1. The Effect of Scope Changes on Project Duration Extensions
Extended Abstract of a PhD Dissertation1
Moshe Ayal
Faculty of Management, Tel Aviv University, Tel Aviv 69978
Abstract
The objective of this research is twofold: first to construct and validate an explanatory model for
project duration extensions, and second to analyze the effect of scope changes and other related
drivers on project delivery times. The research is based on a field study, which draws data from an
engineering service environment, and uses means of structural equation modeling for analysis. The
model contributes to a better understanding of the effect of various scope changes on project
duration, and enables the construction of a practical tool for estimating project duration.
Introduction
Projects frequently finish late and over budget, thus causing organizations heavy penalties and
damage their prestige. Moreover, as projects are hardly ever completed without introducing changes
to their original baseline plan, a major challenge is to accurately estimate the project delivery time,
while understanding the effects of other factors that create the discrepancy between estimated and
actual project completion times. Thus, the intention in this work is to quantify the factors affecting
duration extensions, an issue that has barely been addressed in the literature. One way to quantify
these factors is by generating a descriptive empirical model that includes the major behavioral and
quantitative measures of performance.
Literature
The section reviews the following themes: (1) duration estimation, the basis of duration extension
measures, (2) possible generators of duration extensions, and (3) scope changes, and their effect on
project performance.
Duration Estimation. The tools most commonly used are based upon mathematical models in
which task duration is explained by technical parameters of the task and the experience of the
executing entity. Well known examples of these tools are SLIM (Putnam, 1978), and COCOMO
Softwares (Boehm, 1981; Boehm et al., 2000). Burt and Kemp (1991) proposed predicting task
duration from knowledge about durations of categories of tasks. However, here a potential bias
exists, known in the psychology literature as the planning fallacy, due to the tendency of individuals
to underestimate the amount of time needed to complete a given project. In the words of Buehler,
Griffin and Ross (1994), they tend to focus on the future, ignoring past experience.
Duration Extensions Generators. Several generators are discussed in the literature. Levy and
Globerson (1997) implemented concepts from queuing theory for reducing the impact of waiting
periods of critical work packages on the delivery times of projects executed in parallel. Goldratt
(1997) claimed that task splitting, whether planned or results from preemptive processing might
lead to severe duration extensions. Shenhar (2001) classified technological uncertainty into four
levels, correlating them with overall project duration. Shenhar et al. (2002) also claimed that
1 Thesis Supervisor: Prof. Shlomo Globerson

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projects high in uncertainty must be managed differently, employing means to reduce the
uncertainty. Low experience with technology often results in what we term low structured projects
(Applegate, Austin, and McFarlan, 2003) where risk mitigation is of great need. Based on surveys,
Chan and Kumaraswamy (2002) mentioned: (1) impractical design, (2) labor shortages, (3) poor
performance, (5) unforeseen conditions, and (6) poor communication. Still, literature lacks
empirical quantifications of the effects of the above-mentioned generators on project duration.
Scope Changes. Modification to the agreed upon scope (PMBOK, 2000) are considered as
inherent in the nature of projects because of their complexity and the inevitable appearance of
unforeseen problems (Ertel, 2000). The evidence shows that scope changes have a significant
impact on the cost of projects. Chick (1999) showed that the later a change occurs in a project the
more effect it will have on the project’s cost, and also mentioned a possible effect on project
schedule. Kauffmann et al. (2002) used the earned value method in quantifying scope change
‘magnitude’ for cost adjustments. Barry et al. (2002) showed a correlation between software project
duration and effort. However, a thorough investigation of the effect of scope changes on project
duration has not yet been conducted.
Research Design
Figure 1 illustrates the hypothesized work package duration extensions model. The model
includes three exogenous variables: (1) Technological Uncertainty, (2) Project Priority, and (3)
Unforeseen Stoppages. It also has six endogenous variables: (1) Additional Materials, which refers
to inventory orders, and (2) Additional Labor, both resulting from scope changes, and marked
inside a dashed box; (3) Waiting in Line; (4) Preemptive Processing; (5) Stoppage Period, and (6)
the main dependent variable: Duration Extension, which refers to a work package.
Figure 1. Hypothesized Work Package Duration Extensions Model
Unforeseen
Stoppages
Project
Priority
Preemptive
Processing
Waiting
in Line
Duration
Extension
Stoppage
Period
Technological
Uncertainty
H1 H2
H3
(H7)
(H6)
H5
H4
Additional
Materials
Additional
Labor
Table 1 summarizes the proposed model variables and the rationale for their selection.

