IT Human Resource Need Assessment Study - …unpan1.un.org/intradoc/groups/public/documents/apcity/unpan040689.… · IT Human Resource Need Assessment Study ... 3.8 Time Series Linear

IT Human Resource

Need Assessment Study

Pakistan Software Export Board (PSEB)

March 2006

Prepared By: NU-Consulting (FAST) FAST House, AK Brohi Road H-11/4, Islamabad

IT Human Resource Need Assessment Study

Table of Contents

2

Executive Summary ____________________________________________________ 4

1. Preamble ___________________________________________________________ 7

2. Purpose of the Study _______________________________________________ 9 2.1 The building Block _____________________________________________________ 9 2.2 Objectives ___________________________________________________________ 11 2.3 Study Limitations_____________________________________________________ 11

3. Methodology ______________________________________________________ 13 3.1 Population and Sample ________________________________________________ 13 3.2 Unit of Analysis _______________________________________________________ 14 3.3 Variables and Instrument________________________________________________ 14 3.4 Data Collection ________________________________________________________ 15 3.5 Response ______________________________________________________________ 16 3.6 Model Construction: ____________________________________________________ 17 3.7 Modeling Techniques: ___________________________________________________ 17 3.8 Time Series Linear Regression Analysis: ___________________________________ 17 3.9 Time Series Curvilinear Regression Analysis: ______________________________ 18 3.10 Measuring Goodness of fit ______________________________________________ 18

4. Analysis and Findings ______________________________________________ 19 4.1 Findings_______________________________________________________________ 19

4.1.1 Breakdown of IT Professionals by sector. ___________________________________ 20 4.1.2 Breakdown of IT Professionals by Skills. _____________________________________ 21 4.1.3 Breakdown of IT Professionals by Qualification._______________________________ 22 4.1.4 Estimated new job positions broken down by Sector (2006 – 2015). _____________ 23 4.1.5 Breakdown of Job positions in Non IT Sector. _________________________________ 25 4.1.6 Estimated new job positions in the IT/Non IT Sectors broken down by Skills (2006 – 2015). _________________________________________________________________________ 26 4.1.7 Supply of IT Professionals in the Industry (2006 – 2015). _________________________ 30 4.1.8 Comparison of Demand and Supply (2006-2015). ____________________________ 31

4.2 Tools of Analysis_______________________________________________________ 33 4.2.1 The General Model_________________________________________________________ 33 4.2.2 Equation of Best Fit ________________________________________________________ 34 4.2.3 Use of F and t-test__________________________________________________________ 35 4.2.4 The Coefficient of Determination_____________________________________________ 36 4.2.5 The Normal Probability Plot _________________________________________________ 36

4.3 Underlying Factors:_____________________________________________________ 36 5. Conclusion ___________________________________________________________ 39

5.1 The number game _______________________________________________________ 39


Table of Contents

3

5.2 Supply-Demand Situation ________________________________________________ 39 5.4 The Curriculum Revision_________________________________________________ 40 5.5 The Standardization________________________________________________________ 41

Glossary _________________________________________________________________ 42

Annexure 2 Questionnaires to Industry (IT & Non IT) Annexure 3 Questionnaires to Academia (Universities) Annexure 4 Facts and findings (Tables/Graphs/Pie Charts)

Report on IT Human Resource Need Assessment

4

Executive Summary

As IT sector is growing at a very rapid rate since 1990’s hence the focus of the

study is on the development of the required infrastructure and availability of

relevant human resources. The study is essentially a survey leading to

findings and conclusions regarding the current status and future

requirements of the Pakistani IT industry in terms of Human Resource. Main

variables were the current number of employees, their break down by

qualification, by skills, future job openings, qualification for future positions

and skills for future job openings. The statistics on future job openings from

2006 to 2010 by sector and by skill set are prepared to meet the objectives of

the study.

Table showing Demand (2006-2015)

Based on the results of the survey findings, the IT industry will undergo

tremendous growth in the coming few years. The outsourcing by non IT

companies will allow the growth of more IT companies. 80% or more of the

total job positions expected in the industry will be created in the IT sector.

The bar graph below shows number of future job positions in IT Companies

versus new job positions in the Non IT Companies and Universities.


5

The output of IT Professionals by universities is growing many folds over the

period of years as more and more of new universities and degree awarding

institutes are given charter by HEC. And it is reflected in findings that supply

is in access of the demand in the near future.

The percentage component bar chart below shows the comparison of demand

and Supply for year (2006-2015) and the gap for unemployment as well.

No of Job Positions Sector Wise (2006-2015)

0%20%40%60%80%

100%

1 2 3 4 5 6 7 8 9 10

Years (2006-2015)

%ag

e of

Job

s.

UniversitiesNon ITIT

Demand Vs. Supply of IT Professionals (2006-2015)

0%20%40%

60%80%

100%

1 2 3 4 5 6 7 8 9 10

Years (2006-2015)

Sup

ply

Gap

Totalvacancies


6

It is indeed evident from the above graph that in the long term that gap will

be reduced by hiring in the govt. and defense organizations and due to the

creation of new IT companies in response to the growing IT needs of the non

IT companies.

Current number of IT Professionals in the Industry is estimated at 46,000 out

of which 74% are employed in the IT companies, 17% in non IT (plc)

Companies listed at KSE, and 9% are employed in Universities and degree

awarding institutes. Another 42,0001 are estimated to be employed by

Defense, Government and Private limited companies.

Currently Employed 2005

IT Companies 34,000

Non IT Companies (Public Limited) 8,000

Universities 4,000

Total 46,000

Govt. & Defense Org. (Estimated) 25000

SECP registered Pvt Limited

Companies. (Estimated) 17000

Grand Total 88,0002

The study reflects on many interesting aspects of which the most important

one is the shift of IT professionals amongst various skill sets available. The

study clearly indicates that the quality of fresh graduates entering the

industry is not to the market standards and industry incurs heavy cost on job

training and development and yet the retention of trained professionals is an

issue. Industry desires the govt. to share the cost of training through either

sponsorships or by setting up executive development centers. At the same

time universities need to revise their curriculum along with the changing

needs of the industry to make sure that optimal results can be achieved in the

future. 1 These figures are derived from general feedback received from Government and Defense Organizations, industry specialists, PSEB and other sources.


7

1. Preamble

During the last decade, the IT sector of Pakistan has experienced an

unprecedented growth. That growth; to many analysts is due to the

availability of human resource, domestic socio economic factors, geographic

location and other suitable factors that give the country a comparative

advantage and competitive edge over some other competing countries in the

field of Information Technology.

However, many analysts believe at the same time that the realized potential

fell much below the perceived potential at the beginning and it is still the

case. It is a generally agreed upon notion that we have been unable to tap the

arena of human resource capacity building in the most optimal fashion to

meet the demands of the growing IT sector.

At the moment, all initiatives taken in the field of IT depend heavily, not

upon the technological innovations domestically but the availability of

human resource that is technically proficient to handle the outsourced work

generated by developed nations of the world. Conflicting opinions appear as

to what needs to be done in this respect. At one extreme, it is believed that the

influx of considerable work (mostly from abroad and to a lesser extent local

development) implies that the requisite infrastructure and relevant human

resource is available as per the requirement of the industry. At the other

extreme, the perception is that our infrastructure and human resource is way

short of its optimum scale and all efforts must be made to develop it on a war

footing. The reality, it seems, lies somewhere in the middle. We have enough

infra-structure and human resource to attract a considerable amount of work

from the developed west; however we need to invest a lot in real R&D to


8

bring about the realm of technological innovation and fill the innovation

chasm that has become more and more a serious concern. At the same time,

we need to think in terms of our success on a relative scale. The influx of

work, that we have been referring it is still below the desired level when we

start comparing ourselves with countries like India and China. The planning

process, however can only be initiated once we have a reasonable assessment

of the current strength of our human resource as that becomes the building

block for our future IT vision.

We consider this study as a first step towards formation a bigger vision for IT

development and growth in Pakistan. The compilation of this study in itself is

quite complex. The number, type and scope of HR needed to assess the past

and present performance trend and more importantly, to forecast the future

requirement for the IT industry of Pakistan requires collection of

data/information from a variety of sources.

The potential of IT and its capacity to help human development need not

automatically translate into realization of the true potential that the country

has. Institutional innovations to adopt IT to local needs, creative partnerships

to share expertise and finances, enlightened national and global policy

regimes to provide sustainable macroeconomic and legal framework are

some of the necessary inputs to realize the potential of the IT. This potential

for human development is aided by the significant strides in the technology

sector. It could pave the way for an inclusive economic sphere by lowering

entry barriers for e-commerce and provide two-way flow of goods

knowledge and ideas to any and all.


9

2. Purpose of the Study

2.1 The building Block

As the IT sector is growing at a very rapid rate since 1990’s hence the focus of

the study is on the development of the required infrastructure and

availability of relevant human resources. For this we ought to know the

prevailing situation for employed IT Professionals in the industry. The study

would serve the purpose of establishing a reasonable estimate of our

strengths and weaknesses as defined by our pool of human resource to

address the IT vision of the country.

With the world becoming a true global village, the concept of benchmarking

and relative measurement has become more complex than ever. It must have

always been imprudent to decide on policy issues in isolation but in our

times, the relative yardsticks have become more important than ever. To

develop an IT vision for Pakistan, the most fundamental requirement is the

understanding of our human resource strength. The prime reason for

considering it to be that significant is the fact that it is our most productive

asset in the backdrop of strong forces of globalization that shape the threats

as well as opportunities present in the global environment.


10

The Human Resource Assessment will be best understood in the perspective

of the strong global forces including:

• Migration of millions of service oriented jobs from the developed

countries to developing countries

• Ubiquitous availability of information throughout the globe

• Removal of protection given to industry by political entities leading to

intense competition.

• Increasing role and use of knowledge to enhance productivity

Once we understand these forces completely, the next step is to look closely

at what we have to offer to exploit the opportunities present in the

environment. It must be very clear from the beginning that nothing tangible

can be achieved without enabling government policies and a regulatory

framework that supports a globally connected knowledge based economy.

With these prerequisites in place, the national productivity enhancement rests

on two pillars only and they are:

• A well trained and efficient manpower

• Efficient and cost effective infrastructure

Both of these objectives can be achieved once we have a fair idea of the

direction and the scale at which the enabling actions must be taken. The

findings of this report will help achieve that.


11

2.2 Objectives

The study’s prime objective is to assess the current IT human resource in

Pakistan but from that very prime objective flow a number of specific goals.

These goals are as follows:

2.1.1 To reasonably assess the current number of IT professionals in the

industry broken down by skills and qualification.

2.1.2 To reasonably forecast the IT HR requirements for the near term (next

five years) and long run (from five to ten years)

2.1.3 To understand the growth trends in the realm of IT Human Resource

and to foresee the shifts in near future.

2.3 Study Limitations

The study as mentioned earlier is a complex study and thus carries a number

of limiting factors. The methodology that has been adopted aims at

minimizing all such factors and filtering the best possible information that

can be derived using the available means; however it would be unwise not to

mention those factors that to an extent mitigate the results of this study to a

certain extent. The reason for highlighting these limitations at an early stage

help the reader appreciate the results in their true perspective. These factors

are as follows:

2.3.1 The data related to IT professionals employed by Government

organizations, (i.e., AEC, NDC, KRL) though significant, cannot be

obtained completely due to the secretive nature of most of these

organizations and their charter limiting the freedom on their part to


12

share that information with any outsider. Defense and such other

organizations are, hence not part of the population under study.

2.3.2 The public limited companies form the population but non-IT private

limited companies were excluded. The population under study will

thus not include the Non-IT private limited companies.


13

3. Methodology

The study is essentially a survey leading to findings and conclusions serving

the objectives of the study set forth in the earlier sections. Understanding of

the basic parameters of the methodology will help the reader understand the

findings in their true perspective. The highlights of the methodology are as

follows:

3.1 Population and Sample

The target population consists of three broad clusters. These clusters include

IT Companies, Non-IT companies (public limited mainly) and educational

institutes (Universities mainly).

The IT companies registered with PSEB form the core population. The total

number of such companies is around 700. For Non IT Companies only public

listed companies are considered which are listed at KSE. These companies are

680 in total, as per listing of the Karachi Stock Exchange

(http://www.kse.com.pk/publications/daily). For Universities and degree

awarding institutes, we considered all those universities and institutes, in

both public and private sector, which are given charter by Higher Education

Commission. This figure (110) was taken from HEC

(http://www.hec.gov.pk/htmls/hei/collunilist.htm). Out of these 110

Institutes, 05 do not have relevant programs in the IT domain leaving the

relevant population to be of 105 Universities and Institutes.


14

The stratified random sampling seemed appropriate to cater the naturally

existed grouping. In consultation with PSEB a sample of 400 companies in all

is selected consisting of 100 IT companies, 200 Non-IT companies and 100

universities/institutes. For the purpose of data collection the IT industry is

segmented into public and private sectors and then further classified into IT

Companies, Non-IT Companies and Universities awarding the degrees in IT

related education.

3.2 Unit of Analysis

The unit of analysis for this survey was an organization. Hence, all the

employees of each and every organization included in the sample were made

a part of this study. The analysis included actual data of professionals

employed in these organizations collected through four different types of

questionnaires addressing all clusters of the population. (For reference see

Annexure-2 and 3)

3.3 Variables and Instrument

Questionnaire (attached in the annexure-2 and 3) was used as an instrument

to collect information in a precise and efficient manner. Separate

questionnaires were developed for each of the segments i.e., IT, Non IT and

Universities. Questionnaires so developed covered wide spectrum

information. Each question was so constructed that its qualifying response

should flow from an objective statement.

