# A Range Of Mathematical Techniques Can Be Used For Analysing Accounting Essay

Published: Last Edited:

This essay has been submitted by a student. This is not an example of the work written by our professional essay writers.

## Decision Support System

When it is about decision making concerning complex systems (for instance; Management of organizational operations, investment portfolios or industrial processes, Military command and control systems or plant operational controls for nuclear power generation) the intellectual capabilities get pressed to the limit.

Individual communications amongst the variables of a system may be understood well, but it is quite a intimidating task to predict the system's reaction to external manipulation, exemplar policy decision.

What could be, for instance, the result of beginning an additional shift in a factory? This may increase the plant's output by 50%. Additional consideration will be required for aspects such as extra wages, wear and tear of machine, maintenance, usage of raw materials, logistics, and future demand; as these will have an impact, to some extent, on the financial outcomes of this decision. Many variables are implicated in complicated and usually delicate interdependencies and anticipating the overall outcome may come out as intimidating task.

Significant practical evidence available represents that our conclusions, judgments and decisions are far from perfect and they deteriorate yet further when they involve complexity and stress. For numerous situations, eminence of our decision is of the utmost importance and that is why this area has always been a top priority of scientists. Areas of study namely: Statistics, Economics and Operations Research, have emerged which help us in making the right choice.

Recently, these disciplines, in collaboration with information technology, cognitive psychology and artificial intelligence, have equipped us with computer softwares. These softwares can act as stand-alone programs or in conjunction with other softwares in the form of an integrated computing environment which helps in complex decision making. These environments are more commonly known as decision support systems (DSSs). The DSS concept covers a very broad spectrum and various authors have defined it in their own particular way. If we wanted to define the DSSs in such a way as to roughly cover all the current definitions, we it would be true to say that Decision support system is that system which interacts with its users and provides them help in making more informed, accurate decisions and to make the most rational choice amongst different scenarios. DSSs are sometimes also referred to as knowledge-based systems. This reference is in accordance with the actions being taken to make the domain knowledge susceptible

to mechanized reasoning.

Decision Support Systems are achieving widespread recognition in fields such as Businesses, Engineering, Medicine and Military. These are of paramount importance in circumstances where the variables involved exceed greatly the decision making capabilities of an un-aided human decision maker.

When precision is of the utmost importance, DSSs come in with all the help we require. Decision support systems help us in overcoming our limitations by integrating various sources of information so that intelligible knowledge can be extracted from them. On the basis of this knowledge, the system helps us to make the right choice from a pool of viable alternatives. These systems have gone further to employ artificial intelligence which has enabled these systems to apply heuristic methods where the formal ones fail to give results. Equipped with such a strong arsenal at their disposal, decision makers of today have helped to increase productivity, efficiency and effectiveness of various businesses and have thus provided them with relative advantage over their competitor.Â This advantage allowÂ the businesses to go the extra mile in making the right choices regarding their operations, research and development and investments.

## Telecommunication Sector in Pakistan

The telecommunication sector in Pakistan was initiated by a government-run nopolist, previously called as Telephone and Telegraph department (T&T). It was not only the entire single controller in the industry but was also the policy maker and operator in the country. Later on T& T was turned into a corporation which proved quite profitable but the re-investment done by the company was not enough to meet every day technological advancement and to invest in emerging telecom services and make them available to the public. Consequently Pakistan lagged behind in the telecom sector for many initial years as compared with her neighboring countries.

Cellular mobile services first came into Pakistan in the early 1990s with the commencement of the two cellular companies, Paktel and Pak Com (Instaphone). However these two companies were unable to meet the growing demands of the people. The government of Pakistan thus decided to cut down on the monopolistic atmosphere prevalent in the telecom sector and usher in more competition in the cellular market. Resultantly Mobilink came into Pakistan in 1994 introducing GSM technology for the first time in Pakistan initially under MOTOROLA Inc., and later was bought by ORASCOM, an Egyptian multi-national company. Then Ufone was launched by the Pakistan government in 2001 as a part of its PTCL operations but is owned by Etisalat. After that came the year 2005 which is perhaps the landmark in the cellular history of Pakistan as two giant multinationals, Telenor and Warid successfully launched their cellular services in Pakistan right after the other. The coming of these two multinationals revolutionized the entire telecom industry increasing competition and foreign investment to considerable levels and putting an end to the monopolistic practices.

