chapter 4 analysis and findings -...

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


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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


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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


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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


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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


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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

Eig

en

valu

e

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

Risk Lack 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 Risk Inability to meet specifications 0.598

Inadequate measurement tools for reliability 0.578


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

Eige

nval

ue

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 Clarity There 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


Ph.D. Thesis 89

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


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


Ph.D. Thesis 97

(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


Ph.D. Thesis 99

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


Ph.D. Thesis 106

Role clarity -.276** -0.1908** 4.982

Effective supervision -.177** 0.1002NS 3.200

Multiple R = 0.44

R Square = 0.19

** Significant at 0.01 level

The value of multiple R is 0.44 and the value of R square is 0.19 in the equation. It states that

19% of the SRS variability risk can be controlled by these factors. 19% is a significant value that

explains the causes of this risk as only organizational climate has been taken into consideration.

The rest 81% can be attributed to so many other factors which are scattered and individually

contribute only little to the SRS variability risk. It should be noted here, that the dependent

variable in the equation is the SRS variability risk and three independent variables namely

designation, role clarity and effective supervision are negatively correlated with it. An inverse

relation of designation with SRS variability indicates that team members at the lower levels such

as project lead, technical lead or senior software engineer perceive SRS variability risk as having

a high impact on the project. This has also been recapitulated by many researchers such as [30]

[126]. Besides this, SRS variability risk gets affected by two dimensions of organizational

climate namely role clarity and effective supervision. A clear and lucid understanding of the roles

and responsibilities enables the team members to perform their jobs with utmost care and

consideration.

Besides this, timely and effective feedback from the supervisor helps in controlling the

requirement variability risk in its initial stage only. Feedback is a salient mean of guiding,

motivating, and reinforcing effective behaviours within the team thereby reducing detrimental

effects on the project [31] [218] [231] [240] [249]. To avoid potential miscommunications and

complexity, measures such as the establishment of clear feedback mechanisms (such as weekly

meetings, face-to-face meetings, early prototyping) is a very common mitigation tool which must

be used most effectively. Thus, an appropriate and helpful feedback from the supervisor reduces

this risk to a great extent. The equation explains that, the highest contribution in annihilating this

risk is made by the designation followed by role clarity amongst the team members. Role clarity

includes clear understanding of roles and responsibilities within the team, acceptance of one‘s

responsibilities and open and free consultation amongst the team members. While effective

supervision includes listening to new ideas and concerns by the leader, leader giving valuable


Ph.D. Thesis 107

feedback, appreciation by the leader for the work done and the feeling of a valued employee.

Thus, the alternate hypothesis H1a is accepted. The figure 4.12 explains the relationship of role

clarity, effective supervision and designation with SRS variability risk.

4.5.1.2 Team Composition Risk

To gauge the impact of organizational climate factors and the perception of the team members at

various designation and experience on the team composition risk, a stepwise regression analysis

was conducted. The four climate dimensions and two demographics characteristics were then put

in the model as independent variables and team composition risk was put as the dependent

Role Clarity

Open consultation

Clear understanding of roles

Ownership of responsibility


Good listener

Timely feedback

Appreciation of work

Feeling of valued employee

Designation

Level 1

Level 2

Level 3

SRS VARIABILITY RISK

R2 = 0.19

β = - 0.28**

β = - 0.17**

β = - 0.35**

Figure 4.12: Relationship of Role Clarity, Effective Supervision and Designation with SRS Variability Risk


Ph.D. Thesis 108

variable. The equation which emerged after the process is as follows. Table 4.32 summarizes the

determinants of the equation.

Y2= 4.152 - 0.27X1 - 0.12X2

Where,

Y2= Team Composition risk


Table 4.32: Determinants of Organizational Climate Affecting the Team Composition Risk in the Software Projects

(N=300)

Independent Variables Dependent variable: Team Composition Risk


Designation -.267** -0.258** 4.784

Role clarity -.119* -0.098NS 2.133

Multiple R = 0.28

R Square = 0.08



With the multiple R of 0.28 and R square at 8% this risk significantly gets affected by the

presence of high degree of role clarity in the organization. 8% is a significant value that explains

the impact of the independent variables on team composition risk. It must be noted here that only

a part of the internal aspect of the organization has been considered as independent variables. The

environmental factors such as competition, political, technological socio/cultural factors which

may be contributing the rest 92% were not taken for the analysis. The two variables that

independently contribute towards the team composition risk are designation and role clarity.

