ENTERPRISE RESOURCE PLANNING (ERP) SYSTEMS

SUCCESS MEASUREMENT: AN EXTENDED MODEL

Princely Ifinedo

Department of Computer Science and Information Systems, Univesity of Jyväskylä, Agora Building, Jyväskylä, Finland

Keywords: ERP systems success, IS success, Measurement, Assessment, Structural equation modeling, Private firms.

Abstract: Enterprise Resource Planning (ERP) systems are diffusing globally, and it is important to measure the

success of such software in adopting firms. Evidence suggests that firms investing huge sums of money in

information systems (IS) sometimes do not assess the success of such systems for a variety of reasons,

including the lack of knowledge about what to assess. Also, the IS success evaluations research area is

varied, often providing little succour to practitioners. ERP systems success assessment is just beginning to

surface, and this paper discusses an effort towards extending an available success measurement model.

Essentially, two relevant success dimensions not included in the model proposed by Gable and colleagues

(Gable et al., 2003; Sedera and Gable, 2004) were incorporated and tested using criterion analysis and

structural equation modeling technique. The implications of our findings for practice and research are

discussed.

1 INTRODUCTION

An ERP is a complex business information

technology (IT) package designed to integrate

business processes and functions by permitting the

sharing of common data and practices in a real-time

environment (Davenport, 1998; 2000; Somers et al.,

2000). Organizations adopt them for a variety of

reasons, including the replacement of legacy systems

and cost reductions (Davenport, 1998; 2000).

Assessing the success of ERP in organizations is

difficult because of its complex nature (Sedera et al.,

2002, 2003a; Gable et al., 2003).

Furthermore, some firms appear to have given up

hope of evaluating the benefits or success of their

ERP due to a lack of knowledge regarding such

exercises (Ifinedo, 2005). In-depth interviews with 7

case companies regarding how they evaluate the

success of their ERP revealed that only 3 had any

formal evaluations, the others indicated that they

don’t carry out such evaluations; yet almost all these

firms have adopted costly top brands ERP systems.

Our observations are similar to those made by

Kumar (1990) and Seddon et al. (2002) where these

researches discussed the poor state of IS systems

evaluations in organizations. Seddon et al. (2002, p.

11) concluded, “…firms do not conduct rigorous

evaluations of all their IT investments” perhaps due

to a lack of knowledge in such areas. Participants in

our study (Ifinedo, 2005) echoed a similar view.

IT systems success evaluation issues are critical

for both practitioners and researchers (Ballantine et

al., 1997; Seddon et al., 2002; McLean et al., 2002),

and over the past three decades, evaluating the value

and success of IT systems for organizations has been

a recurring issue (DeLone and McLean, 1992;

Myers et al., 1997). Various assessment approaches

have surfaced. One stream of research focuses on the

use of attitudinal and subjective measures (Ives et

al., 1983; Doll and Torkzadeh, 1988), while another

utilizes financial and objective parameters (e.g.

Brynjolfsson and Hitt, 1996). In both instances,

understanding the success of the IT systems could be

limited when the dimensions and measures of

success are restrictive (Grover et al., 1996; Myers et

al., 1997; Gable et al., 2003). Grover et al. (1996)

argued for measures that are more comprehensive to

be used for information systems (IS) success studies.

Perhaps it was the plethora of IS success assessment

approaches that led Keen (1980) to seek clarification

of the “dependent variable.” In response, DeLone

and McLean [D&M] (1992) developed an

integrated, multi-dimensional, and inter-related IS

success model that is now the dominant model for IS

evaluation research (Ballantine et al., 1997; Seddon,

1997). Please see Figure 1 for the D&M model.

Further, in developing their ERP success

Iﬁnedo P. (2006).

ENTERPRISE RESOURCE PLANNING (ERP) SYSTEMS SUCCESS MEASUREMENT: AN EXTENDED MODEL.

In Proceedings of the Eighth International Conference on Enterprise Information Systems - DISI, pages 71-78

DOI: 10.5220/0002496400710078

 SciTePress

measurement model, Gable and colleagues (Gable et

al., 2003; Sedera et al., 2003a) suggested that

perhaps one of the reasons why there are mixed

results reported with regard to IS success research is

the utilization of limited or narrowly defined

success dimensions. It comes as no surprise,

therefore, that practitioners espouse a lack of

knowledge regarding assessing the success of their

acquired IT systems when the research community

appears to lack a consensual approach on “what to

assess?”