## Significance of the study

This research study will provide comprehensive insight on the impact of Decision Support System on Decision Making in the telecommunication sector of Pakistan. The core of this research is to demonstrate the level of importance of Decision Support System in Decision Making which can considerably improve the performance of the employees of telecom sector in Pakistan. Due to technological innovations which have increased the level of competition amongst companies, the ability of firms, especially those in competitive markets, depends crucially on how fast and accurately decisions are made. This study deals with the telecom sector in Pakistan which is highly competitive. Use of DSS to make better, timely and accurate Decision Making in this industry therefore, is of crucial importance, and thus become the subject of my research.

## Study Objectives

To study the impact as to how DSS improve Decision making in telecom sector of Paksitan?

To examine difference in firms' performance one using DSS for decision making and one which is not in telecom sector?

To identify the impact how DSS improve firm's performance in telecom sector?

To study the impact as how DSS improve firm's performance in telecom sector of Pakistan?

Employees level of understanding of DSS ?

Knowledge level of DSS and its impact on decision making ?

## Research Questions

To determine the relationship between DSS and decision making?

To determine the relationship between decision makings and firm's performance?

To determine the relationship between DSS and firm's performance?

Employee knowledge of DSS verses Decision making?

## Dependent variables:

Decision-Making firm's performance

## Independent variables:

Decision Support System

## Research Hypothesis

Following hypothesis will be formulated and tested by the researcher

## Hypothesis

To test the proposition that Decision Support System and decision making, firm performance has a significant/insignificant relationship

Hâ‚’: Î²â‚ â‚Œ 0

Hâ‚’: Î²â‚ â‰  0

Chapter # 2

## LITERATURE REVIEW

The rationale of this Literature review is to present bases for the later study on the topic of DSS and Decision Making which then lead to improvement in the Organization Performance, for this purpose Articles of well known Professors were studied.

Human Judgment and Decision Making; "It has been rather convincingly demonstrated in numerous empirical studies that human judgment and decision making is based on intuitive strategies as opposed to theoretically sound reasoning rules. These intuitive strategies, referred to as judgmental heuristicsÂ in the context of decision making, help us in reducing the cognitive load, but alas at the expense of optimal decision making. Eï¬€ectively, our unaided judgment and choice exhibit systematic violations of probability axioms" (Marek J. Druzdzel and Roger R. Flynn, University of Pittsburgh)

The core motivation being 'the aspiration to improve the human decision making' led to the development of a range of modeling tools in the disciplines; Operational Research, Economics, Decision Theory, Decision Analysis, and Statistics. For these tools, knowledge is symbolized by the use of algebraic, Logic and variables. Interactions among these are displayed with the aid of equations or order logical rules.

Once a model is formulated, a range of Mathematical techniques can be used for analyzing.

Decision making under certainty has been attended to by economic and operations research methods, for instance cash flow analysis, break-even analysis, scenario analysis, mathematical programming, inventory techniques, and a variety of optimization algorithms for scheduling and logistics. Decision making under uncertainty improves the above methods by aid of statistical approaches, such as reliability analysis, simulation, and statistical decision making.

"AÂ Decision Support System (DSS)Â is a class of information systems (including but not limited to computerized systems) that support business and organizational decision-making activities. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from a combination of raw data, documents, personal knowledge, or business models to identify and solve problems and make decisions."( John Day Reservoir)

As said by Sol (1987), the definition of DSS is changing over time. So its scope also been modified. As per 1970s DSS was illustrated as "a computer based system to aid decision making".Â

According to Hogue and Watson (1983) the most significant rationale managers cited for using a DSS was to obtain precise information. Studies has proven is on many occasions. Advocates of building data warehouses identify the possibility of further and enhanced analysis.

In today's decision-making, it is essential to attain for information. However, it is knowledge that has to be mainly looked for. The foundation for effective business activities is provided by knowledge. Procedural knowledge (explaining how to perform tasks and follow procedures) should be accompanied by declarative knowledge (indicating what has to be done), semantic knowledge (depicting relations between facts) and casuistic knowledge (that refers to some cases from the past). So-called tacit knowledge is a large part of knowledge in anÂ organization. OrganizationsÂ that are interested to use knowledge in decision-making are forced to work out

procedures that allow them to transform tacit knowledge into explicit knowledge. In this situation,Â organizationsÂ find it essential to generate repositories of knowledge and knowledge management systems, concurrently finding the way to match them with decision support systems.

