9. Factors Affecting Erp System Adoption

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JEIM
18,4 Factors affecting ERP system
adoption
A comparative analysis between SMEs and
384 large companies
G. Buonanno, P. Faverio, F. Pigni, A. Ravarini,
D. Sciuto and M. Tagliavini
`
CETIC Universita Cattaneo – LIUC, Castellanza, Italy

Abstract
Purpose – Proposes providing an insight about enterprise resource planning (ERP) adoption,
highlighting contact points and significant differences between the way small to medium-sized
enterprises (SMEs) and large companies approach such a task.
Design/methodology/approach – The research is based on a wide literature review, focused on the
identification of a taxonomy of business and organizational factors influencing ERP adoption. The
deriving research model was incorporated in a questionnaire that was preliminarily tested and finally
provided to a sample of 366 companies of any size. Responses were collected through personal
interviews made by a dedicated team to a top manager.
Findings – The analysis of the empirical data shows that business complexity, as a composed factor,
is a weak predictor of ERP adoption, whereas just company size turns out to be a very good one. In
other words, companies seem to be disregarding ERP systems as an answer to their business
complexity. Unexpectedly, SMEs disregard financial constraints as the main cause for ERP system
non-adoption, suggesting structural and organizational reasons as major ones. This pattern is
partially different from what was observed in large organizations where the first reason for not
adopting an ERP system is organizational. Moreover, the decision process regarding the adoption of
ERP systems within SMEs is still more affected by exogenous reasons or “opportunity of the moment”
than business-related factors, contrary to large companies that are more interested in managing
process integration and data redundancy/inconsistency through ERP implementation.
Research limitations/implications – The research model is based on the assumption that
business complexity and organizational change are the most relevant variables influencing ERP
adoption, and such variables are explained through a set of factors inherently limited by the results of
the literature review.
Practical implications – The results of the empirical research provide indication to SMEs willing
to take into consideration the adoption of an ERP system. The same outcomes could be incorporated
into the development strategies of ERP software houses.
Originality/value – This paper contributes to enhancing the understanding of the factors
influencing the evolution of information systems within SMEs with respect to large companies.
Keywords Manufacturing resource planning, Small to medium-sized enterprises, Organizational change
Paper type Research paper

Journal of Enterprise Information Introduction
Management The capability of enterprise resource planning (ERP) systems to manage a company’s
Vol. 18 No. 4, 2005
pp. 384-426 resources efficiently and effectively by providing a total, integrated solution for its
q Emerald Group Publishing Limited
1741-0398
information processing needs (Fui Hoon Nah et al., 2001) has persuaded both
DOI 10.1108/17410390510609572 practitioners and managers of the importance of integrated systems, not only for large

multinational organizations, but also for small and medium-sized firms (Van Factors affecting
Everdingen et al., 2000). ERP system
The evaluation of the contribution of ERP systems in terms of both value creation and
economic returns is a difficult task, because of the extent of the organizational changes adoption
(Lozinsky, 1999; Shtub, 1999; Willcocks and Lacity, 1998) to which their implementation
leads, as well as the difficulties in predicting the return on investment (Mabert et al.,
2001). The competences required to manage properly the organizational change 385
determined by an ERP system implementation is still a debated issue. The most qualified
literature has always stressed the importance of change and project management
competences as critical success factors for ERP implementation (Davenport, 2000;
Mandal and Gunasekaran, 2003; Motwani et al., 2002), hereby indirectly raising the issue
of small to medium-sized enterprises’s (SMEs’s) lack of organizational preparation. Such
a situation is mainly caused by the low extent of formalization of people’s roles and
responsibilities that is expressed by with their continuous re-shuffle (Dutta and Evrard,
1999). This structural condition makes the identification of ERP implementation’s main
figures, such as the process owner and the key user (Davenport, 2000), extremely difficult
to achieve. Beside this, SMEs generally suffer from a widespread lack of culture, as to the
concept of business process: it is not by chance that the reinforcement of the concept of
business process is often claimed among the critical success factors in ERP
implementation (Beretta, 2002). In particular, the business process concept helps
promoting co-operation and convergence of efforts among managers (i.e. managerial
integration), versus the internal competition induced by the functionally-oriented
organizational models which is typical of SMEs.
One of the most misleading legacies of traditional software project management is
that the company expects to gain value from the use of the software application as soon
as it is installed (Al-Mashari et al., 2003). Since the adoption of an ERP system requires
extensive efforts, both for the technological and business aspects of the
implementation, neither information technology (IT) practitioners nor researchers
have developed a deterministic method to evaluate the related impacts (Al-Mashari,
2002). In spite of the benefits potentially offered by ERP systems (Banker et al., 1998;
Davenport, 1998; Gable, 1998; Hicks and Stecke, 1998; Minahan, 1998) the evaluation
issue plays an essential role regardless the company size; during the planning phase it
is critical for companies to figure out whether a specific ERP system fits their business
practices. When the features of the software application do not correctly fit the
business requirements two possible strategies can be identified:
(1) Change the business processes to fit the software with minimal customization. On
one hand, fewer modifications to the software application should reduce errors
and help to take advantage of newer versions and releases (Fui Hoon Nah et al.,
2001). On the other hand, this choice could mean changes in long-established
ways of doing business (that often provide competitive advantage), and could
shake up important people roles and responsibilities (Dewett and Jones, 2001;
Koch et al., 1999).
(2) Modify the software to fit the processes. This choice would slow down the
project, could affect the stability and correctness of the software application and
could increase the difficulty of managing future releases, because the
customizations could need to be torn apart and rewritten to work with the
newer version (Koch et al., 1999). Conversely, it implies less organizational

JEIM changes, because it does not require dramatically changing the company best
practices, and therefore the way people work.
18,4
Although ERP vendors are concentrating on the customization process needed to match
the ERP system modules with the actual features of existing processes in a number of
different industries, several studies show that configuring and implementing ERP
386 systems is a complex and expensive task (Van Everdingen et al., 2000; Mabert et al., 2000).
Several aspects related to this twofold approach towards ERP adoption and
implementation become even more critical, for their known specificities, within SMEs
(Ravarini et al., 2000; Van Everdingen et al., 2000). Although the effective use of
business information is a strategic goal for companies of any size, nowadays most of
the ERP systems available on the market are too expensive for the financial
capabilities of smaller companies (Chau, 1995; Gartner Group and Dataquest, 1998,
1999). SMEs differ from large companies in important ways affecting their
information-seeking practices (Lang et al., 1997). These differences include the:
.
lack of (or substantially less sophisticated) information system management
(Kagan et al., 1990);
.
frequent concentration of information-gathering responsibilities into one or two
individuals, rather than the specialization of scanning activities among top
executives (Hambrick, 1981);
.
lower levels of resource available for information-gathering; and
.
quantity and quality of available environmental information (Pearce et al., 1982).

Chan (1999) asserts that many SMEs either do not have sufficient resources or are not
willing to commit a huge fraction of their resources due to the long implementation
times and high fees associated with ERP implementation. The resource scarcity, the
lack of strategic planning of information systems (IS) (Cragg and Zinatelli, 1995; Levy
and Powell, 2000; Zinatelli et al., 1996), the limited expertise in IT (Levy and Powell,
2000) and also the opportunity to adopt a process-oriented view of the business are
among the factors that strongly influence, either positively or negatively, ERP
adoption by SMEs. Thus it is necessary to find out alternative solutions providing the
ERP capabilities at an affordable price, including implementation costs (Rao, 2000).
Some ERP vendors have taken up the gauntlet and have been moving their attention
toward SMEs (Gable and Stewart, 1999) by offering simplified and cheaper solutions
(Kirchmer, 1998) from both the organizational and technological points of view,
pre-configured systems based on best-practices at a fraction of the cost originally
required and promising implementation times of 60 days. In spite of such promises,
there is not a general agreement on the effectiveness of such systems. As a result, the
current ERP systems adoption rate in SMEs is still low. Furthermore, even if ERP
implementation differences between large and small organizations are recognized in
literature (Bernroider and Koch, 2001), their focus is on the decision-making process.
Hence, other issues need to be further explored: To what extent SMEs informational
needs are different with respect to large companies? Are SME peculiarities a real
obstacle to ERP adoption? Is it possible to identify a relationship between
organizational change and ERP adoption in companies of different size?
This paper studies the factors influencing ERP systems adoption, and discusses to
what extent the differences between SMEs and larger firms affect such factors,

contributing to the increasing literature on ERP adoption in small businesses. Through Factors affecting
a detailed literature review, a set of indicators are identified as variables which could ERP system
influence the ERP adoption process. These indicators have been tested on the field
through an empirical study carried out on a sample of 366 companies. adoption