3. EFFECT OF SCOPE CHANGES ON PROJECT DURATION EXTENSIONS - MOSHE AYAL
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Table 1. Proposed Model Variables
Variable Description Rationale
Technological
Uncertainty
Level of technological uncertainty associated
with a certain work package
Technological uncertainty is correlated with
duration (Shenhar, 2001)
Project Priority
Priorities assigned to projects by top
management team
May decrease duration extensions by
reducing waiting periods
Unforeseen
Stoppages
Number of unforeseen operational stoppages
caused by internal or external sources
A greater number of operational stoppages
may increase in-process duration
Stoppage
Period
Total time periods of operational stoppages
caused by internal or external sources
Longer periods of operational stoppages may
increase in-process duration
Additional
Materials
Number of material orders resulting from
scope changes
Waiting until materials arrival, if filing for an
external supply, may increase the duration
Additional
Labor
Additional labor resulting from scope changes
Scope changes could affect duration (Chick,
1999)
Waiting in Line
Time from the arrival of a work package to the
beginning of its processing
Waiting periods may extend delivery times
(Levy and Globerson, 1997)
Preemptive
Processing
Number of breaks during processing a work
package
Task splitting increases in-process duration
(Goldratt, 1997)
Duration
Extension
Work package in-process duration extension
relative to planned duration
The main dependent variable of the research
Table 2 provides five hypotheses that are derived directly from the duration extensions model,
based on its flow. The sixth hypothesis involves two exogenous variables, not included in the model
for reasons of parsimony: (1) Internal Scope Changes, and (2) External Scope Changes. The seventh
hypothesis is indicated by a correlation in the duration extensions model, and is tested separately
within the projects’ framework.
Table 2. Hypotheses
Variable Effect on Variable Rationale
H1 Additional Labor +
Duration
Extension
Additional labor calls for resources, which in many cases are not
currently available
H2
Additional
Materials
+
Duration
Extension
In-process duration extensions may result from having to wait until
materials arrive
H3
Preemptive
Processing
+
Duration
Extension
Goldratt's (1997) claim that splitting a task results in extending its in-
process duration
H4 Additional Labor +
Preemptive
Processing
Work package manager needs to wait for available resources and/or
materials to arrive
H5 Waiting in Line -
Preemptive
Processing
The negative effect of waiting periods can be decreased by working
intensively and continuously
External Scope
Changes
++
Internal Scope
Changes
+
H7
Technological
Uncertainty
+ Project Priority
Allocating priority to a project may help in rapidly mitigating the
uncertainties in its work packages
Work package managers who introduce scope changes try to avoid
material orders and use in stock materials, so as not to wait for the
materials to arrive. Thus, external scope changes are expected to
have a greater effect on material orders than Internal scope changes
Additional
Materials
H6

