CSF in Project Management_implication From Vietnam

Critical success factors in project management: implication fromVietnam

Cao Hao Thia and Fredric William Swierczekb*

aSchool of Industrial Management, HoChiMinh City University of Technology, Vietnam; bSchool ofManagement, Asian Institute of Technology, Thailand

This study will assess successful project performance based on key project factors.The indicators of project success are cost, time, technical performance and customersatisfaction as used in previous studies. The sample consisted of 239 project membersand managers currently involved in infrastructure projects in Vietnam. Regressionanalysis was used to test five hypotheses developed from theories on project success.Three groups of factors including manager competencies, member competenciesand external stability have significant positive relationships to the success criteria.The completion and implementation stages in the project life cycle are also positivelyrelated to success. The implementation stage of a project moderates both the effects ofexternal stability and organization support on success. The implication for projectmanagers is that implementation is the key stage in determining the success of projects.

Keywords: implementation; project management; success factors; Vietnam

Introduction

The professionalization of project management, for example, through the Project

Management Institute (PMI), has developed uniform standards that for the most part

ignore specific contexts. A recent search for success factors in the PMI database indicated

only two sources for Asia. Both were the Global Conferences 2005 and 2004. While there

were presentations specific to Asia, most were concentrated on best practices. An

interesting example was in the Keynote address of the 2006 conference held in Bangkok

on Leadership, where the speaker C. N. Chu adapted Sun Tsu to the leadership of projects.

She focused on tao (path), kim (timing), ti (direction) and tsao (balanced leadership). The

socialcultural, political and economic context of a project is critical in understanding

what is successful, particularly in developed countries, but it is largely ignored.

Project managers frequently raise the issues of measuring and managing success and the

factors which affect performance. Although it is widely agreed that the determination of

critical success factors and their impacts on project results is important, the evidence from

developing countries is limited. One major controversial issue is the definition of project

success. Pinto and Slevin (1988) recognized that there are few topics in the field of project

management that are so frequently discussed and yet so rarely agreed upon as project success.

Some researchers consider success factors as ubiquitous, something which affects

all projects. Other scholars have tried to classify critical success factors in groups

(Schultz et al. 1987, Belassi and Tukel 1996). In a counter position, Dvir et al. (1998)

ISSN 1360-2381 print/ISSN 1743-792X online

q 2010 Taylor & Francis

DOI: 10.1080/13602380903322957

http://www.informaworld.com

*Corresponding author. Email: [email protected]

Asia Pacific Business Review

Vol. 16, No. 4, October 2010, 567589

argued that success factors should not be universal to all projects. To define success

factors, a different approach is necessary to develop an improved framework that can

include these widely diverging views.

Context

Vietnam is attractive to Official Development Assistance (ODA) and project assistance,

with 25 bilateral donors and 15 international agencies. Approximately US$ 3 billion is

available each year. Since 1996, the largest share of projects has been in major

infrastructure. As of 2006, 6764 projects have been implemented worth US$ 28.7 billion.

However this is only half of what was counted (US and Foreign Commercial Service

2007). The disbursement of ODA is painfully slow and aid effectiveness is limited

(Jacquemin and Bainbridge 2005).

Problems in completing projects on time and in budget come from vested interests who

fear losing power to related project management units (PMUs) which have more benefits

and opportunities. Project performance is affected by the lack of transparency in bidding,

too many regulations and limited enforcement of anticorruption measures. According to

Transparency International (2006), Vietnam ranks 111 in perceived corruption.

Currently, Vietnams economic situation has improved significantly. As a result, there

have been numerous capital investment projects implemented in Vietnam, especially in

the field of infrastructure development, such as the Asian Highway and Airport Upgrading

in Hanoi and Ho Chi Minh City. Unfortunately, according to outside evaluations, the

number of successful projects is limited (Jacquemin and Bainbridge 2005). How to

enhance the success of projects has become an important task for many international

agencies as well as professionals in Vietnam and throughout Asia.

This study will consider key project factors as they relate to performance criteria.

It assesses infrastructure projects in Vietnam to examine the relationship between these

factors and the success of projects in a developing country. It will also consider the

potential generalization of these findings throughout the AsiaPacific region.

Literature review

Project performance criteria

Lim and Mohamed (1999) proposed a classification of project success. In the broad sense,

a project is successful if it has achieved its objectives. Unfortunately, it is only possible to

know whether the original project objectives were achieved during the operational phase

of the project. This achievement depends on the users or stakeholders. As long as the users

are satisfied, the project is considered successful. The more limited definition of project

success considers specific project results. It usually refers to the conclusion of the project

construction phase and the parties involved in the completion. Both definitions should be

considered (Lim and Mohamed 1999). Two criteria are sufficient to determine project

success: completion and stakeholder satisfaction; whereas, to determine the operational

project success, the completion criterion alone is enough.

Tukel and Rom (2001) reported the performance measures project managers

commonly use to evaluate the success of their projects. Specifically, they identified the

project managers orientation toward using internal and customer-driven measures of

performance. They also investigated the priority given to these measures at four different

stages of a project (conceptual, development, implementation and completion stages) by

568 C.H. Thi and F.W. Swierczek

identifying the primary objective at each of those stages. In general, they found that project

managers primary success measure is quality and their most important objective is

meeting customer needs. The priority given to this objective does not change during

various stages of a project, regardless of the project type and industry classification.

The choice of performance measures, however, is influenced by project type and industry

classification.

The literature concentrates on the implementation stage of a project in which

performance measures are primary and the customers requirements are secondary (Baker

et al. 1988, Pinto and Covin 1989). The important factors in the early stages of a project

are internal: meeting budgets; schedules; and technical performance. Yet, in more

advanced phases of the project, external factors such as customer needs and satisfaction

become more important (Pinto and Slevin 1988). Tukel and Rom (2001) recognized that

although project managers claim that they are customer-focused, they commonly employ

internal performance measures to monitor the progress of the project because internal

measures are quantifiable.

Theory

Project success

Project success has often been measured with a simplistic formula, perceived as

unequivocal and easy to assess. Such measures have often defined success as meeting the

objectives of the project budget and schedule and achieving an acceptable level of

performance.

The assessment of project success may also differ according to the evaluator.

Comprehensive success criteria will reflect different interests and views which lead to the

necessity for a multi-dimensional, multi-criteria approach (Cooper and Kleinschmidt

1987, Pinto and Mantel 1990, Freeman and Beale 1992). From another perspective, Pinto

andMantel (1990) identified three aspects of project performance to use as benchmarks for

measuring the success or failure of a project. These were the implementation process, the

perceived value of the project and client satisfaction with the final product. Paolini and

Glaser (1977) and Pinto and Slevin (1988) added client satisfaction and customer welfare.