Conceptual framework
The literature provides different definitions of ERP systems: Rosemann (1999) defines an 387
ERP system as a customizable, standard application software which includes integrated
business solutions for the core processes (e.g. production planning and control,
warehouse management) and the main administrative functions (e.g. accounting, human
resource management) of an enterprise. Gable (1998) defines it as a comprehensive
package software solution that seeks to integrate the complete range of business
processes and functions in order to present an holistic view of the business from a single
information and IT architecture. Watson and Schneider (1999) define ERP as an
integrated, customized, packaged software-based system that handles the majority of an
enterprise’s system requirements in all functional areas such as finance, human
resources, manufacturing, sales, and marketing. It has a software architecture that
facilitates the flow of information among all functions within an enterprise. It is built on a
common database and is supported by a single development environment. Previous
research works (Gibson et al., 1999; Ryan, 1999) suggested how ERP adoption and
implementation could be an highly complex task in which strong managerial and
strategic competences are required to achieve the best fit between the business
peculiarities and the system itself and to deal with the unavoidable organizational
impact induced by an ERP implementation. Other studies outlined different adoption
patterns depending on company size and also observed that smaller companies face only
subset of the needs and opportunities of larger organizations (Markus and Tanis, 2000).
Furthermore, for a long time ERP adoption reasons within SMEs were explained only by
contingency or exogenous factors (Tagliavini et al., 2002). To investigate these
differences further, the research model presented in this paper explores to what extent
the business complexity (measured from a set of business factors) and the awareness of
the organizational requirements (measured by the extent of organizational change) affect
the extent of ERP adoption. Such an effort seems, in fact, feasible for organizations
experiencing high business complexity and information needs, and expecting, or even
planning, significant organizational changes.
The methodology contribution of this paper is experimentally proved by testing the
relationship between business complexity, organizational change and ERP adoption on
300 SMEs through direct, survey-based, interviews. Such an approach, based on a
statistical analysis on a high number of respondents, implies that its findings are not
easily comparable to other previous research works that are often based on case
studies on a very small set of companies.
The following sections will detail the two main components of the conceptual
framework: the business factors and the organizational change.

Business factors
Although the organizational structure of larger firms could be very different from SMEs,
it is reasonable to assume that companies of any size, characterized by high
organizational complexity (or “business complexity”), also show a critical need for

JEIM coordination and control of business activities which, in turn, is related to the complexity
18,4 of the information system (Grinyer et al., 1986; Lorange, 1980; Vancil and Lorange, 1975).
Since ERP systems have been very often advocated by researchers and practitioners as
“the answer” to manage the complexity of information flows more effectively, this last
interpretation of business complexity, will be used in the research model to investigate if
the “the condition” of being a complex organizations (which is measured by a set of
388 business factors) and a greater extent of ERP adoption are straight related factors. Hence,
the model approaches ERP systems as a sort of “black box” and thus their undeniable
inner complexity (expressed by implementation and technological issues for instance) is
taken for granted, and are therefore considered only an exogenous factor embedded into
the ERP concept itself. In particular, the several issues related to ERP system chartering,
development and maintenance (i.e. project and change management issues or cultural
and organizational un-readiness) are typical of the “critical success factors” stream of
research (Davenport, 2000; Mandal and Gunasekaran, 2003; Motwani et al., 2002) and
generally refer more to the success of the implementation than to the reasons that bring
companies to evaluate the opportunity of implementing an ERP system. Therefore, is
business complexity the possible explanation?
The assessment of the complexity measures is partially based on previous works
(Grinyer et al., 1986; Yasai-Ardekani and Haug, 1997) that have developed and
proposed metrics essentially based on size, diversification, and divisionalization. This
paper neither proposes any new measure nor tests their reliability; instead it studies
their occurrence in ERP adoption. Since the consistency of these indicators is essential
for the theoretical validity of the whole framework, a detailed analysis of the IS
literature has been performed in order to identify a set of additional business factors:
.
Company size (micro, small, medium, large). Existing literature confirms the
existence of a mutual dependence between size and organizational complexity.
Kimberly (1976) stressed the necessity of applying a different approach
depending on the industry the company belongs to: for the services industry the
number of employees has a better fit, while for manufacturing companies the
turnover seems to be a better match. In any case, literature emphasizes size as
one of the issues increasing the need for co-ordination and control of
organizational activities (Howard and Hine, 1997; Yasai-Ardekani and Haug,
1997). Apart from any organizational or strategic remark, other research works
(IDC, 1999) simply suggest a direct relationship between the size of organizations
and the percentage of organizations where ERP has been implemented.
.
The market area (local, regional, national, international). Working on a wider
market area requires the management of more differentiated legal and cultural
issues, thus introducing a higher level of complexity (Davenport, 1998; Hamel
and Prahalad, 1994; Prahalad, 1990; Sanders and Carpenter, 1998), as well as the
facing of competitive pressures characterizing the international markets (Bartlett
and Ghoshal, 1989; Roth and O’Donnell, 1996; Rumelt, 1974). In addition, as
companies become more global and develop international supply chains, the
limitations of MRPII have become apparent. Literature has identified the
attempts being made by many organizations to expand their IS infrastructure
beyond their organizational boundaries through the development of
inter-organizational business systems. Consequently, this has resulted in the
widespread adoption of ERP solutions (Irani, 2002).

.
The membership an industrial group (either as the holding or as a controlled firm). Factors affecting
This variable seems to be strongly related to the co-ordination of dispersed ERP system
business units, in terms of alignment of processes and procedures both between
the holding and the controlled companies and among controlled companies adoption
themselves. However, if the imposition of common operating processes on all
units could lead to a tight coordination between the controlled companies, in a
multiregional context strict process uniformity could be counterproductive in 389
terms of flexibility (Davenport, 1998).
.
The presence of branch offices (localization and number of branches). The
management of information flows is a crucial issue for companies with branch
offices which need to be remotely controlled. In larger organizations the
development of intranets is often characterized by a lack of coordination and
supervision (Horgan, 1997). SMEs face different issues (i.e. the cultural and
technological levels of the entrepreneur): this is one of the aspects that must be
considered to comprehend fully the fall-outs in terms of management complexity,
organizational impact and required competencies.
.
The level of diversification (in terms of products, markets, technologies).
Operating in different product-market combinations introduces another level of
complexity (Yasai-Ardekani and Haug, 1997). In related-diversified firms, an
increase in the number of businesses adds information-processing demands by
increasing business-unit interdependencies (Hill and Hoskisson, 1987; Kerr, 1985;
Michel and Hambrick, 1992; Pitts and Hopkins, 1982). In unrelated-diversifiers,
as the number of businesses increases, the information-processing requirements
associated with maintaining efficient internal capital markets also increase
(Jones and Hill, 1988). Moreover, because of the greater need for co-ordination
and control of activities, complex organizations will tend to have specialized
planning departments, employ a larger number of planners and consequently
devote a substantially larger amount of financial resources to strategic planning
(Grinyer et al., 1986; Kukalis, 1989).
.
The degree of functional extension (number of activities carried out internally).
Many companies prefer to outsource those activities that are not directly related to
the business strategies (non-core processes). The degree of functional extension
refers to the number of strategic functions directly managed within the company,
which should be related to the amount of information to be managed (Price, 1997).
In the light of the identified business factors, it is therefore necessary to verify the
association between these factors and the use of ERP systems by testing the following
six main hypotheses:
H1. The company size affects the adoption of ERP systems.
H2. The market area affects the adoption of ERP systems.
H3. The membership of a group affects the adoption of ERP systems.
H4. The presence of branch offices affects the adoption of ERP systems.
H5. The level of diversification affects the adoption of ERP systems.
H6. The degree of functional extension affects the adoption of ERP systems.

