Posted: October 20th, 2022

Programme/portfolio formulation For your Key Concept Exercise this week, you will analyse the…

PAPERS
February 2012 ■ Project Management Journal ■ DOI: 10.1002/pmj 21
INTRODUCTION ■
T
his article considers the impact of program alignment and related factors
that contribute to successful program delivery. The study
attempted to disclose the key underlying assumptions that connect
program management with related theories of strategic, organizational,
and project management. Exposing the hidden management ideology
and practices that actually inform structure and content requires an
understanding of program success and failure factors (Lycett, Rassau, &
Danson, 2004). The identification of an interaction structure and management
practice that supports continuous alignment may thus provide significant
potential for reducing the unacceptable rate of program failures
(Kotter, 1995; Morris, Crawford, Hodgson, Shepherd, & Thomas, 2006). The
study should be of relevance to professionals who retain or exercise influence
over the formation and execution of programs.
Business strategy is complex and intertwined with all the processes and
systems that are required to effectively manage an organization. Program
management may be considered an effective building block and umbrella
framework in the operationalization of business strategy. The links between
business strategy and program management reside within the alignment of
the strategic processes of formulation and implementation. Strategic alignment
will be unique to a particular organization and will involve a dynamic
and iterative process of mutual adjustment and reshaping (Beer, Voelpel,
Leibold, & Tekie, 2005). Strategic implementation in many companies is an
enigma due to misaligned projects and a lack of a systemic approach to business
strategies.
Understanding the potential contribution of program alignment may
thus further contribute to the improvement of the effectiveness and efficiency
of the delivery of strategic objectives (Burdett, 1994; Chorn, 1991;
Strassmann, 1998). This study has empirically explored implementing strategy
through programs and the need to continually manage program context.
Program management environments are complex, and the uncertainty
that arises from multiple combinations of unique personalities, stakeholder
expectations, assumptions, constraints, changing environments, and human
social systems can provide the impetus for failure (Lehtonen & Martinsuo,
2009). The messy, complex, and multifaceted environment of program management
produces a need to continually realign the program and related
projects to changing environmental and corporate objectives (Thiry, 2004).
Previous research has recommended new directions that focus attention on
causality and complexity in context (Ivory & Alderman, 2005; Morris & Pinto,
2007; Pollack, 2007). The inherent complexity involved in applying structured
program management frameworks to organizational contexts thus warrants
further serious consideration (Pellegrinelli, Partington, Hemingway,
Mohdzain, & Shah, 2007). This study responds to this by applying a dynamic
Successful Programs Wanted:
Exploring the Impact of Alignment
Graeme Ritson, Northumbria University, Newcastle upon Tyne, United Kingdom
Eric Johansen, Northumbria University, Newcastle upon Tyne, United Kingdom
Allan Osborne, Northumbria University, Newcastle upon Tyne, United Kingdom
ABSTRACT ■
Alignment between formulation and implementation
of business strategy can be important for
achieving successful programs. The authors
have explored the development of a program
management alignment theory. Statistical testing
showed that interaction between the study
model variables was found to be multidimensional,
complex, and subtle in influence. Thus,
the authors conclude that programs have both
deliberate and emergent strategies requiring
design and management to be organized as
complex adaptive systems. Program life-cycle
phases of design and transition were often
formed from an unclear and confusing strategic
picture at the outset, which can make those
phases difficult to control. Learning was established
as an underlying challenge. The study
model demonstrated continuous alignment as
an essential attribute contributing toward successful
delivery. This requires program design
and structure to adopt an adaptive posture.
KEYWORDS: corporate strategy; program
management; governance; continuous alignment;
deliberate and emerging strategies
Project Management Journal, Vol. 43, No. 1, 21–36
© 2011 by the Project Management Institute
Published online in Wiley Online Library
(wileyonlinelibrary.com). DOI: 10.1002/pmj.20273
22 February 2012 ■ Project Management Journal ■ DOI: 10.1002/pmj
Successful Programs Wanted
PAPERS
systems perspective to program management.
This may improve the usefulness
and practical application of existing
good practice frameworks (Office of
Government Commerce [OGC], 2007;
Project Management Institute [PMI],
2009).
Theoretical Background
and Model
The study was operationalized through
the core concepts of systems, governance,
innovation and learning, corporate
strategy, environmental factors,
continuous alignment, and successful
delivery (Figure 1). This viewed success
as a multidimensional construct—
program management success, program
success, achieving business objectives,
strategic orientation, and business success
(Shenhar, Dvir, Levy, & Maltz,
2001). These dimensions consider success
from a business, corporate, and
economic level, respectively. Appropriate
hypotheses were advanced to
formulate a reasonable prediction
about the relationship of the variables
contained in the model.
Corporate Strategy, Project, and
Program Management
Program management is strategic in
orientation through delivering outcomes
and benefits related to the organization’s
strategic goals (Association
for Project Management [APM], 2007a;
OGC, 2007; PMI, 2009). This will require
the program and interrelated projects
to have their objectives and strategies
aligned with corporate strategy to create
an iterative hierarchy that develops
into business operations (Dietrich &
Lehtonen, 2005). Some organizations
may be adopting program management
as they develop their strategic
management capability. There will be a
multiplicity of options available in
achieving strategic objectives through
program-driven approaches requiring
intelligent program design. Context will
be crucial in determining appropriate
program formation (Pellegrinelli, 2002;
Pellegrinelli et al., 2007). International,
government, societal, industrial, commercial,
and business programs will
differ in focus and predictability of outcome.
Clearly articulated corporate strategy
will support the prioritization and
execution of the right programs and
projects. This alone will not guarantee
program success. Ill-conceived business
strategy will not necessarily be
redeemed by program and project
management. This will make reliable
prioritization and consistent allocation
of resources based on the greatest
strategic contribution more difficult
(Hrebiniak, 2006). Programs may also
be compromised by strategic business
case misrepresentation. This will result
in misalignment with strategic objectives.
Managing programs will involve
the task of remaining aligned with corporate
strategy (OGC, 2007).
Some organizations have welldeveloped
program management
maturity and organizational management
systems that support continuous
alignment. Well-aligned organizations
will be able to prioritize both activities
and how identified work gets executed.
The greater the alignment between the
operating environment, strategy, structure,
and processes, the more positive
H1. Corporate strategy
H6. Continuous
alignment
H2. Internal and external
environments
H4. Systems, subsystems,
and processes H3. Successful delivery
H5. Program
governance
H7. Learning and
innovation
Figure 1:Theoretical model and hypotheses.
February 2012 ■ Project Management Journal ■ DOI: 10.1002/pmj 23
effects this will have on performance
(Middleton & Harper, 2004). Alignment
will be essential for strategic success,
although nonalignment may exist for
temporary periods through significant
organizational and industry-sector
change. Strategy formulation and
implementation will thus require organizational
alignment with its resource
capability (Engwall & Jerbrant, 2003).
Shenhar et al. (2001) also suggested
that project success will be strongly
linked to an organization’s business
effectiveness.
Research indicates that project failure
is distinctly linked to factors at the
front end of a project through misalignment
with an organization’s key strategic
priorities (Pinto & Slevin, 1998). Hyvari
(2006) and Engwall (2003) supported
this in concluding that organizational
context will be an important factor
in determining success or failure.
Programs should be selected and
formed from organizational strategy by
aligning and coordinating related projects
(Morris & Jamieson, 2005). Program
formation and structure may be unclear
at the outset, requiring flexible
strategic implementation planning and
modular phased projects. This need for
a dynamic interface provides a clear
business case for organizations to
improve their capability in the management
of programs of projects and to
ensure that structure follows business
strategy. These and other contextdependent
and decision-oriented
issues lead to the following hypothesis:
H1: Corporate strategy leads to a
vision and stakeholder strategy that
takes account of the organization,
market, and sector in which it operates.
Environmental Factors
Modern organizations are constantly
analyzing their business activities and
industry sector, searching for business
opportunities (Venkatraman, 1989).
