A Model Of Information Systems Development Project Performance

A model of information systems
development project performance
Philip Yetton*, Andrew Martin†
, Rajeev Sharma‡
& Kim Johnston§
*Australian Graduate School of Management, University of New South Wales, Sydney
2052, Australia, email: phily@agsm.edu.au; †
Warwick Business School, University of
Warwick CV4 7AL, UK, email: Andrew.Martin@warwick.ac.uk; ‡
School of Information
Systems, Technology and Management, The University of New South Wales, UNSW
Sydney 2052, Australia, email: r.sharma@unsw.edu.au, and §
University of Western
Sydney, St Marys 1790, NSW, Australia, email: k.johnston@uws.edu.au
Abstract. Performance in information systems development (ISD) projects can be
critical to business success. But, while project performance has been the subject
of much debate, there has been little empirical research into its determinants. A
survey of IS projects in the UK and New Zealand is analysed to test hypotheses
concerning performance in terms of both project completion and budget (time-
cost) variances. Subsequently, a secondary analysis of the findings is used to
build a more complete empirical model of project performance. The paper helps
to develop the theory of IS development project performance and also has sig-
nificant implications for practice. Discussion of the findings highlights the impor-
tance of project team dynamics, risk management, senior management support
for strategic projects and user participation in ensuring successful IS project
performance.
Keywords: Information systems project management, model-building, project per-
formance, survey research.
IN T RO D U C T I O N
Many organizations are making large investments in information systems designed to deliver
significant performance gains (Sauer & Yetton, 1997; Galliers & Baets, 1998). Success in IS
project development is often a necessary prerequisite for realising these gains in organiza-
tional performance (Ballantine etal., 1996). However, project failures are common and, instead
of capturing the benefits, organizations often incur significant direct and indirect costs (Sauer,
1993a; Johnson, 1995; Flowers, 1996). Despite considerable experience, information systems
development remains a high-risk proposition, projects continue to fail at an alarming rate, and
the problem of runaway development projects is still serious (Lyytinen & Robey, 1999). This
Info Systems J (2000) 10, 263–289 263
© 2000 Blackwell Science Ltd

paper contributes to the literature which seeks to understand why such IS development pro-
jects succeed or fail, and how project performance might be improved.
For practitioners, project performance is typically characterized in terms of schedule, cost,
and functionality (Johnson, 1995; Hallows, 1998). For IS researchers, project performance is
a multi-dimensional construct spanning different phases of the project, including development,
deployment and delivery (Lyytinen & Hirschheim, 1987; Ballantine etal., 1996). This paper,
which focuses on success and failure in the development phase of projects, makes three main
contributions. First, a key distinction is made between those aspects of project performance
that are business driven – that is, in the domain of senior business managers, and those
aspects of project performance that are project driven – that is, in the domain of IS project
managers. By distinguishing two different loci of project decision making, business level and
IS project level, the paper identifies the determinants of project performance for each domain.
Specifically, we initially analyse separately and then integrate the determinants of business-
level decisions to continue, redefine or abandon a project, and the project-level determinants
of budget (time and cost) variances.
Second, Ewusi-Mensah & Przasnyski’s (1991) exploratory analysis of project abandonment
is taken as a point of departure for theory building, questionnaire development and analysis.
Hypotheses are developed to explain project completion and budget variances. Ewusi-Mensah
and Przasnyski’s questionnaire was refined and extended; data were collected from British and
New Zealand managers. A robust factor structure for the data is confirmed. The hypotheses are
tested and, generally, found to be supported. Third, a more exploratory integrated model explain-
ing the joint outcomes of project completion and budget variances is developed. Finally, the
implications of the model for theory development and management practice are discussed.
BACKGROUND AND HYPOTHESES
There is a relative lack of theory on the subject of success and failure at the development
stage of IS projects, although there is no shortage related to the implementation stage. Fol-
lowing Pinto & Slevin (1987), the relevant research has focused on identifying critical success
factors associated with the success and failure of such projects. Ewusi-Mensah & Przasnyski
(1991; 1994) report empirical studies of project abandonment in the United States. Martin &
Chan (1996) report a follow-up study which extended the instrument and included projects
which were ‘redefined’ or successfully completed. While these studies have contributed much
to our understanding of managing IS projects, two limitations can be identified. First, they have
examined project success as a unidimensional construct. Second, they have not tested the
strength of the relationship of the critical success factors with project performance. We extend
this literature and develop a model of success and failure of IS development projects by iden-
tifying a robust set of factors and then testing a number of hypotheses concerning their rela-
tionship to project performance.
To do this, we identify and examine two dimensions of IS project performance. One is
‘project completion’, which is measured on a five-point scale from total abandonment
264 P Yetton et al.
© 2000 Blackwell Science Ltd, Information Systems Journal 10, 263–289

through to smooth completion. The other is ‘budget variances’, represented by cost and
time overruns. Both Ewusi-Mensah & Przasnyski (1991; 1994) and Martin & Chan (1996)
treat budget variances as an independent variable influencing project completion. In contrast,
we model budget variances as a joint outcome with project completion. Essentially, we argue
that ‘project completion’ is a measure of the effectiveness of the initial project scope and that
‘budget variances’ is a measure of the efficiency with which the project is managed.
Below, we extend Ewusi-Mensah & Przasnyski’s (1991; 1994) classification of factors to
include additional factors such as management support and the strategic nature of the project,
and develop hypotheses linking the factors to the joint outcomes of project completion and
budget variances. These hypotheses are presented below under four subheadings: project
performance (project completion, budget variances), project characteristics (size, newness,
strategic nature), technical factors (technical risk) and organizational factors (management
support, planning, conflict, staff instability, user participation). We do not claim that the
hypotheses developed below are an exhaustive listing of the potential relationships; because
of the absence of relevant theory we focus on a set of hypotheses which are intuitively plau-
sible and are implied by related literature.
Not all factors influence both project completion and budget variances. For example, while
size is hypothesized to influence both outcomes, the strategic nature of the project is expected
to affect only project completion, as completion of a business-critical project is likely to be
supported by senior management whether or not budget goals are being met. Later, in the
results section, we integrate these hypotheses into a model with the two dimensions of project
performance as joint outcomes. In doing this, the paper reports the results of formal hypoth-
esis testing followed by exploratory analysis and model building.
Project performance
To build a model of project performance, we begin by differentiating between project com-
pletion and budget variances. The decision to continue, redefine or abandon a project is a
business judgement that is typically made by an organization’s IT investment steering com-
mittee. Such a decision may be influenced by budget overruns, but Lyytinen & Hirschheim
(1987), Sauer (1993a) and Ewusi-Mensah & Przasnyski (1991) agree that senior manage-
ment support is the primary influence on whether the development is completed. Steering
committees mediate this support by monitoring whether the expected business benefits of a
project justify the expected expenditure. Keil & Robey (1999) note that it is top management
who most frequently initiate de-escalation. They may take strategic action either to redefine
the project, to put pressure on project management or even change the project manager, but
would not involve themselves directly in project management. They may choose to continue
with runaway projects or to cancel apparently well-performing projects, for business and/or
political reasons. Consistent with this argument, budget variance was found not to be associ-
ated with project abandonment by Ewusi-Mensah & Przasnyski (1991).
Budget performance is generally the primary concern of the project manager, rather than
the business investment appraisal team. The project manager’s focus is on project efficiency.
A model of ISD project performance 265

