evolution
according to the obtained incomes by year.
Employees Evolution according to Incomes
0
2
4
6
8
10
012345
Years since implementation
<= 2 M€
> 2 M€ & <=
10 M€
> 10 M€ &
<= 50 M€
> 50 M€
Figure 1: Natural logarithm of employees’ number
evolution for ERP adopters according to Incomes in EU
ranking for SMEs, by years since implementation.
Figure 2 shows the personnel costs evolution
according to the SME size by number of employees.
Personnel Costs Evolution according to Number
of Employees
0,00
5,00
10,00
012345
Years since implementation
<= 10
11 - 50
51 - 250
>= 251
Figure 2: Natural logarithm of personnel costs evolution
for ERP adopters according to Number of Employees in
EU ranking for SMEs, by years since implementation.
If we compare the results since ERP implementation
year with the last year evaluated, we note that the
evolution of all categories is similar and they tend to
diminish. Nevertheless, the unique exception is the
last category that shows a slight tendency to increase
after five years of ERP implementation year. This
phenomenon can be justified by the fact that last
category corresponds to medium enterprises with
better incomes in our sample. This kind of SME has
the need to grow for being able to continue
increasing their benefits and the only way is
contracting specialized employees in important areas
of their business. Therefore, it is possible that some
medium enterprises have recruited consultants from
their partners in ERP implementation because only
these SMEs can offer better salaries to specialized
people. In this sense, it is important to highlight that
these preliminary findings show that the increase or
decrease of number of employees will depend on
SMEs size.
ICEIS 2008 - International Conference on Enterprise Information Systems
200

4.2 Panel Data Findings
For the hypothesis H1 testing, we use the first panel
data model. Table 3 shows the estimated panel data
values using random effects.
Table 3: Panel Data Analysis using Random Effects to
evaluate the ERP impact on number of employees.
Random Effects
Variable Coef. Std. Error z Prob>|z|
Ln(EmpCosts) 0.8018913 0.0173561 46.20 0.000
With_ERP -0.1067177 0.0173789 -6.14 0.000
Micro_Inc -0.8371361 0.1518015 -5.51 0.000
Small_Inc -0.4314143 0.0933836 -4.62 0.000
Medium_Inc -0.1751324 0.073455 -2.38 0.017
Constant -1.537214 0.1634938 -9.40 0.000
In this model we obtain a significant overall R-
squared = 0.8750 with a p-value = 0.0000. The
natural logarithm of personnel costs, the dummy
variable to indicate when the SME uses an ERP
system and the dummy variables related with the
size of each SME according its incomes (micro,
small or medium) were found to be significant at the
0.05 level.
The dummy variables With_ERP, Micro_Emp,
Small_Emp and Medium_Emp have different
negative coefficients. Micro enterprises have a
coefficient value that is approximately a double of
small ones and five times of medium ones.
Therefore, SMEs size according to their incomes has
a negative impact in the number of employees. As
expected, the findings support the hypothesis H1;
hence, they suggest that the ERP implementation
and usage has a little negative impact in the number
of employees. Moreover, the evidence in Table 3
shows that this impact depends on the SME size.
For the hypothesis H2 testing, we use the second
panel data model. Table 4 shows the estimated panel
data values using random effects.
Table 4: Panel Data Analysis using Random Effects to
evaluate the ERP impact on personnel costs.
Random Effects
Variable Coef. Std. Error z Prob>|z|
Ln(Incomes) 0.4036251 0.0193436 20.87 0.000
Ln(Assets) 0.3329367 0.0232169 14.34 0.000
With_ERP 0.1315715 0.0178684 7.36 0.000
Micro_Emp -1.357411 0.2549366 -5.32 0.000
Small_Emp -1.142593 0.1397028 -8.18 0.000
Medium_Emp -0.5155082 0.1158403 -4.45 0.000
Constant 1.213851 0.2254311 5.38 0.000
In this model we obtain a significant overall R-
squared = 0.7340 with a p-value = 0.0000. The
natural logarithm of incomes, the natural logarithm
of assets, the dummy variable to indicate when the
SME uses an ERP system and the dummy variables
related with the size of each SME according its
number of employees (micro, small or medium)
were found to be significant at the 0.05 level.
The dummy variable With_ERP has a positive
coefficient whereas Micro_Emp, Small_Emp and
Medium_Emp have different negative coefficients.
Micro enterprises have a coefficient value that is
approximately a 20% higher than small ones and a
250% of medium ones. Therefore, the size of SMEs
according number of employees has a negative
impact in their personnel costs. In this case, the
findings contradict hypothesis H2, because they
suggest that the implementation and use of an ERP
system has a little positive impact in personnel costs;
hence, ERP implementation and usage in SME
suggests an increment in personnel costs. However,
the evidence in Table 4 supports that ERP impact on
personnel costs depends on the SME size.
5 CONCLUSIONS AND
FURTHER WORK
Our preliminary results show that after an ERP
implementations, there is a tendency in SME to
decrease the number of employees but it depends on
the SME size, as bigger the SME as lower will be
the decrease in its number of employees. This result
is interesting because data obtained from Spanish
National Institute of Statistics shows an increase
every year in the economically active population by
business sector. However, according our findings,
SMEs using an ERP system show a reduction in
their number of employees. On the other hand, ERP
implementation and usage are positively associated
with personnel costs. This trend to increase
personnel costs can be explained in the sense that
SMEs that use ERP systems need people not only
with specific operational skills but also with a very
holistic approach and with more managerial skills to
understand and obtain maximum benefits with the
ERP system.
To better understand these results, we will attempt to
conduct case studies and interviews with a
representative sub-sample of SMEs, considering not
only SMEs size but also their business sector.
Moreover, a further research should analyze the
ERP IMPACT ON NUMBER OF EMPLOYEES AND PERSONNEL COSTS IN SMALL AND MEDIUM SIZED
ENTERPRISES - A Panel Data Approach
201

correlation between business sector (e.g. production,
service, retail) and the obtained results.
One of the limitations of this study is that we did not
divide employees by role (e.g. marketing,
accounting, administrative, IT personnel, etc.). The
main reason is that the legal information provided by
SMEs is the total number of employees by company.
SMEs are reluctant to provide the information
disaggregated and, in some cases, they don’t know
the historical evolution of their employees. Also, and
due to some legal benefits, employees are
categorized in general personnel categories that not
always fit with the real function/role within the
company especially when SMEs elaborate the
payroll and assign personnel costs to a certain area.
But, we will try to obtain the disaggregated
information at least for IT and administrative
personnel since it seems that empirically these are
the roles more affected by ERP adoptions not only in
terms of number of employees but also in terms of
personnel costs.
Future work should compare these findings with
other similar studies of SMEs in other countries that
have implemented ERP systems. Nevertheless, ERP
systems do not seem to affect equally all SME’
areas.
Hopefully, the results of this study will have an
impact on the customers’ strategies of ERP vendors
and consultants, but also in the understanding of
ERP business benefits and their perception from the
different ERP stakeholder’ viewpoints. The results
may help to improve the understanding of ERP
success and satisfaction levels (expected and
perceived) from the ERP stakeholders. Currently, we
are in contact with some ERP vendors to extend our
sample to other SMEs and other ERP system to
analyze if these findings can be generalized to all
ERP systems. Furthermore, we only consider for this
study the core ERP modules (FI/CO, MM/SD and
HR) and we will attempt to analyze the impact of the
extended ERP modules (e.g. CRM, SCM) in SMEs.
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