13 chapter 4

Analysis and Findings
Ph.D. Thesis 55
CHAPTER 4
ANALYSIS AND FINDINGS
4.1 INTRODUCTION
The present chapter intends to accomplish the objectives of the study by holistically investigating
the various dimensions of project specific risks and organizational climate in the software
projects. The chapter is divided into four sections. The first section aims to identify the top ten
risks affecting the software projects globally through an indepth and exhaustive study of the
secondary data. It then identifies and explores the project specific risks affecting the Indian
software projects through an extensive survey and interview of the software professionals. A
systematic approach was adopted, wherein firstly, the dimensions of project specific risks were
identified by factor analysis and then these dimensions were compared among the various
personal and project characteristics. This section also describes the demographic characteristics
of the respondents and gives vivid account of the details of the project handled by the
respondents. The second section delineates the dimensions of organization climate present in the
organization through factor analysis and compares these dimensions across various personal and
project characteristics. The third sections details out the correlation between the i) the
organizational climate dimensions, demographic characteristics and project specific risk
dimensions ii) the organizational climate dimensions, project specific risk dimensions and the
project success, and finally iii) the organizational climate dimensions, project specific risk
dimensions and the three performance constructs namely budget, schedule and quality
performance of the software projects. Lastly, regression analysis is done to test the various
hypotheses of the study.
4.2 SECTION I
4.2.1 Identification and Ranking of Software Risks: A Global Perspective
The first objective of the study is to identify and rank the risk factors affecting the success of the
software projects globally. There has been plethora of research in identification of the risks
affecting the software industry but the studies focus primarily on the local software industry

Ph.D. Thesis 56
where the research is conducted. An attempt has been made in the present research to consolidate
these studies by identifying and ranking the risks affecting the software projects globally. A list
of top ten risks has been prepared using the risks identified through literature review. The
researches conducted by Boehm [19], Keil et al. [15], Oz and Sosik [107], Schmidt et al [119],
Addison and Vallabh [121], Demarco and Lister [122], Baccarini et al. [37], Smith et al. [124],
Bannerman [36], Iacovou and Nakatsu [125], Costa et al. [24] and Anudhe and Mathew [22] have
been used for the identification and ranking of risks. These studies have been selected on the
basis of the in-depth analysis of the risks and the elaborateness of the risks in the respective
research papers. The methodology followed for identifying and ranking the risks is as follows:
Each risk was individually evaluated and categorized based on the secondary data. For example:
under stakeholder management; lack of top management support, corporate culture not
supportive, inadequate user involvement, lack of client responsibility and commitment and
friction between client and contractor were the main sub risks identified using the inputs from
various research papers and discussion with the project managers from various software
companies located in Noida. Each sub factor/risk was taken and a weighted average score was
calculated depending upon the rank given to that particular sub-category risk by the respective
researcher. For example; under requirement and schedule the first sub-category risk is
‗miscommunication of requirements‘; in this it was found that out of twelve researchers two
identified this risk as the second most important risk, other two found miscommunication of
requirements to be the third most important risk, while seven researchers gave seventh rank to
this risk. The other risks were identified in a similar manner.
Once the frequencies of risks were placed in the cell according to the ranks given by the
researchers, the next step involved assigning weights and calculating the average. Weights were
assigned according to the ranks; the first rank was given a weight of 10 while second rank was
given the weight of 9 and so on. Once all the weights had been assigned, the weights were
multiplied with the respective number of researchers according to the ranks given by them. For
example: miscommunication of requirements has got the weight of 62 which was calculated as
9*2 + 8*2 + 4*7 = 62, where 9, 8 and 4 are the weights belonging to second, third and seventh
rank and 2, 2 and 7 are the frequency. The scoring model for ranking the risks is shown in Table
4.1.

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Table 4.1: Scoring Model for Ranking the Software Risks
Scoring Model for Ranking of Risks
Stakeholder management 10 9 8 7 6 5 4 3 2 1
Weighted
average of
risk (in %)
Overall
weighted
average of
risk (in %)
1 Lack of top mgmt support 5 1 57 37.50 9.68
2
Corporate culture not
supportive 1 1 1 22 14.47 3.74
3 Inadequate user involvement 1 1 1 2 37 24.34 6.28
4
Lack of client responsibility
and commitment 2 2 32 21.05 5.43
5
Friction between client and
contractor 1 1 4 2.63 0.68
Total 152
Requirement and schedule 10 9 8 7 6 5 4 3 2 1
Weighted
average of
risk (in %)
Overall
weighted
average of
risk (in %)
1
Miscommunication of
requirements 2 2 7 62 28.18 10.53
2 Unclear scope/objectives 1 2 1 1 37 16.82 6.28
3 Changing requirements 1 1 2 2 27 12.27 4.58
4
Improper change
management 1 1 1 18 8.18 3.06
5
Unrealistic schedule and
budget 2 18 8.18 3.06
6
Misunderstanding of
requirements 1 1 8 3.64 1.36
7 Unrealistic expectations 1 4 1.82 0.68
8 Gold platting 1 1 0.45 0.17
9
Inaccurate estimation of
schedule or cost 1 1 2 30 13.64 5.09
10
Importance of schedule not
recognized 1 1 15 6.82 2.55
Total 220
Project management 10 9 8 7 6 5 4 3 2 1
Weighted
average of
risk (in %)
Overall
weighted
average of
risk (in %)
1
Inadequate plans and
procedures 1 1 1 1 2 34 18.09 5.77

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2
Lack of project management
methodology 1 2 1 1 26 13.83 4.41
3
New technology being
introduced 1 1 1 1 14 7.45 2.38
4
Lack of single point
accountability 1 7 3.72 1.19
5 Lack of technical knowledge 2 1 3 2 1 52 27.66 8.83
6 Inappropriate staffing 1 1 1 2 25 13.30 4.24
7 High level of attrition 1 1 18 9.57 3.06
8
Lack of commitment from
project team 1 6 3.19 1.02
9
Lack of mechanism of
validation and verification 1 1 5 2.66 0.85
10
Inadequate tools for
reliability 1 1 0.53 0.17
Total 188
Environment 10 9 8 7 6 5 4 3 2 1
Weighted
average of
risk (in %)
Overall
weighted
average of
risk (in %)
1
Inadequate third party
performance 1 10 34.48 1.70
2 Competition alters schedule 2 12 41.38 2.04
3
Change in scope due to
change in business model 1 6 20.69 1.02
4 Natural Disasters 1 1 3.45 0.17
Total 29
Grand
Total 589
Once all the weights of the sub category of risks had been calculated, the weights were summed
up and the percentages were calculated. For example, under requirement and schedule,
miscommunication of requirements has got a weightage of 28.18% (=62/220*100) and so on. The
same step was adopted for the rest of the categories and sub-categories of risks. This resulted in
identifying the topmost risks under each category. Besides this, the overall topmost risks affecting
the software projects were also identified by adding the total of all the risks and then calculating
the percentage. Hence, for miscommunication of requirements weight of 62 was divided by 589

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(which is the total of all the weights i.e. 152+220+188+29) and thus, the weightage of the same in
the overall risk is 10.53%. The same was done for the rest of the sub categories of risks as well.
Thus according to the analysis, the top ten risk factors impacting the success of the software
projects in congruence with various researchers is shown in table 4.2.
Table 4.2: Top Ten Risks Identified Through Secondary Data Analysis
Ranks Top ten risks Percentage
1 Miscommunication of requirements 10.53%
2 Lack of top management support 9.68%
3 Lack of technical knowledge 8.83%
4 Inadequate user involvement 6.28%
5 Unclear scope/objectives 6.28%
6 Inadequate plans and procedures 5.77%
7 lack of client responsibility and commitment 5.43%
8 Inaccurate estimation of schedule or cost 5.09%
9 Changing requirements 4.58%
10 Lack of project management methodology 4.41%
According to the analysis, miscommunication of requirements, lack of top management support
and lack of technical knowledge are the most crucial risks affecting software projects. Thus, the
first objective is effectively achieved as it results in listing the top ten risks affecting the success
of the software project. In order to validate the findings of the secondary data analysis and to
explore factors from first hand data based on the perspective of the software professionals
working in Indian software companies, the next objective is carried out.
4.2.2 Identification of Software Risks: The Indian Perspective
The second objective of the present research is to identify and explore the project specific risks
affecting the software projects in India. Keeping in mind this objective of the study, a dedicated
questionnaire was developed and was used as an instrument to gauge the risk factors affecting the
project‘s success and its performance constructs (budget, schedule and quality). 340
questionnaires were received out of which, only 300 questionnaires were chosen and 40

Ph.D. Thesis 60
questionnaires were discarded. The questions and responses were coded and entered in the
computer in Microsoft Excel Software. Data analysis in a quantitative research is essential as the
interpretation and coding of responses can be very critical. Therefore, required analysis was done
with the aid of Statistical Package for Social Sciences (SPSS) 17.0 Version.
The analysis of the data has been done in two components: first that deals with the analysis of
risk factors and second that deals with the analysis of organizational climate factors. The
following section of the chapter deals with an in-depth analysis of the risk factors identified
through primary research. It discusses the findings of the second objective i.e. to identify and
explore the various project specific risk factors affecting the success (overall and three
performance constructs) of the software projects based on primary data collected for the same.
The analysis was done on the basis of the i) factor analysis, ii) mean and standard deviation of the
risk factors, and iii) comparison of the risk factors among various personal and project
characteristics of the respondents.
Firstly, reliability of the instrument was measured with the help of cronbach alpha and Kaiser-
Meyer-Olkin Measure of Adequacy. Secondly, factor analysis was done to extract the risk factors
impacting the success of the software projects. Thirdly, these risk factors were compared among
the demographic characteristics and project characteristics using Duncan‘s mean test. To begin
with, the personal profile of the respondents and the profile of the last executed project handled
by the respondents have been discussed in the following points.
4.2.2.1 Personal Profile of the Respondents
The first section of the instrument gathered information about the personal profile of the
respondents which included designation, age and total experience. Each of these demographic
characteristics is described below.
Table 4.3: Demographic Characteristics of the Respondents
Characteristics Number Percentage
Designation
Level 1
(project leads, tech leads, consultants, senior software
engineers, lead consultants)
Level 2
(project managers, senior managers, account managers)
116
141
38.7%
47%

