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The Project Value Mindset of Project Managers
Thomas G. Lechler, Stevens Institute of Technology
Abstract
The triple constraint (TC) paradigm constitutes the conceptual foundation of project
management. Although limited in its effectiveness, as documented by the high project
failure rates, and strong criticism from several authors, the research on the TC paradigm
lacks a theoretical base allowing an integrative perspective of the different lines of
critiques.
As an alternative, the project value paradigm (PVP) is offered enabling a reevaluation
and integration of the widely discussed limitations of the TC paradigm. It is derived from
the economic theories of entrepreneurship and serves as the conceptual base for an
empirical study conducted over the last two years. The main research goal of this study is
to answer the questions why and how project managers are creating project value during
a project’s implementation.
Following the TC paradigm, project managers are charged with reducing, if not avoiding,
project risks and project uncertainties. In contrast, the economic literature argues that
uncertainties are the precondition for entrepreneurial opportunities. It follows therefore
that as projects face more or less uncertainties, the question remains if project managers
who were able to create project value were actually seeking and exploiting opportunities
to improve the value proposition of a project. The disposition or attitude of project
managers towards these decisions and activities seems to be a central variable. These

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dispositions are integrated in the construct of a project manager’s project value mindset
(PVM). The PVM stands in contrast to the basic premises of the TC-paradigm. It
suggests that those project managers with a PVM, will more likely create more project
value. Thus, the central hypothesis investigated in this study proposes a link between a
project manager’s PVM and the created project value.
Over a period of two years, data were collected with a survey instrument developed by
the author and co-investigators. To avoid single informant bias, data were collected for
each individual project from the responsible project manager, three project team
members, and the senior manager (sponsor) responsible for funding the project. The final
sample consists of 594 individual responses related to 114 projects. Using structural
equation modeling, it could be demonstrated that the higher the perceived value of a
project manager’s PVM, the higher the likelihood that value opportunities are exploited
and the higher the achieved project value. The results support the hypothesis positing a
relationship between a project manager’s PVM and project value. The research makes the
theoretical discussion of the entrepreneurship research field accessible to develop a
comprehensive theory of project management. Finally, the research stimulates the
discussion of the TC paradigm limitations as well as it supports the call for changing the
paradigm in order to explain common project management issues.
Introduction
The discrepancy between project management practices and theoretical efforts to explain
them is criticized by many different authors over the past decade. The critique is focused
at questioning the theory and, as a consequence, requesting a new or alternative theory of

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project management. Most of these critiques are based on the general project
management paradigm, resulting in a discussion of symptoms without reflecting on the
specific underlying assumptions of the existing paradigm. This approach leaves still
many questions unanswered. This paper reports on a study that was partially funded by
the Project Management Institute (PMI) in 2008 and 2009 with the objective to offer an
alternative perspective to the general discourse in project management. It is an attempt to
change the thinking about how to explain project management specific phenomena or as
Albert Einstein is quoted as saying “we will never solve problems using the same logic
we were using when we created them.”
This study focuses on the central role that project managers assume when implementing
projects. In recent discussions, authors suggest that the mindset of project managers is an
important variable to understand the pattern of decisions made during a course of action.
But the mindset is not independent of the underlying and accepted paradigm. Despite its
critique the TC paradigm is still dominating the literature and the standardization of
project management related education. In general, the TC paradigm is based on two
fundamental assumptions: satisficing and determinism. These two assumptions will be
changed to maximizing and entrepreneurism to build the foundation for the PVP.
Based on the two project management paradigms, the overarching research question is to
understand to which degree a project manager stays closely to the TC paradigm by
satisficing the triple constraints versus the degree to which a project manager attempts to
maximize the project value and achieves better project results. Differently phrased, the
question could be also described as to which degree a project manager “breaks with” the
TC paradigm and demonstrates a value maximizing mindset.

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The main research objective is to empirically demonstrate the importance of a project
manager’s value mindset on the creation of project value. The empirical establishment of
this relationship will help to understand the interaction of project management specific
procedures (planning, controlling, etc.) with the behavioral decision level of the
management of projects. It will also have significant theoretical and practical
implications for the discussion of improving project performance and for the training of
project personnel in general.
Research Objectives and Contributions
Uncertainties of projects are sources for opportunities and, as such, have to be recognized
and exploited. This is only possible if project managers are alert for these opportunities.
A precondition for this alertness is the motivation to maximize a project’s value, which is
driven by the established paradigm. These arguments are linked to the field of
entrepreneurship that is occupied with the question of opportunity recognition,
evaluation, and exploitation. A comprehensive literature review suggests that research on
entrepreneurial behaviors of project managers is sparse and the possibility of the
occurrence of opportunities on the project level is not systematically explored. This
theoretical gap leads to the second major research objective.
With this research effort the main arguments of the entrepreneurship research field are
integrated into the conceptual discourse of project management. For once, this discussion
will allow the theoretical treatment of the existence and occurrence of uncertainty during
project implementation. The importance of uncertainty and its theoretical differentiation
from the concept of risk is not well understood. In most practical and theoretical

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discussions, uncertainty and risk are treated as closely related theoretical phenomena.
Furthermore, uncertainty is seen in most discussions as a negative consequence for
managing projects. This is the general conclusion when uncertainty is analyzed through
the lens of the TC paradigm. In addition, changing the paradigm perspective to a
maximization paradigm allows the discussion of the potential positive effects of
uncertainties. The treatment of project uncertainty through the lens of the maximization
paradigm leads to the second contribution of this study. The enrichment of the theoretical
foundation of project management by changing the paradigm from a satisficing and risk
avoidance to a maximizing and opportunity-based perspective. This conceptual paradigm
change allows the reevaluation of many empirical results and their importance for project
success. One major question which the empirical research on success factors is concerned
with is the importance of senior managers for project success. In a TC-paradigm driven
project management organization, opportunities could only be perceived and exploited by
senior managers as they make decisions about project changes.
Basic Assumptions of TC Paradigm and PV Paradigm
In the view of Thomas Kuhn (1970), paradigms are guidelines for theoretical thoughts
and scientific research. They represent conceptual views of the world consisting of
formal theories. By choosing a paradigm, its user accepts the actual scientific practice,
which includes law, theory, application, and instrumentation together (Kuhn 1970, p.10).
From the perspective of management, Pfeffer (1982, p. 228) mentions that “A paradigm
is not just the view of the world but embodies procedures for inquiring about the world
and categories into which these observations are collected. Thus paradigms have within

