003-0370

A Framework of Problem Diagnosis for ERP Implementations

Sixteenth Annual Conference of POMS, Chicago, IL, April 29 - May 2, 2005

Yu-Hui Tao

National University of Kaohsiung

700 Kaohsiung University Road, Nan-Tzu District 833, Kaohsiung Count, Taiwan, R.O.C

ytao@nuk.edu.tw

Phone: 886-7-5919326, Fax: 886-7-5919328

Josephine Lee, Shu-Chu Liu

National Pingtung University of Science and Technology

1, Shueh-Fu Road, Nei-Pu Hsiang, Pingtung County 91201, Taiwan, R.O.C.

@msa.hinet.net, @mail.npust.edu.tw

Phone: 886-8-7703202 , Fax: 886-8-7740367

ABSTRACT

Although the development and implementation of Enterprise Resources Planning (ERP)

have accumulated quite some experiences and methodologies, many surveys showed one half to

two third of the ERP implementations are not successful. Kranser (2000) contributed the sources

of failures from the perspectives of management, user and technique, in which technique

contributed the least. Furthermore, many initial technical issues were expanded to final failure

due to the management’s failure in providing problem-monitoring and -solving mechanisms,

such as the classical example of FoxMeyer (Scott and Vesseyge, 2002). Therefore, many

literatures applied retrospective research methods to explore the management and user issues in

order to provide ERP evaluation, critical success/failure factors, or implementation methods. On

the other hand, a real-time problem diagnosis can change an ERP implementation from what

Standish Group called “impaired” to “challenged”, or even “successful” result, whose

importance is obviously seen. Nevertheless, although many practitioners conduct ERP

problem-solving in real world, little is discussed in academic literature. To address the above

concerns, we propose a flexible problem-diagnosis framework for ERP implementations. The

primary objective is to utilize the software development life cycle as a core to integrate existing

models and derive a problem-diagnosis process, in order to establish a generalized framework

that encompasses both the academic theories and practical experiences.

Key Words：Enterprise Resources Planning, Implementation, Problem-Diagnosis, Framework

INTRODUCTION

The rapid development of Information System (IS) and Information Technology (IT) make

business organizations increasingly depending on IS/IT (referred as IS from here on). However,

the investment cost on IS is generally high and risky, which is easily turning into unsatisfied

returns or even financial crisis for closure. For example, Enterprise Resource Planning (ERP) is a

fully integrated IS covering all business internal resources, such that not only the complexity and

risk are high, but also occupying a high percentage of IS budget (Krasner, 2000; Scott and

Shepheard, 2002). Other major ISs extending from ERP, such as customer relationship

management (CRM) systems, supply chain management (SCM) systems or knowledge

management systems are also involving widely as well as complex. Although the ERP

implementation experiences have been accumulated since the 1990, the failure rate is still high

(Hong and Kim, 2002). For ERP implementation alone, the worst scenario of failure may lead

the company bankrupt and closed, such as the classical case of the drug retailer company of

FoxMeyer, which even sued the ERP supplier SAP Inc. and the Andersen and Deloitte consulting

firms (Jesitus, 1997). Higher failure rates also appear in other currently popular ISs, such as data

warehouse systems (Wixom and Watson, 2002), CRM systems (Payton and Zahay, 2003) and

SCM systems (Elmuti, 2002).

In the Chaos report by Standish Group (1995), the results of software projects were

classified into 16.2% of “Successful”, 52.7% of “Challenged”and 31.1% of “Impaired”, which

totals a high 83.8% of unsuccessful rate. Long before the Chaos report, Lyytinen and Hirschheim

(1987) had classified the IS problems into categories of correspondence, process, interaction and

expectation from the literature, in which “correspondence” concerning the target

accomplishment, “process”concerning budget and time, “interaction”concerning user usage,

attitude and satisfaction, and “expectation”concerning stakeholders’needs, expectation and

value. The problem classification by Hyythinern and Hirschheim actually pointed out the issues

encountered by the 52.7% of “challenged”problem type. Because successfully implementing

software projects are not easy, Scott and Vesseyge (2002) constructed a risk model for IS

implementation containing factors of external, internal, IS and information project, which was

illustrated by the impaired type of FoxMeyer and challenged type of Dow Corning, who

implementing SAP/R3 at the same period. Similarly, Vogt (2002) compared the result and

handling process of ERP implementation problems of four large US corporations, in which

FoxMeyer belonged to impaired type, and Hershey Food Corp., Jo-Ann Stores Inc. and W. W.