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Level 3
(Chief Operating Officer, Head IT, Director, Chief
Executive Officer)
43 14.3%
Total experience (in years)
4 – 9 years
10 – 14 years
More than 14 years
112
123
65
37.3%
41%
21.7%
Age group (in years)
26 – 30 years
31 – 35 years
More than 35 years
90
124
86
30%
41.3%
28.7%
Designation: Since the questionnaire was deliberately administered on IT professionals with
experience of more than 4 years in handling software projects, the respondents were primarily
project leads and above. As shown in the table 4.3, out of 300 respondents, 141 (47%) were
primarily project managers, senior managers, account managers etc, who have been specified as
Level 2. While 43 respondents (14%) were from the team of top management (Chief Operating
Officer, Head IT, Director, Chief Executive Officer), who have been specified as Level 3. Such a
wide scale of distribution was necessary to enable a better analysis and interpretation of the data.
Figure 4.1: Graphical Representation of Respondents‘ Designation.
Total Experience: As shown in the table 4.3, the respondents were classified in three categories
depending upon their total experience. The second category with 123 (41%) was dominated by

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project managers and senior project managers with a total experience ranging from 10-14 years.
Few directors and vice presidents were also present in this category. In the last category with
more than 14 years of experience, there were 65 (21.7%) respondents mainly belonging to the
senior management team. Few senior managers and account managers did fall under this
category. The main reason behind this blend is that the software industry being a new-age
industry, have individuals aged 25 to 30 year old who can start their own venture and hire
employees. Therefore, it is easier to reach higher levels at an early age as compared to the
traditional industries such as iron and steel.
Figure 4.2: Graphical Representation of Respondents‘ Total Experience.
Age Group: Out of 300 respondents, 124 (41.3%) belonged to the age group of 31 to 35 as
shown in Figure 4.3. This category was strictly dominated by project managers, technical
managers and senior project managers. This is one of the most important categories for analysis
as these project managers and senior project managers are aware about the risks that affect or
may affect their project as they are directly responsible for handling the project as a whole.
Further, it is the project manager who acts a liaison between top management, client/customer
and the team members and is therefore, most affected by the organizational climate.

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Figure 4.3: Graphical Representation of Respondents‘ Age.
Thus, it can be seen from the demographics that the sample was dominated by project managers
and senior project managers. Further an in depth analysis has been done on gauging the profile of
the projects handled by the respondents.
4.2.2.2 Profile of the Last Executed Project Handled by the Respondents
The respondents were asked to provide the details of the last executed project handled by them.
The instrument contained questions on the team size of the project, total duration of the project
and finally the approximate value of the project in dollars. The details of which are provided in
table 4.4.
Table 4.4: Characteristics of the Projects Handled By the Respondents
Project details Number Percentage
Number of team members in the project
3 – 10
11 – 20
More than 20
100
89
111
33.3%
29.7%
37%
Time taken to complete the project (in months)
1 – 9 months
10 – 19 months
More than 19 months
113
96
111
37.7%
32%
37%
Total value of the project (in million dollars)
0.02 – 0.70 dollars
0.71 – 2.00 dollars
Greater than 2.00 dollars
102
89
109
34%
29.7%
36.3%
Team size: The total team size was divided into three categories. As is clear from the table 4.4
and figure 4.4, 100 projects were handled by a team size of three to ten members while 89 were

Ph.D. Thesis 64
handled by a team size of eleven to twenty members. Thus, 189 (63%) projects handled by the
respondents had a team of 20 members or less. On the higher side, only 25 projects (8.4%) had a
team ranging between 50 to almost 500. Only one project had a total value of 145 million dollars
with a team size of 500 team members.
Figure 4.4: Graphical Representation of Total Number of Team Members in the Project Handled By the Respondent
Duration: As shown in figure 4.5, it was found that 113 projects (37.7%) were completed in less
than 1 year. After detailed conversation with few IT professionals, it was found that projects
cannot really be classified as short term, medium term or a long term project as it is organization
specific. For companies with employee strength of thousands or more, a project with one year
duration would be short term project but for a company with 10 employees the same project
would be considered as a long term project.
Figure 4.5: Graphical Representation of the Duration of the Project Handled By the Respondent

Ph.D. Thesis 65
Value: As can be seen in figure 4.6, the projects were almost evenly distributed amongst the
three groups with most of the projects falling in either the first group of project value (upto
seventy thousand dollars) or in the third group (value above 2 million dollars). However, after a
detailed analysis it was found that most of the projects outsourced to Indian software companies
ranged between 1 - 10 million dollars with 151 projects (50.3%) falling under this category.
Thus, it can be seen from the profile of the project that the sample is a homogenous mix of team,
time and total value of the project. Further study was done on extracting the risk factors as rated
by these respondents.
4.2.2.3 Identification of the Risk Dimensions
For identifying and evaluating the project specific risk factors impacting the success of the
project, the first step is to pool the risks that impact the success of the software projects. On the
basis of extensive literature and the pilot study done, a total of 23 risk variables were chosen for
the study. It must be noted here that some of the risks which were important and were a part of
the top ten risks in the secondary data analysis were not taken in these 23 risks as the nature of
these risks appeared to be similar to some of the already existing risk items identified in the pilot
study. For example, lack of user involvement was to an extent similar to lack of client ownership
and responsibility. Similarly, unclear scope/objective was somewhat a subset of
miscommunication of requirements. Furthermore, lack of project management methodology was
not included in the 23 risk items as this appeared to be a broad risk encompassing a number of
Figure 4.6: Graphical Representation of the Value of the Project in Dollars Handled By the Respondent

Ph.D. Thesis 66
risks. This risk was further sub-divided into various risk items that affect the Software
Development Life Cycle of the software projects. Thus, the 23 risk items used for data collection
and analysis were the result of exhaustive literature review and pilot study and were converted
into a questionnaire. The respondents were asked to rate these risks on a 5 point likert scale
ranging from 1 to 5, 1 being no effect on the success of the project and 5 being too much effect
on the success of the software project. Table 4.5 enlists all the 23 variables that were translated
into items in the questionnaire and were used for factor analysis.
Table 4.5: Risk Variables Chosen For Study
Items
1 Working with inexperienced team
2 Delay in recruitment and resourcing
3 Less or no experience in similar projects
4 Insufficient Testing
5 Team Diversity
6 Lack of availability of domain expert
7 Lack of commitment from the project team
8 High level of attrition
9 Estimation errors
10 Inaccurate requirement analysis
11 Lack of top management support
12 Low morale of the team
13 Miscommunication of requirements
14 Conflicting and continuous requirement changes
15 Language and regional differences with client
16 Lack of client ownership and responsibility
17 Inadequate measurement tools for reliability
18 Third party dependencies
19 Inability to meet specifications
20 Inaccurate cost measurement
21 Poor code and maintenance procedures
22 Poor documentation
23 Poor configuration control
To test the validity of the instrument, cronbach alpha and KMO tests were conducted. Cronbach
alpha was calculated to measure the internal consistency and reliability of the instrument. The

Ph.D. Thesis 67
cronbach alpha came as 0.956 as shown in table 4.6, thus the instrument was considered reliable
for the study. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy is a statistic that indicates
the proportion of variance in the variables that might be caused by underlying factors. High
values (close to 1.0) generally indicate that a factor analysis may be useful with the data. If the
value is less than 0.70, the results of the factor analysis probably will not be very useful. The
KMO value for the instrument was 0.916, which is acceptable as a good value [201]. Similarly,
Bartlett's test of sphericity tests the hypothesis that the correlation matrix is an identity matrix,
which would indicate that the variables are unrelated and therefore unsuitable for structure
detection. Small values (less than 0.05) of the significance level indicate that a factor analysis
may be useful with the data. The Bartlett‘s test showed a significant level and hence the
instrument was accepted for further study.
Table 4.6: Cronbach Alpha and KMO Test Value
Since the risk variables were large in number and were inter-related, factor analysis was done to
extract the factors affecting the success of the projects. Principal Component Analysis was the
method of extraction. Varimax was the rotation method. As per the Kaiser criterion, only factors
with eigenvalues greater than 1 were retained [202] [203]. Four factors in the initial solution have
eigenvalues greater than 1. Together, they account for almost 68% of the variability in the
original variables. The items falling under each of these factors were then dealt with quite
prudently. Table 4.7 shows the communality and eigenvalues of the factors. It is followed by a
screeplot (Figure 4.7).
Reliability Statistics
Cronbach's Alpha No. of Items
.956 23
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
.916
Bartlett's Test of Sphericity Approx. Chi-Square 5309.252
Df 253.000
Sig. .000

Ph.D. Thesis 68
Table 4.7: Table of Eigenvalues of the Factors
Variables
Communality Factor Eigenvalue
Percentage
of Variance
Cumulative
Variance
Working with inexperienced team .542 1 5.356 23.289 23.289
Delay in recruitment and resourcing 1.707 2 4.342 18.877 42.165
Less or no experience in similar projects .550 3 3.459 15.040 57.206
Insufficient Testing .644 4 2.394 10.407 67.613
Team Diversity .598
Lack of availability of domain expert .714
Lack of commitment from the project team .685
High level of attrition .622
Estimation errors .646
Inaccurate requirement analysis .757
Lack of top management support .618
Low morale of the team .574
Miscommunication of requirements .762
Conflicting and continuous requirement
changes
.737
Language and regional differences with
client
.703
Lack of client ownership and responsibility .683
Inadequate measurement tools for reliability .703
Third party dependencies .756
Inability to meet specifications .726
Inaccurate cost measurement .726
Poor code and maintenance procedures .755
Poor documentation .689
Poor configuration control .653

Ph.D. Thesis 69
2322212019181716151413121110987654321
Component Number
12
10
8
6
4
2
0
Eigenvalue
Scree Plot
Figure 4.7: Screeplot of the Components Extracted From Factor Analysis
The factors along with their loadings are mentioned in table 4.8.
Table 4.8: Factor Pattern Matrix: Risk Factors Affecting the Success of the Project
ITEMS FACTOR 1 FACTOR 2 FACTOR 3 FACTOR 4
Working with inexperienced team .206 .651 .071 .266
Delay in recruitment and resourcing .646 .508 -.080 .162
Less or no experience in similar projects .604 .384 .062 .183
Insufficient Testing .190 .508 .556 -.201
Team Diversity .235 .650 .346 -.001
Lack of availability of domain expert .014 .761 .238 .281
Lack of commitment from the project team .429 .628 .299 .131
High level of attrition .446 .613 .210 .062
Estimation errors .687 .234 .329 .106