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them an internal consistency that makes evolutionary change or adoption nearly
impossible.”
Only few authors explicitly analyzed the TC paradigm’s limitations for the
implementation of projects. A major underlying critique of the TC paradigm and its
related tools and techniques is that they cannot explicitly handle uncertainty, for example,
the critical path method is criticized for not being able to model uncertainty appropriately
(Whitty & Maylor, 2009). The underlying assumption is that uncertainty could be
avoided by maximizing determinism, for example, the more effort that is put into
collecting data about a project in the planning stage, the less likely it will face risks.
Uncertainty is not really mentioned and is often mixed with risk.
Another major critique is the definition and measurement of project success. One
problem is, as Freeman and Beale (1992) pointed out, that, “... success means different
things to different people.” This perspective is supported by the view that it could be
ambiguous when determining whether a project succeeded or failed (Belassi &Tukel
1996; Freeman & Beale 1992; Pinto & Slevin 1989). Other authors differentiate between
project success and the project management success (Baccarini, 1999; de Wit, 1988) or
they add criteria that are industry specific. The problems of defining and measuring
project success are related to the conceptual level. Many authors start with the TC
paradigm in mind and try to extend and supplement it. This approach does not change the
basic principles under which the criteria are selected. Project success is still measured as
an adherence to the triple constraints with some other criteria that should be fulfilled as
well. The underlying assumption of these discussions is that project success could be

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achieved by satisficing predefined objectives, for example, it is the measurement against
predefined objectives and not the type of objectives that poses the limitation.
The two identified assumptions pose limitations on the management of projects. From a
project manager’s perspective, the challenge is to avoid variation from the baseline. A
valid performance measure with this approach would be only possible if the project is
implemented under deterministic conditions, for example, uncertainty that leads to a
radical change of a project’s value proposition is encountered neither in the available
management tools nor in the management approach. Consequently uncertainties are not
taken into account even though projects are facing uncertainties. The other problem is
that the baseline has to be “realistic.” If the baseline is too ambitious, a negative deviation
is built into the project plan. Another less obvious and less discussed possibility is when a
baseline is too low and the baseline could be met easily. From the perspective of the TC
paradigm, this conceptually means that the project manager needs to minimize the
negative variation from the baseline, but is not challenged to maximize the value of a
project beyond the baseline. There is no empirical evidence about the practical magnitude
of this conceptual problem.
Conceptual Framework of the Study
This chapter provides an overview of the different components that are considered in this
study. Rather than conceptually developing the different components of the framework
for this study, the study’s framework is shown. This gives the reader a structured
overview of the model discussion that follows in the succeeding chapters of this book.
The research framework of this study consists of four variables:

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1) Project Value Mindset—The project value mindset (PVM) describes the
attitude of a project manager, which maximizes the value of a project, by
making value-focused project decisions and by seeking and exploiting
opportunities beyond the baseline that will lead to increased project value.
2) Exploited Opportunities—Those opportunities recognized and exploited by the
project manager during the project implementation.
3) Project Value—Those values defined by efficiency, scope, stakeholder, and
shareholder satisfaction.
4) Project Situation—Those characteristics of the project which include
uncertainty and complexity.
An overview of the relationships between the model variables and the research
hypotheses are indicated in Figure 1.
Figure 1: Project Value Mindset (PVM) Research Framework
The hypotheses are depicted in Figure 1 by the arrows which describe the proposed
relationships between the model variables. The proposed hypotheses for this study are:
Project Value Mindset
(PVM)
Exploited Opportunities
Opportunities
Project Value
H1
H2
H3

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H1: The higher a project manager scores on the project value mindset (PVM) scale, the
greater the likelihood that project value opportunities are exploited.
H2: The higher a project manager scores on the project value mindset (PVM) scale the
greater the likelihood of an increase in project value.
H3: The more project value opportunities that are exploited by a project manager, the
greater the likelihood of increased project value.
The theoretical basis of the model variables and their proposed relationships are
discussed in the next sections of this article.
Project Success vs. Project Value
Most authors agree that project success is insufficiently defined by the TC paradigm, but
a general definition is still not agreed upon. The problems of defining and measuring
project success are related to a conceptual level as many authors begin with the TC
paradigm in mind and try to extend and supplement it. This approach does not change the
basic principles under which the criteria are selected. Project success is still measured as
an adherence to the triple constraints complemented with other criteria that should be
fulfilled as well. It is measuring project success towards a baseline that is predefined
before a project is started and modified during the project execution. From a project
manager’s perspective, the challenge is avoiding variation from the predefined baseline.
This enforces a satisficing approach that does not necessarily lead to the value
maximization of a project. A valid performance measure with this approach would be
only possible if the project is implemented under completely determined conditions, for
example, negative deviation is then directly related to poor management performance of

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the project manager or stakeholders. This means also that projects are successful if the
baseline is met.
The main problem with this approach is that uncertainties are not taken into account.
Contextual conditions of projects are changing constantly and what once looked like
criteria to define value could dramatically change, for example, a new product
development project now competing against another product, although it was started
without the expectation of a competitor’s product. It is impossible to accurately predict a
project’s objectives because of the inevitable occurrence of uncertainty.
The basic TC principle of determining project success leads to critical conclusions like
successful projects do not appear as time and budget critical (Wateridge 1995). To ask, in
general, which performance criteria are more important than others, is an incomplete
question. This question has to be modified as it depends on the specific circumstances
under which a project is implemented. The question should be “which success criteria
best reflect the achievement of project value?” (e.g., time is a very critical success
criterion for consumer product development projects). A delay leads to significant losses
of market share. Also project budget can be an important success criterion for example,
significant cost overruns in a fixed price contract will lead to an overall loss.
The question has to be changed from a generic normative view on determining project
success with a general set of criteria towards a value-oriented perspective. Several
authors have indirectly addressed this perspective, for example, the Thames Barrier
project took twice as long to build and cost four times the original budget, but it provided
profit for most contractors (Morris & Hough 1987). The success of this project could not

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be explained with the TC-based approach. Turner and Muller (2004) suggest that project
outcome should be measured and remunerated on a wider set of objectives, not just the
achievement of time, cost, and technical requirements.
Although the discussions are TC paradigm-centric, the critiques and suggested extensions
to measure project success have a common thread by pointing at a different paradigm. It
is widely accepted that a project is not necessarily successful if the triple constraints are
met and it is more important to create a certain level of satisfaction. This leads to some
extend away from a satisficing approach and points at a maximizing paradigm.
Definition 1: Project Value
A project’s value is defined by the value a project creates for its stakeholders. The project
value could be represented by a single or any combination of efficiency, technical
effectiveness and the satisfaction of a project’s stakeholder with emphasis on clients and
shareholders.
As definition 1 suggests, project value is not defined in a normative sense, rather it is
defined in a relativistic sense as the value of a project could be determined in many
different ways depending on its specific context and situation. The question of whether
the achieved project value represents a maximum can never be answered accurately. This
question is addressed only by evaluating the management process of a project in the
perspective of the maximization paradigm. The expression project value was chosen to
differentiate clearly from the mainstream discussion of project success.