This may result in constant change of
business priorities and plans. De Wit
and Meyer (2005) argued that the most
common cause of corporate failure is
misalignment between the organization
and its environment. Sheppard
and Chowdhury (2005) strengthened
this argument and emphasize that a
fundamental failure of management
will exist if they do not properly evaluate
the environment. Globalization and
technological innovation create dynamic
and complex environments for many
current businesses where change is
a constant factor. Managing major
changes successfully may require an
organization-wide approach, and this
will impact at both the operational and
strategic levels (Carnall, 2007). The size
and scale of some organizations make it
impracticable for a radical change from
existing practices to be orchestrated at
the same time. Strategic change decisions
must be oriented in the most
appropriate sequence to increase the
likelihood of execution success (Bruch,
Gerber, & Maier, 2005). The OGC (2008)
recommended the adoption of program
and project prioritization categories to
ensure alignment between business
priorities, current capability, and
capacity to deliver.
Well-constructed and well-managed
programs should provide confidence
that the right projects are being sponsored
and that the desired benefits will
be achieved. Managing a program
will be a complex undertaking requiring
both project management acumen
and the capability of a business leader
(Pellegrinelli, 1997). Every program will
be unique to its contextual environment.
Rapidly changing and chaotic
environmental factors will create a high
level of task and organizational complexity.
This will require the impetus to
monitor and challenge program and
project performance. Increasing program
complexity may provide an ever-present
threat of failure. Solving one unyielding
problem may create unexpected drawbacks
elsewhere. The more complex the
program, the greater interaction risk and
interdependence with the internal
and external environment (Verma &
Sinha, 2002). This will necessitate a focus
on the tension between strategic direction,
project delivery, operational effectiveness,
and external influences. The
organization is unlikely to be in total
alignment and will have different
change capabilities. An open outlook
and sense of cooperation would be
ideal but this is seldom realized in practice
(Kotter & Schlesinger, 2008).
Organizational change resistance will
be inherent (Ford & Ford, 2009). The
program will more than likely be differentiated
and gradual rather than radical
and coordinated.
Knowledge of the current and future
environment will influence the choice of
strategic objectives and strategies
employed. Changes to the organizational
and business environment may lead
to significant alterations to the program
scope and priorities. The time horizon
and long-term nature of some programs
can have a significant impact on manageability.
The speed of technical evolution
and communication technology
may require adjustment or even cancellation
of tight-time-frame programs.
The program’s mission should provide a
focus for an integrated continuous decision
alignment framework (Scherpereel,
2006). Major strategic reviews may be
required at different times in the
program’s life cycle to coordinate alignment.
This will be an indicator of organizational
strategic maturity (APM,
2007b). Unpredictable environmental
factors are thus cross-linked to managing
programs and will require a dynamic,
flexible, and adaptive temporary
organization. Program management in
practice will involve top-down strategic
implementation linked with bottom-up
emerging management strategy through
successful project integration in the
host organization (Srivannaboon &
Milosevic, 2006). These emergentshaping
conjectures lead to the following
research hypothesis:
H2: The organization’s strategy is
influenced and reshaped from both
the internal and the external environments.
24 February 2012 ■ Project Management Journal ■ DOI: 10.1002/pmj
Successful Programs Wanted
PAPERS
Systemic Factors
Program management is a management
strategy informed by complexity
thinking, which increases manageability
and coordination. Deliberate and
emergent business strategies will
require flexibility in the program design
(Elizabeth & Ysanne, 2007; Mintzberg,
1994; Mintzberg, Ahlstrand, & Lampel,
1998; Mintzberg & Waters, 1985).
Emergence describes a dynamic
process that is the product of ongoing
system interactions. This refers to the
coexistence and impact of program
management, project management,
business-as-usual activities, environmental
factors, and corporate governance.
The emergent and coevolutionary
dynamic of program management will
require open systems. Open system
refers to the uncontrollable variable of
the environment and the self-organizing
tendency if left unmanaged. This introduces
nonlinear interaction, unpredictability,
and feedback loops in support
of organizational learning theory.
Open systems theory considers the
organization as a number of interdependent
subsystems that are open to
and connected with their environment.
This provides the potential for the
system to take on a new form in
response to environmental factors
requiring the facilitation of informationdriven
activities.
Program management provides an
integration solution for strategic business
management in dealing with
complexity and chaos in multiproject
environments (Pourdehnad, 2007).
Establishing systemic alignment
between people, processes, and technology
will provide benefits. Emerging
technologies can be adopted to enhance
organizational alignment capability and
maturity (Gaddie, 2003). The program
system may be radically unpredictable
beyond its immediate future, requiring a
dynamic approach of emergent planning
(Kash & Rycoft, 2000). The capability
of an organization and the coordinated
presence of critical program elements
will influence integration success.
Critical program elements refer to the
contextual program design or blueprint.
Established program processes
will need to continuously integrate
adaptive decision making through
learning processes (Lindkvist, 2008).
This will require a focus on complex
interactions, interdependency, processes,
and the coevolution of business
systems.
Understanding what management
practices are required for any given program
will be an important challenge. It
will be fundamentally important that
the distinguishing features of the program
are understood, as this should influence
program design (Meyer, Loch, &
Pich, 2002). Uncertainty (structural,
technical, directional, and temporal)
will be inevitable and a basic feature of
this complex system. Leading a program
will thus be multifaceted, situational,
and transient (Uhl-Bien, 2006).
Contextual uncertainty may materialize
in the form of an opportunity or risk.
High uncertainty and complexity will
require a holistic approach in designing
the program (Maylor, Brady, CookeDavies,
& Hodgson, 2006). Influences
from the external environment may be
frequent, accidental, and unpredictable,
with the internal environment
being equally as dynamic (Rybakov,
2001). Drawing together these system
dynamic principles leads to the following
research hypotheses:
H3: The program mission and objectives
support the creation of worthwhile
business benefits and the
successful delivery of the program.
H4: Programs and projects are managed
through a set of interdependent
critical processes and subsystems
that support strategic alignment and
realignment.
Governance
Corporate governance provides the
structure for initiating and determining
the objectives of an organization and the
means of monitoring, evaluating,
and influencing performance. Effective
program governance will be a major
strategic factor and cannot be confined
to a narrow static model that ignores
dynamic complexity. This will require
emphasis toward flexibility with the
organization-program-governance interface
(Rycroft & Kash, 2004). The sponsoring
group will be pivotal for success.
A further critical aspect will be the
determination of structures and control
measures to ensure alignment with
the unique organizational and contextual
environments. The success of the
program will require a flexible governance
structure that can be identified
from contextual design criteria to
ensure that it is fit for purpose. The
program mission will be a critical reference
point for aligning structures,
policies, procedures, behaviors, and
decision making.
Multiowned program governance
will require a strong focus on alignment
(APM, 2007b). This will be derived from
intertwining multiple perspectives of
governance in establishing a self-organizing
complex adaptive system (White,
2001). Various alignment strategies may
be needed in response to stakeholder
objections and agenda-setting behaviors.
Emergent and ill-defined programs
will need to make greater use of alignment
mechanisms and tools. Welltimed,
accurate, and focused reporting
will be central to integrating both performance
and learning loops. These
attributes provide the essential platform
for configuration activities in the
process of actively shaping and reactively
adapting to the shifting contextual
environment. These issue and problembased
suppositions lead to the following
research hypotheses:
H5: Program governance provides
the control framework through
which the objectives are delivered
while remaining within corporate
visibility and control.
H6: A continual process of realignment
ensures that programs and projects
remain linked to corporate objectives
and environmental influences.