Designation is a part of demographic and a negative beta (-0.267**) indicates that as the

employee moves ahead in his career, the risk of working with inexperienced team or lack of

commitment from the project team etc. is perceived as having less impact in the project. Apart

from this, the regression equation also indicates that role clarity significantly contributes in

reducing the team composition risk. Belbin [250] has argued that team members‘ sense of

commitment grows stronger as they better understand their own roles within the team. Role

clarity takes on greater significance since each person has to take on a definite task and complete

it so that the team as a whole can accomplish the implementation effort thereby, ensuring greater

degree of commitment levels from the team. A project with a right mix of team members,

wherein, each member is conscientious towards his roles and responsibilities is an ideal stage.


Ph.D. Thesis 109

Besides this, a free and open environment in the team, where team members can freely discuss

and seek advice from each other, the team composition risk can be eliminated. Thus, lucidity in

defining the roles and responsibilities plays a very important role in controlling the team

composition risk. Therefore, the alternate hypothesis stating that organizational climate

dimensions and demographics affect the team composition risk is accepted. The same has been

diagrammatically represented in the figure 4.13.

4.5.1.3 Control Processes Risk

The third risk factor identified after performing factor analysis was control processes risk. It was

hypothesized that robust organizational climate factors can reduce the impact of this risk on the

software projects. Further, hypothesis was also made regarding the perception of various

demographics characteristics on this risk. A stepwise regression analysis was conducted for

analyzing the relation between the control processes risk (poor documentation, poor code and

maintenance procedures, insufficient testing and poor configuration control), four organizational

Role Clarity

Open consultation



Designation

Level 1

Level 2

Level 3

TEAM COMPOSITION RISK

R2 = 0.08

β = - 0.12**

β = - 0.27**

Figure 4.13: Relationship of Role Clarity and Designation with Team Composition Risk


Ph.D. Thesis 110

climate factors (high standards of work tasks, effective supervision, intrinsic fulfilment and role

clarity) and two demographic characteristics (designation and total experience). The table 4.33

summarizes the determinants of the equation. A diagrammatic representation of the relationship

of control processes risk with organizational climate dimensions and demographic characteristics

has also been presented in figure 4.14.

Y3= 3.743 - 0.28X1 - 0.22X2 – 0.17X3

Where,

Y3= Control processes risk


X3 = High standard of work tasks

Table 4.33: Determinants of Organizational Climate Affecting the Control Processes Risk In the Software Projects

(N=300)

Independent Variables Dependent variable: Control Processes Risk


Designation -.281** -0.283** 5.119

Role clarity -.232** -0.110NS 3.474

High standards of work tasks -.173** 0.721NS 2.580

Multiple R = 0.34

R Square = 0.12


The value of multiple R is 0.34 while the value of R square is 0.12. This means that 12% of the

control processes risk can be explained by the three independent variables. As is clear from the

table 4.33, the control processes risk is negatively affected by the three independent variables

namely designation, role clarity and high standard of work tasks. The analysis revealed the

perception about the control processes risk at various designation levels. It shows that as the team

member climbs the organization‘s hierarchical ladder his/her perception about the software risk

changes and he starts perceiving control processes risk as a controllable risk with less impact on

project. Besides this, the empirical analysis also revealed the two organizational climate factors

that impinge the control processes risk. These two climate factors are role clarity and high

standard of work tasks. High standards of excellence means adoption of well planned and

formalized procedures such as benchmarking, six sigma processes, total quality management and

CMM level 5 processes which are the highest accepted industry standards worldwide. If an


Ph.D. Thesis 111

organization adopts these, the risk of poor control processes can be controlled to a great extent.

Role clarity and high standards of work tasks enables the project manager and his team to clearly

define and follow the processes to be adopted to successfully complete the project. A clear

definition of roles provides clarity and concreteness to the tasks performed by the team members

[241]. This enables the project manager to control the risks of poor configuration and poor

documentation and insufficient testing. Besides, setting up of high standard of excellence in

service and delivery by the management also ensures a check on this risk factor thus ensuring the

success of the project.