JEIM Organizational change factors
18,4 Even though business factors play an important role in determining business complexity
they are not considered sufficient to assure the feasibility of ERP adoption. Another issue
that deserves consideration is the organizational impact of ERP systems as they tend to
impose their own logic on company strategy, organization and culture (Davenport, 1998).
Thus, the ERP adoption decision affects most of the company business functions and
390 directly involves a significant number of people. The project team responsible for ERP
implementation will be challenged to either match the functionality of the application to
business practice or find ways to adapt or change current processes and procedures,
while the project team could face organizational resistance to changing the status quo
(Laughlin, 1999). By providing universal, real-time access to operating and financial
data, ERP systems allow companies to streamline their management structures, creating
flatter, more flexible, and more democratic organizations. On the other hand, they also
involve the centralization of control over information and the standardization of
processes, which are qualities more consistent with hierarchical, command-and-control
organizations with uniform cultures (Davenport, 1998). Are the organizations aware of
such a change and then ready to bear and manage it? Is the alignment between the
desired organizational change and the complexity of the IT solution verified? These
remarks highlight a possible relationship between the extent of organizational change
and the rate of ERP system adoption.
The extent of organizational change represents the degree of company
transformation that the entrepreneur plans as a consequence of a technological
innovation. This measure depends on the evaluation of the organizational and
economic impacts, such as the competence of the internal staff or their expected
resistance to change to the adoption of a new technology. In order to analyze the factors
influencing the adoption of ERP systems, we assume that ERP systems could generate
larger benefits if implemented when a high level of organizational change is planned.
Venkatraman (1994) classifies five main levels of transformation (Figure 1):
(1) Local automation of existing procedures. This strategy is pursued only for
automation of local, independent procedures. It requires minimal efforts and the
corresponding expected results are enhancements in business process
performance. Benefits coming from this strategy are easily duplicable, as

Figure 1.
Levels of business
transformation related to
technological innovation

most of standardized solutions. Therefore, it is unlikely to obtain competitive Factors affecting
advantage by simply automating existing procedures. ERP system
(2) Internal integration of existing business processes. It aims at integrating the adoption
business processes and the company IS in order to create competitive advantage.
The required integration has to be pursued both at the technological and
organizational level: whenever necessary, people belonging to different business
functions have to cooperate to reach common objectives. Together with the 391
necessary automation effort, this strategy requires an integration effort; however,
in both cases the business process structures remain unchanged.
(3) Business process reengineering. It involves the partial or complete redesign of
business processes, affecting not only the company procedures, but also its
organizational structure.
(4) Business network redesign. Changes overcome the boundaries of the company
and could affect the entire network of its external relationships. For instance,
electronic data interchange (EDI) can represent the technology chosen to pursue
this strategy, but a great effort has to be put into business process integration,
through a continuous information exchange and competence sharing. Under
these conditions each partner can exploit the competencies of the business
network instead of adopting expensive solutions of vertical integration.
(5) Redefinition of company boundaries through the creation of inter-organizational
relationships. The information communication technologies (ICT) allow the
redefinition of the competitive environment through the creation of strong
inter-organizational relationships (joint ventures, long-term contracts, licensing
agreements).

Therefore another hypothesis to be tested is focused on the matching between
organizational issues and ERP system adoption:
H7. The extent of planned organizational change is directly related to the use of
ERP systems (the greater is the planned organizational change, the greater is
the rate of adoption of ERP systems).
To develop an effective framework it is necessary to include into the research model (as
control measures) both the endogenous and exogenous reasons that may affect ERP
adoption. According to the literature (Al-Mashari, 2002), among the reasons that may
affect ERP adoption, either positively or negatively, it is possible to distinguish operational
reasons (i.e. improving responsiveness to customers and simplifying ineffective or
complex business processes) and technological reasons (i.e. Y2K compliance requirements,
integration of business processes and systems, replacement of older, obsolete systems).
For those companies which have stated that they do not make use of an ERP system, we
classified each justification for ERP non-adoption into four main categories:
(1) Structural motivations related to the need for coordination and control of business
activities, thus to the complexity of information flows (which means that the
company is not sufficiently complex to need an ERP system to manage the business).
(2) Organizational motivations (the company is not prepared to face and manage
the organizational changes related to the adoption and implementation of an
ERP system).

JEIM (3) Economic motivations (ERP system adoption and implementation would be too
18,4 costly for the company).
(4) Other reasons.

In the light of the control measures introduced the whole framework can be represented
as shown in Figure 2.
392
Methodology
Based on the literature review, focused on the identification of a taxonomy of business
and organisational factors, a questionnaire was designed. It comprised three parts: the
company demographics, the assessment of each business factor and the extent of the
organizational change. Before the complete deployment of the survey a first trial was
carried out on 122 companies suggesting the validity of the proposed approach
(Tagliavini et al., 2002). Responses were collected through personal interviews made by
a dedicated team to a top manager (possibly the entrepreneur him/herself or the CEO)
since the proposed questions required the knowledge of the main business objectives,
as well as of the features of the different business activities. The final questionnaire (an
abstract is shown in Figure 2) was then proposed to a random sample of about 2000
Italian companies of any size and industry, geographically located in northern Italy.
Data were finally analyzed with SPSS v11, in particular the hypotheses (from H1-H7)
have been tested by means of cross tabulations. Pearson chi-square was used to verify
whether the cross-tabulated groups were different, while p-values measure how the
previously mentioned difference is statistically significant. Finally, the value of
Spearman’s R is used to evaluate the reliability of correlations.
A preliminary validation of collected data has been performed by cross-tabulating
ERP adoption with each of the seven factors corresponding to the seven hypotheses
(from H1-H7). Chi-square and p-value tests have been used to verify whether the set of
companies using an ERP system is significantly different from the set of the not
adopters. Then, the connection existing between each factor and ERP adoption has
been assessed through Pearson’s R. A further analysis has been also performed

Figure 2.
Theoretical framework

separating SMEs from large companies to highlight possible differences between the Factors affecting
two subsets. ERP system
adoption
Variable measurement
According to the theoretical framework, the following sets of variables were measured:
(1) The business complexity factors have been evaluated through six indicators 393
detailed in the Figure 3. Respondents were asked to qualitatively assess each
variable of the set. More specifically:
.
Diversification has been measured as a synthetic index of business strategy
by offering only two possible responses: diversification and other strategies
(including cost-based and differentiation strategies).
.
Degree of functional extension, i.e. the number of activities carried out
internally, has been assessed with respect to a set of typical business
activities. The classical representation of the value chain (Porter and Millar,
1985) has been integrated with a more recent measure used to assess the
impact of BPR on manufacturing firms (Guimaraes and Bond, 1996). This
measure has been already adopted in author’s previous research (Tagliavini
et al., 2002).
(2) The extent of organizational change which aims at evaluating the level of
organizational change the company is prepared to face in order to achieve
competitive advantage through the use of IT, has been assessed through a
question suggesting Venkatraman five levels of organizational change. Due to
the academic formulation which undoubtedly characterizes the question in the
survey, all the organizational implications and characteristics related to each
level of the Venkatraman’s model have been thoroughly explained by the
interviewers to respondents, to clearly point out any organizational-related
issue.
(3) The technological and operational drivers have been assessed using the model
proposed by Al-Mashari (2002) integrated with other drivers which have been
identified in a previous research (Chau, 1995). Multiple responses were allowed.
(4) The motivations for ERP non-adoption have been assessed by asking
respondents to select items from a check-list (also in this case multiple
responses were allowed).

Research findings
Of the 2,000 contacted companies only 370 accepted to be interviewed yielding a
response rate of 18.5 percent. Data from this sample were collected and filtered to
resolve inconsistencies and correct anomalies, resulting in 366 valid questionnaires.
The choice of the direct interviews to collect data is accountable for the low rate of
rejected questionnaires: only four questionnaires were discarded.

Demographic data
The first part of the questionnaire dealt with companies’ demographics. Firm size
(number of employees and turnover) was investigated according to the current
definition provided by the European Union (see Table I).

JEIM
18,4

394

Figure 3.
Measures adopted in the
questionnaire

Small sized companies represent 43 percent of respondents (18 percent micro, 25 Factors affecting
percent small) while 42 percent have a medium size. Large enterprises represent 15 ERP system
percent of the sample.
With respect to the industry, the sample can be further categorized into three main adoption
groups: manufacturing (66 percent), services (20 percent) and trade (14 percent). This
distribution is highly representative of the economical characteristics of this
geographic area, where large enterprises and services/wholesaling companies play a 395
secondary role (see Figure 4).

Company size (H1)
The analysis of the correlation between company size (as a composite index between
turnover and number of employees) and ERP adoption shows a very good fit with data.
The two groups are significantly different (chi-square equal to 65,166 and p-value
lower than 0.001) while the Pearson’s R (0.401) shows that firms size and ERP adoption
are significantly correlated. In detail, while only 7 percent of companies not making use
of ERP systems are large-sized, the value corresponding to those large companies
adopting ERP systems seems even more significant. In fact, despite not constituting
the most relevant group in absolute terms (medium-sized companies are the 47 percent
of the whole sample adopting ERP), in relative terms as to the sample composition (in
which large companies are only 15 percent) large companies show an interesting result
(38 percent). The analysis clearly shows also that the rate of ERP system adoption is
quite low among both micro and small firms (3 percent and 12 percent respectively).
This reinforces the persuasion that the company size affects the ERP adoption process.
H1: verified.