Grainger Inc. belonged to the challenged type of over budget or schedule delayed.

In principle, Krasner (2000) contributed the failure to the perspectives of management

(16%), user (62%) and techniques (12%), and largely related to user and management layers.

Take FoxMeyer for example, who was the first US retailer adopting SAP/R3, although SAP/R3

did exist technical problems at the beginning, the critical failure factors were, in fact, due to

incomplete implementation planning, no executive management actively participated, no steering

committee, inadequate communication with the consulting firms for coordinating technical

issues and milestone monitoring (Scott and Vesseyge, 2002). Consequently, FoxMeyer produced

a different outcome than the other four corporations facing challenging problems mentioned in

Scott and Vesseyge (2002) and Vogt (2002). Because the management and user impact the IS

implementation failure the most, many literature actively investigates critical success or failure

factors (Hong and Kim, 2002; Vogt, 2002; Yeo, 2002; Umble, Haft and Umble, 2003), and

appropriate implementation processes or methods (Umble, Haft and Umble, 2003; Kukafka et al.,

2003; Strong and Volkoff, 2004). However, these studies collected the IS implementation data for

so-called retrospective analyses in order to provide the future reference or assessment of

potential IS adopting companies. On the other hand, once any problem occurs during

implementing an IS, a quick problem diagnosis and solving to resolve the crisis by reducing the

risk of failure is equally important but is a less investigated research issue in the literature.

Currently, the literature lacks a complete methodology for dealing with quick

problem-diagnosis during ERP implementations with a strong theoretical evidence. A successful

ERP implementation must succeed in every stage of the project life cycle. Therefore, some

retrospective studies classified IS problems based on the IS project life cycle (Cavaye, 1998;

Krasner, 2000; Rajagopal, 2002; Somers and Nelson, 2004). The industry has accumulated rich

experiences in IS problem diagnosis (Vogt, 2002; Kranser, 2000), but because problem diagnosis

is an implicit knowledge, currently IS problem diagnosis lacks a sound conceptual model and

corresponding step-by-step process as a reference for project managers or consultants. Although

there exists theoretical models such as IS evaluation, IS service quality, technology acceptance

model, technology frame, and innovative fusion, each of them accounts only a segment of the IS

project life cycle or core issues. On the other hand, despite the individual limitation, they can be

integrated to form a complete problem-diagnosis framework as the IT use research in Kukafka et

al. (2003), and produce accordingly a procedural process to be applied in practical IS

implementation problems.

Consequently, the main objective of this research is to propose an integrated ERP

problem-diagnosis framework that can be quickly adopted to diagnose and solve implementation

problems. In order to position it as generalized and valuable, this framework focuses on rational

multiple IS models integration for combined synergies, and flexible structure for adapting to

changes or problem nature.

LITERATURE REVIEW

To cover the based of the whole research spectrum, we briefly review literature in IS service

quality, technology acceptance model, and technology frame in this section.

IS Service Quality

IS service quality originates from the service industry’s service quality studies.

Parasuraman, Zeitharml and Berry (1985) proposed a quality service gap model via focus group

and interviews of customers and managers from banks, credit card, securities and product

maintenance industries. To extend to all service industries, Parasuraman, Zeitharml and Berry

(1988) reduced the ten factors to five which includes tangibles, reliability, responsiveness,

assurance and empathy and produced a SERVQUAL instrument containing twenty-two items.

SERVQUAL continued to evolve after it was first published (Zeitharml, Parasuraman and Berry,

1990; Parasuraman, Zeitharml and Berry, 1991; Cronin and Taylor, 1992; Zeitharml, Berry and

Parasuraman, 1993; Parasuraman, Zeitharml and Berry, 1994).