Thus, the lack of knowledge for some

practitioners about what to measure or assess in the

context of ERP systems (Ifinedo, 2005), is the

primary motivation for this study. As previously

mentioned, Gable and colleagues have stepped up to

this challenge, and this study only serves to

complement their effort. In advancing the

knowledge in this area, we specifically ask: Are the

dimensions of success represented in the ERP

success measurement model proposed by Gable and

colleagues comprehensive? If otherwise, can the

model be extended? The purpose of this study is to

present an extended model that could be used by

practitioners. Our focus is on private organizations

in contrast to the public sector organizations that

Gable and colleagues studied. Mansour and Watson

(1980) note that IT issues for a government

environment differs from those in the private sector

because of the profit oriented nature of the latter.

This research is conducted in Finland and Estonia

- two small neighboring technologically advanced

Northern European countries with a comparable

cultural values (Ifinedo and Davidrajuh, 2005).

Finnish companies began adopting ERP in the late

1990s (van Everdingen et al., 2000; Ifinedo, 2005),

and the software is a “key IS management issue” in

Estonia (Ifinedo, 2005; 2006).

2 BACKGROUND

Here, ERP systems success refers to the utilization

of such systems to enhance organizational efficiency

and effectiveness (DeLone and McLean, 1992;

Grover et al., 1996; Gable et al., 2003), and it is

different from ERP implementation success (Martin,

1998; Tan and Pan, 2002; Markus et al., 2000). Our

scan of the literature of ERP success research

revealed that researchers either use narrowly defined

measures (Nelson and Somers, 2001; Zviran et al.,

2005; Wu and Wang, 2005) or elaborate on broad

conceptualization of the concept (e.g., Tan and Pan,

2002; Markus and Tanis, 2000). Some of the

researchers used the end-user satisfaction instrument

(Doll and Torkzadeh, 1988) that has been criticized

for its limited scope (Saarinen, 1996). Markus and

Tanis (2000) discussed ERP success by including

performance metrics and outcomes, and noted that

their “theoretical framework … is too broad in scope

for direct empirical testing (Ibid, p. 200).

Gable and colleagues (Gable et al., 2003; Sedera

et al., 2003a; Sedera and Gable, 2004) provide

perhaps the most comprehensive ERP systems

success measurement model, to date, and others

have used it (e.g., Sehgal and Stewart, 2004). Gable

and colleagues developed an additive model that

redefines the dimensions in the original D&M IS

success model. They noted that Seddon and Kiew

(1994) tested paths in D&M model finding support

for some and not for the others. And, recently

Iivari’s (2005) study corroborates findings made by

Seddon and Kiew. In brief, Gabel and colleagues

eliminated (through multi-stage data collection and

statistical analysis) the Use and User satisfaction

dimensions. Arguments against dropping them are

also available in the literature (Saarinen, 1996;

Seddon, 1997). Furthermore, in their arguments for

the mutual exclusivity of success dimensions, Gable

et al. (2003) suggested an overarching view of

success in which “each measure [and/or dimension]

only addresses one important aspect of IS success”

(p. 578). In brief, the retained dimensions of ERP

system success in Gable and colleague model are as

follows: System Quality (SQ), Information Quality

(IQ), Individual Impact (II) and Organizational

Impact (OI). Please see Figure 2.

We asked whether this model (Figure 2) can be

extended to include other relevant factors? To that

end, we consulted the literature and conducted case

interviews in 7 ERP adopting private firms in

Finland and Estonia. In-depth discussions of this

study are available elsewhere (Ifinedo, 2005).

Evidence obtained from 16 senior personnel in these

firms revealed that the cooperative role and quality

of service of the ERP providers (vendors and

consultants) is linked to the overall success of their

ERP. One interviewee captured the views of others

when he commented: “As for me, I consider the

support from the vendor, their expertise and

commitment levels to be critical to our ERP success”

(Head of IT, Estonian manufacturing firm).

In this light, we believed that a more comprehensive

ERP success model should incorporate the

Vendor/Consultant quality dimension. The quality of

ERP providers throughout the life span of any ERP

acquisition is imperative, and is recognized in the

literature (Davenport, 1998; Markus and Tanis,

2000; Somers et al., 2000; Ko et al., 2005). Markus

and Tanis (2000) highlighted “dependence on

vendors” as a key issue in ERP implementations that

differentiates these systems from other IT

implementations. Recently, Ko et al. (2005)

ICEIS 2006 - DATABASES AND INFORMATION SYSTEMS INTEGRATION

underscored the crucial role that vendors/consultants

play during ERP implementations. Vendors and

consultants are grouped together because they

represent an external source of expertise to the firm

in ERP implementations.

