A Study on Improving the Joining ratio of Job Seekers (Googolsoft )

ABSTRACT

An interview was conducted with the human resource department's staffing professionals from firms who get acceptance rates ranging from 80%-90% from their extended offers. These organizations are exceptions to the survey made by the National Assn. of Colleges and Employers which reports that only 68% of offers are generally accepted. The main objective of this project is to find the effective methods to improve the joining ratio of job seekers .This could be found out by finding the reasons for which the job seekers are not joining the job which will be our secondary objective. By framing questionnaires for the secondary objectives, the reasons for which the job seekers are not joining the job can be obtained from the candidates (job seekers). Thus by finding the reasons, the areas that is needed to improve the joining ratio could be identified and the methods to improve the joining ratio of job seekers could be found.

CHAPTER-1 INTRODUCTION
1.1) RECRUITMENT :
In the present business environment, organizations are faced with the pressure to produce more with fewer resources. Employees, on whom an organization’s profitability depends, comprise the largest fixed cost that an organization incurs. In the past, individuals used to work with one or two organizations during their entire working life (averaging between 20-30 years). Organizations too used to believe in the lifetime Employment concept. However such concepts are being eroded as a result of the unpredictable business environment. Hence, organizations have to evolve methods not only to improve productivity but to also keep the cost down. On the human resources front, productivity can be improved by ensuring that the organizations attract the best talent at the lower possible cost. This objective translate into the adoption of the best recruitment and selection methods and instituting measures to retain and develop them .Further , a quantitative measurement of recruitment and selection effectiveness has to be conducted to prove ones point . With respect to quantitative measures to improve HR effectiveness, the field is still in its nascent stages. Nevertheless, a few approaches and metrics have been developed and applied among several organizations.

1.2. Recruitment methods
The most common method of recruitment of managerial, professional and skilled manual vacancies is the use of local newspaper advertisements. Recruitment 3 followed by advertisement in the trade press. The third most frequent used medium is national newspapers. The proportional using in the internet has continued to grow. This year for the first time more than half the organizations said that they use the internet for recruitment. Our matched sample confirms this trend, with the proportion of employers using the internet increasing by over half between our 1999 and 2001 surveys. From being the practice of a minority of organizations, online recruitment is now a mainstream activity.

• • •
• •

Local newspaper ads – 86.6 % Ads in specialist/trade press – 80.2 % National newspaper Ads – 71.5 % Job center/Employment Service – 70.8 % Employment Agencies – 60.0 % Speculative applications – 59.7 % Word of mouth – 58.1 % The internet – 56.5 % Links with schools/colleges – 43.5 % Recruitment fairs – 29.2 % Headhunters – 28.9 % Local Radio ads – 7.1 %

• • • • • • •

1.3. CHANGES IN RECRUITMENT PRACTICES:
The increasing globalization of the market place combined with an ever increasing shortage of skilled staff and advances in technologies have resulted in the large scale changes to recruitment practices throughout the world
• •

Giving more attention to the retention of staff -73.3 % Offering candidates extra ways of applying for vacancies by e-mail -70.0 % Being willing to be flexible in the recruitment method used – 68.3 % Attempts to speedup the recruitment process -56.7 % Being more flexible about recruitment process -56.7 % Offering flexible hours of work – 31.7 % Outsourcing some activities – 30.0 % Recruiting overseas – 26.7 %

• • • • •
•

• • •

Introducing or improving relocation expenses – 25.0 % Introducing bounty payments to staff who recommend applicants – 21.7 % Introducing or increasing more market premium or location payments -13.3 % Other – 8.3 %

•

The responses provide a valuable insight into the strategies being adopted in the face of recruitment difficulties.

1.4. TRENDS IN DIFFERENT SELECTION METHODS:
Almost all organization use interviews in the selection process for managerial, professional and skilled manual vacancies. Also frequently used are application forms CVs and candidates covering letters. Our matched sample responses indicate that the use of these selection techniques ha remain consistent. Selection Tests • • • • Test of specific skills – 60. 1 % General ability tests – 54.5 % Literacy /numeracy tests – 44.6 % Personality questionnaires 40.7 %

Unlike recruitment practices, there are no marked trends in employers’ use of different selection methods for managers, professionals and skilled workers. Many employers are responding to recruitment difficulties by being prepared to be more flexible in the way they conduct their recruitment and selection.

1.4.1. Electronic Recruitment:
As we saw above, one of the most notable trends in recruitment practice has been the adaptation of online methods. Usage has increased by half from 41.8% of employers in 1999 to 54.3% in 2001. In this year’s survey, we explore this issue in greater depth to find out more precisely what forms this usage takes.

1.4.2. Electronic Mail:
At present, the most common form of electronic recruitment involves the use of e-mails. Overall, two thirds of employers (61.3%) make some use of e-mail message for recruitment processes. Usage is highest in the private service sector where around three-quarters of organizations (74.2%) use e-mail. In the public sector, 62.6% of organizations use e-mail, with the lowest proportion (43.8%) being found among manufacturing firms. Emails are used in several different ways. Most commonly, organizations that make use of e-mails in recruitment do so in order to receive enquiries about vacancies from potential applications; nearly all user (94.8%) use e-mail for this purpose. The majorities of users go further and accept applications sent via e-mail; three-quarters of users (72.9%) accept CV’s sent by e-mail, while just over half (56.8%) accept complete application forms returned by e-mail. Respondents in the public sector are far less likely to accept CV’s by e-mail. Only (47.4%) do so, compared with (93.8%) in manufacturing and (84.8%) in the private sector services. In contrast, similar proportion of respondent in each sector accepted enquiries by e-mail or complete application forms by e-mail.

1.4 THE INTERNET
The internet is slightly less commonly used for recruitment purposes than electronic mail (54.2% of participants in the 2001 survey, against 61.3% using e-mails). The highest proportion of recruiters using the Internet (67%) is in the public sector, followed by 58.4% in private sector service firms. Most commonly, recruiters use the internet by posting vacancies on their organizations own corporate web site Uses of the Internet for recruiting purposes:

Types of usage
Put vacancies on the company’s own internet site 80.3 Put background information for candidates on the company’s own Internet site 77.4 Put vacancies on internal intranet network 72.3 Put vacancies on external provider’s Internet Recruitment site 63.5 Offer form of selfselection questionnaire on company’s own Internet site 11.7 Administer some selection test via the internet 2.9 In addition, over three-quarter of Internet users (77.4%) put background information for candidate on the company’s site. Nearly as many (72.3%) put vacancies on the internal intranet network. External providers’ Internet Recruitment sites are used by 63.5% of those using the Internet. What is relatively uncommon is offering a form of self selection questionnaire on the company’s own Internet site. Only 11.7% say they do this and only 2.9 %

administer some selection tests via the Internet. The pattern of usage is similar in different sector. However, the public sector respondents said they do this, compared with 88.5% of private sector service firms, and 83.3% of manufacturers.

Problems using electronic media
Surprisingly few organizations using the Internet or e-mails in recruitment have encountered significant difficulties – only one in seven 15.5%. of the relatively small number of respondent that had experienced problem, the largest proportion 55.6% said that they had received a large number of unsuitable applications. Two other problems were mentioned by 29.6% of the respondent: resolving technical/technological problems, and receiving few or no applications. Cost was mentioned as a problem by 14.8% of Internet and e-mail users.

1.5 RECRUITMENT DIFFICULTIES
Just over half of the survey participants had experienced difficulties in filling one or more of their vacancies over the past year. The analysis of trends among the matched sample indicates that recruitment difficulties eased somewhat in 2000 but have now increased again.

Reason for Recruitment Difficulties:
Lack of required experience in candidates continues to be the most common reason for difficulties in filling vacancies, mentioned by nearly three-quarters of respondent. Lack of technical skills was cited by two third of respondent, followed by just over half who stated that applicants ‘wanted more pay than we could offer’. Our matched sample analysis points to a doubling in the proportion of recruiters failing to receive any applications between 1999 and 2001 surveys. Reason for Recruitment Difficulties • •
•

Lack of required experience – 72.1% Lack of technical skills - 67.6% Wanted more pay than we could offer – 52.2% No applicant – 42.6% Lack of personal abilities – 26.5%

• •

• •

Lack of formal qualification - 19.1% Attitude to work – 13.2%

Changes resulting from recruitment difficulties:
Over four in 10 employers who had experienced recruitment difficulties said that they had made changes to their personal policies and practices as a result of these difficulties. Employers in the public sector were more likely to have done so than those in the private sector. Three quarters of employers making changes are giving more attention to the recruitment of staff. Nearly one third have responded to recruitment difficulties by outsourcing some of their organizations activities, followed by a quarter that have looked to overseas to fill vacancies. Employers are also taking measures in connection with their recruitment practice. Just over two-third are making it easier for candidates to apply, and a similar proportion is being more responsive and flexible in respect to specific recruitment situations Employers differ in the breadth of their focus when identifying the recruitment specifications for vacancies. Around two-fifth focus on the requirements of the immediate vacancy as it exist now, while a similar proportion consider additionally how it might change in the future. However, a fifth of recruiters consider both the needs of the vacancy and the competencies of the potential recruits in terms of related jobs that the person might perform. One-third go further and consider the more demanding jobs that the potential recruit’s competencies might enable them to do in the future. Over 90% of organizations involve their line managers in recruitment, compared with three quarter that continues to involve their central personnel staff. Of those involving their line managers in recruitment, over two-fifth do so, on the basis that they are equal partners, while in over one-third, they take the leading role.

1.6 IMPORTANCE OF RETENTION AND MOTIVATION
Retention of excellent employees is one of the most important challenges in organizations today. In today’s turbulent, often chaotic, environment, commercial success depends on employees using their full talents. In addition, these are times when delaying and the flattering of hierarchies can create insecurity and lower staff morale. Moreover, more staff than ever before

are working part time or on a limited-term contract, and these employees are often especially had to motivate. Twyla Dell writes of motivation, “The heart of motivation is to give people what they really want most from work. The more you are able to provide what they want, the more you should what you really want, namely: productivity, quality, and service”. (An Honest Day’s Work (1988)).

Advantages
A positive motivation philosophy and practice should improve productivity, quality, and service. Motivation helps people: Achieve goals Gain a positive perspective; Create the power to change Build self-esteem and capability, Manage heir own development and help others with theirs.

Disadvantages
There are no real advantages to successfully motivating employees, but there are many barriers to overcome. Barriers may include unaware or absent managers, inadequate buildings, outdated equipment, and enriched attitudes, for example: “We don’t get paid extra to work harder.” “We’ve always done it this way.” “Our bosses don’t have a clue what we do.” “It doesn’t say that in my job description.” “I am going to do as little as possible without getting fired.” Such views will take persuasion, perseverance, and the proof of experience to break down.