Membership of a group (H2)
The cross tabulation for H2 shows an inverse correlation between membership of a
group and ERP adoption (Pearson’s R ¼ 20:277). Moreover, the 55 percent of companies

Micro Small-sized Medium-sized
Criteria enterprises enterprises enterprises

Maximum number of employees ,10 ,50 ,250
Maximum turnover in e million – 7 40
Maximum balance-sheet total in e million – 5 27
Table I.
Source: European Commission (1996) SMEs definition

Figure 4.
Sample definition by size,
industry and enterprise
application

JEIM belonging to a group prefer other management systems rather than ERP (Tables II-IV).
A correct interpretation of such result requires considering that distribution of the
18,4 sample according to this factor as unbalanced (237 firms in a standalone configuration
compared to 53 belonging to an industrial group). Nonetheless, it is reasonable to
conclude that, despite what suggested in the existing literature, the membership of a
group seems not to be directly related to the use of ERP systems.
396 The Pearson’s R index of correlation computed on SMEs (2 0.169, Tables V-VII) and
large enterprises (0.060, Tables VIII-X) did not show consistent results.
H2: rejected.

Market area (H3)
At a first glance, a wider market area of a company seems to be related to the use of
ERP systems. Only a small subset of companies with a limited market area make use of
ERP systems, while this value is higher for companies with a national market area (22
percent) and even more for those companies acting on international markets (74
percent, see cross-tabulation in Tables XI-XIII). Nevertheless, the high percentage of
companies with an international market area which do not use ERP systems (68
percent) clearly shows that the ERP system is far from being the only solution adopted
as stated in H3. The significant difference between the cross-tabulated groups,

Company size and ERP adoption
(whole sample)
Size Other management system ERP Total

Micro Count 62 3 65
% within size 95.4 4.6 100.0
% within ERP 23.6 3.3 18.4
Small Count 77 11 88
% within size 87.5 12.5 100.0
% within ERP 29.3 12.2 24.9
Medium Count 105 42 147
% within size 71.4 28.6 100.0
% within ERP 39.9 46.7 41.6
Large Count 19 34 53
% within size 35.8 64.2 100.0
% within ERP 7.2 37.8 15.0
Total Count 263 90 353
Table II. % within size 74.5 25.5 100.0
Company size and ERP % within ERP 100.0 100.0 100.0
adoption (whole sample) % of total 74.5 25.5 100.0

Value df Asymptotic significance (two-sided)
a
Pearson chi-square 65.166 3 0.000
Likelihood ratio 65.121 3 0.000
Table III. Linear-by-linear association 56.552 1 0.000
Company size and ERP n of valid cases 353
adoption (whole sample)
– Chi-square tests Note: a 0 cells (0.0 percent) have expected count less than 5. The minimum expected count is 13.51

(chi-square ¼ 14:538, p-value ¼ 0:000, Tables XI-XIII), is contradicted by the Factors affecting
unsatisfactory value of the Pearson’s R index (0.190), which confirms the lack of a ERP system
correlation between market area and ERP adoption.
adoption
H3 has also been tested on both SMEs and large companies, pointing out the same
trend (Tables XIV-XVI and Tables XVII-XIX).
H3: rejected.
397

Asymptotic Approximate Approximate
Value standard errora Tb significance

Interval by interval Pearson’s R 0.401 0.042 8.197 0.000c
Ordinal by ordinal Spearman correlation 0.405 0.044 8.287 0.000c
n of valid cases 0.353 Table IV.
Company size and ERP
Notes: a Not assuming the null hypothesis; b Using the asymptotic standard error assuming the null adoption (whole sample)
hypothesis; c Based on normal approximation – symmetric measures

Membership of a group and ERP
Other management system ERP Total

Member of a group Count 54 44 98
% within membership 55.1 44.9 100.0
% within ERP 20.4 48.9 27.6
Standalone company Count 211 46 257
% within ERP 79.6 51.1 72.4
% within membership 74.6 25.4 100.0 Table V.
% within ERP 100.0 100.0 100.0 Membership and ERP
% of total 74.6 25.4 100.0 adoption (whole sample)

Asymptotic Exact Exact
significance significance significance
Value df (two-sided) (two-sided) (one-sided)

Pearson Chi-square 27.327b 1 0.000
Continuity correctiona 25.919 1 0.000
Fisher’s exact test 0.000 0.000
Linear-by-linear association 27.250 1 0.000
n of valid cases 355 Table VI.
a b
Membership and ERP
Notes: Computed only for a 2 £ 2 table; 0 cells (0.0 percent) have expected count less than 5. The adoption (whole sample)
expected count is 24.85 – Chi-square tests

JEIM Presence of branch offices (H4)
18,4 According to the literature, the presence of branch offices could be a factor that
positively influences the complexity of information flows and that, consequently, could
lead to a larger adoption of ERP systems. The empirical analysis shows a correlation
between the extent of geographical dispersion of the company and the use of ERP
systems. These systems have been adopted by only 15 percent of respondents with no
398 branch offices and by 42 percent of companies with geographically dispersed offices


Interval by interval Pearson’s R 20.277 0.056 2 5.426 0.000c
Ordinal by ordinal Spearman correlation 20.277 0.056 2 5.426 0.000c
Table VII. n of valid cases 355
Membership and ERP
adoption (whole sample) Notes: a Not assuming the null hypothesis; b Using the asymptotic standard error assuming the null
– symmetric measures hypothesis; c Based on normal approximation

Membership of a group and ERP
adoption (SMEs)

Member of a group Count 36 17 53
% within ERP 15.2 32.1 18.3
Stand-alone company Count 201 36 237
% within ERP 84.8 67.9 81.7
Table VIII. % within membership 81.7 18.3 100.0
Membership and ERP % within ERP 100.0 100.0 100.0
adoption (SMEs) % of total 81.7 18.3 100.0

b
Pearson chi-square 8.269 1 0.004
Table IX. n of valid cases 290
Membership and ERP
adoption (SMEs) – Notes: a Computed only for a 2 £ 2 table; b 0 cells (0.0 percent) have expected count less than 5. The
Chi-square tests expected count is 9.69

(Tables XX-XXII). The evaluation of the behavior of companies making use of ERP Factors affecting
systems confirms this relationship: 62 percent of them have geographically dispersed ERP system
offices, while only 38 percent of ERP users have no branch offices to manage. Although
adoption
this interesting trend, a correlation between the two variables cannot be fully claimed:
the empirical verification shows a Pearson’s R value equal to 0.297 that is not
statistically reliable to state that the presence of branch offices directly affects a higher
rate of ERP system adoption (Tables XX-XXII).
399


Interval by interval Pearson’s R 20.169 0.067 2 2.907 0.004c
Ordinal by ordinal Spearman correlation 20.169 0.067 2 2.097 0.004c
n of valid cases 290 Table X.
Membership and ERP
Notes: a Not assuming the null hypothesis; b Using the asymptotic standard error assuming the null adoption (SMEs) –
hypothesis; c Based on normal approximation symmetric measures

Membership and ERP adoption (large
companies)

Member of group Count 15 25 40
% within ERP 78.9 73.5 75.5
Standalone company Count 4 9 13
% within ERP 21.1 26.5 24.5
Total Count 19 34 53 Table XI.
% within membership 35.8 64.2 100.0 Membership and ERP
% within ERP 100.0 100.0 100.0 adoption (large
% of total 35.8 64.2 100.0 companies)


Linear-by-linear association 0.190 1 0.663 Table XII.
n of valid cases 53 Membership and ERP
adoption (large
Notes: a Computed only for a 2 £ 2 table; b 1 cell (25.0 percent) has expected count less than 5. The companies) – Chi-square
expected count is 4.66 tests

JEIM The empirical veriﬁcation of H4 on the subsets obtained by separating the companies
18,4 by size does not show any interesting result (Tables XXIII-XXV and
Tables XXVI-XXVIII). In particular, the statistical analysis on large companies
(Tables XXVI-XXVIII) does not show any meaningful difference between the two
groups (chi-square ¼ 0:348 and p-value ¼ 0:409).
H4: rejected (weak signiﬁcance on the whole sample).
400


