Factors Influencing Project Success: A Statistical Report

This report presents a statistical analysis of factors influencing project success, based on a survey of 276 managers from various industries. The study investigates the impact of leadership characteristics, educational levels, gender differences, and project management experience on project outcomes. The analysis includes descriptive statistics, Chi-square tests, ANOVA, and regression analysis to explore the relationships between independent variables (education, gender, experience) and dependent variables (project success, design requirements, client satisfaction). The findings reveal insights into the influence of educational levels, gender, and other factors on project success, providing valuable information for project managers and organizations seeking to improve project outcomes. The report includes an introduction, conceptual model, methodology, descriptive and inferential analyses, and a conclusion, along with references and appendices.

RUNNING HEAD: PROJECT SUCCESS AND ITS ASSESSING FACTORS
Project Success and its Assessing Factors

Project Success and its Assessing Factors
Abstract
The reason for this report is to experimentally examine the attributes of the undertaking
achievement, and execution of administrators dependent on the back ground factors. The model
proposed and tried in this investigation recommends that the administration position of the group
pioneer or supervisor significantly affects the learning inside the group, which accelerates the
productivity for effective effect available. 276 managers from different businesses were
considered as a satisfactory connection for learning of this exploration. To be incorporated into
the overview, the officials needed to answer addresses identified with their proficiency and
execution in the association to expand the undertaking achievement caused by foundation and
demographical factors. The ultimate result or ward variable was the Project Success, which was
assessed from the consolidated score of five ward factors. No huge distinction at 5% level
between the two sexes in evaluating the customer necessities and fulfilling the structure needs of
clients. The ANOVA display set up the case that at 5% level undertaking achievement relied
upon dimensions of instruction. The impact of number of courses in project management was
observed to be unfavorable on the undertaking achievement composite score.
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Project Success and its Assessing Factors
Table of Contents
Abstract............................................................................................................................................2
Introduction.....................................................................................................................................4
Conceptual Model and Hypotheses................................................................................................5
Methodology...................................................................................................................................6
Descriptive Analysis.........................................................................................................................6
Summary of Nominal Predictors..................................................................................................6
Summary of Categorical (Scale) Outcome Variables...................................................................9
Inferential Analysis........................................................................................................................11
Conclusion......................................................................................................................................14
References.....................................................................................................................................16
Appendices....................................................................................................................................17
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Project Success and its Assessing Factors
Introduction
New innovative products and rapid commercialization is often the key to survival and
success in a competitive market environment. More and more companies utilize the
multifunction managers to improve the performance in new product development and project
success (Turner, 2014). Some executives from different functional areas or departments come
together to jointly develop and improve new products. The excellence of such decision making
persons has been associated with their background qualification and qualities like project
management experience, educational level, and major of study along with number of courses
partaken. In addition, the success rate in the development of new products also relies on total
budget, time taken, quality of work produced, uniqueness of design, and moreover client
satisfaction. Many researchers see a learning process in the development of becoming a
successful manager (Coleman, 2014).
Although some studies have analyzed the reasons within the perimeter of external causes,
none of them has examined the relationship between background learning and performance
measures. Recent studies also indicate that the background characteristics of the managers can
have a significant impact on the work climate and learning within the teams (Mir, & Pinnington,
2014).
However, the relationship between the behavior of the team leader and the use of team
learning and knowledge is still very broad. The knowledge-based business view indicates that the
goal of an organization is to promote the effective application of individual knowledge, although
it is not clear which leadership features are useful in office environment (Hoxha, 2017). In
summary, while the above arguments may suggest intuitively that leadership behavior can be a
combined causal effect of the learning, knowledge and performance, the critical relationship with
age and educational background has not been empirically tested.
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Project Success and its Assessing Factors
Conceptual Model and Hypotheses
Effective project manager has always been identified as one of the key persons to lead
and oversee projects that have been successful throughout the development process. Recent
studies that the characteristics of team leaders have a significant impact on the work environment
and team learning. Team leaders not only influence the behavior of individual members of the
team, but also achieve organizational goals by applying the knowledge to minimize budget in
optimum time producing quality of work with unique design, and moreover client satisfaction.
The article suggests that the effectiveness of a guide based on three sources: personal
traits, status of education, and project success influenced by project management experience
along with previously stated factors. In this study, the focus was on the three hypotheses of the
leader's effectiveness:
 The influence of different educational levels in relation to project success.
 The differences between the two genders have been identified for project design.
 A cross-sectional regression effect of age, major of study, educational level,
number of courses, project management experience on project success.