On IS service quality measurement, Kettinger and Lee (1994) first applied SERVQUAL to

IS domain for lifting the satisfaction from the management users. Follow-up studies also

continued, such as Pitt, Watson and Kavan (1995), Van Dyke, Kappelman and Prybutok (1997),

and Pitt et al. (1997). To its counterpart of the model, Kettinger and Lee (1995) modified the

PZB service quality gap model to adapt the IS context to an IS service quality gap model, in

which the SERVQUAL was still used to measure the gap between the user’s expectation and

perceived service. Pitt et al. (1995) also proposed a decision factor model of IS user expectation

as seen in Figure 1, which pointed the key decision factors to word-of-mouth communications,

personal needs, past experiences, vendor communications, and IS communications. Continuing

studies included the works by Kettinger and Lee (1997) and Jayasuriya (1998).

ExpectedService

Word-of MouseCommunications

ISCommunications

PersonalNeeds

PastExperiences

PerceivedService

User ISDepartment

Gap

VendorCommunications

Vendor

Figure 1 Decision Factors of User’s Expectation (Pitt et al., 1995)

Service quality is only one part of the overall IS quality measurement. An earlier key study

on IS success was DeLone and McLean (1992) who collected over 100 papers since 1981, and

proposed a six-factor IS success model, including system quality, information quality, use, user

satisfaction, individual impact and organizational impact. However, Pitt et al.(1995) thought that

the model of DeLone and McLean (1992) emphasize more on the technical view, and ignored IS

department in a service role. Therefore service quality is added to the IS success model. Myers,

Kappelman and Prybutok (1997) agreed with Pitt et al. but added work group impact since which

was a middle layer between the organization and the individual as shown in Figure 2. Lately,

DeLone and McLean (2003) collected 144 papers referred their original IS success model and

confirmed the addition of the service quality as an important factor as Pitt et al.(1995) and Myers,

Kappelman and Prybutok.(1997) suggested.

SystemQuality

ServiceQuality

Use

GroupImpact

IndividualImpact

InformationQuality

UserSatisfaction

OrganizationalImpact

Figure 2 Revised IS Success Model (Myers et al., 1997)

Technology Acceptance Model

Davis (1986) first proposed Technology Acceptance Model (TAM) based on the Theory of

Reasoned Action (TRA) by Fishbein and Ajzen (1975), which explored the relationship between

perception, attitude and technology use. The purpose of TAM was to simplify the TRA by

eliminating subjective norm and look for a more general model for explaining users’behavior in

computer technology. Legris, Ingham and Collerette (2003) analyzed twenty-two TAM papers

between 1980 and 2003 and found out that despite different external factors were included to

different scenarios, only less than 40% of variances were explained in average. In order to adapt

to different types of business and ISs, Davis had modified TAM several times (Davis, 1989; 1993;

1995; Venkatesh & Davis, 1996; 2000) and clarified other’s question (Davis, 1996) for making

TAM more matured. Because the explanation power of TAM varied to different technology or IS,

many other scholars had also proposed different integrated models (Goodhue and Thompson,

1995; Taylor and Todd, 1995a; Dishaw and Strong, 1999) or model comparison (Mathieson,

1991; Taylor and Todd, 1995b; Plouffe et al., 2001).

Extension of TAM（TAM2）was proposed by Venkatesh and Davis (2000) to include social

influence and cognitive instrumental factors regarding user perceptions. Social influence

includes subjective norm, voluntariness and image; cognitive instrumental includes job relevance,

output quality, result demonstrability, and perceived ease of use. The studies showed that these

two variables explained 51% of the variance in perceived ease of use, and the whole model

explained 49% of the variance in behavioral intention. Except extending the TAM forward,

TAM2 also raised the completeness of the theoretical background. More importantly, TAM2

brought the research scene into internal business operations.