Freeman and Beale (1992) identified seven specific criteria used to measure project

success. Five of these are frequently used: technical performance; efficiency of execution;

customer satisfaction; personal growth; organizational ability; and business performance.

A project can be considered to be successful by meeting the internal performance

measures of cost, time and technical performance, but also insuring that the project is

accepted by the customer (Kerzner 1998).

According to Kerzner (2001), project success was defined as the completion of an

activity within the constraints of cost, time and performance. This definition of project

success has been modified to include completion:

. within the allocated time period;

. within budgeted cost;

. at the proper performance or specification level;

. with acceptance by the customer/user;

. when you can use the customers name as reference;

. with minimum or mutually agreed upon changes in scope;

. without disturbing the main work flow of the organization;

. without changing the corporate culture.

Asia Pacific Business Review 569

Project performance in this paper will concentrate on four criteria: cost; time; technical

performance; and customer satisfaction. Success will be determined by the level of

performance achieved.

Success factors usually described in the literature of project management are usually

not based on actual performance. To determine whether these factors are actually

successful depends on their effect on project performance. In this review, the term critical

success factor is based on the definition of performance in the literature. PMBOK (Project

Management Institute 2004) defined project performance baseline as an approved plan

against which deviations are compared for management control. PMBOK also defined

cost performance index as the cost efficiency ratio of earned value to actual costs, and

schedule performance index as the schedule efficiency ratio of earned value

accomplished against the planned value.

Key project management factors

The search for critical project factors has been continuing for more than three decades.

Most early studies in this area focused on the reasons for project failure rather than project

success. Rubin and Seeling (1967) investigated the relationship of the project managers

experience on the projects success or failure. The findings indicate that a project

managers previous experience had a minimal impact on the projects performance

(Abdul-Rahman et al. 2006). The size of the previously managed projects did not influence

the managers performance. Avots (1969) identified the reasons for project failure and

concluded that the wrong choice of project manager, the unplanned projects termination

and unsupportive top management were the main reasons for failure. Hughes (1986)

conducted a survey to identify the factors that affect project performance. He concluded

that projects fail because of the improper focus of the management system, by rewarding

the wrong actions and the limited communication of goals. However, to understand failure

does not guarantee success in the future. Replicating the critical success factors in new

projects has been suggested as the more effective approach to improve project

performance (Hawk 2006).

Dvir et al. (1998) suggested that project success factors are not universal for all

projects. Different types of projects are affected by different sets of success factors. Thus,

a project-specific approach is appropriate for subsequent research into the practice and

theory of project management (Hyvari 2005).

Belassi and Tukel (1996) grouped critical success factors into four areas: external

environment; project manager and team members; organization; and the project.

The identification of critical factors would lead to the better evaluation of projects. Critical

factors are linked to their effects which lead to project success or failure. The identification

of this causeeffect relationship would improve project performance (Karlsen et al. 2006).

Schultz et al. (1987) classified critical success factors as strategic or tactical. Strategic

factors consist of factors such as project mission, top management support and

project scheduling, whereas tactical factors relate to client consultation and personnel

selection and training. These two groups of factors affect project performance at different

phases of implementation (Belout and Gauvreau 2004).

Pinto and Slevin (1987) discovered ten critical success factors, including project

mission, top management support, project schedule/plan, client consultation, personnel,

technical tasks, client acceptance, monitoring and feedback, communication and trouble

shooting. This research only identified the critical success factors, but did not measure the

strength of their relationship with project performance. Morris and Hough (1987) and


Cooke-Davies (2002) suggested a range of critical success factors relevant to performance,

including project team and management competencies relevant to large, complex projects,

but also to projects in general.

Belout and Gauvreau (2004) constructed a model in which the relationship between

the independent variables and project performance will be affected by the project life

cycle, project structure and project sectors (intervening effect). The project life cycle

consists of conceptualization, planning, execution and completion (Dvir et al. 2003). The

types of project structure include functional, functional matrix, balanced matrix, project

matrix and project team. The project sector is business or industrial. This framework is

presented in Figure 1.

In the model, the ten factors developed by Pinto and Slevin (1987) are defined as

independent variables in order to assess the impact of these factors, especially the

personnel factor. Project success is measured from three viewpoints: sponsors view;

project managers view; and client or customers view. Success is defined as the level of

satisfaction expressed by at least one of these three viewpoints (Freeman and Beale 1992).

This would include specific evaluation criteria: technical performance; efficiency of

project execution; managerial and organizational implications; personal growth; project

termination; technical innovativeness; business performance; and users satisfaction.

Project Life Cycles

Project SuccessDependent Variable

Project Mission

Project schedule

Client consultation

Technical tasks

Client acceptance

Monitoring

Communication

Troubleshooting

Management support

Personnel

Independent Variables

ProjectStructures

ProjectSectors

Figure 1. Hierarchical regression analysis of success criteria.Source: Belout and Gauvreau (2004).


Yasin et al. (2000) recognized that significant gaps exist between the existing body of

knowledge and the practice of international project management in relation to variables

that are relevant to construction project management success.

Most previous studies consider that project success criteria should include cost, time,

technical performance and customer satisfaction. This means the success of a project was

defined as the completion of an activity within budgeted cost, within the allocated time

period, at the proper technical performance and with acceptance of the customer

(Hawk 2006).

Many studies of project management have presented lists of critical success factors.

The success factors were usually discussed as either general or specific factors that

affected a particular project. Rather, it is the characteristics of a project which would

determine the critical factors, and success is defined as a result.

There is limited research on the strength of the relationship between the critical

factors and success criteria. There is even less analysis of the causal effects between

these factors and the performance of projects in Asia. Key project factors need to be

determined as successful based on the level of performance accomplished in a variety of

real projects.

Hypotheses

Based on the theories of success factors in the literature, five specific hypotheses will be

developed. The dependent variables are the four widely agreed project performance

criteria: cost; time; technical performance; and customer satisfaction. These are used for

evaluating the success of projects in this study.

The four independent variables as developed by Belassi and Tukel (1996) are key

project factors which might have an influence on the project success. These are the

external environment: the competencies of the project manager and the project team

members. Factors related to the project will be treated as intervening variables because of

how they influence the process of project management.