4.5.1.4 Dependability Risk

A regression analysis was conducted to comprehend the relation of the demographic

characteristics and understand the impact organizational climate factors on the dependability risk

of the software project. The six dimensions were then put in the model as independent variables

Role Clarity

Open consultation



High Standards of work tasks

On time completion

Adequate tools

Clear understanding

High standards of excellence

Designation

Level 1

Level 2

Level 3

CONTROL PROCESSES RISK

R2 = 0.12

β = - 0.23**

β = - 0.17**

β = - 0.28**

Figure 4.14: Relationship of Role Clarity, High Standards of Work Tasks and Designation with Control Processes Risk


Ph.D. Thesis 112

and dependability risk was put as the dependent variable. The equation which emerged after the

process is as follows. Table 4.34 summarizes the determinants of the equation.

Y4= 3.985 - 0.31X1 - 0.33X2 – 0.16X3 – 0.18X4

Where,

Y4= Dependability risk

X1 = Designation X2 = Effective supervision

X3 = Role clarity X4 = High standards of work tasks

Table 4.34: Determinants of Organizational Climate Affecting the Dependability Risk In the Software Projects

(N=300)

Independent Variables Dependent variable: Dependability Risk


Designation -.308** -0.286** 5.827

Effective supervision -.333** -0.198** 4.573

Role clarity -.164** -0.136* 3.789

High standards of work tasks -.179** -0.063NS 2.497

Multiple R = 0.43

R Square = 0.18



18% of the dependability risk can be controlled by these factors thus, leading to the acceptance of

alternate hypothesis H1d. The rest 82% can be attributed to so many other factors such as

managerial issues, people issues, procedural issues and technical competency of the third party

vendors which individually contribute to the dependability risk of the software project. It should

be noted here that the dependent variable in the equation is the dependability risk and four

independent variables are negatively correlated with it. The first independent variable showing an

inverse relation with dependability risk is designation. This means that as the employee climbs

the organizational hierarchy his/her perception about the risk changes. Hence the perception of a

project manager about the dependability risk is different from that of a senior software engineer.

A negative beta (-0.308**) shows that the project manager perceives dependability risk as a

controllable risk with little impact on the project. Besides this, better clarity in roles, an adept

supervisor and setting up of high standards of work tasks contributes positively in reducing the

risk of dependability. If the team member has clear understanding of roles and responsibilities


Ph.D. Thesis 113

and a will to accept his responsibilities he will notify the manager well in advance, in case, if he

sees any deviation from the third party vendor thus reducing the risk of dependability.

Furthermore, it is extremely important for the organization to have a disciplined approach and a

well laid down plan where the work is finished much before time and is tested for reliability

before it is made available to the client [251]. Finally, the presence of a proficient supervisor,

who listens to the concerns of the team members and gives valuable and timely feedback, can to a

great extent control the risk of dependability. The equation shows that highest contribution in

abrogating this risk is made by effective supervision. Effective supervision includes listening of

new ideas and concerns by the leader, leader giving important feedback, recognition of the work

done by the team members and the feeling of a valued employee. The relationship of role clarity,

effective supervision, high standards of work tasks and designation with dependability risk has

been explained with a help of a figure 4.15 presented below.


On time completion

Adequate tools

Clear understanding


Role Clarity

Open consultation




Good listener

Timely feedback



Designation

Level 1

Level 2

Level 3

DEPENDABILITY RISK

R2 = 0.18

β = - 0.16**

β = - 0.33**

β = - 0.31**

β = - 0.18**

Figure 4.15: Relationship of Role Clarity, Effective Supervision, High Standards of Work Tasks and Designation with Dependability

Risk


Ph.D. Thesis 114

4.5.2 Regression Model for Predicting the Overall Success of the Project

This section works out the regression model of the project specific risk dimensions and

organizational climate dimensions that impact the software project‘s success. It considers the

regression equation in the model and examines the strength of the independent variables in

predicting the dependent variable. It was assumed that there is a linear relationship between the

risk dimensions and organizational climate dimensions extracted through factor analysis and their

impact on the project‘s success. A regression analysis was conducted with the dependent variable

as overall success of the project and the independent variables as risk and organizational climate

dimensions. The eight dimensions were then put in the model as independent variables and

overall success of the project was put as the dependent variable. Out of eight, five variables

contributed significantly to the equation. The equation which emerged after the process is as

follows. Table 4.35 summarizes the determinants of the equation.