Ph.D. Thesis 70
Inaccurate requirement analysis .786 .248 .249 .127
Lack of top management support .268 .561 .269 .399
Low morale of the team .268 .596 .221 .313
Miscommunication of requirements .765 .225 .304 .186
Conflicting and continuous requirement changes .789 .120 .218 .231
Language and regional differences with client .612 .138 .501 .242
Lack of client ownership and responsibility .533 .262 .488 .303
Inadequate measurement tools for reliability .280 .233 .486 .578
Third party dependencies .250 .196 .099 .803
Inability to meet specifications .324 .408 .312 .598
Inaccurate cost measurement .635 .153 .480 .263
Poor code and maintenance procedures .342 .290 .716 .201
Poor documentation .194 .227 .733 .251
Poor configuration control .394 .355 .529 .302
The four factors extracted for further study are shown in table 4.9. These 4 factors that were
extracted included the items which have loadings of more than 0.5 and have been referred as the
risk dimensions in further analysis. Table 4.9 is followed by the explanation of all these four risk
dimensions.
Table 4.9: Factor Analysis of the Risk Dimensions
Factor Item
Factor
Loading
Factor Name
(Risk Dimensions)
1
Conflicting and continuous requirement changes 0.789
SRS Variability Risk
Inaccurate requirement analysis 0.786
Miscommunication of requirements 0.765
Estimation errors 0.687
Less or no experience in similar projects 0.646
Inaccurate cost measurement 0.635
Language and regional differences with client 0.612
Delay in recruitment and resourcing 0.604
Lack of client ownership and responsibility 0.533
2
Lack of availability of domain expert 0.761
Working with inexperienced team 0.651

Ph.D. Thesis 71
Team Diversity 0.650
Team Composition
RiskLack of commitment from the project team 0.628
Low morale of the team 0.613
High level of attrition 0.596
Lack of top management support 0.561
3
Poor documentation 0.733
Control Processes
Risk
Poor code and maintenance procedures 0.716
Insufficient Testing 0.556
Poor configuration control 0.529
4
Third party dependencies 0.803
Dependability RiskInability to meet specifications 0.598
Inadequate measurement tools for reliability 0.578
SRS Variability Risk
The software requirement specification (SRS) variability risk is the name given to the first risk
dimension identified through factor analysis. The items included in this are conflicting and
continuous requirement changes, inaccurate requirement analysis, miscommunication of
requirements, estimation errors, less or no experience in similar projects, inaccurate cost
measurement, language and regional differences with client, delay in recruitment and resourcing
and lack of client ownership and responsibility. All these variables had a factor loading of more
than 0.5. All these items have one commonality, lack of proper flow of information leading to
requirement variability. The first step for any project is to gauge correct requirements from the
client. If the first step goes wrong the project is bound to get delayed or fail. Most often it is
observed that language problems and little or no experience in handling similar projects affect the
project manager‘s capability in gauging correct set of requirements. The same has been reiterated
by [10] [134] [136] [137] [150]. Besides this, lack of client ownership and lack of drive to specify
requirements is a major contributor to vague requirements and not enough clarifications is done
to de-bottleneck them in time. Agency cost mainly arise due to contracting costs, divergence of
control, separation of ownership and control and the different objectives among the managers at
client end. Although this seems quite a paradox, this is one of the biggest reasons of SRS
variability risk. Researchers even state that the project managers fail to make correct estimation in

Ph.D. Thesis 72
the initial stages of the software development and sometimes distort or become too optimistic,
thus creating a gross estimation errors [112] [113]. Iacovou and Nakatsu [125] have very well
explained the consequences of requirement variability in their research work. According to them,
variability in requirements is one of the biggest risk factors as they can complicate the
transmission of the original set of requirements and subsequent information exchanges and
change requests. Thus, it can be seen how crucial it is to understand the requirements correctly
for the success of the project.
Team Composition Risk
This emerged as the second factor and has variables as lack of availability of domain expert,
working with inexperienced team, team diversity, lack of commitment from the project team, low
morale of the team, high level of attrition and lack of top management support falling under it.
All these variables have factor loading greater than 0.5. This factor deals with the risks related to
the team members responsible for the development and execution of the project. The major
contributors to these risks are the lack of top management support and unavailability of a
competent project manager in handling the team. Any show of disinterestedness on the part of the
top management will result in hiring of an inexperienced team or a highly diverse team. To add to
this, if the top management is not keen in investing in training or hiring the subject matter expert
it will lead to a risk of unavailability of a domain expert which will create problems for the
project [15] [127] [128] [129]. Besides lack of interest of the senior management, project
manager is also responsible in contributing to the team composition risk. Project manager acts as
a liaison between top management and the team. All the issues related to promotion, performance
appraisals are handled by the project manager. If the manager is inept in handling the team issues
it is bound to create dissatisfaction amongst team members resulting in low morale, lack of
commitment and finally turnover [28] [43] [204] [205]. According to a number of researchers
people leave managers not companies. Mostly manager drives people away [204]. Besides this,
risk variables such as lack of availability of domain expert and working with inexperienced team
can also be attributed to the project manager‘s ineptness in estimating the human resource
requirement correctly. It is the job of the manager to plan well in advance the time and the type of
resources needed for the project, failing to do so results in project delays and escalation of cost
and time.

Ph.D. Thesis 73
Control Processes Risk
This factor includes poor documentation, poor code and maintenance procedures, insufficient
testing and poor configuration control as they are all related to the control mechanism of the
project. Pressman [90] states that configuration control is "a set of activities designed to control
change by identifying the work products that are likely to change, establishing relationships
among them, defining mechanisms for managing different versions of these work products,
controlling the changes imposed, and auditing and reporting on the changes made." To enable any
changes successfully the developer must understand how making changes will affect the system,
how the system is build and what all the different parts are doing and how they are connected.
Therefore an up-to-date documentation and configuration control is extremely important [58]
[186] [206] [207] [208] [209]. In one of the surveys done by Jansson [210], lack of
documentation and lack of up to date documentation was indicated as the primary reason for poor
maintenance of the project. Besides poor documentation, it has also been seen that the software
developer does not perform adequate testing. After a detailed discussion with the Vice President
of Quality of a reputed company in Noida (India), it was found that lack of time and coordination
during testing phase were the primary reasons especially because different members of the
maintenance team work on different problems at the same time without proper coordination. This
is especially true for the Indian software companies. Kavitak Ram Shriram, founder-director of
Google admitted in an interview that Indian IT professionals deliver low quality application
software that needs thorough testing. All these issues are related to control processes as proper
and regular audit of the on-going project would highlight the problem of poor code and poor
configuration control well in advance. It will enable the project manager to check whether a
proper documentation is being done and all the versions of the code are being saved in the central
repository of the company or not. Thus, this is a very crucial risk that affects the success of the
project.
Dependability Risk
Dependability risk is the name given to the fourth and the last factor. The items included in this
are third party dependencies, inability to meet specifications and inadequate measurement tools
for reliability. All the items had a factor loading greater than 0.5. It is extremely vital for a

Ph.D. Thesis 74
software project to be dependable and reliable. Software dependability is defined as the ability to
avoid service failures that are more frequent and more severe than is acceptable. Dependability is
a broad term which includes availability, reliability, safety, integrity and maintainability of the
software. [211]. Dependability of the software is therefore very crucial for the success of the
project. For a successful completion of the project all components (hardware and software) must
be available at the right time and at the right place. Sometimes to make things easier, a part of the
project is outsourced by the company to the third party vendor. This creates a third party
dependency and if the sub-contractor fails to deliver the part on time it results in the inability of
the project team to meet specifications. It has often been observed, especially in the Indian
software industry that even when the project is delivered on time, yet it fails on reliability tests,
which means it fails to meet the desired quality standards. Many reasons have been attributed to
this phenomenon by the Indian software professionals. They are inability of the third party vendor
to meet the specifications, wrong choice of the sub-contracting vendor, or third party component
not reliable. This finding about dependability and reliability is supported by numerous studies and
is also in conformity with studies of [26] [116] [142] [154].
4.2.2.4 Comparison of Risk Factors across Various Personal and Project Characteristics
The dimensions of project specific risk so formulated after the factor analysis were then
compared across the various personal characteristics of the respondents and the project
characteristics handled by the respondents chosen for the study. The personal characteristics
included experience and designation while project characteristics included total team size, total
time taken to complete the project and the total value of the project. The comparisons are
discussed in the following section.
Personal characteristics
Designation
Duncan‘s Mean Test was applied to compare the dimensions of project specific risks among the
three designation groups of the respondents. All risk dimensions viz. SRS variability, team
composition, control processes and dependability showed significant differences in mean and