Moreover, Sedera et al. (2003b) found that

“consultant and vendor items loaded together

yielding a new factor named External knowledge

player” (p. 1411).

Furthermore, we argue that the underlying

philosophy of ERP systems that facilitates the

harmonization and integration of organizational

functions and departments (Davenport, 1998; 2000;

Markus and Tanis, 2000) makes a case for the

incorporation of a dimension relating to the issue of

inter-departmental or cross-functional impacts.

Along this similar line of reasoning, Myers et al.

(1996) argued that any IS success model should

incorporate Workgroup Impact in light of the

contributions made by work teams/groups toward

organizational productivity, and these authors added

it to the D&M model. Workgroup encompasses the

sub-units and/or functional departments of an

organization. Furthermore, “interdepartmental co-

operation” and “interdepartmental communication”

ranked 3rd. and 6th. respectively in a study of 22

critical success factors (CSFs) of ERP

implementation by Akkermans and van Helden

(2002). Other CSFs studies have produced

comparable results (see Esteves and Pastor, 2001).

Thus, our conceptualization of ERP systems success

measurement model is shown in Figure 3 with two

new dimensions: Vendor/Consultant Quality (VQ)

and Workgroup Impact (WI).

Figure 3: Extended ERP Systems Success.

3 METHODOLOGY

This study is a part of three-stage research effort

using both qualitative and quantitative research

approaches. Here, we report the main survey.

Admittedly, it was impossible for us to determine

the number of firms adopting ERP in Finland and

Estonia due to the unavailability of such a sampling

frame. Rather, we sampled firms generated from

local contacts, ERP User Groups and vendors lists,

as well as published lists of Top Enterprises for 2004

for both countries. Firms were chosen by our ability

to obtain contact addresses for key organizational

personnel. We identified 350 firms in Finland and

120 firms in Estonia. In order to ensure data validity

and reliability, four knowledgeable individuals

completed the questionnaire prior to our mailing it,

and their comments helped us improve the quality.

Respondents in our survey indicated agreement with

statements using a 7-point Likert-type scale, where 1

= strongly disagree and 7 = strongly agree (the

questionnaire is omitted due to space restrictions).

Since the unit of analysis of this study was at the

functional and organizational levels only key

organizational informants including chief finance

officers, unit managers, and IT managers received a

packet consisting of a cover letter, questionnaire,

and a self-addressed, stamped envelope. 40% of the

mailings were matched pairs (two questionnaires in

the packet), and the recipients were encouraged to

give one of the questionnaires to an appropriate

person within their organization. It was felt that

multiple respondents from one organization would

enhance the validity of the study, as common source

variance would be reduced. The other 60% included

only one questionnaire. We encouraged the subjects

to present views representative of their organization.

ERP

Systems

Success

Figure 2: Gable et al. (2003) ERP Systesm Success Model.

Use

User

Satisfaction

Figure 1: DeLone & McLean (1992) IS Success Model.

ERP

Systems

Success

ENTERPRISE RESOURCE PLANNING (ERP) SYSTEMS SUCCESS MEASUREMENT: AN EXTENDED MODEL

3.1 Results

Our overall response rate is 9.5% (44 firms)

combined for the two countries, namely, 29 and 15

firms for Finland and Estonia, respectively. In total,

we received 62 individual responses: 39 from

Finland and 23 from Estonia. Of which, there were

26 (42%) top-level management and 36 (58%) mid-

level managers. These groups of respondents are

among the most knowledgeable informants

regarding ERP success (Shang and Seddon, 2002;

Gable et al., 2003, Sedera et al., 2004). There were

35 (56.5%) men and 27 (43.5%) women in our

sample. On average, they had 9 years of work

experience in their respective organizations. Of the

respondents, 40% had college degrees, and 43

(69.3%) were aged between 31 and 50 years. Of the

62 respondents, 33.9% of them had SAP in their

organizations, 14.5% had Movex, 9.6% had Scala,

8.1% had Hansa, and the remaining 33.9% had other

mid-market ERP (including Concorde, Scala, etc.).