Two Musts for Motivation!
Every person is motivated. The challenge at work is to create an environment in which people are motivated about work priorities. Too often organizations fails to pay attention to the employees relations, communication, recognition, and involvement issues that are most important to people. The first step in creating a motivating work environment is to stop taking action that is guaranteed to demotivate people. Identify and take the actions that will motivate people. It’s a balancing act. Employers walk a fine line between the needs of the organization and its customers and meeting the needs of its internal staff. Do both well and thrive! An attention getting Gallup Poll about disengaged employees was highlighted in a recent wall Street Journal. Gallup found 19 percent of 1,000 people interviewed “actively disengaged” at work. These workers complain that they don’t have the tools they need to do their jobs. They don’t know what is expected of them. Their bosses don’t listen too them. Based on these interviews and survey data FROM ITS CONSULTING PRACTICES, Gallup says actively disengaged worker cost employers $292 billion to $355 billion a year. Further more, Gallup conclude that disengaged workers miss more days of work and are less loyal to employers. With this in mind, let’s look at a couple of areas in which balance is critically needed for employee motivations in organization today. I’ll cover more aspects of creating a motivating work environment in future articles. The subject is too important and wide ranging to cover in one article with a few bullet points.

Rules and Policies
Want to be a cop? That’s how some supervisors feel in organizations that operate on the assumptions that people are untrustworthy. You’ve seen the company handbooks that list pages of rules. Step out of line? Fifty-seven potential infractions, with resultant punishment, are listed on page 74.Need time off for our Grandma’s funeral? You get three days off to travel 600 miles. Have a question? We have answer. In fact we have got policies that answer almost every question. Supervisory discretion what’s that? We’ve got employees who, left to their own devices, will choose to do bad things. You can’t trust supervisors to treat employees fairly and consistently either. John in Accounting is a softy. People who work for him get away with anything and everything. If you work for Beth in sales however, you can count on the rule book guiding every decision. Sound familiar? I’ve heard these reasons and many more to justify the need for hundreds of rules and policies in organizations.

1.7) GUIDELINES ENVIRONMENT:

FOR

A

MOTIVATING

WORK

Make only the minimum number of rules and policies needed to protect your organizations legally and create order in the work place. Publish the rules and policies and educate all employees. With the involvement of many employees, identify organizational value and write value statements and a professional code of conduct. Develop guidelines for supervisors and educate them about the fair and consistent application of the few rules and policies. Address individual dysfunctional behavior on a “need- to” basis it counseling, progressive discipline, and performance improvement plans. Clearly communicate work place expectations and guidelines for professional behavior.

Helpful Hints
Solicit employee feed back on potential policies, areas in which policies are needed, and so on.(Do not ,as one company did recently, announce a new attendance policies by posting it on a bulletin board. If you decide to adhere to and hold employees accountable for existing policies, don’t ambush your company members. If you have not enforced the policy in the past, meet the employees and explain the policy, the intent of the policy, why the policy is necessary, and why it was not enforced in the past. Then tell everyone that following the meeting, everyone is accountable for adhere to the policy. You will be surprised how much support for legitimate policies and rules you receive from the people in your organization. People like a well organized work place in which expectations are clear. People thrive in a work place in which all employees live by the same rules. If you create an environment that is viewed as fair and consistent, you give people little to push against. You open up a space in which people are focused on contribution and productive activities rather than gossip, unrest, and unhappiness. Which work place would you use?

Trust Rules! The Most Important Secret
Trust. You know when you have it, you know when you don’t. Yet, what is trust and how is trust usefully defined for the work place? Can you build trust when it doesn’t exist? How do you maintain and build upon the trust you may currently have in your workplace? These are important questions for today rapidly changing world. Trust forms the foundation for effective communication, employee retention and employee motivation and contribution of discrenationary energy, the extra effort that people voluntarily invest in work. When trust exists in an organization or in a relationship, almost everything else is easier and more comfortable to achieve. In reading about trust I was struck by the number of definition that purportedly describe trust in understandable ways-but don’t. According to Dr.Duane C.Tway ,jr. in his 1993 dissertation ,A Construct of Trust, ”Their exists today, no practical construct of trust that all of us to design and implement organizational interventions to significantly increase trust labels between people. We all think we know that trust is from our own experience, but we don’t know much about how to improve it. Why? I believe it is because we have been taught to look at trust as if it single entity.” Tway defines trust as, “the state of readiness for unguarded interaction with someone or something.” He developed a model of trust that includes three components. He calls trust a construct because it is “constructed” of these three components “the capacity for trusting, the perception of competence, and the perception of intentions.” Thinking about trust as made up of the interactions and existence of these three components makes ”trust” easier to understand. The capacity for trusting means that your total life experiences have developed your current capacity and willingness to risk trusting others. The perception of competence is made up of your perception of your ability and ability of others with whom you work to perform competently at whatever is needed in your current situation. The perception of intentions, as defined by Tway, is your perception that the actions, words, directions, missions, or decisions are motivated by mutually- serving rather than self serving motives.

1.8) WHY TRUST ORGANIZATION:

IS

CRITICAL

IN

A

HEALTHY

How important is building a trusting work environment? According to Tway, people have been interested in trust since Aristotle Tway states ,”Aristotle(384-322 BC),writing in the Rhetoric, suggested that Ethos ,the Trust of the speaker by the listener, was based on the listener perception of three characteristics of the speaker, Aristotle believe these three characteristic to be the intelligence of the speaker(correctness of the opinions, or competence),the character of the speaker (reliability--a competence factor ,and honesty—a measures of intentions towards the listener).”I don’t think this has changed much even today. Additional research by Tway and others shows that trust is the basis for much of the environment you want to create in your work place. Trust is the necessary precursor for : Feeling able to rely upon a person, Cooperating with and experiencing teamwork ith a group, Taking thoughtful risk, and Experiencing believable communication.

Employee Survey Pinpoints Recognition
The question about whether the company cared about the welfare and happiness of its employees drew divergent views. Some people agreed other disagreed. So, the culture and the communications team put out a second survey asking what would make the employees feel as if the company cared about them. We developed several answers employees could check and supplied room for their comments and additional thoughts. Fifty-five percent of the respondents said that praise and attention from their supervisors would make them feel as if the company cared about them and their well-being. As you might also expect, money, benefits, and events such company lunches ranked high too. But recognition from the supervisor ranked above all other choices. I have sponsored similar surveys in different organizations. The findings are always similar. Employees want to know that they have done a good job – and that you noticed. Employees want to be thanked and appreciated. A leader of employees makes other people feel important as appreciated. The leaders excels at creating opportunities to provide rewards, recognition and thanks to his or

her staff a leader creates a work environment in which people feel important and appreciated.

You can reinforce powerfully the recognition you provide in these ways.
Write out the recognition ,what the employee did, why it was important ,and how the actions served your organization give a copy of the letter to the employee and to the department head or COE, depending on the size of your company. Place a copy in the employee’s file. Write a personal note to the employee perhaps have your supervisors sign it, too. photo copy the note and place the recognition in the employees file. Accompany the verbal recognition with the gift .Engrave dplaques, merchandise that carries the companies logo ,even certificates of appreciation reinforce the employee recognition. Everyone likes cash or the equivalent in gift cards, gift certificates, and checks .If you use a consumable form of employee recognition, accompany the cash with a note or letter. When the money has been spent ,you want the employee to remember the recognition. Present the recognition publicity, at an employee meeting ,for example. Even if the employee is uncomfortable with publicity, it is important for the other employees to know that employees are receiving recognition.

1.9) TOP TEN WAYS TO RETAIN YOUR GREAT EMPLOYEES:
Key employees retention is critical to the long term health and success of your business. Managers readily agree that retaining your best employees ensures customer satisfaction, product sales ,satisfied coworkers and reporting staffs ,effective succession planning and deeply embedded organizational knowledge and learning. If managers can cite these fcts so well, why do they behave in ways that so frequently encourage great employees to quit their jobs? Employee retention matters .Organizational issues such as training time and investment; lost knowledge; mourning, insecure coworkers and a costly candidates search asides, and failing to retain a key employee is costly. Various estimates suggest that losting a middle manager cost an organization up to 100 percent of the salary. The loss of a senior executive is even more costly. Employee retention is one of the primary measures of the health of your organization. If you are losing critical staff members, you can safely bet that other people in their department are looking as well. Exit interviews with departing employees. You’ll never have amore significant source of data about the health of your organization.

A satisfied employee knows clearly what is expected from him everyday at work. Changing expectations keep people on edge and create unhealthy stress. They rob the employee of internal security and make employee feel unsuccessful. I’m not advocating jobs just the needs for a specific frame work within which people clearly know what is expected from them. The quality of the supervision an employee receives is critical to the employee retention. People have manage and supervisors more often than they leaves company or the jobs. It is not enough that the supervisors is well–liked or a nice person, startingith clear expectations of the employees, the supervisors has the critical role to play in retention. Anything the supervisor does to make an employee feel unvalued ill contribute to turnover .Frequent employee complaints center on these areas. • • • • • Lack of clarity about expectations Lack of clarity about earning potential Lack of feedback about performance Failure to hold scheduled meetings Failure to provide a framework within which the employee perceives he can succeed

The ability of the employee to speak his or her mind freely within the organizations is another key factor in employee retention. Does your organization solicit ideas and provide an environment in which people are comfortable providing feedback? If so, employees offer ideas, feel free to criticize and commit to a continuous improvement. If not, they bite their tongues or find themselves constantly “in trouble” – until they leave. Talent and skill utilization is another environment factor your key employees seek in your workplace. A motivated employee wants to contribute to work areas outside of his specific job description. How many people could contribute far more than they currently do? You just need to know their skills, talent and experience, and take the time to tap into it. As an example, in a small company, a manager pursued a new marketing plan and logo with the help of external consultants. An internal sales rep, with seven years of ad agency and logo development experience, repeatedly offered to help. His offer was ignored and he cited this as one reason why he quit his job. In fact, the recognition that the company didn’t want to take advantage of his knowledge and capabilities helped precipitate his job search.

The easiest to solve, and the ones most affecting employee retention, are tools, time and training. The employee must have the tools, time and training necessary to do their job well or they will move to an employer who provides them.