February 2012 ■ Project Management Journal ■ DOI: 10.1002/pmj 25
Innovation and Learning
Organizational learning is essentially
about an organization increasing its
ability to explore opportunities and
undertake effective action (Carlile,
2004). This may lead to far-reaching
changes and the formulation of new
organizational strategies. Learning and
continuous improvement is attributed
as the highest level of management
maturity. There must be defined roles,
functions, and procedures for learning
to become organizational (Lipshitz,
Popper, & Friedman, 2002). The various
bodies of knowledge incorporate the
need to learn from projects. Learning
through programs and projects is thus a
subset of organizational learning
(Brady & Davis, 2004). This will need
the systemic integration of data, information,
and knowledge. Sense (2007)
suggested that projects are an embryonic
structure that develops a new
community of practice through situational
learning and negotiation of
emergent opportunities. This provides
an experientially constructed temporary
system for solving problems and
knowledge transfer.
Learning from both success and
failure will thus be essential in a program
of projects. Programs provide
enhanced opportunity for learning
through the batching of related projects,
interdependency, and the
increased socialization from resource
sharing. Project management practices
will differ between industry sectors and
organizations, providing a potential
gulf in language and learning that must
be considered. The ill-defined requirements
of some programs will challenge
the linear stage-gate process of innovation
through inherent iteration (Smith &
Winter, 2005). Programs may require
differing distinctive leadership styles at
different junctures in the program life
cycle. Multidisciplinary learning will
emerge through a process of activity
and alignment decision making (Fong,
2003). The following research hypothesis
captures this strong learning relationship:
H7: Within the life cycle, program
and project processes embrace
change and realignment by using
learning to create innovation and
improvement opportunities to support
the successful delivery of the
program.
Method
The study was designed to bring to the
forefront critical issues that supported
the advancement of alignment theory
for program management. The need for
a rigorous theory-building process led
to the selection of a mixed-method
study design that involved both statistical
and text analysis.
Sample and Data Collection
The participants were selected from
a population profile that was established
from the Rethinking Project
Management Network, accredited project
management training and consulting
organizations, the e-mail list for the
Association for Project Management
Programme Management Specific
Interest Group, and specific e-mail
groups from a UK-wide service-led
organization. The Rethinking Project
Management Network was a UK government-funded
research initiative that
aimed to develop, extend, and enrich
mainstream project management ideas
in relation to developing practice. This
included leading academics and practitioners
in the field of project management.
The program concluded with five
directions established for future
research, which were outlined in a 2006
special issue of the International Journal
of Project Management. These themes
complemented this study through a
growing emphasis on programs and
managing collections of projects.
Association for Project Management
Accredited Project Management training
providers specialize in the delivery of
project-based training that is aligned to
APM qualifications. These companies
influence developing practice and project
professionals through their consultancy
practice. The APM Programme
Management Specific Interest Group
aims to be the leading internationally
recognized group for program management.
This study contributed to their
mission to promote the science and discipline
of program management. The
specific e-mail groups from the serviceled
organization consisted of those
involved in both transformational
change and information system programs.
All respondents were further
classified by their role within programs
and the context of their practice-related
experience. This targeted professional
groups who were classified as consultants
or experts, senior managers that are
actively involved in programs, program
managers, project managers, and those
holding project-related job functions.
This ensured the respondents were representative,
knowledgeable, and appropriate
to the study.
Data collection was by means of a
standardized questionnaire and semistructured
interviews. The quantitative
data was collected through a multimode
administration method primarily
from an e-mail-driven strategy supported
by a web survey. The web survey
mirrored the self-administered questionnaire.
This did not include advance
notification, as it was administered
through the Programme Management
Significant Influence Group monthly
newsletter. The e-mail-driven list consisted
of 264 subjects, while the web
survey provided 2,005 additional subjects.
Six volunteer informants were
randomly selected for interviews that
classified themselves as either program
consultants or experts and program
managers to ensure absolute knowledge
of the dynamics of the study model.
Each interview volunteer further represented
a different program management
context—information technology,
organizational change, new product
development, civil engineering, and
someone who had diverse experience
with different types of programs. These
interviews further explored the causal
relationships of the research model to
understand how participants actually
constructed theory and determined
26 February 2012 ■ Project Management Journal ■ DOI: 10.1002/pmj
Successful Programs Wanted
PAPERS
emerging phenomena in relation to the
study. This provided the opportunity for
the interviewee to introduce issues that
they conceived as important. The quantitative
and qualitative phases were
integrated by an iterative process, with
each influencing the other accordingly.
Measures
The questionnaire design was structured
to gather information and understanding
about organizational, environmental,
program, and project management
alignment. Respondents were requested
to respond based on their practicerelated
experience and expertise. This
required questions to be answered by
respondent experience and insight.
Closed-ended questions were used to
classify the professional orientation
(program management consultant/
expert; senior manager involved in programs;
program manager/director;
project manager involved in programs;
other—please specify) and program
management context of participants
(IT/software development; organizational
or management change; new
product development; construction/
civil engineering; generalized). These
differing characteristics and attributes
were coded by a five-category nominal
level of measurement. The model variables
and hypotheses were included in
the questionnaire as closed-ended
questions to validate the respondent’s
opinion of the statements. These were
measured on a four-point Likert scale
ranging from 1 (never) to 4 (always).
This was a deliberate decision in
removing the availability of a middle
alternative to ensure respondents indicated
the direction of their viewpoint.
The model variables and hypotheses
needed to be constructed into a
structural equation model to test and
confirm proposed relationships. This
involved translating the proposed
alignment theory into a structural
model. Learning and innovation, program
governance, and systemic factors
were classified as independent variables
in the study model. Successful
delivery was the dependent variable,
which was hypothesized to be influenced
by the independent variables
and intervening variables of environment,
continuous alignment, and corporate
strategy. The general sample
characteristics and size of the study
data determined the measurement and
interpretation of the statistical analysis.
The selection of structural equation
modeling ensured that measurement
error was taken into account in the procedures
(Schumacker & Lomax, 2004).
The model was identified by including
an error parameter for each variable
that fixed the factor loading to 1.
This multivariate statistical approach
combined the application of both
path and confirmatory factor models in
analyzing the causal model and study
data. Analysis of Moment Structures
(AMOS) is an add-on module for SPSS
that allows structural equation models
to be specified by using a simple dragand-drop
drawing tool to test proposed
causal relationships. The specified
structural model follows standard
drawing conventions to show the causeand-effect
relationships (Figure 2). The
variables measured in the study model
are depicted by enclosed rectangles.
Unobserved variables or model measurement
errors are denoted by circles.
This ensured that measurement errors
were explicitly considered in statistical
calculations. Straight-line singleheaded
arrows from one variable to
another indicate a direct influence
from that variable to the other. Zero rating
values would indicate that there
was no direct impact. The absence of a
straight-line single-headed arrow
between variables indicates that there
are no direct effects hypothesized.
Double-headed curved lines between
variables indicate a covariance. These
coefficients detect and measure the
relationship between two variables
through an index range, with zero indicating
no relation and 1.0 suggesting a
perfect relationship. The strength and
impact of each model parameter estimate
is illustrated by the numerical output
beside an arrowhead or variable in
the study model. Problems in specifying
Study Variable
Study Variable
Parameter value Error
indicating no relation
Parameter value
indicating a perfect relationship
1.0
0.0
Circle used to draw
the unobserved
variables/model
measurement error
Study Variable
Single-headed arrows
used to draw the causeeffect
relationship
between variables
Rectangle used to draw
the study variables
Double-headed arrow
used to draw the
covariance between
variables
Error
Error
Figure 2: Structural equation model drawing conventions.
Note. Model measurement error factor loading fixed to 1.0.
February 2012 ■ Project Management Journal ■ DOI: 10.1002/pmj 27
the drawn model structure are highlighted
by an error message or by the
AMOS text output not calculating.
Measurement and study model modification
involved identification with and
linking of qualitative data.
Reliability and Validity
The mixed-method study design combined
the different criteria used for
validity and reliability in both qualitative
and quantitative research. There
were a number of prerequisites that
needed to be satisfied before a multivariate
analysis could be undertaken.
Exploratory data analysis validated the
appropriateness of statistical methods
and techniques (Table 1). Kurtosis outputs
established that outliers in the
data sample were not problematic, as
this can provide misleading values with
statistical methods and techniques. The
presence of outliers would affect structural
equation model fit significance tests.