External environment

This first group consists of factors, external to the organization but which have an

impact on project performance, either positively or negatively. A number of external

environmental factors, such as political, economic, social, legal and those related to

advances in technology or even factors related to nature, may affect project performance

(Jin and Ling 2006). In their empirical study, Pinto and Slevin (1989) found that most

of the environmental factors affect projects during the planning stage of a projects life

cycle. However, some of the factors affect a project at all phrases of the life cycle, such

as weather conditions or the social environment. Sometimes these factors are so

influential that they cause a project to be terminated at the implementation stage.

Belassi and Tukel (1996) recognized that a client of a project can be from outside or

inside the organization to which the project belongs. If a client is from outside the

organization, the client should also be considered as an external factor influencing the

project performance. For functional projects, clients are usually part of the organization,

such as top management. In such cases, a client is from inside the organization and factors

related to the client can be grouped under the factors related to the organization.

Additional external factors affecting project performance include competitors in the

market and subcontractors. They should also be considered as factors related to the


external environment. External factors influence the availability of resources and project

performance. The variability in the external environment factors will influence the project

planning and thus project performance (Soja 2006). For these reasons, stability is the

main attribute of these factors that affects the project success criteria (Legris and

Collerette 2006).

Based on these considerations, the following hypothesis is proposed:

Hypothesis 1: In a project, the more stable the external environment, the better the

project performance.

Competencies of the project manager and team members

In the literature, many factors related to the competencies of project managers and team

members have been proposed for the successful completion of projects. These factors not

only affect project performance but they also have an impact on client satisfaction and

project acceptance (Procaccino and Verner 2006). Pinto and Slevin (1989) demonstrated

the importance of selecting project managers who possess the necessary technical

and administrative skills for successful project completion. The project managers

competence becomes most critical during the planning and termination stages (Zwikael

and Globerson 2006). The competence of the team members is also found to be a critical

factor throughout the project cycle (Puthamont and Chareonngam 2007). Similarly, well-

established communication channels between the project manager, the organization and

the client are necessary for the acceptance of the project outcome by the client.

Thamhain (2004) conducted a field study to examine the influences of the project

environment on team performance and resulting project performance. He found a

positive relationship between team involvement and performance especially in complex

project environments.

This discussion leads to specific hypotheses about the relationships between the

project manager, team members competencies and project performance:

Hypothesis 2: In a project, the higher the competencies of the project manager, the better

the project performance.

Hypothesis 3: In a project, the higher the competencies of the project team members, the

better the project performance.

Organizational factors

From the literature, the factors related to the organization include top management

support, project organization structure, functional managers support and project

champion. Tukel and Rom (1995) found that one of the most critical factors for the

successful completion of projects is top management support. Support is usually strongest

if there is a project champion and if this champion is from top management. Top

management helps project managers understand and achieve the projects objectives,

which are specified by the client and/or top management. Top management usually

controls a project managers access to resources, which are supervised by functional

managers. The level of support from top management usually determines the level of

support provided the functional manager. Performance depends on the project

organizational structure. For projects with a functional form, the availability of resources

is not usually an obstacle, because the functional manager is usually also the project


manager. But for projects with a pure project form, or with a matrix organizational form,

adequate resources can be a difficult issue. It requires skilled negotiation and positional

power within the organization. In that case, full support from the organization for the

project helps to facilitate and implement strategies for the successful completion of

projects (Zwikael and Globerson 2006).

The following hypothesis is proposed:

Hypothesis 4: In a project, the more support from the organization, the better the project

performance.

Project characteristics

In the literature, project characteristics have long been overlooked as being critical

success factors. In one of the few studies available, Morris and Hough (1987) identified

schedule duration and urgency as critical factors. However, many projects fail due to

problems within projects. Belassi and Tukel (1996) specified the size and the value of a

project, the uniqueness of project activities, the density of a project network, project life

cycle and the urgency of the project outcome. Tukel and Rom (1995) found that many

large projects (more than 100 activities) exceed their deadlines. There are usually

penalties imposed on projects when deadlines are exceeded. The most common penalties

are monetary and credibility loss. The project managers performance on the job can be

heavily influenced by the uniqueness of the activities. It is easier for project managers to

plan, schedule and monitor their projects if a project has more standard tasks. Project

density also influences performance. Tukel and Rom (1995) defined project density as the

ratio of total number of preceding actions to the total number of activities. The density will

affect the allocation of resources, especially man hours. Because of resource constraints,

project managers are often forced to use overtime, which exceeds the estimated budget,

or they are forced to delay activities competing for the same resources, which results in

delays in project completion.

The urgency of a project relates to success. Pinto and Slevin (1989) defined urgency as

the need to implement the project as soon as possible. In many cases, project performance

criteria are not met because of the urgency of a project (Puthamont and Chareonngam

2007). Projects with political purposes are typical examples. In these situations, not

enough time is allocated for planning and scheduling projects, and as a result they are more

likely to exceed budgets and fail.

Finally, two more characteristics of the project will be considered in the conceptual

framework. These are the stages in project life cycle (concept, development,

implementation and completion) and the type of project (Soja 2006). The priority given

to the internal and customer-driven measures of performance at different stages of a

project depends on the primary objective at each stage (Pinto and Slevin 1988, Tukel and

Rom 2001). Tukel and Rom (2001) also identified that the choice of performance

measures is influenced by the project type and industry classification. This issue was also

identified as very important in IT projects (Legris and Collerette 2006).

From these considerations, the following hypothesis is proposed:

Hypothesis 5: In a project, the strength of relationship between project factors and project

performance will be significantly influenced by the characteristics of the

project.

The hypotheses are summarized in the conceptual framework shown in Figure 2.


Research methodology

Operationalization and measurement

This study examined the relationship between project factors and the successful

performance of projects. The indicators of project success and its antecedent factors are

measured by the perception of managers and professionals in infrastructure projects in

Vietnam.

The indicators of project performance are cost, time, technical performance and

customer satisfaction as used in previous studies. The measurement of these concepts

captures the extent to which a project has been successful.

The indicators of project factors are mainly based on the four sets of factors developed

by Belassi and Tukel (1996). These indicators are: the external environment; the project

manager and team; the organization; and project characteristics. Additionally, the factors

related to the project manager and team members were divided into two dimensions to

capture the level of competencies of project manager and team members.

The measurement of the external environment focuses on the level of stability, in terms

of political, economical, social, legal and technological aspects, ecological (climate or

weather) factors, client consultation, competitors and sub-contractors involvement.

The competencies of a project manager are measured by the ability to delegate

authority, to negotiate, to coordinate, to make decisions and to understand their role and

responsibilities.

ProjectPerformance

Organization Support

Team Member Competencies

Manager Competencies

External Stability

H4 +

H3 +

H2 +

H1 +

H5

Project Characteristics Project target Project size Project value Financial control Budget authority Uniqueness of project activities Density of project Urgency Stage of project Type of project

Figure 2. Conceptual framework.