Table XIII. Ordinal by ordinal Spearman correlation 0.060 0.134 0.432 0.668c
Membership and ERP n of valid cases 53
adoption (large
companies) – symmetric Notes: a Not assuming the null hypothesis; b Using the asymptotic standard error assuming the null
measures hypothesis; c Based on normal approximation

Market area and ERP adoption
(whole sample)
Market area Other management system ERP Total

Local Count 19 2 21
% within market area 90.5 9.5 100.0
% within ERP 7.0 2.2 5.8
Regional Count 25 2 27
% within ERP 9.2 2.2 7.4
National Count 83 20 103
% within ERP 30.6 21.7 28.4
International Count 144 68 212
% within ERP 53.1 73.9 58.4
Table XIV. % within market area 74.7 25.3 100.0
Market area and ERP % within ERP 100.0 100.0 100.0


Pearson Chi-square 14.358a 3 0.002
Table XV. Linear-by-linear association 13.113 1 0.000
Market area and ERP n of valid cases 363
– Chi-square tests Note: a 0 cells (0.0 percent) have expected count less than 5. The minimum expected cost is 5.32

Diversification (H5) Factors affecting
Despite the support provided by the literature for diversification as a factor affecting ERP system
the complexity of information flows, the empirical verification does not show
meaningful correlations with the adoption of ERP systems (Tables XXIX-XXXI). Only adoption
27 percent of diversified companies make use of an ERP system. Moreover, 55 percent
of companies adopting an ERP system pursue another kind of strategy. The low
significance of diversification as a factor affecting ERP adoption is also confirmed by 401
the results of the statistical analysis: the two groups are not significantly different


n of valid cases 363 Table XVI.
Market area and ERP
Notes: a Not assuming the null hypothesis; b Using the asymptotic standard error assuming the null adoption (whole sample)

Market area and ERP adoption (SMEs)

Local Count 19 19
% within market area 100.0 100.0
% within ERP 7.9 6.4
Regional Count 21 2 23
% within ERP 8.7 3.6 7.7
% within ERP 30.6 23.6 29.3
% within ERP 52.9 72.7 56.6
% within market area 81.5 18.5 100.0 Table XVII.
% within ERP 100.0 100.0 100.0 Market area and ERP
% of total 81.5 18.5 100.0 adoption (SMEs)

a
Pearson Chi-square 9.643 3 0.022
Linear-by-linear association 9.561 1 0.002 Table XVIII.
n of valid cases 297 Market area and ERP
adoption (SMEs) –
Note: a Two cells (25.0 percent) have expected count less than 5. The minimum expected count is 3.52 Chi-square tests

JEIM (Chi-square equal to 0.261, with a p-value of 0.610) and the very low value of Pearson’s
18,4 R correlation index (0.027) bears out that no correlation occurs. Accordingly to the
methodology, the analysis has been carried out on both SMEs and large companies to
verify possible different behaviors in the sub-groups (Tables XXXII-XXXIV and
Tables XXXV-XXXVII), but no statistical evidence has been found.
H5: rejected.
402


Table XIX. n of valid cases 297
Market area and ERP
adoption (SMEs) – Notes: a Not assuming the null hypothesis; b Using the asymptotic standard error assuming the null
symmetric measures hypothesis; c Based on normal approximation

Market area and ERP adoption
(large companies)

Local Count 1 1
Regional Count 3 3
% within ERP 21.1 20.6 20.8
% within ERP 63.2 76.5 71.7
Table XX. Total Count 19 34 53
Market area and ERP % within market area 35.8 64.2 100.0
adoption (large % within ERP 100.0 100.0 100.0
companies) % of total 35.8 64.2 100.0

a
Table XXI. Likelihood ratio 7.351 3 0.062
Market area and ERP Linear-by-linear association 1.397 1 0.237
adoption (large n of valid cases 53
companies) – Chi-square
tests Note: a Five cells (62.5 percent) have expected count less than 5. The minimum expected count is 0.36

Functional extension (H6) Factors affecting
Even though most of respondents (88 percent) manage all the business activities ERP system
internally, the functional extension does not seem to affect the rate of ERP system
adoption. For each meaningful value of functional extension (cross-tabulation cells adoption
with more than five companies, Tables XXXVIII-XL), the percentage of companies
making use of ERP systems is at most equal to the 26.1 percent. In particular, only 25.9
percent of the 321 companies characterized by the maximum level of functional 403


Ordinal by ordinal Spearman correlation 0.163 0.141 1.180 0.244c Table XXII.
n of valid cases 53 Market area and ERP
adoption (large
Notes: a Not assuming the null hypothesis; b Using the asymptotic standard error assuming the null companies) – symmetric
hypothesis; c Based on normal approximation measures

Branch offices and ERP adoption
(whole sample)
Branch offices Other management system ERP Total

No Count 193 35 228
% within branch offices 84.6 15.4 100.0
% within ERP 70.7 37.6 62.3
Yes Count 80 58 138
% within ERP 29.3 62.4 37.7
% within branch offices 74.6 25.4 100.0 Table XXIII.
% within ERP 100.0 100.0 100.0 Branch offices and ERP
% of total 74.6 25.4 100.0 adoption (whole sample)

b
n of valid cases 366 Table XXIV.
a b
Branch offices and ERP
Notes: Computed only for a 2 £ 2 table; 0 cells (0.0 percent) have expected count less than 5. the adoption (whole sample)
maximum expected count is 35.07 – Chi-square tests

JEIM extension claim to make use of an ERP system. The unbalanced distribution of the
18,4 sample undoubtedly challenges the reliability of the analysis; nevertheless SMEs do
not consider ERP systems as the solution needed to improve their organizational
performance yet. The analysis of both correlation and statistical significance with
respect to SMEs and large companies does not show any meaningful change in of the
main indexes (Tables XLI-XLI and Tables XLIV-XLVI).
404 H6: rejected.


Table XXV. n of valid cases 366
adoption (whole sample) Notes: a Not assuming the null hypothesis; b Using the asymptotic standard error assuming the null
– symmetric measures hypothesis; c Based on normal approximation

adoption (SMEs)

No Count 181 29 210
% within ERP 74.2 51.8 70.0
Yes Count 63 27 90
% within ERP 25.8 48.2 30.0
Table XXVI. % within branch offices 81.3 18.7 100.0
Branch offices and ERP % within ERP 100.0 100.0 100.0
adoption (SMEs) % of total 81.3 18.7 100.0


Table XXVII. n of valid cases 300
adoption (SMEs) – Notes: a Computed only for a 2 £ 2 table; b 0 cells (0.0 percent) have expected count less than 5. the
Chi-square tests maximum expected count is 16.80

Extent of organizational change (H7) Factors affecting
Unlike previous factors (from H2-H6), the results of the data analysis on H7 ERP system
(Table XLVII-XLIX) highlights that the extent of organizational change the company
wishes to achieve is likely to affect the decision of whether adopting an ERP system or not. adoption
Even though the Pearson’s R value (0.306) shown by the whole sample is just acceptable,
other considerations support this statement. In particular (Tables L-LII), companies
adopting other management systems reveal a lower mean for the organizational change 405
factor (1.2) in comparison with those companies which have adopted an ERP system (2.3).


n of valid cases 300 Table XXVIII.
a b
Notes: Not assuming the null hypothesis; Using the asymptotic standard error assuming the null adoption (SMEs) –

Presence of branch offices and
ERP adoption (large companies)

No Count 4 5 9
% within ERP 21.1 14.7 17.0
Yes Count 15 29 44
% within ERP 78.9 85.3 83.0
Total Count 19 34 53 Table XXIX.
% within branch offices 35.8 64.2 100.0 Branch offices and ERP
% within ERP 100.0 100.0 100.0 adoption (large
% of total 35.8 64.2 100.0 companies)


Linear-by-linear association 0.342 1 0.559 Table XXX.
n of valid cases 53 Branch offices and ERP
adoption (large
Notes: a Computed only for a 2 £ 2 table; b One cell (25.0 percent) has expected count less than 5. The companies) – Chi-square
maximum expected count is 3.23 tests

JEIM A detailed analysis of the cross-tabulation between organizational change and ERP
adoption has pointed out some interesting remarks (see Figure 5):
18,4
. Companies seem to privilege the ERP solution when their need to integrate,
reengineer or redesign business processes becomes a priority (respectively the
17.4 percent, 25 percent and the 16.3 percent). Only 11 percent of companies
which make use of ERP systems exploit this solution just for local automation
406 purposes, while the number of companies considering a more advanced solution
(business network redesign levels) is quite negligible (8.7 percent).