Ph.D. Thesis 75
standard deviation values. Table 4.10 shows all the values of mean and standard deviation of the
dimensions of project specific risk across the various designation groups.
Table 4.10: Comparisons of Risk Factors among Three Designation Groups
(D1= level 1, D2= level 2, D3 = level 3) Duncan‘s Mean Test
Risk factors
D1 (N=116)
Mean S.D
D2 (N=141)
Mean S.D
D3 (N=43)
Mean S.D
D1
v/s
D2
D1
v/s
D3
D2
v/s
D3
F-value
SRS Variability 3.58 0.87 2.72 1.04 2.74 1.01 * * - 27.05**
Team Composition 3.19 0.98 2.41 0.94 2.67 1.05 * * - 20.89**
Control Processes 2.97 0.95 2.19 0.98 2.32 1.12 * * - 20.47**
Dependability 3.26 1.18 2.62 1.08 2.42 0.82 * * - 14.60**
*Significant at .05 level. ** Significant at .01 level.
It can be seen from the table 4.10 that the F value is highest in case of SRS variability. This factor
has been ranked highest by respondents of level 1 (project leads, technical leads, consultants and
analyst) with a mean of 3.58 and a standard deviation of 0.87, which implies that level 1
respondents perceive this risk to have a high effect on the success of the project. Dependability
and team composition risk with a mean of 3.26 and 3.19 respectively are again considered
significant risks by level 1 respondents than compared to the other two groups which are
dominated by Project managers (level 2) and Directors (level 3). This is because level 1
respondents have neither sufficient experience nor expertise in handling and mitigating these
risks effectively compared to the other two levels. Most of the respondents falling under level 1
have an experience of 4-7 years, which is not sufficient in understanding the nitty-gritty of the
project and the risks associated with it. Moreover, they do not really have any authority of
controlling these risks other than informing the project manager or technical manager about it.
Another interesting fact that emerged out of the analysis was that the difference in perception
about these factors was significant only in two groups i.e. level 1 and level 2; and level 1 and
level 3. It should be noted here that there was no significant difference between level 2 and level
3 respondents, thus testifying that these two levels have almost similar opinion. Neither of the
two (level 2 and 3) regarded these factors as high risk for the success of a project. However, when
compared with level 1 employees, both these groups showed significant difference. Hence, it can
be said with statistical confidence that there exists a difference in perception of these risks among

Ph.D. Thesis 76
the various designation groups. Level 1 employees perceive more risks than other two
designation groups. This finding is in conformity with many other previous researches also.
Stephen et al. [196] have testified that IT project managers with more experience have risk
perceptions that differ from those of more junior managers. Warkentin et al. [30] have also
concluded that instead of viewing risks as separate or discrete categories, managers at higher
levels, due to their more comprehensive organizational perspective, are more likely to consider
risks essentially organizational in nature as compared to their junior managers. The same has
been reiterated by [124].
Total experience
Duncan‘s Mean Test was applied to compare the risk dimensions among three groups formed on
the basis of total experience. Significant difference was found in the mean values of all the
dimensions of risk. Table 4.11 shows all the values of mean and standard deviation of the
dimensions of risk across the various experience groups.
It can be seen that F value was highest in case of SRS variability risk, followed by team
composition, control processes, and dependability. It should be noted again that the difference
was significant only between two groups i.e. between E1 (upto 9 years of experience) and E2 (10
to 14 years of experience); and E1 and E3 (more than 14 years of experience). E2 and E3 had no
significant difference between them as far as these four risk factors were concerned.
All four risks were ranked highest by E1 respondents, followed by E2 and then E3. This is not
much surprising as employees with fewer years of experience have a completely different
perception about risks as compared to veterans of the industry. It is because employees with few
years of experience are not much well versed with managing such issues or even mitigating them.
As years go by and employees get more experience in handling projects, such issues do not
emerge as risks but minor challenges that need to be faced. Respondents in E2 and E3 category,
therefore, have similar opinion about such risks and hence there is no significant difference
between the two. Another point to be noted here is that control processes had the lowest mean of
2.91, ranked by E1 respondents. This suggests that control processes did not have much effect on
the success of the last executed project as perceived by the respondents in that category. This

Ph.D. Thesis 77
finding also has congruence with few previous studies like [30] [124] wherein it was concluded
that employees with higher experiences in project leadership were more likely to view projects,
and their associated risks, more holistically and assign and resolve risk as if they were
organizational in nature.
Table 4.11: Comparisons of Risk Factors among Three Experience Groups
(E1= upto 9 years, E2= 10 - 14, E3 = more than 14) - Duncan‘s Mean Test
Risk factors
E1 (N=112)
Mean S.D
E2 (N=123)
Mean S.D
E3 (N=65)
Mean S.D
E1
v/s
E2
E1
v/s
E3
E2
v/s
E3
F-value
SRS Variability 3.51 0.93 2.82 1.07 2.74 1.01 * * - 18.17**
Team Composition 3.14 0.99 2.52 0.97 2.51 1.02 * * - 13.61**
Control Processes 2.91 1.02 2.31 0.89 2.23 1.09 * * - 13.91**
Dependability 3.19 1.17 2.74 1.09 2.44 0.98 * * - 10.42**
Project characteristics
After comparing the dimensions of software risks across the various personal characteristics, the
same were compared across the various project characteristics. The project characteristics
included total team size, total time taken to complete the project and the total value of the project.
The comparisons are discussed as follows:
Total Team Size
Size refers to the magnitude of the resources that are needed to complete the project [212].
According to this definition, human resources engaged in a project make the team size. Past
research also illustrates that the level of resources has association with the complexity of the
development, which in other words is project related risks [86] [213] [214]. The team size of the
projects is an important variable that is associated with the risk dimensions. In this study, team
size has been divided into three categories viz. T1 (upto 10 members), T2 (11-20 members) and
T3 (more than 20 members). Duncan‘s mean test was done to find out significant difference
among the means of these three categories. The findings in table 4.12 show that none of the F
values were significant. Thus, it cannot be said with statistical confidence that the risk dimensions
vary with the team size.

Ph.D. Thesis 78
Table 4.12: Comparisons of Risk Factors among Three Team Size Groups
(T1 = upto 10, T2 = 11-20, T3 = more than 20) Duncan‘s Mean Test
Risk factors
T1 (N=100)
Mean S.D
T2 (N=89)
Mean S.D
T3 (N=111)
Mean S.D
T1
v/s
T2
T1
v/s
T3
T2
v/s
T3
F-value
SRS Variability 2.99 1.11 3.11 1.03 3.08 1.03 - - - 0.3304
Team Composition 2.71 1.13 2.74 1.01 2.79 0.96 - - - 0.1772
Control Processes 2.64 1.13 2.43 0.84 2.47 1.13 - - - 1.1641
Dependability 2.77 1.22 2.89 1.06 2.85 1.10 - - - 0.2921
Total Duration
The total time taken for the completion of a project is an important attribute which is associated
with risks as it is an extensive resource for any project [30]. Total duration of a project was
categorized under three heads viz. TT1 (upto 9 months); TT2 (10-19 months); and TT3 (more
than 19 months). The risk factors were, thus, compared across these three categories using
Duncan‘s Mean Test. Only team composition had significant difference among the three
categories, with an F-value of 3.1201 (table 4.13). None of the other risks had any significant
difference among the three groups.
Team composition had significant difference only between TT2 and TT3 category i.e. between
projects with duration of 10-19 months and projects with duration of more than 19 months.
Duncan‘s mean test shows that there is a difference in mean values of risk between these two
categories. Projects with longer duration have a higher mean compared to projects with shorter
ones. This is because as the duration of the project increases, the level of morale and motivation
of the employees tend to diminish as such projects are generally maintenance projects. With low
or almost no challenge in work along with high attrition, employees lack commitment for the
project and thus the team composition emerges as a significant risk for projects with longer
duration [26] [30]. Warkentin et al. [30] have pointed out that considering the time issue of a
project, the team relationships have to be managed. As quoted by Rogers in Warkentin et al. [30]
―ultimately you need effective communication channels with your vendors and technology
partners. Mutual respect and understanding play a large role in the relationship‖. This clearly

Ph.D. Thesis 79
defines that team composition is associated with the duration of a project and that it has a larger
impact on projects with longer duration as compared to shorter ones.
Table 4.13: Comparisons of Risk Factors among Three Total Time Groups
(TT1 = upto 9 months, TT2 = 10-19 months, TT3 = more than 19) Duncan‘s Mean Test
Risk factors
TT1 (N=113)
Mean S.D
TT2 (N=96)
Mean S.D
TT3 (N=111)
Mean S.D
TT1
v/s
TT2
TT1
v/s
TT3
TT2
v/s
TT3
F-value
SRS Variability 2.99 1.13 2.99 1.06 3.18 0.98 - - - 1.1998
Team Composition 2.70 1.12 2.56 1.00 2.93 0.94 - - * 3.1201*
Dependability 2.76 1.15 2.75 1.19 2.98 1.07 - - - 1.4238
Total Dollar Value
Money is a critical resource that should be allocated and monitored for successful software and
information systems development projects [30] [215]. The total dollar value thus becomes an
important attribute for any project, and it has been selected for comparing the risk factors. The
total dollar value of projects in which the respondents were involved are divided in three
categories viz. V1 (upto 0.70 mn dollars); V2 (0.71-2.00 mn dollars); and V3 (more than 2.00 mn
dollars). Duncan‘s mean test was applied to see if there was any difference in the mean values of
the risk factors among the three categories of dollar value associated with the last executed
projects. As shown in table 4.14 none of the differences came significant. Thus, it cannot be said
with statistical confidence that there exists a difference in the mean value of the risk factors
across the three categories of project value.
Table 4.14: Comparisons of Risk Factors among Three Value Groups
(V1 = upto 0.70 mn dollars, V2 = 0.71-2.00 mn dollars, V3 = more than 2.00 mn dollars) Duncan‘s Mean Test
Risk factors
V1 (N=102)
Mean S.D
V2 (N=89)
Mean S.D
V3 (N=109)
Mean S.D
V1
v/s
V2
V1
v/s
V3
V2
v/s
V3
F-value
SRS Variability 2.93 1.17 3.12 1.06 3.13 0.94 - - - 1.1468

Ph.D. Thesis 80
Team Composition 2.66 1.14 2.79 1.01 2.80 0.94 - - - 0.6530
Dependability 2.72 1.23 2.87 1.16 2.93 1.00 - - - 0.9201
After identifying the risk dimensions, assigning appropriate names to them and comparing them
across various personal and project characteristics, the next section elaborates on the
identification and exploration of the organizational climate factors that affect the project specific
risk dimensions and the success of the project and its three performances constructs.
4.3 SECTION II
4.3.1 Identification of Organizational Climate Dimensions
In project management, the trend is to focus on the technical issues of the project, the timeline,
the project plan, the resources, budget etc. When in fact, if a project is going to fail, in most cases
a good deal of the problem can be traced back to leadership, lack of teamwork and other ―soft‖ or
cultural issues [216]. Thus, organizations play a very crucial role in ensuring the success of the
project by providing the correct set of tools needed to control and alleviate the impact of the risk
factors on the project. More is the freedom and openness in the organization, more is the chance
of success of the project. Numerous researchers have tried to establish association between
various dimensions of organization‘s climate factors and the success of the project [31] [32] [49]
[50] [51] [52] [53].
However, most of the above mentioned studies have concentrated on establishing relationship of
just few dimensions of organizational climate factors and performance of the team members. A
holistic view of the organizational climate factors affecting the software projects is still missing
in the literature. Therefore, this section deals with identification of the organizational climate
factors using factor analysis and a comparison of organizational climate factors across various
demographics and project characteristics which is also the third objective of the study.