In huge enterprises, enormous volumes of data are generated and consumed, and considerable fractions of the data change rapidly. Managers from businesses require up-to-date information in order to make decisions. Unfortunately, conventional decision support systems do not offer the low latencies required for decision making in this uncertain environment.

So importance of using a computer base system which helps in decision making increases and DSS is one mode.

## (Business psychology and organisational behaviour

Â By Eugene F. McKenna)

## "

(Decision support systems: concepts and resources for managers

Â By Daniel J. Power)

## "

(Decision support systems: concepts and resources for managers

Â By Daniel J. Power)

## Introduction to chapter

The introduction to chapter will be about the research we are going to held. Which methods we shall use in order to determine whether our model is perfect and how these variables are related to each other. The introduction will be followed by the research approach, methodology and the data we have used.

## Research Design:

The research method used to carry out this study was descriptive.Â To describe the descriptive category of research, Creswell (1994) ''stated that the descriptive method of research is to gather information about the present existing condition''.(Creswell 1994)

Prominence isÂ laidÂ onÂ the descriptionsÂ rather than onÂ judgementsÂ orÂ interpretations. By usingÂ theÂ descriptive method, weÂ aim to achieve a verification of the formulated hypothesis in reference to the present situation so that it may be elucidated. This approach allows more flexibility for the introduction of new questions and issues into the study as they arise. This helps in conducting further investigations regarding important matters.

This method focuses on describing the nature of the situation. The researcher tries to gain knowledge about the current situation. He does this by profiling people, events and situations. For this kind of research, researcher collect data on raw bases.

This can be collected from various sources such as respondents. This approach allows him to formulate his own opinions and conclusions which are not affected by any other factor. Therefore the results produced are unbiased, free from any external influence and represent solely the views of the researcher in their purest form.

For the purpose of this study, the descriptive method of research was used in showing that a positive co-relation exists between organisation performance and the use of the decision support system. This method was chosen for its flexibility in providing the researcher with the option to work with both quantitative and qualitative data. This opens up a whole new world of possibilities for the researcher to gather first hand information using an array of data gathering tools. The study further aims to provide the merits and demerits of using a DSS. As the researcher wishes to make knowledgeable conclusions using first hand data therefore the descriptive research method is best equipped to meet his needs.

Employees from 4 telecom companies in Lahore are being used as respondents. The idea is to identify the similarities and dissimilarities in the answers provided by the respondents. To benefit fully from the choice of research methodology, it was decided to work with both quantitative and qualitative data. This would not only help to eliminate the discrepancies in each but also provide the researcher with the full merits of both types of data. The primary sources of data were the respondents which took part in the survey. Secondary data was

collected from published annual results of the telecom companies working with and without an effective DSS.

Quantitative methods used in this research relied solely on the figures provided in the published documents. The relationship between variables was studied without any context and conclusions were reached which were unbiased and achieved with the help of techniques such as measurement, analysis of numerical data and the use of statistical methods.

## Purpose of research

The purpose of the research of this research is to determine the affect of the independent variable we have chosen on Decision making of telecom sector in Pakistan and how they are interconnected to each other. To check the results of 125 questionnaires we have floated in main companies of telecom sector of Pakistan.

## Data Processing and Analysis

Data for research is collected from forms. For using the interpretation of the Linkert-scale, weighted means representing each question were calculated. In this process, weights are assigned to each quantity. These weights give representation to the significance of the quantities in the average. Each value is multiplied by its weight to calculate the weighted mean. The multiplied results are then added. The weights are also added. The sum of the products is then divided by the sum of the weights to obtain the final weighted average.

## Primary and Secondary data

The primary data was gathered with the help of questionnaire and was interpreted using frequency distribution, cross- tabulation, linear regression, ANOVA, descriptive (mean, median) and multivariate regression. Whereas, the secondary data was gathered with the help of literature reviews and with that we made our theoretical framework which shows the relationship between dependent and independent variables.

## Regression Equation

Y= ï¡ + ï¢X

Y: Y is our dependent variable which is "Decision making in the Telecom Sector of Pakistan". Since it is my dependent variable, therefore it is the denoted by Y. We want to check that how other variables effect on it and how they are interrelated.