Thus, while intuitively it may be expected that budget variances would be associated (nega-
tively) with project completion, these outcomes are conceptually distinct and their relationship
is an empirical question.
Hypothesis 1: Project completion and budget variances are joint dimensions of project per-
formance in which project completion is independent of budget variances.
Project characteristics
Large projects are likely to be characterized by high complexity and high levels of task
interdependence. Other research has shown that the capacity to perform to tight budgets
is a negative function of task interdependence (Hirst, 1987; Hirst & Yetton, 1999). In fact,
Hirst & Yetton (1999) specifically find that budget variances are a positive function of
task interdependence – that is, budget variances increase on complex, interdependent tasks.
Because complexity and interdependence typically increase with project size, we propose
that:
Hypothesis 2a: Budget variances are a positive function of project size.
Furthermore, large projects are likely to have project elapsed times which extend
across annual budget cycles. Not only are they subject to potential budget variances, they
are also likely to be redefined or abandoned as business demands and external conditions
change over time (McFarlan, 1981; Redmill, 1997; Sauer & Yetton, 1997; Willcocks etal.,
1997).
Hypothesis 2b: Project completion is a negative function of size.
The newness of a project to the organization is associated with a lack of relevant knowl-
edge and experience. This lack of experience with similar projects increases the likelihood of
the project being redefined over time. Furthermore, newness and low project-specific knowl-
edge are associated with a higher risk of failure (McFarlan, 1981). Thus, we argue that
newness is likely to have a negative relationship with project completion. Elsewhere this is
referred to as the ‘liability of newness’ (Stinchcombe, 1965). Furthermore, because newness
of a project increases project risk, and thus its outcomes are more unpredictable, we hypoth-
esize that newness will also be associated with higher budget variances.
Hypothesis 3a: Project completion is a negative function of newness.
Hypothesis 3b: Budget variances are a positive function of newness.
When a project is considered to be of strategic importance, senior managers are likely to
ensure it is supported through to completion. For strategic projects, which are seen as busi-
ness critical, the organization would be unwilling to compromise on their success. In contrast,
at the project management level we do not hypothesize a direct relationship between a
project’s strategic nature and its budget performance. Although it could be argued that gen-
erous resources are initially allocated for strategic projects (tending to enhance budget per-
266 P Yetton et al.

formance), it can also be argued that overruns will be associated with strategic projects simply
because the project has to be completed, irrespective of budget performance.
Hypothesis 4: Project completion is a positive function of the strategic nature of the project.
Technical factors
Ewusi-Mensah & Przasnyski (1991) found that technical factors did not contribute significantly
to project abandonment. Similarly, Pinto & Slevin (1987) argue that technical problems are not
a significant determinant of project failure. Technical risk is therefore unlikely to influence
decisions to abandon, redefine or complete a project. Technical risk could, however, have a
significant influence on budget (time-cost) overruns, because of the unproven availability, per-
formance, timeliness and functionality of new products and services. In that case, the tech-
nical complexity of the project would lead to unforeseen delays and higher costs during a
project than anticipated in initial estimates.
Hypothesis 5: Budget variances are a positive function of technical risk.
Organizational factors
There is widespread agreement in the project management literature that a fundamental deter-
minant of project failure or abandonment is the lack of senior management support (Lyytinen
& Hirschheim, 1987; Pinto & Slevin, 1987; Edwards, 1989; Sauer, 1993a). Thus, successful
completion of a project is likely to be associated with management support, independently of
its budget performance. In addition to the commitment of resources, active senior manage-
ment support includes clarifying and communicating project objectives. Keider (1984) cites
both inadequate definition and lack of communication as causes of project failure. Conversely,
Pinto & Slevin (1987) cite clear project mission and communication as critical success factors.
McFarlan (1981) expresses clarity of objectives in terms of the degree of structure of the
project and suggests that this leads to lower project risk. Defining senior management support
for the purposes of this study as the joint outcome of clarity and communication of objectives
and resource commitment, we propose that:
Hypothesis 6: Project completion is a positive function of senior management, support.
Similarly, planning is also frequently cited as a critical factor in project performance (Keider,
1984; Pinto & Slevin, 1987; Deephouse etal., 1995/6; Hallows, 1998). Specifically, poor plan-
ning is likely to be associated with inefficiencies in development and thus lead to high budget
variances. It is unlikely, however, to threaten the viability of a strategic project or organiza-
tional support for its completion.
Hypothesis 7: Budget variances are a negative function of planning.
A number of researchers suggest that project team conflict and organizational politics
threaten project performance (Turner, 1982; Ewusi-Mensah & Przasnyski, 1991; Yourdon,