To assess the relationship between demand and supply of IT professionals in

the market, the questionnaire developed for universities was broken down

into two sections; first form was used to collect information on students’


15

passing out ratio to assess the supply for the market and the second form was

used to tap the IT Professionals employed in the universities and the demand

for professionals by the universities in the future. The questionnaires for IT

and Non IT companies tap the present employment status of the companies

in terms of IT Professionals, their qualifications and their skills. Same

questionnaires also tap the future requirements by these companies in terms

of employee’s qualification and skill.

Main variables were the current number of employees, their break down by

qualification, by skills, future job openings, qualification for future positions

and skills for future job openings. The statistics on future job openings were

demanded from 2006 to 2010 to meet the objective of a realistic trend for these

five years and to extend the same trends to the next five year phase,

extrapolation techniques were employed.

3.4 Data Collection

The data collection is governed by Delphi Technique. The Delphi Technique

is supplemented in many ways to increase the response rate to the most

optimal possible. Questionnaires were initially transmitted to the respondent

agency, institute or company through Courier Services. In the second phase

email, faxes and telephones were extensively used not only to retransmit the

questionnaire but to keep a constant follow up. All the questionnaires were

sent in both soft copy and hard copy format.

The questionnaires were accompanied by PSEB and HEC endorsement letters

to convey their authenticity to the respondents and to persuade a better

response rate.


16

All possible modes (fax, e-mail, courier and physical follow-up) of collecting

the filled questionnaires were employed to increase the response rate by

facilitating the respondent end. From respondents’ point of view these

methods allowed the information (requested in the questionnaire which was)

not available at a single desk to be collected from several desks and then be

compiled at a single access point. Hence, access to questionnaires by different

departments within the same institute was made possible.

Apart from using questionnaires as mode of collecting data, interview of

several IT professionals at the leading positions in their organizations were

also held. These interviews proved helpful in taking the feedback on the

quality of current IT Professionals coming in the market and they were also

helpful in developing trends for the future needs for the IT Professionals in

the industry. Interviews were generally held on a concurrent basis but some

are held post-collection as well. The interviews were meant to validate the

results obtained through questionnaires and to have a subjective opinion

along side the objective close-end data in order to extrapolate the information

to create trend lines and forecasts.

3.5 Response

The total response rate has been 41%. In terms of numbers, data from 165

organizations was received in total. Questionnaires received from Universities totaled

35, from IT companies 46 and from Non-IT public limited companies 93 respectively.

In terms of percentages, 29% of the total response came from the IT companies

whereas 58% came from the non-IT sector and 21% was covered by the response

from the Universities. The response was supplemented with interviews from the

leading professionals of the industry and extrapolation techniques (time series

regression) were employed to extend the sample data to the entire population and to

assess the trends in various dimensions.


17

3.6 Model Construction:

The models were constructed through the use of time series regression

analysis. These regression models so constructed were then analyzed for their

predictive ability at 95% level of confidence (as indicated by the regression

and individual regression variable P-values). Models were constructed

applying Ordinary Least Square to the data on future job positions,

qualifications and skills in IT, Non IT sectors and universities.

3.7 Modeling Techniques:

The SPSS version 11.0.1 software package was used to perform the statistical

analysis for this project. SPSS can perform a variety of statistical operation

including regression analysis (linear, multiple linear, curvilinear, and curve

estimation). The software provided tools to fit 11 regression models: linear,

logarithm, inverse, quadratic, cubic, power, compound, S, logistic, growth

and exponential.

3.8 Time Series Linear Regression Analysis:

Simple Time Series linear regression analysis was conducted to examine

relationship between time (independent variable) and job positions

(dependent variable), skills for jobs and qualification for jobs. The best fit

equation was developed for each response pair.


18

3.9 Time Series Curvilinear Regression Analysis:

In addition to standard linear models, several curvilinear models were

considered where scatter plots showed a non-linear relationship between

time periods and skills, qualification, total job positions. Detailed discussion

of these analyses is given in annexure-04.

3.10 Measuring Goodness of fit

Measuring how well the model fits the data, three statistics were commonly

used.

1. co-efficient of determination r2

2. t-test for the significance of regression co-efficient

3. F-test for the significance of fitted model.

The goodness of fit is developed through appropriate descriptive statistics

and graphs attached in the annexure-04.


19

4. Analysis and Findings

Once all the parameters are identified, the analysis is undertaken with respect

to the specific objectives of the study and the focus remained on developing a

trend model to reflect the HR demand and supply situation for near term and

long run.

The definition under which an IT professional shall fall during this study is as

follows:

“Professionals involved in the design/development, services, installation, and

implementation of computer systems and applications, including those personnel that

have a degree or certification and experience in the information technology arena.”

With this definition well in place and having a complete comprehension of the

importance of the study, the analysis is conducted that revealed the following

findings.

4.1 Findings

The current study appears to be first of its kind. We are confident that the

actual figures are within a 5% range of the estimations given below. The

current number of IT Professionals in the Industry (within our defined

population) turns out to be 46,000. Another 42,000 is estimated to be

employed by Defense, government and private limited companies.


20

4.1.1 Breakdown of IT Professionals by sector. (Rounded off to nearest 000)

Currently Employed 2005

IT Companies 34,000

Non IT Companies (Public Limited) 8,000

Universities 4,000

Total 46,000

Govt. & Defense Org. (Estimated) 25000

SECP registered Pvt Limited

Companies. (Estimated) 17000

Grand Total 88,0003

The pie chart below shows that of total IT Professionals in the industry 74%

are employed in the IT companies, , 17% in non IT (plc) Companies listed at

KSE, and 9% are employed in Universities and degree awarding institutes.

That breakdown is for the reviewed population and estimated figures for

Defense and Government organizations and Private Limited companies is

excluded from this and all subsequent analyses.

3 These figures are derived from general feedback received from Government and Defense Organizations, industry specialists, PSEB and other sources.


21

Figure: Pie Chart showing the breakdown of IT Professionals by sector.

Segmented Distribution

74%

17%9% IT Companies

Non IT Companies(Public Limited)Universities

4.1.2 Breakdown of IT Professionals by Skills.

The other important breakdown would be according to the skill set. The pie

chart below depicts that 31% IT professionals have other skills, 19% belong to

the field of computer sciences, 17% are software engineers, 7% networking

specialists, 5% database administrators and project managers each, 4% system

administrators, 3% from the field of electronics; 2% each of hardware, system

design, MIS and telecom; and 1% computer engineers employed in the

industry. The terms “other skills” become quite misleading in this respect. It

merely shows that IT professionals employed by the industry serve the

reason for which a particular company operates rather than their true

specialization. Many specialists work in areas that at times is remote from

their area of expertise and most of the companies prefer to categorize their

human resource as ‘others’ based on the job at hand at a given point of time.

That is particularly true for Call Centers, Business Solution Providers and

Financial Institutions.


22

Figure: Pie Chart showing the breakdown of IT Professionals by Skills.

Breakdown of IT Professionals by Skills

1%19%

5%

3%

2%

2%

7%31%

5%

17%

4%

2%

2%

Computer Engr.

Computer Science

Database

Electronics

Hardw are Design

MIS

Netw orking

others

Project managers

Softw are Engineering

System Adm

System Design

Telecom

4.1.3 Breakdown of IT Professionals by Qualification.

Another important breakdown is by qualification. The pie-chart below

depicts that 1% are PhD foreign qualified, 25% are MS local qualified, 4% are

MS foreign qualified, 4% BS foreign Qualified, 24% BS local qualified, 1% BE

foreign qualified, 6% BE local qualified, 1% foreign diploma holders, another

19% local diploma holders, 1% other foreign qualified and 14% are other local

qualified professionals in the industry.


23

Figure: Pie-Chart showing breakdown of IT Professionals by qualification.

Breakdown of IT Professionals by Qualification.

1% 19%1%

14%

0%

4%25%

4%

24%1% 6%

1%

PhdForeign

DipLocal

OtherForeignOtherLocal

PhdLocal

MSForeign

MSLocal

BSForeign

BSLocalBEForeign

BELocal

DipForeign

4.1.4 Estimated new job positions broken down by Sector (2006 – 2015).

The new job positions in the industry and in each sector (IT companies, Non

IT Companies and Universities) for year 2006 to 2015 are summarized below.

4.1.4 Table: New job positions in the industry and each sector. (Rounded off

to nearest 000)

In the coming few years IT industry will undergo tremendous growth. The

outsourcing by non IT companies will allow the growth of more IT

companies. 80% or more of the total job positions expected in the industry

will be created in the IT sector as shown in the component bar chart below.


24

Figure: Percentage bar chart showing job positions in each sector (2006 –

2015).

The bar graph below shows number of future job positions that will be

created in the IT Companies versus new job positions in the Non IT

Companies and Universities.


0%20%40%60%80%

100%

1 2 3 4 5 6 7 8 9 10

Years (2006-2015)

%ag

e of

Job

s.



25

Figure: Bar Chart showing number of job positions in each sector.

4.1.5 Breakdown of Job positions in Non IT Sector.

Off all the new job positions that are expected in the Non IT Sector of the

industry, 34% will be in the financial sector alone including banks, insurance

companies, leasing companies etc. remaining job positions will be created in

all the other sectors of Non IT sector including sugar mills, textile industries,

energy sector, petroleum sector, oil and gas, automobiles and other consumer

goods industries etc.

No. of Job Positions in Each Sector (2006 - 2015)

05000

100001500020000

2500030000350004000045000

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015Years

No.

of J

obs.

IT

Non IT

Universities


26

Figure: Hiring in Financial Institutions vs. all other sectors in Non IT Sector

No. of IT Professionals in Non IT Sector.

34%

66%

Financial Sector

Other sectors

Note: a breakdown of other sectors is not given due to the fact that the

demand for IT professionals in these sectors was homogeneous and on

average was 2-3. So in the absence of considerable variation these were

combined in the form of others.

4.1.6 Estimated new job positions in the IT/Non IT Sectors broken down by Skills (2006 – 2015).

The table on the next page summarizes the number of required IT

Professionals (by skills) in the IT/Non IT sector. The trend clearly indicates a

shift towards business solutions including call centers and e-commerce

solutions. The traditional fields of IT i.e., software development will continue

to grow and along with that growth is expected in the field of embedded

systems as well. The declining trends for positions in the System analyst,

Automation and MIS category (that are categories addressed primarily for

the non IT sector) depict that NON-IT sector would gradually reduce these


27

positions as their permanent workforce and outsource these functions. IT

sector will eventually provide the resources in this function however they

would not label their HR exactly according to these classifications. That

implies that the growing number of Software Developers, Networking

Specialists and Business Solution Providers would cover this skill set in the

future. Another significant trend is observed in the E-Commerce category and

the Security category. This trend is evident of the changing priorities of the

local business environment and their understanding of the critical success

factors in today’s global setting. Hardware development sees a significant

rise, indicating again the outsourcing trends and the shifts in local indigenous

development regime.


28

4.1.6 Table: Number of IT Professionals required in each skill (2006 – 2015).

(Rounded off to nearest 00)

Skills 2,006 2,007 2,008 2,009 2,010 2,011 2,012 2,013 2,014 2,015 Business Solutions 2,900 3,000 4,100 4,700 5,400 6,800 7,400 7,900 7,400 7,300 Software Development 4,700 5,600 6,500 7,400 8,300 9,200 10,100 11,100 11,900 12,400 Embedded System 2,000 2,400 2,900 3,400 3,900 4,900 5,000 5,500 5,900 6,400 Networking 1,400 1,700 1,900 2,100 2,200 2,300 2,600 2,700 2,900 3,100 Industrial Automation 200 200 300 400 300 - - - - - E-commerce 1,600 1,100 1,600 2,400 3,000 3,000 3,500 2,900 3,100 3,400 Hardware Development 100 300 300 200 300 500 1,200 2,400 4,300 6,100 Security 400 600 800 1,000 1,200 1,200 1,300 1,400 1,600 1,600 Others 1,600 2,000 2,300 2,500 2,900 3,000 3,200 3,600 3,800 4,100 System Analyst 100 200 - - - - - - - - System Administration 200 300 300 300 200 - - - - - MIS 200 300 300 300 200 - - - - - Support Staff 300 300 400 400 500 500 500 600 600 600 Total 15,800 18,100 21,800 25,100 28,200 31,400 34,800 38,200 41,600 45,000


29

The following bar graph depicts the skills most required in the future by the

industry. It clearly reflects a shift towards business solutions and call centers,

e-commerce solutions, software development and embedded systems.

Figure: Multiple bar chart showing number of IT Professionals by skills.

No. of IT Professionals by Skills (2006 - 2015)

0

2000

4000

6000

8000

10000

12000

14000

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Years

No.

of I

T P

rofe

ssio

nals

BusinessSolutionsSoftw areDevelopmentEmbeddedSystemNetw orking

IndustrialAutomationE-commerce

Hardw areDevelopmentSecurity

Others

SystemAnalystSystemAdministrationMIS

Support Staff


30

4.1.7 Supply of IT Professionals in the Industry (2006 – 2015).

By the year 1999, 61 registered universities in public and private sector were

imparting computer and IT relating education where as till 2005 they are 110

in number. Five of these universities are chartered recently and have yet to

send a batch to the market. The supply will gradually increase many folds

(growth rate stood at 16-20% on average).

Public Sector: 47 (Universities) + 9 (Degree Awarding Institutes) = 56

Private Sector: 36 (Universities) + 18 (Degree Awarding Institutes) = 54

Total: 83 (Universities) + 27 (Degree Awarding Institutes) = 110

Table: Yearly student output by universities (2006 – 2015). (r.o.t. 000)

Figure: Bar chart showing yearly student output.

Years 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Student

Output 26000 29000 32000 34000 37000 40000 43000 46000 48000 51000

Student Output (2006-2015)

0100002000030000400005000060000

1 3 5 7 9

Years (2006-2010)

No.

of S

tude

nts.


31

(Here 1-10 are time periods representing years from 2006 to 2015

respectively.)