Y1= 3.302 - 0.35X1 + 0.29X2 - 0.25X3 - 0.27X4 - 0.19X5

Where,

Y1 = Success of the software project

X1 = SRS variability risk

X2 = High standard of work tasks

X3 = Team composition risk

X4 = Control processes risk

X5 = Dependability risk

Table 4.35: Determinants of Risk Factors and Organizational Climate Affecting the Success of the Software Projects

(N=300)

Independent Variables Dependent variable: Overall Success


SRS Variability risk -.349** -0.4647** 4.463

High standard of work tasks .286** 0.3009** 6.091

Team Composition risk -.250** -0.4347** 3.247

Control Processes risk -.269** -0.2717** 3.574

Dependability risk -.196** -0.4493** 2.766

Multiple R = 0.60

R Square = 0.37



Ph.D. Thesis 115


37% of the project success gets affected by these factors. As is clear from the table 4.35, the

probability of project‘s success reduces due to variability in requirement gathering, low morale

and high attrition in the team, poor documentation and control and finally too much dependability

on third party. While the probability of success of the project increases with setting up high

standards of work tasks. Thus, the hypothesis H2 is accepted. However, it should be noted here,

that the dependent variable in the equation is the overall success of the software project and four

independent variables are negatively correlated with it while one is positively correlated. That

means the pessimistic dimensions like SRS variability risk, team composition risk, control

processes risk and dependability risk are negatively correlated and negatively affect the success

of the software project. While positive dimensions like high standards of work tasks are

positively correlated and contribute positively to the success of the software project. The equation

explains that highest contribution to delay in project is made by the variability in the requirement

gathering and this includes conflicting and continuous requirement changes, inaccurate

requirement analysis, miscommunication of requirements etc. Similarly poor team composition

which includes lack of availability of domain expert, working with inexperienced team, team

diversity, lack of commitment from the project team, also contribute to the delay or failure of the

software project. Only one factor contributes positively to the success of the project i.e. high

standard of work tasks. All these factors have been discussed below:

SRS variability risk

It is a well known fact that variability in requirement gathering and analysis impacts the success

of the project to an extent that it can lead to a complete failure of the project. With a beta value of

0.35 and a correlation coefficient of 0.46, significant at 0.01 level, this factor impacts

significantly and quite largely on the success of the project. This is quite evident from the fact,

that the entire project is dependent on the correct set of requirements and any miscommunication,

misunderstanding or error can prove fatal to the project. Another important point that needs a

mention here is the ability of the project manager to make correct estimation of budget, schedule,

human resource requirements etc. Any error in estimation can prove extremely deadly during the

later stages of the project when both the budget and schedule are at stake. One risk which is

peculiar to the Indian software industry is the delay in resourcing and recruitment. Many project


Ph.D. Thesis 116

managers highlighted in the interview, that they were forced to start the project without any team,

which is very surprising. This of course, limits the ability of the project manager in correctly

estimating the schedule and budget as he is not even aware of the nature of team with whom he

has to work.

High standard of work tasks

This factor with a beta coefficient of 0.29 and a correlation coefficient of 0.31, significant at 0.01

level, also emerged as an imperative determinant of success of the software project. Availability

of adequate tools and technologies, a well defined work breakdown structure and sufficient and

clear understanding of work tasks enables the team to perform their best. This is also evident

from the fact that clear and robust understanding of what is expected from the team members will

improve their productivity and the ability to deliver the project modules on time, while

simultaneously reducing the wastage of time, resources and duplication of work. This will not

only certify the success of the project but will also ensure a satisfied customer.

Team composition risk

The variable that negatively correlates with the success of the project is the team composition.