The competencies of project team members are measured by technical background,

communication skills, troubleshooting, commitment, problem-solving and teamwork.

The measurement of the organization factors includes top management support, project

organizational structure, functional manager support and project champion support.

The factors related to the project measure the level of influence of project

characteristics. Project characteristics are divided into two groups of variables. The first is

the project situation. This consists of project goal, size and value, financial control, budget

authority of the project manager, uniqueness of project activities, density of project

network and the urgency of the project outcome. Each of the items in the group was

measured using a seven-point Likert scale ranging from 1 to 7. The second group, project

background characteristics, includes ownership, number of activities, total budget, project

form and stages of project. In the regression analysis, the situational characteristics are

treated as independent variables and the background characteristics are treated as dummy

variables.

Research design and data collection

There are four main phases of the study. The first phase was conducted via literature

review and interviews. The purpose of this phase was to assess the content and

applicability of each measure based on the literature. Also, the interviews helped identify

key indicators for the research. The second phase was a pilot survey to pre-test

the measures developed for this study. The purpose of this pre-test was to provide a

preliminary evaluation and refinement of the measurement scales. It was

conducted with 40 potential respondents, in charge of infrastructure projects in Ho Chi

Minh City. Third, a survey was conducted with a sample of 239 project professionals in

infrastructure projects in Vietnam. This was the main phase in the research design.

Finally, a focus group was also conducted with project professionals in infrastructure

projects in Ho Chi Minh City to discuss the results and implications. There were 21

participants in the focus group. This also served as a check on the results of the interview

and the survey.

The data received from each respondent in the survey consisted of the description

of the project in terms of background information, perceptions about the projects

performance and the project factors that influenced the performance. The questionnaire

required respondents to think of a project in which they were currently involved

or had recently completed. This project was to be their frame of reference for

completing the questionnaire. The questionnaire included 46 multiple-choice and

open-ended questions. Each of the items about project performance criteria and

project factors were measured on a scale ranging from 1 (strongly disagree) to 7

(strongly agree).

In this research, the population is infrastructure projects in Vietnam. Based on the

list of professionals who are in charge of infrastructure projects in Vietnam, 1000

questionnaires were sent to potential respondents through a nationwide mail survey. 239

usable questionnaires were returned, a response rate of approximately 24%. Bollen (1989)

proposed an empirical ratio of at least five observations per estimated parameter.

In addition, it is generally agreed that the minimum sample for appropriate use of

maximum likelihood is 100 (Hair et al. 1995). Based on the questionnaire, the estimated

parameters are 46. The sample size required is about 230 (46 x 5). The sample size of 239

is adequate for this study. Missing data was minimal and related to background in

formation of the project.


The inability to secure responses from all elements of the population selected for a

sample can produce a non-response bias (Churchill 1991). Specific analysis of the sample

is required to determine whether the responses are representative. One method of doing

this is the comparison of responses across and within the sample (Amstrong and Overton

1977). For this sample, non-response bias was estimated by comparing respondents from

the survey and the pilot survey. A T-test was conducted to test differences between the

means for each of the research variables in the survey and pilot survey. All p-values in the

test statistics were greater than 5% except for the variable ability to coordinate

(p 4.41%). The T-test results presented in Table 1 confirmed that the responses from thesurvey were not significantly different from the pilot survey. This reduces the possibility of

non-response bias in this study.

Data analysis

A frequency analysis is conducted for indicators related to the background information

about the project. This information includes the position of respondents in the project, the

type of project, ownership of the project, the number of project activities, total budget of

the project, organizational structure of the project, stage of the project and work duration

of project members. These background characteristics are presented in Table 2.

Sixty-nine per cent of the projects were related to state-owned enterprises. Nearly half

had budgets in the range of 500,000 to 5 million US dollars per project. Forty-four per cent

were in the implementation stage and 22% were in completion. Most of the projects were

in industrial plants or infrastructure (roads, etc.). The majority of projects had specific

activities that ranged from 50 to 150 in number, which indicates they were of a medium to

large scale. The dominant project structure was functional. The majority of projects also

tended to be between 2 and 10 years in duration. These background criteria are

representative of this type of project throughout Vietnam. They are predominantly public,

medium-sized and in implementation or already completed.

The means and standard deviations of the dependent variables (4 performance criteria)

and independent variables (32 project characteristics) and the correlation coefficients

between dependent variables and independent variables are presented in Table 3.

The means range from 5.42 for technical performance to 4.04 for time. This represents

satisfactory performance.

The findings from the descriptive analysis present some interesting patterns. The most

important manager competencies are understanding the role and responsibilities of a

project manager. Clearly, simply being in the position is not sufficient. The project

manager through experience, training and development or from appropriate delegation,

has to understand what is required in their role and the actions expected of them in their

performance. The ability to make decisions and understanding trade-offs are related

competencies which can affect the project managers performance.

Members of project teams primarily need an appropriate and sufficient technical

background, but they need to be able to apply this expertise flexibly through problem-

solving and troubleshooting.

Specific characteristics of organizational support that were identified include the need

for a project champion, top management support and an appropriate organizational form

which tends to be functional. This level of support would improve the performance of the

project.

In terms of project environment, the political, economic and social environment has

the most relevance for project success. For the project itself, the highest-rated factor is


Table

1.

Non-response

biasassessment.

Survey

Response

PilotSurvey

Response

TestingStatistics

No

Variables

Size

Mean

St.dev.

Size

Mean

St.dev.