1997). Conflict within the project team is likely to result in delays. It should be noted that there
could be a mutually reinforcing relationship whereby poor budget performance produces man-
agement pressures which exacerbate conflict within the team. In contrast, at the strategic level,
project team conflict would only jeopardise the completion of the project if it resulted in a
decrease in senior management support. This seems unlikely. Rather, we would expect that
if project team conflict threatened project performance, senior management would replace key
team members. This relationship between conflict and team stability is examined in the
exploratory secondary analysis and model building.,
Hypothesis 8: Budget variances are a positive function of project team conflict.
Willcocks & Griffiths (1994) and Willcocks etal. (1997) suggest that staffing stability can be
a risk factor in information systems projects. Against this, Ewusi-Mensah & Przasnyski (1991)
found that project staff turnover was not strongly associated with project abandonment. It is,
however, more likely to be associated with poor budget performance because of the delays
and learning costs of replacing staff. This also may be a reciprocal relationship, with poor
budget performance leading to management action to replace project staff.
Hypothesis 9: Budget variances are a positive function of project staff instability.
User participation in analysis and design of IS projects has long been of interest to IS
researchers (e.g. Franz & Robey, 1984; Hunton & Beeler, 1997). Involving business users in
project definition and design has been found to be an important potential contributor to project
effectiveness (Ives & Olsen, 1984). The involvement of business managers in this way
increases the likelihood that the project is of value to them and therefore is supported through
to completion. It could be argued that user involvement tends to increase budget variance by
encouraging suggestions for changes to specification, but also tends to decrease budget vari-
ance by managing expectations and quickly resolving potential problems. Therefore no hypoth-
esis is made with respect to the association between user participation and budget variances.
Hypothesis 10: User participation contributes to project completion.
The above hypotheses are presented in Figure 1. The next section discusses the method-
ology and data we used to test these hypotheses. In addition to the independent variables
which are hypothesized above to influence project performance, the questionnaire measures
two other variables: ‘management risk perception’ and ‘end-user resistance’. No hypotheses
concerning these variables are presented here. Instead, they are examined later in the
exploratory secondary analysis and model building.
RESEARCH METHOD
The hypotheses proposed above are tested using data collected through mail surveys of IS
project managers in medium- to large-sized organizations in New Zealand and the United
Kingdom. The UK was chosen as the home base of one of the authors, who also had the
268 P Yetton et al.

opportunity to survey New Zealand companies while on a period of study leave. The survey
questionnaire asked respondents to answer a number of questions with respect to a recent
IS project within their organization. Overall, 72 usable responses were obtained from the two
surveys. The following sections describe the sample, the dependent and independent vari-
ables and analysis.
New Zealand survey
The organizations included in the survey were selected using a number of sources. The
primary source was the top 107 companies and the top 19 financial institutions in New
Zealand, ranked by revenue. Seventeen additional organizations were identified by graduate
students at Victoria University, Wellington, and a further six public bodies were identified by
one of the authors. In all, questionnaires were sent to 149 organizations. Each organization
was contacted by phone to identify the person closest to the role of IS Development Manager,
to whom the questionnaire was addressed. Forty-six responses were received of which two
were incomplete. This represents a usable response rate of 29.5%, which compares well with
a usable response rate of 8.7% reported by Ewusi-Mensah & Przasnyski (1991) and 5.6%
reported by Ewusi-Mensah & Przasnyski (1994).
All respondents to the New Zealand survey were practitioners from IS departments. Of
these, 50% were IS directors or IS managers, 25% were systems development managers,
15% were project managers or project staff and 10% were administrators. The industry profile
of the respondent organizations was spread almost evenly across a range of industry sectors
including banking and finance, communications, government, insurance, manufacturing, min-
erals, primary producers, retail and distribution, and utilities. This combination of sources
identifies a reasonably representative cross-section of organizations of significant size. The
average duration of the reported projects was 13months and the average budget was $NZ2
million; the approximate exchange rate at the time of data collection was £UK1=$NZ2.5.
UK survey
A mailing list of industry contacts maintained by the Warwick Business School was used to
identify 300 participants to be included in the survey. Twenty-eight usable responses were
Figure1. Hypothesized relationships to project performance.

received. This represents a response rate of 9.3%, which is similar to that reported by Ewusi-
Mensah & Przasnyski (1991; 1994). However, the response rate for the UK survey is a little
low compared with other unsolicited surveys that use mailing lists to identify potential respon-
dents. Inspecting over 50 published studies from a meta-analysis conducted by two of the
authors, it was found that the response rates for such studies were typically between 10% and
20%.
Respondents from the UK survey were also all from IS departments. Of these, 30% were
IS directors, 44% were IS managers, 22% were project or team leaders and 4% were con-
sultants. The industry profile of the respondent organizations was diverse and included manu-
facturing, banking, insurance, retail, transportation, utilities and government, which again is
believed to be reasonably representative. The average duration of the reported projects was
32months and the average budget was £UK2.2 million.
Measurement of variables
Ewusi-Mensah & Przasnyski (1991; 1994) report some of the earliest work in deve-
loping instruments for studying IS projects, which we took as the point of departure for devel-
oping the measures used in this study. An examination of their results suggested that
significant work needed to be carried out to develop instruments with a high degree of
reliability.
In particular, two aspects of the results of their factor analysis lead us to this conclusion.
First, the amount of variance extracted by the factor analysis is low, indicating the absence of
a strong underlying factor structure. The cumulative variance explained by the 12 factor solu-
tion reported by Ewusi-Mensah & Przasnyski (1994) is less than 25% of the variance in the
data. Second, an analysis of the items included in the factors reported by Ewusi-Mensah &
Przasnyski (1994, p. 193) suggests the absence of a coherent underlying construct in many
of the factors generated. For instance, their Factor 1, labelled as ‘escalating project costs and
completion schedules’, includes diverse items such as ‘project time frame continuously revised
up’ and ‘organizational structural changes reduced perceived benefits of the project to the
organization’. Similarly, Factor 3, labelled as ‘actual project expenditures and duration below
estimates’, includes diverse items such as ‘time spent on project when abandoned was lower
than the total estimated project time’ and ‘insufficient training was available for majority of end-
users’. Factors 2 and 4, which relate to technical difficulties and inadequacies, include items
such as ‘project staff turnover contributed to abandonment’ and ‘project was first of its size
or kind’. Given the need to improve the instruments used by Ewusi-Mensah & Przasnyski
(1991; 1994), we selected items with high-face validity from their measures and supplemented
these with items from other sources. These variables are presented below.
Dependent variables
We identify two distinct dimensions of IS project performance, project completion and budget
variances (time and cost overruns). Whereas Ewusi-Mensah & Przasnyski (1991; 1994) con-
270 P Yetton et al.