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A Project Manager’s Project Value Mindset
The specific characteristics of a project manager’s value mindset are derived from
conceptual differences of the TC paradigm and the PV paradigm. One fundamental
characteristic of the PV paradigm is its maximization premise in contrast to the
satisficing premise of the TC paradigm. Another premise is that uncertainty occurs during
the implementation of projects and that these uncertainties are a potential source for
opportunities to increase a project’s predefined value. The definition of PVM is based on
these two fundamental assumptions. It is obvious that PVM cannot be derived from one
singular perspective rather it is a complex concept that is related to different attitudes and
traits of a project manager as discussed in the following sections.
Project Manager’s Opportunity Disposition
Uncertainties during the implementation of projects are more likely a general rule and not
the exception. The strategic management literature amply points out that developing the
plan addresses the market while the implementation of the plan is an operations-based
endeavor. The plan will most likely face uncertainties. The economists argue that
uncertainty is the conditio sine qua non for the existence of business opportunities
(Knight, 1948). Furthermore, entrepreneurs only evolve if individuals are “alert” to
opportunities, that is, they are actively seeking or are open for opportunities (Kirzner
1973). Following this line of thought, it is imperative for project managers to seek for
those opportunities that could significantly change the value proposition of a project. The
disposition for seeking and creating opportunities to maximize the project value beyond
the predefined value proposition is a necessary attitude. These attitudes express the quest
of a project manager to seek a project value maximum beyond the baseline of a project.

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Project Manager’s Overachievement Disposition
The main critique of the TC paradigm is its inherent premise of satisficing, as evidenced
by the concept of trade-offs. But if projects face more or less uncertainties, it is
impossible to set project objectives that reflect the future “reality” of a project. This
would be possible only in a deterministic world and the main objective for a project
manager would be to satisfy these objectives.
The conditions are completely different under the premise of uncertainty. It is impossible
to define ultimate project objectives. Changing objectives is a given and a project
manager, who strictly follows the rules of the TC paradigm, would most likely not
achieve the full value potential of a project.
Most discussions see uncertainty negatively related to a project’s value proposition, for
example, the targets of the objectives have to be lowered. However, uncertainty, as
discussed earlier, could also open the door for opportunities that could lead to a higher
value proposition of a project. What if the initial project targets were set too low? In this
situation, the initial value proposition of a project should be completely changed. Under
these conditions, it is obvious that a satisficing approach would not lead to achieving the
potential project value. Only if seeking to increase the initial value proposition, would the
project manager be alert for opportunities and be willing to exploit opportunities. Thus,
the attitude for overachievement is a defining component of the PVM
Project Manager’s Dialectic Requirement Disposition
For any project manager, it is imperative to understand the “constraints” of a project, for
example, the requirements and the specifications laid out in a project charter. Often a
project manager is charged with fulfilling the requirements for which involvement during

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their definition was likely minimal, if at all. Under these conditions, project managers are
charged to “find” the best fit between the requirements and the management process
necessary to fulfill them. The traditional way to do so is to create a project baseline that
best meets the requirements. In acting in compliance with the TC paradigm, project
managers are calculating the critical path under the given resource constraints to assure a
most likely project implementation process with a high likelihood of fulfilling the
predefined requirements. This planning process that complies with the constraints follows
the satisficing premise. As discussed earlier, the best possible compliance for a given set
of requirements would not necessarily lead to the best possible project results or as it is
called here—project value.
However, if the premise is changed from satisficing to maximizing a project’s value, as
the PV paradigm suggests, it is imperative for the project manager to question the given
project requirements and specifications. It is most likely that the derived requirements are
not representing an “optimal” set of constraints defining a “solution space” in which the
maximal potential project value lies. Given that project managers have a certain level of
expertise about the relationship between intended requirements and the implementation
of specific problems, it seems important to question the given set of requirements and to
put some effort into modifying and prioritizing requirements before and during the
implementation process, in particular with the occurrence of uncertainty. Only a constant
search for modifying and exceeding predefined specific requirements will lead to a
potential possibility to maximize a project’s value. The questioning and constant
validation of project requirements and technical specifications under the maximization
premise seems to be an essential attitude in describing a project manager’s PVM.

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Project Manager’s Ambiguity Disposition
The creation and exploitation of opportunities is related to situations of ambiguity. Only
those project managers who are accepting ambiguous situations will be able to exploit
opportunities. The disposition to tolerate ambiguous situations was defined by Budner
(1962) as an individual's propensity to view ambiguous situations as either threatening or
desirable first. A project manager’s disposition towards ambiguity could be seen as a
precondition to exploit opportunities. A disposition towards ambiguity is related to the
direct treatment of uncertainty and thus an essential facet in describing a project
manager’s PVM as it is supported by the general literature on ambiguity.
Project Manager’s Personality Traits
The attitude which an individual displays and acts upon is intertwined with one’s
personality and to some degree with individual’s intelligence. Intelligence is a general
concept that underlies nearly every human behavior and is not specific enough for the
PVM concept. The traits of a project manager are more specific and are easier to be
monitored. The common traits typology consists of extraversion and stability as well as
conscientiousness, openness, and agreeableness. Other personality traits that are
important to the project manager include an ability to be comfortable in making decisions
under ambiguous situations.
Under the conditions of uncertainty and value maximization, the personality of a project
manager to seek and exploit opportunities is an important characteristic of the person’s
mindset. A PM’s traits are linked to the decision process and thus an important facet of
the PVM concept.

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Project Value Mindset Definition and Conclusion
The discussion of the different components of a project manager’s PVM suggests that it
is a complex mix of personal characteristics, dispositions, attitudes, and context, the
result of which is strongly situation-dependent.
Definition 2: Project Manager’s Project Value Mindset (PVM)
The PVM of a project manager is a mental state involving several dispositions and
attitudes resulting in activities to seek, discover, and create opportunities beyond a pre-
defined value proposition with the intent to create and maximize a project’s value.
The VPM is defined in contrast to a TC paradigm by questioning the underlying premises
of the satisficing concept. It is based on a maximization premise and focuses on the
maximization of project value.
Project Value Opportunities
The creation of economic value is closely related to the exploitation of opportunities and
consequently an important variable for the project value paradigm. The existence and
nature of opportunities traditionally occupied the economic literature and were not yet
considered by project management researchers. It is argued that occurrence of
uncertainties is one of the major preconditions for the existence of opportunities within
an economy (Kirzner, 1973; Knight, 1948; and Schumpeter, 1934), and and it is
understandable that without uncertainties, entrepreneurial profits (e.g., extraordinary
profits) would be impossible.
The relevance of this topic for project management is obvious. The management of
projects is challenged with managing uncertainties. If projects are unique, as all formal
project definitions suggest, then uncertainties (unknown-unknowns) are inevitable no
matter how much information is gathered before a project is initiated. This means that the