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Analysis. We analyze the model using means of structural equation modeling (SEM) in order to
elicit the partial correlations amongst the variables, and to establish the causal relations. Two of the
variables are dummy ones: (1) Project Priority, which takes the value of ‘1’ for prioritized project,
and ‘0’ otherwise, and (2) Technological Uncertainty, which takes the value of ‘1’ for work
package with technological uncertainty, and ‘0’ otherwise. We conduct linear multiple regression in
examining Hypothesis 6, and use means of binomial regression to test Hypothesis 7, on the effect of
technological uncertainty on project priority. We use the EQS software package (Byrne, 1994) for
conducting the SEM analysis. In order to bring all data to the same reference point, the model’s
variables, except for the dummies, are divided by the planned duration of the work package.
Data collection. The study draws data from 714 work packages comprising the 56 systems
engineering projects being performed at the time by a leading engineering services corporation. The
projects ranged in value from several thousand dollars to one hundred thousand dollars, while the
work packages comprising these projects ranged in duration from several days to a month. The
projects had a sequential PERT/CPM structure, thus above 90% of the work packages where
critical. Top management team, department managers, project managers and professional section
managers were involved in data gathering. They used an interactive data collection interface, which
was part of the project control system of the corporation.
Results
Table 3 shows the means, standard deviations, variables ranges and bivariate correlations for the
variables of the proposed model. Note that in some cases total labor hours invested in a work
package were decreased as a result of scope changes. However, in our data it was the rare case, as
most of the time scope changes resulted in additional labor, which sometimes accumulated to as
high as several hundreds of percents of the allocated labor hours!
Table 3. Descriptive Statistics and Pearson Correlation Matrix
Variables Mean s.d. Min Max 1 2 3 4 5 6 7 8
1 Tech. Uncertainty 0.29 0.46 0 1
2 Project Priority 0.37 0.48 0 1 .22**
3 Unforeseen Stoppages 0.04 0.10 0 0.5 -.08* -.02
4 Stoppage Period 0.10 0.36 0 6.5 -.05 -.05 .53**
5 Additional Materials 0.02 0.06 0 0.5 .18** .04 .01 -.01
6 Additional Labor 0.23 0.63 -0.47 7 .29** .06 -.03 -.05 .54**
7 Waiting in Line 0.30 0.29 0 1.5 -.08* -.21** -.003 .002 .03 .12**
8 Preemptive Processing 0.15 0.14 0 1 .09* .01 .28** .08* .11** .27** -.05
9 Duration Extension 0.31 0.68 -0.66 7 .11** -.08* .31** .60** .41** .48** .28** .25**
Note . n=714 (work packages)
* p < .05
** p < .01

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Model Fit. Using structural equation modeling means of analysis, the hypothesized model of
work package duration extensions is found significant: 6.322
=χ , with 18 degrees of freedom,
which rejects the null model hypothesis. In addition, the goodness of fit indices: NFI=0.98;
NNFI=0.98; CFI=0.99; and RMSEA=0.034 (for n=714 work packages), indicating a good fit of the
model to the data (Bagozzi & Yi, 1988; Bagozzi & Yi, 1989).
Hypotheses 1 to 5. From the partial correlations given in Table 4, Hypotheses 1 to 5 are seen to
have statistical significance. Note that two of the hypothesized relations are proved not to be
significant: (1) additional material orders affect preemptive processing, but only indirectly, via the
resulted additional labor; and (2) project priority affects duration extension only indirectly, via
shorter waiting in line periods. In addition, out of the three correlations tested, only the one between
project priority and technological uncertainty is found significant (r =0.22).
Table 4. Direct Relations in the Work Packages Duration Extensions Model
Hyp. From To
Standardized
coefficients
t-values
H1 Additional Labor Duration Extension .34 13.49
H2 Additional Materials Duration Extension .21 8.80
Waiting in Line Duration Extension .23 10.92
H3 Preemptive Processing Duration Extension .10 4.81
Stoppage Period Duration Extension .61 29.83
Project Priority Duration Extension -.03 -1.62(*)
Technological Uncertainty Additional Labor .20 6.38
Additional Materials Additional Labor .51 16.36
Technological Uncertainty Additional Materials .18 4.82
Project Priority Waiting in Line -.21 -5.74
Unforeseen Stoppages Preemptive Processing .23 8.44
H4 Additional Labor Preemptive Processing .32 7.76
Additional Materials Preemptive Processing -.06 -1.46(*)
H5 Waiting in Line Preemptive Processing -.08 -2.31
Unforeseen Stoppages Stoppage Period .53 16.62
Note. n=714; NFI=0.98; NNFI=0.98; CFI=0.99; RMSEA=0.034.
(*) path not significant
Duration Prediction. Using means of linear multiple regression, we derive a mathematical
model for the prediction of work package duration based on its predetermined variables,
performance variables, and disruptions like forced stoppages and scope changes (R2
=0.71). Table 5
shows the predictors of the best-fitting model. Note that if we ignore the two scope changes
variables – additional material orders, and additional labor - we explain only 51% of the variance in
the duration extension. Conducting a partial F-test we again confirm the hypothesis that the two
variables representing scope changes are significant in the model.