Ph.D. Thesis 81
In order to identify and evaluate the factors affecting the success of the project based on primary
data, the respondents were asked to rate the climate factors that were present in their organization
while executing their project. These factors were identified after exhaustive literature review and
focused group interviews with the software professionals and were put on a 5 point likert scale
ranging from 1 as never present to 5 as always present. There were in all total 17 items in this part
of the instrument. These 17 items were chosen based on the data provided by the project
managers during the pilot study and extensive literature review. Table 4.15 enlists all these
factors that were translated into items in the questionnaire and were used for factor analysis.
Table 4.15: Variables of Organizational Climate Chosen for the Study
Items
1. There was clear understanding of roles and responsibilities within the group.
2. There was full utilization of my skills and abilities in the project.
3. There were opportunities to further develop my skills and abilities.
4. There were challenging tasks in my job role.
5. Employees consulted with one another when they needed support.
6. I felt valued as an employee.
7. There was a good balance between work and personal life
8.
High standards of excellence in service and delivery were set by senior
management
9. There was fair and just treatment of the employees by the management
10. My direct supervisor gave me helpful feedback on how to be more effective
11. My direct supervisor listened to my ideas and concerns
12. My direct supervisor appreciated the work I did.
13. There was clear understanding of work tasks which were to be performed.
14. Everyone took responsibility of his/her actions.
15. Work tasks were completed on time
16. There were adequate tools and technologies needed for performing work
17. Our products/services met our customers' expectations

Ph.D. Thesis 82
Cronbach alpha was calculated to measure the internal consistency and reliability of the
instrument. The Kaiser-Meyer-Olkin test was done to measure the homogeneity of variables and
Bartlett's test of sphericity was done to test for the correlation among the variables used. Table
4.16 summarizes the cronbach and KMO test values of this part of the instrument. As the value of
cronbach‘s alpha was greater than 0.7 and the value of KMO was greater than 0.7, the instrument
was considered reliable and was used for further analysis.
Table 4.16: Cronbach Alpha and KMO Test Value
Reliability Statistics
Cronbach's Alpha No. of Items
.903 17
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
.817
Bartlett's Test of Sphericity Approx. Chi-
Square
2945.270
df 136.000
Sig. .000
Since the organizational climate factors were large in number and were inter-related, factor
analysis was done to extract the factors responsible for the success of the software project.
Principal-component analysis was used as a pre-processing step to obtain a smaller number of
orthogonal domain metrics. Varimax was the rotation method. As per the Kaiser criterion, only
factors with eigenvalues greater than 1 were retained. Four factors in the initial solution had
eigenvalues greater than 1. Together, they accounted for almost 67% of the variability in the
original variables. The items falling under each of these factors were then dealt with quite
judiciously. Table 4.17 shows the communality and eigenvalues of the factors. It is followed by a
screeplot as shown in Figure 4.8.
Table 4.17: Table of Eigenvalues of the Factors
Variable Communality Factor Eigenvalue
Percentage
of Variance
Cumulative
Variance
There was clear understanding of roles
and responsibilities within the group.
.652 1 3.450 20.297 20.297
There was full utilization of my skills
and abilities in the project.
.622 2 3.271 19.241 39.538
There were opportunities to further
develop my skills and abilities.
.717 3 2.786 16.387 55.925
There were challenging tasks in my job
role.
.613 4 1.940 11.414 67.339

Ph.D. Thesis 83
Employees consulted with one another
when they needed support.
.728
I felt valued as an employee. .540
There was a good balance between
work and personal life
.620
High standards of excellence in
service and delivery were set by
senior management
.586
There was fair and just treatment of
the employees by the management
.635
My direct supervisor gave me helpful
feedback on how to be more
effective
.750
My direct supervisor listened to my
ideas and concerns
.779
My direct supervisor appreciated the
work I did.
.794
There was clear understanding of work
tasks which were to be performed.
.535
Everyone took responsibility of his/her
actions.
.761
Work tasks were completed on time .757
There were adequate tools and
technologies needed for performing
work
.660
Our products/services met our
customers' expectations
.698
1716151413121110987654321
Component Number
7
6
5
4
3
2
1
0
Eigenvalue
Scree Plot
The factors along with their loading are mentioned in table 4.18
Figure 4.8: Screeplot of the Components Extracted From Factor Analysis

Ph.D. Thesis 84
Table 4.18: Factor Pattern Matrix- Factors Responsible For the Success of the Software Project
ITEMS FACTOR 1 FACTOR 2 FACTOR 3 FACTOR 4
There was clear understanding of roles and
responsibilities within the group.
.418 .101 .146 .668
There was full utilization of my skills and abilities
in the project.
.222 .189 .713 .168
There were opportunities to further develop my
skills and abilities.
-.021 .265 .779 .199
There were challenging tasks in my job role. .034 .142 .748 .181
Employees consulted with one another when they
needed support.
.004 .039 .219 .824
I felt valued as an employee. .228 .555 .402 -.136
There was a good balance between work and
personal life
.671 .322 -.130 .222
High standards of excellence in service and
delivery were set by senior management
.564 .428 -.076 .282
There was fair and just treatment of the
employees by the management
.599 .484 -.064 .197
My direct supervisor gave me helpful feedback
on how to be more effective
.149 .832 .177 .065
My direct supervisor listened to my ideas and
concerns
.112 .848 .156 .154
My direct supervisor appreciated the work I did. .116 .826 .309 .059
There was clear understanding of work tasks which
were to be performed.
.489 .400 .307 .207
Everyone took responsibility of his/her actions. .454 .156 .313 .657
Work tasks were completed on time .784 -.024 .358 .117
There were adequate tools and technologies
needed for performing work
.790 .045 .181 .028
Our products/services met our customers'
expectations
.561 .160 .591 .090
The four factors extracted for further study are shown in Table 4.19. These 4 factors extracted
have been referred to as organizational climate dimensions in further analysis. The table 4.19 is
followed by the explanation of all these four dimensions.
Table 4.19: Factor Analysis of the Organizational Climate Factors
Factor Item
Factor
Loading
Factor Name
(Organizational
Climate
Dimensions)
1
There were adequate tools and technologies needed for
performing work
.790
High standard of
Work tasks were completed on time .784
There was a good balance between work and personal life .671
There was fair and just treatment of the employees by the
management
.599

Ph.D. Thesis 85
High standards of excellence in service and delivery were
set by senior management
.564
work tasks
There was clear understanding of work tasks which were
to be performed.
.489
2
My direct supervisor listened to my ideas and concerns .848
Effective supervision
My direct supervisor gave me helpful feedback on how to
be more effective
.832
My direct supervisor appreciated the work I did. .826
I felt valued as an employee. .555
3
There were opportunities to further develop my skills and
abilities.
.779
Intrinsic fulfilment
There were challenging tasks in my job role. .748
There was full utilization of my skills and abilities in the
project.
.713
Our products/services met our customers' expectations .591
4
Employees consulted with one another when they needed
support.
.824
Role ClarityThere was clear understanding of roles and
responsibilities within the group
.668
Everyone took responsibility of his/her actions .657
High standard of work task
This name was given to factor 1. The items that strongly correlated with factor 1 are:
There were adequate tools and technologies needed for performing work.
Work tasks were completed on time.
There was a good balance between work and personal life.
There was fair and just treatment of the employees by the management.
High standards of excellence in service and delivery were set by senior management.
There was clear understanding of work tasks which were to be performed.
All these items had a loading of more than 0.4. All these variables had one thing in common and
that was high standard of work task were maintained by the respondents while executing the
project. High standard of work task not only encompasses quality of work done but also the level
of commitment of the employees, clear definition of work tasks and life interest and work

Ph.D. Thesis 86
compatibility. A project can be successful only when the team members feel connected with the
project [31]. When there is group ―ownership,‖ project team members are more likely to treat the
plan and milestones seriously and put forth the necessary effort to get the work done. The most
effective way to achieve this ownership is to use the entire project team when putting together the
plan. The project team members should identify the tasks and should produce the work
breakdown structure. If the entire team estimates task duration and rates the dependency
relationships among the tasks, then there is more understanding and ownership in the team
resulting in on time completion of tasks and ensuing project success. The presence of fair and
just treatment of the employees by the management plays a very crucial role in motivating the
employees to give their best. When an organization has a free and open climate and a cooperative
spirit is embodied in the team members, it spreads to users so that all are more willing and ready
to contribute to project success. Studies have shown that effective management of the project,
team work, team autonomy, creative-thinking skills, team coordination, using support
technologies, identifying clear goals and assigning tasks to competent team members have been
proven to engender the software project success [55] [217] [218] [219] [220] [221] [222] [223]
[224].
Effective Supervision
This name was given to factor 2. All the items that strongly associated with this factor are:
My direct supervisor listened to my ideas and concerns.
My direct supervisor gave me helpful feedback on how to be more effective.
My direct supervisor appreciated the work I did.
I felt valued as an employee.
All the variables had a factor loading greater than 0.5. All the items had one commonality and
that is effective and facilitative supervision. Extraordinary demands are placed on software person-
nel—demands that require extraordinary commitments in order to accomplish the task at hand.
Generating this level of commitment through the process of team building is a primary responsibility
of any supervisor [225]. Software is mostly invisible and software projects also tend to be
invisible. To be successful, the supervisor whether he is a team lead, project lead or a project

Ph.D. Thesis 87
manager must make the product (the software being developed) and the project (the development
process) visible. Project goals, system requirements, project plans, project risks, individual
responsibilities, and project status must be visible and understood by all parties involved. Only
then can the project team make informed decisions and have a reasonably good opportunity for
success. Finding a person who can handle these challenges successfully is not easy. Few people
have the qualifications and attitudes necessary to succeed in managing complex projects. Having
a certain level of technical competence is helpful, but managerial and interpersonal skills are the
most important attributes of an effective supervisor. Researchers have stated that the employees
do not leave the organizations or projects, they leave their supervisor. Previous studies have laid
great emphasis on characteristics of an effective supervisor, team commitment and the success of
the software projects [56] [225] [226] [227] [228] [229] [230] [231]. Therefore, finding the right
minded person who values the team members, listens to their ideas and facilitates their
development is very crucial.
Intrinsic Fulfilment
This name was given to Factor 3. The items that were strongly associated with factor 3 are as
follows:
There were opportunities to further develop my skills and abilities.
There were challenging tasks in my job role.
There was full utilization of my skills and abilities in the project.
Our products/services met our customers' expectations.
All these factors had factor loadings of more than 0.5. Intrinsic fulfilment is when an individual is
motivated by internal factors, as opposed to external drivers of motivation. Intrinsic motivation is
the means by which the potent wellsprings of human energy and creativity are directed toward
people‘s desired goals [233]. Herzberg [234] describes motivation as being ―…based on growth
needs. Motivation is an internal engine, and its benefits show up over a long period of time.
Because the ultimate reward (of) motivation is personal growth, people don‘t need to be rewarded
incrementally (such as through raises and promotions).‖ As an internal growth need, motivation
stands in contrast to a ‗surface‘ ―fear of punishment or failure to get extrinsic rewards‖ [234].