The Pearson’s correlation coefficient
and two-tailed significance-level tests
indicated that all variables were significantly
correlated. Correlation is a
measure of linear dependence between
variables. The stronger the correlations,
the more power structural equation
modeling has to detect an incorrect
fitting model. Reliability testing
provided an indication of the general
quality of the study data. The
Cronbach’s alpha statistic (1951) was
adopted as a measure of the internal
consistency and reliability of the study
data. This statistical output is from any
value less than or equal to 1 and provides
an unbiased estimate of the ability
of the data to be generalized. The
value of above 0.70 is recommended,
although values exceeding 0.80 are
desirable for higher reliability test studies.
The Cronbach’s alpha statistic output
of 0.831 thus validated that the
internal consistency reliability of this
analysis was good. The exploratory
data analysis procedure concluded that
the data were approximately multivariate
normal in distribution and suitable
for application to structural equation
modeling. This classification was
essential, as small deviations from
multivariate normality can lead to a
large difference in the Chi-square test.
AMOS labels this test global model fit
(CMIN). This label will now be adopted
throughout the remainder of this article
when referring to the Chi-square test.
Multiple pilot-testing methods
were used to refine the standardized
questionnaire and qualitative interview
structure to validate that the designs
were clear, simple, and elicited the
appropriate responses. Missing data
were eliminated with participants of
the online survey by adopting a computer
questionnaire, which needed a
response before completing the survey.
The quality and completeness of the
returned e-mail questionnaires was
extremely high. The only incidence of
missing data was clarified by a follow-up
0 2 3 4 5 6 7 Mean SD
Strategy Correlation 0.407** 0.402** 0.366** 0.360** 0.441** 0.386** 2.89 0.695
Sig. (two-tailed) 0.000 0.000 0.000 0.000 0.000 0.000
Environment Correlation 0.297** 0.309** 0.301** 0.403** 0.336** 3.23 0.673
Sig. (two-tailed) 0.002 0.001 0.001 0.000 0.000
Successful Delivery Correlation 0.503** 0.485** 0.442** 0.454** 2.93 0.726
Sig. (two-tailed) 0.000 0.000 0.000 0.000
Systemic Factors Correlation 0.490** 0.453** 0.406** 2.89 0.782
Sig. (two-tailed) 0.000 0.000 0.000
Governance Correlation 0.531** 0.354** 2.89 0.758
Sig. (two-tailed) 0.000 0.000
Continuous Alignment Correlation 0.522** 2.70 0.761
Sig. (two-tailed) 0.000
Learning and Innovation Correlation 2.50 0.751
Sig. (two-tailed)
Cronbach’s Alpha Reliability Statistics Based on Standardized Items 0.831
**Pearson’s correlation is significant at the 0.01 level (two-tailed).
Table 1: Pearson’s correlation and Cronbach’s alpha reliability tests.
28 February 2012 ■ Project Management Journal ■ DOI: 10.1002/pmj
Successful Programs Wanted
PAPERS
e-mail. Structural equation modeling
used interval data from the questionnaire
for testing purposes. Randomized
selection was adopted for the interview
informants to address the amount of
diversity bias evident from the quantitative
data phase. This ensured that a
representative sample could be generalized
across the wider program management
community. The semistructured
interviews focused on rigorous subjectivity
by respondent validation of the
quantitative research findings. This
involved asking questions such as
“Does the model structure make
sense?” and “Are the relationship paths
representative of your experience?”
Respondents were then encouraged to
justify opinions and provide alternative
explanations. This facilitated understanding
and interpretation of relationships
between study variables.
Structural equation modeling validation
essentially involved statistical
and theoretical Assessment of model fit.
The selection of appropriate statistical
fit measures considered issues such as
sample size and overall complexity of
the model. These tests are based on the
assumption that the correct and complete
relevant data have been modelled.
The study sample size adopted a
lower limit of 100 respondents as proposed
by some authors (Chen, Bollan,
Paxton, Curran, & Kirby, 2001; Gagne &
Hancock, 2006). Unobserved error variables
were included to explicitly depict
the unreliability of measurement in the
model. This allows the structural relations
between variables to be accurately
estimated. Criteria for study model fit
and testing were determined from the
size of the study data and sample multivariate
characteristics. Model validation
was determined through global
model fit (CMIN), root mean square
error of approximation (RMSEA), goodness-of-fit
index (GFI), and other normalized
model fit measures. CMIN of
zero illustrates a perfect model fit,
although it is generally accepted that
this is impractical in reality. For reasonable
sample sizes, a difference enough
to produce a CMIN in the region of the
degrees of freedom (DF) would suggest
a close model fit. RMSEA was adopted
because it provides an output that
does not penalize model complexity.
Modification index results following a
specification search illustrated the reliability
of the relation paths drawn in a
specified structural equation model.
Data Analysis
Study model modification followed an
iterative process between the structural
equation modeling statistical analysis
(Arbuckle, 2007) and model structure
theoretical validation through semistructured
interviews (Silverman,
2006). The best-fitting model that was
also consistent with theory was selected.
Structural equation models combine
measurement models (e.g., reliability
tests) with structural models (e.g.,
regression weights). This is based on
data-driven model fitting. AMOS provided
automated modification indices
as an alternative to manual model
building and model trimming. Model fit
was first measured on the closeness of
the study-sample variance-covariance
matrix. Modification needed to satisfy
these measurement criteria and also
meet the need for theoretical meaningfulness.
Statistical model cross-validation
through semistructured interviews
established problems in the structure of
the model. This resulted in the model
structure being redefined with the variables
arranged differently, affecting
path relations (Figure 1).
The model estimates were then
recalculated. This involved the AMOS
automated modification function, indicating
that all the model correlations
and direct relations from the independent
variables to the intervening variables
were optional. This supported the
potential of further model refinement by
removing poorly weighted relationship
parameters following the specification
search. This provided a multiplicity of
other models that fitted the data and
identified potential adjustments that
could be made to the model. The AMOS
text output indicated the estimated
change and reliability in the new path
coefficient for each alternative model
proposed. Improvement in model fit
was measured by a reduction in CMIN.
The statistical model output indicated
that overall model fit was adequate and
could not be further statistically
improved (Table 2 and Figure 2). The
model structure was then further
refined to provide a theoretically validated
model. This introduced a new
variable that could not be statistically
measured.
Results
The e-mail survey provided a response
rate of 31% (81). The web survey had a
larger population but significantly
lower response rate of 1% (29), reducing
the overall study response rate to 5 percent
(110). This provided some concern
regarding statistical significance.
Various studies have concluded that a
lower response rate does not necessarily
differentiate reliably between accurate
and inaccurate data (Keeter,
Kennedy, Dimock, Best, & Craighill,
2006; Visser, Krosnick, Marquette, &
Curtin, 1996). The findings of these
studies found that much lower
response rates were only minimally less
accurate. The 29 web survey respondents
were included, as they provided a
rich source of expert data. More importantly,
their responses also enhanced
the sample size to improve statistical
model significance. Survey respondents
were reasonably dispersed over
four program management practicerelated
groups—experts (25%), senior
managers (27%), program managers
(21%), and project-related roles (27%).
An Assessment of Hoelter’s (1983) critical
N from the model output suggests
that the largest model sample size
required at a significance level of 0.05 is
a threshold of 37. This provided reasonable
confidence of sample-size adequacy
(N 110) against concerns of statistical
significance.