Your best employees, those employees you want to retain, seek frequent opportunities to learn and grow in their careers, knowledge and skill. Without the opportunity to try new opportunities, sit on challenging committees, attend seminars and read and discuss books, they feel they will stagnate. A career-oriented, seminar employee must experience growth opportunities within your organization. A common place complaint or lament I hear during an exit interview is that the employee never felt senior managers knew he existed. By senior manager I refer to the president of a small company or a department or division head in a larger company. Take time to meet with new employees to learn about their talents, abilities and skills. Meet with each employee periodically. You will have more useful information and keep your finger on the pulse of your organization. It’s a critical tool to help employees feel welcomed, acknowledged and loyal. No matter the circumstance, never ever threaten an employee’s jobs or income. Even if you know layoffs loom if you fail to meet production or sales goals, it is a mistake to for shadow this information with employees. It makes them nervous; no matter how you phrase the information; no matter how you explain the information; even if you are absolutely correct, your best staff member will update their resumes. I’m not advising that makes people feel they need to search for another job. I place this final tip on every retention list I develop because it is so key and critical to retention success. Your staff member must feel rewarded, recognized and appreciated. Frequently saying thank you goes a long way. Monetary rewards, bonuses and gifts make the thank you even more appreciated. Understandable rises, tied to accomplishments and achievement, help retain staff. Commissions and bonuses that are easily calculated on a daily basis, and easily understood, raise motivation and help retain staff. Annually, I receive from staff member that provide information about raise nationally.

1.10 NEED FOR THE PRESENT STUDY, RECRUITMENT & RETENSION TASK FORCE:
Responsible – Responsive H.R. Policies & Programs We need policies that enable rather than restrict individuals Flexibility is the key Total compensation, working condition, work-life balance, flexible work arrangements, personal and career development opportunities (including “grow your own” or promote from within we do not have to replace with zebras) Flex benefits (one size does not fit all) Different pension options Creative non-taxable benefits (e.g. reimbursement of loss on the sale of a home, moving allowances for furniture/drapes etc.) Concierge service Start-up research grants for faculty T.A support Research Space Equipment and time Competitive compensation (lag/meet/exceed) Finding the solution for Recruitment & Retention Quick fixes do not work – the solutions may be about how we treat employees, not just about compensation. A national survey /study of the changing workplace – asked employees what they considered to be very important in their decision to take an employment offer.

CHAPTER 2.2 COMPANY PROFILE ABOUT US
Googolsoft Technologies is a software development and a soft skills training company which offers world class application development services and an unparalleled training in recruitment and soft skills. Our comprehensive portfolio of services includes Technology Staffing, Training in Recruitment, Employment skills Training, Presentation skills training, Soft skills training, Web Designing, Web Development and Web Hosting. Our company pioneered the concept of providing a certification course in recruitment to the MBA HR students. Real time recruitment specialists train the students on the basics and the core recruitment techniques to make them ready for employment. During this course, job seekers are given special training on how to conduct the interviews and how to face the interviews. All our industry endorsed training materials are tailor-made to create employable students. Googolsoft training division has crafted a first of its kind employment skills training program for the job seekers. This offers a unique combination of personality development skills and interview preparation skills training to the students. Our employment oriented training covers the entire gamut of skills needed to get the employment. With our expertise developed through the profound experience, we help the businesses to bridge the gap between their business and information technology driven goals. Periodical workshops offer soft skills training programmes to the corporate executives. As a web development and web designing company, we have been redefining the market standards with utmost consistency. Googolsoft Technologies is powered by skill professionals and driven by its mission to create values to all its clients. Our forte is the well qualified trainers and the innovative methodologies used to provide training of international standards.

SERVICES:
1. SOFTWARE DEVELOPMENT 2. WEB DEVELOPMENT 3. WEB DESIGNING 4. WEB HOSTING 5. TECHNOLOGY STAFFING 6. CERTIFICATION IN RECRUITMENT 7. SOFT SKILLS TRAINING 8. JOB SKILLS TRAINING

1. SOFTWARE DEVELOPMENT:
Googolsoft creates custom software applications for the clients to meet their specific business needs. Clients leverage our experience and knowledge base to improve their businesses with the latest technologies. Our development team works with them to identify inefficiencies and bottlenecks in their workflow processes, and create custom IT tools to address their needs. At Googolsoft, our Java application development team takes full responsibility and ownership of the entire project. Our expert team identifies the client’s application development needs and then delivers ready-to-use solutions.

Java Application Development
Googolsoft has a dedicated Java application development team with good proficiency across various industry verticals. Our company offers a wide range of Java Development Services which includes Java Application Development, Java Software Development, Java Web Development, J2ME application Development, J2EE Application Development, Java Mobile Application Development and Java Enterprise Portal Development.

Java Application Development Expertise
Googolsoft’s Java application development team develops world-class Java applications which run across different platforms, adding value to our customers’ products and organizations. As a Java development company, we help our clients realize the potential and benefits of J2EE and J2ME applications and related technologies including Enterprise Java Application Development, Web Portal Development and Mobile Application Development. Our Java Development expertise includes:

• • • • • • • •

Enterprise Software Application Development SaaS Application Development Web Portal Development Java Software Development J2ME Mobile Application Development Migration, Enhancement, and Integration of Java Applications Java Application Maintenance Java Application Testing

Technology Expertise:
Googolsoft’s Java application development team is well experienced in technologies and tools necessary to design, develop, and test a robust and scalable Java application: J2EE Application development Client/Desktop EJB, Servlets, JSP, JDBC, JNDI, RMI/IIOP, JTA/JTS, JMS, JavaMail, JSF, Struts, Spring, Hibernate HTML/XHTML, JavaScript, J2ME, J2SE, SWING, SWT, Macromedia Flex, ActiveX

Web/Application Servers Apache, Tomcat, JBoss, IBM WebSphere Web Services Java Mobile Application Development Databases IDEs WSDL, SOAP J2ME, Blackberry, Android Oracle, PostgreSQL, MS SQL Server, MySQL Eclipse, Netbeans, JCreator, JBuilder

Development Methodologies:
Our company follows CASE tools software methodology which leads to effective execution of Java application development plans. Our Java development plans are geared towards flexible, secure and portable Java application development. Our technical Java software development team strives to do more than just application programming. We make efforts to take complete ownership of your Java application development project, to be your technology partner for Java development. Our Java application development approach is a world-class experience for business in the following ways:
• • •

Our expert team focuses on a secure and scalable application architecture Experienced professionals use the latest cutting-edge technology for Java application development Our analytical approach looks at the broader picture of your business objectives for Java software development

• •

Our priority is on overall security of the system Unique development process and a global delivery model enable us to address your most pressing technology needs

2. WEB DEVELOPMENT
Googolsoft specializes in various web technologies like PHP, ASP.NET, LAMP, Joomla and back-end databases including MySQL and SQL Server. We generally follow the agile method for project management. The entire project is first split up into several small milestones with deliverables and time frame defined. Our team works on each milestone and the progress report is sent on a daily basis. Modules once completed are uploaded to our test server for client feedback. The project manager assigned communicates with the client and sends the status reports. An experienced web project manager is assigned for each project and holds the below responsibilities: - Act as a single point of contact between the client and the project team. - Monitor the design and the development activities. - Communicate the project updates and delivery milestones to the client regularly and participate in project discussions. - Update the internal project monitoring software on a daily basis - Track all the bug reports in Mantis and update the Mantis accordingly - Communicate with client for overall feedback and post launch support We generally use the following tools for different aspects of a project development process:Bug Tracking – Mantis Feature Request Documentation – GoogleDocs Source Code Repository – SVN We use a development server for hosting the web portal during the entire development and testing process. The URL to the web portal at this phase will not be crawlable by Google and it is password protected. So only our development team and the client have access to it. We are committed to create a successful and profitable website for our clients.

3. WEB DESIGNING
Googolsoft has one of the best web design teams in South India, a dedicated design team to create professional Web2.0 designs for our clients. Our designs are targeted to the end users who will use the website. We are creative, experimental and at the same time address the functional specifications of the website. We believe that a great design is the key to making a successful websites. We study the client requirements and research other websites in terms of usability and popularity. Based on outcome of the study, we prepare the design mockups which are sent to the client for approval. Once the home page is approved by the client, we design the inner sections based on the basic theme.

4. WEB HOSTING
Googolsoft offers affordable and cheap web hosting plans to all its clients. It includes all the basics and the flexibility to customize with options tailored to your needs. Our hosting services are perfect for small static websites to big dynamic portals. All this comes in an affordable price that is packed with power features. Our company provides Linux & Windows based hosting plans.

5. TECHNOLOGY STAFFING
Googolsoft specializes in providing the technology staffing services to all its IT clients on any fixed duration basis. Our technology staffing services extends across all the verticals in the IT sector. Clients leverage on our experience and knowledge base in the Information technology sector to avail the best staffing resources with the least turn around time. Our company strongly believes that human assets play a crucial part in the growth of an organization. In this dynamic market, competitive advantage of every company depends upon the competent and committed employees. Depending on the needs of our clients, we provide skilled manpower on a temporary or contract basis. Technology staffing ensures “Just-in time recruitment” for short-term or critical projects without the overheads associated with a full time employee. It enables our clients to manage their head count with ease. Technology staffing acts as a flexible, cost effective and a quick solution for all our IT clients. Contract staffing service also provides an opportunity for our clients to evaluate the on –the – job performance of a temporary staff and rewarding the good performance with a permanent job. All of our professionals are guaranteed to meet or exceed our client’s expectations.

BENEFITS OF OUR STAFFING SERVICES:

•

Our expertise in developing complex software applications enable us to understand the IT staffing requirements and provide pre-screened candidates that best fit our clients’ technology staffing needs. Googolsoft possesses a unique combination of time tested processes and innovative recruitment techniques with the state of the art infrastructure Technology professionals who match both the job profile and the culture of the client’s organization are only suggested. Our team of highly qualified recruiters with strong experience and expertise in recruitment across all the domains acts as an extended partner for our clients. Flexibility for our clients to get temporary staffing of qualified professionals on any fixed duration basis. Contract duration of our professionals can also be extended based on the demand of the projects

• • • •

6. CERTIFICATION IN RECRUITMENT
First time in India, an organization has stepped up to provide the “Training in Recruitment skills” to MBA HR students. Researches conducted across India have confirmed that majority(60%) of the jobs available in HR field are basically in recruitment. With the phenomenal growth of IT and Telecom sectors in the last decade, there is a big need for the employable MBA HR students in the recruitment arena. During this training, students are empowered with the world class manpower staffing processes and the industry endorsed techniques. In addition, the secrets of core recruitment practices are also shared to the young budding HR students. Students are given a practical chance to do the real time recruitment. State of the art classroom provides extra support to the the practice sessions. Course covers a wide array of hot topics of recruitment including : ? Decoding of “Human Psychology” ? How to face interviews and how to conduct interviews. ? Basics of recruitment and core recruitment process. ? Time tested ways to frame hiring plan strategies and make market analysis. ? Methods to source the right candidates using Job portals, Networking, Innovative Reference building methods, Headhunting, Job ads in Naukri, Recruitment drives etc ? Tips for screening the profiles based on Technical skills, Attitude, Communication, CTC, Offers, and so on.