Ph.D. Thesis 88
Thus, intrinsic motivation drives one to do things from the soul. Factors like growth
opportunities, substantial learning during and after the project, challenging tasks and feeling of
self-fulfilment arouse the instinct in an individual internally. Autonomy, proper feedback,
intellectually challenged work enables the team members to bring out the best in them. Mc
Connell [235] has also cited in his study that software professionals place higher value in the
intrinsic value of the work itself rather than in extrinsic factors, which include compensation,
working conditions and appropriate technical resources. Much of the research that focused on the
practitioner‘s perception of software project success explored, to some extent, employee
motivation [81] [236]. According to the researchers, the practitioner‘s perception of project
success is, at least in part, determined by components that are related to their motivation and that
motivation had the single largest impact on practitioner productivity [233] [235]. Therefore,
project managers need to establish a vision for the development team, hold the team accountable
for results, delegate tasks to the team in a manner that are ―challenging, clear and supportive‖ and
remove barriers to team productivity when necessary [80] [235] [237] [238].
Role Clarity
This name was given to Factor 4. The items that were strongly associated with factor 4 are as
follows:
Employees consulted with one another when they needed support.
There was clear understanding of roles and responsibilities within the group.
Everyone took responsibility of his/her actions.
All these factors had factor loadings of more than 0.6. All these items rated by the respondents
had one thing in common and that is clarity in roles and responsibilities. Role clarity is defined as
the "fit between the amount of information that a person has and the amount he (or she) requires
to perform the role adequately" [239]. A clear definition of roles and responsibilities provides a
mechanism to distinctly assign accountability to team members for carrying out a task. When
roles and responsibilities remain unclear, multiple untested assumptions often displace them
[240]. Opportunities realised or opportunities lost can be linked to how well an individual grasps
his/her role and the level of commitment to accountabilities, even the slightest vagueness here can
hurt an entire teams‘ ability to meet its objectives. Without a clear articulation of roles, a team

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can be sent sputtering whenever a new idea or problem presents itself. Not only does this result in
missed opportunities, rework and delays, it also creates an atmosphere of uncertainty and lack of
predictability. However, a clear and lucid definition of roles and responsibilities promotes
autonomy, ownership, and self-accountability. When team members are confident about what is
in their control and what is not, they can step forward to accept responsibility with full knowledge
of what is expected from them. Roles and responsibilities exercised out of a sense of ownership
inspire commitment towards the project and organization [241]. Furthermore, role clarity also
increases job satisfaction amongst the team members thereby, further strengthening the
commitment levels. Numerous studies have been conducted on establishing relationship between
role clarity, job satisfaction and commitment towards the project and organizations [242] [243]
[244] [245] [246] [247] [248]. Therefore, to ensure the success of the project, it is the
responsibility of the project manager to define specific, clear and lucid roles for the members of
the development team [232] [238].
4.3.2 Comparison of organizational climate factors across various personal and project
characteristics
The dimensions of organizational climate factors so formulated after the factor analysis were then
compared across the various personal characteristics of the respondents and the project
characteristics handled by the respondents chosen for the study. The personal characteristics
taken for the analysis included experience and designation while the project characteristics
included total team size, total time taken to complete the project and the total value of the project.
The comparisons are discussed as follows:
Personal characteristics
Designation
Duncan‘s Mean Test was applied to compare the dimensions of organizational climate among
three designation groups. Significant difference was found in the mean values of only two of the
dimensions of organizational climate as perceived by respondents of the various categories of
designation. High standards of work tasks and effective supervision showed significant

Ph.D. Thesis 90
differences in mean and standard deviation values. Table 4.20 shows all the values of mean and
standard deviation of the dimensions of software risk across the various designation groups.
Table 4.20: Comparisons of Organizational Climate Factors among Three Designation Groups
(D1= level 1, D2= level 2, D3 = level 3) Duncan‘s Mean Test
Organizational climate factors
D1 (N=116)
Mean S.D
D2 (N=141)
Mean S.D
D3 (N=43)
Mean S.D
D1
v/s
D2
D1
v/s
D3
D2
v/s
D3
F-value
High standard of work tasks 3.68 0.68 3.65 0.79 3.37 0.56 - * * 3.13*
Effective supervision 3.77 0.89 3.97 0.75 3.52 0.52 * - * 5.99**
Intrinsic fulfilment 3.89 0.61 4.01 0.68 4.04 0.64 - - - 1.38
Role clarity 4.00 0.62 3.99 0.76 3.80 0.68 - - - 1.47
*Significant at .05 level. ** Significant at .01 level
It can be seen from the table 4.20 that high standards of work tasks and effective supervision had
an F-value of 3.13 (significant at .05 level) and 5.99 (significant at .01 level) respectively. High
standards of work tasks had the highest mean (3.68) at D1 level. It was then followed by D2 with
a mean of 3.65. The differences were significant between two groups i.e. between D1 and D3 and
then D2 and D3. This means that respondents at relatively low designation such as level 1 and
level 2, share the same opinion about organizational climate. Their difference of opinion lies with
respondents from level 3. This is because the dynamics of organizational climate has a varied
effect on employees at different levels. High standards of work tasks are perceived more deeply
by employees at lower designations because they are the ones who are functionally more attached
to a given project. Similarly, in case of effective supervision, employees at lower levels perceive
its impact more than employees at higher levels. It can be seen from the table 4.20 that the mean
is highest in case of D2 and the difference is also significant in D1 v/s D2 and D2 v/s D3. This
means employees at D2 level have a different opinion regarding effective supervision and that the
presence of this factor in the organizational climate impacts the success of a software project.
Normally it is seen that employees at D2 level which comprises of project managers, senior
managers, account managers etc, supervise teams and are also supervised by COOs, CEOs,
Directors etc. Thus, such middle level respondents understand the impact of effective supervision
on success of a software project most deeply.
These findings are also supported by previous studies. Research has established that interactions
between risk factors are often driven by organizational factors and it varies with people at junior
or senior level [30].

Ph.D. Thesis 91
Total Experience
Duncan‘s Mean Test was applied to compare the dimensions of organizational climate among
three groups formed on the basis of total experience. Significant difference was found only in the
mean values of high standards of work task. Table 4.21 shows all the values of mean and standard
deviation of the dimensions of organizational climate across the various experience groups. It can
be seen that high standard of work tasks had an F value of 3.4, significant at 0.05 level. The
difference was significant only between two groups i.e. between E1 (upto 9 years of experience)
and E2 (10 to 14 years of experience); and E1 and E3 (more than 14 years of experience). E2 and
E3 had no significant difference between them. High standard of work tasks was ranked highest
by E1 respondents, followed by E2 and then E3. This is not much surprising as employees with
fewer years of experience have a completely different perception about the standards of work
tasks framed by the company as compared to veterans of the industry. It is because employees
with few years of experience are more involved with the work tasks and perceive them to be
highly present in the organization. As years go by and employees get more versed with
organizational climate, the perception regarding these factors changes as they start understanding
the nitty-gritty of the system and getting the holistic framework. Respondents in E2 and E3
category, therefore, have similar opinion about such attributes of organizational climate and
hence there is no significant difference between the two.
Table 4.21: Comparisons of Organizational Climate Factors among Three Experience Groups
(E1= upto 9 years, E2= 10 - 14, E3 = more than 14) Duncan‘s Mean Test
Organizational climate
factors
E1 (N=112)
Mean S.D
E2 (N=123)
Mean S.D
E3 (N=65)
Mean S.D
E1
v/s
E2
E1
v/s
E3
E2
v/s
E3
F-value
High standard of work tasks 3.76 0.72 3.57 0.74 3.49 0.66 * * - 3.40*
Effective supervision 3.85 0.91 3.89 0.73 3.65 0.67 - - - 2.09
Role clarity 4.03 0.64 3.91 0.76 3.98 0.70 - - - 0.903
Project characteristics
After comparing the dimensions of organizational climate across the various personal
characteristics, the same were compared across the various project characteristics. The project