With a beta value of 0.25 and a negative correlate coefficient of 0.43, significant at 0.01 level,

team composition emerged as a very important variable that impacts the success of the software

project. It is an assortment of inexperienced team, low morale, lack of availability of subject

matter expert, lack of commitment from team and top management. This independent variable is

bound to affect the success of the project because if the team is not good no matter how accurate

the estimation and requirement gathering has been done the project is bound to get delayed or

failed. This is quite evident from the fact, that the project is a result of human endeavour and if

the efforts are not applied in correct manner by the right resources with a right attitude and frame

of mind, the project is bound to get affected.

Control processes risk

Yet, another important factor that affects the success of the project is improper control processes.

The beta value of this variable is 0.26 with a negative correlate coefficient of 0.27 with the

success of the project. Control processes encompass poor documentation, insufficient testing and


Ph.D. Thesis 117

poor configuration control. Although most of the variables of this risk factor come later in the

project but have a surmountable effect on the delivery of the project. Skipping the testing phase

because the project is way behind schedule has been observed as a very common practice among

the software professionals. Moreover, most of the Indian software professionals have not been

exposed to proper technical documentation training. As a result, even when a good code is

written, without proper explanation it fails to satisfy the client and results in the delay of the

project, thus, affecting its success.

Dependability risk

The last independent variable that negatively affects the success of the software project is

dependability. With a beta value of 0.16 and a negative correlation of 0.45 this risk factors plays a

dominant role in reducing the chances of success of the project. Whenever the team rely upon

third-party tools or systems to make their applications work, they are putting the customer‘s

perception of the quality of their product into someone else‘s hands. Most often it has been

observed, that many of these third-party components are the cause for the customer‘s

dissatisfaction. Not only this, maintenance nightmares, security woes and installation pain are all

associated with third party association. The entire problem of third party arises when the project

team fails to meet the desired specifications set by client either due to lack of availability of

subject matter expert or unavailability of licensed software or hardware. Whatever may be the

cause, this risk definitely adds a pessimistic dimension to the success of the project.

The figure 4.16, elucidates the determinants of success in form of a diagrammatic relationship.


Ph.D. Thesis 118

OVERALL SUCCESS

R2 = 0.37


Estimation errors Inaccurate requirement analysis No experience in similar project

Delay in resourcing


Lack of commitment High level of attrition

Low morale Team diversity


Poor documentation

Poor configuration control Poor code and maintenance

Insufficient testing

Dependability Risk

Third party dependencies Inability to meet specifications

Inadequate measurement tools for

reliability

Figure 4.16: Relationship between the Software Risk Dimensions, Organizational Climate Dimensions and Overall Success of the

Project


On time completion

Adequate tools

Clear understanding High standards of excellence

β =0.29**

β = - 0.35**

β = - 0.25**

β = - 0.27**

β = - 0.19**

ORGANIZATIONAL CLIMATE DIMENSIONS

SOFTWARE RISK DIMENSIONS


Ph.D. Thesis 119

4.5.3 Regression Model for Predicting the Three Success Performance Constructs:

Budget, Schedule and Quality Performance

This section works out the regression model of the risk factors and organizational climate

factors that impact the three success‘s constructs namely budget, schedule and quality

performance. It was assumed that there is a linear relationship between the risk

dimensions, organizational climate dimensions extracted in section I and II respectively,

and their impact on the three success performance constructs. A regression analysis was

conducted individually with the dependent variable as budget performance followed by

schedule and then quality performance of the software project and the independent

variables as the risk and organizational climate dimensions that got extracted from the

factor analysis.

4.5.3.1 Budget performance

A regression analysis was conducted to comprehend the impact of risk and organizational

climate factors on the budget performance of the software project. The eight dimensions

were then put in the model as independent variables and budget performance of the

project was put as the dependent variable. The equation which emerged after the process

is as follows. Table 4.36 summarizes the determinants of the equation.