Table XXXI. Ordinal by ordinal Spearman correlation 0.081 0.141 0.581 0.564c
Branch offices and ERP n of valid cases 53
adoption (large

Diversification and ERP adoption

Other strategy Count 158 51 209
% within diversification 75.6 24.4 100.0
% within ERP 57.9 54.8 57.1
Diversification Count 115 42 157
% within ERP 42.1 45.2 42.9
Table XXXII. % within diversification 74.6 25.4 100.0
Diversification and ERP % within ERP 100.0 100.0 100.0


Pearson chi-square 0.261b 1 0.609
Table XXXIII. n of valid cases 0.366
Diversification and ERP
adoption (whole sample) Notes: a Computed only for a 2 £ 2 table; b 0 cells (0.0 percent) have expected count less than 5. The
– Chi-square tests maximum expected count is 39.89

.
A quite unexpected outcome of the analysis is the relevant percentage of Factors affecting
companies (21.7 percent) declaring that no organizational change occurred, or ERP system
has been foreseen, as a consequence of ERP adoption. This result is even more
surprising in the light of the support given by IS literature to the thesis that ERP adoption
adoption both requires and provokes an unavoidable organizational reshuffling
of roles and tasks (Dewett and Jones, 2001).
.
The comparative analysis of means and distribution for the organizational 407
change factor (Table XLVII-XLIX) also shows that the underestimation of the


n of valid cases 366 Table XXXIV.
a b Diversification and ERP
Notes: Not assuming the null hypothesis; Using the asymtopic standard error assuming the null adoption (whole sample)

(SMEs)

% within ERP 58.2 57.1 58.0
% within ERP 41.8 42.9 42.0
Total Count 244 56 300 Table XXXV.
% within diversification 81.3 18.7 100.0 Diversification and ERP
% within ERP 100.0 100.0 100.0 adoption (SMEs)
% of total 81.3 18.7 100.0

b
n of valid cases 300 Table XXXVI.
Notes: a Computed only for a 2 £ 2 table; b 0 cells (0.0 percent) have expected count less than 5. The adoption (SMEs) –
maximum expected count is 23.52 Chi-square tests

JEIM organizational change factor becomes even more relevant among those
18,4 companies that make use of another management system (50.4 percent).
The previous analysis has been performed also separating SMEs and large companies
in order to highlight possible differences in the behavior of the two sub-groups.
Organizational change shows a stronger correlation with ERP adoption in the case of
408 large companies (Pearson’s R ¼ 0:386, Tables LIII-LV) with respect to SMEs
(Pearson’s R ¼ 0:218, Table LVI-LVIII). To explore the reasons underlying the
different correlation shown by the two sub-groups, the previous outcome has been


Table XXXVII. n of valid cases 300
adoption (SMEs) – Notes: a Not assuming the null hypothesis; b Using the asymptotic standard error assuming the null
symmetric measures hypothesis; c Based on normal approximation

(large companies)

% within ERP 63.2 50.0 54.7
% within ERP 36.8 50.0 45.3
Table XXXVIII. Total Count 19 34 53
Diversification and ERP % within diversification 35.8 64.2 100.0
adoption (large % within ERP 100.0 100.0 100.0
companies) % of total 35.8 64.2 100.0


Table XXXIX. Linear-by-linear association 0.836 1 0.361
Diversification and ERP n of valid cases 53
adoption (large
companies) – Chi-square Notes: a Computed only for a 2 £ 2 table; b 0 cells (0.0 percent) have expected count less than 5. The
tests maximum expected count is 8.60

further investigated by means of the analysis of both frequencies for the organizational Factors affecting
change factor and ERP non-adoption reasons:
ERP system
.
The evaluation of frequencies (Figure 5) points out that SMEs are more inclined to a
strategy of incremental innovation (local automation and internal integration) when
adoption
adopting ERP systems, whereas the use of ERP among large companies seems to
be more related to a radical-oriented attitude towards organizational innovation
(business process reengineering and business network redesign). As suggested by 409
literature, an incremental and too prudential approach towards organizational
innovation could endanger the survival itself of the small organization. This is due
to the mismatches that could occur between the assumptions about organizational
structure implicitly embedded in the reference models the ERP software makes use
of and the actual organization (Kumar and Hillegersberg, 2000). This remark gains


Ordinal by ordinal Spearman correlation 0.127 0.135 0.913 0.366c Table XL.
n of valid cases 53 Diversiﬁcation and ERP
adoption (large
Notes: a Not assuming the null hypothesis; b Using the asymptotic standard error assuming the null companies) – ymmetric
hypothesis; c Based on normal approximation measures

Functional extension and ERP
adoption
Number of internally Other management
managed activities system ERP Total

0 Count 1 1
% within functional extension 100.0 100.0
3 Count 3 3
% within ERP 1.1 1 0.8
5 Count 3 25.0 4
% within functional extension 75.0 1.1 100.0
6 Count 11 21.4 14
7 Count 17 26.1 23
% within ERP 6.2 83 6.3
8 Count 238 25.9 321
% within ERP 87.2 93 87.7
Total Count 273 25.4 366 Table XLI.
% within functional extension 74.6 100.0 100.0 Functional extension and
% within ERP 100.0 25.4 100.0 ERP adoption (whole
% of total 74.9 100.0 sample)

JEIM even more importance in the light of the 25.5 percent of SMEs adopting ERP
claiming that no organizational change occurred or has been foreseen, compared
18,4 with the only 14.7 percent of large companies.
.
The incremental attitude towards organizational innovation shown by SMEs is
strengthened by the analysis of both the reasons justifying ERP non-adoption

410
a
Table XLII. Likelihood ratio 2.506 5 0.776
Functional extension and Linear-by-linear association 1.057 1 0.304
ERP adoption (whole n of valid cases 366
sample) – Chi-square
tests Note: a Seven cells (58.3 percent) have expected count less than 5. The minimum expected count is 0.25


Table XLIII.
Functional extension and
n of valid cases 366
ERP adoption (whole
sample) – symmetric Notes: a Not assuming the null hypothesis; b Using the asymptotic standard error assuming the null

Number of internally adoption (SMEs)
managed activities Other management system ERP Total

3 Count 3 3
5 Count 2 2
6 Count 9 2 11
% within ERP 3.7 3.6 3.7
7 Count 16 2 18
% within ERP 6.6 3.6 6.0
8 Count 214 52 266
% within ERP 87.7 92.9 88.7
Table XLIV. % within functional extension 81.3 18.7 100.0
Functional extension and % within ERP 100.0 100.0 100.0
ERP adoption (SMEs) % of total 81.3 18.7 100.0

and the stated extent of organizational innovation (Figure 6). In the 58.2 percent Factors affecting
of cases (multiple response allowed) SMEs declare their business is not complex ERP system
enough to justify the adoption of an ERP system (structural reasons), while the
organizational un-readiness (i.e. organizational skills are not sufﬁcient to manage
adoption

Value df Asymptotic signiﬁcance (two-sided) 411
a
Linear-by-linear association 1.336 1 0.304 Table XLV.
n of valid cases 300 Functional extension and
ERP adoption (SMEs) –
Note: Six cells (60.0 percent) have expected count less than 5. The minimum expected count is 0.37 Chi-square tests


n of valid cases 300 Table XLVI.
a b
Notes: Not assuming the null hypothesis; Using the asymptotic standard error assuming the null ERP adoption (SMEs) –

Number of internally adoption (large companies)
managed activities Other management system ERP Total

0 Count 1 1
5 Count 1 1 2
% within ERP 5.3 2.9 3.8
6 Count 2 1 3
% within ERP 10.5 2.9 5.7
7 Count 1 3 4
% within ERP 5.3 8.8 7.5
8 Count 14 29 43
% within ERP 73.7 85.3 81.1
Table XLVII.
% within ERP 100.0 100.0 100.0
ERP adoption (large
% of total 35.8 64.2 100.0
companies)

JEIM the change in the organization induced by an ERP implementation) is cited in
only the 29.5 percent of cases. Despite the emphasis that the literature has always
18,4 put on the scarcity of financial resource of SMEs as one the most important
factors affecting ERP non-adoption (Cragg and Zinatelli, 1995; Levy and Powell,
2000; Themistocleous et al., 2001; Zinatelli et al., 1996), quite surprisingly
economic reasons (15,2 percent) are not as important as structural and
412 organizational motivations.
.
The same analysis performed on large companies (Table LIX and Figure 7)
shows a supremacy of organizational reasons (45.5 percent of cases) whereas
structural reasons are mentioned in the 36.4 percent of cases. These results are
merely indicative due to the lack of numerical consistency of the sub-group itself
(only 11 valid cases and 12 total responses).