? Scheduling and co-ordination methods for technical and HR interviews. ? Offer negotiation and offer discussion styles. ? Job offer to On-board joining ratio conversion techniques. ? Real life Candidate Relationship Management (CRM).

This certification course enables a job seeker to have an edge over others during the campus interviews. It also provides a good opportunity to earn lakhs of money through the highly paid freelancer or part-time jobs in recruitment industry.

7. SOFT SKILLS TRAINING:
Soft skills are the interpersonal skills and the associated non-technical skills that help individuals deal with everyday challenges at the work place effectively. In this regard, soft skills play a crucial role to increase the employment and growth opportunities in the competitive global environment. Every human being is pre-programmed with a set of interpersonal behavioral patterns. People acquire them over a period of time through their own experience. During the soft skills training sessions, the old programs in the brain are replaced with a new set of programs. Success of training depends on the amount of permanent changes made to the behavioral patterns. To make this happen, frequent reinforcement of concepts backed with encouragement, feedback and guidance is mandatory. In the current global business environment, it is essential for everybody to have the right skills to reach the point of self actualization. Backed by experienced trainers, our company offers interactive personality development sessions in the following areas: ? Stress management ? Time Management ? Team Management ? Email etiquettes and Telephone etiquettes ? Presentation skills ? Public Speaking skills ? Vision Setting ? Confidence Boosters

? Human Relations Management ? Business games ? Motivation Googolsoft training division is committed to the task of producing efficient professionals. Our training solutions are customized based on the need of the corporate sectors.

8. JOB SKILLS TRAINING:
Googolsoft training division has crafted a first of its kind training program for the job seekers. Employment skills training offer a unique combination of personality development skills and interview preparation skills training to the students. Our practice oriented training covers the entire gamut of skills needed to get the employment. Job seekers are empowered with the innovative job search method techniques to widen their job opportunities. Tips to improve the vocabulary and communication are given to make them an effective communicator. Our training module has been framed on the basis of the research conducted to identify the root cause of the interview failure. Voice recording and video taping of practice sessions produces a dramatic improvement in employment skills among the job seekers. Our training process has four different stages. At the first stage, the inner desire for the dream career has been identified and counseled accordingly. Plan for the career and the interview is drafted at the second stage. Necessary self belief on the career plan is provided at the third stage to facilitate a gradual lead up to the last stage. In the last stage, job-seekers are trained in the core interview skills and the presentation skills necessary for success in the interviews. Stage 1(Self Awareness): ? Self-Analysis sessions ? ? Stage 2 (Planning) ? Vision & Goal Setting ? Plan of action ? Stage 3 (Confidence Boosters) ? Self- Confidence Enhancement Visualization techniques Ice – breaker games One to one Career Counseling

? Presentation Skills ? Vocabulary building tools Stage 4 (Interview skills): ? Resume Preparation ? Group Discussion skills ? Interview questions handling ? Interview preparation ? Mock Interviews ? Innovative Job Search techniques

CLIENTS:
A partial list of our IT clients : ? HCL Technologies (CMMI Level 5) ? Mascon Global Limited (CMMI Level 5) ? CSC India (Fortune 500 company) ? Calsoft Labs (CMM Level 5)
? Bahwan Cybertek (CMMI Level 5)

? Ness Technologies (CMM Level 5) ? Cybernet Slash Support (CMM Level 5) ? Sword Global (CMM Level 5) ? GAVS Technologies (CMM Level 5) ? Aspire Systems (CMMI Level 4) ? Kumaran Systems (CMMI Level 3)
? Photon InfoTech (CMM Level 3)

CHAPTER 3 REVIEW OF LITERATURE
Studies on Recruitment and Retention done in various companies:

1. Job offer arrival rates and acceptance probabilities:
In the search model, the process of entry into a new job from unemployment is governed by the job offer arrival rate together with the probability of acceptance of a job offer by the unemployed worker (and acceptance of the applicant by the firm). The job offer arrival rate could either be endogenous, dependent on search intensity, or as in the standard search model be exogenous, determined by demand for labor in the relevant part of the market. Finally, mixed cases could occur where an exogenous base level of the offer arrival rate may be influenced by individual search intensity. Possible effects from UI benefits through the arrival rate will occur if search intensity is sensitive to benefits and if the arrival rate of job offers is sensitive to search intensity. If, on the other hand, the arrival rate is purely exogenous, possible benefit effects must work through an impact on the probability that a job offer is accepted. The acceptance probability, given a job offer arrives, depends on the wage offer in relation to the reservation wage. A number of empirical studies report estimates of the acceptance probability.22 Among the studies surveyed here, Warren with U.K. data and van den Berg (1990) with Dutch data conclude that virtually every job offer is accepted by individuals in their samples. Devine and

Kiefer (1991, p. 137 ff.) in their summary of results from studies of structural models conclude that unemployed workers almost always accept an offer, once an offer is received. Devine and Kiefer reach the same conclusion in their summary of results from three-state models.23 They conclude p. 158) that variations in the transition into employment by and large reflect variations in arrival rates, as opposed to systematic variations in the willingness to accept offers. As a consequence of these findings, longer durations of unemployment for some groups of workers are interpreted as reflecting more infrequent arrival of offers. This will shift the explanation of longer spells of unemployment to the demand side if the arrival of job offers is exogenous as assumed in the standard job search model. If, on the other hand, the arrival rate can be significantly affected by individual search intensity, this will be a channel through which UI benefits can influence unemployment durations. In their survey of results from studies with direct evidence on search activity, Devine and Kiefer (1991) conclude - tentatively - that there is some evidence that search intensity declines with the duration of an unemployment spell. Wadsworth’s (1 990) result, referred to above, that benefit claimants search more than nonrecipients, goes in the opposite direction. A possible interpretation is that benefits have opposite effects on the level and the duration dependency of search intensity.

Studies estimating the parameters in structural search models are still in an early stage. Available evidence does not support very firm conclusions. On balance, the results concerning the very high acceptance probabilities point to variations in arrival rates as very important thereby shift the weight to demand side factors in the explanation of unemployment durations.

D.Muthukumaran (2005): Studied on retention practices followed Ambattur Clothing Factory Ltd. The main objective is to study the retention practices followed to retain the employees. They had used the structured questionnaire with 25 items in it and administer the sample of 75 people. Researchers used percentile method. Exploratory Research with sample random sampling method was used. The result of the study shows the recruitment practices done through personnel references is the key factor to the retention of employees in the organization.

K. Girja Devi (2004): Studied of the effectiveness of recruitment in Fenner India Ltd. TZhe objective of the study is to understand and help the process of recruitment and its effectiveness . Questionnaire methods were administered among the 50 employees containing 35 items . The researcher to manipulates the data used sample random sampling and percentile method . The result of the study shows effectiveness of the recruitment process in finding the appropriate candidate foe the organization.

T. GopalanSrinivasan (2004):Made a study on the effectiveness of recruitment and selection process in GAVS Information System Pvt Ltd. The objective of the study is to find out the effectiveness of the selection and recruitment process done in the organization. Questionnaire methods were administered for a sample odd 50 containing 35 items. Pie and bars diagrams are used in the manipulation of data so collected. The result showed that proper selection process helped in building the effectiveness of the recruitment program.

S. Vijay Kumar (2003): Attempted to study on recruitment effectiveness among the IT professionals in Dax Network Pvt Ltd. The objective of the study is to find the effectiveness of the present recruitment process and to suggest improvement on the same. The sample size of 40 employees was randomly selected. The questionnaire method was used to collect the required data . The questionnaire contain 25 items . Pie and bar diagrams are used . Percentile methods are used to manipulate the data collected . The result concluded with various suggestions to be implemented to make the requirement process much more effective .

P. Swami Nathan (2002): Studied on retention practices among the employees of Futura Info Tech Private Limited , Taramani Chennai . The objective of the study is to analyze the retention practices followed to retain the employees in the organization and to provide suggestions to improve the practices. A structure questionnaire is distributed on sample random sampling basis to 50 employees. The questionnaire contains 30. The result of the study concluded with a brief analysis of the retention practices and suggestions to improve the process

CHAPTER 4 PROBLEM & HYPOTHESIS

4.1 STATEMENT OF THE PROBLEM: After detailed review of studies were collected from different sources, it is highly useful to formulate the hypothesis for the present research. The present study is aimed to find the techniques to increase the joining ratio of the job seekers. 4.2 HYPOTHESIS: Based on availability of review of literature in the field of recruitment the investigators have formulated the following hypothesis for the present study and they are as follows. 1. There will be no significant difference between the joining ratio and personal factors of the job seekers. 2. There will be a significant difference between the joining ratio and personal factors of the job seekers. 4.3 OPERATIONAL DEFINITION:

Recruitment: An activity in which the organization attempt to identify and attract candidates to meet the requirements of anticipated and actual job openings. Recruitment refers to the process of finding possible candidates for a job or function. It may be undertaken by an employment agency or a member of staff at the business or organization looking for recruits. Either way it may involve advertising, commonly in the recruitment section of a newspaper or in a newspaper dedicated to job adverts. Employment agencies will often advertise jobs in their windows. Posts can also be advertised at a job center if they are targeting the unemployed.

CHAPTER 5 RESEARCH METHODOLOGY
5.1 OBJECTIVES OF THE STUDY: PRIMARY OBJECTS ? To identify the effective methods to increase the offer joining ratio of job seekers. SECONDARY OBJECTIVES ? To identify the reason for dropping an offer. ? To identify the expectation of job seekers out of an offers. ? To identify the factors that decides the offer acceptance. ? To identify the expectation of three categories of candidates.

Formulation of suitable methodology for the study is indispensable for a meaningful and systematic analysis of the problematic situation and to find solution to the problem identified .

5.2. RESEARCH DESIGN: This project consisted of descriptive researchers. The descriptive research is used by questionnaire method. By this, the job seekers are questioned through mail by sending framed questionnaires to their mail. The observational findings were obtained through the researcher’s personal observation of the respondents’ reply, after analyzing the reply. Population Universe refers to the total number of items in any field of enquiry whereas population refers to the total number of items about which the information is required. Here the population is the job seekers who have uploaded their profiles in portals. Sample universe The sample universe is the entire group of items the researcher wishes to study and about which they plan to generalize. The samples of this study are the job seekers with minimum experience of 2 years and above 8 years of experience in IT sector. Universal sampling is done in this study. Sample size The questionnaire was given to100 respondents but 50 respondents didn’t reply and therefore the remaining 50 samples were considered as sample size. 5.3. QUESTIONNAIRE DESIGN: The required information was collected through a non-disguised structured questionnaire. The structured questionnaire of the study included Likert scale, the degree of verbal description used was for Agree. 5.4. STATISTICAL TOOLS USED After completion of the data sampling all the particulars obtained were coded and transferred into charts and tabulation and analysis was done in the research using SPSS software package. The statistical tools used are: ? Independent sample t-test,

? One way Anova,

? Factor Analysis,
? Cluster analysis.

