Project success and new ventures’ outcomes: How often do partners’ potential benefits and losses really converge?

Full text

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Project success and new ventures‘ outcomes: How often do
partners‘ potential benefits and losses really converge?
___________________________________________________________________
Philippe Ruiz, PhD
____________________________________________________________________________________________________________________
Abstract: The core interests of new business ventures‘ partners do not always converge. When individual
stakeholders‘ expected benefits and losses are not aligned with each other, the success of their common
projects can be compromised. This work explores the typical partnership misalignment cases and what
their impact on project success may be. A growing body of evidence indicates that projects fail because of
changing and diverging interests among the parties involved. This paper offers a theoretical explanation of
the observed facts. Based on a systematic analysis of 256 scenarios (e.g., payoff matrices differences), the
average divergence was found to be equal to the maximum expected returns for both partners. Essentially,
31% of all possible partnerships have converging partners, 38% have neither converging nor diverging
partners, and 31% have diverging partners (e.g., partners pulling in opposite directions). These findings
are fully consistent with the literature on the topic.
Keywords: new ventures, partnerships, project success, project failure, payoff matrix, converging and
diverging interests
_____________________________________________________________________________________

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1. Introduction
When two or more partners undertake a business venture, cooperation among them is expected, not
competition. However, partnerships often fail and the business world is rife with litigations and
arbitrations.
The present research will focus on the behavior of agents involved in new ventures. Corporate
entrepreneurship, project networks, and inter-organizational relationships will be particularly considered.
Hence, the word ‗project‘ is used here to indicate a specific new venture with at least two different
partners (also called ‗agents‘ or ‗stakeholders‘). Of particular relevance to the current study is the
definition by Murtoaro and Kujala (2007): ―A project can be conceived as a single continuum of recurring
negotiations with multiple participants with varying concerns.‖
2. Literature review, hypothesis and research question
A great number of projects suffer from various problems such as high fragmentation, resource
discrepancies, complexity and dynamicity, cost and time overruns, or conflicts and disputes (San
Cristóbal, 2015).
Many studies have investigated the nature of the expression ‗Project Success‘ (Mir & Pinnington, 2013).
Some conceptualize it as a one-dimensional construct concerned with meeting budget, time and quality
(Brown & Adams, 2000; Bryde, 2008; Fortune et al., 2011) while others consider project success a
complex, multi-dimensional concept encompassing many more attributes (Ika, 2009; Jugdev & Muller,
2005; Shenhar et al., 2001).
A study by Muller and Jugdev (2012) focuses on the evolution of the project success literature and
reviews this subject by emphasizing that it has become a multidimensional, networked construct. When
contemplating the richness of the field, Ika and Bredillet (2016) considers the ontological and
epistemological perspectives as well as the diversity we face. Ruiz (2013) emphasizes the importance of
entropy to measure project complexity.
Individual partners and stakeholders will often construe project success in different ways (Cleland and
Ireland, 2006). Moreover, viewpoints about performance also vary between industries (Chan and Chan,
2004). Projects differ in size, uniqueness and complexity, thus the standards for measuring success differ

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from project to project, making it improbable that a general set of project success criteria can be agreed
upon (Westerveld, 2003).
Traditional PM systems (Pinto & Slevin, 1987) which solely consider the success criteria of cost, time,
quality and meeting technical requirements are often unsuccessful (Walton and Dawson, 2001) and are
considered obsolete by some (Mir & Pinnington, 2013). A better approach is to focus on multiple
stakeholders' expectations (Bryde, 2003; Tukel & Rom, 2001). However, this has led to a new series of
complications in developing models for evaluating performance – agents or stakeholders' requirements are
sometimes difficult to regulate and measure (Maylor, 2001). There is often resistance to go beyond the
old-style criteria due to management forces (Chan et al., 2003). These issues have limited the research and
literature on different performance assessment frameworks for project environments.
There is in particular a limited coverage of conflict management and negotiation in the standards for
project management (San Cristóbal, 2015). The Australian National Competency Standard for Project
management, one of the most widely recognized and referenced project management standards based on
the nine areas of the American Body of Knowledge (Project Management Institute, 2013) concentrates on
the mechanisms of communication within a project, but the only reference to negotiation is that of contract
negotiation.
The wide range of stakeholders and multiple objectives in large-scale projects inevitably cause conflicts
(Kuphal & Bode, 2000). There are different opinions on conflict and its causes. Levinson (1994) describes
conflict as a disagreement over resources, while others (Rahim, 2002) think that conflicts are either
interpersonal (affective) or task/goal oriented (substantive). Interpersonal conflicts are noticeably more
unmanageable than task/goal conflicts and can lead to entrenched resistance (Hudson et al., 2005). Jehn
(1995) indicates that while task/goal conflict may increase performance under certain conditions, the
disadvantages are the same as for interpersonal conflicts. Conflict can arise from numerous causes such as
cross-cultural differences (Yatim, Bredillet, & Ruiz, 2009).
Ironically, although project success is very difficult to predict or define theoretically, project failure is
fairly easy to establish empirically. Numerous studies, particularly in IT, have been conducted over the
last two decades.
Let‘s take a closer look at a few documented facts and figures:

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- 75% of IT project leaders believe their projects are ―doomed from the start‖ (Geneca, 2011).
- 17% of large IT projects (budgets over $15m) go so badly that they threaten the existence of the
company (McKinsey & Company, 2012).
A recent study by The Standish Group (2013) indicates that 43% of all IT projects are challenged while
18% truly fail (e.g., are either cancelled prior to completion or never satisfactorily finished).
The Standish Group (2013) has further established that large IT projects (more than $10 million) are twice
as likely to be late, over budget, and missing critical features as small IT projects (less than $1 million). A
large project is more than 10 times more likely to fail outright, meaning it will be cancelled or will not be
used because it outlived its usefulness prior to implementation. Thus, while on the one hand 20% of all
small projects are challenged and 4% fail, on the other hand 52% are of large projects are challenged and
38% fail.
The Bull Survey (Spikes Cavell, 1998) revealed that the major cause of project failure during the lifecycle
of an IT project is a breakdown in communications (57%), well before lack of planning (39%) and poor
quality control (35%).
A more recent study by the Project Management Institute (2015) indicates the most common causes of
project failure (not just in IT but across the board):
1- Changing priorities within organization – 40%
2- Inaccurate requirements – 38%
3- Change in project objectives – 35%
4- Undefined risks/opportunities – 30%
5- Poor communication – 30%
6- Undefined project goals – 30%
Changing priorities within organization lead to change in project objectives and also to inaccurate
requirements, poor communication and undefined risks and project goals (Project Management Institute,
2015).
To summarize as a hypothesis: Partnership failure is a direct consequence of naturally evolving and
often diverging interests among the various agents involved in new business ventures or projects.

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Main research question: Theoretically, how often do partners‘ potential benefits and losses really
converge?
3. Conceptual framework and methods
3.a. Game theory and the payoff matrix
In strategic project management, game theory is still in the beginning of its practical applications (San
Cristóbal, 2015). The mathematical theory of games was invented by John von Neumann and Oskar
Morgenstern (1944).
According to the Stanford Encyclopedia of Philosophy (Ross, 2014):
Game theory is the study of the ways in which interacting choices of economic agents produce
outcomes with respect to the preferences (or utilities) of those agents, where the outcomes in
question might have been intended by none of the agents.
In sum, game theory is a branch of applied mathematics which looks at decision-making among groups of
people where the outcome for each person or ‗player‘ in a given situation or ‗game‘ depends on the
actions of all. Game theory attempts to predict a company‘s success in decision-making, depending on the
choices of others.
The Prisoners' Dilemma game is a classic example of basic game theory from which we will borrow the
concept of ‗payoff matrix‘:
Gains of
Partner 1
Partner 2
C
D
Partner
1
C
+5
0
D
-2
-4
Table 1. Partner 1 Payoff Matrix
Gains of
Partner 2
Partner 2
C
D
Partner
1
C
+4
+2
D
+1
-1
Table 2. Partner 2 Payoff Matrix
Let‘s consider the simplest form of business venture with only two partners. Table 1 above summarizes
the expected gains and losses (in million dollars) of a given partnership for Partner 1 (P1) whereas Table 2