Framed set of hypotheses based on the above outline are:
 Null Hypothesis: H01: There was no significant influence of different educational
levels on project success.
Alternate Hypothesis: HA1: There was significant and positive influence of
educational levels (nominal) on project success (continuous) in ANOVA model.
 Null Hypothesis: H02: No significant differences were identified between the two
genders for impact on project design.
Alternate Hypothesis: HA2: Significant differences were identified between the
two genders (nominal) for impact on project design (scale and categorical) in Chi-
square analysis.
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Project Success and its Assessing Factors
 Null Hypothesis: H03: Age, educational level, major of study, project management
experience, and number of courses were found to have no significant impact on
project success.
 Alternate Hypothesis: HA3: Statistically significant achieve of age, educational
level, major of study, project management experience, and number of courses
were found to have on project success in the multivariate backward regression
model.
Methodology
This study was conducted as part of a broader evaluation of the evaluation of project
success due to the associated multifunctional qualities of managers. 276 managers from various
industries were considered as an adequate link for learning of this research. To be included in the
survey, the executives had to answer questions related to their efficiency and performance in the
organization to increase the project success caused by background and demographical factors. As
a result of the cross-sectional analyses of the responses, the effect of the independent factors such
as project management experience, educational level, and major of study along with number of
courses partaken has been included in the study. Missing information for any particular field was
excluded at the time of specified analyses (Macfarlane et al., 2015).
Descriptive Analysis
Summary of Nominal Predictors
From figure 1 the demographic details of 286 managers was apparent. The graphical
representation clearly exhibits that the sample had more female managers (N = 150, P = 50%)
compared to male managers (N = 136, P = 45.33%). Age distribution was almost normal with a
mean of 41.13 years (SD = 10.64 years). Average age for male (M = 40.86, SD = 10.11, CI =
[39.07, 42.66]), and female managers (M = 41.36, SD = 11.11, CI = [39.51, 43.21]) was almost
equal. This provided a scenario that almost all the managers were aged around 40 years.
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Project Success and its Assessing Factors
Figure 1: Gender and Age Distribution of Managers
From Figure 2 it can be easily noted that the majority of managers were having their
bachelor degrees (N =181, P = 63.3%). Some 26.57% of them (N = 76) also had masters in their
respective fields. As a major in degree courses engineering (N = 211, P = 73.8%) was noted to be
the dominant field of expertise, followed by other various categories.
Figure 2: Highest Educational Qualification and Major of Study
The summary of managers’ specialization in project management courses has been
presented in Figure 3. From figure 3 it can be identified that most of the managers had PMP (N =
146, P =51.0%) as their specialized field. 35 managers were identified to have done CAPM (N =
35, P 12.2%) courses. But, there was a group of managers who had no such specialized degrees
to come up with (N = 81, P = 28.3%). This trend was not an encouraging fact for the
management of a respective company.
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Project Success and its Assessing Factors
Figure 3: Qualification in Project Management Courses
In depth analysis was done regarding the number of courses partaken by the managers
during their project management course tenure. It was noted that most of them has zero to five
courses in project management (N = 95, 33.2%), which was followed by 28.3% of managers (N
= 81) with 6 to 10 number of courses in their project management program. Experience of
managers was evaluated to grasp an idea about the understanding capabilities of managers. Most
of the sample observations or managers were comparatively new (N = 116, P = 40.6%) with
experience of less than 5 years. Among the rest, 20.63% (N = 59) were experienced between 6 to
10 years. Some of them also were very experienced with more than 16 years of familiarity with
the intricacies of their jobs (N = 68, P = 23.8%).
Figure 4: Number of Courses Taken and Experience of the Managers
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Project Success and its Assessing Factors
Summary of Categorical (Scale) Outcome Variables
Figure 5 shows us that 26% (N = 78) of managers opined that they frequently completed
their projects within budget, where 22.33% (N = 67) said that sometimes they were successful to
complete their projects within given budget. The trend reflected presence of some efficient
managers in the sample data. Regarding meeting quality issues, 43.67% (N = 131) agreed that
they always met the quality requirements. Some 36.33% (N = 109) managers also expressed that
they were able to meet the quality requirements frequently.