4.1.8 Comparison of Demand and Supply (2006-2015).

The following bar graph shows a comparison between the demand of IT

professionals vs. the supply in the industry. The graph also highlights the gap

in demand and supply.

Figure: percentage bar graph showing gap between demand and supply.

(Here 1-10 are time periods representing years from 2006 to 2015

respectively.)

Apparently during the coming years the supply of IT Professionals will be

more than its demand in the IT industry as more universities are given

charters and current universities would expand their programs. The trend

shows a gap that implies a supply surplus. In this context it needs to be

understood that this study covers supply side in a comprehensive fashion but

certain clusters from the demand side are not part of the population (Pvt.

limited companies in the non IT sector and absence of data from govt.

organizations). That particular fact indicates that the gap would be mitigated

Demand Vs. Supply of IT Professionals (2006-2015)

0%20%40%

60%80%

100%

1 2 3 4 5 6 7 8 9 10

Years (2006-2015)

Sup

ply

Gap

Totalvacancies


32

to a certain extent. However, it would be difficult to mention a specific

percentage reduction in the gap covered by the aforementioned clusters.

If quality standards in education are employed properly, more productive

work force may be created and the gap could be minimized with effective

utilization through exploring new avenues. In that case the excess supply will

not only generate competition but also give new direction to users to employ

manpower trained and adept in multiple software application and IT

management.

This envisaged atmosphere of competition will produce quality manpower

that expects better job prospects, remuneration and positions. Data findings

have reflected that in the near future, the focus of IT will be more towards

business solutions, call centers and applied technologies. Hence, universities

need to revise their curriculum to adjust it to market needs. Universities must

focus on preparing entrepreneurs for providing business solutions, running a

call centre etc. Present situation reflects that universities are producing more

of software engineers, computer engineers, system administrators, database

administrators, networking specialist etc. where as industry demands are

growing more for business solution providers, software development and e-

commerce solutions providers. That stresses the need for curriculum revision

in line with industry trends and demands for the near term and the future.


33

4.2 Tools of Analysis

Time series regression analysis is used to analyze trends that appear to be

related to time. An estimated equation is developed using time as an

independent variable. The time period is divided in subsequent years and job

positions are used as dependent variable. To identify the HR with various

skills and qualifications, (current and new) job positions were broken down

by skill set. Qualification sets were treated as dependent variable and plotted

with time to develop trend for forecasts.

We must remember that time does not cause change in the demand or supply

of human resources. These changes are caused by a variety of economic

factors. Hence, time series regression analysis will not automatically identify

changes in a trend (i.e., it cannot predict a period of low demand for IT

professionals when the available data trace a trend of increasing demand for

IT professionals.). The forecasts made in this report are thus, based on an

identifiable set of economic trends that are assumed to remain consistent over

the forecast period. The reader must keep in mind that a significant change in

economic indicators could bring about an unpredicted change in the

forecasts, however the study of economic trends reveal that such volatile

fluctuations are highly unlikely.

4.2.1 The General Model The general situation involved a set of n paired observations to be denoted:

(Xi, Yi) for i= 1, 2, 3… n.

Here the forecast variable Yi represents the job positions for IT Professionals

and the explanatory variable is time. Scatter plots were constructed by

plotting time against x-axis and job positions along y-axis to assess the nature


34

of relationship among variables. A linear relationship was considered

between job positions in IT sector and time. Hence the fitted model was:

Y = a + bX + e

Where a is the intercept, b is the slope of the line, and e denotes the error. The

objective was to find the values of a and b so the line ŷ = a + bX presents the

best fit to the data.

4.2.2 Equation of Best Fit To obtain an overall goodness of fit, we calculated the sum of squared errors.

The line of best fit is chosen to be the one which yields the smallest value for

this sum of squared errors. The correlation coefficient plays an important role

in multivariate data analysis and has strong ties with regression analysis.

Coefficient of determination (r²), the squared correlation coefficient is used as

a measure for the adequacy of the fitted model. r² can be interpreted as a

proportion of the variation in dependent variable Y (job positions) which is

explained by independent variable X (time). In the case of new job positions

and time data, the calculated r² = 0.997 or 99.7%. So 99.7% variations in the

job positions will be explained by the straight line model (see table 4.1). The

errors (e) also called residuals cannot explained by the fitted model hence we

also examined the residuals for the adequacy of the fitted model and for that

matter we constructed the normal P-P plots (see figure 4.1 in annexure).


35

4.2.3 Use of F and t-test Certain statistical tests were conducted to test the significance of the overall

models, to test the individual coefficients in the equation, and to develop the

prediction intervals for the forecasts that were being made using the fitted

models. F-test was used for the significance of overall model. The large value

of F statistics means regression explains a substantial proportion of the

variance. The p-value gives the probability of obtaining an F-statistic as large

as the one calculated for the data. Hence we concluded that the model was

significant for the p-value was smaller than 0.05. t-test was setup to test the

intercept and the slope values. For a p-value smaller than 0.05 we concluded

that an estimated parameter was different from zero.

The b coefficients and the constants are used to create the prediction equation

(regression). The t-test tests the significance of each b coefficient. In this case

all b coefficients are significant at 0.05 significance level. However, it is

possible to have a regression model which is overall significant as supported

by the F-test, but where a particular coefficient is not significant.

Beta coefficients are the standardized regression coefficients. Their relative

absolute magnitude reflects their relative importance in predicting dependant

variable (new job position).


36

4.2.4 The Coefficient of Determination The coefficient of determination is also used as measure of goodness of the

fitted models. For business solutions it is 98.2% meaning 98.2 % of variations

are

explained by the model. Similarly, for software development it is 94.2%,

embedded systems 95.9%, networking 83.1%, industrial automation 79.1% e-

commerce 93.5%, hardware 99.57% and security 90.5%. All the fitted models

are significant at 5% with the exception of networking which is significant at

17%.

4.2.5 The Normal Probability Plot The normal probability plot (z residual normal P-P plot) is another test of

normally distributed residual error. Under perfect normality, the plot will be

a 45-degree line, if a standard line model is fitted. It is clearly evident in the P-

P plots of all new job positions (Business Solutions, Networking, Software

Development, Embedded System, E-commerce, Security and Others) that the

fitted models are significant. By looking at this plot, it is evident that the

transformation of variables leads to a good reproduction of the shape of the

true function.

In addition to standard linear regression, several curvilinear regression

models were considered. A summary of the results of the curvilinear

regression analysis is given in the result section.

4.3 Underlying Factors:


37

Questionnaires and interviews revealed a number of significant underlying

factors leading up to the shifts in industry expectations and priorities.

Identification of these factors proved effective in developing trends for the

demand of IT professionals in various companies. Analysis of these factors

revealed that discernable trend in Non IT companies is that, in spite of hiring

IT professionals they prefer opting for outsourcing of their IT needs. Thus, for

any complex technical development or redesigning of system these business

organizations will solicit the services of expert IT Company. Hence, we may

say that as in the near future the demands of these companies in such service

areas grow, they will rely heavily on outsourcing. As a result, new IT

companies will emerge to cater to the needs of Non IT sector particularly for

providing e-commerce solutions, industrial automation and networks

maintenance.

Industry Professionals are not found satisfied with the quality of fresh

graduates entering the industry. The theoretical knowledge base of these

graduates is, no doubt, very strong but they lack the skill to apply the

acquired knowledge to practical environments, even though these graduates

have completed their internships as well to get a hand on experience on

practical applications.

These organizations claim that they had to train their fresh entry of graduates

in order to groom them to acquire market standards before they could render

desired output. This training period is usually spanned over 3 – 6 months. It

involves heavy cost of expenditures during on job training and development

of new recruits before they mature to render valuable output for the parent

organization. After successful completion of on the job training, skilled and

trained employees become most attractive human resource for any needing


38

organization that are prepared to offer them a lucrative package to subvert

their loyalty to the other institution.

The private entities are of the opinion that the Govt. must share the cost of

training for their respective inductees through either sponsorships or by

setting up an executive development center to train these fresh graduates.

Thus, a pool of trained and skilled work force can be groomed to meet their

anticipated demand by sharing their cost of training.

The study suggests that we must anticipate a significant growth in the IT

sector. As a result, more entrepreneurs will emerge with new ideas and new

companies. This trend will serve to lower the unemployment rate in the IT

Industry. To facilitate this tremendous growth Govt. should also setup a

venture capital fund. More Software technology parks should be established

to facilitate the infrastructural requirements.

It is not that the public sector is not making efforts in this regard. PSEB itself

has launched initiatives like Young Professional Program that aims at

providing a platform to train the young manpower in the IT sector to the

extent of becoming a tailor-made product as per the needs of the industry.

Another initiative by the name of PSEB Venture Capital Fund is in the process

of design by which incubation could be provided to new companies and

support would be made available to innovative ideas to make sure that

emergence of new companies would not face the usual hazards. STPs are

already functioning at various locations and in the near future the number

would grow manifolds. The issue really is to bridge the gap between

Industry, academia and public sector and to achieve that all have to make an

effort.


39

5. Conclusion

5.1 The number game Based on the results of the survey findings, there are approximately 88,000 IT

professionals employed in the industry. In the coming few years IT industry

will undergo tremendous growth. The outsourcing by non IT companies will

allow the growth of more IT companies. 80% or more of the total job positions

expected in the industry will be created in the IT sector (breakdown given

below).

5.2 Supply-Demand Situation

The output of IT Professionals by universities is growing many folds over the

period of years as more universities and degree awarding institutes are given

charter by HEC. As per HEC records, 50 registered universities both in pubic

and private sector were awarding bachelor and masters degrees in computer

sciences and software engineering till mid nineties. The number has risen to

105 universities by year 2005 and the disciplines have grown from computer

science and software engineering to telecom engineering, IT, computer

engineering etc. Similarly there has been a growth in the IT sector as new

companies have emerged and existing companies have expanded their

businesses.


40

5.3 Future requirements

The total number of job positions that will be created in the industry in year

2006 are approximately 16,000, in year 2007 are 18000, in year 2008 are 22000,

in year 2009 are 25000 and in year 2010 are 28000 for the near term.

The somewhat abstract (predicted) requirements for IT professionals for the

long term are as follows: in year 2011 are 31000, in year 2012 are 35000, in

year 2013 are 38000, in year 2014 are 42000 an in year 2015 are 45000.

5.4 The Curriculum Revision

Apparently it seems that the supply is much more than the demand of IT

professionals in the industry, but considering that the Govt. organizations

and non-it private limited companies are excluded from the population, it

would be fair to assume that the gap will be filled to a reasonable extent by

these entities. Hence, the job prospects in the market are good for graduates

of well reputed universities, whereas each year 5-10% of the graduates

(mostly from weaker universities) will remain unemployed not because of the

absence of job opportunities but due to the lack of adequate training to meet

the changing demands of the industry.

Hence, universities need to revise their curriculum along with the changing

needs of the industry. Also Govt. should join hands with the private sector to

meet their demand of a more mature, skilled and trained work force by

sharing the cost of training and re-training of IT Professionals in the industry

and also by setting up an executive development centre where these fresh

graduates equip themselves with market standards.


41

5.5 The Standardization

Another important factor in this regard is the standardization issue. It is

imperative that the IT (in industrial as well as educational perspective)

should be taken as a profession like medicine, law or engineering and an

independent body needs to be set up to standardize the education by

considering all its dimensions. This entity should be formed in line with PEC

or PMDC but with a more pro-active approach. HEC has established NCEAC

(National Computing Education Accreditation Council) as a first step in this

regard but its evolution and effective role is yet to be observed. More concrete

efforts in this regard are needed to cope with challenges of tomorrow.


42

Glossary

IT Professional:

Professionals involved in the design/development, services, installation, and

implementation of computer systems and applications, including those

personnel that have a degree or certification and experience in the

information technology arena.

System Administration:

The term system administrator (abbreviation: sys-admin) designates a job

position of engineers involved in computer systems. They are the people

responsible for running the system, or running some aspect of it.

Database administration:

Database administration is the process of establishing computerized

databases, and insuring their recoverability, integrity, security, availability,

reliability, and performance.

System Analyst:

A person responsible for studying the requirements, feasibility, cost, design,

specification, and implementation of a computer based system for an

organization/ business.

Networking:

The linking of a number of devices, such as computers, workstations,

printers, and AV gear into a network (system) for the purpose of sharing

resources.

Software Developers:

The professional and amateur programmers who create software for use on

computers.

Software engineering:

Software engineering is the profession concerned with creating and

maintaining software applications by applying technologies and practices


43

from computer science, project management, engineering, application

domains, and other fields.


44

Computer Science:

The body of knowledge resulting from this discipline contains theories for

understanding computing systems and methods; design methodology,

algorithms, and tools; methods for the testing of concepts; methods of

analysis and verification; and knowledge representation and implementation.

E-Commerce:

E-commerce is the buying and selling of goods and services, and the transfer

of funds, through digital communications.

Industrial automation:

Industrial automation is the use of computers to control industrial machinery

and processes, replacing human operators.

Support staff:

Support staff is a technician and other support personnel who resolve

incidents assigned to them within the system.

MIS

This stands for Management Information Systems, and is really just another

name for data processing by computer to produce current, accurate, and

informative reports for decision makers.

Security

Protection of networks and their services from unauthorized modification,

destruction, or disclosure, and provision of assurance that the network

performs its critical functions correctly and there are no harmful side-effects.

Network security includes providing for data integrity.

Embedded Systems:

Large engineering systems containing a computer to monitor its whole

behavior.

Others:

IT professionals employed by the industry serve the reason for which a

particular company operates rather than their true specialization.