The statistically modified model
is illustrated in Figure 3 following
February 2012 ■ Project Management Journal ■ DOI: 10.1002/pmj 29
Parameter Standardized
Description of Path Estimate SE CR P Estimate
Environment d Governance 0.134 0.091 1.473 0.141 0.151
Environment d Systems 0.124 0.090 1.378 0.168 0.144
Environment d Learning 0.201 0.087 2.293 0.022 0.224
Alignment d Systems 0.122 0.084 1.457 0.145 0.125
Alignment d Environment 0.191 0.088 2.171 0.030 0.169
Alignmentd Governance 0.312 0.084 3.705 *** 0.311
Alignment d Learning 0.308 0.082 3.741 *** 0.304
Strategy d Environment 0.233 0.093 2.515 0.012 0.226
Strategy d Alignment 0.160 0.099 1.621 0.105 0.175
Strategy d Governance 0.086 0.092 0.932 0.352 0.094
Strategy d Systems 0.101 0.087 1.167 0.243 0.114
Strategy d Learning 0.129 0.090 1.436 0.151 0.140
Successful delivery d Environment 0.084 0.102 0.826 0.409 0.078
Successful delivery d Alignment 0.293 0.091 3.201 0.001 0.307
Successful delivery d Strategy 0.246 0.100 2.454 0.014 0.236
Table 2: Regression weights and standardized regression weights.
Program governance
Systemic factors
Learning and innovation
Environment
Continuous alignment
Corporate strategy
0.57
e1
1
0.61
e2
1
0.56
e3
1
Successful delivery
0.37 e4
0.32
e5
e6 0.34
1
1
1
0.12
0.23
0.19
0.16
0.08
0.29
0.25
0.13
0.31
0.12
0.10
0.20
0.13
0.31
e7 0.39
1
0.24
0.29
0.20
Program Management Alignment Theory
Chi-square 23.156 (3 df)
p 0.000
0.09
Figure 3: Statistically modified structural equation model.
30 February 2012 ■ Project Management Journal ■ DOI: 10.1002/pmj
Successful Programs Wanted
PAPERS
measurement model reliability testing.
An analysis of the number of distinct
parameters (NPAR 25) and the number
of degrees of freedom (DF 3)
determined that the model was complex.
This outcome will provide conflicting
results with some model fit
measures that attempt to balance parsimony
or simplicity against model
complexity. RMSEA provides an output
that does not penalize model complexity.
The modified model has an output
of 0.248 that exceeds the reasonable
error approximation of 0.1 suggested by
Browne and Cudeck (1989). This suggests
poor model fit, although RMSEA
can be misleading when the minimum
sample discrepancy function (CMIN/
DF) is small and sample size is not large
( 200). Figure 3 illustrates the modified
model, which provides an adequate
CMIN of 23.156, with the minimum
sample discrepancy function
being satisfactory (CMIN/DF 7.719).
The minimum sample discrepancy
function (CMIN/DF) attempts to make
CMIN less dependent on sample size.
The minimum sample discrepancy
function (CMIN/DF) should be close to
1 for perfect fitting models. CMIN for
models involving 75 to 200 cases are a
reasonable measure of model fitness.
However, complex models are more
likely to have a good CMIN. The p values
for CMIN for sample sizes less than
200 are also useful as this measure is a
function of sample size (p 0.000).
Other measures of model fit were
considered in addition to the CMIN
standard. The objective was to find the
most parsimonious model, which was
well fitting by a selection of GFI tests.
Comparison against a baseline model
allows a further Assessment against the
saturated (e.g., guaranteed to fit any set
of data perfectly) and independence
(e.g., severely constrained to provide a
poor fit) models through a number of
indices. These fitness measures are normalized
to fall between 0 and 1, with an
output close to 1 indicating a good fit.
Jöreskog and Sörbom’s (1984) GFI supports
this outcome of good model fit
(GFI 0.948). The Bentler-Bonett
(1980) normed fit index (NFI) further
suggests a good model fit (NFI 0.900),
although results less than this value
indicate that substantial improvement
is required. Bollen’s (1989) incremental
fit index (IFI) and Bentler and Weeks’
(1980) comparative fit index (CFI) both
indicated a very good model fit (IFI
0.912 and CFI 0.904).
Parsimony-adjusted measures provide
an estimate of the required parameters
to achieve a specific level of model
fit. This rewards parsimonious models
with relatively few parameters to estimate
in relation to the number of variables
and relationships in the model.
Some researchers oppose penalizing
models with more parameters. There is
no commonly agreed-upon cut-off
value for an acceptable model,
although some authors use above 0.50
and others 0.60 (Preacher, 2006). The
James, Mulaik, and Brett (1982) parsimonious
normed index (PNFI 0.129)
and parsimonious comparative fit
(PCFI 0.129) suggest poor model fit.
These results are influenced by model
complexity. This measure of fit thus
offers little in contributing to the selection
of the best-fitting model other than
for assessment after consideration of
goodness-of-fit measures among proposed
competing models.
Path correlation coefficients in the
model were interpreted after a well-fitting
model had been accepted.
Regression weight significance tests for
each parameter relationship in the
structural model are given in Table 2.
The first column is labelled Parameter
Estimate (PE), with the next column
indicating the Standard Error (SE) for
each parameter. The Critical Ratio (CR)
is the parameter estimate divided by
the SE. Any critical ratio that exceeds
1.96 in size would be identified as significant,
using a significance level of
0.05. The SE is only an approximation
and therefore may not be the best
approach in determining parameter
significance, and caution was used in
its interpretation. Individual parameter
values can also be affected by sample
size, with Anderson (1984) recommending
that sample sizes exceed 150
for reasonable and stable parameter
relationship estimates. Structural path
coefficients are the effect sizes calculated
by AMOS. These are displayed above
their respective arrows in the structural
drawing diagram (0.8: high, 0.5: moderate,
less than 0.2: low).
All correlation coefficients for the
variables that represent the critical program
elements are positive in direction
and have moderate strength (0.20 to
0.29). The CRs for each of these are statistically
significant (CR 3.927, 4.595,
and 3.487). This indicates that there is a
closely defined relationship between
the critical program variables, suggesting
a finely balanced direct correlated
effect on the intervening variables. The
continuous alignment intervening variable
is directly influenced the greatest
from the collective strength of all the
independent variables (0.31, 0.12, and
0.31). Both the governance and learning
and innovation variables are
deemed statistically significant (CR
3.705 and 3.741, respectively), although
the systemic variable is insignificant
(CR 1.457, p 0.145). The dominant
independent program variable in relation
to strength impact is learning and
innovation (0.13, 0.31, and 0.20)
The relationship paths from the
environment variable to continuous
alignment and strategy are both statistically
significant (CR 2.171, p 0.030
and 2.515, p 0.012, respectively).
The environment variable has a direct
(CR 0.826, p 0.409) and indirect
effect on the successful delivery dependent
variable through both the continuous
alignment and corporate strategy
intervening variables. Continuous
alignment also has a direct (CR 3.201,
p 0.001) and indirect effect on the
successful delivery dependent variable
through corporate strategy. Continuous
alignment and corporate strategy have a
moderate direct individual effect but a
strong collective influence on successful
delivery (0.29 and 0.25, respectively).
February 2012 ■ Project Management Journal ■ DOI: 10.1002/pmj 31
The environment variable has a low
direct impact on successful delivery
(0.08) but moderately contributes indirectly
through two other intervening
variables (0.19 and 0.23). Table 3 further
summarizes the testing of hypothesized
relation pathways in the accepted
study model (CR 1.96, significant at
the p 0.05 level).
The findings of the statistical investigation
suggest that the independent
variables are predicators of successful
program delivery through the complex
interaction of the intervening variables.
The outcome of the analysis illustrates
that the model is complex, with relationships
among indicators precariously
balanced. The modified model offers
an empirical explanation of the critical
relationships involved for continuous
alignment and successful delivery. The
results of this mathematical maximization
procedure are sample-specific and
can only be generalized to the study
population. This provides a causal
model that articulates but does not
conclude causal assumptions. The
semistructured interviews further validated
the relationship and importance
of the statistical analysis impact
weighting for each parameter. An
underlying theme that emerged from
each interview was the need to make
more clearly explicit the activity of program
design. This provided a strong
justification for the importance and
inclusion of program design in the
study model. The inclusion and visibility
of this variable were further supported
by expanding the necessary dynamic
feedback from corporate strategy.