Figure 5: Distribution of within Budget Completion and Requirement Satisfaction of Managers
From figure 6 we can see that a high number of executives (N = 121, P = 40.3%) always
met design requirements, whereas 34.3% (N = 98) were able frequently to meet the
requirements. Very low frequency (N = 4, P = 1.3%) of managers were identified who failed to
meet the design requirements. Client satisfaction has always been a major criterion for a
successful business. Here, N = 111 (P = 37.0%) managers were found to always satisfying the
client demands. 83 managers (P = 27.7%) were identified who could not achieve such accuracy
and were able to satisfy their customers frequently. But, some were not efficient compared to
others, and N = 61 (P = 20.3%) of them said to have been able to satisfy sometimes.
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Project Success and its Assessing Factors
Figure 6: Design Requirement and Client Satisfaction Review of Managers
Regarding focus on the business objectives, 107 managers were identified to have
fulfilled the overall objective of the problem. Among them, N= 71 (P = 23.7%) were able to
satisfy frequently compared to N = 36 (P = 12%) of the sampled managers who were always
focused on the business objectives.
Figure 7: Views on Business Objective of Managers
The final outcome or dependent variable was the Project Success, which was evaluated
from the combined score of five dependent factors, which are reviews on completed within
budget, meeting quality requirements, design requirements, client satisfaction, and business
objectives. The validity of the Project Success was checked with correlation coefficients, and
moderate to very high positive and significant correlations were observed.
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Project Success and its Assessing Factors
Figure 8: Outlier Classification from Project Success and Normal Curve Fitted to the Distribution
Inferential Analysis
After checking the reliability with Cronbach’s alpha ( α=0 . 495 ), the inferential analyses
were conducted. The data set was moderately reliable for statistical analyses on the evaluated
outcome variable, project success.
 Chi-square test to examine hypothesis test to examine the differences between gender and project
design. From Table 1 we can see that females were a bit ahead (P = 52.4%) compared to men (P
= 47.6%) in complying with the project design requirements of the customers. Overall, 42.3% of
managers were always able to satisfy the design requirements, and 34.3% were able to frequently
satisfy the terms of the clients. The Chi-square test statistics indicated that ( χ2 , p=0 . 465 ) there
was no significant difference at 5% level between the two genders in assessing the client
requirements and satisfying the design needs of customers. Hence, H01 failed to get rejected at
5% level of significance.
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Project Success and its Assessing Factors
Table 1: Contingency Table in Chi-Square Table to examine the differences between gender and project design
Total
Male Female
Meeting
design
requiremen
Never Count 3 1 4
% within Meeting design
requirements
75.0% 25.0% 100.0%
% within Gender 2.2% .7% 1.4%
Seldom Count 0 1 1
% within Meeting design
requirements
0.0% 100.0% 100.0%
% within Gender 0.0% .7% .3%
Sometimes Count 18 28 46
% within Meeting design
requirements
39.1% 60.9% 100.0%
% within Gender 13.2% 18.7% 16.1%
Frequently Count 50 48 98
% within Meeting design
requirements
51.0% 49.0% 100.0%
% within Gender 36.8% 32.0% 34.3%
Always Count 59 62 121
% within Meeting design
requirements
48.8% 51.2% 100.0%
% within Gender 43.4% 41.3% 42.3%
Unsure Count 6 10 16
% within Meeting design
requirements
37.5% 62.5% 100.0%
% within Gender 4.4% 6.7% 5.6%
Total Count 136 150 286
% within Meeting design
requirements
47.6% 52.4% 100.0%
% within Gender 100.0% 100.0% 100.0%
Gender
Meeting design requirements * Gender Crosstabulation
 ANOVA test for the differences between different educational levels in relation to project
success was conducted at 5% level of significance. Table 2 clearly shows that Doctoral degree
holders (M = 24.86, SD = 2.46) were the most efficient managers, followed by the managers
with master degrees (M = 23.43, SD = 3.46). This claim was tested at 5% level of significance
with a pre test for homogeneity of variances. From Levene test statistic (L = 7.88, p < 0.01) we
identified that the variances in project success for four education levels were significantly
different. The ANOVA model established the claim that at 5% level project success depended on
levels of education (F = 4.27, p < 0.05). The post hoc analysis by Tukey was used for pair wise
comparison. We were interested in the comparison of average project success scores of doctoral
and master degree holders, which was found to be not statistically significant (MD= 1.422, p =
0.51) at 5% level. Therefore, H02 was rejected against the alternate HA1 5% level.
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Project Success and its Assessing Factors
Table 2: Descriptive Summary of Project Success for Educational Levels
Lower
Bound
Upper
Bound
Secondary 8 19.00 0.00 19.00 19.00
B.A/ BSc 181 22.81 4.62 22.13 23.49
M.A/ MSc 76 23.43 3.46 22.64 24.23
Doctorate 21 24.86 2.46 23.74 25.97
Total 286 23.02 4.22 22.53 23.51
ProjectSuccess N Mean Std.