45

Class Interval: In plotting a histogram, one starts by dividing the range of values into a set of non-overlapping intervals, called class intervals, in such a way that every datum is contained in some class interval. Cluster Sample: In a cluster sample, the sampling unit is a collection of population units, not single population units. For example, techniques for adjusting the U.S. census start with a sample of geographic blocks, then (try to) enumerate all inhabitants of the blocks in the sample to obtain a sample of people. This is an example of a cluster sample. (The blocks are chosen separately from different strata, so the overall design is a stratified cluster sample.) Confidence Interval: A confidence interval for a parameter is a random interval constructed from data in such a way that the probability that the interval contains the true value of the parameter can be specified before the data are collected. Confidence Level: The confidence level of a confidence interval is the chance that the interval that will result once data are collected will contain the corresponding parameter. If one computes confidence intervals again and again from independent data, the long-term limit of the fraction of intervals that contain the parameter is the confidence level. Correlation: A measure of linear association between two (ordered) lists. Two variables can be strongly correlated without having any causal relationship, and two variables can have a causal relationship and yet be uncorrelated. Correlation coefficient: The correlation coefficient r is a measure of how nearly a scatterplot falls on a straight line. The correlation coefficient is always between -1 and +1. To compute the correlation coefficient of a list of pairs of measurements (X,Y), first transform X and Y individually into standard units. Multiply


46

corresponding elements of the transformed pairs to get a single list of numbers. The correlation coefficient is the mean of that list of products. Dependent Variable: In regression, the variable whose values are supposed to be explained by changes in the other variable (the independent or explanatory variable). Usually one regresses the dependent variable on the independent variable. Discrete Variable: A quantitative variable whose set of possible values is countable. Typical examples of discrete variables are variables whose possible values are a subset of the integers, such as the number of vehicles that cross a particular bridge on a given day, the number of people in a family, ages rounded to the nearest year, etc. Discrete variables are "chunky." C.f. continuous variable. A discrete random variable is one whose set of possible values is countable. A random variable is discrete if and only if its cumulative probability distribution function is a stair-step function; i.e., if it is piecewise constant and only increases by jumps. Distribution: The distribution of a set of numerical data is how their values are distributed over the real numbers. It is completely characterized by the empirical distribution function. Similarly, the probability distribution of a random variable is completely characterized by its probability distribution function. Sometimes the word "distribution" is used as a synonym for the empirical distribution function or the probability distribution function. Estimator: An estimator is a rule for "guessing" the value of a population parameter based on a random sample from the population. An estimator is a random variable, because its value depends on which particular sample is obtained, which is random. A canonical example of an estimator is the sample mean, which is an estimator of the population mean. Expectation, Expected Value: The expected value of a random variable is the long-term limiting average of its values in independent repeated experiments. The expected value of the random variable X is denoted EX or E(X). For a discrete random variable (one that has a countable number of possible values) the expected value is the


47

weighted average of its possible values, where the weight assigned to each possible value is the chance that the random variable takes that value. One can think of the expected value of a random variable as the point at which its probability histogram would balance, if it were cut out of a uniform material. Taking the expected value is a linear operation: if X and Y are two random variables, the expected value of their sum is the sum of their expected values (E(X+Y) = E(X) + E(Y)), and the expected value of a constant a times a random variable X is the constant times the expected value of X (E(a×X ) = a× E(X)). Histogram: A histogram is a kind of plot that summarizes how data are distributed. Starting with a set of class intervals, the histogram is a set of rectangles ("bins") sitting on the horizontal axis. The bases of the rectangles are the class intervals, and their heights are such that their areas are proportional to the fraction of observations in the corresponding class intervals. That is, the height of a given rectangle is the fraction of observations in the corresponding class interval, divided by the length of the corresponding class interval. A histogram does not need a vertical scale, because the total area of the histogram must equal 100%. The units of the vertical axis are percent per unit of the horizontal axis. This is called the density scale. The horizontal axis of a histogram needs a scale. If any observations coincide with the endpoints of class intervals, the endpoint convention is important. Independent Variable: In regression, the independent variable is the one that is supposed to explain the other; the term is a synonym for "explanatory variable." Usually, one regresses the "dependent variable" on the "independent variable." There is not always a clear choice of the independent variable. The independent variable is usually plotted on the horizontal axis. Independent in this context does not mean the same thing as statistically independent. Population: A collection of units being studied. Units can be people, places, objects, epochs, drugs, procedures, or many other things. Much of statistics is concerned with estimating numerical properties (parameters) of an entire population from a random sample of units from the population. Regression, Linear Regression: Linear regression fits a line to a scatterplot in such a way as to minimize the sum of the squares of the residuals. The resulting regression line, together


48

with the standard deviations of the two variables or their correlation coefficient, can be a reasonable summary of a scatter-plot if the scatter-plot is roughly football-shaped. In other cases, it is a poor summary. If we are regressing the variable Y on the variable X, and if Y is plotted on the vertical axis and X is plotted on the horizontal axis, the regression line passes through the point of averages, and has slope equal to the correlation coefficient times the SD of Y divided by the SD of X. Sample: A sample is a collection of units from a population. Standard Deviation (SD): The standard deviation of a set of numbers is the rms of the set of deviations between each element of the set and the mean of the set t-test: An hypothesis test based on approximating the probability histogram of the test statistic by Student's t curve. t tests usually are used to test hypotheses about the mean of a population when the sample size is intermediate and the distribution of the population is known to be nearly normal.

Extrapolation:

Extrapolation is when the value of a variable is estimated at times which have not yet been observed. This estimate may be reasonably reliable for short times into the future, but for longer times, the estimate is liable to become less accurate.

ANOVA:

(Analysis of variance): A test for significant differences between multiple means by comparing variances. It concerns a normally distributed response (outcome) variable and a single categorical explanatory (predictor) variable, which represents treatments or groups. ANOVA is a special case of multiple regression where indicator variables (or orthogonal polynomials) are used to describe the discrete levels of factor variables. The term analysis of variance refers not to the model but to the method of determining which effects are statistically significant.


49

Covariance models:

Models containing some quantitative and some qualitative explanatory variables, where the chief explanatory variables of interest are qualitative and the quantitative variables are introduced primarily to reduce the variance of the error terms. [Models in which all explanatory variables are qualitative are called analysis of variance -ANOVA- models.] Analysis of covariance -ANCOVA- combines features of ANOVA and regression.

F test:

The F test for linear regression tests whether the slope is significantly different from 0, which is equivalent to testing whether the fit using non-zero slope is significantly better than the null model with 0 slope.

General linear model:

A group of linear regression models in which the response variable is continuous and normally distributed, the response variable values are predicted from a linear combination of predictor variables, and the linear combination of values for the predictor variables is not transformed (i.e., there is no link function as in generalized linear models). Linear multiple regression is a typical example of general linear models whereas simple linear regression is a special case of generalized linear models with the identity link function.

Linear regression models:

In the context of linear statistical modeling, 'linear' means linear in the parameters (coefficients), not the explanatory variables. The explanatory variables can be transformed (say, x2), but the model will still be linear if the coefficients remain linear. When the overall function (Y) remains a sum of terms that are each an X variable multiplied by a coefficient, the function Y is said to be linear in the coefficients.

Annexure 02 Questionnaires to Industry (IT & Non IT)

Pakistan Software Export Board (PSEB) & NU-Consulting (FAST) Joint Study 1

IT Human Resource Need Assessment Study Questionnaire

1- Name of the Institute/Organization: 2- Authorized Person: (name) 3- Designation: 4- Email: 5- Telephone: 6- Year of Commencement: 19___ / 20___ 7- Field of Specialization: (tick the appropriate box)

Business Solutions

E- Commerce Solutions

Embedded Systems

Hardware Development

Software Development

Industrial Automation

Net-working

Others Security

8- Break Down the IT Human resource employed by the organization in terms of Highest Qualification:

(write the number in the relevant box) Year Qualification Origin

Year of establishment

2004 At Present

(Foreign Qualified) PhD (Local Qualified) (Foreign Qualified) MS/MIT/MCS (Local Qualified) (Foreign Qualified) BS

(Local Qualified) (Foreign Qualified) BE (Local Qualified) (Foreign Qualified) Diploma/ Certification

(Local Qualified) (Foreign Qualified) Others

(Local Qualified) 9- Break Down in terms of Specialization: (write the number in the relevant box; each employee must

only be reported once) Year Specialization

Year of establishment

2004 At Present

Software Engineering Computer Sciences Telecom Networking System Administration Databases Computer Engineering MIS Hardware Designing Electronics/ Electrical System Designing Project Managers Others (please specify)


10- How many new positions will be created in your organization in the future? (Give fair estimates)

11-What ratio of each skill set will be hired for all future positions? (Give percentages and relate the number of positions to your answer in Q-10)

Year 2005 2006 2007 2008 2009 2010

Business Solutions


Embedded Systems

Networking


E-Commerce Solutions


Security

Others

Total 100% 100% 100% 100% 100% 100%

12- What ratio of each terminal qualification will fill the future positions in your organization? (Give percentages and relate the number of positions to your answer in Q-10)

Year 2005 2006 2007 2008 2009 2010

PhD (Foreign)

PhD (Local)

MS/MIT /MCS

BS

BE

Other Qualification

Diploma/Certificate

Total 100% 100% 100% 100% 100% 100%

12- How many of your employees have their current position as their first job? (Tick the appropriate box)

No One 1-10% 11-20% 21-30% 31-40% 41-50% 51-60% 61-70%

71-80% 81-90% 91-100%

Year 2005 2006 2007 2008 2009 2010

No. of Positions/ Jobs

Please Return to: Uzma Javed, Associate Consultant UAN: 051-111-128-128, FAST/PSEB Joint Task Team Direct: 051-7102005 FAST House, A K Brohi Road, H-11/4, Islamabad Email: [email protected]


IT Human Resource Need Assessment Study Questionnaire

1- Name of the Institute/Organization: 2- Authorized Person: (name) 3- Designation: 4- Email: 5- Telephone: 6- When was the company IT/ IT related department(s) formed: 7- What is the total number of employees in the IT /

IT related department (s), at present?

8- What areas IT department focuses upon: (tick all the appropriate box (es)) Networking Databases System

Analysis Industrial Automation


General Maintenance

MIS Others

9- Break Down the IT Human resource employed by the organization in terms of Highest Qualification:

(write the number in the relevant box) Year Qualification Origin

2003 2004 At Present (Foreign Qualified) PhD (Local Qualified) (Foreign Qualified) MS/MIT/MCS (Local Qualified) (Foreign Qualified) BS



(Local Qualified) 10- Break Down in terms of Specialization: (write the number in the relevant box; each employee must only

be reported once) Year Specialization

2003 2004 At Present System Analyst System Administrator MIS Specialists System Auditor Database Administrators Developers Support Staff MIS Others 11- How many future positions will be created in your organization for IT related areas? (Write a number)

Year 2005 2006 2007 2008 2009 2010

No. of Positions/ Jobs


12- What ratio of each skill set will be hired for all future positions in the IT related fields? (Give percentages and relate the number of positions to your answer in Q-11)

Year 2005 2006 2007 2008 2009 2010

Business Solutions


System Analyst

Networking

System Administration

Security Specialist

MIS Specialist

Support Staff


Others

Total 100% 100% 100% 100% 100% 100%

13- What ratio of each terminal qualification will fill the future positions in your organization fro IT related fields? (Give percentages and relate the number of positions to your answer in Q-11)

Year 2005 2006 2007 2008 2009 2010

PhD (Foreign)

PhD (Local)

MS/MIT

BS

BE

Other Qualification

Diploma/Certificate

Total 100% 100% 100% 100% 100% 100%

14- How many of your employees in IT related fields have their current position as their first job? (Tick the appropriate box)

No One 1-10% 11-20% 21-30% 31-40% 41-50% 51-60% 61-70%

71-80% 81-90% 91-100%


Annexure 03 Questionnaires to Academia (Universities)

Pakistan Software Export Board (PSEB) & NU-Consulting (FAST) Joint Study

1

IT Human Resource Need Assessment Study Questionnaire-1 (Student Output Data)

1- Name of the Institute/University: 2- Authorized Person: (name) 3- Designation: 4- Email: 5- Telephone: 6- Please fill for the departments that exist in your institution/university? (state the year)

Department Date of Commencement

Computer Science Electronics Engineering Mechatronics Computer Engineering Telecom Engineering Software Engineering MIS IT Bioinformatics Other

7- What has been the output of passing students for the following years? (Write the numbers for each cell. Write “0” for non-applicable cells”)

Year Department Programs 2004 2002 2000 1995 1990 1985

Bachelors Masters

Computer Sciences

Ph.D. Bachelors Masters

Electronics Engineering


Computer Engineering


Telecom Engineering


Software Engineering


MIS /MIT


Bioinformatics


Mechatronics


Others

Ph.D.


2

8- How long does it take for the passing out batch to get employment? (Give percentages)

Duration within which employment secured Total Department from which graduate pass out 06

months 6-12 months

More than a year

Remain Unemployed

Computer Science 100% Electronics Engineering 100% Mechatronics 100% Computer Engineering 100% Telecom Engineering 100% Software Engineering 100% MIS 100% IT 100% Bioinformatics 100% Other 100%



1

IT Human Resource Need Assessment Study Questionnaire-2 (IT Human Resource Data)

1- Name of the Institute/University: 2- Authorized Person: (name) 3- Designation: 4- Email: 5- Telephone: 6- How many employees does the institute have with IT related Qualifications, at the moment? Faculty Non-Faculty Total

7- Break Down in terms of Highest Qualification: (write the number in the relevant box; each

employee must only be reported once and only according to the highest qualification that he/she has)

Year Qualification Origin 2000 2002 2004 At Present

(Foreign Qualified) PhD (Local Qualified) (Foreign Qualified) MS/MIT/MCS (Local Qualified) (Foreign Qualified) BS



(Local Qualified) 8- Break Down in terms of Specialization: (write the number in the relevant box; each employee

must only be reported once)

Year Specialization 2000 2002 2004 At Present

Software Engineering Computer Sciences Telecom Networking System Administration Databases Computer Engineering MIS Hardware Designing Electronics/ Electrical Mechatronics Support Staff Others


2

9- What number of persons will be hired for all future positions in IT? (Give fair estimates) Year 2006 2007 2008 2009 2010

Faculty

Non-Faculty

10- How many of your employees in the IT disciplines have their current position as their first job? (Tick the appropriate box)

No One 1-10% 11-20% 21-30% 31-40% 41-50% 51-60% 61-70%

71-80% 81-90% 91-100%


Annexure 04 (Facts & Figures)

A. Human Resource in IT Companies

Table 4.1 Statistics for No. of Employees.