Gender Age

BehavioralIntention

UseIntention

PerformanceExpectancy

EffortExpectancy

SocialInfluence

FacilitatingConditions

Experiences Voluntarinessof Use

Figure 3 UTAUT Model (Venkatesh et al., 2003)

Venkatesh, Morris, Davis & Davis (2003) reviewed years of TAM related models and

proposed Unified Theory of Acceptance and Use of Technology (UTAUT), which proved to have

a 70% high of explanation. UTAUT covered perspectives in psychology, sociology, and

technique by incorporating eight models, including TRA, TAM, motivation model, theory of

planned behavior (TPB), combined TAM and TPB, model of PC utilization, innovation diffusion

theory, and social cognitive theory as shown in Figure 3. Previous studies had some constraints,

such as using student subjects, personal-oriented and simple IS, voluntarily adoption, and

experiences of IS. In order to conquer these constraints, Venkatesh et al. (2003) used four

adjusting variables of gender, age, experience, and voluntariness of use, and four factors of

performance expectancy, effort expectancy, social influence and facilitating conditions. The

experiment covered four different lines of large corporations and was conducted in six months

with three different testing stages, which demonstrated that UTAUT explained 69% of the

variance in intention. A follow-up experiment was done to two different types of corporations

and again obtained a 70% level, much effective than any individual or combined models in the

past.

Technological Frame Model

Orlikowski and Gash（1994）proposed a Technological Frame (TF) to explain the source of

IS implementation difficulties by extending the theory into social cognition, which studied the

incongruence between members of a large consulting firm in adopting Notes groupware. The

incongruence came from the technical staff and user group who had different interpretations

about technology use, especially in their understandings of technological assumption,

expectation and knowledge.

TF consists of three domain areas, which are nature of technology, technology strategy and

technology in use. Nature of technology explored people’s impression on technology and

understanding of the functionality and performance of the technology in use, including

motivation and success principles. Technology strategy explored people’s opinions in why their

organization implementing a specific technology, including the motivation and vision to adopt

the technology and their mission to the organization. Technology in use explored how people use

technology in daily routines, and the criteria and expected outcome for using such a technology,

including priority and resource, educational training, difficulty level and policy of safety quality.

Khoo (2001) emphasized that there were three implications from the TF. First, the content

of TF is not fixed and will evolve through the research questions; second, TF can be related to

any specific stakeholder group or community of practice; and third, incongruence in technology

will lead to big discrepancy in the expectation, assumption and knowledge. Accordingly, Khoo

(2001) applied TF analysis to a collection review policy of a digital library, which innovatively

adopted the content and participated groups of TF. In the analysis of adopting electronic data

interchange (EDI) in London insurance market, Brett (1999) successfully combined TF with

structural culture by Thompson (1990; 1995) to develop a research foundation for a

process-based methodology, which illustrates the possibility of integrating TF with other theories.

When investigating technology changing issues, McLoughlin, Badham and Couchman (2000)

used technology allocation and TF to rethink the role of business political process and validated

by three case companies. Also in studying how political process affects technology adoption,

McGoverna and Hicks (2004) used a small make-to-order manufacturer to confirm the decisive

power of the frame on IS decisions. In a comparative study by Davison (1997) on TF impacting

technology adoption, one case company’s TF evolved with organization changes and thus an IS

application could be successfully adopted in a new functional area, but another case company

had a strong resistance in IS development which could be contributed to continuously changing

TF in the participant group.

THE FRAMEWORK

Most IS theories are model-based, as seen in the literature review, while the industrial

practices are mainly process-oriented. Therefore, to meet the objective of having a

problem-diagnosis solution, a natural approach is a framework that consists of a static model

rationally integrating multiple IS models, and a dynamic process containing correspondent

methods to the static model. The static model and dynamic process are designed to meet

prerequisite principles so that the overall framework can be effective and preserve some fine

characteristics for problem diagnosis.

Static Model

To achieve a generalized and flexible static model, the guidelines under the goal of ERP

problem diagnosis as follows:

a. Regarding quick diagnosis, all the IS models must be fully validated and include

corresponding instrumental measurement and questionnaire items.

b. Regarding ERP project life cycle, all stages in the life cycle need to be covered.

c. Regarding nature of problem diagnosis, problem detection should be multi-stage and

repetitive to reduce the risk.

d. Regarding flexibility, original IS model assumptions should not constrain the different

nature of problem-diagnosis.

e. Regarding generalization, adaptable to include other models not considered or thought of,

and adjustable to replace part of the component to meet special ERP application domains

are demanded. Combining d with e, this static model obeys the quality criteria of

programming modules of tight internal cohesion and loose module coupling.