Deviation
95% Confidence
Interval for Mean
 Hypothesis test for association between project success (continuous) and educational level, age,
project management experience, major of study, and number of courses was conducted by OLS
regression model. The project success was estimated by the predictors in a multiple regression
model. It was noted that age of the managers, major of study, experience as a project manager
were the three independent factors which were nowhere statistically significant in assessing
project success of the managers. The regression model was modified by backward approach and
statistically non-significant predictors were removed from the model. The final model was found
to be statistically significant with two predictors. Highest education level (t = 4.035, p < 0.05)
and number of courses in project management (t = - 2.97, p < 0.05) were found to be the two
statistically significant predictors. Highest level education was positively associated, whereas the
effect of number of courses in project management was found to be adverse on the project
success composite score. Hence, the hypothesis H03 was rejected for the final model (F = 9.51, p
< 0.05) with two predictors. The estimated linear equation was,
Pr oject Success=1 . 49∗Highest Level Education −0 .58∗No. of Courses in Pr oject Management + 19. 38
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Project Success and its Assessing Factors
Table 3: Initial and Final Regression Models
B Std. Error
(Constant) 19.741 1.567 12.597 .000
How old are you? -.034 .029 -1.175 .241
Highest educational level 1.533 .371 4.134 .000
Major of study .066 .220 .298 .766
Experience have worked as a
project manager .189 .217 .872 .384
Number of courses in project
management -.429 .226 -1.894 .059
Regression Model
Unstandardized
Coefficients t Sig.
B Std. Error
(Constant) 19.383 1.186 16.344 .000
Highest educational level 1.488 .369 4.035 .000
Number of courses in
project management -.575 .194 -2.970 .003
Final Regression Model
Unstandardized
Coefficients t Sig.
Conclusion
The study extends this research flow by exploring how managers can manage the success
of delivering projects of new and multifunctional development teams based on the knowledge of
project management, educational level, which focuses on the use of knowledge from an
individual perspective. In particular, the scholar investigated how the characteristics of the
background knowledge of the leader influence the team and then their function. All team leaders
who have proven to be responsible for this study are monitored by leaders or senior management.
Many of these skills, for example, minimization of the total budget, time taken with high quality
of work produced have been scrutinized.
It has been established that there is a lack of empirically validated research into
organizational learning in general and team learning in particular. This study goes beyond
learning organizations and literature about knowledge management by presenting one of the few
empirical studies on learning and knowledge management in team teams. Results show that age,
educational level of leaders can influence the decision-making, moreover, major of study, project
management experience, and number of courses also identified to encourage managers to get a
more complete picture of their work and to gather a wide range of information, inputs and
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Project Success and its Assessing Factors
limitations in their decision-making process. Moreover, this behavior helps to transform the
implicit knowledge of the individual into the explicit knowledge of many team members.
The scholar expected that the gender of the team leader would have no effect on the
efficiency of successful project completion, but it was discovered that gender was not
significantly related. It is possible that the behavior to promote open communication and
information exchange within the teams for both the genders equally lead to satisfaction of the
team. Team leaders with a higher education level are like an instrument that tries to expedite any
process with efficient resource handling (Badewi, 2016). In such circumstances, benefits of this
leadership can therefore be compensated by the fluctuating learning environment that arises as
the involuntary consequence of such a leadership style.
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Project Success and its Assessing Factors
References
Badewi, A. (2016). The impact of project management (PM) and benefits management (BM)
practices on project success: Towards developing a project benefits governance
framework. International Journal of Project Management, 34(4), 761-778.
Coleman, R. A. (2014). The relationship between project managers' competence, professional
experience, and education on career success: A correlation study (Doctoral dissertation,
Capella University).
Hoxha, L. (2017). Relationship between Project Managers' Age, Years of Project Experience,
and Project Success(Doctoral dissertation, Walden University).
Macfarlane, M. D., Kisely, S., Loi, S., Macfarlane, S., Merry, S., Parker, S., ... & Looi, J. C.
(2015). Getting started in research: designing and preparing to conduct a research
study. Australasian Psychiatry, 23(1), 12-15.
Mir, F. A., & Pinnington, A. H. (2014). Exploring the value of project management: linking
project management performance and project success. International journal of project
management, 32(2), 202-217.
Turner, J. R. (2014). Handbook of project-based management(Vol. 92). New York, NY:
McGraw-hill.
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Appendices
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