T-stat

p-value

1Cost

233

4.89

1.26

40

5.05

1.22

20.594

55.29%

2Tim

e234

4.04

1.63

40

4.33

1.65

20.636

52.55%

3Technical

perform

ance

235

5.42

1.04

40

5.68

1.00

21.421

15.65%

4Customer

satisfaction

231

5.32

1.12

40

5.67

1.20

21.595

11.19%

5Politicalenvironment

239

5.99

1.23

40

6.15

0.98

20.653

51.45%

6Economic

environment

239

5.24

1.18

40

5.65

1.03

21.783

7.56%

7Social

environment

238

5.24

1.21

40

5.30

1.14

20.244

80.77%

8Legal

environment*

198

4.98

1.47

40

9Technological

environment

237

4.86

1.32

40

5.13

1.38

20.895

37.17%

10

Nature

237

4.88

1.36

40

5.13

1.17

20.821

41.24%

11

Clientconsultation

224

4.88

1.32

40

4.69

1.14

0.660

50.97%

12

Competitors

221

4.69

1.53

40

4.66

1.78

0.071

94.36%

13

Subcontractors

222

4.59

1.44

40

4.76

1.50

20.471

63.78%

14

Abilityto

delegateauthority

234

4.94

1.39

40

5.28

1.38

21.031

30.36%

15

Abilityto

negotiate

231

5.18

1.26

40

5.53

1.01

21.358

17.57%

16

Abilityto

coordinate

235

5.11

1.34

40

5.69

0.98

22.023

4.41%

17

Abilityto

makedecisions

237

5.38

1.37

40

5.70

1.24

21.024

30.69%

18

Perceptionofresponsibility

238

5.42

1.31

40

5.65

0.92

20.845

39.90%

19

Technical

background

237

5.28

1.11

40

5.50

1.15

21.034

30.21%

20

Communicationskills

238

4.96

1.06

40

5.15

1.12

20.974

33.11%

21

Troubleshooting

239

5.03

1.21

40

5.33

1.31

21.171

24.26%

22

Commitment

239

5.15

1.25

40

5.38

1.05

20.899

36.95%

23

Problem

solving

239

5.25

1.14

40

5.50

0.93

21.182

23.83%

24

Teamwork

239

51.32

40

5.33

1.05

21.169

24.34%

25

Topmanagem

entsupport

236

5.15

1.51

40

5.53

1.09

21.046

29.65%

26

Org.structure

support

233

5.09

1.34

40

5.31

1.10

20.751

45.32%

27

Functional

managers

support

234

4.94

1.48

40

5.25

1.06

20.889

37.46%

28

Project

cham

pion

235

5.7

1.26

40

5.98

1.12

21.063

28.87%

29

Target

ofproject

isclear

238

6.37

0.95

40

6.53

0.78

21.088

27.78%

30

Thesize

ofproject

islarge

236

5.33

1.51

40

5.18

1.41

0.392

69.54%


31

Thevalueofproject

ishigh

235

5.03

1.53

40

5.18

1.43

20.382

70.31%

32

Financial

controlisstrict*

198

5.54

1.39

40

33

PM

has

budget

authority*

196

4.63

1.78

40

34

Project

isunique

233

4.35

2.00

40

4.85

1.95

20.736

46.26%

35

Network

ofproject

isdense

221

4.6

1.40

40

4.67

1.46

20.205

83.76%

36

Urgency

ofoutcomeishigh

233

5.46

1.35

40

5.73

1.04

20.919

35.87%

Note:*Thesevariableswereadded

inthesurvey.


a clear target or goal. This is supported by the urgency of the outcome or result of the

project and the project manager having sufficient budget authority to make decisions.

These results are supportive of the findings developed from the literature. They suggest

the key role of the project manager, the capability and flexibility of project members and the

critical requirement of a clear goal with the involvement of a project champion. As shown

in Table 3, these variables are all significantly correlated with performance criteria.

Table 2. Background characteristics.

PositionPresident/Director 25.0%Division/Department 32.0%Supervisor/Staff 26.0%Professional consultant 14.0%

Ownership of projectState owner 69.0%Private 10.9%Joint venture/Foreign 19.0%Others 0.8%

Total budget of projectLess than 500,000 USD 20.9%500,000 to 1,000,000 USD 23.4%1,000,000 to 5,000,000 USD 23.0%5,000,000 to more than 10,000,000 USD 32.0%

Stage of projectConception/Development 19.0%Implementation 44.4%Termination 13.0%Completed 21.8%

Type of ProjectBuilding 15.0%Industrial plants 28.0%Road 20.5%Infrastructure 30.0%

Number of activitiesLess than 50 activities 37.7%50100 activities 28.5%100150 activities 11.7%150more than 200 activities 14.0%

Organizational structureProject form 11.7%Functional project 55.2%Matrix project 27.6%

Work durationLess than 2 years 17.2%2 to 5 years 33.9%6 to 10 years 28.0%11 to 20 years 14.0%


Table

3.

Descriptivestatistics:factors

andperform

ance.