The majority of firms implemented their ERP

between 1998 and 2002. We received responses

from a wide range of industries, including

manufacturing, financial services, retail businesses.

Our sample included 15 small firms, 25 medium-

sized firms, and 22 large companies using the

workforce categorization guidelines provided by the

European Commission (2003) and 32. Laukkanen

et al. (2005).

3.2 Instrument Development and

Validity

The research instrument was developed from

measures and constructs that have been validated in

the literature (Gable et al., 2003; Sedera et al.,

2003a; Sedera and Gable, 2004). Although for one

construct – Workgroup Impact – we used guidelines

provided by Myers et al. (1996, 1997), and

information garnered from our case interview

(Ifinedo, 2005). We used 45 measures for the 6

dimensions and 3 measures to assess the ERP

systems success construct. SQ comprised 10

measures such as “Our ERP has accurate data”, and

IQ comprised 9 measures, including “The

information on our ERP is understandable” (Gable et

al., 2003; DeLone and McLean, 1992). VQ consists

of 5 measures, including “Our ERP

vendor/consultant is credible and trustworthy”

(Thong et al., 1994; Ko et al., 2005). II consists of 6

measures, including “Our ERP improves individual

productivity” (Gable et al., 2003, DeLone and

McLean, 1992; Myers et al., 1997). WI comprised 7

measures, including “Our ERP helps to improve

workers’ participation in the organization” (Myers et

al., 1996; 1997; Ifinedo, 2005). “Our ERP reduces

organizational costs” is among the 8 measures

included in the OI dimension. The ERP systems

success construct has 3 measures from Gable et al.

(2003) (see below: Criterion analysis). The content

validity of the study is enhanced over stages in the

study, including the pilot test. Regarding the

reliability of our measures, the Cronbach Alpha for

each dimension ranged from 0.769 to 0.942, which

is above the 0.70 limit recommended by Nunnally

(1978), thus indicating a reasonably high reliability

of the research measures.

4 DATA ANALYSIS

4.1 Additivity of the ERP Systems

Success Dimensions

Following guidelines in Gable et al. (2003), we

assessed the additive nature of our model by

investigating the criterion validity of the measures in

our instrument. We assessed our ERP systems

success using the following three statements: (A)

“Overall, the impact of our ERP on me has been

positive,” (B) “Overall, the impact of our ERP on

my workgroup has been positive,” and (C) “Overall,

the impact of our ERP on my organization has been

positive.” To assess the content and the criterion

validity of ERP success, we computed the following

composite measures: (D) “criterion average” is the

average of the three criterion items, and (E)

“dimensions average” is the average of the six

success dimensions. Table 1 shows the correlation of

(A), (B), (C), and (D) with the six dimensions and

their average (E). Gable et al. (2003, p. 585) stated,

“The extent to which each dimension or the

dimension average correlates with the criterion

scores is evidence of their criterion validity” (see

also, Kerlinger, 1988).

The correlations are significant at the 0.01 level

(two-tailed), with the exception of the correlation

between “Organization Impact” and “Impact on

Individual,” which is 0.70. The three largest

correlations are for (A), (C), and (D) with (E), which

are respectively 0.70, 0.72, and 0.74. Consistent with

Gable et al. (2003), the largest correlation (0.74) is

between (D) criterion average and (E) dimension

average, which suggests that (D) and (E) are the

strongest measures of overall ERP success. Gable et

al. (2003, p. 585) noted, “that the dimension average

yields the largest correlation with all the criteria

further supports the view that the dimensions are

additive, and thus when combined yield a stronger

overall measure of success than possible from any

ICEIS 2006 - DATABASES AND INFORMATION SYSTEMS INTEGRATION

single dimension.” In this regard, our data supports

the work of Gable et al.

Table 1: Correlations: Criteria and Dimensions.

Dimensions A B C D

1 SQ .55 .54 .64 .61

2 IQ .59 .58 .63 .64

2 VQ .41 .42 .40 .43

4 II .51 .57 .60 .59

5 WI .60 .58 .57 .62

6 OI .70 .61 .67 .69

E Dimension

Average

.70 .68 .72 .74

A: Impact on Individual, B: Impact on Workgroup,

C: Impact on Organization, D: Criterion Average.

We also used PLS Graph 3.0 to assess our model.