The model structure was revised
accordingly (Figure 4). Quantitative
data had already been gathered, so the
modified and theoretically validated
Path Coefficients Critical
Description of Relationship Path (Estimate) Ratio (CR) p-value Result
H1 Successful delivery d Strategy 0.246 (Moderate) 2.454 0.014 Significant
Design d Strategy No data No data No data Untested
H2 Alignment d Environment 0.191 (Low) 2.171 0.030 Significant
Strategy d Environment 0.233 (Moderate) 2.515 0.012 Significant
Successful delivery d Environment 0.084 (Low) 0.826 0.409 Insignificant
H3 Successful delivery d Strategy 0.246 (Moderate) 2.454 0.014 Significant
Successful delivery d Environment 0.084 (Low) 0.826 0.409 Insignificant
Successful delivery d Alignment 0.293 (Moderate) 3.201 0.001 Significant
H4 Environment d Systems 0.124 (Low) 1.378 0.168 Insignificant
Alignment d Systems 0.122 (Low) 1.457 0.145 Insignificant
Strategy d Systems 0.101 (Low) 1.167 0.243 Insignificant
H5 Environment d Governance 0.134 (Low) 1.473 0.141 Insignificant
Alignment d Governance 0.312 (Moderate) 3.705 *** Significant
Strategy d Governance 0.086 (Low) 0.932 0.352 Insignificant
H6 Strategy d Alignment 0.160 (Low) 1.621 0.105 Insignificant
Successful delivery d Alignment 0.293 (Moderate) 3.201 0.001 Significant
H7 Environment d Learning 0.201 (Moderate) 2.293 0.022 Significant
Alignment d Learning 0.308 (Moderate) 3.741 *** Significant
Strategy d Learning 0.129 (Low) 1.436 0.151 Insignificant
H8 Learning d Design No data No data No data Untested
Systems d Design No data No data No data Untested
Governance d Design No data No data No data Untested
Table 3:Tests of hypothesized relationship pathways (p-value 0.05).
32 February 2012 ■ Project Management Journal ■ DOI: 10.1002/pmj
Successful Programs Wanted
PAPERS
model could not be statistically tested
further.
Discussion
The study was designed to advance the
development of an alignment theory for
program management through a rigorous
theory-building process. Structural
equation modeling was selected as a
technique that was used to estimate,
analyze, and test the study model that
specified relationships among variables.
This allowed testing and validation
of already constructed theories
involving an Assessment of structure and
model fit. Semistructured interviews
were discovery-focused, which uncovered
contradictions and new ways of
thinking in the study model. This resulted
in the respecification of the structural
model variables and validation of
the accepted conceptual model. The
strength of structural coefficient paths
in the model needed assessment, as
goodness-of-fit measures do not provide
an absolute guarantee that each
particular part of the model fits well.
The structural model was evaluated by
modification indices that report the
improvement in fit that results from
adding or deleting an additional path to
the model. This introduced competing
models and the Assessment of individual
parameters. Modification indices results
suggested that model adjustments
would make no further improvements
to CMIN. Modifications also needed to
have substantive sense and theoretical
validation.
Model Assessment is one of the most
disputed and difficult issues connected
with structural modeling (Arbuckle,
2007). Structural equation models are
generally considered a good fit if the
value of the global model fit (CMIN
23.156, p 0.000) and badness of fit
index (RMSEA 0.248) test is adequate,
and at least one incremental fit index
(GFI 0.948) and one baseline fit measure
(NFI 0.900; IFI 0.912 and CFI
0.904) meet the predetermined criteria.
The study model satisfies and in some
cases exceeds this convention with the
exception of badness of fit index
(RMSEA). The minimum sample discrepancy
function (CMIN/DF) and
study sample size make RMSEA difficult
to interpret. This may be deceptive and
not necessarily indicate a poorly fitting
model. The structural model could be
considered adequate against prescribed
measures of fit providing a model that
conveys causal assumptions. The meaning
of “causal,” in this study, should be
interpreted with care, as structural equation
modeling does not confirm that
an accepted model produces validated
causal conclusions. The research
framework provided convergence and
Program governance
Systemic factors
Learning and innovation
Environment
Continuous alignment
Corporate strategy
e1
1
e2
1
e3
1
Successful delivery
e4
e5
e6
1
1
e7
1
1
Program design
e8
1
Figure 4: Statistically modified and theoretically validated model.
February 2012 ■ Project Management Journal ■ DOI: 10.1002/pmj 33
corroboration of findings resulting in
the statistically modified and theoretically
validated model. This was responsive
to changes in the unfolding of the
study. The study model was only partly
statistically tested due to insufficient
data, which revealed weaknesses in the
research design and methodology. These
were addressed to validate the structural
model and hypotheses before administrating
the test instrument to the wider
study population.
The qualitative research aspects of
the study design offered a richness and
depth of understanding unlikely to be
achieved with a stand-alone quantitative
approach. Some interesting underlying
issues, most notably relationships
among strategy, learning, and program
design, were exposed. There was reasonably
clear demonstration that
strategic vision was being translated
into programs. Program life-cycle phases
of design and transition were found
to be particularly problematic in practice
by interview respondents. This
emphasized that strategy was a rather
ambiguous phenomenon in practice.
The creation of strategy was seen to be
an easier process than implementation.
This reinforced that organizations were
complex systems. Strategic management
was principally perceived by
interview respondents as providing
required organizational direction in
dealing with success and failure from a
business context. There was recognition
that program success would not be
guaranteed even when a clearly articulated
business strategy was apparent
from the outset. Strategy was generally
seen to be emergent, affecting the program
as it moves down the organization.
There was recognition that
absolute organizational alignment may
be difficult and unrealistic as a consequence.
Interview respondents confirmed
that this made it necessary to
view programs as dynamic and evolving
structures.
The front-end of programs were
identified to be frequently ill defined,
with low levels of formation constraining
the early definition of success. This suggested
that program design and structure
was a dynamic process that needed
to be continually assessed from program
formation through to program
close. Interview respondents emphasized
that programs of projects should
continually use the best knowledge
obtainable to inform a systems view. In
their view, this was necessary to align
practice with stated goals. Other viewpoints
emphasized the need to move
away from the linear, milestone-based
processes of some business activities,
because integration was seen to go
hand-in-hand with experienced complexity.
The different practice-related
views of the interview respondents
demonstrated that in contextual detail
every program will be unique. This further
confirmed the presence of a high
level of execution-complexity with a
high level of organizational and environmental
complexity, since a wide
variety of variables need to be considered.
There was general consensus that
many organizations were not designed
for project management. Program
design was stated to be much more
complex than a static process. Practical
challenges were identified when the
host organization did not have the requisite
project management capability.
This further emphasized the highly
complex nature of effectively designing
programs of projects. Interview respondents
stated that program design was a
significantly important pre-implementation
activity. This led to its greater
prominence in the study model, as
inappropriate setup was seen as something
that would negatively impact
implementation and management of
the program. Interview respondents
suggested that programs need to be
designed to acknowledge the complexity
and emergent details of the program.
The study further exposed that program
culture was often underscored by
learning and innovation in responding
to inherent program complexity. This
strong underlying profile for learning
and knowledge-sharing practices was
occasionally underrated in the interview
phase of the study. There was
some evidence of systemic learning,
driven by a project management
approach, with people who had similar
levels of knowledge. Nonetheless, there
was a general tendency for learning to
be classified as a low priority. However,
the statistical model findings strongly
suggest that learning and innovation in
programs are fundamentally important
for success. The different types of learning
that emerged related less to structured
approaches and more to satisficing
and improvisational outcomes. There
was some recognition that increasing
program complexity will make organizational
learning a primary measure of
program management effectiveness.
Examples were given where program
learning had been effective but had
not been transferred to the wider
organization.