Correlation

Mean

SD

Cost

Tim

eTechnical

perform

ance

Customer

satisfaction

Dependentvariables

1Cost

4.89

1.26

1.000

2Tim

e4.04

1.63

0.383**

1.000

3Technical

perform

ance

5.42

1.04

0.468**

0.377**

1.000

4Customer

satisfaction

5.32

1.12

0.374**

0.341**

0.635**

1.000

Independentvariables

5Politicalenvironment

5.99

1.23

0.253**

0.156**

0.241**

0.200**

6Economical

environment

5.24

1.18

0.343**

0.203**

0.364**

0.300**

7Social

environment

5.24

1.21

0.264**

0.213**

0.328**

0.305**

8Legal

environment

4.98

1.47

0.208**

0.192**

0.213**

0.218**

9Technological

environment

4.86

1.32

0.329**

0.224**

0.324**

0.208**

10

Nature

4.88

1.36

0.163*

0.220**

0.265**

0.239**

11

Clientconsultation

4.88

1.32

0.197**

0.255**

0.195**

0.417**

12

Competitors

4.69

1.53

0.171*

0.071

0.116

0.163*

13

Sub-contractors

4.59

1.44

0.188**

0.195**

0.288**

0.221**

14

Abilityto

delegateauthority

4.94

1.39

0.248**

0.359**

0.382**

0.348**

15

Abilityto

tradeoff

5.18

1.26

0.326**

0.271**

0.262**

0.301**

16

Abilityto

coordinate

5.11

1.34

0.277**

0.408**

0.323**

0.338**

17

Abilityto

makedecisions

5.38

1.37

0.260**

0.397**

0.370**

0.332**

18

Perceptionofhisrole

andresponsibility

5.42

1.31

0.255**

0.333**

0.306**

0.329**

19

Technical

background

5.28

1.11

0.197**

0.236**

0.324**

0.245**

20

Communicationskills

4.96

1.06

0.244**

0.248**

0.279**

0.301**

21

Troubleshooting

5.03

1.21

0.312**

0.317**

0.322**

0.275**

22

Commitment

5.15

1.25

0.258**

0.330**

0.385**

0.328**

23

Problem

solving

5.25

1.14

0.297**

0.381**

0.358**

0.330**

24

Teamwork

5.00

1.32

0.289**

0.351**

0.288**

0.360**

25

Topmanagem

entsupport

5.15

1.51

0.151*

0.213**

0.253**

0.202**

26

Project

organizational

structure

5.09

1.34

0.184**

0.251**

0.370**

0.304**

27

Functional

managers

support

4.94

1.48

0.219**

0.256**

0.330**

0.278**


Table

3continued

Correlation

Mean

SD

Cost

Tim

eTechnical

perform

ance

Customer

satisfaction

28

Project

cham

pion

5.70

1.26

0.246**

0.193**

0.321**

0.294**

29

Thetarget

ofproject

isclear

6.37

0.95

0.299**

0.139*

0.287**

0.234**

30

Thesize

ofproject

islarge

2.67

1.51

20.174**

0.036

20.146*

20.053

31

Thevalueofproject

ishigh

2.97

1.53

20.091

0.018

20.040

0.054

32

Thefinancial

controlisstrict

2.46

1.39

20.336**

20.237**

20.269**

20.306**

33

ThePM

has

fullbudget

authority

4.63

1.78

0.143*

0.384**

0.213**

0.203**

34

Project

isunique

3.65

2.00

20.118

20.176**

20.243**

20.264**

35

Network

ofproject

isdense

3.40

1.40

20.099

20.160*

20.187**

20.243**

36

Theurgency

ofproject

outcomeishigh

5.46

1.35

0.269**

0.197**

0.274**

0.258**

Notes:**Correlationissignificantat

the0.01level(2

tailed);*Correlationissignificantat

the0.05level(2

tailed).


Factor analysis of key project factors

Factor analysis was used to reduce the 32 project characteristics into meaningful factors.

Table 4 shows the results in which a five-factors solution emerged. These are member

competencies, manager competencies, organization support, external stability and project

situation.

The five factors in Table 4, with the variables grouped under the relevant factor, confirm

that these are appropriate scales. Overall, the measures of different constructs are clearly

distinguishable fromeach other. This provides evidence for satisfactory discriminant validity.

The second purpose of this factor analysis was to assess the multi-dimensional nature

of perceived project performance. The project performance criteria were operationalized

as cost, time, technical performance and customer satisfaction. A factor analysis was

conducted to determine if performance could be considered a uni-dimensional construct.

Table 4. Factor analysis of the key project factors.

Factor 1:Member

Competencies

Factor 2:Manager

Competencies

Factor 3:Organization

Support

Factor 4:ExternalStability

Factor 5:ProjectSituation

Problem solving .827Troubleshooting .798Commitment .758Technical background .727Teamwork .722Communication skills .667

Ability to coordinate .765Ability to make decisions .752Ability to negotiate .742Ability to delegate authority .715Ability to perceive role andresponsibility

.439 .595

Functional managers support .753Project organizationalstructure support

.744

Top management support .734Project champion .625

Social environment .815Political environment .768Economical environment .746Technological environment .563Nature .440

The value of project is high .839The size of project is large .756Network of project is dense .551Project is unique .502

Eigenvalues 9.264 2.648 1.577 1.414 1.164Variance explained (%) 38.60 11.03 6.57 5.89 4.85Cumulative varianceexplained (%)

38.60 49.63 56.20 62.09 66.94

Cronbachs alpha 0.9006 0.9092 0.8951 0.7628 0.6806KMO 0.906Notes: Rotation method Varimax with Kaiser normalization. Factor loadings less than 0.40 have been omitted.


This analysis shows that only one factor was extracted. This is specified as success

criteria. The results of this analysis are presented in Table 5.

The analysis identified five groups of key project factors with an eigenvalue greater

than 1, accounting for 66.94% of the cumulative variance, and one group of success

criteria with an eigenvalue greater than 1, accounting 57.51% of the variance.

The common accepted decision criteria in research were applied: an eigenvalue more than

1, at least 50% variance being explained, and the simplicity of the factor structure (Hair

et al. 1995).

Cronbachs alpha scores were used to assess the reliabilities for the five groups

of key project factors and the success criteria. The member competencies, manager

competencies, organization support, external stability and project situation had

reliabilities ranging from 0.90 to 0.68. Project situation has marginal reliability. Project

success had a reliability of 0.72. All principal components had a Cronbachs alpha value

more than 0.60, a threshold acceptable in exploratory research (Nunnally 1978, Yoon

et al. 1995).

Hierarchical regression analysis

To examine the direct relationships between the project factors (member competencies,

manager competencies, organization support, external stability and project situation) and

project success, regression analysis was used. The moderating influences of project

characteristics (ownership, number of activities, total budget, project form, implemen-

tation stage and completed stage) were also included. Three hierarchical models were

developed. The first introduced the five groups of project factors in Model 1; six

background characteristics are included in Model 2 as dummy variables. The third model

added eight interactive terms to the significant groups of factors and two statistically

significant dummy variables in Model 3. The results of this analysis are presented

in Table 6.

Model 1 in Table 6 indicates that four groups of factors including manager

competencies, member competencies, external stability and organization support have a

significant positive relationship with success criteria. Only the project situation has a

negative effect on success criteria, but is not statistically significant.

When the six background characteristics treated as dummy variables are entered

in Model 2, manager competencies, member competencies, external stability and

Table 5. Results of factor analysis for success criteria.

Factor 1: Project success criteria

Technical performance .839Customer satisfaction .789Cost .727Time .667

Eigenvalues 2.300Variance explained (%) 57.51Cumulative variance explained (%) 57.51Cronbachs alpha 0.7259KMO 0.718Notes: Rotation method Varimax with Kaiser normalization. Factor loadings less than 0.40 have been omitted.


organization support continue to be significant, along with the addition of the completed

stage and the implementation stage.

Adding the eight interactive relationships between the four significant groups of

factors and the two significant dummy variables in Model 3, the three factors of manager

competencies, member competencies and external stability and two project characteristics

of completion stage and implementation stage still have statistical significance. There are

only two interactive variable terms which are significant. The implementation stage of

the project moderates both the effects of external stability and organization support on

success.

The overall model explains the data reasonably well, with 40.2% of the total variance

in the data being explained by Model 3. At each stage in transition from one model to the

next, the increment in adjusted R2 is also significant (Table 6). Manager competence

(.358) has the strongest relationship to performance, followed by member competencies

(.309). In the project environment context, external stability (.284) is the most important.

Organizational support (.119) is also positively related.

It is interesting that the stage of the project (completed or implementation) relates

significantly to success. This suggests that project managers do not recognize the

importance of results until the later stages of a project. This issue indicates that better and

continuous performance monitoring in projects is necessary.

Table 6. Hierarchical regression analysis of success criteria.