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summarizes the expected gains and losses for Partner 2 (P2). Each partner has two options titled C for
‗Cooperate‘ and D for ‗Decline to cooperate‘ or ‗Default‘.
Classical game theory indicates that both partners should cooperate because it is in their best interests
(+5, +4), while a common decline to cooperate would result in a loss (-4, -1) for both. However, there is
more in this situation than meets the eye.
First, P1 has a lot more to lose than P2 in the case both partners decide to default on their promises
(-4, -1). Second, if P2 decides to default but P1 still cooperates, P2 can gain (+2) whereas P1 only breaks
even (0). Finally, if P1 defaults but P2 cooperates, P2 still gains something (+1) but P1 loses (-2).
A simple and efficient way to capture the asymmetries in the scenario described above is to measure the
divergence or convergence of possible gains and losses for P1 and P2. A distance matrix is calculated by
subtracting each corresponding cells in Tables 1 and 2 and by obtaining the absolute values of these
differences.
Partners‘
distances
Partner 2
C
D
Partner
1
C
1
2
D
3
3
Table 3. Partners’ Distance Matrix
Table 3 above is the result of this computation. The numbers are then added and expressed as a percentage
of the maximum expected gains for both P1 and P2. We get: (1 + 2 + 3 + 3) / (5 + 4) = 100% which
indicates substantial divergence in this particular partnership scenario.
3.b. Simplification of the model
To gain a comprehensive understanding of all the possible different scenarios (a scenario is a
confrontation of two partners‘ payoff matrices), we reduced the possible four outcomes of a partner payoff
matrix (which were previously expressed as gains or losses in millions of dollars) to only two values:
0 (for a loss) and 1 (for a gain). There are thus 24 = 16 possible dichotomous payoff matrices for each
partner (Table 4).

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Table 4. All 16 possible dichotomous payoff matrices for a given partner
Given that they are 16 possible payoff matrices for P1 and 16 for P2, there are a total of exactly 162 = 256
possible scenarios in this simplified version.
The main question to ask at this point is whether the set of 16 payoff matrices listed in Table 4 makes
sense for any given partner and whether this set can be confronted with an equivalent set on the other
partner‘s side.
Even if a few payoff matrices may appear surprising at the very beginning of a new venture (e.g., four
zeros or four ones), such matrices can quickly become real possibilities as the project unfolds. For
instance, the four zero matrix [0, 0, 0, 0] – in this horizontal format, the top two numbers are listed first –
means that no matter what is done, the venture will fail for that particular partner. Termination is the only
option and it may happen anytime in the lifecycle of a project. Similarly, when the end is near, [1, 1, 1, 1]
is highly possible because no matter what the partners do, the project cannot be derailed.
Correspondingly, because partnerships are so dynamic by nature, it is impossible to assert that some
confrontations are impossible or less likely than others. For example, [1, 0, 0, 1] vs. [0, 1, 1, 0] seems a
bizarre scenario at first. P1 can only gain if both partners either fully cooperate or fully default whereas P2
is exactly the opposite: P2 can only gain if either partner cooperates, but not if both do or if they both
default. How could two partners ever be involved in such a strange situation?
The position of P1 is less odd than it may appear at first. It is easy to understand that if both partners
cooperate, P1 may succeed and the opposite can also be explained: if both partners decide to default and
part cleanly, it is still possible for P1 to gain something, maybe for the work already performed.
As for P2, he cannot do anything productive with P1, maybe due to interpersonal or task/goal conflicts, he
can only gain if either he or P1 does something alone in the venture. However, if nobody does anything,
he will fail.
1
1
1
1
1
1
1
0
1
0
1
1
0
1
1
1
1
1
0
1
1
1
0
0
0
0
1
1
1
0
1
0
0
1
0
1
1
0
0
1
0
1
1
0
0
1
0
0
0
0
0
1
0
0
1
0
1
0
0
0
0
0
0
0

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Of course, P1 and P2 are exact opposites and they will not be able to solve their problems because what
one needs is precisely what the other one cannot provide. Not only project failure can be expected but
litigation is likely. P1 will look for the exit while P2 will try to maintain this impossible partnership as
long as possible.
Anyhow, there is no reason to assert at this stage that the [1, 0, 0, 1] vs. [0, 1, 1, 0] scenario is less likely to
occur than the ideal [1, 0, 0, 0] vs. [1, 0, 0, 0].
3.c. Simulation and computations
The 256 scenarios described in 3.b. can now be tested and the corresponding distance matrices calculated.
The numbers in the distance matrices are then added and expressed as a percentage of the maximum
expected gains for both P1 and P2 (as explained in 3.a.).
Example 1:
[1, 0, 0, 0] vs. [1, 0, 0, 0] gives a distance matrix of [1-1, 0-0, 0-0, 0-0] = [0, 0, 0, 0] thus a total distance of
0 + 0 + 0 + 0 = 0. Given maximum expected gains for both P1 and P2 of 1 + 1 = 2, the computed
divergence is 0/2 = 0% or total convergence. P1 and P2 have exactly the same profit and loss profiles.
Example 2:
[1, 0, 0, 1] vs. [0, 1, 1, 0] gives a distance matrix of [1-0, 0-1, 0-1, 1-0] = [1, -1, -1, 1] thus a total distance
of 1 + 1 + 1 + 1 = 4. Given maximum expected gains for both P1 and P2 of 1 + 1 = 2, the computed
divergence is 4/2 = 200% or total divergence. P1 and P2 have exact opposite profit and loss profiles
(irreconcilable differences).
Most of the scenarios will fall somewhere between the two extremes illustrated by Examples 1 and 2
above (e.g., between 0% and 200%).
Given the properties of the binomial distribution, all the necessary Bernouli trials were performed and the
256 possible scenarios fully tested.