5.4.1. ANOVA Analysis of Variance, a statistical technique for examining the differences in the mean values of the dependent variable associated with the effect of the controlled independent variables, after taking into account the influence of the uncontrolled independent variables. Essentially, ANOVA is used as a test of means for two or more populations. The null hypothesis, typically, is that all means are equal. ANOVA must have a dependent variable that is, metric. There must also be one or more independent variables. The independent variables must be all categorical. Categorical independent variables are also called factors. Marketing researchers are often interested in examining the difference in the mean values of the dependent variable for several categories of a single independent variable or factor. The statistics associated with one-way ANOVA are eta2, F statistic, Mean square, SSbetween, SSwithin, SS
y

. The procedure for conducting one-way analysis of variance is: ? Indentify the dependent and independent variables. ? Decompose the total variation. ? Measure the effects. ? Test the significance. ? Interpret the results.

5.4.2. FACTOR ANALYSIS Factor analysis is a general name denoting a class of procedures primarily used for data reduction and summarization. In marketing research, there may be a large number of variables, most of which are correlated and which must be reduced to a manageable level. Relationships

among sets of many interrelated variables are examined and represented in terms of a few underlying factors. Factor analysis is used in the following circumstances:
? To identify underlying dimensions, or factors, that explains the correlations among a set

of variables.
? To identify a new, smaller set of uncorrelated variables to replace the original set of

correlated variables in subsequent multivariate analysis.
? To identify a smaller set of salient variables from a larger set for use in subsequent

multivariate analysis. Factors – An underlying dimension that explaining the correlations among a set of variables. The steps involved in conducting factory analysis are: Formulate the problem: The objectives of factor analysis should be identified. The variables to be included in the factor analysis should be specified based on past research, theory, and judgment of the researcher. It is important that the variables be appropriately measured on an interval or ration scale. An appropriate sample size should be used. Construct the correlation matrix: The analytical process is based on a matrix of correlations between the variables. Valuable insights can be gained from an examination of this matrix. For the factor analysis to be appropriate, the variables must be correlated. If the correlations between all the variables are small, factor analysis may not be appropriate. We would also expect that variables that are highly correlated with each other would also highly correlated with the same factory or factors. Formal statistics are available for testing the appropriateness of the factor model. Bartlett’s test of sphericity can be used to test the null hypothesis that the variables are uncorrelated in the population. A large value of the test statistic will favor the rejection of the null hypothesis. If this hypothesis cannot be rejected, then the appropriateness of factor analysis should be questioned. Another useful statistic is the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy. Generally, a value greater than 0.5 is desirable. Determine the method of factor analysis: once it has been determined that factor analysis is an appropriated technique for analyzing the data, an appropriate method must be selected.

Principle components analysis- an approach to factor analysis that considers the total variance in the data. Common factor analysis- an approach to factor analysis that estimates the factors based only on the common variance. Determine the Number of factors: Several procedures have been suggested for determining the number of factors. These include a priori determination and approaches based on Eigen values, screen plot, percentage of variance accounted for, split-half reliability, and significance tests. Eigen values- In this approach, only factors with Eigen values greater than 1.0 are retained; the other factors are not included in the model. An Eigen value represents the amount of variance associated with the factor. Hence, only factors with a variance greater than 1.0 are included. Factors with variance less than 1.0 are no better than a single variable, because, due to standardization, each variable has a variance of 1.0. If the number of variables is less than 20, this approach will result in a conservative number of factors. Rotate Factors: The variance explained by the individual factors is redistributed by rotation. Difference methods of rotation may result in the identification of different factors. Orthogonal rotation- Rotation of factors in which the axes are maintained at right angles. Varimax procedure- An orthogonal method of factor rotation that minimizes the number of variables with high loadings on a factor, thereby enhancing the interpretability of the factors. Oblique rotation- Rotation of factors when the axes are not maintained at right angles. Interpret factors: Interpretation is facilitated by identifying the variables that have large loadings on the same factor. That factor can then be interpreted in terms of the variables that load high on it. Another useful aid in interpretation is to plot the variables using the factor loadings as coordinates. Variables at the end of an axis are those that have high loadings on only that factor, and hence describe the factor. Variables near the origin have small loadings on both the factors. Variables that are not near any of the axes are related to both the factors. If a factor cannot be

clearly defined in terms of the original variables, it should be labeled as an undefined or a general factor. Calculate the factor scores: Following interpretation, factor scores can be calculated, if necessary. Factor analysis has its own stand-alone value. However, if the goal of factor analysis is to reduce the original set of variables to a smaller set of composite variables for use in subsequent multivariate analysis, it is useful to compute factor scores for each respondent. A factor is simply a linear combination of the original variables.

CHAPTER 6 DATA ANALYSIS AND INTERPRETATION
AGE: Age of respondents TABLE 6.1
Age Cumulative Frequency Valid 22-24 25-30 above 30 Total 63 112 25 200 Percent 31.5 56.0 12.5 100.0 Valid Percent 31.5 56.0 12.5 100.0 Percent 31.5 87.5 100.0

GRAPH 6.1.

Inference: About 56% of the respondents are in the age group of 25-30 GENDER: Gender of the respondents
TABLE 6.2 Gender

Frequency Valid male female Total 125 75 200

Percent 62.5 37.5 100.0

Valid Percent 62.5 37.5 100.0

Cumulative Percent 62.5 100.0

GRAPH 6.2

Inference: About 62.5% of the respondents are male

DESIGNATION: Designation of the respondents TABLE 6.3
Designation Cumulative Frequency Valid software engineer analysts leads Total 97 79 24 200 Percent 48.5 39.5 12.0 100.0 Valid Percent 48.5 39.5 12.0 100.0 Percent 48.5 88.0 100.0

GRAPH 6.3

Inference: About 48% of the respondents are software engineers.

EXPERIENCE: Experience of the respondents TABLE 6.4
Experience Cumulative Frequency Valid fresher 1-5 yrs 5-8 yrs above 8 yrs Total 37 68 74 21 200 Percent 18.5 34.0 37.0 10.5 100.0 Valid Percent 18.5 34.0 37.0 10.5 100.0 Percent 18.5 52.5 89.5 100.0

GRAPH 6.4

Inference: About 37% of the respondents have an experience of 5-8 year SALARY: Salary is the most important factor. TABLE 6.5
Salary Cumulative Frequency Valid strongly agree Agree Neutral Total 87 97 16 200 Percent 43.5 48.5 8.0 100.0 Valid Percent 43.5 48.5 8.0 100.0 Percent 43.5 92.0 100.0

GRAPH 6.5

Inference: From the above table it shows that 48.5% of the respondents agree that salary is the important factor to accept the offer. COMPANY BRAND: Brand of the company is very important.
TABLE 6.6 Brand of Company Cumulative Frequency Valid strongly agree Agree Neutral Disagree strongly disagree Total 36 85 62 7 10 200 Percent 18.0 42.5 31.0 3.5 5.0 100.0 Valid Percent 18.0 42.5 31.0 3.5 5.0 100.0 Percent 18.0 60.5 91.5 95.0 100.0

GRAPH 6.6

Inference: From the above table it shows 42% of the respondents agree that brand name of the company is very important for the respondents to accept the offer. RELATIONSHIP: Relationship with the point of contact(HR) is an important factor. TABLE 6.7

HR Contact Cumulative Frequency Valid strongly agree Agree Neutral Disagree strongly disagree Total 49 90 43 9 9 200 Percent 24.5 45.0 21.5 4.5 4.5 100.0 Valid Percent 24.5 45.0 21.5 4.5 4.5 100.0 Percent 24.5 69.5 91.0 95.5 100.0

GRAPH 6.7

Inference: From the above table it shows 45% of the respondents agree that relationship with the point of contact (HR) is one of the important factors to accept an offer. ROLE : I prefer a better role.

TABLE 6.8 Better Role Cumulative Frequency Valid strongly agree agree neutral disagree Total 117 64 16 3 200 Percent 58.5 32.0 8.0 1.5 100.0 Valid Percent 58.5 32.0 8.0 1.5 100.0 Percent 58.5 90.5 98.5 100.0

GRAPH 6.8

Inference: From the above table it shows, 58% of the respondents strongly agree that they prefer the offer with better role in an organization. WORK CULTURE: I prefer better work culture.

TABLE 6.9 Work Culture Cumulative Frequency Valid strongly agree Agree Neutral Total 121 68 11 200 Percent 60.5 34.0 5.5 100.0 Valid Percent 60.5 34.0 5.5 100.0 Percent 60.5 94.5 100.0

GRAPH 6.9

Inference: From the above table it shows, 60% of the respondents strongly agree that they prefer the offer in an organization with a better work culture. LOCALITY: I prefer the offer which is near my locality.

TABLE 6.10 Locality Cumulative Frequency Valid strongly agree agree neutral disagree Total 68 49 69 14 200 Percent 34.0 24.5 34.5 7.0 100.0 Valid Percent 34.0 24.5 34.5 7.0 100.0 Percent 34.0 58.5 93.0 100.0

GRAPH 6.10

Inference: From the above table it shows, 35.5% of the respondents were neutral in their thinking in order to accept the offer which is nearer to their locality. STATUS OF COMPANY: I give priority to CMMI level status of company.

TABLE 6.11 CMMI level Cumulative Frequency Valid strongly agree Agree Neutral Disagree strongly disagree Total 50 68 40 36 6 200 Percent 25.0 34.0 20.0 18.0 3.0 100.0 Valid Percent 25.0 34.0 20.0 18.0 3.0 100.0 Percent 25.0 59.0 79.0 97.0 100.0

GRAPH 6.11

Inference: From the above table it shows, 34% of the respondents confirm that they accept the offer which is based on the CMMI level of the organization SALARY COMPONENTS: I prefer an offer with better salary components.

TABLE 6.12 Salary components Cumulative Frequency Valid strongly agree Agree Neutral Disagree strongly disagree Total 51 90 47 9 3 200 Percent 25.5 45.0 23.5 4.5 1.5 100.0 Valid Percent 25.5 45.0 23.5 4.5 1.5 100.0 Percent 25.5 70.5 94.0 98.5 100.0

GRAPH 6.12

Inference: From the above table it is very clear that 45% of the respondents will accept the offer in the company where the employees are paid with better salary components. TECHNOLOGY: Prefer project with better technology.