Variables Model 1 Model 2 Model 3

Antecedent variables (project factors)Manager competencies 0.358** 0.325** 0.305**Member competencies 0.309** 0.315** 0.308**External stability 0.284** 0.286** 0.146*Organization support 0.119* 0.116* 0.001Project situation 20.400 20.051 20.036

Moderators (project background characteristics)Completed 0.290** 0.294**Implementation 0.149* 0.157*Ownership 20.119 20.115No. activities 0.081 0.049Budget 0.027 0.032Project form 0.028 0.015

Interaction with moderatorsManager competencies* Implementation 0.005Member competencies * Implementation 20.008External stability * Implementation 0.209**Organization support * Implementation 0.154**Manager competencies * Completed 20.004Member competencies * Completed 0.029External stability * Completed 20.118Organization support * Completed 20.007

Constant/Intercept term 20.01145 20.301** 20.309**F-value 22.453** 19.622** 19.831**R2-value 0.319 0.383 0.423Adjusted R2-value 0.305 0.363 0.402

Notes: *p , 0.05; ** p , 0.01.


The implementation stage is influenced very much by the project environment as it

influences performance. Both external stability and organization support are significantly

related.

Other background characteristics such as ownership, number of activities, budget and

organizational form do not have any influence on performance.

Discussion

A total of five hypotheses were developed to examine the relationships in the conceptual

framework. The hierarchical regression results provided statistical evidence to support

three hypotheses related to the factors of external stability, manager competencies and

member competencies and rejected the hypothesis related to the factor of organizational

support. The results confirmed that the factors of external stability, manager competencies

and member competencies had a positive and significant impact on project performance.

These also provide confirmatory support to the success factors developed by Belassi and

Tukel (1996) and Pinto and Slevin (1987). For the moderating influences of project

characteristics, the hypothesis concerning the project characteristics significantly

influencing the relationship between project factors and project performance was

supported. This provides support confirming the intervening effect of the project stage on

project results as proposed by Belout and Gauvreau (2004).

However, the hypothesis on the impact of organizational support on performance was

rejected by the analysis. This indicates that the organizational support factor does not have

a significant impact on project performance. In the focus group, 52.4% of participants

agreed with this result; 38.1% disagreed and 9.5% gave other comments. Almost all

participants agreed that the influence of organization support on project performance

depends on the project stage and project scale. Organizational support is less important

than other factors in the implementation stage and in small- and medium-scale projects.

But in the beginning stage (project conception and planning) or completion of large-scale

projects, organization support is very important. The research results are supported by

these comments because 44.0% of the projects of the sample were in the implementation

stage and nearly half of projects were of small scale (the number of activities less than 100

with a total project budget less than 1 million US dollars). In addition, enterprises

implementing infrastructure projects in Vietnam were not directly responsible for the

administration of projects. Projects are normally organized as an independent project

management unit (PMU). The cooperation, collaboration and communication between the

PMU and the enterprise are in many cases ineffective. Consequently, the support of the

organization is often less important related to project performance.

Implications

In summary, there are significant implications from the results obtained. First, from the

literature review of project management, key project factors were identified. The criteria

for success determined for projects in Vietnam were assessed to examine the relationship

between these factors and the performance of projects in a developing country. The results

supported and confirmed the project factors and criteria developed in past research

conducted in advanced economies. This supports the inclusiveness of the project

management approach. Second, this study demonstrated that factors related to manager

competencies and member competencies affect success criteria. It suggests that more

emphasis on developing these competencies through more appropriate training for


managers and professionals in skills and certifications may be very important for the future

success of projects (White and Fortune 2002). Appropriate training has an important part

to play in enhancing professional development and adaptability (Hansson et al. 2003).

Cheng et al. (2007) found implementing new project management solutions required

training and monitoring of the performance of projects on a continuous basis to enhance

the success of projects. Third, the manager and team competencies are more important

to the success of a project in the implementation and completion stages. This is where

projects managers have a major role in effective performance.

Limitations

The research has identified the causal effects between some critical factors and the success

of a project, but did not examine the specific inter-relationships between these factors. This

could be done in the future using structural equation modelling. Another limitation

concerns the use of an altitudinal survey. The focus of this study was designed to assess

how managers in infrastructure projects in Vietnam perceived project success and its

related factors. There is certainly the possibility of bias in their responses. Observations of

project managers and team members in actual projects would provide an interesting

perspective on these findings. The study only included a sample from Vietnam. A wider

survey of project professionals in AsiaPacific would guarantee greater external validity.

Conclusions

This study developed and tested a comprehensive model of key project factors relevant to

developing countries like Vietnam and identified the project characteristics that affect

project success. Hierarchical regression analysis indicates that the model explains 40% of

the total variance in project success.

From a theoretical perspective, the results of the factor analysis in this study provide

some confirmation of the success factors developed by Belassi and Tukel (1996) and Pinto

and Slevin (1987) relevant to advanced economies. These are the external environment,

project manager, team members, organization and project characteristics. The results also

confirmed that success is defined uni-dimensionally, including cost, time, technical

performance and customer satisfaction. These criteria are widely used in previous

research, but only as separate indicators.

A new contribution from the analysis of the overall model is that three factors

manager competencies, member competencies, and external stability have significant

positive relationships to success.

For the moderating influences of project characteristics, the stages of completion

and implementation in the project life cycle are also positively related. It means the

relationships between the project management factors and success is stronger in the

completion and implementation stages of project. Only two interactive relationships

emerge as significant. The implementation stage of a project moderates both the effects of

external stability and organization support on success. The relationships between these

factors and success are stronger in the implementation stage. This provides some

confirmation for the intervening effect of the project stage proposed by Belout and

Gauvreau (2004). Infrastructure projects throughout East Asia have similar project

environmental factors like public investment, significant bureaucracy, limited

transparency and lack of sufficiently trained project managers. The findings of this

study can be generalized for projects throughout the region, if applied with caution.


Notes on contributors

Cao Hao Thi is currently a Faculty member at the School of Industrial Management, HoChiMinhCity University of Technology, Vietnam. He holds a PhD in International Business Administrationfrom the Asian Institute of Technology (AIT) (Thailand), an ME in Water Resource Developmentfrom AIT, and a BSc in Civil Engineering from HCMC University of Technology.

Fredric William Swierczek is an Associate Professor of Management at the School of Managementof the Asian Institute of Technology, Thailand. He is the coordinator of International BusinessProgram and coordinator of the Executive MBA, AIT Vietnam.

References

Abdul-Rahman, H., et al., 2006. Delay mitigation in the Malaysian construction industry. Journal ofconstruction engineering and management, 132 (2), 125133.

Amstrong, S.J. and Overton, T., 1977. Estimating non-response bias in mail survey. Journal ofmarketing research, 14, 396402.