Chapter # 3

## Dss and Comapny

Which DSS system your company currently using

Cares

IVC

Micro Strategy

No DSS

Oracle

Count

Count

Count

Count

Count

Company

Brain Net

0

0

0

17

0

0

0

30

0

8

Telenor

0

9

18

0

4

PTML

41

0

0

0

0

Waridtel

0

0

0

0

15

Age of Respodent

30 and less

31-35

36-40

41-45

46-50

51 and above

Count

Count

Count

Count

Count

Count

Company

Brain Net

11

6

0

0

0

0

19

15

4

0

0

0

Telenor

9

15

4

3

0

0

PTML

15

13

7

5

1

0

Waridtel

6

2

3

2

2

0

Sex of respodent

Female

Male

Count

Count

Company

Brain Net

3

14

9

29

Telenor

3

28

PTML

13

28

Waridtel

1

14

Work experience

<1

1-3

3-5

5<

Count

Count

Count

Count

Company

Brain Net

0

13

4

0

0

7

20

11

Telenor

1

12

15

3

PTML

1

6

15

19

Waridtel

0

0

6

9

## Respondent Qualification

Qualification of Respodent

Bachulars

Masters

other

Count

Count

Count

Company

Brain Net

13

4

0

17

21

0

Telenor

13

18

0

PTML

21

20

0

Waridtel

3

12

0

## Question 1

Descriptive Statistics

N

Min

Max

Mean

Std. Dev

Has your firm implemented any computerized system to support decision making

142

1

5

4.23

1.207

## Question 2

Descriptive Statistics

N

Min

Max

Mean

Std. Dev

Does your company actively manage decision-relevant information

142

1

5

4.19

1.129

## Question 3

Descriptive Statistics

N

Min

Max

Mean

Std. Dev

Does your firm have any strategic informative system

142

1

5

4.34

.858

## Question 4

Descriptive Statistics

N

Min

Max

Mean

Std. Dev

Are information system planning and strategy focused on strategic questions

142

1

5

4.25

.837

## Question 5

Descriptive Statistics

N

Min

Max

Mean

Std. Dev

Are business processes designed to support use of Decision Support System

142

1

5

4.27

.764

## Question 6

Descriptive Statistics

N

Min

Max

Mean

Std. Dev

142

1

5

4.14

.896

## Question 7

Descriptive Statistics

N

Min

Max

Mean

Std. Dev

Has your firm examined its business processes for Decision Support System perspective

142

1

5

4.11

1.276

## Question 8

Descriptive Statistics

N

Min

Max

Mean

Std. Dev

Has a problem with decision process led managers to consider developing or improving DSS

142

1

5

1.83

1.045

## Question 9

Descriptive Statistics

N

Min

Max

Mean

Std. Dev

Have the key decision processes done through DSS

142

1

5

4.33

.928

## Question 10

Descriptive Statistics

N

Min

Max

Mean

Std. Dev

Does your firm have user interface for DSS

142

3

5

4.42

.698

## Question 11

Descriptive Statistics

N

Min

Max

Mean

Std. Dev

your company involve positional users in the design and development of DSS

141

1

5

3.28

1.470

## Question 12

Descriptive Statistics

N

Min

Max

Mean

Std. Dev

Are users satisfied with the DSS

142

2

5

3.93

.888

## Question 13

Descriptive Statistics

N

Min

Max

Mean

Std. Dev

use of DSS overall improve the decision making

142

2

5

4.37

.719

## Question 14

Descriptive Statistics

N

Min

Max

Mean

Std. Dev

Age of Respodent

142

1

5

1.91

1.010

## Question 15

Descriptive Statistics

N

Min

Max

Mean

Std. Dev

Work experience

142

1

4

3.00

.790

## Question 1

Has your firm implemented any computerized system to support decision making

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Never

13

9.2

9.2

9.2

Sometimes

4

2.8

2.8

12.0

usually

45

31.7

31.7

43.7

always

80

56.3

56.3

100.0

Total

142

100.0

100.0

Interpretation

Above pie chart show 56.34% of the employees say there company is using computerized system for making decision making and 31.69% of the employees say there company usually use computerized system. While 2.81% say sometime. Whereas only 9.155% says there company is not using computerized system to support decision making

## Question 2

Does your company actively manage decision-relevant information

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

never

8

5.6

5.6

5.6

sometimes

10

7.0

7.0

12.7

not sure

1

.7

.7

13.4

usually

51

35.9

35.9

49.3

always

72

50.7

50.7

100.0

Total

142

100.0

100.0

Interpretation

Above pie chart show 50.70% of the employees say there company is actively managing decision-relevant information and 35.92% of the employees say there company usually manage decision-relevant information. While 7.042% say sometime and 0.704% are not sure. Whereas only 5.634% says there company is not actively managing decision-relevant information.

