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. This advantage allow 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.
PROBLEM DEVELOPMENT
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 Research Query#s
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?
Relevant Variables
Dependent variables:
Decision-Making firm's performance
Independent variables:
Decision Support System
Research Hypothesis
Following hypothesis will be formulated and tested by the researcher
Use of Decision Support System
Hypothesis # 1
To test the proposition that Decision Support System and decision making has a significant/insignificant relationship
Hₒ: β₠₌ 0
Hₒ: β₠≠0
Hypothesis # 2
To test the proposition that Decision making and Firm's performance has a significant/insignificant relationship
Hₒ: β₠₌ 0
Hₒ: β₠≠0
Hypothesis # 3
To test the proposition that Decision Support System and Firm's performance has a significant/insignificant relationship
Hₒ: β₠₌ 0
Hₒ: β₠≠0
Hypothesis # 4
To test the proposition that Employee knowledge of DSS and Decision making has a significant/insignificant relationship
Hₒ: β₠₌ 0
Hₒ: β₠≠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 heuristics in the context of decision making, help us in reducing the cognitive load, but alas at the expense of optimal decision making. Effectively, 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.
"A Decision Support System (DSS) 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".
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 an organization. Organizations 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, organizations 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
By Eugene F. McKenna)
"
"
(Decision support systems: concepts and resources for managers
By Daniel J. Power)
"
"
(Decision support systems: concepts and resources for managers
By Daniel J. Power)
RESEARCH METHODOLOGY
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. 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 is laid on the descriptions rather than on judgements or interpretations. By using the descriptive method, we 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 Research Query#s 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 Research Query#naires 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 Research Query# 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 Research Query#naire 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
Research Findings and Data Analysis
Comparative Analysis
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
Mobilink
0
0
30
0
8
Telenor
0
9
18
0
4
PTML
41
0
0
0
0
Waridtel
0
0
0
0
15
Respondent age
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
Mobilink
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
Respondent sex
Sex of respodent
Female
Male
Count
Count
Company
Brain Net
3
14
Mobilink
9
29
Telenor
3
28
PTML
13
28
Waridtel
1
14
Respondent Work Experience
Work experience
<1
1-3
3-5
5<
Count
Count
Count
Count
Company
Brain Net
0
13
4
0
Mobilink
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
Mobilink
17
21
0
Telenor
13
18
0
PTML
21
20
0
Waridtel
3
12
0
Research Query's Analysis
Research Query# 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
Research Query# 2
Descriptive Statistics
N
Min
Max
Mean
Std. Dev
Does your company actively manage decision-relevant information
142
1
5
4.19
1.129
Research Query# 3
Descriptive Statistics
N
Min
Max
Mean
Std. Dev
Does your firm have any strategic informative system
142
1
5
4.34
.858
Research Query# 4
Descriptive Statistics
N
Min
Max
Mean
Std. Dev
Are information system planning and strategy focused on strategic Research Query#s
142
1
5
4.25
.837
Research Query# 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
Research Query# 6
Descriptive Statistics
N
Min
Max
Mean
Std. Dev
Has your company's culture had a positive impact on the IS strategy your firm is implementing
142
1
5
4.14
.896
Research Query# 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
Research Query# 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
Research Query# 9
Descriptive Statistics
N
Min
Max
Mean
Std. Dev
Have the key decision processes done through DSS
142
1
5
4.33
.928
Research Query# 10
Descriptive Statistics
N
Min
Max
Mean
Std. Dev
Does your firm have user interface for DSS
142
3
5
4.42
.698
Research Query# 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
Research Query# 12
Descriptive Statistics
N
Min
Max
Mean
Std. Dev
Are users satisfied with the DSS
142
2
5
3.93
.888
Research Query# 13
Descriptive Statistics
N
Min
Max
Mean
Std. Dev
use of DSS overall improve the decision making
142
2
5
4.37
.719
Research Query# 14
Descriptive Statistics
N
Min
Max
Mean
Std. Dev
Age of Respodent
142
1
5
1.91
1.010
Research Query# 15
Descriptive Statistics
N
Min
Max
Mean
Std. Dev
Work experience
142
1
4
3.00
.790
Research Query# Frequencies
Research Query# 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
Research Query# 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.
Research Query# 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.
Research Query# 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.
Research Query# 5
Are information system planning and strategy focused on strategic Research Query#s
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 .
Research Query# 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.
Research Query# 7
Has your company's culture had a positive impact on the IS strategy your firm is implementing
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.
Research Query# 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.
Research Query# 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.
Research Query# 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.
Research Query# 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.
Research Query# 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.
Research Query# 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.
Research Query# 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
Research Query# 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
Research Query# 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.
Research Query# 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
Research Query# 18
Company
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
Brain Net
17
12.0
12.0
12.0
Mobilink
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.
Research Query# 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
Adjusted 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
Scattor plot
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 Research Query# 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 this Topic. 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.
After critical analyzing each and every point of this research lead to conclusion
The quantitative results of my thesis shows that the dependent variable, decision-making and organization performance have a positive relationship with DSS.
The scope of this thesis is to show the relationship between telecom sector of paksitan and the use o Decision support systems, and to analysis this relationship the data was collected through questionnaires. Which were distributed in all companies of Telecom sector what are Ufone, Mobilink, Telenor, Warid.
The questionnaire was design such to show
The impact of Decision Support System, its positive impacts as well as its drawbacks.
Which organization is utilizing decision support system effectively which is not.
Are decision support systems used by companies are up to mark to handle the work load.
Are they fulfilling the requirements of the organization decision making.
Does use of Decision support systems for decision making improving organization performance or not.
What benefits organizations are getting from use of Decision Support System?