sider budget overruns as a factor contributing to the abandonment of projects, we model
budget performance as a joint outcome with project completion.
Two dependent variables were therefore examined in this study. The first is ‘project com-
pletion’, which captures a judgement of the extent to which the project development suc-
cessfully meets its original scope. A single-item five-point ordinal scale was used to
operationalize project completion. The five rating options on the scale were rank ordered as
‘totally abandoned’, ‘substantially abandoned’, ‘significantly redefined’, ‘partially abandoned’
and ‘smoothly completed’. Of these, the partially abandoned, substantially abandoned and
totally abandoned categories were defined as per Ewusi-Mensah & Przasnyski (1991; 1994).
Following Sauer (1993b), the measure was further delineated by including the category ‘sig-
nificantly redefined’, recognizing that organizations frequently use this as a strategy to manage
failing projects. Finally, ‘smoothly completed’ was included as an outcome category, recog-
nizing that some projects are indeed completed according to plan. Complete definitions of
these categories and a copy of the survey instrument are available from the authors.
The second dependent variable is ‘budget variances’. For practitioners, schedule and cost
overruns are key aspects of measuring the performance of project managers (Johnson, 1995;
Hallows, 1998). Whereas ‘project completion’ captures the extent to which the project outcome
meets its original scope, ‘budget variances’ captures the efficiency with which the project was
managed. The latter was operationalized as a two-item measure, capturing time overruns and
cost overruns. In IS projects the major component of cost is personnel cost, which varies as
a function of person-hours spent on the project (Rakos, 1990; Kemerer, 1996).
Independent variables
The items for ‘resource commitment’, ‘technical risk’ and ‘instability of project team’ were
adapted from Ewusi-Mensah & Przasnyski (1991; 1994). Measures for ‘strategic nature of
project’, ‘conflict in project team’, ‘clarity of objectives’, ‘communication of objectives’, ‘plan-
ning’, ‘project size’ and ‘risk perception’ were developed for this study. In addition to the inde-
pendent variables included in the factor analysis, single-item measures were used for
‘end-user participation’ and ‘newness of project’.
Table1 reports the results of a factor analysis. Factor loadings less than 0.40 are sup-
pressed. Items for single-item factors (end-user participation, newness and post-mortem,
along with Country) were excluded from the factor analysis. Eleven factors with eigen-values
greater than 1.0 were extracted. These factors cumulatively explain 73% of the variance in
the data. The high level of variance explained suggests a strong underlying factor structure.
Further, all items, except one, load well on their respective constructs with most factor load-
ings being in excess of 0.7. (Note ‘Project scope and objectives were made clear and well
communicated to other IS development teams’, which was expected to load on the construct
‘Clear communication of objectives’, has a weak loading on this construct but a loading of
0.49 on the construct ‘Clarity of objectives’. A subsequent test of reliability indicated that this
item correlated well with the other three items on the ‘Clear communication of objectives’ con-
struct. Cronbach alpha for the four-item scale with this item included was 0.71 and for the

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Systems
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Table 1. Rotated factor matrix
Conflict Unstable
project Strategic Tech. Clarity End-user Commn project Res. Risk
Factor name/Item description team nature risk object. resist. object. Size Planning team commit percept
Risk of noncompletion 0.41 0.62
Perceived complexity 0.86
Perceived benefits 0.81
Strategic importance 0.74
Urgency of project 0.80
Technical feasibility unproven 0.83
Technical expertise lacking 0.40 0.58
Design incompatibility 0.80
Technology not ready 0.75
Management committed resources 0.41 0.64
Adequate resources allocated 0.80
Organizational politics and 0.63
disagreements
Disagreements within project group 0.67
Difficulties between sponsor and team 0.58
Difficulties between project manager 0.79
and team
Difficulties within project team 0.76
Difficulties between team and end-users 0.42 0.40
Difficulties between team and IS group 0.75

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End users felt threatened 0.83
End users resistant 0.87
Project staff turnover –0.44 0.58
Loss of project personnel 0.86
Loss of management personnel 0.65
Project well defined 0.53
Clear strategic and business needs 0.55
Cost-benefit evaluation 0.66
Clear scope and objectives 0.76
Object. communicated to those who 0.79
need to know
Object. communicated within project team 0.78
Object. communicated to other IS 0.49
development teams
Object. communicated to senior 0.66
management
Allowance for developer learning 0.77
Realistic plan and schedule 0.77
Initial budget 0.85
Initial duration 0.92
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Factor loadings less than 0.40 are suppressed.
Single item measures and control variables are not included in the factor analysis
Cumulative variance extracted 73%.

three-item scale with this item excluded was 0.69. Hence, it was decided to retain this item
with the ‘Clear communication of objectives’ construct. In addition, there were only four
instances of cross-loadings greater than 0.40. This suggests that the instruments used have
satisfactory levels of convergent and discriminant validity. As the extracted factor structure
corresponds closely to the expected factor structure and as the factor analysis indicates the
factors to be distinct, the findings reported below are not likely to be subject to potential con-
struct validity threats arising from the instruments used in the study.
The results of the factor analysis suggest significant improvements in the set of instruments
used in this study compared with that adopted by Ewusi-Mensah & Przasnyski (1994). Further,
the factor structure emerging in this study suggests a much higher coherence in the underly-
ing constructs than is evident in the previous studies. However, it should be noted that Ewusi-
Mensah & Przasnyski (1994) were conducting an exploratory study which is expected to report
weak results. The instruments used in this study are refined versions of the instruments devel-
oped by Ewusi-Mensah & Przasnyski (1994) and have benefited from their developmental work.
Table2 reports the reliabilities for the 11 factors presented in Table1. Cronbach alpha
indices for 10 of these 11 measures are greater than 0.70. These are considered to be accept-
able levels of internal reliability for instruments used in basic and preliminary research (Nun-
nally, 1978). Management risk perception has low internal validity (a=0.54) and the findings
concerning this variable in the secondary analysis must be interpreted with caution. Table3
reports the means, standard deviations and correlations of the independent variables.
Analysis
Nine of the 10 hypotheses listed above specify a simple main effect of a variable, such as
project team instability, on one of the two dimensions of project performance, in this case
budget variances. The test statistic for these hypotheses is the correlation coefficient (r) with
a one-tailed test – r is significantly/nonsignificantly different from zero in the direction speci-
fied in each hypothesis. In contrast, hypothesis 6 – that project completion is a positive
274 P Yetton et al.
Table 2. Reliabilities of instruments
Factor Number of items Cronbach alpha
Strategic nature of project 3 0.78
Technical risk 4 0.77
Resource commitment 2 0.74
Clarity of objectives 4 0.73
Communication of objectives 4 0.71
Planning 2 0.71
Conflict in project team 7 0.82
Instability of project team 3 0.73
End-user resistance 2 0.84
Management risk perception 2 0.54
Project size 2 0.80