Y1= 2.905 + 0.36X1 - 0.36X2 + 0.28X3 + 0.13X4

Where,

Y1 = Budget performance of the software project

X1 = High standards of work tasks X2 = Team composition risk

X3 = Effective supervision X4 = Intrinsic fulfilment

Table 4.36: Determinants of Risk Factors and Organizational Climate Affecting the Budget Performance

of the Software Projects (N=300)

Independent Variables Dependent variable: Budget Performance




Effective supervision -.289** 0.0387NS 4.531

Intrinsic fulfilment .134** 0.1604** 2.186

Multiple R = 0.50

R Square = 0.26



Ph.D. Thesis 120

The value of multiple R is 0.50 and the value of R square is 0.26 in the equation. It states

that 26% of the budget performance gets affected by these factors. The probability of

project meeting the pre-estimated budget reduces due to inexperienced, low morale and

high attrition in the team and effective supervision. While the probability of project

getting completed within the pre-estimated budget increases with setting up high

standards of work tasks and providing intrinsic fulfilment to the team. It should be noted

here that the dependent variable in the equation is the budget performance of the software

project and two independent variables are positively correlated with it while two are

negatively correlated. That means the positive dimensions like high standards of work

task and intrinsic fulfilment are positively correlated and contribute positively to the

budget performance of the software project.

While, pessimistic dimension like poor team composition is negatively correlated and

contribute negatively to the budget performance of the software project. The equation

explains that highest contribution in derailing the budget performance of the project is

made by the poor team composition and this includes lack of availability of domain

expert, working with inexperienced team, team diversity, lack of commitment from the

project team etc. All these factors have been discussed as follows:

With a high beta coefficient of 0.36 and correlation of 0.26, significant at 0.01 level, high

standards of work tasks is one of the major factors that help in achieving the pre-

estimated budget. Clarity in work tasks, high standards of excellence and adoption of

CMM levels ensures that the budget performance of the project is on right track. To add

to this, another important variable that helps in meeting the pre-estimated budget is the

intrinsic fulfilment of the team members. The intrinsic motivation encourages a sense of

ownership in the team member and enables him to deliver his best without any wastage

of resources. Thus, with a beta value of 0.13 and a correlation coefficient of 0.16

significant at 0.01 level, this factor has emerged as a strong influencer of the budget

performance.

However, on the flip side the entire budget can collapse if the right composition of the

team is missing. Expenditure incurred on training an inexperienced team or hiring new

recruits due to attrition, enormous amount of wastage due to non-commitment towards


Ph.D. Thesis 121

the projects are some of the critical points that can derail the budget of the project. One

very intriguing finding that came into light is the negative effect of effective supervision.

At the first look, it seems as oxymoron but a detailed analysis gives a completely

different picture. Constant feedback and advice from the supervisor is exceptionally good

for the project‘s quality, but too much time spent on this can actually derail the budget

construct of the project. Too much importance given to the team members concerns and

issues will result in shift in focus of the manager from the bigger picture to small

concerns. Thus, with a beta value of 0.29 this risk negatively impacts the budget

performance of the project. Therefore, the hypothesis H3 is accepted. Figure 4.17 explain

the relationship in a diagrammatic form.


Ph.D. Thesis 122

Figure 4.17: Relationship between the Software Risk Dimensions, Organizational Climate Dimensions and Budget Performance of the Project

BUDGET PERFORMANCE

R2 = 0.26



Low morale Team diversity


On time completion

Adequate tools Clear understanding





Good listener Timely feedback



Intrinsic Fulfilment

Opportunities of growth

Challenging tasks Full utilization of capabilities

Customer expectation

β = - 0.36**

β = 0.13**

β = 0.36**

β = - 0.29**


Ph.D. Thesis 123

4.5.3.2 Schedule performance

A similar approach was adopted to gauge the impact of risk and organizational climate

factors on the schedule performance of the software project. The eight dimensions were

put in the model as independent variables and schedule performance of the project was

put as the dependent variable. The equation which emerged after the process was as

follows. Table 4.37 summarizes the determinants of the equation.

Y2= 3.63 - 0.50X1 + 0.16X2 - 0.20X3

Where,

Y2 = Schedule performance of the software project


X2 = High standards of work tasks

X3 = Control processes risk

Table 4.37: Determinants of Risk Factors and Organizational Climate Affecting the Schedule Performance


Independent Variables Dependent variable: Schedule Performance




Control Processes risk -.207** -0.1421*

2.716

Multiple R = 0.41

R Square = 0.17

* Significant at .05 level.