According to the above guidelines, the static problem-diagnosis model uses the ERP

project life cycle as the core (guideline b) to integrate UTAUT model and IS success model

covering before and after adoption point on the life cycle, and TF (guideline a) covering the

whole life cycle. The considerations are as described in the literature review: TAM and IS

service quality both have a series of models targeting different perspectives, for

problem-diagnosis purpose, the maximum coverage is the best option. Therefore, UTAUT is the

ultimate integrated TAM version with the highest variation explained, and IS success model

covers the service quality as well as the traditional information and system quality factors. As

for TF itself, it is stand alone but covering the whole life cycle. Therefore, not only TF is closed

to practical problem-diagnosis mechanism, but also its flexibility in its content and structure

makes it highly complement to UTAUT and IS success model (guideline c).

Because the chosen IS models do not interact with each other, and thus maintain their own

validity and completeness without the need for further testing (guideline e). However, as see in

Figure 4, UTAUT and IS success model contain only the first half of their original models,

which is because from the problem-diagnosis perspective, detecting potential problem sources is

the main concern rather than their original purposes (guideline d). In addition, the content of TF

is not fixed but depends on the problem domain for appropriate selection and confirmation

(guideline d).

UTAUT Model

Technological Frame

Technology in Use（Priorities and Resources, Training,Ease of Use, Policies around Security and Quality）

Technology Strategy (Motivation, Criteria of Success）

…Nature of Technology

Key Groups (Users, Technologists,… )Domain

IS Success Mode

SystemQuality

InformationQuality

ServiceQuality

UserSatisfaction

UseBehavioralIntention

PerformanceExpectancy

EffortExpectancy

SocialInfluence

Gender Age Experiences Voluntarinessof Use

FacilitatingConditions

UseBehavior

Initiation Adoption Adaptation Acceptance Routinization Infusion

Software

LifeCycle

Figure 4 ERP Problem-Diagnosis Framework

The six stages of life cycle by Rajagopal (2002), namely initiation, adoption, adaptation,

acceptance, routinization, and fusion, is used in this model because Somers and Nelsons (2004)

thought the last two stages represent the behavior after the adoption, which meets the nature of

problem-diagnosis. Nevertheless, from Figure 4 we can tell that currently the only critical cutoff

point on the life cycle is the adoption. In other words, other IS project life cycle with this

particular cutoff point can replace Rajaogpal’s version. Besides, if other IS models can cut in to

other stages of the life cycle with little interactions on the existing models, as long as it

significantly increases the benefits of problem-diagnosis, they can be included into the current

structure of this static model without difficulties (guideline e).

Dynamic Process

In order to fully amplify the referential value of the static model, a corresponding dynamic

process that can be quickly executed to precede the problem diagnosis is important. Therefore, a

dynamic model is part of the overall problem-diagnosis framework for laying out the actual

problem-diagnosis processes. Same as the static model, there are a few guidelines to be

considered as follows:

a. Regarding research attribute, this process can be used for the purpose of exploratory or

explanatory.

b. Regarding data collection, this process needs to contain both quantitative and qualitative data

collection methods.

c. Regarding research process formation, this process should be flexibly combined from basic

constructs in order to meet various complexities of different problems.

Based on the above requirements, this IS problem-diagnosis process model composes of

three fundamental constructs that can be flexibly combined for the problem on hand:

Construct I: Secondary data collection, including public available or internal business data

sources, which can be documents, manuals, report, programs, records, files, and any IS

related references or messages. This secondary data can be used for a qualitative or

quantitative content analysis or other means for providing some initial propositions or

hypotheses of the problems.

Construct II: Combining UTAUT model and IS success model together, they cover the entire life

cycle before and after the cutoff point of adoption, and associate validated measurement of

questionnaire items for a quick exploration of potential problem detection. Or they can be

used at the final stage of problem-diagnosis to confirm the validity of the potential

problems from the target user groups.