TABLE 6.13 Better projects Cumulative Frequency Valid strongly agree Agree Neutral Disagree Total 100 66 25 9 200 Percent 50.0 33.0 12.5 4.5 100.0 Valid Percent 50.0 33.0 12.5 4.5 100.0 Percent 50.0 83.0 95.5 100.0

GRAPH 6.13

Inference: From the above table it shows, 50% of the respondents will accept an offer in an organization only if they are provided a project with a better technology.
FIRST OFFER: I prefer the first offer.

TABLE 6.14 First offer Cumulative Frequency Valid strongly agree agree neutral disagree strongly disagree Total 31 26 57 74 12 200 Percent 15.5 13.0 28.5 37.0 6.0 100.0 Valid Percent 15.5 13.0 28.5 37.0 6.0 100.0 Percent 15.5 28.5 57.0 94.0 100.0

GRAPH 6.14

Inference: From the above table it shows, 37% of the respondents were not ready to accept the first offer provided by the company. OPPORTUNITY: I prefer onsite opportunity.

TABLE 6.15 Onsite opportunity Cumulative Frequency Valid strongly agree Agree Neutral Disagree strongly disagree Total 83 61 25 19 12 200 Percent 41.5 30.5 12.5 9.5 6.0 100.0 Valid Percent 41.5 30.5 12.5 9.5 6.0 100.0 Percent 41.5 72.0 84.5 94.0 100.0

GRAPH 6.15

Inference: From the above table it shows, 41.5% of the respondents were very much ready to accept the offer where there are onsite opportunities. OFFER WITH JOINING BONUS: I prefer an offer with joining bonus.

TABLE 6.16 Joining bonus Cumulative Frequency Valid strongly agree agree neutral disagree strongly disagree Total 26 51 87 30 6 200 Percent 13.0 25.5 43.5 15.0 3.0 100.0 Valid Percent 13.0 25.5 43.5 15.0 3.0 100.0 Percent 13.0 38.5 82.0 97.0 100.0

GRAPH 6.16

Inference: From the above table it shows, 43.5% of the respondents were neutral in their thinking in order to accept the offer in a company where joining bonus is provided. DAY SHIFTS: I prefer day shifts.

TABLE 6.17 Dayshifts Cumulative Frequency Valid strongly agree Agree Neutral Disagree Total 100 66 25 9 200 Percent 50.0 33.0 12.5 4.5 100.0 Valid Percent 50.0 33.0 12.5 4.5 100.0 Percent 50.0 83.0 95.5 100.0

GRAPH 6.17

Inference: From the above table it shows, 50% of the respondents will accept the offer only when they are provided with dayshifts. JOB PROFILE: I prefer better job profile.

TABLE 6.18 Job Profile Cumulative Frequency Valid strongly agree agree neutral Total 73 75 52 200 Percent 36.5 37.5 26.0 100.0 Valid Percent 36.5 37.5 26.0 100.0 Percent 36.5 74.0 100.0

GRAPH 6.18

Inference: From the above table it shows, 37.5% of the respondents will accept the offer in a company when they are provided with a better job profile. DESIGNATION: I prefer the offer with better designation.

TABLE 6.19 Better Designation Cumulative Frequency Valid strongly agree agree neutral disagree Total 78 75 44 3 200 Percent 39.0 37.5 22.0 1.5 100.0 Valid Percent 39.0 37.5 22.0 1.5 100.0 Percent 39.0 76.5 98.5 100.0

GRAPH 6.19

Inference: From the above table it shows, 39% of the respondents strongly agree to an offer with better designation. PROJECT DOMAIN: I prefer better Project Domain

TABLE 6.20 Project Domain Cumulative Frequency Valid strongly agree Agree Neutral Disagree Total 36 83 60 21 200 Percent 18.0 41.5 30.0 10.5 100.0 Valid Percent 18.0 41.5 30.0 10.5 100.0 Percent 18.0 59.5 89.5 100.0

GRAPH 6.20

Inference: About 41.5% of the respondents agree that they like to join a company with better project domain. PRODUCT BASED COMPANY: I prefer Product based company

TABLE 6.21 Product Based Cumulative Frequency Valid strongly agree Agree Neutral Disagree Total 100 66 25 9 200 Percent 50.0 33.0 12.5 4.5 100.0 Valid Percent 50.0 33.0 12.5 4.5 100.0 Percent 50.0 83.0 95.5 100.0

GRAPH 6.21

Inference: About 50% of the respondents strongly agree that they like to join the product based company. POSITION: I prefer permanent position only

TABLE 6.22 Permanent Position Cumulative Frequency Valid strongly agree agree neutral Total 92 66 42 200 Percent 46.0 33.0 21.0 100.0 Valid Percent 46.0 33.0 21.0 100.0 Percent 46.0 79.0 100.0

TABLE 6.22

Inference: About 46% of the respondents strongly agree that they like to join the company were they get permanent position. OFFER BASED ON PROJECT: I select the offer based on Project phase

TABLE 6.23 Project phase Cumulative Frequency Valid strongly agree Agree Neutral Disagree Total 45 80 51 24 200 Percent 22.5 40.0 25.5 12.0 100.0 Valid Percent 22.5 40.0 25.5 12.0 100.0 Percent 22.5 62.5 88.0 100.0

GRAPH 6.23

Inference: About 40 % of the respondents agree that they accept the offer based on the project phase. TAKE HOME SALARY: I prefer the offer with Better take home salary

TABLE 6.24 Take home Cumulative Frequency Valid strongly agree Agree Neutral disagree Total 42 81 56 21 200 Percent 21.0 40.5 28.0 10.5 100.0 Valid Percent 21.0 40.5 28.0 10.5 100.0 Percent 21.0 61.5 89.5 100.0

GRAPH 6.24

Inference: About 40% of the respondents agree that they prefer an offer with better take home salary NON MONETARY BENEFITS: I prefer an offer were I get more Non monetary benefits

TABLE 6.25 Nonmonetary Cumulative Frequency Valid strongly agree Agree Neutral Disagree Total 24 80 75 21 200 Percent 12.0 40.0 37.5 10.5 100.0 Valid Percent 12.0 40.0 37.5 10.5 100.0 Percent 12.0 52.0 89.5 100.0

GRAPH 6.25

Inference: About 40% of the respondents agree that they prefer the offer were they can get better nonmonetary benefits. CARRER GROWTH: I prefer the offer were I can get better career growth.

TABLE 6.26 Career Growth Cumulative Frequency Valid strongly agree Agree neutral disagree Total 100 66 25 9 200 Percent 50.0 33.0 12.5 4.5 100.0 Valid Percent 50.0 33.0 12.5 4.5 100.0 Percent 50.0 83.0 95.5 100.0

GRAPH 6.26

Inference: About 50% of the respondents strongly agree that they accept the offer were they can get better career growth. FACTOR ANALYSIS:

In this study factor analysis is done to compress or reduce the number of factors that are taken into study. As the factors are quite high, analysis of all the factors with the demographic variables will be time consuming. Hence to reduce the time constraints and make the study easy factor analysis is done.

TABLE 6.27 KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity Approx. Chi-Square Df Sig. .519 332.378 231 .000

The above table shows the Kaiser Mayer Oyen’s test and Bartlets test of the given data of the study which shows the significance as .000.

TABLE 6.28

Initial Salary Brand of company HR contact Better role Work culture Locality CMMI level Salary components Better projects First offer Onsite opportunity Joining bonus Dayshifts Job profile Better designation Project domain Product based company Permanent position Project phase Take-home salary Better career growth 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Extraction .624 .650 .689 .518 .657 .697 .632 .595 .574 .487 .536 .654 .586 .613 .667 .666 .621 .587 .620 .651 .543 .661

Non-monetary benefits 1.000

This table shows the communalities i.e each value attained by the factors that are taken into the study. The values are assigned to each factor as they are extracted by the method of principal component analysis.

TABLE 6.29 Total Variance Explained Initial Eigenvalues Extraction Sums Squared Loadings of Rotation Sums of Squared Loadings

% of Cumulative % of Cumulative % of Cumulative Component Total Variance % Total Variance % Total Variance % 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 2.10 9.583 8 1.66 7.559 3 1.53 6.968 3 1.33 6.053 2 1.24 5.643 1 1.22 5.578 7 1.21 5.507 2 1.11 5.049 1 1.06 4.829 2 1.03 4.724 9 .986 4.481 .910 4.138 .839 3.815 .827 3.759 .791 3.595 .732 3.328 .684 3.109 .644 2.926 .603 2.743 .540 2.452 9.583 17.142 24.110 30.163 35.806 41.384 46.892 51.940 56.769 61.493 65.974 70.112 73.928 77.686 81.281 84.608 87.717 90.644 93.386 95.838 2.108 9.583 1.663 7.559 1.533 6.968 1.332 6.053 1.241 5.643 1.227 5.578 1.212 5.507 1.111 5.049 1.062 4.829 1.039 4.724 9.583 17.142 24.110 30.163 35.806 41.384 46.892 51.940 56.769 61.493 1.55 7.077 7 1.44 6.570 5 1.43 6.535 8 1.42 6.480 5 1.39 6.324 1 1.28 5.837 4 1.27 5.808 8 1.26 5.761 7 1.22 5.552 1 1.22 5.550 1 7.077 13.647 20.182 26.661 32.985 38.822 44.631 50.392 55.944 61.493

TABLE 6.30 Component Matrix Component 1 First offer Better role Work culture Better projects Dayshifts HR contact Salary components Better designation Joining bonus CMMI level Brand of company Locality Product based company Onsite opportunity Job profile Take-home salary Project phase Permanent position Nonmonetary benefits Project domain Salary Better career growth .531 .606 .489 .617 .486 .561 .473 -.548 .522 .513 .483 2 3 4 5 6 7 8 9 10

This table shows the factors that are reduced from the factors that is taken into study. Out of the 23 factors taken into study by using factor analysis the factors are reduced and ten components similar to each other are extracted. The extraction is done by principal component analysis. TABLE 6.31 Rotated Component Matrix Component 1 HR contact Better role Salary Better projects Work culture Project domain First offer Better designation Job profile Joining bonus Brand of company CMMI level Permanent position Project phase . 802 . 568 . 695 . 623 . .471 550 .738 -.56 3 .701 .568 -.54 5 . 723 . 680 . 696 . 504 2 3 4 5 6 7 8 9 10

Rotated Component Matrix Product based company Nonmonetary benefits Better career growth Salary components Take-home salary Locality Onsite opportunity Dayshifts This table shows the components which are the factors that are grouped based on the extraction values. Here we can see the 23 factors are reduced into 10 components (new factors). This rotated component matrix is done by the method of Varimax with Kaiser Normalization . 769 . 460 .748 -.65 6 . 766 .780

TABLE 6.32 Component Transformation Matrix Component 1 1 2 3 4 5 6 2 3 4 5 6 7 8 9 10 -.09 .281 .120 .164 .002 .182 5

.478 .465 .540 .318

-.44 -.19 -.35 -.29 -.23 .430 .312 .358 .232 -.178 5 9 6 2 2 .397 .168 -.16 -.61 -.01 -.31 .137 .418 .202 .307 -.136 1 1 6 1 -.24 -.09 -.11 -.21 .351 .727 .057 .110 .416 5 6 9 8

-.24 -.02 -.27 -.03 .299 .197 .316 .743 .132 -.256 3 7 7 8 .346 -.03 -.07 -.21 -.01 -.49 .370 .068 .525 -.402 5 7 1 7 9

Component Transformation Matrix 7 8 9 10 -.41 -.14 -.01 -.10 .069 .637 .186 .470 .323 .172 0 6 2 5 -.06 -.43 -.43 -.09 -.00 .187 .254 .006 .566 .432 4 4 9 1 4 .101 .461 .164 .392 -.58 -.26 -.08 -.18 -.22 .201 .188 .444 7 0 0 3 9 -.40 -.10 -.41 .114 .218 .492 .259 -.321 8 4 2

The above table shows the component transformation matrix. This explains how the 23 factors are transformed into 10 components.