The PLS (Partial Least Squares) procedure is a

second-generation multivariate technique used to

estimate structural models (Chin, 1998; 2000). This

approach is suitable for this study because of our

small-sized data, and the developing knowledge

regarding the additive nature of IS success

measurement. PLS is capable of testing complex

models consisting of multiple interactions measured

with multiple indicators. PLS recognizes two

components of a casual model: the measurement

model and the structural model (Chin, 1998; 2000).

The measurement model consists of relationships

among the conceptual factors of interest (the

observed items or variables) and the measures

underlying each construct. This model demonstrates

the construct validity of the research instrument, i.e.

how well the instrument measures what it purports

to measure. The two main dimensions are the

convergent validity (composite reliability) and the

discriminant validity. PLS Graph 3.0 computed the

composite reliability of each dimension or construct.

The composite reliability of each construct in the

model with the highest predictive power in this

study are as follows: SQ - 0.73; IQ - .62, VQ - 0.51,

II - 0.58, WI - 0.50, OI - 0.63, and ERP success -

0.77 (please see the discussions below). This is

adequate for this study (Hair et al., 1998). The

discriminant validity is assessed by checking the

extent to which items measure a construct. This is

assessed by checking the square root of the average

variance extracted (AVE) for each construct. In no

case was any correlation between the constructs

equal to or greater than the squared root of AVE

(Fornell and Larcker, 1981; Chin, 1998). This

suggests that our measures are distinct and

unidimensional (The result is omitted due to space

restrictions, but available upon request). Thus, we

can say that the convergent and discriminant validity

of our data are psychometrically sound and adequate

for an explanatory study such as this one (Fornell

and Larcker, 1981; Chin, 1998; Hair et al., 1998).

The structural model gives information as to how

well the theoretical model predicts the hypothesized

paths or relationships. PLS Graph 3.0 provides the

squared multiple correlations (R

) for each

endogenous construct in the model and the path

coefficients. The R

indicates the percentage of a

construct’s variance in the model, while the path

coefficients indicate the strengths of relationships

between constructs (Chin, 1998; 2000). PLS does

not generate a single goodness of fit metric for the

entire model, unlike other structural modeling

software, but the path coefficients and the R

are

sufficient for analysis (Chin, 1998; 1999).

4.2 Alternative Models

The examination of alternative models in structural

modeling could facilitate insights (Doll and

Torkzadeh, 1988; Hair et al., 1998; Sedera and

Gable, 2004). Thus, we developed alternative Model

1 to Model 6, and checked their path coefficients

and R

s. The details of these models are shown in

Table 2 (See also the Appendix for their

illustrations). The R

of Models 1, 2, 3, 4, 5, and 6

respectively are 0.335, 0.366, 0.050, 0.305, 0.362,

and 0.316. Clearly, Model 2 has the best R

suggesting its relative strength in predicting ERP

success in comparison to the other models. It has to

be noted that all the models but Model 3 explained

more than 30% of the variance in the ERP success

model, which is adequate for this study. Our

extended ERP success framework represented in

Model 2 suggests that ERP success is a second-

order factor. This is consistent with results in the

work of Gable and colleagues (Gable et al., 2003;

Sedera and Gable, 2004). Even though our ERP

success model has more dimensions than do Gable

et al. (2003), the conclusions seem to be comparable.

Further, Figure 4 shows the path coefficients in

Model 2. Chin (1998) recommends that path

coefficients should be at least 0.20, and ideally

above 0.30 to be considered meaningful. Apparently,

SQ and OI predict “success” more than do any other

dimensions with their relatively better path

coefficients. Again, this result corroborates the

results by (Sedera et al., 2002) in which these two

dimensions were noted as the most important in

assessing ERP success. These researchers sampled

the views of key organizational stakeholders in 23

Australian public sector organizations using the four

dimensions in the Gable et al. model.

ENTERPRISE RESOURCE PLANNING (ERP) SYSTEMS SUCCESS MEASUREMENT: AN EXTENDED MODEL

Table 2: Structural models and their Corresponding R

Structural

Model

Description

0.335

Model 1 One first-order factor, with

all the 45 items

0.366

Model 2 Six first-order factor (SQ,

IQ, VQ, II, WI, OI), One 2

order factor

0.050

Model 3 Six first-order factor, Two

order factors, One 3

order factor

0.305

Model 4 Four first-order factor (SQ,

IQ, II, OI), One 2

order

factor (Gable et al. (2003)

2 =

0.362

Model 5 Five first-order factor (SQ,

IQ, II, WI, OI), One 2

order

factor (without VQ)

2 =

0.316

Model 6 Five first-order factor (SQ,

IQ, VQ, II, OI), One 2

order factor (WI)

Figure 4: Results of PLS Graph 3.0 for Model 2.