Ph.D. Thesis 92
characteristics included total team size, total time taken to complete the project and the total value
of the project. The comparisons are discussed as follows:
Total Team Size
In this study, team size has been divided into three categories viz. T1 (upto 10 members), T2 (11-
20 members) and T3 (more than 20 members). Duncan‘s mean test was done to find out
significant difference among the means of these three categories. The findings in table 4.22
clearly show that only role clarity has a significant difference in all the three groups. There was
considerable difference in the mean values in all the three categories. Role clarity has the highest
mean in the category of T-1, which is the team size upto 10 members. It is closely followed by T2
and then T3. There is a significant difference in all three groups suggesting that perception of
respondents with different team sizes is quite different when it comes to role clarity. It is quite
natural also as role clarity is an attribute that is quite specific to number of employees working in
a team. F-test here denotes that teams with less than 10 members feel that presence of role clarity
in the organizational climate is more as compared to team sizes of 11-20 members or more than
20 members. Attributes like having clear understanding of roles and responsibilities, and
consulting with one another during a project is more visible when there are fewer members in a
group.
Table 4.22: Comparisons of Organizational Climate Factors among Three Team Size Groups
(T1 = upto 10, T2 = 11-20, T3 = more than 20) Duncan‘s Mean Test
Organizational climate
factors
T1 (N=100)
Mean S.D
T2 (N=89)
Mean S.D
T3 (N=111)
Mean S.D
T1
v/s
T2
T1
v/s
T3
T2
v/s
T3
F-value
High standard of work tasks 3.66 0.75 3.54 0.71 3.66 0.71 - - - 0.7616
Intrinsic fulfillment 3.91 0.78 3.93 0.53 4.05 0.58 - - - 1.5515
Role clarity 4.17 0.68 3.73 0.69 3.97 0.67 * * * 9.7588**
Total Duration
Total duration of a project was categorized under three heads viz. TT1 (upto 9 months); TT2 (10-
19 months); and TT3 (more than 19 months). The organizational climate factors were then
compared across these three categories using Duncan‘s Mean Test. The findings as shown in

Ph.D. Thesis 93
table 4.23, shows that none of the F-values were significant. Thus, it can not be said with
statistical confidence that the organizational climate dimensions vary with the time duration taken
by a project.
Table 4.23: Comparisons of Organizational Climate Factors among Three Total Time Size Groups
(TT1 = upto 9 months, TT2 = 10-19 months, TT3 = more than 19 months) Duncan‘s Mean Test
TT1 (N=113)
Mean S.D
TT2 (N=96)
Mean S.D
TT3 (N=111)
Mean S.D
TT1
v/s
TT2
TT1
v/s
TT3
TT2
v/s
TT3
F-value
Intrinsic fulfillment 4.05 0.71 3.91 0.65 3.93 0.57 - - - 1.2368
Role clarity 3.98 0.48 4.08 0.67 3.87 0.63 - - - 2.2195
*Significant at .05 level.
** Significant at .01 level.
Total value
The total dollar value of projects in which the respondents were involved are divided in three
categories viz. V1 (upto 0.70 mn dollars); V2 (0.71-2.00 mn dollars); and V3 (more than 2.00 mn
dollars). Duncan‘s mean test was applied to see if there was any difference in the mean values of
the organizational climate factors among the three categories of dollar value associated with the
last executed projects. As is clear from the table 4.24, only role clarity had significant difference
between V1 and V3.
There was considerable difference in the mean values in all three categories. Role clarity has the
highest mean in the category of V1, which is value up to 0.70mn dollars. It is followed by V2 and
then V3. There is a significant difference in V1 and V3 suggesting that the role clarity is higher in
the project of value less than seventy one thousand dollars than compared to higher value
projects. The difference can be attributed to the size of the project. In India support and
maintenance projects are generally of higher value. These projects involve a large number of

Ph.D. Thesis 94
team members and many interdependencies with the client, user and other third party vendors.
Thus role clarity is bound to diminish with more expensive projects.
Table 4.24: Comparisons of Organizational Climate Factors among Three Value Groups
(V1 = upto 0.70 mn dollars, V2 = 0.71-2.00 mn dollars, V3 = more than 2.00 mn dollars) Duncan‘s Mean Test
V1 (N=102)
Mean S.D
V2 (N=89)
Mean S.D
V3 (N=109)
Mean S.D
V1
v/s
V2
V1
v/s
V3
V2
v/s
V3
F-value
Role clarity 4.08 0.79 3.99 0.61 3.84 0.67 - * - 3.1635*
Thus, the identification, assigning of names and comparison of the software risk dimensions and
organizational climate dimensions across various personal and project characteristics are
complete. The next step involves calculating the mean and standard deviations of the four risk
dimensions, organizational climate dimensions and the success (overall and the three
performance constructs) of the project. Besides this, the correlation between project specific risk
dimensions, organizational climate dimensions, and the success of the software projects and its
three performance constructs namely budget, schedule and quality have also been calculated.
These all have been presented in section III of the chapter.
4.4 SECTION III
This section deals with the computation of mean and standard deviation of the project specific
risk dimensions, organizational climate dimensions and success (overall and the three
performance constructs) of the software project. Besides this, the correlation between i) four risk
dimensions, four organizational climate dimensions and overall success of the project, ii) four
risk and organizational climate dimensions and the three success performance constructs and
finally iii) four organizational climate dimensions, designation and project specific risk
dimensions have been calculated.

Ph.D. Thesis 95
4.4.1 Mean and standard deviations of the project specific software risk dimensions,
organizational climate dimensions and the success of the software project and its three
constructs.
4.4.1.1 Project Specific Risk Dimensions
Before determining the correlates and impact of the project specific risk dimensions on the
success of the project, mean and standard deviations of the risk dimensions were calculated, as
this helps in understanding them better. The respondents were asked to rate the effect of each risk
on the success of their last executed project on a scale of 5, where 5 was too much effect and 1
was no effect at all. After the factor analysis, when four factors emerged, the score of each of the
factors was computed by taking out the mean of the items falling under each factor. For e.g. in
order to calculate the mean of dependability, the score of all the items i.e. third party
dependencies, inability to meet specifications and inadequate measurement tools for reliability
were first added and then mean was calculated. Similarly, means and standard deviations were
calculated for all the factors.
The ranking of the dimensions based on the means and standard deviations is shown in table 4.25.
Figure 4.9 gives the graphical representation of the same.
Table 4.25: Means and Standard Deviation of the Risk Factors
S. No. Factor Name Mean Standard Deviation
1 SRS Variability risk 3.06 1.06
2 Dependability risk 2.84 1.13
3 Team Composition risk 2.75 1.03
4 Control Processes risk
2.52 1.05
It is clear from table 4.25 that SRS variability risk has the highest mean of 3.06, stating that most
of the respondents consider Software Requirement Specification (SRS) variability as the most
important risk affecting the software projects. Standard deviation for SRS variability risk is 1.06.
The SRS variability risk is closely followed by dependability risk with a mean of 2.84, team
composition risk mean 2.75 and finally control processes risk mean 2.52.

Ph.D. Thesis 96
Figure 4.9: Graphical Representation of Mean and Standard Deviations of the Risk Factors
4.4.1.2 Organizational Climate Dimensions
The mean and standard deviation helps in explaining the organizational climate dimensions in a
more lucid manner. The organizational climate dimensions identified were as follows:
1. High standards of work tasks,
2. Effective supervision,
3. Intrinsic fulfilment,
4. Role clarity.
The respondents were asked to rate the presence of the organizational climate factor during the
execution of the project on a scale of 5, where 5 was always present and 1 was never present.
After the factor analysis, the score of each of the factors was computed by taking out the mean of
the items falling under each factor. The mean and standard deviation of each of the factors are
shown in table 4.26
It is clear from the table 4.26, that intrinsic fulfilment factors has the highest mean of 3.98,
thereby meaning that intrinsic fulfilment was present most of the times during the execution of
the project. Standard deviation for the same is 0.65. It is closely followed by role clarity factors

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(mean=3.97, sd= 0.70), then effective supervision factors (mean=3.82, sd= 0.79) and finally high
standards of work tasks factors (mean=3.62, sd=0.72). The primary reason behind the low value
of high standards of work tasks is the balance between the work and personal life. Most of the
respondents irrespective of the designation ranked this variable as seldom present which means
that software industry is plagued with improper work and life balance. The mean and standard
deviation of each of the factors are shown in table 4.26 and also graphically represented in figure
4.10.
Table 4.26: Means and Standard Deviation of the Organizational Climate Factors
1 Intrinsic fulfilment
3.98 0.65
2 Role Clarity
3.97 0.70
3 Effective supervision
3.82 0.79
4 High standard of work tasks
3.62 0.72
Figure 4.10: Graphical Representation of Means and Standard Deviations of Organizational Climate Factors

Ph.D. Thesis 98
4.4.1.3 Overall Success and the Three Performance Constructs
Having calculated the mean and standard deviation of the independent variables i.e. risk factors
and organizational climate, the next step is to calculate the mean and standard deviations for the
dependent variables i.e. success of the project and its three performance constructs. There are
many different definitions of project success and success today is defined on the basis of the
stakeholders [80]. However for the present study the traditional definition of success has been
used which covers meeting time, cost and quality [9] [82] [86] [236]. The instrument contained
questions on the overall success of last executed project and on the three performance constructs.
The respondents were asked to rate the overall success and the performance constructs. The
question had five options ranging from 1- less than 50%, 2 - 50-60%, 3 – 60-80%, 4 – 80-90%
and 5 – more than 90% success. The mean and standard deviation of the project success and the
three parameters is shown in table 4.27 followed by a graphical representation of the same in
figure 4.11.
The analysis reveals a very interesting finding. Although the overall success rate of the project as
perceived by the IT professionals is 3.19 with a standard deviation of 1.28, the quality
performance of the project has a higher mean at 3.70 (1.15) as shown in the table 4.27. This
shows that the Indian software professionals pay more attention to the quality aspect of the
software and feel that meeting the quality performance of the project is most important followed
by schedule and budget performance respectively. This is also in conformity with the study done
by Agarwal and Rathod [74] on the Indian software professionals. Besides this, it is also very
interesting to note that the means of all the three performance constructs are more than the overall
success rate. This means that there are more performance constructs of success other than budget,
schedule and quality in the minds of the software professionals. After a detailed discussion with
few project managers of reputed software companies in Noida, it was found that besides these
three performance constructs, there were many intrinsic factors associated with the success of the
project. For example, sense of achievement, learning, challenging work, satisfaction of the client
etc. This means that wherein the software professionals were able to meet the three success
parameters successfully they lagged somewhere in having a sense of achievement or learning
anything new from the project. The mean and standard deviation of the other two performance