## Question 3

Which DSS system your company currently using

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Cares

41

28.9

28.9

28.9

IVC

9

6.3

6.3

35.2

Micro Strategy

48

33.8

33.8

69.0

No DSS

17

12.0

12.0

81.0

Oracle

27

19.0

19.0

100.0

Total

142

100.0

100.0

Interpretation

Above pie chart show 28.9% of the employees say there company is using CARES as DSS and 6.3% of the employees say there company is using IVC. While 33.8% say thay are using MICRO STRATEGY and 19% using ORACLE. Whereas only 12% says there company is not using any DSS.

## Question 4

Does your firm have any strategic informative system

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

never

2

1.4

1.4

1.4

sometimes

4

2.8

2.8

4.2

not sure

12

8.5

8.5

12.7

usually

50

35.2

35.2

47.9

always

74

52.1

52.1

100.0

Total

142

100.0

100.0

Interpretation

Above pie chart show 52.11% of the employees say there company is using strategic informative system and 35.21% of the employees say there company usually use informative system. While 2.817% say sometime and 8.451% are not sure. Whereas only 1.4% says there company is not using any informative system.

## Question 5

Are information system planning and strategy focused on strategic questions

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

never

1

.7

.7

.7

sometimes

3

2.1

2.1

2.8

not sure

21

14.8

14.8

17.6

usually

51

35.9

35.9

53.5

always

66

46.5

46.5

100.0

Total

142

100.0

100.0

Interpretation

Above pie chart show 46.48% of the employees say always and 35.92% of the employees say there company usually focus on strategic. While 2.113% say sometime and 14.79% are not sure. Whereas only 0.7% says there company is not focusing on strategic .

## Question 6

Are business processes designed to support use of Decision Support System

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

never

1

.7

.7

.7

sometimes

2

1.4

1.4

2.1

not sure

15

10.6

10.6

12.7

usually

63

44.4

44.4

57.0

always

61

43.0

43.0

100.0

Total

142

100.0

100.0

Interpretation

Above pie chart show 42.96% of the employees say always and 44.37% of the employees say there company usually support. While 1.408% say sometime and 10.56% are not sure. Whereas only 0.7% says there company is not support DSS.

## Question 7

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

never

1

.7

.7

.7

sometimes

9

6.3

6.3

7.0

not sure

15

10.6

10.6

17.6

usually

61

43.0

43.0

60.6

always

56

39.4

39.4

100.0

Total

142

100.0

100.0

Interpretation

Above pie chart show that 39.44% of respondents say there company has positive impact on IS and 42.96% say there company usually has it. Where as .338% say sometimes and 10.56% are not sure of it. While only 0.7% say there company don't has positive impact.

## Question 8

Has your firm examined its business processes for Decision Support System perspective

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

never

17

12.0

12.0

12.0

sometimes

1

.7

.7

12.7

not sure

3

2.1

2.1

14.8

Usually

50

35.2

35.2

50.0

Always

71

50.0

50.0

100.0

Total

142

100.0

100.0

Interpretation

Above pie chart show that 50% on respondents say that there company do and 35.21% say there company usually do it. Where as 2.113% are not sure of it. While only 11.97% say there company didn't do it.

## Question 9

Has a problem with decision process led managers to consider developing or improving DSS

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Never

63

44.4

44.4

44.4

sometimes

61

43.0

43.0

87.3

not sure

4

2.8

2.8

90.1

Usually

7

4.9

4.9

95.1

Always

7

4.9

4.9

100.0

Total

142

100.0

100.0

Interpretation

4.930% say always and 4.930% say sometime there company led to improve DSS due to some problem. Whereas 2.817% are not sure of it and 42.96% say there company sometimes consider of improvement it DSS. While 44.37% say there company never consider of improving the DSS due to some problem in DSS.

## Question 10

Have the key decision processes done through DSS

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

never

5

3.5

3.5

3.5

not sure

15

10.6

10.6

14.1

usually

45

31.7

31.7

45.8

always

77

54.2

54.2

100.0

Total

142

100.0

100.0

Interpretation

Above pie chart show that 54.23% of employees say that there company always do decision making through DSS and 31.69% say company usually decision making is done through DSS. Whereas 10.56% are nor sure of it and only 3.521 say there company never do decision making through DSS.