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4- Findings
Figure 1. Frequency of partners’ computed divergence based on all 256 scenarios
Figure 1 above illustrates the outcome of the simulation:
The mean of 100 (which is also the mode and the median of the distribution) indicates an average
divergence equal to the maximum expected returns for both partners.
- 16 partnerships (6.25%) have a computed divergence (CD) of 0, which indicates perfect convergence
and total alignment. Yet, the given projects may fail for reasons not linked to the alignment of the
partner‘s payoff matrices.
- 64 partnerships (25%) have a computed divergence (CD) of 50, which indicates moderate divergence
and good alignment.
- 96 partnerships (37.5%) have a computed divergence (CD) of 100, which indicates significant
divergence and challenged alignment.

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- 64 partnerships (25%) have a computed divergence (CD) of 150, which indicates severe divergence and
very imperfect alignment.
- 16 partnerships (6.25%) have a computed divergence (CD) of 200, which indicates total divergence and
no alignment. What is beneficial to one partner is detrimental to the other. Failure of the partnership is a
de facto reality.
Simply put, 25 + 6.25 = 31.25% of all possible partnerships have converging partners, 37.5% have neither
clearly converging nor diverging partners, and 25 + 6.25 = 31.25% have diverging partners (e.g., partners
pulling in opposite directions).
5- Discussion, limitations, and future research
The developed theoretical model predicts that, based on the partners‘ possible return patterns, 31.25% of
all projects should go smoothly, 31.25% should be seriously challenged, and 37.5% should fall in
between. This is a clear answer to our main research question: Theoretically, how often do partners’
potential benefits and losses really converge?
Importantly, the empirical findings of The Standish Group are remarkably similar to ours: The Standish
Group (2013) has established that 39% of all IT projects are quite successful, 43% are challenged, and
18% fail.
Likewise, these new findings are fully congruent with the expectations and evidence discussed in the
literature review, namely that partnership success and project failure should be the primary result of
naturally evolving and often diverging interests among the involved partners. Consequently, our main
hypothesis is strongly supported.
Our results further indicate that, except in extreme cases of perfect convergence (6.25%) or perfect
divergence (6.25%) where nothing can be done, most situations (87.5%) necessitate the ability to
communicate and negotiate. Since organizations are becoming flatter, culturally richer, geographically
diverse, and intensely competitive, the possibilities for conflict in such environments are greater.
Therefore, all the involved partners must recognize conflict, understand the sources of conflict, and to do
so they must be able to understand the basics of negotiation theory. Indeed, negotiation is an important
aspect of any partnership or project – it plays an important role in resolving claims, preventing disputes,
and keeping a harmonious relationship between partners, as already explained by Ren et al. (2003).

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While the present study makes several noteworthy contributions, it is important to qualify these in light of
design limitations.
First, if the consequences of diverging partners‘ interests were investigated, nothing was said about the
origin of such discrepancies.
Second, although the simulation was very comprehensive, it was based on a simplified and binary model
of reality (0/1): A more fine-grained and continuous simulation should be undertaken to clarify the
primary results obtained.
Additionally, the model considered was static, not dynamic, and the different phases of the partnership
(e.g., beginning, middle, and end) were not considered.
Last but not least, only two agents were taken into account while some projects and partnerships involve
many more agents.
Even so, our theoretical data supported the original hypotheses of evolving and diverging interests among
the various stakeholders involved in new business ventures or projects, sometimes resulting in partnership
total failure (e.g., mutually exclusive interests).
Undoubtedly, replication of these findings using more advanced and dynamic measures of partners‘
convergence and divergence patterns can instill further confidence in these results.
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