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Table 5. Predictors of Work Package Duration Extentions
Models:
Predictor Estimate St. Error Estimate St. Error
Project Priority -0.039 0.039
Technological Uncertainty 0.218 0.041
Unforeseen Stoppages -0.470* 0.214
Stoppage Period 1.159** 0.039 1.188** 0.059
Additional Materials 2.466** 0.282
Additional Labor 0.363** 0.027
Waiting in Line 0.552** 0.049 0.686** 0.063
Preemptive Processing 0.519** 0.107 1.133** 0.139
Intercept -0.171** 0.026 -0.212** 0.038
R^2 0.707 0.513
Adj. R^2 0.705 0.509
Note . n=714. Partial F test for Model(2) = 328, p<0.001
* p < .05
** p < .01
(1) Best Fitting (2) without Scope Changes
Hypothesis 6. To test Hypothesis 6, on the effects of internal and external scope changes on
additional material orders, we regressed the additional material orders against the number of
external and internal scope changes, dividing both by the planned duration, as with all the variables.
The results showed in Table 6 indicate that external scope changes result in a significantly higher
amount of material orders than do internal scope changes. Note, however, that the number of
internal and external scope changes is almost the same.
Table 6. Additional Material Orders by Number of External and Internal Scope Changes
Predictor Number Estimate St. Error
Internal Scope Changes 113 0.065** 0.02
External Scope Changes 120 0.267** 0.021
Intercept 0.003 0.002
R^2 0.19
Adj. R^2 0.187
Note . n=714 (work packages)
** p < .01
Hypothesis 7. Figure 2 shows the number of prioritized and non-prioritized projects by the
number of work packages with technological uncertainty. The data shows that the majority of the
projects of five or more work packages with technological uncertainty are prioritized. To formally
corroborate Hypothesis 7, we regressed project priority against the number of work packages with
technological uncertainty in the project, using means of binomial regression.

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Figure 2. Prioritized and Non-Prioritized Projects by Work Packages with Technological Uncertainty
0
5
10
15
20
0-1 2-3 4 5 6-11
Work Packages with Technological Uncertainty
Projects
Regular Projects
Prioritized Projects
Table 7 presents the results of the binomial regression, which indicate that a project consists of
five or more work packages with technological uncertainties is likely to get priority by top
management. This might shed some new light on the way top management reduces technological
uncertainties in projects.
Table 7. Project Priority by Number of Work Packages with Technological Uncertainties
Predictor Estimate St. Error Significance
Technological Uncertainty 0.88 0.26 0.001
Intercept -4.19 1.13 0.001
-2LL 45.88
Note . n=56 (projects).
Implications and Conclusions
Functional Level. Functional managers usually do not have a picture of the entire projects.
Rather they see various work packages arriving from different project managers that must be
processed according to certain priority rules. The model developed here can help them to better
assess the effects of the various disruptions that occur while the work packages are being processed.
They can also use the model to compensate for duration extensions, thus enabling better
performance of overall workload.

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Project / Multi-Project Level. At the project level, we should distinguish between critical work
packages that need to be tightly controlled, and non-critical work packages. At the multi-project
level, top management has to differentiate between prioritized and non-prioritized projects, giving
top priority to critical work packages belonging to prioritized projects. This will assure that the
projects with the highest priority will be delivered on time. The proposed model assesses the effect
of unforeseen disruptions on critical work packages of high-priority projects, and provides better
tools for estimating final duration and compensating for delays.
Summary
The study contributes to an improved understanding of duration extensions and their causes, by
quantifying scope changes, differentiating amongst the various types of scope changes, and
constructing an integrated model of work package duration extensions. The study pointed to the
greater effect of external scope changes, compared with internal ones, on material orders, and thus
on the total duration extensions. Generally, the model can be implemented for forecasting work
package duration extensions, and estimating the effect on the project’s duration. Finally, several
implications at the functional, the project, and the multi-project levels are suggested.
Acknowledgments
This work is based on my PhD dissertation, supervised by Prof S. Globerson. I wish to expresses
my gratitude to him, and to all whose insights have contributed to this thesis.
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