H7: verified (in particular for large companies).

Discussion
The empirical verification of the framework shows the difficulties in describing the
relationship between the growth in business and organizational complexity and ERP
adoption: only two measures were considered reliable (Table LX).
The data analysis supports the existence of a strong correlation between company size
(evaluated as a composed measure of number of employees and turnover) and ERP
adoption. All the other hypotheses regarding possible business complexity measures have
been rejected. No other studies seem to have tested the relationship between business
complexity and ERP adoption on such a relative high number of companies, and on SMEs
in particular. The methodology itself differentiates this research from previous efforts,
being based on an extensive questionnaire deployed through direct interviews, instead of


Pearson Chi-square 3.610a 4 0.461
Table XLVIII. Likelihood ratio 3.813 4 0.432
Functional extension and Linear-by-linear association 2.678 1 0.102
ERP adoption (large n of valid cases 53
tests Note: Eight cells (80.0 percent) have expected count less than 5. The minimum expected count is 0.36


Table XLIX. Ordinal by ordinal Spearman correlation 0.162 0.143 1.173 0.246c
Functional extension and n of valid cases 53
ERP adoption (large

Organizational change and ERP adoption
Local Internal Business process Business Redeﬁnition of
Nothing automation integration reengineering network redesign company boundaries Total

Other Count 122 29 40 25 21 5 242
management % within
system organizational
change 50.4 12.0 16.5 10.3 8.7 2.1 100.0
% within ERP 85.9 74.4 71.4 52.1 58.3 38.5 72.5
ERP Count 20 10 16 23 15 8 92
% within 21.7 10.9 17.4 25.0 16.3 8.7 100.0
organizational
change
% within ERP 14.1 25.6 28.6 47.9 41.7 61.5 27.5
Total Count 142 39 56 48 36 13 334
% within 42.5 11.7 16.8 14.4 10.8 3.9 100.0
organizational
change
% within ERP 100.0 100.0 100.0 100.0 100.0 100.0 100.0
% of total 42.5 11.7 16.8 14.4 10.8 3.9 100.0
adoption
Factors affecting

sample)
and ERP adoption (whole
Organizational change
ERP system

Table L.
413

JEIM the commonly used approach based on case study. Despite the positive outcomes of such
18,4 choices, one main side effect could be a reduced comparability of the results.
The ﬁndings on the relationship between ERP adoption and organizational change
(H7) show that companies making use of an ERP system expect a wider extent of
business transformation (business process reengineering and business network
redesign) with respect to companies making use of other software applications. SMEs
414 always scheduled a limited organizational change in the case of ERP adoption, thus
they seem not to consider ERP systems as a keystone for organizational innovation. If
the testing on H7 reveals an incremental and conservative approach to organizational
change by SMEs, on the contrary, H7 suggests a different innovation strategy by large

a
Table LI. Likelihood ratio 33.650 5 0.000
Organizational change Linear-by-linear association 31.147 1 0.000
and ERP adoption (whole n of valid cases 334
sample) – Chi-square
tests Note: a One cell (8.3 percent) has expected count less than 5. The minimum expected count is 3.58


Table LII. Ordinal by ordinal Spearman correlation 0.304 0.052 5.822 0.000c
Organizational change n of valid cases 334
and ERP adoption (whole
sample) – symmetric Notes: a Not assuming the null hypothesis; b Using the asymptotic standard error assuming the null

Figure 5.
Frequencies for
organizational change and
adopted software solution
(whole sample)

Organizational change and ERP adoption (large companies)

Other Count 8 5 3 1 17
management % within
change 47.1 29.4 17.5 5.9 100.0
% within ERP 61.5 50.0 23.1 12.5 33.3
ERP Count 5 3 5 10 7 4 34
% within 14.7 8.8 14.7 29.4 20.6 11.8 100.0
organizational
change
% within ERP 38.5 100.0 50.0 76.9 87.5 100.0 66.7
Total Count 13 3 10 13 8 4 51
% within 25.5 5.9 19.6 25.5 15.7 7.8 100.0
organizational
change
% within ERP 100.0 100.0 100.0 100.0 100.0 100.0 100.0
% of total 25.5 5.9 19.6 25.5 15.7 7.8 100.0

companies)
adoption
Factors affecting

and ERP adoption (large
Table LIII.
ERP system

415

JEIM companies since they are especially aware of the organizational implications of ERP
18,4 adoption and, in particular, of the need of more radical changes.
Firms that are planning to change their information system radically are more
inclined to adopt ERP systems due to the expected “integration” outcomes. However,
SMEs result to be less inclined to radical change and less aware of the organizational
impact caused by the implementation of an ERP system. SMEs’ traditional focus on
416 operations and day-by-day management, coupled with a lack of strategic view of ICT,
could be partially accountable for these findings. Indeed, SMEs could simply prefer to
continue doing business as they are used to, by refusing to adopt solutions that could
change their “course”. On one hand, this approach has opened up the SME market to
personalized integrated solutions able to respond adequately both to their information
needs and extreme flexibility, not necessarily provided by the ERP market’s big
players. On the other hand, it is a clear sign for ERP big players that the SME market
requires simplified and less expensive solutions. This has been the way ERP vendors
tried to cope with the issues connected to the dualism between business processes
change and software customization issues. In particular, the first answer of ERP
vendors was the proposal of products with a range of functionalities on a smaller scale
to lower the overall costs and vertical solutions to achieve a concrete reduction in
customization costs without a dramatic change in the way people currently work.
It is still questionable whether ERP packages are really keeping their promises. The
subject is still debated but, by looking at vendors’ scenery, there is a general agreement
on the role played by company size (H1) as the main clustering variable for the ERP
market. The ERP market evolution has clearly shown that differences in company size
have an influence not only on the adoption of ERP systems (H1), but also on the inner
characteristics of ERP packages themselves. In particular, experiences on the field
seem to confirm that both the extent of business complexity and the sustainable level
of organizational change may dramatically vary according to company size, hence
SMEs could be no longer treated by the same standard as large companies. Therefore,

a
Pearson’s Chi-square 11.582 5 0.041
Table LIV. Likelihood ratio 13.665 5 0.018
Organizational change Linear-by-linear association 7.454 1 0.006
and ERP adoption (large n of valid cases 51
tests Note: a Eight cells (66.7 percent) have expected count less than 5. The minimum expected count is 1.00


Table LV. Ordinal by ordinal Spearman correlation 0.393 0.116 2.994 0.004c
Organizational change n of valid cases 51
and ERP adoption (large

Organizational change and ERP adoption (SMEs)

Other Count 108 29 34 21 19 5 216
management % within
change 50.0 13.4 15.7 9.7 8.8 2.3 100.0
% within ERP 88.5 80.6 75.6 63.6 70.4 62.5 79.7
ERP Count 14 7 11 12 8 3 55
% within 25.5 12.7 20.0 21.8 14.5 5.5 100.0
organizational
change
% within ERP 11.5 19.4 24.4 36.4 29.6 37.5 20.3
Total Count 122 36 45 33 27 8 271
% within 45.0 13.3 16.6 12.2 10.0 3.0 100.0
organizational
change
% within ERP 100.0 100.0 100.0 100.0 100.0 100.0 100.0
% of total 45.0 13.3 16.6 12.2 10.0 3.0 100.0
adoption
Factors affecting

(SMEs)
and ERP adoption
Table LVI.
ERP system

417

JEIM a first wave in the ERP market segmentation occurred: for example, SAP AG
18,4 introduced Business One, a solution for small firms, by keeping R/3 for the medium
and large companies. This was only the first wave in the vendors’ strategy: some
intrinsic characteristics of medium companies, such as organizational flexibility,
availability of financial resources and dynamism, make them a specific and hybrid
economic subject which has to face the typical issues of both large and small
418 businesses. The characteristics of medium companies led to a second wave in the
evolution of the ERP systems. Software packages such as mySAP All-in-One should be
able to minimize customization costs through an enhanced availability of vertical
solutions, relying on the same technology platform of the traditional ERP systems
(such as mySAP Business Suite or Peoplesoft EnterpriseOne) to ensure the scaling up
of the ERP package.

a
Pearson’s Chi-square 14.547 5 0.012
n of valid cases 271
Table LVII.
Chi-square tests Note: a One cell (8.3 percent) has expected count less than 5. The minimum expected count is 1.62