Avots, I., 1969. Why does project management fail? California management review, 12, 7782.Baker, B.N., Murphy, D.C., and Fisher, D., 1988. Factors Affecting Project Success. In: D.J. Cleland

and W.R. King, eds. Project management handbook. New York: Van Nostrand-Reinhold,902919.

Belassi, W. and Tukel, O.I., 1996. A new framework for determining critical success/failure factorsin projects. International journal of project management, 14 (3), 141151.

Belout, A. and Gauvreau, C., 2004. Factors influencing project success: the impact of HRM.International journal of project management, 22 (1), 111.

Bollen, K.A., 1989. Structural equations with latent variables. New York: John Wiley and Sons.Cheng, M.I., Dainty, A., and Moore, D., 2007. Implementing a new performance management

system within a project-based organization. International journal of productivity andperformance management, 56 (1), 6075.

Churchill, N.C., 1991. Market research methodological foundation. Chicago, IL: Dryden Press.Cooke-Davies, T., 2002. The real success factors on project. International journal of project

management, 20 (3), 185190.Cooper, R.G. and Kleinschmidt, E.J., 1987. Success factors in product innovation. Industrial

marketing management, 16 (3), 215224.Dvir, D., Lipovetsky, S., Shenhar, A., and Tishler, A., 1998. In search of project classification: a non-

universal approach to project success factors. Research policy, 27, 915935.Dvir, D., Raz, T., and Shenhar, A., 2003. An empirical analysis of the relationship between

project planning and project success. International journal of project management, 21 (2),8995.

Freeman, M. and Beale, P., 1992. Measuring project success. Project management journal, 13 (1),916.

Hair, J.F., Anderson, R.E., Tatham, R.L., and Black, W.C., 1995. Multivariate analysis withreadings. Englewood Cliff, NJ: Prentice-Hall.

Hansson, J., Backlund, F., and Lycke, L., 2003. Managing commitment: increasing the odds forsuccessful implementation of TQM, TPM or RCM. International of quality and reliabilitymanagement, 20 (9), 9931008.

Hawk, D., 2006. Conditions of success: a platform for international construction development.Construction management and economies, 24 (7), 735742.

Hughes, M.W., 1986. Why projects fail: the effects of ignoring the obvious. Industrial engineering,18, 1418.

Hyvari, I., 2005. Project management effectiveness in project-oriented business organizations.International journal of project management, 23, 101112.

Jacquemin and Bainbridge, 2005. Perceptions of development partners and evidence on AIDeffectiveness. London: DFID.

Jin, X. and Ling, F., 2006. Key relationship determinants of project performance in China.Building and environment, 41 (7), 915925.

Karlsen, J., et al., 2006. An empirical study of critical success factors in IT projects. Internationaljournal of management and enterprise development, 3 (4), 292311.

Kerzner, H., 1998. In search of excellence in project management. New York: Van Nostrand Reinhold.


Kerzner, H., 2001. Project management a systems approach to planning, scheduling, andcontrolling. 7th ed. New York: John Wiley and Sons.

Legris, P. and Collerette, P., 2006. A road map for IT project implementation: integratingstakeholders and change management issues. Project management journal, 37 (5), 6475.

Lim, C.S. and Mohamed, M.Z., 1999. Criteria of project success: an exploratory re-examination.International of project management, 17 (4), 243248.

Morris, P.W. and Hough, G.H., 1987. The anatomy of major projects. New York: JohnWiley and Son.Nunnally, J.C., 1978. Psychometrics theory. New York: McGraw-Hill.Paolini, A. Jr. and Glaser, M.A., 1977. Product selection methods to pick winners. Research

management, 20, 2629.Pinto, J.K. and Covin, J.G., 1989. Critical factors in project implementation: a comparison of

construction and R&D projects. Technovation, 9, 4962.Pinto, J.K. and Mantel, S.J., 1990. The causes of project failure. IEEE transactions of engineering

management, 37 (4), 269276.Pinto, J.K. and Slevin, D.P., 1987. Critical factors in successful project implementation.

IEEE transactions of engineering management, 34 (1), 2227.Pinto, J.K. and Slevin, D.P., 1988. Project success: definitions and measurement techniques.

Project management journal, 19 (3), 6773.Pinto, J.K. and Slevin, D.P., 1989. Critical success factors in R&D projects. Research technology

management, 32 (1), 3133.Procaccino, J. and Verner, J., 2006. Software project managers and project success: an exploratory

study. The journal of systems and software, 79, 15411551.Project Management Institute, 2004. A guide to the project management body of knowledge

(PMBOK). Newton Square, PA: Project Management Institute.Puthamont, S. and Chareonngam, C., 2007. Strategic project selection in the public sector.

International journal of project management, 25, 177188.Rubin, I.M. and Seeling, W., 1967. Experience as a factor in the selection and performance of project

managers. IEEE transactions of engineering management, 14 (3), 131134.Schultz, R.L., Pinto, J.K., and Slevin, D.P., 1987. Strategy and tactics in a process model of project

implementation. Interfaces, 17 (3), 3436.Soja, P., 2006. Success factors in ERP systems implementations: lessons from practice. Journal of

enterprise information management, 19 (6), 646661.Thamhain, H., 2004. Linkages of project environment to performance: lessons for team leadership.

International of project management, 22, 533544.Transparency International, 2006. Corruption Perception Index. Available from: http://www.

transparency.org [Accessed 30 September 2010].Tukel, O.I. and Rom, W.O., 1995. Analysis of the characteristics of projects in diverse industries.

Working paper, Cleveland State University, Cleveland, OH.Tukel, O.I. and Rom, W.O., 2001. An empirical investigation of project evaluation criteria.

International journal of operation and production management, 21 (3), 400416.US and Foreign Commercial Service, 2007. Doing business in Vietnam: a country commercial guide

for US company. Washington, DC: Department of State.White, D. and Fortune, J., 2002. Current practices in project management an empirical study.

International journal of project management, 20, 111.Yasin, M., Martin, J., and Czuchry, A., 2000. An empirical investigation of international project

management practices: the role of international experience. Project management journal, 31 (2),2030.

Yoon, Y., Guimarraes, T., and ONeal, Q., 1995. Exploring the factor associated with expert systemssuccess. MIS quarterly, 19, 83106.

Zwikael, O. and Globerson, S., 2006. Benchmarking of project planning and success in selectedindustries. Benchmarking: an international journal, 13 (6), 688702.


Copyright of Asia Pacific Business Review is the property of Routledge and its content may not be copied oremailed to multiple sites or posted to a listserv without the copyright holder's express written permission.However, users may print, download, or email articles for individual use.

Documents

CSF in Project Management_implication From Vietnam