## Question 11

Does your firm have user interface for DSS

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

not sure

17

12.0

12.0

12.0

usually

48

33.8

33.8

45.8

always

77

54.2

54.2

100.0

Total

142

100.0

100.0

Interpretation

54.23% of respondent say there firm has Interface of DSS. Whereas 33.90% say upto some extend they have user interface for the use of DSS. While 11.97% say there company don't has any user interface for DSS.

## Question 12

your company involve positional users in the design and development of DSS

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

never

26

18.3

18.4

18.4

sometimes

21

14.8

14.9

33.3

not sure

19

13.4

13.5

46.8

usually

37

26.1

26.2

73.0

always

38

26.8

27.0

100.0

Total

141

99.3

100.0

Missing

System

1

.7

Total

142

100.0

Interpretation

Above pie chart represent that 26.95% of employees say that there company involve positional users in the design and development of DSS and 26.24% say usually. Whereas 14.89% say sometime there company involve them and 13.48 are not sure of it. While only 18.44% say no.

## Question 13

Are users satisfied with the DSS

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

sometimes

4

2.8

2.8

2.8

not sure

49

34.5

34.5

37.3

usually

42

29.6

29.6

66.9

always

47

33.1

33.1

100.0

Total

142

100.0

100.0

Interpretation

Above pie chart represent that 33.10% of respondents are satisfied with the DSS they are using and 29.58% are usually satisfied with it. Whereas 34.51 are not sure of it and only 2.817% say sometime.

## Question 14

use of DSS overall improve the decision making

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

sometimes

1

.7

.7

.7

not sure

17

12.0

12.0

12.7

usually

53

37.3

37.3

50.0

always

71

50.0

50.0

100.0

Total

142

100.0

100.0

Interpretation

Above pie chart represent 50% of respondent say use of DSS overall improve the decision making and 37.32% are usually satisfied. While 11.97% are not sure of it. While only 0.704% say sometime use of DSS overall improve the decision making

## Question 15

Sex of respondent

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Female

29

20.4

20.4

20.4

Male

113

79.6

79.6

100.0

Total

142

100.0

100.0

Interpretation

79.58% of respondents are male while 20.42 are female

## Question 16

Age of Respodent

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

30 and less

60

42.3

42.3

42.3

31-35

51

35.9

35.9

78.2

36-40

18

12.7

12.7

90.8

41-45

10

7.0

7.0

97.9

46-50

3

2.1

2.1

100.0

Total

142

100.0

100.0

Intrepretation

42.35% of respondent are of age 30 or below. Whereas 35.92% are of in-between 31 to 35 while 12.68% are of 36 to 40 and 7.042% of 41 to 45. While only 2.113% are of age 46 to 50.

## Question 17

Qualification of Respodent

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Bachulars

67

47.2

47.2

47.2

Masters

75

52.8

52.8

100.0

Total

142

100.0

100.0

Interpretation

above pie chart show 52.83% of employees are of Masters Degree and 47.18% of bachelors

## Question 18

Company

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Brain Net

17

12.0

12.0

12.0

38

26.8

26.8

38.7

Telenor

31

21.8

21.8

60.6

ufone

41

28.9

28.9

89.4

warid

14

9.9

9.9

99.3

Waridtel

1

.7

.7

100.0

Total

142

100.0

100.0

Interpretation

Above pie chart show that 28.9% of employees are of Ufone, 21.8% are of Telenor, 26.8% are of Mobilink, 10.6% are of Warid and only 12% are of BrainNet.

## Question 19

Work experience

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

1 year or less

2

1.4

1.4

1.4

more than 1 year

38

26.8

26.8

28.2

3 to 5 years

60

42.3

42.3

70.4

more then 5 years

42

29.6

29.6

100.0

Total

142

100.0

100.0

Interpretation

Above pie chart show that 42.25% of employee have work experience of 3 to 5 years and 29.58% have more than 5 year work experience. While 26.76 have work experience of between 1 to 3 and only 1.408 have less than 1 year work experience.

## Correlations

In process of finding the relationship between these variables data was collected through survey aiming at DSS and users. The results of the surveys were gathered and appropriate statistical methods were used to form inferences.

For this purpose co-relation test was applied to validate or reject H0.