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263–289
Table 3. Means, standard deviations and correlations of independent variables
Factor Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14
1. Project size 5.44 1.15 1.00
2. Newness of project 3.31 1.50 0.12 1.00
3. Strategic nature 3.87 0.75 –0.15 –0.07 1.00
4. Technical risks 2.53 0.96 0.09 0.21 0.02 1.00
5. Resource commitment 3.21 1.06 –0.06 –0.15 0.43 –0.07 1.00
6. Clarity of objectives 2.86 0.88 –0.07 0.01 0.26 0.00 0.34 1.00
7. Communication of objectives 3.53 0.73 –0.02 0.06 0.30 –0.08 0.25 0.53 1.00
8. Planning 2.68 1.08 0.06 0.04 –0.05 –0.01 0.31 0.31 0.28 1.00
9. Conflict in project team 2.80 0.83 0.25 0.15 –0.07 0.31 0.03 –0.11 –0.19 –0.12 1.00
10. Instability in team 2.31 1.10 0.28 0.14 –0.12 0.09 –0.18 –0.03 –0.12 –0.36 0.31 1.00
11. Participation 2.53 1.17 0.23 0.11 –0.12 –0.05 –0.03 0.00 0.10 –0.03 0.12 0.00 1.00
12. End-user resistance 2.60 1.17 0.17 0.21 0.11 0.21 0.11 0.29 0.07 –0.05 0.17 0.18 0.06 1.00
13. Risk perception 2.85 0.93 0.14 0.26 0.22 –0.02 0.12 0.07 0.19 –0.01 –0.04 –0.13 0.11 0.28 1.00
14. Country 0.50 0.03 –0.30 0.07 –0.08 –0.11 –0.10 0.17 0.15 0.05 0.45 –0.02 0.06 –0.05
r = 0.20, P < 0.05; r = 0.28, P < 0.01: one-tailed test.

function of senior management support – is more complex. Senior management support is
operationalized here as a linear function of resource commitment, clarity of objectives and
communication of objectives. In this case, the appropriate test statistic is the variance
explained (DR2
) when project completion is regressed on the three factors entered as a block
into a simple least squares regression model.
After the formal testing of hypotheses, the data were further analysed in order to identify
other relationships and to develop a more complete model of project performance. Theories
of IS project outcomes are not yet sufficiently developed to a priori propose complex causal
models that can be validated by empirical testing. Current theories of IS project outcomes
typically hypothesize direct effects, such as those represented in Figure 1. Therefore this sec-
ondary analysis is based on an exploratory approach rather than the more formal techniques
of causal modelling. The approach adopted here explores observed patterns in the data;
essentially it lets the data speak for itself. Subsequent research can validate the resultant
model using the more rigorous techniques of structural equation modelling.
RESULTS
Project performance
The results reported in Table 4 support H1. Project completion and budget variances are inde-
pendent (H1: r=–0.10, NS).
Project characteristics
The results presented in Table4 support H2b, H3a and H4, but do not support H2a or H3b.
Contrary to H2a, the size of the project does not have a direct influence on budget variances
(H2a: r=0.09, NS). Compared with small projects, large project completion is more problem-
atic and, at a minimum, there is a tendency to redefine the project (H2b: r=–0.30, P<0.05).
The greater the newness of the project to the organization, the more likely that completion is
problematic and that the project is redefined as the threats and opportunities become appar-
ent (H3a: r=–0.21, P<0.05). In contrast, the relationship between newness and budget vari-
ances is not significant (H3b: r=0.15, NS). The secondary analysis reported below, however,
shows that the relationship is significant when controlling for the effect of perceived risk. Pro-
jects which are strategic to the business are less likely to be redefined than non-strategic pro-
jects (H4: r=0.28, P<0.05).
276 P Yetton et al.
Table 4. Correlation coefficients for project performance, project characteristics and technical risk
Budget variances Project size Newness of project Strategic value Technical risk
Project completion –0.10 –0.30 –0.21 0.28 n/h*
Budget variances 1.00 0.09 0.15 n/h 0.05
*n/h = no hypothesis. See Table 7 for full list of correlations between independent and dependent variables.

Technical risk
The results reported in Table4 do not support H5. The technical risk of the project does not
influence budget variances (H5: r=0.05, NS).
Organizational factors
H6 is supported, as shown in Table5. Senior management support, as measured by re-
source commitment, clarity of objectives and communication of objectives, increases the like-
lihood that the project is completed and not redefined or abandoned (H6: DR2
=0.14, F=3.6,
P<0.05).
The results presented in Table6 support H7, H8 and H9. Planning has a strong negative
influence on budget variances, i.e. planning reduces budget overruns (H7: r=–0.39, P<0.05).
Budget variances are a positive function of both team conflict (H8: r=0.28, P<0.05), and
project team instability (H9: r=0.49, P<0.05). Hypothesis 10 is also supported by the results
reported in Table6. User participation increases the likelihood that the project is completed
and not redefined or abandoned (H10: r=0.36, P<0.05).
SECONDARY ANALYSIS: AN INTEGRATED MODEL
In general, the results reported above support the hypotheses derived from the literature.
These are expressed as the main effects of factors on either project completion or budget
variances. Here we first complete the above analysis by reviewing the relationships between
the independent variables and dependent variables not tested above. Table7 reports the cor-
relations of project completion and budget variances with the independent variables. There
are no ‘unanticipated’ (in the sense that no hypotheses were developed) significant relation-
Table 5. Regression of project completion on senior management support
Independent variables DR2
F Significance
Senior management support 0.14 3.6 P<0.05
Resource commitment
Clarity of objectives
Clear communication of objectives
Table 6. Correlation coefficients for project performance and organizational factors
Planning Team conflict Team instability User participation
Project completion n/h* n/h n/h 0.36
Budget variances –0.39 0.28 0.49 n/h
*n/h = no hypothesis.