5 DISCUSSIONS AND

CONCLUSION

This paper discusses ERP success measurement

model as proposed by Gable and colleagues (Gable

et al., 2003, Sedera et al., 2003a, Sedera and Gable,

2004). Specifically, we asked whether the Gable and

colleagues’ model is comprehensive. We found

through literature review and interviews with case

companies that their ERP systems success

measurement model might be limited in scope as

two important dimensions are not considered. To

that end, this paper presents perhaps the first attempt

at validating and extending their model, and in a

different setting (private sector) and geographical

location. Importantly, this paper draws from the

issues of additivity and mutually exclusivity of ERP

success measures discussed by Gable and colleagues

as we incorporated two relevant dimensions,

namely, Workgroup Impact and Vendor/Consultant

Quality, which we found to be relevant in the

discourse.

With regard to research, this endeavor could

entice further studies. Our operationalized set of

ERP dimensions (and measures) offers perhaps a

more comprehensive model in the literature. The

proposed ERP systems success measurement model

(Figure 3) has sound psychometric properties as

assessed through structural equation modeling

technique, and criterion validity. Particularly, this

effort might engender the development of an

appropriate scale to assess ERP system success for

adopting organizations. Further, we find support for

the claim that ERP systems success is a second-

order factor (Sedera and Gable, 2004), and our data

shows that a six-factor construct outperforms the one

with four as proposed by Gable and colleagues.

Additionally, our model offers other useful insights,

for example, System Quality and Organizational

Impact were found to be perhaps the two most

important dimensions to watch out for in evaluating

ERP systems success, this finding adds credence to a

previous study (Sedera et al., 2002) carried out in

public sector organizations. Admittedly, our findings

are not conclusive and further testing and

refinements is expected. Future research might need

to focus on utilizing confirmatory factor analysis as

knowledge is accumulated in this area of research.

Our study has implications for practice as well.

As noted, this study is motivated by the need to

present practitioners with guidelines for assessing

the success of their ERP software. It is not claimed

that our guideline is the final word regarding ERP

success measurement, evaluation or assessment for

ERP adopting firms; however, our comprehensive

list of success dimensions could be valuable

especially for firms with no formal means of

conducting such an exercise. It is worth noting that

anecdotal evidence exists indicating that our

research instrument is already in used for such

purposes in our research settings. Management can

use the dimensions of Systems Quality and

Organizational Impact of acquired systems in

assessing the effectiveness or success of such

technologies in instances where a more

comprehensive instrument or formal evaluation

techniques are not readily available. Our model

could be modified for other enterprise systems,

including Customer Relationship Management

(CRM), and Supply Chain Management (SCM).

To conclude, we highlight the limitations of this

study. It is exploratory, and our sample is not

random. Nor can we rule out personal bias, even

though the respondents claimed to present an

average view for their respective organizations on

selected issues. Our sample comprises mixed ERP

software, including top-brand names (e.g. SAP and

ERP

Systems

Success

0.213

0.092

0.084

0.135

-0.242

0.296

= 0.366

ICEIS 2006 - DATABASES AND INFORMATION SYSTEMS INTEGRATION

Oracle) and mid-market products (e.g. Scala and

Nova). It is possible that the heterogeneous nature of

the ERP systems used for our study are limiting.

Finally, our sample consists of small, medium, and

large companies. The diversity in the sample is

good, but it may affect our findings. A homogenous

sample of only large or small firms might yield

results different from the ones discussed herein.

Future studies could improve the findings of this

study by addressing some of these limitations.

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APPENDIX

Illustrations of the alternative ERP systems success

models

ERP

Success

Measure 1

Measure 45

…

Quality

Impac

ERP

Success

ERP

Success

Model 1: One firs

-order factor, with

all the 45 items

Model 2: Six firs

-order factor and one 2

order factor

Model 3: Six firs

-order factor, Two 2

order factors, one 3

order factor

Model 4: Four firs

-order factor (Gable

et al.)

Model 5: Five firs

-order factor, one

order factor (without VQ)

Model 6: Five firs

-order factor, one 2

order

factor (without WI)

ERP

Success

ERP

Success

ERP

Success

ICEIS 2006 - DATABASES AND INFORMATION SYSTEMS INTEGRATION