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concept of opportunity and its recognition and exploitation could be adopted for the
management of projects. This argument is related to the unique nature of all projects
which face, more or less, a specific level of uncertainty during their implementation.
Thus, following the arguments of the economists, these uncertainties represent potential
sources for opportunities to increase a project’s value.
The bottom line is that projects, as they are facing uncertainties, are predestined to
experience the emergence of opportunities and subsequently the recognition, evaluation,
and exploitation of an opportunity during a project’s implementation is a major concept
of the project value paradigm as it is the source for maximizing project value.
Definition 3: Project Value Opportunity (PVO)
Project Value Opportunities represent a potential to exceed the predefined stakeholder
value of a project during a project’s implementation.
The project management literature acknowledges the concept of project risks (known-
unknowns) and suggests many different approaches to analyze possible sources of project
variation. The TC paradigm addresses these sources of variation by analyzing trade-offs
and their consequences for the project results; however, it does not address the concept of
uncertainty. But as Baumol (1993) pointed out, situations of uncertainty defy any
optimization calculus. Thus, under a paradigm of maximization, project uncertainties
could also be linked to opportunities and if exploited, will exceed the predefined project
value.
Research Hypotheses
The guiding question of this study is the relationship between a project manager’s PVM
and the creation of project value. The discussion of the potential TC paradigm constraints

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is a good starting point. One of the main critiques of the TC paradigm is that it is not
constructed to deal effectively with uncertainty. It also does not support a maximization
approach, instead it encourages a satisficing attitude. The consequences are indicated by
the relatively high failure rates of projects. The main proposition is that uncertainty has to
be managed effectively with a maximization approach in mind.
Project Value Mindset – Project Value Opportunity Relationship
The relationship between PVM and project value opportunities is substantiated by the
theoretical arguments of the entrepreneurship research field. Opportunities have to be
created, discovered, selected, and exploited to enable an entrepreneur or an organization
to generate entrepreneurial profit (Shane & Venkataraman, 2001). One major
precondition is the alertness of entrepreneurs (Kirzner, 1973) to seek for opportunities.
The alertness, as Kirzner defined it, is reflected by the presented definition of the PVM
construct. This leads to the first research hypothesis:
Hypothesis 1: PVM – PVO Relation
The higher a project manager scores on the project value mindset scale, the greater the
number of project value opportunities that were exploited during a project’s
implementation.
This hypothesis describes the relationship between the PVM and opportunity variables
that are represented by specific measurement scales. The measurement scales are
developed in the next section.
Project Value Opportunity – Project Value Relationship
The second relationship in the introduced research framework is the direct influence of a
project manager’s PVM on project value. As expressed with Hypothesis 1 and shown in
the research model in Figure 1, the influence of PVM on project value is mediated by the

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exploitation of project value-related opportunities. However, the recognition and
exploitation of opportunities is not the only source to maximize project value. The PVM
is also related to the decisions and specific activities that project managers make when
implementing a project. Thus a direct relationship between PVM and project value is
proposed.
Hypothesis 2: PVM – PV Relation
The higher a project manager scores on the project value mindset(PVM) scale the
greater the created project value.
Exploited Project Value Opportunities – Project Value Relationship
The third relationship in the introduced research framework represents the impact of
exploited project value opportunities on the created project value. The conceptual
treatment of this hypothesis is straightforward. It builds on the assumption that the PVM
represents the attitude of a project manager to create and seek project value opportunities.
But the search alone would not necessarily lead to an increase in project value. It is
actually the opportunity that was exploited that has the potential to increase a project’s
value.
Hypothesis 3: PVO – PV Relationship
The more project value opportunities (PVO)that are exploited, the greater the created
project value.
This relationship is a result of the underlying assumption that opportunities which are
exploited were chosen under the maximization premise.

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Research Methodology and Data Collection
The chosen research method to address the raised research questions and to finally test
the research hypotheses are discussed in this section. A correlational research design was
chosen to analyze quantitatively the role and influence of a project manager’s PVM
across many different projects within different industries.
Data Collection Method
A survey instrument was developed and data were collected during 2008 and 2009 from
many different projects across a variety of industries including manufacturing, software,
and telecommunication industries and many different organizations. One of the major
goals was to obtain a large-scale sample allowing for advanced statistical analyses. To
achieve a high return rate and to have some control over the data collection process,
participants of this study were recruited from a part-time graduate program of
professional who were studying and/or earning their certifications in project
management. They were instructed on how to conduct the data collection and their
questions about the data collection process were directly answered by the principal
investigators of this study. The questionnaires were distributed to them as hardcopies or
on request as files via email.
For the purpose of this research, it was necessary and important to gather data from
multiple sources. As the focus of this study is a project manager’s PVM, it was important
to collect data on the perceptions from several members of the project manager’s team as
to which project-related decisions were made by the project managers, what
considerations where taken, and which decisions could have or should have been made.
To prevent single-informant bias issues, the contacts of this study were asked to identify

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five members of a project and to return five completed questionnaires for each project:
from three key members of the project team, one from the project manager, and one from
the senior manager (sponsor) responsible for the funding of the project. All respondents
received the same questionnaire to avoid confusion in the data collection process on the
side of the contact person. Due to their direct involvement, these respondent groups were
believed to be the most knowledgeable about the project decisions and processes. Further,
given that project team members and project sponsors were directly affected by project
decisions and processes, understanding their views about the PVM and the project results
was most important.
Finally, to ensure a reasonably comparable level of familiarity with the projects across
the sample, project members were instructed by the contact person to choose a project
with which they were most familiar and involved with throughout its implementation.
Many empirical studies conducted in project settings use retrospective methods for
reasons of feasibility (Meyer & Utterback, 1995; Tatikonda & Montoya-Weiss, 2001). To
improve the accuracy of retrospective reports, respondents were asked by the contact
person to select recent projects to control the elapsed time between the events of interest
and the data collection.
Data for this study were mainly collected in the U.S. The total sample obtained thus far is
596 questionnaires. The sample includes responses from 387 core team members, 114
project managers and 95 senior managers. Actually, the original number of collected
questionnaires was higher but 21 cases had to be rejected because they were incomplete
or had other serious quality issues (e.g. questionnaires filled out by incorrect respondents
or each respondent reported on different projects) or they did not meet the required