** Significant at .01 level.

Here, the multiple R is 0.41 and R square is 0.17. This states that 17% of the variation in

schedule performance of the project can be explained by these variables. The rest 83%

can be attributed to so many other factors which are scattered and individually affect only

little to the schedule performance of the software project. It should be noted here that out

of three, two independent variables negatively contribute to the quality performance of


Ph.D. Thesis 124

the project. While, only one independent variable contribute positively to the

performance. Inadequate and inexperienced team along with poor documentation and

inadequate testing delays the project and affects the schedule performance of the software

projects. While, setting up of high standards of work tasks assist in keeping the software

project on track and meeting the pre-estimated guidelines. The equation explains that

highest contribution in derailing the schedule performance of the project is made by the

poor team composition and this includes lack of availability of domain expert, working

with inexperienced team, team diversity, lack of commitment from the project team etc.

The independent variables are further explained below. Thus, it can be clearly seen that

the hypothesis H4 is accepted. The relationship has also been explained diagrammatically

in figure 4.18.

Team composition has once again emerged as a major contributor in off-tracking the

schedule performance of the project. With beta coefficient of 0.50 and a strong negative

correlation of 0.35 at a significance level of 0.01, this risk has emerged as a strong

detriment to the schedule performance of the project. High level of attrition and non-

availability of domain expert strongly impacts the schedule of the project to an extent that

it can lead to shutdown of the project. Besides this, poor control processes like poor

code and maintenance, poor documentation and insufficient testing also affects the

schedule of the project as it will lead to development of a software which is full of bugs

and most of the time will be spent on removing them. However, if the organization is able

to set high standards of work task with proper process and role definition this risk can

be controlled. Adoption of CMM level and PCMM levels is the right step in this

direction.

Thus, with a beta value of 0.165 and a correlation coefficient of 0.16, at 0.01 significance

level high standards of work tasks positively affects the schedule performance of the

software project.


Ph.D. Thesis 125

Figure 4.18: Relationship between High Standards of Work Tasks, Team Composition Risk, Control Processes Risk and Schedule Performance of the Project



Low morale

Team diversity


Poor documentation

Poor configuration control

Poor code and maintenance Insufficient testing


On time completion

Adequate tools

Clear understanding High standards of excellence



β = 0.16**

β = - 0.50**

β = - 0.21**

SCHEDULE PERFORMANCE

R2 = 0.17


Ph.D. Thesis 126

4.5.3.3 Quality performance

A regression analysis was also conducted to measure the impact of risk and

organizational climate factors on the quality performance of the software project. The

eight dimensions were then put in the model as independent variables and quality

performance of the project was put as the dependent variable. The equation which

emerged after the process is as follows. Table 4.38 summarizes the determinants of the

equation. A diagrammatic representation is also presented in figure 4.19.

Y3= 3.46 - 0.25X1 + 0.16X2

Where,

Y3 = Quality performance of the software project


X2 = Role clarity

Table 4.38: Determinants of Risk Factors and Organizational Climate Affecting the Quality Performance


Independent Variables

Dependent variable: quality performance



Role clarity .157** 0.1823** 2.841

Multiple R = 0.36

R Square = 0.11


The value of multiple R is 0.36 and the value of R square is 0.11 in the equation. It

should be noted here that the dependent variable in the equation is the quality

performance of the software project and one independent variable is negatively correlated

with it while another is positively correlated to the quality performance. While, the poor

and inefficient team reduces the quality of the software project, the clarity in roles and

responsibilities in a team contribute positively to the quality of the software project. The


Ph.D. Thesis 127

equation explains that the highest contribution in reducing the quality of the project is

made by poor team composition which includes lack of availability of domain expert,

working with inexperienced team, team diversity, and lack of commitment from the

project team. All the independent variables are explained below.

With a beta coefficient of 0.25 and negative correlation of 0.27 significant at 0.01 level,

team composition factor negatively contributes to the quality performance of the project.