ships reported in Table7. For instance, while strategic value influences completion (Hypothe-
sis 4: r=0.28), it does not influence budget variances (r=–0.05, NS). Similarly, while planning
reduces budget variances (Hypothesis 7: r=–0.39), it does not increase project completion
rates (r=0.06, NS). There is a marginally significant relationship between senior management
support and budget variances – higher support leads to lower variances. In the following
exploratory analysis, this is shown to be an indirect effect on budget variances mediated by
planning.
We now explore more complex relationships, where two or more factors interact to influ-
ence the outcome. For example, management support might be an intermediary variable
between the strategic nature of the project and project completion. In this case, the relation-
ship between strategic nature and project completion, controlling for management support,
would be non-significant. We also control for country effects and examine the influence of risk
perceptions and end-user resistance on which data were collected but about which no
hypotheses were developed.
Because project completion and budget variances are independent (H1: r=–0.10), we ini-
tially build separate submodels for each performance outcome. To do this, we begin by inspect-
ing the data for significant relationships. These include any significant correlations among the
factors reported in Table3, and any significant correlations between the two dependent vari-
ables and factors for which hypotheses were not derived and tested. We then develop a model
of project completion which incorporates those relationships. This process is repeated to
develop a model of budget variances. The models are then integrated into a model of project
performance.
An inspection of the data revealed three sets of relationships indirectly influencing project
completion. First, the strategic nature of the project correlates with clarity (r=0.26), commu-
nication (r=0.30) and commitment (r=0.43), which together underpin senior management
support. Second, there is a country effect. The UK projects are larger (r=0.50), less strate-
gic (r=–0.30), and have higher levels of user participation (r=0.45) than the NZ projects.
278 P Yetton et al.
Table 7. Correlation of project completion and budget variances with indepen-
dent variables
Independent variables Budget variances Project completion
Project size 0.09 –0.30
Newness 0.15 –0.21
Strategic nature –0.05 0.28
Tech. risk 0.05 –0.12
Senior mgt support –0.20 0.37
Planning –0.39 0.06
Conflict proj. team 0.28 –0.03
Unstable proj. team 0.49 –0.08
User participation –0.01 0.36
The statistic for ‘senior management support’ is the multiple r for the regression model on project
completion reported in Table 5, and for the equivalent model on budget variances.

Finally, end-user participation is positively associated with project size (r=0.23). As specu-
lated above, senior management support mediates the effect of the strategic nature of a
project on project completion. Table8 shows that, controlling for management support, the
effect of strategic nature on completion is nonsignificant (DR2
=0.03, F=2.6, NS). Combining
the results of this secondary, exploratory analysis with the findings from Hypotheses 2b, 3, 4,
6 and 10, gives the submodel of project completion presented in Figure 2.
The submodel for budget variances is considerably more complex than the submodel for
project completion. An inspection of the data reveals three patterns. First, planning is asso-
ciated with senior management support (DR2
=0.15, F=4.12, P<0.05) and project team insta-
bility (r=–0.36). Second, there is a set of factors associated with conflict in, and the dynamics
Figure2. Submodel of project completion.
Table 8. Regression of project completion on strategic nature and senior man-
agement support
Independent variable DR2
F Significance
Senior management support
Clarity of objectives 0.14 3.6 <0.05
Resource commitment
Communication of objectives
Strategic nature 0.03 2.6 NS

of, the project team. Conflict correlates with team instability (r=0.31), technical risk (r=0.31)
and project size (r=0.25). Third, budget variance is a function of perceived risk (r=–0.24),
which covaries with newness (r=0.26). Perceived risk in turn is a function of end-user resis-
tance (r=0.28) and the strategic nature of the project (r=0.22); and end-user resistance is a
function of newness (r=0.21), technical risk (r=0.21) and senior management support (clarity
of objectives: r=0.29).
Considering the second pattern of relationships in more detail, Table9 shows that team
instability mediates the effect of conflict on budget variances. Controlling for team instability,
conflict has a nonsignificant effect on budget variances (p =0.14, t=1.25, ns). The relation-
ship between planning, team stability and budget variances is more complex. Table10 shows
that both good planning and team instability, controlling for the effects of each other, have
independent effects on budget variances (planning: b=–0.25, t=–2.26, P<0.05; team insta-
bility: b=0.40, t=3.67, P<0.05).
Finally, Table11 shows that both perceived risk and newness have independent and oppo-
site effects on budget variances, controlling for the effect of the other (newness: b=0.23, t=
1.91, P=0.06; perceived risk: b=–0.30, t=–2.54, P<0.05). Because newness and perceived
risk covary (r=0.26), the simple effect of newness on budget variances as measured by the
correlation coefficient is not significant (r=0.15).
Combining this exploratory analysis with the findings for Hypotheses 2a, 5, 7, 8 and 9, gen-
erates the submodel of budget variances presented in Figure 3. Note that two of the rela-
tionships, between team instability and team conflict, and between team instability and budget
280 P Yetton et al.
Figure3. Submodel of budget variances.

variances, are presented as reciprocal, as the directions of causality can be argued to be
mutually reinforcing.
The two submodels presented in Figures 2 and 3 are integrated into a model of project per-
formance which is presented in Figure 4. This can be compared with the initial modelling of
the hypotheses in Figure 1. The resulting empirical model is much more complex, and incor-
porates a number of relationships not considered in the initial analysis. This model highlights
the centrality of senior management support to successful project management, and the com-
plexity associated with developing and managing a successful project team.
DISCUSSION AND CONCLUSIONS
This paper reports three major findings which we review below. We then examine whether
they are subject to any major validity threats. Next we review and integrate them with the
extant literature. Finally, we explore the implications for management.
Underpinning this study is an important proposition that project performance can be parti-
tioned into two independent dimensions: budget variances and project completion. This propo-
sition is expressed in Hypothesis 1 and supported by the data, forming the first contribution
of the paper.
The study develops 11 hypotheses concerning the different determinants of these two
dimensions of project performance. With the exception of project size and newness, the
Table 9. Regression of budget variance on team conflict and instability
Independent variable beta t Significance
Instability 0.45 4.07 <0.05
Conflict 0.14 1.25 NS
Table 10. Regression of budget variance on planning and instability
Planning –0.25 –2.26 <0.05
Instability 0.40 3.67 <0.05
Table 11. Regression of budget variances on newness and perceived risk
Newness 0.23 1.91 0.06
Perceived risk –0.30 –2.54 < 0.05

factors hypothesized to influence budget variances are different from those influencing project
completion. Nine of the hypotheses are supported. Budget variances are a function of plan-
ning, team conflict and instability, and project completion is a function of a project’s strategic
nature, senior management support, size, newness and user participation. The hypotheses
concerning the influence of size and technical risk on budget variances, however, are not sup-
ported. This is the second contribution of the paper.
Subsequently, the data were further analysed to begin the process of building a more com-
plete model of project performance; this forms the third contribution of the paper. On the basis
of this further analysis, the evidence suggests that in the case of three hypotheses, the pro-
posed relationship with the dependent variable is an indirect one, whereby the effect of the
independent variable on project performance is mediated by a third variable. Figure4 shows
that if a project is strategic (Hypothesis 6), its effect on project completion is mediated through
senior management support. That is, the strategic nature of a project increases the level of
management support, which in turn increases the probability of project completion. Second,
the level of conflict in the project team (Hypothesis 8) influences budget variances only indi-
rectly by exacerbating the level of project team turnover (instability). Third, while planning has
a direct effect on budget performance (Hypothesis 7), it also has an indirect effect by reduc-
ing project team instability. Furthermore, the empirically derived model of project performance
presented in Figure 4 identifies a number of complex relationships in addition to the hypoth-
eses developed earlier in the paper. In particular, the effect of newness on budget variance
(Hypothesis 3b) is supported; its direct effect is masked due to its covariance with risk
perception.
282 P Yetton et al.
Figure4. Combined model of project performance.