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project characteristics. These 21 cases were discarded from further analyses. The final
sample used in the data analyses includes the data of 114 projects and consists of 104
mainly successful projects and only 10 unsuccessful projects. Some unsuccessful projects
were possibly not reported because they were never completed. Such restriction in range
tends to impact correlations more than regression weights and path coefficients and
consequently do not expect seriously distorted results. All projects had a budget of at
least $500,000 and their duration was at least three months.
Research Methodology
One of the main challenges this research project faced was the measurement of a project
manager’s project value mindset (PVM). However, the PVM is not directly observable
andan adequate measurement tool that could be applied for this study was not existent.
Over the course of this research project, many different steps were incorporated to
develop a valid and reliable measurement tool.
In behavioral organizational science, it is quite common to use Likert-scales to measure a
respondent’s feelings or attitudes about a variable that is not directly observable. Each
Likert-scale consists of several questions that are called items. Each item describes a
continuum between two extremes from “strongly disagree” to “strongly agree”. The
respondents indicate how closely their feelings match the question or statement on a
rating scale consisting of numbers ranging between 1 and 5 or more. During the summer
of 2006, a 118 item questionnaire was developed. A pilot-scale investigation was
performed in the fall of 2006 and spring of 2007 to evaluate the quality of the survey
instrument. In total, 30 respondents with project experience were asked to provide
detailed feedback of how well they understood the questions. Based on this investigation,

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several minor modifications were made to improve the survey instrument. Besides
rewording some items, the survey instrument was extended to 120 items.
The analyses of the data were conducted in several steps. In the first step, the quality and
final configurations of the measurement scales were determined. This step was conducted
on the full sample of 596 surveys. Several statistical tests were used to analyze the quality
of the measurement scales. In the second step the constructed scales were used to analyze
the proposed relationships between the model variables depicted in Figure 1.
The first step tests validity and reliability of the developed measurement scales.
Reliability refers to the consistency of a measure and a measure is considered reliable if
the same result is received repeatedly. Cronbach’s Alpha test was used to test for
reliability of the measurement scales. It is a test commonly used as a measure of the
internal consistency reliability of Likert-scales. Values below 0.70 would lead to a
rejection or modification of the constructed scale. Values above 0.80 are acceptable and
values above 0.90 very good. The value of alpha is not related to the factorial
homogeneity because it depends on the size of the average inter-item covariance, while
unidimensionality depends on the pattern of the inter-item covariances.
For the latter reason, Principal Component Analysis was used to investigate the construct
validity of the measurement scales. Only if the resulting factor achieved an explained
variance of at least 60% and all factor loadings were at least 0.7 for each individual item
or each component of a scale, it was accepted and not further modified.
In a second step, all scales were aggregated to the project level. In this step of the data
analyses, the responses of the project managers were used for the specific project
information (name, size, etc.) and to replace missing values in the individual answers.

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Otherwise, to avoid any respondent specific biases, only the responses of the team
members and the senior managers were aggregated by calculating the mean across the
developed scales of project value, PVM, and exploited opportunities. The aggregated
scales were used as the input for the final step. To test the derived research hypotheses, a
structural equation modeling (SEM) technique was employed. This technique allows
testing simultaneously interactions between several model variables. Furthermore, it is a
combination of a factor analysis combined with a regression analysis, allowing the
measurement model (different scales) of a latent variable (not directly observable
variable) and testing its influence on other similar constructed variables. This has an
advantage in that the estimates are more accurate since a total aggregation of different
scales measuring a variable is not necessary and specific measurement errors could be
included in the estimation without distorting the estimates. This is in particular necessary
for the PVM variable. The results of the SEM are direct tests for the hypotheses. For the
model estimation LISREL version 8.51 (linear structural relationships) was used.
LISREL is a statistical method that allows simultaneous analyses of hypothesized causal
relationships for multiple variables (Jöreskog & Sörbom, 1993). The use of SEM also
offers several test statistics to evaluate several aspects of the validity of the measurement
scales. In particular, it was used to test for criterion validity by checking if the proposed
causal relations are indeed of statistical significance (predictive validity) and if the
constructs (model variables) are clearly explained by the several scales (concurrent
validity).

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Measures for Project Value
The achieved project value is a complex and multidimensional construct that is difficult
to measure and many different alternative measures for project success are suggested.
Pinto and Mantel (1990) identified three distinct aspects of project performance: the
implementation process, the perceived value of the project, client satisfaction with the
delivered project outcome. Shenhar, et al. (1997) suggested four different criteria to
assess project success: meeting design goals, benefits to customers, c) commercial
success, and future potential. Even though there is no convergence about the scales to be
used to determine project success, it is commonly agreed, as the examples from the
literature show, that multiple measures are necessary to determine project success.
Following this line of thought, a project’s value was measured by four different scales
derived from the literature review and the conceptual discussion of the project value
paradigm by using and developing 19 different items as shown in Table 1.
Individual PVM Scale Number of Items Cronbach´s Alpha Variance Explained
Project_Value 4 0.97 0.78
1. The project was an economic success for the organization that completed it.
2. All things considered, the project was a success for the organization that completed it.
3. The project will achieve a positive net present value (NPV) for the organization that completed it.
4. The project will achieve a positive return on investment (ROI) for the organization that completed it.
Client_Satisfaction 4 0.88 0.74
1. The clients were satisfied with the project implementation process.
2. Clients using this project’s outcomes will experience more effective decision making and / or
improved performance.
3. The project results led to an improvement in client performance.
4. The clients are satisfied with the results of the project.
Scope_Satisfaction 3 0.87 0.79
1. The project outcome met all technical requirements.
2. The planned project scope was fully met.
3. The project outcome does what it is supposed to do.
Technical_Quality 3 0.83 0.74

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1. A high number of defects were discovered after initial acceptance by the client.
2. The number of hours of rework to previously completed deliverables was high.
3. The total support costs after project completion are expected to significantly exceed the original
estimates.
Efficiency 5 0.86 0.64
1. The project was completed on schedule.
2. The project was completed within budget.
3. The scheduled milestones had a high on-time completion rate.
4. This project was finished faster than comparable projects.
5. The process by which this project was completed was very efficient.
Table 1: Quality measures of the project value scales
The scales of efficiency, satisfaction with scope and technical quality are standard
measures of the TC paradigm. The statistics for the efficiency scale testing reliability and
construct validity reach satisfying levels. Originally, six items were developed to measure
a project’s effectiveness. The first and the third item were used from an effectiveness
scale developed by Pinto (1986). Although an orthogonal factor analysis with varimax
rotation of the effectiveness items yielded in two different factors representing different
aspects of project effectiveness. One scale measures the achieved technical quality of the
project output and the other scale measures the level of satisfaction with the fulfillment of
a project’s scope requirements. In summary, both effectiveness scales satisfy the
statistical criteria for reliability and validity.
Client satisfaction as a success criterion is mentioned by many authors and represents an
extension of the TC paradigm. The perceived client satisfaction was measured with four
items. The first three items were adopted from Pinto’s (1986) client satisfaction scale. As
demonstrated in Table 1, the client satisfaction scale satisfies all statistical requirements.
The fifth scale measures the perception of the value the project has created for the
organization that implemented the project. The implementing organization might not be
necessarily the project owner. The project value scale consists of four items and, as
shown in Table 1, satisfies the discussed requirements for reliability and validity.