Conclusions
The selected model provides a conceptual
framework to support the understanding
of programs. The strength of
the model is in the illustration of the
systemic characteristics that will make
programs particularly challenging to
understand and manage. The hypothesized
statements can be conceived to be
a plausible set of interconnected narratives
that describe the relationships that
support the conceptualization of the
study model. This needs to include
the program design variable to allow
recursive feedback in the model. The
study model does highlight the importance
of effective program design and
transition management. The model
suggests that successful program delivery
will be an elusive concept in practice
that requires flexibility for strategic and
environmental adaptation.
The findings of the study conclude
that programs have both deliberate and
emergent strategies, requiring program
design and management to be organized
as complex adaptive systems. This
integrates theoretical concepts from
34 February 2012 ■ Project Management Journal ■ DOI: 10.1002/pmj
Successful Programs Wanted
PAPERS
systems thinking, organizational, and
project management theory. Complex
adaptive systems are often illustrated
by unclear strategies from the outset
and influential constant changes.
Interviewee knowledge of the concept
of complex adaptive systems was limited,
although descriptions and viewpoints
supported this system dynamic. Senior
managers and program managers need
to recognize the importance of all the
study model variables, how they align,
and the program capability required in
successfully delivering business strategy.
The adoption of program management
should thus be a well-considered
strategic decision.
This study contributes to program
management by the understanding of
complex adaptive systems and its application
to the project management field
in many ways. First, the study identified
several high-level variables that should
be considered to ensure the successful
alignment of programs. These variables
can be used to analyze project and program
failures contributing to organizational
learning. Second, the exploration
of these variables has led to the development
of a model that reveals an interaction
structure depicting program formation
and implementation in practice.
Finally, the results of validating the
study model have identified some of
the managerial problems that should be
considered when designing and managing
programs of projects.
Recommendations for Further
Research
Based on the literature review, study
results and emergent issues identified in
the study, there were some insights that
provide direction for future research. The
emergent reality of program management
requires a clearer understanding
on the impact of structured, incremental,
and contextual learning. Learning
within programs is also an identified gap
within the published literature. The
study also identified a significant need
to identify the effective practices and
approaches that support successful
program design. Research in this field
should consider how organizations
effectively apply an adaptive posture to
environmental factors.
Limitations
The study has a number of limitations.
Structural equation modeling cannot
test directionality in relationships. The
directions of arrows in the accepted
structural equation model represent the
researcher’s hypotheses of causality
within a program management system.
This is limited to the choice of selected
variables and hypothesized relation
pathways. Increasing the sample size
would improve the statistical model
convergence and parameter estimate
accuracy, providing greater confidence
in the model outcome. This may directly
affect the model path regression
weightings. The findings of the statistical
model are also influenced by the
researcher’s organization, which was
undergoing a significant organizationwide
change program. This potential
bias was adequately accounted for in
the selection strategy for the semistructured
interviews. Change programs are
vision-led and emergent. This puts
greater emphasis on culture change and
organizational readiness, which may
have enhanced the model path relationship
regression weightings for learning.
Every program classification will have
an inherent need to remain aligned with
business strategy, regardless of issues
relating to program context. ■
References
Anderson, T. W. (1984). An introduction
to multivariate statistical analysis. New
York, NY: Wiley.
Arbuckle, J. L. (2007). AMOS™ 16.0
user’s guide. Chicago, IL: SPSS, Inc.
Association for Project Management.
(2007a). Programme management SIG:
APM introduction to programme management.
Retrieved from http://
www.e-programme.com/progm.thm
Association for Project Management.
(2007b). Governance SIG: Co-directing
change: A guide to the governance of
multi-owned projects. Retrieved from
https://monkessays.com/write-my-essay/apm.org.uk
Beer, M., Voelpel, S. C., Leibold, M., &
Tekie, E. B. (2005). Strategic management
as organizational learning:
Developing fit and alignment through
a disciplined process. Long Range
Planning, 38(5), 445–465.
Bentler, P. M., & Bonett, D. G. (1980).
Significance tests and goodness of fit
in the analysis of covariance structures.
Psychological Bulletin, 88(3),
588–606.
Bentler, P. M., & Weeks, D. G. (1980).
Linear structural equations with latent
variables. Psychometrika, 45(3),
289–308.
Bollen, K. A. (1989). A new incremental
fit index for general structural equation
models. Sociological Methods and
Research, 17(3), 303–316.
Brady, T., & Davis, A. (2004). Building
project capabilities: From exploratory
to exploitative learning. Organization
Studies, 25(9), 1601–1621.
Browne, M. W., & Cudeck, R. (1989).
Single sample cross-validation indices
for covariance structures. Multivariate
Behavioral Research, 24, 445–455.
Bruch, H., Gerber, P., & Maier, V.
(2005). Strategic change decisions:
Doing the right change right. Journal
of Change Management, 5(1), 97–107.
Burdett, J. O. (1994). The magic of
alignment. Management Decision,
32(2), 59–63.
Carlile, P. (2004). Transferring, translating,
and transforming: An integrative
framework for managing knowledge
across boundaries. Organization
Science, 15(5), 555–568.
Carnall, C. (2007). Managing change in
organizations (5th ed.). Harlow, Essex,
UK: Pearson Education.
Chen, F., Bollan, K. A., Paxton, P.,
Curran, P. J., & Kirby, J. B. (2001).
Improper solutions in structural equation
models: Causes, consequences
and strategies. Sociological Methods &
Research, 29(4), 468–508.
February 2012 ■ Project Management Journal ■ DOI: 10.1002/pmj 35
Chorn, N. H. (1991). The alignment
theory: Creating strategic fit.
Management Decision, 29(1), 20–24.
Cronbach, L. J. (1951). Coefficient
alpha and the internal structure of
tests. Psychometrika, 16(3), 297–334.
De Wit, B., & Meyer, R. (2005). Strategy
synthesis: Resolving strategy paradoxes
to create competitive advantage (2nd
ed.). London, UK: Thompson Learning.
Dietrich, P., & Lehtonen, P. (2005).
Successful management of strategic
intentions through multiple projects—
Reflections from an empirical study.
International Journal of Project
Management, 23(5), 386–391.
Elizabeth, M., & Ysanne, C. (2007).
Strategy as order emerging from chaos:
A public sector experience. Long Range
Planning, 40(6), 574–593.
Engwall, M. (2003). No project is an
island: Linking projects to history and
context. Research Policy, 32(5), 789–808.
Engwall, M., & Jerbrant, A. (2003). The
resource allocation syndrome: The
prime challenge of multi-project management.
International Journal of
Project Management, 21(6), 403–409.
Fong, P. S. W. (2003). Knowledge creation
in multidisciplinary project
teams: An empirical study of the
processes and their dynamic interrelationships.
International Journal of
Project Management, 21(7), 479–486.
Ford, J. D., & Ford, L. W. (2009).
Decoding resistance to change—
Strong leaders can hear and learn from
their critics. Harvard Business Review,
87(4), 99–103.
Gaddie, S. (2003).Enterprise programme
management: Connecting strategic
planning to project delivery. Journal of
Facilities Management, 2(2), 177–189.
Gagne, G., & Hancock, G. R. (2006).
Measurement model quality, sample
size, and solution propriety in confirmatory
factor models. Multivariate
Behavioral Research, 41(1), 65–83.
Hoelter, J. W. (1983). The analysis of
covariance structures: Goodness-of-fit
indices. Sociological Methods and
Research, 11(3), 325–344.
Hrebiniak, L. G. (2006). Obstacles to
effective strategy implementation.
Organizational Dynamics, 35(1), 12–31.
Hyvari, I. (2006). Success of projects in
different organizational conditions.
Project Management Journal, 37(4),
31–41.
Ivory, C., & Alderman, N. (2005). Can
project management learn anything
from studies of failure in complex systems?
Project Management Journal,
36(3), 5–16.
James, L. R., Mulaik, S. A., & Brett, J. M.
(1982).Causal analysis: Assumptions,
models, and data. Beverly Hills, CA: Sage.
Jöreskog, K. G., & Sörbom, D. (1984).
LISREL-VI user’s guide (3rd ed.).