A right mix of candidates in a team is mandatory to achieve desired outcome. Working

with inexperienced team is same as working with highly qualified team as both may

result in poor performance and poor delivery. A novice team may fail to deliver the

desired quality product due to their inexperience while ego, overconfidence and conflict

may affect the performance delivery of the highly qualified team, thus resulting in a poor

quality of the software. Besides this, high level of attrition and lack of availability of

domain expert are yet another factors that may affect the quality performance of the

project. Another important variable that affects the quality performance of the project is

role clarity. Clarity in roles and responsibilities and the acceptance of one‘s

responsibilities help greatly in instilling a sense of ownership amongst the team members.

The moment a sense of belongingness is developed among the team members they start

putting extra efforts in delivering a quality product to the customer or client.

Thus, role clarity significantly contributes to the quality performance of the project. With

a beta coefficient of 0.15 and correlation of 0.18 significant at 0.01 level, this factor

positively contributes in achieving desired quality performance.


Ph.D. Thesis 128

Role Clarity

Open consultation Clear understanding of roles




Low morale

Team diversity



QUALITY PERFORMANCE

R2 = 0.11

β = - 0.25**

β = 0.16**

Figure 4.19: Relationship between the Role Clarity, Team Composition Risk and Quality Performance of the Project


Ph.D. Thesis 129

4.6 CONCLUDING REMARKS

Based on the outcome of various statistical tests done for analysis many interesting

findings have come to light. Tests like factor analysis; Duncan‘s mean test; correlations

and regressions analysis have been instrumental in analyzing the data and inferring out

from it. The detailed analysis of the secondary data unfolds the list of top ten software

project risks that are present in the software projects globally. Weighted average method

has been used and miscommunication of requirements, lack of top management support

and lack of technical knowledge have been identified as the top three most important

risks affecting the software projects globally. The Principal Component factor analysis

has extracted four risk dimensions namely a) SRS variability risk, b) Team composition

risk, c) Control processes risk and d) Dependability risk, as the key project specific risk

factors that are present in the Indian software industry. Similarly, four organizational

climate dimensions have also been extracted and they are a) climate of high standards of

work tasks, b) climate of effective supervision, c) climate of intrinsic fulfilment and d)

climate of role clarity. In order to compare the means of the perceptions about risk

dimensions and organizational climate dimensions across the various demographic

characteristics, Duncan‘s mean test has been done. It is found that the perception about

software risks greatly varies amongst the three designations and experience group with

employees having an experience of upto 9 years perceiving the risks to have greater

degree of impact on the success of the project than compared to the more experienced

employees. The perception about the software risks does not show much variation as far

as the team size and total value of the project characteristics are concerned. Only the team

composition risk shows a significant difference between projects with duration of 10-19

months and projects with duration of more than 19 months. The perception about the

organizational climate factors also varies greatly amongst the three designations and

experience groups.

Moving ahead to the regression analysis for identifying the organizational climate factors

that contribute in reducing the software risks reveals that clarity and lucidity in roles can

help in reducing all the identified project specific software risk dimensions namely SRS

variability risk, team composition risk, intrinsic fulfilment risk and dependability risk,


Ph.D. Thesis 130

while an effective and proficient supervisor can contribute effectively in reducing the risk

of SRS variability and Dependability. Lastly, setting up of high standards of work tasks

in the organization and project can help in controlling and annihilating the control

processes risk and dependability risk.

Last of all, the regression analysis done to find out the determinants of project success

reveals the project specific software risk dimensions and organizational climate

dimensions that affect the overall success and the three performance constructs of the

software project. The overall success of the software project gets affected by all the four

dimensions of risk but in varying degree. The team composition risk negatively affects

the budget, schedule and quality performance of the software projects on the other hand,

the control processes risk affects the schedule performance and the dependability risk

affects the budget performance of the software project negatively. As far as the

organizational climate dimensions are concerned, out of four only high standards of work

tasks contributed positively to the overall success of the software project. The climate of

high standards of work tasks also contributed positively to the budget and schedule

performances of the project, while dimensions of intrinsic fulfilment and role clarity

showed a significant positive impact on the budget and quality performances

respectively, the effective supervision – a climate dimension showed a negative impact

on the budget performance of the project. This is an interesting finding as it shows that

too much interference by the manager can actually derail the project. Thus all the

research objectives have been successfully attained and all the alternate hypotheses have

been accepted.

chapter 4 analysis and findings -...

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