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The scores for each scale were calculated by averaging the scores across the single items
of each scale. These values were used as a basis for the following statistical analyses.
Measures for Project Value Mindset Components
The PVM definition indicates that the operationalization of the variable PVM to test the
research hypotheses is complex and requires several scales representing the different
aspects of PVM. This problem constitutes one of the major methodological challenges of
this study: How to measure the PVM of a project manager?
In this step a comprehensive measurement model consisting of seven scales was
constructed. In total, seven different scales were developed based on 29 items to measure
the PVM of a project manager. The scales correspond with the different conceptual
components discussed in the previous sections.

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Conceptual PVM
Components
Empirical PVM Measurement Scales
Opportunity Disposition Value Opportunity Search—Project manager seeks out
ways to improve efficiency and effectiveness, which
should result in lowering the budgeted costs,
shortening the time, or improving the functionality for
the benefit of the managing firm and client.
Overachievement
Disposition
Overachievement—Project manager seeks to
overachieve the original objectives.
Dialectic Requirement
Disposition
Strategic Gap Analysis—The Project manager puts
effort into understanding the requirements that are
beyond the written scope statement and that are
relevant for the achievement of project value.
Specifications Analysis—At the project start, project
manager looks for inconsistencies, seeks accuracy, and
anticipates missing data or project requirements.
Ambiguity Disposition Change Anticipation—The project manager constantly
tries to anticipate sources of change.
Ambiguity—The project manager is not risk averse
and, if an opportunity occurs, accepts uncertainty in
order to increase the value of the project.
Traits Traits—Adopted from the five-factor personality
characteristic, attitudinal views, and attention to detail.
Table 2: PVM component—PVM scale relations
Two components of the PVM concept are measured by two scales. The dialectic
requirement analysis is measured with the strategic gap analysis and the specifications
gap analysis. The first scale measures attitudes and activities to specify or seek for project
requirements that are not formally defined. These activities consume resources that are
most likely not considered in the project plan and therefore it is the project manager’s
decision to “invest” in these activities. Since the investment of resources in these
activities could be seen as a strategic decision the scale is called strategic gap analysis.
The other scale measures activities and attitudes to adjust, modify, and identify project
specifications and is therefore called specifications gap analysis. The literature
differentiates between specifications as technical issues from requirements that are
broader including nontechnical issues as well.

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The ambiguity deposition is also measured with two scales. The ambiguity scale
represents the positive attitude of a project manager towards uncertainty. The change
anticipation scale represents the avoidance of changes by anticipation. The latter stands to
some extent as a contrast to ambiguity.
The only scale that was not developed for this study is the traits scale. It was adopted
from a five-factors personality scale to measure a project manager’s traits.
The following analyses to construct a scale to measure a project manager’s PVM were
conducted in two steps: First, the individual scales measuring the different components of
the PVM were developed. The second step tests if these scales are representing the
mindset of project managers as a whole.

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Individual PVM Scale Number of Items Cronbach´s Alpha Variance Explained
Project Manager_Value Opportunity
Search
4 0.87 0.71
1. The project manager was always seeking solutions to improve the project value (beyond the
original plans).
2. The project manager contacted experts to find opportunities to exceed project requirements.
3. The project manager was open to new ways to achieve better project results.
4. During the project implementation the project manager was seeking opportunities to exceed the
planned functionality of the output.
Project Manager Overachievement 3 0.80 0.70
1. The project manager routinely tries to exceed stated specifications.
2. The project manager considers giving clients more than is specified.
3. The project manager exhibits awareness of opportunities to improve or “over achieve” project
performance.
Strategic Gap Analysis 5 0.87 0.66
1. The project manager spent a significant amount of time to identify the needs of the different
stakeholders (clients, management, and shareholders).
2. The project manager invested effort to identify stakeholder needs (clients, management, and
shareholders), that were not included in the original project plan or contract.
3. The project manager routinely tried to anticipate new risks or changes to project requirements.
4. The project manager exceeded the project requirements.
5. The project manager prevented conflicts by putting a significant effort into the requirement
analysis.
Specifications Gap Analysis 5 0.88 0.67
1. The project manager critically evaluates or challenges the project specifications
2. The project manager routinely corrects project specifications.
3. The project manager routinely modifies project specifications.
4. The project manager looks for weaknesses or inconsistencies in project specifications.
5. The project manager tries to identify a mismatch between project specifications with the company’s
capabilities.
Traits 5 0.90 0.71
1. The project manager is sociable, talkative, dominant, exhibition, confident, andactive.
2. The project manager is good natured, nurturing, gentle, warm, cooperative, andforgiving.
3. The project manager is careful, thorough, detailed, responsible, dependable, organized, and self-
disciplined.
4. The project manager is calm, enthusiastic, poised, in control, and secure.
5. The project manager is imaginative, creative, aware, sensitive, cultured, curious, intellectual, and
polished.
Change Anticipation 3 0.80 0.70
1. The PM looks for unrealistic goals in project specifications.
2. The PM anticipates unwritten (undocumented) specifications.
3. The PM anticipates new or a change in project specifications.
Ambiguity 4 .82 .69
1. The project manager accepts uncertainty in an attempt to improve the project.
2. The project manager is open to take chances to improve the project.
3. The project manager has the vision to see opportunities for project improvement.
4. The project manager invites the views of others to improve project performance.

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Table 3: Quality of individual PVM scales
The developed individual PVM scales achieve good to very good values, as the statistical
values in Table 3 demonstrate, indicating that the developed scales could be used for
further analyses.
Aggregated Measures for Project Value Mindset
In this section, the different scales are combined to measure the project-value-mindset
scale. Several statistical tests were conducted to test if these scales are indeed
representing the mindset of project managers.
Both statistical scale tests, the Cronbach’s Alpha test as well as the PCA indicated that
the scale project manager change anticipation had to be removed from the final PVM
scale. The inclusion of this scale lowered the Alpha to an unacceptable level and it
lowered also the portion of explained variance estimated with the PCA concluding that
the project manager’s change anticipation could not be statistically integrated into the
overall PVM scale. From a practical perspective, this result is consistent with the entire
discussion. The relatively low variance of the project manager’s change anticipation scale
indicates that the anticipation of changes is an act that all project managers, independent
of their mindset, are considering. After this step, the number had to be reduced from
seven subscales to finally six subscales. The final results of the analyses are demonstrated
in Table 4.