Mooresville: IN: Scientific Software.
Kash, D. E., & Rycoft, R. W. (2000).
Patterns of innovating complex technologies:
A framework for adaptive
network strategies. Research Policy, 29,
819–831.
Keeter, S., Kennedy, C., Dimock, M.,
Best, J., & Craighill, P. (2006). Gauging
the impact of growing nonresponse on
estimates from a national RDD telephone
survey. Public Opinion
Quarterly, 70(5), 759–779.
Kotter, J. P. (1995). Leading change:
Why transformation efforts fail.
Harvard Business Review, 73(2), 59–67.
Kotter, J. P., & Schlesinger, L. A. (2008).
Choosing strategies for change.
Harvard Business Review, 86(7/8),
130–139.
Lehtonen, P., & Martinsuo, M. (2009).
Integrating the change program with
the parent organization. International
Journal of Project Management, 27(2),
154–165.
Lindkvist, L. (2008). Project organization:
Exploring its adaptation properties.
International Journal of Project
Management, 26(1), 13–20.
Lipshitz, R., Popper, M., & Friedman, V.
(2002). A multifaceted model of organizational
learning. Journal of Applied
Behavioral Science, 38(1), 78–98.
Lycett, M., Rassau, A., & Danson, J.
(2004). Programme management: A
critical review. International Journal of
Project Management, 22(4), 289–299.
Maylor, H., Brady, T., Cooke-Davies, T.,
& Hodgson, D. (2006). From projectification
to programmification.
International Journal of Project
Management, 24(8), 663–674.
Meyer, A. D., Loch, C. H., & Pich, M. T.
(2002). Managing project uncertainty:
From variation to chaos. MIT Sloan
Management Review, 43(2), 60–67.
Middleton, P., & Harper, K. (2004).
Organizational alignment: A precondition
for information systems success?
Journal of Change Management, 4(4),
327–333.
Mintzberg, H. (1994). The fall and rise
of strategic planning. Harvard Business
Review, 72(1), 107–114.
Mintzberg, H., Ahlstrand, B., &
Lampel, J. (1998). Strategy safari: The
complete guide through the wilds of
strategic management. Harlow, Essex,
UK: Pearson Education.
Mintzberg, H., & Waters, J. A. (1985).
Of strategies, deliberate and emergent.
Strategic Management Journal, 6(3),
257–272.
Morris, P. W. G., Crawford, L., Hodgson,
D., Shepherd, M. M., & Thomas, J.
(2006). Exploring the role of formal
bodies of knowledge in defining a profession:
The case of project management.
International Journal of Project
Management, 24(8), 710–721.
Morris, P. W. G., & Jamieson, A. (2005).
Moving from corporate strategy to
project strategy. Project Management
Journal, 36(4), 5–18.
Morris, P. W. G., & Pinto, J. K. (2007).
The Wiley guide to project, program &
portfolio management. Hoboken, NJ:
Wiley.
Office of Government Commerce
(OGC). (2007). Managing successful
programmes (3rd ed.). London: The
Stationery Office.
Office of Government Commerce (OGC).
(2008). OGC prioritization categories.
36 February 2012 ■ Project Management Journal ■ DOI: 10.1002/pmj
Successful Programs Wanted
PAPERS
Retrieved from https://monkessays.com/write-my-essay/ogc
.gov.uk/documents/
Prioritisation_Categories.pdf
Pellegrinelli, S. (1997). Programme
management: Organizing project
based change. International Journal of
Project Management, 15(3), 141–149.
Pellegrinelli, S. (2002). Shaping context:
The role and challenge for programmes.
International Journal of
Project Management, 20(3), 229–233.
Pellegrinelli, S., Partington, D.,
Hemingway, C., Mohdzain, Z., & Shah,
M. (2007). The importance of context
in programme management: An
empirical review of programme practices.
International Journal of Project
Management, 25(1), 41–55.
Pinto, J., & Slevin, D. (1988). Critical
success factors across the project life
cycle. Project Management Journal,
19(3), 72–84.
Pollack, J. (2007). The changing paradigms
of project management.
International Journal of Project
Management, 25(3), 266–274.
Pourdehnad, J. (2007). Synthetic (integrative)
project management: An idea
whose time has come. Business
Strategy Series, 8(6), 426–434.
Preacher, K. J. (2006). Quantifying parsimony
in structural equation modeling.
Multivariate Behavioral Research,
41(3), 227–259.
Project Management Institute. (2009).
The standard for program
management—Global standard (2nd
ed.). Newtown Square, PA: Author.
Rybakov, L. A. (2001). Environment
and complexity of organizations.
Emergence, 3(4), 83–94.
Rycroft, R. W., & Kash, D. E. (2004).
Self-organizing innovation networks:
Implications for globalization.
Technovation, 24(3), 187–197.
Scherpereel, C. M. (2006). Alignment:
The duality of decision problems.
Management Decision, 44(9),
1258–1276.
Schumacker, E. R., & Lomax, G. R.
(2004). A beginner’s guide to structural
equation modeling (2nd ed.). Mahwah,
NJ: Lawrence Erlbaum Associates.
Sense, A. J. (2007). Structuring the project
environment for learning.
International Journal of Project
Management, 25(4), 405–412.
Shenhar, A. J., Dvir, D., Levy, O., &
Maltz, A. C. (2001). Project success:
A multidimensional strategic concept.
Long Range Planning, 34(6), 699–725.
Sheppard, J. P., & Chowdhury, S. D.
(2005). Riding the wrong wave:
Organizational failure as a failed turnaround.
Long Range Planning, 38(3),
239–260.
Silverman, D. (2006). Interpreting qualitative
data (3rd ed.). London: Sage.
Smith, C., & Winter, M. (2005).
Rethinking project management:
Actuality and uncertainty. Engineering
and Physical Sciences Research
Council Network. Retrieved from
https://monkessays.com/write-my-essay/rethinkingpm.org.uk
Srivannaboon, S., & Milosevic, D. Z.
(2006). A two-way influence between
business strategy and project management.
International Journal of Project
Management, 24(6), 493–505.
Strassmann, P. A. (1998, August). What
is alignment? Alignment is the delivery
of the required results. Cutter IT
Journal, p. 108.
Thiry, M. (2004). For DAD: A programme
management life-cycle
process. International Journal of
Project Management, 22(3), 245–252.
Uhl-Bien, M. (2006). Relational leadership
theory: Exploring the social
processes of leadership and organizing.
Leadership Quarterly, 17(6),
654–676.
Venkatraman, N. (1989). Strategic orientation
of business enterprises: The
construct, dimensionality, and measurement.
Management Science, 35(8),
942–962.
Verma, D., & Sinha, K. K. (2002).
Toward a theory of project interdependencies
in high tech R&D environments.
Journal of Operations
Management, 20(5), 451–468.
Visser, P., Krosnick, J., Marquette, J., &
Curtin, M. (1996). Mail surveys for
election forecasting? An Assessment of
the Colombia dispatch poll. Public
Opinion Quarterly, 60(2), 181–227.
White, L. (2001). Effective governance
through complexity thinking and management
science. Systems Research and
Behavioral Science, 18(3), 241–257.
Graeme Ritson is a construction lecturer at
Northumbria University. His background is in
construction and business-related projects. He
has 10 years of practical experience in managing
business improvement projects in a large
service organization.
Eric Johansen is the director of construction at
Northumbria University. After 20 years of project
management experience in the construction
industry, he became an academic in 1990. He
manages the Construction Group and teaches
and researches in lean construction, planning,
project, program, and portfolio management.
Allan Osborne is director of project management
at Northumbria University. He manages
the Project Management Group at the university
and teaches and researches in project team
dynamics and interorganizational relationships.
Copyright of Project Management Journal is the property of John Wiley & Sons, Inc. and its content may not be
copied or emailed 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.

Order | Check Discount

Tags: AI Plagiarism free essay writing tool, Australian best tutors, best trans tutors, buy essay uk, cheap dissertation writer, Do my essay assignment