Age Vs factors Hypothesis: H0: There is no association between age and the reduced components. H1: There is association between age and the reduced components. ANOVA TABLE 6.33 Sum Squares Locality Between Groups 11.179 Within Groups 231.301 Total Onsite opportunity 242.480 Between Groups 17.060 Within Groups 264.458 Total HRBR 281.517 Between Groups 14.138 Within Groups 95.809 Total Sal BPWC 109.947 Between Groups 2.124 Within Groups 72.137 Total PDFO 74.262 Between Groups 3.485 Within Groups 92.393 Total Design JPJB 95.878 Between Groups .361 Within Groups 45.293 Total Brand CMMI 45.655 Between Groups 6.204 Within Groups 133.052 Total 139.256 ppPB Between Groups 1.691 199 5 .338 .749 .587 of Df 5 194 199 5 194 199 5 194 199 5 194 199 5 194 199 5 194 199 5 194 1.241 .686 1.809 .113 .072 .233 .309 .907 .697 .476 1.464 .203 .425 .372 1.143 .339 2.828 .494 5.726 .000 3.412 1.363 2.503 .032 Mean Square F 2.236 1.192 1.875 Sig. .100

Age Vs factors Hypothesis: H0: There is no association between age and the reduced components. H1: There is association between age and the reduced components. ANOVA TABLE 6.33 Within Groups 87.558 Total NMCG 89.249 Between Groups .786 Within Groups 61.891 Total salTH INFERENCE: ? There is no association between age and locality. ? There is association between age and onsite opportunity. ? There is association between age and human relation, better role. ? There is no association between age and salary, better projects and work culture. ? There is no association between age and project domain, first offer. ? There is no association between age and designation, job profile, joining bonus. ? There is no association between age and brand of company, cmmi level status.
? There is no association between age and permanent position, product

194 199 5 194 199 5

.451 .157 .319 .589 1.401 .225 .493 .781

62.677

Between Groups 2.946

based company. ? There is no association between age and non monetary benefits, career growth. ? There is no association between age and salary components, take home.

Gender Vs factors Hypothesis: H0: There is no association between gender and the reduced components (factors) H1: There is association between gender and the reduced components (factors) ANOVA TABLE 6.34 Sum Squares Locality Between Groups 7.135 Within Groups 235.345 Total Onsite opportunity 242.480 Between Groups 20.632 Within Groups 260.885 Total HRBR 281.517 Between Groups 2.916 Within Groups 107.032 Total SalBPWC 109.947 Between Groups 4.225 Within Groups 70.036 Total PDFO 74.262 Between Groups 3.478 Within Groups 92.400 Total Design JPJB 95.878 Between Groups .927 Within Groups 44.728 Total 45.655 of Df 5 194 199 5 194 199 5 194 199 5 194 199 5 194 199 5 194 199 .185 .231 .804 .548 .696 .476 1.461 .205 .845 .361 2.341 .043 .583 .552 1.057 .386 4.126 1.345 3.068 .011 Mean Square F 1.427 1.213 1.176 Sig. .322

ANOVA TABLE 6.34 Brand cmmi Between Groups 4.591 Within Groups 134.664 Total ppPB 139.256 Between Groups 1.292 Within Groups 87.957 Total NMCG 89.249 Between Groups 2.376 Within Groups 60.301 Total salTH INFERENCE:
? There is association between gender and locality. ? There is association between gender and onsite opportunity. ? There is no association between gender and human relation, better role. ? There is association between gender and salary, better projects and work

5 194 199 5 194 199 5 194 199 5

.918 .694 .258 .453 .475 .311 .772

1.323

.256

.570

.723

1.529

.182

62.677

Between Groups 3.860

1.857

.104

culture.
? There is no association between gender and project domain, first offer. ? There is no association between gender and designation, job profile,

joining bonus.
? There is no association between gender and brand of company, cmmi level

status.
? There is no association between gender and permanent position, product

based company.
? There is no association between gender and non monetary benefits, career

growth.
? There is no association between gender and salary components, take

home.

Designation Vs Factors Hypothesis: H0: There is no association between designation and the reduced components (factors) H1: There is association between designation and the reduced components (factors) ANOVA TABLE 6.35 Sum Squares locality Between Groups 1.559 Within Groups 240.921 Total Onsite opportunity 242.480 Between Groups 12.499 Within Groups 269.019 Total HRBR 281.517 Between Groups 3.495 Within Groups 106.452 Total SalBPWC 109.947 Between Groups 1.532 Within Groups 72.729 Total PDFO 74.262 Between Groups 1.909 Within Groups 93.969 Total 95.878 of Df 4 195 199 4 195 199 4 195 199 4 195 199 4 195 199 .477 .482 .990 .414 .383 .373 1.027 .394 .874 .546 1.601 .176 3.125 1.380 2.265 .064 Mean Square F .390 1.235 .315 Sig. .867

ANOVA TABLE 6.35 Design JPJB Between Groups .872 Within Groups 44.783 Total Brand cmmi 45.655 Between Groups 1.936 Within Groups 137.320 Total ppPB 139.256 Between Groups 1.977 Within Groups 87.272 Total NMCG 89.249 Between Groups 1.051 Within Groups 61.627 Total salTH INFERENCE:
? There is no association between designation and locality. ? There is association between designation and onsite opportunity. ? There is no association between designation and human relation, better

4 195 199 4 195 199 4 195 199 4 195 199 4

.218 .230 .484 .704 .494 .448 .263 .316 .943

.949

.437

.687

.602

1.105

.356

.831

.507

62.677

Between Groups 3.771

2.277

.062

role.
? There is no association between designation and salary, better projects and

work culture.
? There is no association between designation and project domain, first

offer.
? There is no association between designation and designation, job profile,

joining bonus.
? There is no association between designation and brand of company, cmmi

level status.
? There is no association between designation and permanent position,

product based company.

? There is no association between designation and non monetary benefits,

career growth.
? There is association between designation and salary components, take

home.

Experience Vs Factors Hypothesis: H0: There is no association between experience and the reduced components (factors) H1: There is association between experience and the reduced components (factors) ANOVA TABLE 6.36 Sum Squares Locality Between Groups 3.852 Within Groups Total Onsite opportunity Within Groups Total HRBR Within Groups Total SalBPWC Within Groups Total PDFO 238.628 242.480 264.687 281.517 99.774 109.947 71.776 74.262 of Df 3 196 199 3 196 199 3 196 199 3 196 199 3 1.016 2.144 .096 .829 .366 2.263 .082 3.391 .509 6.662 .000 5.610 1.350 4.154 .007 Mean Square F 1.284 1.217 1.055 Sig. .370

Between Groups 16.830

Between Groups 10.173

Between Groups 2.486

Between Groups 3.047

ANOVA TABLE 6.36 Within Groups Total DesigJPJB Within Groups Total Brand cmmi Within Groups Total PpPB Within Groups Total NMCG Within Groups Total SalTH INFERENCE:
? There is no association between experience and locality. ? There is association between experience and onsite opportunity. ? There is association between experience and human relation, better role. ? There is association between experience and salary, better projects and

92.831 95.878 44.676 45.655 137.038 139.256 87.747 89.249 58.660 62.677

196 199 3 196 199 3 196 199 3 196 199 3 196 199 3

.474 .326 .228 .739 .699 .501 .448 1.339 .299 1.362 3.319 .021 4.474 .005 1.118 .343 1.057 .369 1.431 .235

Between Groups .979

Between Groups 2.217

Between Groups 1.502

Between Groups 4.017

Between Groups 4.086

work culture.
? There is association between experience and project domain, first offer. ? There is association between experience and designation, job profile,

joining bonus.
? There is no association between experience and brand of company, cmmi

level status.
? There is no association between experience and permanent position,

product based company.

? There is association between experience and non monetary benefits, career

growth.
? There is association between experience and salary components, take

home.

One sample t-test for gender Hypothesis: H0: There is no significant difference between population and sample mean H1: There is significant difference between population and sample mean TABLE 6.37 One-Sample Statistics N Gender 200 One-Sample Test Test Value = 200 Sig. tailed) .000 (2- Mean Difference -198.34997 95% Confidence Interval of the Difference Lower -198.4505 Upper -198.2494 Mean 1.6500 Std. Deviation .72119 Std. Mean .05100 Error TABLE 6.38

T

df

Gender -3.890E3 199

INFRENCE: Since the significance value is .000. there is a no significant difference between the population and the sample mean.

This shows that the sample mean comes from the population.