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PVM_Scale
Cronbach´s Alpha 0.90 Variance
Explained0.67
PVM Subscales Factor Loadings
Strategic requirement gap 0.787
Traits 0.861
Requirement analyses 0.771
Ambiguity 0.820
Over achievement 0.771
Opportunity recognition 0.895
Table 4: PVM—Overall scale
Overall, the resulting PVM scale to measure the value mindset of project managers
represents a very good base for further analyses to test the research hypotheses.
Measures for Exploited Project Value Opportunities
The existence of a specific mindset could only be validated by the specific decisions that
could be related to it. Seven items were developed to measure if opportunities were
exploited during the project implementation. Surprisingly, the analyses resulted in two
different scales measuring two kinds of exploited opportunities. One scale measures
exploited opportunities that are directly related to the TC paradigm as they describe
opportunities to increase schedule, budget, and scope objectives (exploited TC
opportunities). The second scale measures exploited opportunities with the purpose to
increase the value of a project.
Exploited TC Opportunities Scale Number of Items Cronbach´s Alpha Variance Explained
Items 3 0.82 0.74
1. During the project implementation, opportunities were exploited to exceed the planned
functionality of the output.
2. During the project implementation, opportunities were exploited to shorten the project duration.
3. During the project implementation, opportunities were exploited to lower the project budget.
Table 6: Quality of exploited TC opportunities

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The statistical parameters of the Exploited TC Opportunities scale indicate a very good
scale quality to measure if TC opportunities were exploited during the project
implementation.
Exploited Value Opportunities Scale Number of Items Cronbach´s Alpha Variance Explained
Items 4 0.84 0.68
1. During the project implementation, opportunities were exploited to increase the satisfaction of the
client.
2. Implemented project changes were financially beneficial to the project owner (the group
implementing it) or client.
3. During the project implementation, several opportunities were exploited to increase the project
value.
4. During the project implementation, opportunities were exploited to significantly increase the
shareholder value.
Table 7: Quality of exploited value opportunities
The second scale measuring exploited opportunities to increase in general the project
value (Exploited Value Opps Scale) demonstrates also a good statistical quality.
Several statistical tests were conducted, like a PC analysis across all seven items to
confirm these results. Despite the exploratory nature of this study, these results are
consistent with the initial idea about the constraints of the TC-paradigm and its
consequences for managing projects and achieving project results.
Empirical Results
For this step, all variables were aggregated by using the ratings of four responding project
participants, for example, three project team members and the project sponsor. Due to the
risk of response bias, the responses of the project managers were excluded from further
analyses. These responses were only used to fill in for missing values. The data
aggregation led in total to 114 projects. All further analyses are based on the aggregated
data of the responses of the different respondents of 114 projects.

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Component related descriptive results
The descriptive statistics of the scales show that means are pretty high across all
developed scales. This is not very surprising as most reported projects were classified by
the respondents as successful. Interesting are also the standard deviations of the scales.

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Descriptive Statistics
Minimum Maximum Mean
Standard
Deviation
Strategic requirements gap 2.95 7.00 5.1178 0.76792
Traits 3.30 7.00 5.5924 0.80222
Requirement analyses 2.50 6.75 4.9814 0.81029
Ambiguity 2.94 6.67 5.1240 0.81555
Overachievement
Opportunity recognition 2.88 7.00 5.1557 0.85906
Business result 1.00 7.00 5.5154 1.02267
Exploited TC opportunities 1.00 7.00 4.0293 1.05726
Exploited value opportunities 1.94 6.88 4.6405 1.00356
Quality 1.00 6.67 3.3198 1.32320
Effectiveness 1.78 7.00 5.7879 1.00863
Client satisfaction 2.12 7.00 5.5939 1.00134
Efficiency 1.15 6.75 4.7048 1.15819
Table 8: Descriptive statistics of the measurement scales

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Thomas G. Lechler
Business Quality Effectiveness
Client
Saqtisfaction Efficiency
Strategic requirements
gap
0.553 -0.242 0.510 0.562 0.388
Sig. (2-tailed) 0.000 0.011 0.000 0.000 0.000
Traits 0.614 -0.322 0.631 0.628 0.446
Sig. (2-tailed) 0.000 0.001 0.000 0.000 0.000
Requirements analyses 0.434 -0.214 0.434 0.448 0.360
Sig. (2-tailed) 0.000 0.026 0.000 0.000 0.000
Ambiguity 0.504 -0.357 0.522 0.608 0.498
Sig. (2-tailed) 0.000 0.000 0.000 0.000 0.000
Over achievement 0.480 -0.042 0.362 0.454 0.425
Sig. (2-tailed) 0.000 0.663 0.000 0.000 0.000
Opportunity recognition 0.591 -0.247 0.564 0.662 0.397
Sig. (2-tailed) 0.000 0.010 0.000 0.000 0.000
Project Value Mindset 0.644 -0.329 0.633 0.693 0.496
Sig. (2-tailed) 0.000 0.001 0.000 0.000 0.000
Exploited TC
opportunities
0.216 0.129 0.088 0.224 0.177
Sig. (2-tailed) 0.024 0.187 0.362 0.019 0.065
Exploited value
opportunities
0.543 -0.035 0.325 0.504 0.277
Sig. (2-tailed) 0.000 0.723 0.001 0.000 0.004
Table 9: Correlations of mindset scales and exploited opportunities with project
value
All correlations between the mindset scales with the project value scales are significant
and very strong.
Empirical Tests of Hypotheses
The developed measurement scales are the basis for testing the research hypotheses. In
the final step of the statistical analyses, the three research hypotheses were
simultaneously tested with a structural equation model (SEM) by using LISREL VIII for

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the model estimation. The SEM shown in Figure 2 displays the structural model as well
as the measurement model of the PM’s Project Value Mindset consisting of the six
scales. The error terms are not shown.
Figure 2
Standardized Path Coefficients of the Structural Equation Model a
* p < 0.05;
Fit Statistics b
RMSEA AGFI NNFI NFI CFI X 2
df
0.002 0.93 1.00 0.99 1.00 14.98 (p=0.53)
16
a
T-values are in brackets.
b
RMSEA = Root-mean-square error of approximation; NNFI = Nonnormed Fit Index;
NFI = Normed Fit Index; CFI = Comparative Fit Index; X 2
chi-square, df = degrees of
freedom
The evaluation of a structural equation model is quite complex since no single test offers
sufficient evidence to accept or reject a model. Recognizing the problems associated with
the evaluation of linear structural equation models (Anderson &Gerbing, 1988; Bagozzi,
1980; Baumgartner & Homburg, 1996; Bollen, 1989), a comprehensive set of tests was
0.63* (7.49)a
0.19 *
(2.04)
0.59 * (5.93)
Strat_Req_Gap
Traits
Req_Analyses
Overachievement
Ambiguity
Opp_Recognition
0.59
*
0.65
*
0.59
*
0.60
*
0.64
*
0.77
*
PM’s Value
Mindset
Project
Value
R2
=0.52
Exploited Value
Opportunities
R2
=0.41