N Valid 39

Missing 0

Mean 47.38

Median 26.00

Mode 14

Std. Deviation 63.565

Skewness 3.176

Std. Error of

Skewness

.378

Minimum 4

Maximum 342

Percentiles 10 6.00

20 11.00

25 11.50

30 12.00

40 12.35

50 13.00

60 34.00

70 43.00

75 52.00

80 64.00

90 107.00

A Multiple modes exist. The smallest value is shown.

Figure 4.1

No. of Employees

350.0325.0

300.0275.0

250.0225.0

200.0175.0

150.0125.0

100.075.0

50.025.0

0.0

No. of Employees

Freq

uenc

y

20

10

0

Std. Dev = 63.57 Mean = 47.4

N = 39.00

Table 4.2: Segmentation of IT Companies with respect to number of employees.

% of Companies

Average

Employees

No. of

Companies

No. of

Employees

46% 12.5 322 4025

28% 34.73 196 6807

15.40% 73.5 108 7938

10.30% 200 74 14800

100% 47.38 700 33570

Table 4.3: T- Test for the significance of Mean for IT Companies.

One-Sample Statistics

N Mean Std. Deviation Std. Error

Mean

No. of

Employees

39 47.38 63.565 10.179

One-Sample Test

Test Value =

0.05

t df Sig. (2-tailed) Mean

Difference

95%

Confidence

Interval of

the

Difference

Lower Upper

No. of

Employees

4.650 38 .000 47.33 26.73 67.94

Table 4.4: Qualification/Origin * Years Cross tabulation

Year 2005

Sample Population

Qualification/Origin PhdForeign Count 16 302

Expected Count 22.2

% within Years 0.9

DipLocal Count 359 6513

Expected Count 372.8

% within Years 19.4

OtherForeign Count 16 302

Expected Count 9.6

% within Years 0.9

OtherLocal Count 299 5438


% within Years 16.2

PhdLocal Count 1 33

Expected Count 0.5

% within Years 0.1

MSForeign Count 42 772

Expected Count 49.4

% within Years 2.3

MSLocal Count 404 7352


% within Years 21.9

BSForeign Count 84 1511

Expected Count 70.5

% within Years 4.5

BSLocal Count 479 8695


% within Years

25.9

BEForeign Count 15 269

Expected Count 11.6

% within Years 0.8

BELocal Count 124 2249


% within Years 6.7

DipForeign Count 9 134


% within Years 0.4

Total Count 1848 33570

Expected Count 1848

% within Years 100

Figure 4.2: Pie Chart

Percentage of IT Professionals in 2005 by Qualification

1% 21%1%

14%0%2%22%5%

25%1%7% 1%

PhdForeign DipLocal OtherForeign OtherLocal PhdLocal MSForeignMSLocal BSForeign BSLocal BEForeign BELocal DipForeign


IT Professional in 2005 by Specialization.

17%3%3%

5%

30%19%

2%7%4%5%1%2%2%

Software Engineering Electronics System DesignProject managers others Computer ScienceTelecom Networking System AdmDatabase Computer Engr. MISHardware Design

Table 4.5: Specialization * Years Cross tabulation

Year 2005

Sample Population

Specialization

Software

Engineering Count 317 5774

% within Years 17.2

Electronics Count 62 1141

% within Years 3.4

System Design Count 47 839

% within Years 2.5

Project managers Count 84 1511

% within Years 4.5

others Count 578 10507

% within Years 31.3

Computer Science Count 357 6479

% within Years 19.3

Telecom Count 28 504

% within Years 1.5

Networking Count 126 2283

% within Years 6.8

System Adm Count 75 1376

% within Years 4.1

Database Count 91 1645

% within Years 4.9

Computer Engr. Count 23 436

% within Years 1.3

MIS Count 28 504

% within Years 1.5

Hardware Design Count 32 571

% within Years 1.7

Total Count 1848 33570

% within Years 100

Table 4.5.1 Statistics

Year 2006

N Valid 39

Missing 0

Mean 18.54

Median 10.00

Mode 10


Skewness 1.377

Std. Error of Skewness .378

Minimum 0

Maximum 65

Percentiles 10 2.00

20 5.00

30 7.00

40 10.00

50 10.00

60 15.00

70 20.00

80 30.00

90 58.00

Table 4.5.2 Descriptive Statistics

Years N Minimum Maximum Mean Std.

Deviation

No. of Job

Positions

2006 39 0 65 18.54 18.466 12978

2007 39 0 100 21.79 25.251 15253

2008 39 0 140 25.95 33.350 18165

2009 39 0 160 30.49 41.219 21343

2010 38 0 225 34.84 52.717 24388

Table 4.6 Number of IT Professionals by Skills/ specialization.

Skills Years

2006 population 2007 population 2008 population 2009 population 2010 population

Business Solutions 15.5 2010 15.2 2315 14.6 2657 16.3 3482 16.7 4071

Software

Development 37.5 4865 35.4 5401 34.2 6211 37.3 7970 33.2 8086

Embeded Systems 16.0 2082 15.2 2315 15.2 2764 15.4 3285 16.8 4089

Networking 8.4 1095 9.8 1489 10.0 1813 7.7 1651 8.4 2045

Industrial

Automation 1.2 162 1.4 215 1.5 269 0.8 180 0.6 147

E-Commerce

Solutions 5.8 754 4.7 718 6.9 1256 7.1 1508 7.9 1916

Hardware

Development 1.0 126 2.2 341 1.8 323 1.2 251 1.1 258

Security 2.6 341 4.4 664 3.5 628 4.1 880 4.0 976

Others 11.9 1544 11.8 1794 12.4 2244 10.0 2136 11.5 2800

100 12978 100 15253 100 18165 100 21343 100 24388


IT Professionals in 2006

15%

38%16%

8%

1%

6%

1%

3%

12%Business Solutions

SoftwareDevelopmentEmbeded Systems

Networking

IndustrialAutomationE-CommerceSolutionsHardwareDevelopment



15%

36%

15%

10%

1%

5%

2%

4%

12% BusinessSolutions

Softw areDevelopmentEmbededSystems

Netw orking

IndustrialAutomation



15%

35%

15%

10%

1%

7%

2%

3%

12%

BusinessSolutions


Netw orking



IT Professional in 2009

16%

38%15%

8%

1%

7%

1%

4%

10%BusinessSolutions


Netw orking




17%

33%

17%

8%

1%

8%

1%

4%

11% Business Solutions

SoftwareDevelopmentEmbeded Systems

Networking

IndustrialAutomationE-CommerceSolutionsHardwareDevelopmentSecurity

Others

Table 4.7: Summaries of Results for Extrapolating IT Professionals for year 2011 _ 2015

Skills Years

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Business Solutions 1849.2 2378.1 2907 3435.9 3964.8 4493.7 5022.6 5551.5 6080.4 6609.3

Software

Development 4704.4 5605.5 6506.6 7407.7 8308.8 9209.9 10111 11012.1 11913.2 12814.3

Embeded Systems 1910 2408.4 2906.8 3405.2 3903.6 4402 4900.4 5398.8 5897.2 6395.6

Networking 1206 1412.2 1618.4 1824.6 2030.8 2237 2649.4 2649.4 2855.6 3061.8

Industrial

Automation 162.6 223.6 239.6 210.6 136.6 17.6 0 0 0 0

E-Commerce

Solutions 607.6 919 1230.4 1541.8 1853.2 2164.6 2476 2787.4 3098.8 3410.2

Hardware

Development 126.65 338.36 326.93 248.36 258.65 513.8 1169.81 2382.68 4308.41 7103

Security 400.6 549.2 697.8 846.4 995 1143.6 1292.2 1440.8 1589.4 1738

Others 1532.8 1818.2 2103.6 2389 2674.4 2959.8 3245.2 3530.6 3816 4101.4

Total 12643.4 15534.4 18425.4 21316.4 24207.4 27098.4 29989.4 32880.4 35771.4 38662.4

4.8 Time Series

Table 4.8: Overall Job Positions in IT Sector.

Variables Entered/Removed

Model Variables

Entered

Variables

Removed

Method

1 X . Enter

a All requested variables entered.

b Dependent Variable: EMPLOYEE

Model Summary

Model R R Square Adjusted

R Square

Std. Error

of the

Estimate

1 .998 .997 .995 312.130

a Predictors: (Constant), X


ANOVA

Model Sum of

Squares

df Mean

Square

F Sig.

1 Regression 83578810.0

00

1 83578810.0

00

857.878 .000

Residual 292275.200 3 97425.067

Total 83871085.2

00

4



Coefficients

Unstandar

dized

Coefficient

s

Standardiz

ed

Coefficient

s

t Sig. 95%

Confidenc

e Interval

for B

Model B Std. Error Beta Lower

Bound

Upper

Bound

1 (Constant) 12643.400 241.775 52.294 .000 11873.965 13412.835

X 2891.000 98.704 .998 29.290 .000 2576.879 3205.121

a Dependent Variable: EMPLOYEE

Residuals Statistics

Minimum Maximum Mean Std.

Deviation

N

Predicted

Value

12643.40 24207.40 18425.40 4571.072 5

Residual -281.40 334.60 .00 270.312 5

Std.

Predicted

Value

-1.265 1.265 .000 1.000 5

Std.

Residual

-.902 1.072 .000 .866 5


Figure 4.9: Normal P-P Plot for Overall IT Professionals in the IT Sector.

Normal P-P Plot of Regression Standa

Dependent Variable: EMPLOYEE

Observed Cum Prob

1.00.75.50.250.00

Exp

ecte

d C

um P

rob

1.00

.75

.50

.25

0.00

4.9 Overall Job Positions in IT Sector by Skills.

Table 4.9: Business Solutions

Model Summary


R Square

Std. Error

of the

Estimate

1 .982 .964 .951 187.743


b Dependent Variable: Business Solutions

ANOVA

Model Sum of

Squares

df Mean

Square

F Sig.


0

1 2797352.10

0

79.364 .003

Residual 105741.900 3 35247.300

Total 2903094.00

0

4



Coefficients

Unstandar

dized

Coefficient

s

Standardiz

ed

Coefficient

s

t Sig. 95%

Confidenc

e Interval

for B


Bound

Upper

Bound

1 (Constant) 1849.200 145.425 12.716 .001 1386.393 2312.007

X 528.900 59.369 .982 8.909 .003 339.960 717.840




Deviation

N

Predicted

Value

1849.20 3964.80 2907.00 836.264 5

Residual -250.00 160.80 .00 162.590 5

Std.

Predicted

Value

-1.265 1.265 .000 1.000 5

Std.

Residual

-1.332 .856 .000 .866 5


Figure 4.10: Normal P-P Plot for Business Solutions


Dependent Variable: EMPLOYEE

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b

1.00

.75

.50

.25

0.00

Table 4.10: Software Development


Model Variables

Entered

Variables

Removed

Method

1 X . Enter


b Dependent Variable: Software Development

Model Summary


R Square

Std. Error

of the

Estimate

1 .969 .940 .920 416.659



ANOVA

Model Sum of

Squares

df Mean

Square

F Sig.


0

1 8119812.10

0

46.772 .006

Residual 520813.100 3 173604.367

Total 8640625.20

0

4



Coefficients

Unstandar

dized

Coefficient

s

Standardiz

ed

Coefficient

s

t Sig. 95%

Confidenc

e Interval

for B


Bound

Upper

Bound

1 (Constant) 4704.400 322.742 14.576 .001 3677.290 5731.510

X 901.100 131.759 .969 6.839 .006 481.784 1320.416

a Dependent Variable: Software Development



Deviation

N

Predicted

Value

4704.40 8308.80 6506.60 1424.764 5

Residual -295.60 562.30 .00 360.837 5

Std.

Predicted

Value

-1.265 1.265 .000 1.000 5

Std.

Residual

-.709 1.350 .000 .866 5

a Dependent Variable: Software Development

Figure 4.11: Normal P-P Plot


Dependent Variable: Software Develo

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b1.00

.75

.50

.25

0.00

Table 4.11: Embedded Systems


Model Variables

Entered

Variables

Removed

Method

1 X . Enter


b Dependent Variable: Embeded Systems

Model Summary


R Square

Std. Error

of the

Estimate

1 .979 .959 .945 189.315



ANOVA

Model Sum of

Squares

df Mean

Square

F Sig.


0

1 2484025.60

0

69.308 .004

Residual 107520.400 3 35840.133

Total 2591546.00

0

4



Coefficients

Unstandar

dized

Coefficient

s

Standardiz

ed

Coefficient

s

t Sig. 95%

Confidenc

e Interval

for B


Bound

Upper

Bound

1 (Constant) 1910.200 146.643 13.026 .001 1443.517 2376.883

X 498.400 59.867 .979 8.325 .004 307.878 688.922

a Dependent Variable: Embeded Systems



Deviation

N

Predicted

Value

1910.20 3903.80 2907.00 788.040 5

Residual -143.00 185.20 .00 163.952 5

Std.

Predicted

Value

-1.265 1.265 .000 1.000 5

Std.