Table LVIII. Ordinal by ordinal Spearman correlation 0.222 0.059 3.741 0.000c
Organizational change n of valid cases 0.271
and ERP adoption
(SMEs) – symmetric Notes: a Not assuming the null hypothesis; b Using the asymptotic standard error assuming the null

Figure 6.
Frequencies for
organizational change and
adopted software solution
(SMEs vs large
companies)

Count Percentage of responses Percentage of cases
Factors affecting
ERP system
Economic reasons 26 15.2 17.8
Structural reasons 85 49.7 58.2 adoption
Organizational reasons 43 25.1 29.5
Other reasons 17 9.9 11.6
Total responses 171 100 117.1
98 missing cases; 146 valid cases
419
Economic reasons 1 8.3 9.1
Structural reasons 4 33.3 36.4
Organizational reasons 5 41.7 45.5 Table LIX.
Other reasons 2 16.7 18.2 Reasons for ERP
Total responses 12 100 109.1 non-adoption (SMEs and
8 missing cases; 11 valid cases large companies)

Figure 7.
Extent of organizational
change

Hypothesis Result Remarks

Company size (H1) Verified Extremely significant for the whole sample
Membership of an industrial group (H2) Rejected
Market area (H3) Rejected
Presence of branch offices (H4) Rejected Some significance for the whole sample
Level of diversification (H5) Rejected Table LX.
Degree of functional extension (H6) Rejected Outcomes of the
Extent of organizational change (H7) Verified Extremely significant for the whole sample empirical verification

On the other hand, the urgent need for integrating ERP systems with the existing
legacy systems could not only br met by offering vertical and lighter ERP solutions.
Thus, a third wave in the ERP vendor’s strategy has taken place, by offering
component-based solutions allowing SMEs gradually to acquire and assemble a lower
cost ERP systems, by gradually integrating components that are reasonably

JEIM customized to their specific needs. This would help reducing the problem of mismatch
18,4 between organizational requirements and ERP solutions and finally migrations should
become more gradual as outdated components are upgraded individually instead of the
whole system (Kumar and Hillegersberg, 2000). The same component strategy marks
the ERP systems for large companies and is based on the composition of large systems
from largely independent components that are assembled to meet situation-specific
420 requirements (e.g. mySAP Business Suite, Peoplesoft Enterprise and Oracle Business
Suite includes the Customer Relationship Management, Supplier Relationship
Management, Business Intelligence and Product Lifecycle Management modules
among others).
The adequacy of the behaviour of ERP big players seems to find a confirmation in
the findings related to company size (H1), organizational change (H7) and ERP
adoption. After a period in which ERP packages were generally planned with a great
emphasis on the reduction of the organizational-related costs through vertical and less
expensive solutions, now vendors seem to have understood that the deployment of
independent business modules could address the same issues in a different way. The
role of ERP modules has gradually shifted from the automation of specific processes
(invoicing and billing, HR management) to a view in which ERP modules manage the
“stakeholders-centric” requirements of the company also through the so-called
enterprise portals. In particular, ERPII (or “second generation” ERP) systems are built
in a modular way, thus allowing companies the possibility of acquiring only the
business modules needed (instead of a monolithic package) and to ensure also the
presence of the technological platforms (e.g. SAP NetWeaver or Oracle Data Hub)
which are needed to achieve an effective interaction between the ERP and the existing
legacy systems.

Impact of technological and operational drivers on ERP adoption
One of the most interesting outcomes of the analysis on H7 is that SMEs are less
inclined to evaluate and plan the organizational change when implementing an ERP
system than large companies. Yet, the differences in the approach towards ERP
adoption are confirmed by the outcome of the analysis which has been performed on
the technological and operational drivers that affect ERP adoption in 90 of the 366
companies the whole sample is made of. The results (Table LXI) show that SMEs still
consider contingency factors such as the millennium bug (Y2K, 30.4 percent), the
introduction of the Euro (30.4 percent), hardware or software obsolescence (44 percent)
as the prevailing reasons leading to ERP adoption, whereas strategic and
organizational drivers (i.e. limited support to decisions, dissimilarity of procedures)
are stated as less important. The unsatisfying process integration (30.4 percent) is the
only exception to this trend.
The same analysis has been carried out on large companies and results point out a
greater balance between exogenous and endogenous reasons. Large companies seem to
devote more attention to endogenous reasons as motivating factors for ERP adoption:
the unsatisfying process integration is claimed to be the most relevant factor (44.1
percent of cases in comparison with the 30.4 percent claimed by SMEs), while the high
relevance of data redundancy/inconsistency and dissimilarity of procedures (29.4
percent and 23.5 percent respectively) seems to highlight a greater attention by large

companies to both the process management issues and the related information flows in Factors affecting
comparison to SMEs (19.6 and only 1.8 percent respectively). ERP system
Despite the greater awareness of organizational and information-related issues if
compared to SMEs, also among large companies the decision regarding the ERP adoption
system adoption is strongly affected by exogenous reasons. 38.2 percent of large
companies declare that the ERP system adoption has been imposed by the controlling
company. In accordance with the outcomes of other studies on the same topic, Euro and 421
Y2K issues turn out to be important drivers also for large companies (26.5 percent and
29.4 percent respectively).
A further proof of how differently the two sub-groups managed exogenous reasons
(such as Y2K and Euro) is given by the analysis of ERP adoption curves for both SMEs
and large companies. Figure 8 clearly points out how large companies’ strategy seem to
anticipate ERP adoption with respect to the occurrence of external events: in detail, the
adoption curve for large companies in years 1999 (Y2K issue) and 2001 (Euro issue)
shows a greater gradient in comparison with SMEs’ curve, whereas the rate of
adoption is constantly lower than SMEs upon the occurrence of the external event. It is
also interesting to notice that the ERP adoption curve of large companies is flattening
showing the difficulties for ERP vendors to penetrate large companies’ market further,
while in the last two years SMEs’ market has been showing a steady growth.

Conclusions and further research
This research has attempted to study the relationship between business complexity,
organizational change and ERP adoption. The analysis of the empirical data clearly
shows that business complexity, as a composed factor, is a weak predictor of ERP
implementation, whereas just company size results to be a very good one. In other
words, companies seem to be disregarding ERP systems as an answer to their business
complexity. This outcome could depend both on the validity of this index or the

Percentage of cases Percentage of cases
Drivers (SMEs) (large companies)

HW/SW obsolescence 44.6 26.5
Euro issue 30.4 26.5
Y2K issue 30.4 29.4
Unsatisfying process integration 30.4 44.1
Unsatisfying order management 26.8 8.8
Data redundancy and/or inconsistency 19.6 29.4
Limited support to decisions 19.6 26.5
Lack of flexibility 17.9 23.5
Forced decision (by a controlling company) 16.1 38.2
Logistics and transportation issues 16.1 14.7
High cost of data distribution 14.3 8.8
Other reasons 14.3 11.8
Over-dimensioning of stock 12.5 5.9 Table LXI.
CRM issues 8.9 14.7 Technological and
Unsatisfying time-to-market 3.6 8.8 operational drivers (90
Dissimilarity of procedures (i.e. rules on quality SMEs and large
management) 1.8 23.5 enterprises adopting ERP
Total responses 307.1 341.2 systems)

JEIM
18,4

422

Figure 8.
ERP adoption curves
(SMEs and large
companies)

erroneous perception of SMEs of their business complexity: the reason they cite most
for discounting ERP systems. However, no large differences in terms of complexity
were found among these two groups. Unexpectedly, SMEs disregard financial
constraints as the main cause for ERP system non-adoption, suggesting structural and
organizational reasons as major ones. This pattern is partially different from what
observed in large organizations where the first reason for not adopting ERP system is
organizational. These results could imply that SMEs structural peculiarities are a real
obstacle to ERP diffusion if and adequate strategy by vendors lacks. Moreover, the
decision process regarding the adoption of ERP systems within SMEs is still more
affected by exogenous reasons or “opportunity of the moment” rather than on
business-related factors contrary to large companies that are more interested in
managing process integration and data redundancy/inconsistency through ERP
implementation. This paper investigates whether business complexity could influence,
and somehow explain, the different extent to which companies of any size adopt ERP
systems. The focus has been largely appointed to a comparative analysis of the
business characteristics of the companies themselves, intentionally leaving out of
consideration a closer investigation of ERP packages. Accordingly to the previous
section remarks, a further research could consider a sort of reverse engineering of this
approach. In particular, it could be interesting to analyze how vendors interpret the
concepts of “business complexity” and “organizational change” by exploring in detail
the characteristics of the ERP systems they currently offer.

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