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Thomas G. Lechler
employed to assess the goodness of fit. To accept the model, the following criteria have
to be satisfied: a chi square (p > 0.05), which tests the null hypothesis that the estimated
variance-covariance matrix deviates from the sample variance-covariance matrix only
because of sampling errors. The chi-square test is limited to the extent that it is dependent
on the sample size. Browne and Cudeck (1993) showed that with an increase of the
sample size, any model could be rejected. Because of these weaknesses of the chi-square
test, Jöreskog and Sörbom suggested two global fit indices, GFI (goodness of fit index)
and AGFI (adjusted goodness of fit index). To evaluate a model’s fit the AGFI was used,
since its calculation is based on the GFI but it also accounts for the degrees of freedom.
Values below 0.90 indicate that the model should be rejected (Baumgartner& Homburg,
1996). The RMSEA (root mean square error of approximation) is a measurement of non-
centrality and estimates how well the fitted model approximates the population
covariance matrix per degree of freedom. Browne and Cudeck (1993) suggest that a
RMSEA <= 0.05 indicates a close fit and the model should be accepted. The CFI
(comparative fit index) assesses the relative reduction in lack of fit as estimated by the
chi square of a target model versus a baseline model in which all of the observed
variables are uncorrelated (Bentler, 1990). Models with a CFI below the 0.85 should be
rejected (Bentler & Bonett, 1980).
The table of the fit statistics demonstrates that the estimated model fulfills all requested
statistical benchmarks and demonstrates an acceptable fit. All path coefficients are
significant (p < 0.05) suggesting that all model variables have to be included in the
structural equation model. Furthermore, the PVM variable explains 41% of the variance
of the exploited value opportunities during a project’s implementation. Both, the PVM

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variable and the exploited opportunity variable explain 52% of the variance in the project
value variable. This percentage is very high given the many other possible influences on
the achievement of project success.
The SEM also supports the proposed measurement model for a project manager’s
mindset. The path coefficients between the latent variable (project manager’s value
mindset) and the six different measurement scales are very high and significant on the 1%
level (p < 0.01). In sum, the SEM demonstrates that the three research hypotheses should
not be rejected. It could be empirically demonstrated that links between the project
manager’s value mindset the exploited opportunities during a project’s implementation
and the perceived project value exist.
Discussion of Empirical Results
Overall the empirical results of this study are surprising. The initial expectation was that
many project managers follow the dominant TC paradigm and try to create project value
within the predefined constraints of their projects. Instead the strong relations
demonstrate that it is more common than not that project managers strive to maximize
project value in seeking for opportunities to improve the value proposition of their
projects. Given all other possible influences on project value, as mainly discussed by
success factor studies, the project value mindset of a project manager turns out to be an
important contributor in achieving project success.
The empirical results demonstrate that the PVM should be measured with at least the six
different scales measuring a project manager’s traits, attitudes, and behaviors. This
means, that project managers who are perceived by team members and peers to have a
PVM receive simultaneously high scores on all six scales. It is possible that individual

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project managers could receive a high score on a specific scale but lower scores on other
scales. In these cases project managers demonstrate some aspects of the value mindset
but in summary they do not demonstrate a PVM as a whole.
The results show in summary how important each of the six scales are to measure a
project manager’s PVM. The importance of each scale was tested with two different tests
(principle component analysis and SEM) with two different levels of data aggregation
and both methods show similar results. The results also demonstrate the complex nature
of the PVM variable and they underline the necessity to operationalize the construct in
dependence of the specific context, in this case the management of projects.
The main hypothesis of this study describes the importance of a project manager’s PVM
for the creation of project value. This hypothesis was tested with the SEM method,
estimating the strength of the direct path of the PVM variable on project value while
simultaneously considering the influence of exploited value opportunities on project
value. The high path coefficient is surprising and shows how important it is that project
managers manage their project continuously towards improving the value proposition of
their project. The strong impact indicates also that project managers who seek to satisfice
the initial project value proposition will likely fail to capture the potential project value.
The openness towards change by questioning the initial requirements and seeking for
improving the value proposition of a project is an effective strategy to face uncertainty.
This well-known companion of many projects could be addressed by seeking for
opportunities to improve the initial value proposition of a project.
The relatively high mean of the scale for exploited opportunities suggests that
opportunities to increase the project value obviously occur and are exploited to achieve

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and create value. The strong impact of exploited value opportunities on the created
project value, as suggested by the high path coefficient in Figure 2, demonstrates how
important it is to exploit opportunities during a project’s implementation.
An important precondition to exploit project value opportunities is a project manager’s
disposition to seek and identify opportunities by constantly questioning a project’s
requirements and the motivation to maximize a project’s value. The proposed relationship
between the PVM and the exploited value opportunities is supported by the high path
coefficient (Figure 2) and the high level of explained variance (R2
=43%). This means that
without a mindset to maximize the project value, promising opportunities to extend a
project’s value proposition won’t be exploited. This link is crucial as it points to the
fundamental limitations of the TC paradigm. The creation of project value by means of
the exploitation of opportunities to increase stakeholder satisfaction is beyond the TC
paradigm. The results support the basic critique of the TC paradigm in that only the quest
to maximize the value of a project will, in the end, lead to achieving a project’s value
potential.

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Benefits of the Study
The role of a project manager is often seen as an implementer with an operational focus,
implicitly excluding the responsibility or the need for opportunity recognition and
exploitation. After studying the impact of a project manager’s mindset on the creation of
project value, the perspective on a project manager’s responsibilities and functions should
be changed. Project managers have much more influence on the creation of project value
than the TC paradigm conceptually could explain.
This study redefines the role of a project manager: from an implementer to a manager
with strategic responsibilities in generating value for the organization. Consequently the
empirical results demonstrate the need for a greater empowerment of project managers
and encouragement of project managers to use more entrepreneurial behaviors. It also
suggests changes in criteria used to evaluate project managers’ performance beyond the
established triple constraint measures. The consequences of this study for project
management are manifold and could be related to three different perspectives: The
practice of project management, the education and the research.
Thomas G. Lechler was educated at the University of Karlsruhe, Germany and received the
degrees of Diplom Wirtschaftsingenieuer and PhD in Management. He was the cofounder and
CEO of the Vivatech GmbH. He was a NASA research fellow in project management from 2003–
2005. At present he is an associate professor at the Howe School, Stevens Institute of
Technology. His research focuses on the value creation through innovation with particular
emphasis on the management of projects and the recognition and exploitation of business
opportunities. He received several research grants from PMI (Project Management Institute) to

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investigate the value creation of project managers. He is a member of the GPM and the Academy
of Management.

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