Validity threats
The findings appear robust against the three validity threats discussed below – construct, inter-
nal and external validity threats (Cook & Campbell, 1979). The factor analysis reported in
Table1 shows that the items load appropriately and strongly onto meaningful constructs. The
emergent factor structure corresponds very closely with the expected factor structure, and
there is good evidence of convergent and discriminant validity. Single-item constructs are a
threat to validity, and further development of the instrument is planned to address this issue.
It is also accepted that the ordinal scale for project completion is somewhat subjective, nev-
ertheless the general robustness of the results provides indirect evidence for the construct
validity of this measure.
One potential internal validity threat to the study’s findings is that all respondents were IT
managers. Given that we found that technical risk had little effect on project performance
(Hypothesis 5), it could be argued that this finding is due to IT managers protecting them-
selves from blame for poor budget performance or project failure, by attributing other than
technical reasons for poor project performance. Against this interpretation, the data show that
there is no range restriction in the levels of technical risk reported by the managers. Thus,
the evidence does not support the conclusion that the respondents ‘hid’ an actual relationship
between these factors. Again, further work could control for this threat by sampling the project
manager, IS director and client manager for each project.
We acknowledge the risk in exploratory model building of identifying ‘significant’ relation-
ships that are actually chance findings, because these relationships were not hypothesized
ex ante, and thus the statistical controls are not rigorous. Against that, the model building
allows us to examine the relationships observed among the variables. This begins with an
analysis of the hypothesized relationships. Some factors are shown to have an indirect effect
on the relevant dependent variable through another mediating factor. The exploratory analy-
sis also identifies a complex set of potential relationships, incorporates them into a model of
the project team and its context, and examines their effects on budget variances.
Another potential internal validity threat to the non-significant findings reported here arises
from the low power of the tests. The overall sample size of 72, while typical of such studies,
could result in a high Type II error rate. Against this, nine of the 11 hypotheses are supported.
To examine this issue formally, we estimate the power of the tests. To do this, we conducted
a post-hoc power analysis after the procedure suggested by Cohen & Cohen (1983,
pp. 154–164). The analysis finds that for a sample size of 72, the tests conducted have a
power of greater than 0.90 for medium and large-sized effects. This exceeds the minimum
acceptable power of 0.70 recommended for this type of research (Cohen & Cohen, 1983, p.
162).
The study is cross-sectional, so there is no evidence for the hypothesized causal direction
of the relationships between variables. In addition, as shown in Figures 3 and 4, we specu-
late that some of the relationships are likely to be bi-directional. The findings and exploratory
model therefore need to be subjected to further testing either under experimental conditions
or in longitudinal field studies.

With regard to the generalizability of the findings, it is noted that the sample includes orga-
nizations from a wide range of industries, and a range of different sized projects, across two
countries. It is not felt that cultural differences between New Zealand and UK are a serious
threat to validity, in particular with respect to the issues under investigation (Hofstede, 1980).
Rather, including respondents from two countries and spread across different industries
enhances the external validity of the findings (Cook & Campbell, 1979). This diversity
of subjects reduces the possibility of bias in findings arising out of a skewed and non-
representative sample as a consequence of low response rates, in particular for the UK sub-
sample. Furthermore, there is no evidence of range restriction in the independent variables
(see Table3). There is no evidence that the sampling frame used here exposes the findings
to a major external validity threat.
Implications
In integrating the study’s findings, four powerful influences on project performance are sug-
gested – project team dynamics, risk management, management support and user participa-
tion. First, project team dynamics have a strong effect on budget variances: conflict and
instability in the project team lead to poor budget performance. Technical risk can also exac-
erbate team conflict. Good planning facilitates and assists a stable and skilled project team
to perform more efficiently. Effective management of both social and technical processes thus
produces a stable, competent team which is a prerequisite for good project performance. This
is consistent with the organizational psychology literature on group dynamics, which finds that
both effective group social processes and technical expertise jointly predict performance
(Shaw, Robbin & Beber, 1981; Yetton & Bottger, 1983; Hackman, 1990).
Second, risk management reduces budget variances. Recently, Keil & Robey (1999) con-
clude that ‘research has generated little insight into means by which managers may avoid
risks and prevent project failure’. Here, we begin to address this omission. Clarity of objec-
tives, newness and technical risk stimulate end-user resistance and influence management’s
perception of risk. Newness and technical risk clearly signal a need for risk management
(McFarlan, 1981). While it is perhaps counter-intuitive to find a negative relationship between
risk perception and budget variances (Figure 3), it can be argued that a perception of risk is
an essential prerequisite to managing risk down (Martin & Chan, 1996). Lack of awareness
of risk may lead to inadequate risk management and high budget variances. In addition, size
and newness, which are known risk factors (Stinchcombe, 1965; McFarlan, 1981), also influ-
ence project completion and can lead to project redefinition or failure. Increasingly, to reduce
risk, IS projects are being broken down into smaller quasi-independent modules (Feeny, 1997;
Redmill, 1997). Newness of the project means that the organization may not have the requi-
site skills and competencies to successfully complete the project, and so seek to recruit or
outsource them. Part of the current attraction of outsourcing may be an attempt by firms to
locate these skills and competencies outside the firm, in an attempt to reduce project risk.
Third, senior management support and the strategic nature of a project have a powerful
influence on its successful completion. When a project is considered to be strategic to the
284 P Yetton et al.