Construct III: TF can be used to identify the incongruence between different user groups, which

is a qualitative analysis method in nature via in-depth interviews to confirm some potential

problems.

As building blocks, the above process constructs can be combined, reordered, and repeated,

in order to customize for meeting the needs of a specific problem (guideline c). Data collect can

be customized as well (guideline b). When applying to the problem diagnosis, they can be not

constrained by the original model of different perspectives. For examples:

1. Because the purpose is not to validate the completeness of the models in Construct II, but to

quickly detect potential problem sources, only the impacting factors in the original models

are concerned.

2. Construct III can be used to confirm known proposition as well as the incongruence between

user groups (guideline a).

JUSTIFICATION

The proposed framework was designed to meet required guidelines, and thus contains the

following desirable characteristics for ERP problem-diagnosis as listed below. In particular, the

flexibilities in either its structure or applications are the focal points.

1. Theoretically sound while practically feasible. Currently, the static model integrates

UTAUT model, IS success model, and TF model, which have been evolving throughout at

least a decade, and extensively adopted and discussed in the literature. Most importantly,

they are still widely applied in practice via the three proposed constructs covered in the

dynamic process. The fusion of theories and practices is obviously seen.

2. Comprehensive in scope and double secured. The software project life cycle was brought

into measure the adequate coverage in time. Currently, two sets of models and processing

steps are included to repeatedly cover the complete life cycle. The first complete coverage is

achieved by the UTAUT and IS success model on the cutoff point of adoption stage, while

the second is by the TF model that can be used at any stages of the life cycle as suggested

by its inventors. The comprehensiveness and repetition are then realized.

3. Flexibility in its structure. The structural flexibility has many meanings that make this

framework scalable and sustainable to content changes over time:

a. The separation of the static model and dynamic process makes it easy to be

manipulated and mapped between the theoretical foundation and practical procedure.

b. The stages around the core of the project life cycle makes the static model not bounded

to any specific model, and can be extended to cover more IS models.

c. The feasibility to replace the IS models and/or their components of the static model. As

a result, the content of the static model can be evolved with related theories and not

affecting the overall referential value.

d. The three constructs in the dynamic process can not only be combined according a

specific problem domain, but also adjustable to the changes in the static model.

To sum up, this framework preserves the quality of high cohesive content modules that

are loosely coupled.

4. Flexibility in its applications. The application flexibility is naturally inherited from the

characteristic of structural flexibility:

a. Static model –depending on the complexity, the IS models can be freely selected based

on the judgment of appropriateness of the context of the problem. For example, if the case

problem is only on its initial deployment stage and not formally implemented, then only

the UTAUT or together with the TF can be considered. Another example is that if all the

user groups in the case problem had used the IS, then only the IS success model or

together with the TF can be considered. In other words, this static model can be degraded

to only a subset of the embedded IS models.

b. Dynamic process–same as above, the three fundamental constructs can be adopted based

on actual needs. For example, if there is no appropriate or enough secondary data,

construct I can be omitted. Or if the person who conducts the diagnosis is very familiar

with the case problem, and has a pretty good idea what might be the problems, then

construct III can be apply directly to confirm the hypotheses without going through the

first two stages of constructs.

CONCLUSIONS

We have proposed a problem-diagnosis framework for ERP implementations, which fuses

theoretical models with practical approaches. In the academic world of retrospective studies on

ERP implementations, this real-time ERP problem-diagnosis framework can not only bring in

valuable reference to both academic and practice domains, but will also bring out more insightful

research about IS implementations on the dimension of real-time problem diagnosis. As argued

in the JUSTIFICATION section, there is no need to validate any new or modified IS models in

this proposed integrated framework. But a comprehensive case study to demonstrate the process

of adopting this IS problem-diagnosis framework would add a good referential value, which is

underway. Finally, because the proposed problem-diagnosis framework is not bounded to any

particular ERP theories and practices, it can also be effectively extend to cover other IS

implementations, such as the data warehouse, SCM and CRM mentioned in the

INTRODUCTION.

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