Residual

-.755 .978 .000 .866 5

a Dependent Variable: Embeded Systems



Dependent Variable: Embeded System

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b1.00

.75

.50

.25

0.00

Table 4.12: Networking Variables Entered/Removed

Model Variables

Entered

Variables

Removed

Method

1 X . Enter


b Dependent Variable: Networking

Model Summary


R Square

Std. Error

of the

Estimate

1 .912 .831 .775 169.732



ANOVA

Model Sum of

Squares

df Mean

Square

F Sig.

1 Regression 425184.400 1 425184.400 14.759 .031

Residual 86426.800 3 28808.933

Total 511611.200 4



Coefficients

Unstandar

dized

Coefficient

s

Standardiz

ed

Coefficient

s

t Sig. 95%

Confidenc

e Interval

for B


Bound

Upper

Bound

1 (Constant) 1206.200 131.474 9.174 .003 787.792 1624.608

X 206.200 53.674 .912 3.842 .031 35.386 377.014

a Dependent Variable: Networking



Deviation

N

Predicted

Value

1206.20 2031.00 1618.60 326.031 5

Residual -173.80 194.40 .00 146.992 5

Std.

Predicted

Value

-1.265 1.265 .000 1.000 5

Std.

Residual

-1.024 1.145 .000 .866 5

a Dependent Variable: Networking



Dependent Variable: Networking

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b1.00

.75

.50

.25

0.00

Table 4.13: Industrial Automation


Model Variables

Entered

Variables

Removed

Method

1 X . Enter


b Dependent Variable: Industrial Automation

Model Summary


R Square

Std. Error

of the

Estimate

1 .211 .045 -.274 54.987



ANOVA

Model Sum of

Squares

df Mean

Square

F Sig.

1 Regression 422.500 1 422.500 .140 .733

Residual 9070.700 3 3023.567

Total 9493.200 4



Coefficients

Unstandar

dized

Coefficient

s

Standardiz

ed

Coefficient

s

t Sig. 95%

Confidenc

e Interval

for B


Bound

Upper

Bound

1 (Constant) 207.600 42.593 4.874 .017 72.051 343.149

X -6.500 17.388 -.211 -.374 .733 -61.838 48.838

a Dependent Variable: Industrial Automation



Deviation

N

Predicted

Value

181.60 207.60 194.60 10.277 5

Residual -45.60 74.40 .00 47.620 5

Std.

Predicted

Value

-1.265 1.265 .000 1.000 5

Std.

Residual

-.829 1.353 .000 .866 5

a Dependent Variable: Industrial Automation



Dependent Variable: Industrial Automa

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b1.00

.75

.50

.25

0.00

Table 4.14: E-Commerce Variables Entered/Removed

Model Variables

Entered

Variables

Removed

Method

1 X . Enter


b Dependent Variable: E Commerce

Model Summary


R Square

Std. Error

of the

Estimate

1 .967 .935 .913 150.084



ANOVA

Model Sum of

Squares

df Mean

Square

F Sig.

1 Regression 969699.600 1 969699.600 43.050 .007

Residual 67575.600 3 22525.200

Total 1037275.20

0

4



Coefficients

Unstandar

dized

Coefficient

s

Standardiz

ed

Coefficient

s

t Sig. 95%

Confidenc

e Interval

for B


Bound

Upper

Bound

1 (Constant) 607.600 116.255 5.226 .014 237.626 977.574

X 311.400 47.461 .967 6.561 .007 160.359 462.441

a Dependent Variable: E Commerce



Deviation

N

Predicted

Value

607.60 1853.20 1230.40 492.367 5

Residual -201.00 146.40 .00 129.977 5

Std.

Predicted

Value

-1.265 1.265 .000 1.000 5

Std.

Residual

-1.339 .975 .000 .866 5

a Dependent Variable: E Commerce



Dependent Variable: E Commerce

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b1.00

.75

.50

.25

0.00

Table 4.15: Curve Fit Hardware Development MODEL: MOD_1.

_

Dependent variable.. HD Method.. CUBIC

Listwise Deletion of Missing Data

Multiple R .99947

R Square .99894

Adjusted R Square .99577

Standard Error 5.49805

Analysis of Variance:

DF Sum of Squares Mean Square

Regression 3 28540.571 9513.5238

Residuals 1 30.229 30.2286

F = 314.71960 Signif F = .0414

-------------------- Variables in the Equation --------------------

Variable B SE B Beta T Sig T

X 375.285714 13.886220 7.021030 27.026 .0235

X**2 -189.571429 8.816497 -14.794024 -21.502 .0296

X**3 26.000000 1.448864 8.269154 17.945 .0354

(Constant) 126.657143 5.458639 23.203 .0274



X

543210-1

400

300

200

100

Observed

Cubic

Table 4.16: Security Variables Entered/Removed

Model Variables

Entered

Variables

Removed

Method

1 X . Enter


b Dependent Variable: Security

Model Summary


R Square

Std. Error

of the

Estimate

1 .951 .905 .874 87.737



ANOVA

Model Sum of

Squares

df Mean

Square

F Sig.

1 Regression 220819.600 1 220819.600 28.686 .013

Residual 23093.200 3 7697.733

Total 243912.800 4



Coefficients

Unstandar

dized

Coefficient

s

Standardiz

ed

Coefficient

s

t Sig. 95%

Confidenc

e Interval

for B

Model B Std. Error Beta Lower Upper

Bound Bound

1 (Constant) 400.600 67.961 5.895 .010 184.319 616.881

X 148.600 27.745 .951 5.356 .013 60.304 236.896

a Dependent Variable: Security



Deviation

N

Predicted

Value

400.60 995.00 697.80 234.957 5

Residual -69.80 114.80 .00 75.982 5

Std.

Predicted

Value

-1.265 1.265 .000 1.000 5

Std.

Residual

-.796 1.308 .000 .866 5

a Dependent Variable: Security



Dependent Variable: Security

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b

1.00

.75

.50

.25

0.00

Table 4.17: Others Variables Entered/Removed

Model Variables

Entered

Variables

Removed

Method

1 X . Enter


b Dependent Variable: Others

Model Summary


R Square

Std. Error

of the

Estimate

1 .944 .890 .854 182.764



ANOVA

Model Sum of

Squares

df Mean

Square

F Sig.

1 Regression 814531.600 1 814531.600 24.385 .016

Residual 100207.600 3 33402.533

Total 914739.200 4



Coefficients

Unstandar

dized

Coefficient

s

Standardiz

ed

Coefficient

s

t Sig. 95%

Confidenc

e Interval

for B


Bound

Upper

Bound

1 (Constant) 1532.800 141.568 10.827 .002 1082.267 1983.333

X 285.400 57.795 .944 4.938 .016 101.471 469.329

a Dependent Variable: Others



Deviation

N

Predicted

Value

1532.80 2674.40 2103.60 451.257 5

Residual -253.00 140.40 .00 158.278 5

Std.

Predicted

Value

-1.265 1.265 .000 1.000 5

Std.

Residual

-1.384 .768 .000 .866 5

a Dependent Variable: Others



Dependent Variable: Others

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b

1.00

.75

.50

.25

0.00

Table 4.18: Curve Fit MODEL: MOD_3.

_

Dependent variable.. IA Method.. QUADRATI


Multiple R .88943

R Square .79109





Regression 2 7510.0000 3755.0000

Residuals 2 1983.2000 991.6000

F = 3.78681 Signif F = .2089



X 83.500000 35.105799 2.710068 2.379 .1405

X**2 -22.500000 8.415971 -3.046147 -2.673 .1161

(Constant) 162.600000 29.635693 5.487 .0317



X

543210-1

280

260

240

220

200

180

160

140

120

Observed

Quadratic

B. Analysis for Demand in Educational Institutes

Table 4.19: Qualification/Origin * Years Cross tabulation

% within Years

Years

2000 2002 2004 2005

Qualification

/Origin

PhdForeign 3.1% 4.5% 6.7% 7.4%

DipLocal 3.7% 5.8% 8.7% 12.4%

OtherForeign 0% .2% .3% .2%

OtherLocal 1.2% 2.5% 2.2% 4.2%

PhdLocal 1.8% .7% .5% 1.0%

MSForeign 8.6% 5.8% 4.7% 8.4%

MSLocal 55.2% 55.4% 56.9% 49.9%

BSForeign 0% .2% 1.2% 2.1%

BSLocal 9.8% 15.6% 8.5% 7.8%

BEForeign .6% .2% 1.7% 1.1%

BELocal 16.0% 9.2% 8.5% 5.1%

DipForeign 0% 0% .2% .2%

Total 100.0% 100.0% 100.0% 100.0%

Figure 4.21: Bar Chart

IT Professionals by Skills 2000 - 2005

0.010.020.030.040.050.060.0

2000 2002 2004 2005

Years

%ag

e of

IT

Prof

essi

onal

s.

PhdForeign

DipLocal

OtherForeignOtherLocal

PhdLocal

MSForeign

MSLocal

BSForeign

BSLocalBEForeign

BELocal

DipForeign


IT Professionals by Qualification in Universities 2005

7%

13%

0%

4%

1%

8%51%

2%8% 1%5%0%

PhdForeign

DipLocal

OtherForeign

OtherLocal

PhdLocal

MSForeign

MSLocal

BSForeign

BSLocal

BEForeign

BELocal

DipForeign


% within Years

Specialization Years

2000 2002 2004 2005

Software

Engineering

8.0% 6.7% 6.4% 8.9%

Electronics 3.1% 1.6% 2.8% 4.0%

Mechatronics 1.2% 2.0%

Support Staff 4.9% 3.6% 6.4% 8.3%

Others 11.0% 4.9% 6.2% 6.7%

Computer Science 39.9% 59.6% 52.7% 38.0%

Telecom .6% 1.3% 3.3% 3.7%

Networking 9.8% 7.1% 6.5% 6.9%

System Adm 2.5% 3.3% 3.2% 4.0%

Database 8.6% 5.8% 3.5% 4.2%

Computer Engr. 8.0% 4.5% 5.0% 7.6%

MIS .7% 1.8% 3.4%

Hardware Design 3.7% .9% 1.0% 2.3%

Total 100.0% 100.0% 100.0% 100.0%


IT Professionals in Universities by Skills in 2000

8%

3%

0%

5%

11%

39%1%

10%

2%

9%

8%

0%

4%Softw areEngineeringElectronics

Mechatronics

Support Staff

Others

ComputerScienceTelecom

Netw orking

System Adm

Database

Computer


IT Professionals by Skills in 2002

7%2%0%4%5%

59%

1%7%

3%6%4%1%1%

Softw areEngineeringElectronics

MechatronicsSupportStaffOthers


Netw orking

System Adm

Database



6%

3%

1%

6%

6%

53%

3%7%3%4%5% 2%1%


Mechatronics

Support Staff

Others

Computer Science

Telecom

Netw orking

System Adm

Database



9%

4%

2%

8%

7%

38%4%

7%

4%

4%

8%

3%

2%Softw areEngineeringElectronics

Mechatronics

Support Staff

Others

Computer Science

Telecom

Netw orking

System Adm


IT Professionals by Skills 2000 - 2005

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

2000 2002 2004 2005


Mechatronics

Support Staff

Others


Netw orking

System Adm

Database

Computer Engr.

MIS

Table 4.21: Statistics Year 2005

N Valid 32

Missing 0

Mean 30.34

Median 15.00

Mode 15


Skewness 4.057


Minimum 1

Maximum 272

Percentiles 10 2.30

20 7.00

30 9.90

40 12.20

50 15.00

60 23.00

70 27.30

80 35.20

90 65.90


Y2005

275.0250.0

225.0200.0

175.0150.0

125.0100.0

75.050.0

25.00.0

Y2005Fr

eque

ncy

16

14

12

10

8

6

4

2

0

Std. Dev = 49.90 Mean = 30.3

N = 32.00

Table 4.22: One-Sample Statistics

N Mean Std.

Deviation

Std. Error

Mean

Y2005 32 30.34 49.896 8.821

One-Sample Test

Test Value

= 0.05

t df Sig. (2-

tailed)

Mean

Difference

95%

Confidence

Interval of

the

Difference

Lower Upper

Y2005 3.434 31 .002 30.29 12.30 48.28

Public Sector: 47 (Universities) + 9 (Degree Awarding Institutes) = 56

Private Sector: 36 (Universities) + 18 (Degree Awarding Institutes) = 54

Total: 83 (Universities) + 27 (Degree Awarding Institutes) = 110

Table 4.23:

Universities/institutes

Average

employees Universities/Institutes

IT

Professionals

9.4% 6 10 60

50.93% 28 56 1568

26.57% 33 29 957

3.10% 66 4 264

10% 100 11 1100

Total 110 3949

Table 4.24: Qualification/Origin * Years cross tabulation % within Years

Qualification/Origin

%age by

Qualification in 2005

No. of IT

Professionals

PhdForeign 7.42 293

DipLocal 12.36 488

OtherForeign 0.21 8

OtherLocal 4.22 167

PhdLocal 1.03 41

MSForeign 8.44 333

MSLocal 49.95 1972

BSForeign 2.06 82

BSLocal 7.83 309

BEForeign 1.13 45

BELocal 5.15 203

DipForeign 0.21 8

Total 100 3949


% within Years

Specialization

% in Year

2005

No. of IT

Professionals

Software Engineering 8.9 350

Electronics 4.0 159

Mechatronics 2.0 77

Support Staff 8.3 329

Others 6.7 264

Computer Science 38.0 1501

Telecom 3.7 146

Networking 6.9 272

System Adm 4.0 159

Database 4.2 167

Computer Engr. 7.6 301

MIS 3.4 134

Hardware Design 2.3 89

Total 100 3949

Table 4.26: Statistics

Y2006 Y2007 Y2008 Y2009 Y2010

N Valid 27 26 26 26 26

Mean 7.81 8.38 9.85 10.00 10.08

Median 4.00 5.00 7.50 6.00 6.00

Mode 4 4 4 2 4

Std. Deviation 9.111 7.879 8.201 13.239 11.482

Skewness 2.973 1.650 1.296 2.799 2.052

Std. Error of

Skewness

.448 .456 .456 .456 .456

Minimum 1 0 1 0 0

Maximum 45 32 31 60 46

Percentiles 10 1.80 1.70 2.00 .70 .70

20 2.60 3.00 3.00 2.00 2.00

30 3.40 4.00 4.00 3.10 3.10

40 4.00 4.00 5.60 4.80 4.00

50 4.00 5.00 7.50 6.00 6.00

60 6.60 8.00 10.20 8.40 10.00

70 8.00 9.90 12.90 10.00 11.00

80 10.00 12.60 15.60 12.80 15.60

90 20.20 21.50 23.00 26.30 26.30

a Multiple modes exist. The smallest value is shown


YEARS

20102009200820072006

Cou

nt160

140

120

100

80

future positions

Faculty

Non Faculty

Figure 4.30: Histogram

Y2006

45.040.035.030.025.020.015.010.05.00.0

Y2006

Freq

uenc

y

14

12

10

8

6

4

2

0

Std. Dev = 9.11 Mean = 7.8

N = 27.00

Table 4.27: New Faculty in 2006


Y2007

30.025.020.015.010.05.00.0

Y2007

Freq

uenc

y

12

10

8

6

4

2

0

Std. Dev = 7.88 Mean = 8.4

N = 26.00

%age of universities

Average

Employees

No. of

Universities

Total

Employees

62% 8 68 544

28% 15 31 465

10% 23 11 253

Total 1262

Table 4.28 : New Faculty in 2007


Average

Employees

No. of

Universities

Total

Employees

62% 8 68 544

26.50% 11 29 319

11.50% 20 13 260

Total 110 1123


Y2008

30.025.020.015.010.05.00.0

Y2008

Freq

uenc

y

10

8

6

4

2

0

Std. Dev = 8.20 Mean = 9.8

N = 26.00



Average

Employees

No. of

Universities

Total

Employees

82% 10 90 900

18% 20 20 400

Total 110 1300


Y2009

60.050.040.030.020.010.00.0

Y2009

Freq

uenc

y

14

12

10

8

6

4

2

0

Std. Dev = 13.24 Mean = 10.0

N = 26.00



Average

Employees

No. of

Universities

Total

Employees

82% 10 90 900

18% 23 20 460

Total 110 1360


Y2010

45.040.035.030.025.020.015.010.05.00.0

Y2010

Freq

uenc

y

10

8

6

4

2

0

Std. Dev = 11.48 Mean = 10.1

N = 26.00



Average

Employees

No. of

Universities

Total

Employees

80% 11 88 968

20% 20 22 440

Total 110 1408

Table 4.32: Estimated IT professionals after Model Fitting:

Years 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

IT Professionals 1262 1123 1300 1360 1408

Est. IT

Professionals 1221 1219 1253 1325 1433 1577 1759 1977 2232 2523

Table 4.33: MODEL on Faculty Requirements

Dependent variable.. EMP Method.. QUADRATI

List wise Deletion of Missing Data

Multiple R .82891

R Square .68708





Regression 2 32701.886 16350.943

Residuals 2 14893.314 7446.657

F = 2.19574 Signif F = .3129



X -20.528571 96.203592 -.297562 -.213 .8508

X**2 18.357143 23.063046 1.109937 .796 .5095

(Constant) 1221.514286 81.213365 15.041 .0044

Figure 4.35: P-P Plot for model adequacy to estimate faculty requirements 2010 – 2015.

No. of IT Professionals

X

543210-1

1500

1400

1300

1200

1100

Observed

Quadratic

Table 4.34: Future positions * YEARS Crosstabulation

% within YEARS

YEARS

2006 value 2007 value 2008 value 2009 value 2010 value

future

positions

Faculty 61.1% 771 56.8% 638 54.5% 709 58.8% 800 57.0% 803

Non

Faculty

38.9% 491 43.2% 485 45.5% 590 41.2% 560 43.0% 605

Total 100.0% 1262 100.0% 1123 100.0% 1300 100.0% 1360 100.0% 1408


IT Professionals in Universities in 2006

61%

39%FacultyNon Faculty



57%

43% FacultyNon Faculty



54%




59%




57%



YEARS

20102009200820072006

Cou

nt

160

140

120

100

80

future positions

Faculty

Non Faculty

C. Human Resource in Non IT Sector.

Table 4.35 Statistics

No. of Employees

N Valid 61

Missing 0

Mean 9.00

Median 6.00

Mode 2


Skewness 5.211


Minimum 1

Maximum 100

Percentiles 25 3.00

50 6.00

75 9.00


No. of Employees

100.090.0

80.070.0

60.050.0

40.030.0

20.010.0

0.0

No. of EmployeesFr

eque

ncy

30

20

10

0

Std. Dev = 13.72 Mean = 9.0

N = 61.00

Table 4.36 a.

Companies No. of comp Avg Employees Total Emp

banks 24 100 2400

Leasing Comp 21 12 252

others 635 8 5080

Total 680 7732

Table 4.36 b.

Specialization * YEARS Crosstabulation

% within YEARS

Specialization 2005

%age value

others 19.5 1507

System Analyst 11.1 859

System Auditor 0.5 42

Developers 11.1 859

Support Staff 22.6 1746

System Adm 17.3 1338

Database 10.6 817

MIS 7.3 563

Total 100 7732

Table 4.36 c.

Qualification and Origin * YEARS Cross tabulation % within YEARS

Qualification 2005

%age value

DipLocal 19.13 1479

OtherForeign 0.55 42

OtherLocal 10.56 817

MSForeign 6.56 507

MSLocal 32.97 2549

BSForeign 1.28 99

BSLocal 22.77 1760

BEForeign 0.18 14

BELocal 4.92 380

DipForeign 1.09 85

Total 100 7732

Table 4.37


N Mean Std. Deviation Std. Error

Mean

No. of

Employees

61 9.00 13.725 1.757

Table 4.38

One-Sample Test

Test Value =

0.05

t df Sig. (2-tailed)Mean

Difference

95%

Confidence

Interval of

the

Difference

Lower Upper

No. of

Employees

5.093 60 .000 8.95 5.43 12.47

Table 4.39

Specialization * YEARS Crosstabulation

Specialization YEARS

2006 2007 2008 2009 2010

%age value %age value %age value %age value %age value

Business Solution 5.921 94 9.4 158 9.942 230 13.25 318 12.8 313

System Analyst 8.553 136 15.4 259 9.942 230 8.434 203 12.2 299

System

Administration 18.42 293 14.8 248 10.53 243 12.65 304 11.6 284

MIS Specialists 5.921 94 9.4 158 14.04 325 13.25 318 11 270

Database

Administrators 0.658 10 0 0 0 0 0

Support Staff 17.11 272 16.1 270 21.05 487 13.86 333 19.2 469

Software

Development 23.68 376 9.4 158 11.11 257 6.627 159 11.6 284

Networking 11.84 188 12.1 203 14.04 325 10.84 261 6.98 171

Industrial

Automation 0.658 10 2.01 34 0.585 14 7.831 188 6.4 156

E-Commerce

Solution 0 0 0 0 0.58 14

Security

Specialists 2.632 42 2.68 45 2.924 68 6.627 159 4.65 114

Others 4.605 73 8.72 146 5.848 135 6.627 159 2.91 71

Total 100 1588 100 1678 100 2313 100 2403 100 2445


Bar Chart Showing Skills for IT Professionals from Year 2006 to 2010

0.05.0

10.015.020.025.0

2006

2007

2008

2009

2010

Years

%ag

e of

IT

Prof

essi

onal

s

BusinessSolutionSystem Analyst

SystemAdministrationMIS Specialists

DatabaseAdministratorsSupport Staff

SoftwareDevelopmentNetworking

IndustrialAutomationE-CommerceSolutionSecuritySpecialistsOthers

Figure 4.49: Pareto Chart

Pareto Chart for Skills

2006 - 2010

Specialization

Database Administrat

Industrial Autimatio

Security Specialist

Others

Business Solutions

System Analyst

MIS Specialist

Networking

Software Developers

System Administrator

Support Staff

Cou

nt

1000

800

600

400

200

0

Percent

100

50

084868891101110142

Figure 4.50 Pie Chart

Skills required in 2006

6%9%

17%

6%

1%17%

23%

12%

1%

0%

3%

5% Business Solution

System Analyst




IndustrialAutomationE-CommerceSolutionSecurity

Figure 4.51: Pie chart

Skills Required in 2007

9%

15%

15%

9%0%17%

9%

12%

2%

0%

3%9%

BusinessSolution

SystemAnalyst



SoftwareDevelopment

Networking




10%

10%

11%

14%0%20%

11%

14%

1%

0%

3%

6%

Business Solution

System Analyst




IndustrialAutomationE-CommerceSolutionSecuritySpecialistsOthers



13%

8%

13%

13%0%13%

7%

11%

8%

0%

7%

7%Business Solution

System Analyst


MIS Specialists

Database Administrators

Support Staff

Softw are Development

Netw orking


E-Commerce Solution

Security Specialists

Others


Skills required in 2010

13%

12%

12%

11%0%18%

12%

7%

6%

1%

5%

3%Business Solution

System Analyst


MIS Specialists

Database Administrators

Support Staff


Networking


E-Commerce Solution

Security Specialists

Others

Qualification in Non IT

Table 4.40; Qualification and Origin * YEARS Cross tabulation

YEARS

Qualification 2006 2007 2008 2009 2010

%age value %age value %age value %age value %age value

PhdForeign 0.00 0 0.00 0 0.00 0 0.00 0 1.16 28

DipLocal 23.03 366 13.42 225 25.15 582 20.48 492 23.26 569

OtherForeign 0.00 0 3.36 56 0.00 0 0.00 0 0

OtherLocal 2.63 42 6.71 113 9.94 230 13.86 333 12.79 313

PhdLocal 0.00 0 1.34 23 1.75 41 1.81 43 1.16 28

MSForeign 0.00 0 0.00 0 2.34 54 0.00 0 0 0

MSLocal 44.08 700 43.62 732 34.50 798 29.52 709 39.53 967

BSLocal 24.34 387 21.48 360 23.39 541 26.51 637 16.86 412

BELocal 5.92 94 10.07 169 2.92 68 7.83 188 5.23 128

Total 100 1588 100 1678 100 2313 100 2403 100 2445

Figure 4.55: Bar chart

Bar Chart for Qualification of IT Professionals Required from Year 2006 to 2010.

0

10

20

30

40

50

2006 2007 2008 2009 2010Years

Perc

enta

ges

PhdForeign

DipLocal

OtherForeign

OtherLocal

PhdLocal

MSForeign

MSLocal

BSLocal

BELocal


Qualification of IT Professionals in 2006

0%

23%

0%

3%

0%

0%

44%

24%

6%

PhdForeign

DipLocal

OtherForeign

OtherLocal

PhdLocal

MSForeign

MSLocal

BSLocal

BELocal



0% 13%3%7%1%0%

45%

21%

10%

PhdForeign

DipLocal

OtherForeign

OtherLocal

PhdLocal

MSForeign

MSLocal

BSLocal

BELocal



0%25%

0%

10%

2%

2%35%

23%

3%PhdForeign

DipLocal

OtherForeign

OtherLocal

PhdLocal

MSForeign

MSLocal

BSLocal

BELocal



0%20%

0%

14%

2%

0%29%

27%

8%

PhdForeign

DipLocal

OtherForeign

OtherLocal

PhdLocal

MSForeign

MSLocal

BSLocal

BELocal


Qualification of IT professionals in 2010

1%23%

0%

13%

1%

0%40%

17%

5% PhdForeign

DipLocal

OtherForeign

OtherLocal

PhdLocal

MSForeign

MSLocal

BSLocal

BELocal

Statistics

Table 4.41 Y2006 Y2007 Y2008 Y2009 Y2010

N Valid 61 60 61 61 61

Missing 0 1 0 0 0

Mean 2.49 2.48 2.80 2.72 2.82

Median 2.00 1.00 1.00 1.00 1.00

Mode 1 1 1 1 1

Std. Deviation 2.592 2.902 3.290 3.489 3.784

Skewness 2.555 2.653 2.629 2.798 2.839

Std. Error of Skewness .306 .309 .306 .306 .306

Minimum 0 0 0 0 0

Maximum 12 16 19 20 21

Percentiles 10 1.00 1.00 1.00 1.00 1.00

20 1.00 1.00 1.00 1.00 1.00

30 1.00 1.00 1.00 1.00 1.00

40 1.00 1.00 1.00 1.00 1.00

50 2.00 1.00 1.00 1.00 1.00

60 2.00 2.00 2.00 1.20 2.00

70 3.00 2.00 3.00 2.00 2.00

80 4.00 4.00 4.60 4.60 4.60

90 5.00 5.00 7.00 6.80 8.00

T-Test

Table 4.42


N Mean Std.

Deviation

Std. Error

Mean

Y2006 61 2.49 2.592 .332

Y2007 60 2.48 2.902 .375

Y2008 61 2.80 3.290 .421

Y2009 61 2.72 3.489 .447

Y2010 61 2.82 3.784 .484

One-Sample Test

Table 4.43 Test Value

= 0.05

t df Sig. (2-

tailed)

Mean

Difference

95%

Confidence

Interval of

the

Difference

Lower Upper

Y2006 7.356 60 .000 2.44 1.78 3.11

Y2007 6.494 59 .000 2.43 1.68 3.18

Y2008 6.535 60 .000 2.75 1.91 3.60

Y2009 5.980 60 .000 2.67 1.78 3.56

Y2010 5.717 60 .000 2.77 1.80 3.74


Y2006

12.510.07.55.02.50.0

Y2006Fr

eque

ncy

40

30

20

10

0

Std. Dev = 2.59 Mean = 2.5

N = 61.00


Y2007

15.012.510.07.55.02.50.0

Y2007

Freq

uenc

y

40

30

20

10

0

Std. Dev = 2.90 Mean = 2.5

N = 60.00


Y2008

20.017.515.012.510.07.55.02.50.0

Y2008Fr

eque

ncy

40

30

20

10

0

Std. Dev = 3.29 Mean = 2.8

N = 61.00


Y2009

20.017.515.012.510.07.55.02.50.0

Y2009

Freq

uenc

y

40

30

20

10

0

Std. Dev = 3.49 Mean = 2.7

N = 61.00


Y2010

20.017.515.012.510.07.55.02.50.0

Y2010

Freq

uenc

y

40

30

20

10

0

Std. Dev = 3.78 Mean = 2.8

N = 61.00

Time Series Regression for Future Jobs in Non IT Sector.