business, it creates a level of senior management support that drives project completion.
When an IS project is considered to be critical to business success, budget (time-cost) per-
formance is secondary to project completion. Management support, does, however, set the
context for the project team’s budget performance. In particular, management support leads
to good planning, which enables management to monitor ongoing performance. Planning
leads both directly and indirectly to low budget variances (see Figure 3). Indirectly, via its
effects in reducing team instability, senior management support ensures provision of quality
resources which are not bid away or reassigned, and therefore reduces budget variances. So
management support drives both project completion and budget variance components of
project performance.
Finally, user participation assists the successful completion of development projects.
Threats to completion can be reduced by actively involving the user or client at all stages of
project development. Only the user or client can make the business tradeoffs which keep
project complexity within feasible limits. User participation increases the level of business
understanding of the project, as well as ensuring its business relevance. This is consistent
with the research on user participation in systems development (Franz & Robey, 1984; Ives &
Olsen, 1984; Hunton & Beeler, 1997). By ensuring the project’s relevance and usefulness,
user participation in development also helps to build support for the project during
implementation.
In fact, the importance of both user participation and senior management support in the
above analysis suggests that jointly they can perform an integrative role across both the devel-
opment and deployment stages of IS projects, which have been typically seen as distinct func-
tions (Lyytinen & Hirschheim, 1987; Ballantine etal., 1996; Willcocks etal., 1997). In essence,
senior management support and user/client participation integrate across these two functions
by building implementation capability during the development phase. While the role of man-
agement support in the successful implementation of IS innovations is acknowledged (Jar-
venpaa & Ives, 1991; Sauer, 1993a; Yetton, Sharma & Southon, 1999), there has been little
critical examination of how the role of management support in the development stage could
contribute to the effect of management support on successful implementation in this next
stage. We speculate that both senior management support and user participation provide con-
tinuity to, and ensure the business value of, the project through both the development and
deployment stages. In that case, by contributing to quality and acceptance of the system
through both design and implementation phases, user/client participation and senior man-
agement support would play an important role in project success. This interdependence across
the two domains should be explored in further research. An integration between the model
presented here and models of information systems implementation such as those developed
by Lucas etal. (1990) might be a good starting point.
These four influences highlight the complexity of the project management process as mod-
elled in Figure 4. A comparison between Figure 1, which presents the initial hypotheses,
and Figure 4, which presents an empirically-derived model, shows a shift from a relatively
simple to a highly complex model of project management. It shows the gap between current
models of IS project management and the complexity of project management practice. Impor-

tantly, the complexity presented in Figure 4 makes a strong case for the argument that appoint-
ing a good project manager and following a standard methodology would not begin to resolve
the many relationships which need to be managed effectively to deliver high project perfor-
mance. This helps to explain the relative failure in the IT industry to develop IS project man-
agers who consistently deliver projects on time, to functionality and to budget. The challenge
is too complex to resolve by a single intervention.
This failure has within it the source of its resolution. If it is difficult for a project manager to
resolve the complexity presented in Figure 4, then the solution must lie in re-framing the
problem. One of the authors has observed over many years that, rather than having better
managers, successful organizations experience fewer problems relative to unsuccessful orga-
nizations. So, one approach is to reduce the number of problems, or the level of complexity
of the task. An inspection of Figure 4 shows that the implicit ‘best practice’ project profile
(leading to successful project completion with low budget variances) requires stable project
teams with strong support from senior management, good planning, low conflict, a strategic
focus, experience with this type of project (low newness), user participation and small scale.
This profile is similar to that found by Sauer etal. (1998) for successful project management
in the Australian construction industry.
This profile is not typical of IS project management. There is considerable anecdotal evi-
dence that IS projects are often characterized by a lack of management support, inadequate
project manager authority and responsibility, low project team skills and experience, high team
instability, and a perception that IS projects are not central to the business (Yourdon, 1997;
Hallows, 1998). The ‘best-practice’ profile described above suggests that the way ahead is
not just better project managers. While good project management skills are obviously impor-
tant, they are a necessary but not sufficient condition for best practice. The critical issue is
developing the management context which enables them to learn to be productive, rather than
repeating failures (Lyytinen & Robey, 1999).
SUMMARY
This paper contributes to and reframes the existing limited empirical research on the deter-
minants of successful IS project management. It began by partitioning project performance
into budget (time-cost) variances and project completion, and separately analysing their deter-
minants. The level of budget variances were found to be a function of planning, instability,
newness and risk perception; while the level of project completion was found to be a function
of a project’s newness, management support, size and user participation.
The responsibilities of the project manager are found to be critical to successful project
management. The role of top management is also shown to be critical, and it is suggested
that project managers can only succeed in meeting their responsibilities within the context of
a culture that supports and cultivates professional project management practice. We have
identified four key influences on project performance which require management attention:
project team dynamics (stable, experienced, cohesive), effective management of risk, senior
286 P Yetton et al.

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Biographies
Philip Yetton is Commonwealth Bank Professor of Man-
agement and Executive Director of the Fujitsu Centre for
Managing Information Technology in Organizations, at the
Australian Graduate School of Management in the Uni-
versity of New South Wales. He has degrees from Cam-
bridge University and Liverpool University, and received
his MBA and PhD from Carnegie-Mellon University. His
research interests include strategic IT management,
dynamics of IT-based change, decision-making and stra-
tegic leadership. Philip has published over 60 research
articles and books in these areas. In addition, he has

extensive international management consulting
experience.
Andrew Martin is Lecturer in Information Systems at
Warwick Business School. He has an MA from Cambridge
University, and has extensive IS project management
experience. His research interests focus on computer-
based management simulations and the theory and
practice of IS project management. He has built several
computer-based project management simulations for
teaching and research. Andrew has published his research
in several journals, including International Journal of
Project Management, Information Resource Management
Journal, Journal of Economic Behavior and Organization,
Journal of Behavioral Decision Making and the Australian
Computer Journal.
Rajeev Sharma is Senior Lecturer in Information
Systems at The University of New South Wales. He has a
BE from the University of Delhi, PGDM from the Indian
Institute of Management, Bangalore and is a doctoral can-
didate at the Australian Graduate School of Management.
Rajeev’s research interests include implementation of IS
innovations and managing IS project performance. His
published research includes articles in the Journal of Infor-
mation Technology, the Proceedings of the International
Conference on Information Systems and Proceedings of
the European Conference on Information Systems.
Kim Johnston is currently a Strategy and Review
Officer at the University of Western Sydney. He has a
BA(Hons) from the University of Western Australia and an
MBA and PhD from the University of New South Wales.
Kim’s research interests include organizational designs for
IT management, managing strategic change and IS project
management. He has published articles in Sloan Man-
agement Review, Journal of Strategic Information
Systems, International Journal of Technology Manage-
ment, and in many international conference proceedings.

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