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constructs of success are: budget performance 3.27 (1.35) and schedule performance 3.61(1.20).
Figure 4.11 gives the graphical representation of the same.
Table 4.27: Means and Standard Deviation of the Project Success and Its Various Performance Constructs
1 Quality performance 3.70 1.15
2 Schedule performance 3.61 1.20
3 Budget performance 3.27 1.35
4 Success of the project 3.19 1.28
Figure 4.11: Graphical Representation of Mean and Standard Deviations of the Project Success and Its Performance
Constructs
4.4.2 Correlates of Software Risk Dimensions and Organizational Climate Dimensions on
the Success of the Software Project
The next step involved computing the correlations of four dimensions of project specific risk,
four dimensions of organizational climate with the overall success (and three performance
constructs) of the project. This was done to find out the relationship between the overall success,
three performance constructs and the various risk and organizational climate dimensions. The

Ph.D. Thesis 100
correlation coefficient of the eight independent variables and the overall success of the project - a
dependent variable are shown in table 4.28.
Table 4.28: Relationships (Correlation Coefficients) of Risk Factors and Organizational Climate Factors with the
Success of the Project
(N= 300)
** Significant at .01 level. NS – not significant
The table 4.28 clearly shows that out of the eight independent variables seven variables have
significant correlations with the dependent variable that is success of the project. All the
correlations of the risk factors with the success of the project are negative, while all the
correlations are positive between the organizational climate factors and success of the project. It
should be noted here that the dependent variable in the equations are strongly correlated with
most of the independent variables. These findings align with many previous researches done in
the same domain. The four factors of risk; SRS variability [15] [125] [133], team composition
[15] [17] [42] [153], control processes [58] [59] [206] and dependability [41] [142] [154]
negatively affect the success of the project. The more is the variability in the requirement, the
more is the chance of the project getting delayed or failed. Similarly, if there is no commitment
from the project team, there is high attrition in the team, there is insufficient testing, the subject
matter expert is not available or there is too much dependency on the third party the chances of
the project getting delayed or failed increases.
While, on the other hand the organizational climate dimensions positively correlates with the
success of the project. These findings align with many previous researches done in the same
Risk and Organizational Climate Dimensions Success of the Project
SRS Variability Risk -0.4647**
Team Composition Risk -0.4347**
Control Processes Risk -0.2717**
Dependability Risk -0.4493**
Climate of High standard of work tasks 0.3009**
Climate of Effective supervision 0.0162NS
Climate of Intrinsic fulfilment 0.2186**
Climate of Role clarity 0.2313**

Ph.D. Thesis 101
domain. These four factors of organizational climate; high standards of work tasks [31] [55] [218]
[219], effective supervision [56] [230] [231] [232], intrinsic fulfilment [235] [237] and role
clarity [242] [243] [244] [245] [246] positively affect the success of the project. Higher is the
standard of work tasks set by the organization, more is the chance of the project success.
Similarly, if the project has a clear division of roles and responsibilities and the team members
are intrinsically motivated and committed to the project, the project is bound to be a success.
4.4.3 Correlates of Software Risk Dimensions and Organizational Climate Dimensions on
the Three Performance Constructs of Success of the Software Project
After assessing the impact of the project specific risk dimensions and the organizational climate
dimensions on the overall success of the project, the correlations between the project specific risk
dimensions, organizational climate dimensions on the three success constructs were also
calculated.
Table 4.29: Relationships (Correlation Coefficients) of Risk Factors and Organizational Climate with the Three
Performance Constructs of Success of the Project
(N= 300)
* Significant at .05 level. ** Significant at .01 level. NS – not significant
The table 4.29 shows all the correlations between the eight independent variables with the three
success performance constructs. As is clear from the table 4.29, all the risk dimensions have
significant correlations with all success performance constructs i.e. the budget, schedule and
quality. All the four risk dimensions negatively correlate with the budget, schedule and quality
performance of the project. This means that the budget, schedule and quality performance of the
Software Risk and Organizational Climate
Dimensions
Budget
performance
Schedule
performance
Quality
performance
SRS Variability Risk -0.3532** -0.2559** -0.2345**
Team Composition Risk -0.3633** -0.3476** -0.2699**
Control Processes Risk -0.2178** -0.1421* -0.2165**
Dependability Risk -0.3688** -0.2536** -0.2270**
Climate of High standard of work tasks 0.2579** 0.1616** 0.0997NS
Climate of Effective supervision 0.0387NS 0.0168NS 0.0176NS
Climate of Intrinsic fulfilment 0.1604** 0.1201* 0.1019NS
Climate of Role clarity 0.1954** 0.1673* 0.1823**

Ph.D. Thesis 102
project will decrease with the increase in requirement variability, poor control processes,
inexperienced or incompetent team composition and dependability on the third party. While on
the other hand, out of four organizational climate dimensions only few dimensions show a
significant positive relation with the three success constructs. Out of which, role clarity shows a
significant positive correlation with all the three dependent variables namely budget, schedule
and quality. While effective supervision shows no relation with any of the three constructs.
4.4.4 Correlates and Impact Assessment of Organizational Climate Dimensions and
Demographics on the Software Risk Dimensions
In order to identify relationship between demographics characteristics and organizational climate
factors with the software risk dimensions, correlation between these were computed. The
independent variables were the three demographic characteristics namely designation, total
experience and age and four organizational climate dimensions namely high standards of work
tasks, effective supervision, intrinsic fulfilment and role clarity. While, the dependent variables
were four project specific risk dimensions namely SRS variability risk, team composition risk,
control process risk and dependability risk. The correlation coefficients between the seven
independent variables and the four dependant variable are shown in table 4.30.
Table 4.30: Relationships (Correlation Coefficients) of Demographics and Organizational Climate Dimensions with
the Project Specific Risk Dimensions
(N= 300)
Demographics and Organizational
Climate Dimensions
SRS
Variability
Risk
Team
Composition
Risk
Control
Process Risk
Dependability
Risk
Designation -0.340** -0.258** -0.283** -0.286**
Total experience -0.173** -0.174** -0.152** -0.255**
Age -0.224** -0.177** -0.172** -0.241**
Climate of High standards of work tasks 0.021NS 0.037NS 0.072NS -0.063NS
Climate of Effective supervision 0.100NS 0.028NS 0.053NS 0.197**
Climate of Intrinsic fulfilment -0.082NS -0.031NS -0.108NS -0.043NS
Climate of Role clarity -0.191** -0.098NS -0.110NS -0.136*
* Correlation is significant at the 0.05 level. ** Correlation is significant at the 0.01 level. NS – not significant
As is clear from the table 4.30, all the background variables have a significant correlation with all
the dependent variables. All the variables negatively correlate with the four risk dimensions. This

Ph.D. Thesis 103
means that the perception about the risks greatly vary as the employees move ahead in their
career and gain more experience. A negative correlation indicates that the project managers and
senior project managers with an experience of 11-15 years perceive these risk factors as having
less impact on the success than perceived by the project leads with an experience of 4 -7 years.
While on the other hand, out of four organizational climate dimensions only few dimensions
show a significant relation with the four risk dimensions. Role clarity shows a significant
negative correlation with two dimensions of risk namely SRS variability risk, and dependability
risk while effective supervision show a significant positive correlation with one dimension, that is
dependability risk.
4.5 SECTION IV
After calculating the correlates and determinants of the overall success and the three performance
constructs and four risk dimensions the next section details out the regression process carried out
to test the hypothesis.
A) To test the relation of the organizational climate dimensions and demographic characteristics
with software risk dimensions following hypotheses were formulated.
Hypothesis related to SRS variability risk
H1a. The demographic characteristics and the organizational climate dimensions affect the
SRS variability risk.
Hypothesis related to Team composition risk
H1b. The demographic characteristics and the organizational climate dimensions affect the
team composition risk.
Hypothesis related to Control processes risk
H1c. The demographic characteristics and the organizational climate dimensions affect the
control process risk.

Ph.D. Thesis 104
Hypothesis related to the Dependability risk
H1d. The demographic characteristics and the organizational climate dimensions affect the
dependability risk.
B) To test the relation of the organizational climate dimensions and software risk dimensions
characteristics with the overall success and three performance constructs following
hypotheses were formulated.
Hypothesis related to the overall success of the project
H2. The organizational climate and project specific risk dimensions affect the overall success
of the software projects.
Hypothesis related to the budget performance of the project
H3. The organizational climate and project specific risk dimensions affect the budget
performance of the software projects.
Hypothesis related to the schedule performance of the project
H4. The organizational climate and project specific risk dimensions affect the schedule
Hypothesis related to the quality performance of the project
H5. The organizational climate and project specific risk dimensions affect the quality
4.5.1 Regression Model for Predicting the Affect of Organizational Climate Dimensions and
Demographic Characteristics on the Software Risk Dimensions
This section works out the regression model of the demographics and organizational climate
dimensions that impact the project specific software risk dimensions. It considers the regression
equation in the model and examines the strength of the independent variables in predicting the

Ph.D. Thesis 105
dependent variable. It was assumed that there is a linear relationship between the organizational
climate dimensions, demographics and the software risk dimensions. A stepwise regression
analysis was conducted with the dependent variable as the four dimensions of software risk
namely SRS variability, team composition, control processes and dependability risk, and the
independent variables as the demographics and organizational climate factors. It must be noted
that to avoid multi-collinearity , out of the three demographics characteristics, only two, namely
designation and total experience were taken as independent variable while age was ignored as it
showed a very high correlation with designation (0.690**) and total experience (0.782**).
Further, the project specific risk dimensions showed significant relation with the demographic
characteristics. In order to strengthen this relationship and know the direction of perception, the
regression analysis was conducted. The regression model between the organizational climate
factors and demographic characteristics with the SRS variability risk, team composition, control
processes and dependability risk has been examined in the following section.
4.5.1.1 SRS Variability Risk
A regression analysis was conducted to comprehend the impact of designation, experience and
organizational climate factors on the SRS variability risk affecting the software project. The four
climate dimensions and the two demographics characteristics were then put in the model as
independent variables and SRS variability risk was put as the dependent variable. The equation
which emerged after the process is as follows. Table 4.31 summarizes the determinants of the
equation.
Y1= 4.776 - 0.35X1 - 0.28X2 – 0.177X3
Where,
Y1 = SRS variability risk
X1 = Designation X2 = Role clarity
X3 = Effective supervision
Table 4.31: Determinants of Organizational Climate Affecting the SRS Variability Risk in the Software Projects
(N=300)
Independent Variables
Dependent variable: SRS Variability Risk
Beta Simple r t-value
Designation -.355** -0.340** 6.777

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