Table 4.42: Variables Entered/Removed

Model Variables

Entered

Variables

Removed

Method

1 X . Enter


b Dependent Variable: JOBS

Model Summary


R Square

Std. Error

of the

Estimate

1 .925 .855 .807 183.024



ANOVA

Model Sum of

Squares

df Mean

Square

F Sig.

1 Regression 594872.100 1 594872.100 17.759 .024

Residual 100493.100 3 33497.700

Total 695365.200 4



Coefficients

Unstandar

dized

Coefficient

s

Standardiz

ed

Coefficient

s

t Sig. 95%

Confidenc

e Interval

for B


Bound

Upper

Bound

1 (Constant) 1597.600 141.770 11.269 .001 1146.426 2048.774

X 243.900 57.877 .925 4.214 .024 59.709 428.091

a Dependent Variable: JOBS



Deviation

N

Predicted

Value

1597.60 2573.20 2085.40 385.640 5

Residual -163.50 227.60 .00 158.503 5

Std.

Predicted

Value

-1.265 1.265 .000 1.000 5

Std.

Residual

-.893 1.244 .000 .866 5

a Dependent Variable: JOBS

Figure 4.66:


Dependent Variable: JOBS

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b

1.00

.75

.50

.25

0.00

Table 4.44: Predicted Job Positions in Non IT Industry (2006 – 2015)

Years 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

No. of Job positions 1598 1842 2085 2329 2573 2817 3061 3304 3548 3793

Table 4.45: Model Fit on Business Solutions MODEL: MOD_1.

_

Dependent variable.. BUSINESS Solutios Method.. QUADRATI


Multiple R .98389

R Square .96804





Regression 2 36823.543 18411.771

Residuals 2 1215.657 607.829

F = 30.29106 Signif F = .0320



X 94.657143 27.485343 1.534750 3.444 .0750

X**2 -8.714286 6.589107 -.589374 -1.323 .3170

(Constant) 85.571429 23.202639 3.688 .0663

_

Figure 4.67:

BUSINESS

X

543210-1

400

300

200

100

0

Observed

Quadratic

Table 4.46: Model Fit on MIS Dependent variable.. MIS Method.. QUADRATI


Multiple R .94523

R Square .89346





Regression 2 37528.971 18764.486

Residuals 2 4475.029 2237.514

F = 8.38631 Signif F = .1065



X 164.914286 52.734340 2.544562 3.127 .0888

X**2 -28.428571 12.642090 -1.829719 -2.249 .1535

(Constant) 73.742857 44.517394 1.656 .2395

Figure 4.68:

MIS

X

543210-1

400

300

200

100

0

Observed

Quadratic

Table 4.47: Model Fit on Networking

Dependent variable.. NET Method.. QUADRATI


Multiple R .83992

R Square .70547





Regression 2 11258.743 5629.3714

Residuals 2 4700.457 2350.2286

F = 2.39524 Signif F = .2945



X 115.542857 54.046261 2.892261 2.138 .1660

X**2 -28.285714 12.956599 -2.953495 -2.183 .1607

(Constant) 168.228571 45.624895 3.687 .0663

_

Figure 4.69:

Networking

X

543210-1

340

320

300

280

260

240

220

200

180

160

Observed

Quadratic

Table 4.48: Model Fit on System Analyst

MODEL: MOD_2.

_

Dependent variable.. SYSTANAL Method.. CUBIC


Multiple R .99948

R Square .99897





Regression 3 15045.643 5015.2143

Residuals 1 15.557 15.5571

F = 322.37374 Signif F = .0409



X 238.940476 9.961853 6.156869 23.986 .0265

X**2 -141.214286 6.324878 -15.178312 -22.327 .0285

X**3 22.916667 1.039402 10.038541 22.048 .0289

(Constant) 136.471429 3.915980 34.850 .0183

Figure 4.70:

System Analyst

X

543210-1

400

300

200

100

Observed

Cubic

Table 4.49: Model Fit on System Administration

Dependent variable.. SA Method.. CUBIC


Multiple R .92593

R Square .85735





Regression 3 2569.6429 856.54762

Residuals 1 427.5571 427.55714

F = 2.00335 Signif F = .4692



X -116.059524 52.224261 -6.703829 -2.222 .2692

X**2 68.785714 33.157694 16.573530 2.075 .2860

X**3 -10.083333 5.448988 -9.901368 -1.850 .3154

(Constant) 295.471429 20.529228 14.393 .0442

Figure 4.71:


X

543210-1

310

300

290

280

270

260

250

240

230

Observed

Cubic

Table 4.50: Model Fit on Support Staff

Model Summary


R Square

Std. Error

of the

Estimate

1 .686 .470 .294 88.141


b Dependent Variable: Support Staff

ANOVA

Model Sum of

Squares

df Mean

Square

F Sig.

1 Regression 20702.500 1 20702.500 2.665 .201

Residual 23306.700 3 7768.900

Total 44009.200 4


b Dependent Variable: Support Staff

Coefficients

Unstandar

dized

Coefficient

s

Standardiz

ed

Coefficient

s

t Sig. 95%

Confidenc

e Interval

for B


Bound

Upper

Bound

1 (Constant) 275.600 68.274 4.037 .027 58.322 492.878

X 45.500 27.873 .686 1.632 .201 -43.204 134.204

a Dependent Variable: Support Staff



Deviation

N

Predicted

Value

275.60 457.60 366.60 71.942 5

Residual -79.10 120.40 .00 76.333 5

Std.

Predicted

Value

-1.265 1.265 .000 1.000 5

Std.

Residual

-.897 1.366 .000 .866 5

a Dependent Variable: Support Staff

Figure 4.72:


Dependent Variable: Support Staff

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b

1.00

.75

.50

.25

0.00

Table 4.51: Model Fit on Security

Dependent variable.. SECU Method.. CUBIC


Multiple R .95381

R Square .90976





Regression 3 9146.0000 3048.6667

Residuals 1 907.2000 907.2000

F = 3.36052 Signif F = .3766



X -78.000000 76.072334 -2.460042 -1.025 .4920

X**2 76.000000 48.299068 9.998528 1.574 .3604

X**3 -13.000000 7.937254 -6.970118 -1.638 .3490

(Constant) 45.600000 29.903846 1.525 .3695

Figure 4.73:

Security

X

543210-1

160

140

120

100

80

60

40

20

Observed

Cubic

Table 4.52: Curve Fit on Industrial Automation MODEL: MOD_8.

_

Dependent variable.. IA Method.. CUBIC


Multiple R .89340

R Square .79817





Regression 3 22996.286 7665.4286

Residuals 1 5814.914 5814.9143

F = 1.31824 Signif F = .5521



X -94.928571 192.595830 -1.768546 -.493 .7085

X**2 86.857143 122.280975 6.749933 .710 .6068

X**3 -13.500000 20.095111 -4.275649 -.672 .6234

(Constant) 19.114286 75.708943 .252 .8426

Figure 4.74:


X

543210-1

200

100

0

-100

Observed

Cubic

Table 4.53: Curve Fit on Others MODEL: MOD_10.

_

Dependent variable.. OTHERS Method.. QUADRATI


Multiple R .96561

R Square .93241





Regression 2 7185.8857 3592.9429

Residuals 2 520.9143 260.4571

F = 13.79476 Signif F = .0676



X 91.471429 17.991971 3.294947 5.084 .0366

X**2 -22.642857 4.313245 -3.402270 -5.250 .0344

(Constant) 72.714286 15.188503 4.787 .0410

Figure 4.75:

Others

X

543210-1

180

160

140

120

100

80

60

Observed

Quadratic

Table 4.54: Specialization YEARS

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Est. Est. Est. Est. Est. Est. Est. Est. Est. Est.

Business Solution 86 171 240 291 324 341 339 321 285 232

System Analyst 137 234 49 0 0 0 0 0 0 0

System

Administration 238 258 294 286 175 0 0 0 0 0

MIS Specialists 210 290 312 278 188 40 0 0 0 0

Database

Administrators

Support Staff 275 321 366 412 457 503 548 593 639 685

Software

Development 0 0 0 0 0 0 0 0 0 0

Networking 168 255 286 260 178 39 0 0 0 0

Industrial Automation 19 0 69 152 165 29 0 0 0 0

E-Commerce Solution 0 0 0 0 0 0 0 0 0 0

Security Specialists 46 31 90 145 118 0 0 0 0 0

Others 73 142 165 143 76 0 0 0 0 0

Table 4.44: Job Positions in Industry

Required 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

IT 12978 15253 18165 21343 24388 27098 29989 32880 35771 38662

Non IT 1588 1678 2313 2403 2445 2817 3061 3304 3548 3793

Universities 1262 1123 1300 1360 1408 1577 1759 1977 2232 2523

Total 15828 18054 21778 25106 28241 31492 34809 38161 41551 44978



No of IT Professionals required in Year 2006

82%

10% 8%ITNon ITUniversities


0%20%40%60%80%

100%

1 2 3 4 5 6 7 8 9 10

Years (2006-2015)

%ag

e of

Job

s.



No. of IT Professionals in Year 2007.

85%

9%6%

IT

Non IT

Universities


No. of IT Professionals in Year 2008

83%

11% 6%

ITNon ITUniversities


No. of IT Professionals in Industry in Year 2009.

85%

10% 5%ITNon ITUniversities


No. of IT Professionals in Year 2010.

86%

9% 5%IT

Non IT

Universities

D. Supply in terms of Student Output

Table 4.56a : Variables Entered/Removed

Model Variables

Entered

Variables

Removed

Method

1 PERIOD . Enter


b Dependent Variable: Student Output

b. Model Summary


R Square

Std. Error

of the

Estimate

1 .992 .985 .980 1808.61918

a Predictors: (Constant), PERIOD


c. ANOVA

Model Sum of

Squares

df Mean

Square

F Sig.


00

1 641761210.0

00

196.191 .001

Residual 9813310.000 3 3271103.333

Total 651574520.0

00

4

a Predictors: (Constant), PERIOD


d. Coefficients

Unstandar

dized

Standardiz

ed

t Sig.

Coefficient

s

Coefficient

s

Model B Std. Error Beta

1 (Constant) 25922.000 1400.950 18.503 .000

PERIOD 2801.000 571.936 .992 14.007 .001

a Dependent Variable: Student Output

e. Residuals Statistics


Deviation

N

Predicted

Value

25922.0000 57966.0000 41944.0000 12666.50317 5

Residual -1773.0000 1528.0000 .0000 1566.31015 5

Std.

Predicted

Value

-1.265 1.265 .000 1.000 5

Std. Residual-.980 .845 .000 .866 5

a Dependent Variable: Student Output

Figure 4.82:


Dependent Variable: Student Output

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b

1.00

.75

.50

.25

0.00

Table 4.57: Estimate Yearly Output of IT professionals by Universities.

Years 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Student

Output 25922 28723 31524 34325 37126 39927 42728 45529 48330 51131

Table 4.58: current position as first job * Type of Company Cross tabulation

% within Type of Company Type of

Company

current position

as first job

IT Company Non IT Company University

No one 7.9% 11.3% 12.9%

81 _ 90% 8.1% 6.5%

91 _ 100% 5.3% 12.9% 3.2%

1 _ 10% 21.1% 9.7% 25.8%

11 _ 20% 10.5% 12.9% 3.2%

21 _ 30% 15.8% 8.1% 6.5%

31 _ 40% 7.9% 14.5% 6.5%

41 _ 50% 7.9% 8.1% 12.9%

51 _ 60% 5.3% 6.5% 6.5%

61 _ 70% 5.3% 4.8% 6.5%

71 _ 80% 13.2% 3.2% 9.7%

Total 100.0% 100.0% 100.0%

Figure 4.83: Number of IT Professionals having their current job as their first job position in IT Companies

IT Company8%

0%

5%

21%

11%16%

8%

8%

5%5%

13%

No one

81 _ 90%

91 _ 100%

1 _ 10%

11 _ 20%

21 _ 30%

31 _ 40%

41 _ 50%

51 _ 60%

61 _ 70%

71 _ 80%

Figure 4.84: Number of IT Professionals having their current job as their first job position in Non IT Companies.

Non IT Company

10%

13%

8%

15%8%

6%

5%

3%

8%

13%

11%

1 _ 10%

11 _ 20%

21 _ 30%

31 _ 40%

41 _ 50%

51 _ 60%

61 _ 70%

71 _ 80%

81 _ 90%

91 _ 100%

No one

Figure 4.85: Number of IT Professionals having their current job as their first job position in Universities.

University

14%

6%

3%

27%

3%6%

6%

13%

6%

6%

10%

1 _ 10%

11 _ 20%

21 _ 30%

31 _ 40%

41 _ 50%

51 _ 60%

61 _ 70%

71 _ 80%

81 _ 90%

91 _ 100%

No one

Documents

IT Human Resource Need Assessment Study - …unpan1.un.org/intradoc/groups/public/documents/apcity/unpan040689.… · IT Human Resource Need Assessment Study ... 3.8 Time Series Linear