Cluster analysis: In this study cluster analysis is used to group the newly formed components. The 10 components that are formed by factor analysis is grouped on the basis of certain distribution. This will help to find the intensity of the factors that decide the fulfillment of the job seekers TABLE 6.39 Cluster Distribution N Cluster 1 Combined Excluded Cases Total 200 200 2 202 % of Combined % of Total 100.0% 100.0% 99.0% 99.0% 1.0% 100.0%

Cluster profiles TABLE 6.40

Centroids HRBR SalBPW C PDFO desigJPJ Brand B cmmi ppPB NMCG salTH

Std. Std. Std. Std. Std. Std. Std. Std. Me Devi Me Devi Me Devi Me Devi Me Devi Me Devi Me Devi Me Devia an ation an ation an ation an ation an ation an ation an ation an tion Clu 1 ster . . . . . . . . 2.1 1.8 2.5 2.2 2.2 2.3 2.0 2.0 7433 6108 6941 4789 8365 6696 5612 6516 975 783 900 084 850 775 400 880 0 8 2 8 3 9 1 7

Com . . . . . . . . 2.1 1.8 2.5 2.2 2.2 2.3 2.0 2.0 bined 7433 6108 6941 4789 8365 6696 5612 6516 975 783 900 084 850 775 400 880 0 8 2 8 3 9 1 7

TABLE 6.41 Initial Cluster Centers Cluster 1 HRBR SalBPWC PDFO Brand cmmi ppPB NMCG salTH 1.00 1.00 3.50 2 4.00 2.67 1.50 2.00 4.50 2.00 2.00 1.50 3 1.00 3.00 2.50 1.67 1.00 3.00 2.00 2.00

desigJPJB 1.00 5.00 2.50 1.00 3.50

TABLE 6.42

Iteration Historya Iterati Change in Cluster Centers on 1 2 3 1 2 3 4 5 6 7 8 9 2.144 .466 .256 .099 .125 .078 .044 .030 .000 2.150 .128 .129 .033 .000 .000 .055 .037 .000 1.979 .131 .101 .036 .049 .032 .028 .000 .000

From the above table it could be seen that there is a little deviation or convergence in the cluster. Convergence achieved due to no or small change in cluster centers. The maximum absolute coordinate change for any center is .000. The current iteration is 9. The minimum distance between initial centers is 4.720. TABLE 6.43 Final Cluster Centers Cluster 1 HRBR SalBPWC PDFO Brand cmmi ppPB NMCG salTH 1.55 1.69 3.04 2 2.82 2.03 2.22 2.39 3.10 2.45 2.19 1.72 3 2.26 1.91 2.53 2.20 1.73 2.36 1.97 2.02

desigJPJB 2.07 2.94 2.36 2.08 2.54

TABLE 6.44

ANOVA Cluster Mean Square df HRBR SalBPWC PDFO desigJPJB Brand cmmi ppPB NMCG salTH 17.764 1.349 7.823 1.131 40.930 .124 .782 7.900 2 2 2 2 2 2 2 2 Error Mean Square df F Number of Cases in each .378 197 47.023 Cluster .363 197 Cluster 1 48.000 3.713 .407 2 .220 3 Valid .291 Missing .452 .310 .349 197 39.000 19.207 197 5.133 113.000 Sig. .000 .026 .000 .007

200.000 140.483 .000 197 2.000 197 .273 .001 197 197 2.521 22.650 .003 .000 This table 10 shows that

The above table shows the ANNOVA of the final cluster centers. The value of the the ANNOVA is seen from the significance in which the significance of all the clusters is less than .05. This means that the clusters obtained are correct and could be further taken for the approach. components are clustered into 3 clusters and their values are shown in the above table.

TABLE 6.45 Case Processing Summary Cases Valid N Cluster Number of Case 200 * Age Cluster Number of Case 200 * Designation Cluster Number of Case 200 * Experience Percent 99.0% 99.0% 99.0% Missing N 2 2 2 Percent 1.0% 1.0% 1.0% Total N 202 202 202 Percent 100.0% 100.0% 100.0%

Chi-Square tests: In this study Chi-square test is used at this stage to see that whether there is any relation between the clusters and the demographic variables. Hypothesis: H0: There is no relationship between age and the clusters. H1: There is relationship between age and the clusters

Age vs cluster Clusters Cluster Case Total Number of 1 2 3

22-24 7 21 49 77

25-30 24 12 42 78

above 30 16 6 20 42

Total 48 39 113 200

Chi-Square Tests Value Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases 21.830a 23.700 7.884 200 Df 10 10 1 Asymp. Sig. INFERENCE: (2-sided) Since the significance .016 attained is less than .05.There is .008 relationship between age and the .005 clustered factors. Designation vs clusters

Hypothesis: H0: There is no relationship between designation and the clusters. H1: There is relationship between designation and the clusters

Clusters Cluster Case Total Number of 2 3

Software engineer 15 19 43 77

Analyst 15 13 46 74

Leads 14 5 18 37

Total 48 39 113 200

Chi-Square Tests Value Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases 8.463a 8.392 2.562 200 Df 8 8 1 Asymp. Sig. (2-sided) .390 .396 .109

INFERENCE: Since the significance value is greater than .05 there is significant difference between designation and the clustered factors. Hence there is no relationship between the designation and clusters.

Experience Vs clusters

Hypothesis: H0: There is no relationship between experience and the clusters. H1: There is relationship between experience and the clusters

Clusters Cluster Case Total Number of 1 2 3

Experience Freshers 1-5 years 5-8 years Above8 years Total 7 11 30 48 13 19 50 82 15 6 25 46 13 3 8 24 48 39 113 200

Chi-Square Tests Value Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases 19.916a 18.511 11.420 200 Df 6 6 1 Asymp. Sig. (2-sided) .003 .005 .001

INFERENCE: Since the significant value is less than .05 there is no significant difference between experience and the clustered factors. Hence there is relationship between experience and clusters.

CHAPTER 7

FINDINGS AND CONCLUSION
7.1 FINDINGS:
? Respondents are ready to accept an offer which has a better job role and work culture.

Technology, project and career growth are some of the other reasons for accepting an offer. They are very much ready to accept an offer for a product based company with day shifts.
? Respondents expect an offer from a company which provides good salary, joining bonus,

and better salary components. They expect to join a company with high brand value and a very good HR relationship with employees. A permanent position in the organization is expected by the employees before accepting the offer.
? Onsite opportunities and project domain are the most important factors that decide the

offer acceptance. Better job profile, project phase and CMMI level of the company are some of the factors that affect the acceptance of the offer. Non monetary benefits and better take home salary are other factors that decide the offer acceptance. 7.2. CONCLUSION: From the analysis the above factors have a direct influence over the categorical variables and hence the techniques can be framed from these factors.

CHAPTER 8

TECHNIQUES TO INCREASE THE JOINING RATIO
SWEETENING THE OFFER:
This is an innovative technique adopted by many MNC’s to increase the joining ratio. This technique of sweetening the offer is done either by increasing the offer value or package, by understanding the competitive offers which the job seeker gets in due course of time (joining time). This competitive offers could be identified by doing regular follow ups and creating a good relationship with the candidate. In case if there is a deadlock between the mandatory offer value and the competitive offer then joining bonus of minimum of 10% of the billing value which is affordable to the company could be provided, so that a valuable candidate is not lost. Thus by sweetening the offer the candidate can be made to join, thus increasing the joining ratio.

CUMULATING THE DESIGNATION:
It is found that there is association between the age and designation. As there is an increase in age there is an inverse relationship between age and salary. Most job seekers demand for better roles and designation. For example a 4 years experienced software engineer looks for a lead role. This could be utilized effectively by the companies by providing better roles and designation so that the candidates joining ratio could be improved. In case if the job seeker is not convinced by the sweetened offer, this cumulating designation technique could be adopted to increase the joining ratio.

OFFER DISCUSSION BEFORE RELEASE:
This is an effective way to increase the joining ratio which most companies fail to adopt. As from the analysis it is found that there is an association between the age, gender, designation, experience and the HR contact and non-monetary benefits. Most companies just release the offer letter without any discussion. The benefits of the offer should be discussed clearly before releasing in an attractive manner that the job seeker should make him capable to get all the monetary and non-monetary benefits. This will give a hike in the career of the candidate and an increase in productivity of the company.

INTENSION BALANCE:

This technique is used after identifying the needs of the job seeker from the company which he is offered. The candidates demands and the company’s provisions, if they match together or if the candidate is very much delighted with the offer then the candidate will surely join in the company. The company’s intension should go on par with the candidate’s expectation. Hence if there is a balance in the intension of both job seeker and the company then the joining ratio of the job seeker can be increased to the greater amount. This technique is adopted in Googolsoft technologies. To make the intension balanced negotiation is carried out with the candidate and the company people by doing proper follow up till the candidate joins with the company.

CHAPTER 9

LIMITATIONS OF THE STUDY
9.1 LIMITATIONS:
1. The study is limited by several factors, especially owing to the fact that except for collecting of data through the questionnaire adequate direct contact with the respondents was not possible. 2. The main limitation of the study is the relatively lesser number of respondents as compared to the population. 3. The duration of the study has been limited with which the researcher cannot go in depth of the study

CHAPTER 10

SCOPE OF THE STUDY
Scope of the study:

? Based on the techniques framed the IT companies can increase the offer to joining

ratio of the job seekers. ? Further studies could be done beside this about the untold factors that decide the joining ratio of job seekers.
? This study will create a tremendous change in the growth of HR departments in

every corporate as based on these factors they can retain their employees thereby reducing the employee turnover.
? IT companies can increase their productivity by adopting these techniques. ? The same study can be taken as a base and the joining ratio of job seekers in core

companies could be studied.
?

This will lead to a better HR environment.

REFERENCE
Retention practices - D. Muthukumaran (2005) Effectiveness of recruitment- K. Girja Devi (2004) Effectiveness of recruitment and selection process- T. Gopalan Srinivasan (2004) Recruitment effectiveness among IT companies- S. Vijay Kumar (2003) Man power strategies for flexible organizations, Sage publications – J. Atkinson. Recruitment and selection, sage publications – T. Watson Human resources and personnel management, Tata McGraw Hill publications – K. Ashwatappa. Personnel management and industrial relations – Tripathy. Work place employee relations – S. Woodland. Sources of referral and employee turnover – M.J. Cannon Studies on Recruitment and Retention done in various companies: Wikipedia.

Websites used

www.Naukri.com www.google.com

QUESTIONNAIRE

Name Age Gender Location Designation Category Job offers holding

: : : : : : : Fresher ( ) 1-5 years ( ) 5-8years ( ) 8+years ( ) 18-25 Male 26-35 Female 36-45 above 45

(Please fill these questions-on what basis you will select an offer .Type “S” in the appropriate boxes to select your answer.)
S. No Questions Strongl y agree Agre e
Neutr al

Disagre e

Strongly disagree

1

Salary is the most important factor.

2

Brand of important.

the

company

is

very

3

Relationship with the point of contact (HR) is an important factor. I prefer a better role. I prefer better work culture.
I prefer the offer which is near my locality.

4 5 6

7

I give priority to CMMI level status of company.

8

I prefer an offer with better salary components. Prefer project with better technology.
I prefer the first offer.

9 10 11 12 13 14 15

I prefer onsite opportunity. I prefer an offer with joining bonus. I prefer day shifts. I prefer better job profile. I prefer the designation. offer with better

16 17 18

I prefer better Project Domain I prefer Product based company I prefer permanent position only

19

I select the offer based on Project phase

20

I prefer the offer with Better take home salary I prefer an offer were I get more Non monetary benefits I prefer the offer were I can get better career growth.

21

22



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