H0: r = 0.00: There exists no relationship DSS and better decision making.

H1: r =/= 0.00: There exist relationship between DSS and better decision making.

Correlations

Dss_Mean

User_Mean

Dss_Mean

Pearson Correlation

1

.762**

Sig. (2-tailed)

.000

N

142

142

User_Mean

Pearson Correlation

.762**

1

Sig. (2-tailed)

.000

N

142

142

**. Correlation is significant at the 0.01 level (2-tailed).

Since the "Sig" level for the Pearson correlation coefficient between these variables is ".000", we can reject the null hypothesis.

## Hence there exist a relationship between DSS and better decision making.

And as the value of Pearson correlation co-efficient is positive +.762 so it can be concluded that there exist a strong positive relationship between DSS and Better decision making. This means that implementation of DSS increases the performance for organization by helping in better decision making.

## Regression

Model Summary

Model

R

R Square

Std. Error of the Estimate

1

.762a

.580

.577

.478

Predictors: (Constant), Dss_Mean

Dependent Variable: User_Mean

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

44.154

1

44.154

193.312

.000a

Residual

31.977

140

.228

Total

76.131

141

a. Predictors: (Constant), Dss_Mean

b. Dependent Variable: User_Mean

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

1.124

.211

5.331

.000

Dss_Mean

.726

.052

.762

13.904

.000

a. Dependent Variable: User_Mean

## T-Test

One-Sample Statistics

N

Mean

Std. Deviation

Std. Error Mean

Dss_Mean

142

3.97

.771

.065

User_Mean

142

4.00

.735

.062

One-Sample Test

Test Value = 0

t

df

Sig. (2-tailed)

Mean Difference

95% Confidence Interval of the Difference

Lower

Upper

Dss_Mean

61.299

141

.000

3.966

3.84

4.09

User_Mean

64.916

141

.000

4.003

3.88

4.12

## T-Test

One-Sample Statistics

N

Mean

Std. Deviation

Std. Error Mean

use of DSS overall improve the decision making

142

4.37

.719

.060

One-Sample Test

Test Value = 0

t

df

Sig. (2-tailed)

Mean Difference

95% Confidence Interval of the Difference

Lower

Upper

use of DSS overall improve the decision making

72.330

141

.000

4.366

4.25

4.49

Chapter # 4

## Discussion

Use of DSS has many more benefits but due to the limitation of time and resources this thesis will on focus on Telecom sector of Pakistan and use of DSS resolution in Better Decision Making and Organization Performance.

Other benefit of use DSS are as follows.

Time savings

One of the main advantages decision support systems is reduced decision making time which benefits organization, which increase employee. When we documented decision making done through decision support system is often substantial. But the question remain that the decision make through decision support system are always quantitative or not. More research is required for this

Enhance effectiveness

Second major advantage of using decision support system which is most talked and seen is much improved decision making better decisions and effectiveness and decision in due time or in timely manner. But these two advantages of using decision support system which are effectiveness and decision making are hard to measure and documented.

Improve interpersonal communication

Major advantage of decision support system is that it provide better communication and collaboration among decision makers primly involved in the core decision making of organization.

Competitive

If we rank the advantages of decision support system then the competitive advantage will make it way to top 3. As whenever we take about decision support system the most common and widely know advantage is competitive advantage for computerized decision support.

Cost reduction

When using computer based system in decision maing one more benefit we get is cost saving. As less labor is use in decision making so less labor cost which means less expense organization has to bear and so t can generate more revenue

Increase decision maker satisfaction

When human make decision there is always some concern that there will be some biasness.

But use of Decision support system removes this concern

More research is needed on these questions. But I will not go in that because that out of these thesis boundaries and for it new research is needed.

Chapter #5

## CONCLUSION

The reason of selecting this topic is to analyze use of DSS in the telecommunication sector of Pakistan and how it contributes towards increasing the overall organization performance. Decision Support System hold a great significance in today's corporate world. Not only because there is a lot of competition, but also to augment the company's profitability. The telecom sector in Pakistan is fast gaining momentum after its privatization. The result of the government's policies of deregulation, liberation and privatizing the telecom sector has enhanced the company's overall performance and approved entrance of innovative technology and improved competitiveness. The telecom sector now has become a major employer of DSS.

The viewpoint of the researchers clearly states that use of DSS improve decision-making which leads to increase in organization performance. The research I have conducted will further justify this point of view.