ACKNOWLEDGEMENT

In the Name of Allah the most merciful and the most gracious lord of the Day of Judgment. Without His will this would not have been possible. The accomplishment of this task depends on the contribution of many people without whom we could not do anything first of all we would like to thanks Allah almighty without whom I was unable to complete and manage this project “Comparative analysis on-job & off-job training effects on employee performance”.

Secondly I am thankful to all my teachers who have up till now provided me with the standards of excellence. I would especially like give me thanks to Sir Salman Zia who helped me hard and it is because of his guidance that gets motivated and completed my research work in very fine manner. Finally but more specifically we want to thanks our parents and friends without them, I would not be able to complete our project effectively.

Table of Contents

Abstract……………………………………………………………………………………………………………………..….………4

chapter 1 5 Introduction…………………………………………………………………………………………………………………………………5

chapter 2 7 Review Of Literature…………………………………………………………………………………………………………….7 chapter 3 22 Methodology…………………………………………………………………………………...…………………………………22 chapter 4………………………………………………………………………………………………………………………..25 Analysis………………………………………………………………………………………………………..…………………….25 chapter 5………………………………………………………………………………………………………………………..40 Summary &Conclusion…………….…………………………………………………………..…………………………..40 Policy Recommendations…….….…………………………………………………………..…………………………..…41 References…………………………………………………………………………………………………………………..………43 Appendix………………………………………………………………………………………………………………………..…….45

ABSTRACT

This study about Comparative Analysis of on-job & off-job training effects on employee performance. There are many factors which are influence on performance or employees. Like training, motivation level, organization environment, trainers, technology. But I have selected training, environment organizational and training centre and trainer. My dependent variable is employee performance. There are two main method of training on job training and off job training. I conducted a survey which method of training is more effective to other. I conduct these to factor as comparative and others like environment and trainers moderate variable.

First I try to give an introduction about my topic and variables. Collect data through different related sources and conduct some possibility statistical analysis like mode (the most frequent value) which represent that mostly people select which type of training. I have defined regression and correlation to find out relation between all hypotheses. At the end I have provided a summary of all statistical analysis which shows that on job training is better than off job training according to conducted survey and defined analysis. This study is beneficial for all type of organization which is want to improve the performance of their employees. They should provide on job training method to their employees. Because according to conducted research’s result on job training is most efficient as compare to off job training. Other moderate variables also influenced performance of an employee. But this is comparative analysis of on and off job training method.

Chapter# 1

INTRODUCTION

Introduction of Topic

The subject study is aimed to investigate an issue i.e. “Comparative Analysis of on-job & off-job training effects on employee performance” for this I have select to method of training off job training and two moderate variables environment and trainers which control on employee performance. On job training and off job training variable are conducted as comparative variable while other variable conducted as moderate variables. In general the organizations are using performance appraisals in order to appraise their employees and to assess their annual performance. Performance Appraisals have become a management craze over the past decade whereby every organization seems to think that by faithfully adhering to this practice, people within the organization will grow and develop in the company image. Nothing could be further from reality. Experience has proved to me that since those who are carrying out the assessment are usually poorly trained, poorly prepared and with an ' I am your boss so I must be more effective than you' attitude. The result is often than not highly subjective and of very little value to either the organization or to assess.

Every human resource manager knows that through training they can improve the skills or performance of employees or work force. But I want to clear which training method is most beneficial for work force. So that human resource manger would provide that training method which is most suitable for improve the performance of employees. For this purpose I conduct my research and comparative analysis that which training method is efficient and which factor influence more on performance of an employee.

Problem Statement:

Comparative Analysis of on-job & off-job training effects on employee performance

Objective Statement:

From side to side training can get better the skills or achievement of employees. My objective of conduct a comparative research is to find out efficient method of training to enhance or improve the skill of employee.

Introduction to Variables:

Dependent variable: Performance of Employee

Independent Variables: On job training, Off job training

Moderate Variables: Environment, Trainers

Chapter 2

REVIEW OF LITERATURE

Training on the job has become a major source of skill buildup for workers in the last two decades due to the rapid pace of technological change. Studies by Bishop (1994) and Bartle and Lichtenburg (1987), among others, establish that a well-trained workforce provides returns to employers in the form of higher productivity and better flexibility to technological change. Hence, there is a strong incentive for employers to sponsor training for their workforce. However, employers also need to think with the possibility that workers may quit before employers can fully realize the benefits of the training that was provided.
For nearly three decades since Becker (1964) classified training as general training (training that builds skills transferable to other firms) or specific training (training that builds firm-specific skills), researchers have assumed that employers would be more willing to sponsor specific training as opposite to general training that could be used elsewhere. However, many recent experimental studies have failed to provide any evidence for this idea. Instead, experimental research has consistently found that most employer-sponsored training does, in fact, provide employees with skills that are transferable to other employers (i.e., that most employer-sponsored training should be classified as general training).
Studies by Barron, Berger and Black (1999), Lowenstein and Splatter (1999), Booth and Bryan (2002) find, that most or all the training that is sponsored by employers result in workers acquiring general skills that can be used at other firms. Such recent findings have sparked a changed interest in the following question: do workers who acquire transferable skills from employer-sponsored training continue working in the same job or do they seek better returns for their newly acquired skills from other employers? While this question has been explored in many recent studies, most of this research (possibly due to the nature of the survey data available) has focused on younger workers or a cross-section of workers.
In doing this, workers who are in the middle of their careers – a subset of workers who enjoy a considerable benefit from training – have been overlooked; it is well-understood that the type of training undergone by young workers is considerably different from the re-training of mid-career workers. De Grip and Van Loo (2002) detail the various ways, in which a workers skill may degrade over the course of a career, necessitating corrective on-the-job training is often essential to maintaining worker productivity. In this context, it could be reasonably expected that the nature of training mid-career employees receive would take fundamentally different forms than training for the labor force at large. Also, workers tend to be highly mobile between jobs early in their careers; Topel and Ward (1992), for example, show that a typical worker holds seven different jobs during the first 10 years of his/her career, with the rate of job mobility then declining significantly. Hence, employers may be more willing to sponsor general training for such workers, believing that they would be more likely to experience the rewards of this training due to the decreased job mobility at this stage of the worker’s career.
Effective training programmers’ require the dedicated support of top management (Motwani, Frahm et al. 1994). Such organizations provide training mapped to employee and organizational needs (Mann, 1997), and provide this at the proper time. Yet, not all companies place the same emphasis on, or show the same commitment to employee training (Roberts and McDonald, 1995; Hughey and Mussnug, 1997). Some companies work hard to recruit the best people and yet spend relatively little effort to retain them once hired (Cappelli, 2000). There is evidence to show that benefits follow to organizations that are committed to employee training (Wills, 1994).
Organizations that place a high value on training give resources to the management of the training process. They devote time to ensuring that employees get the training programmers’ that is most appropriate for them given their existing IT skill sets (Eighteen, 1999). Such firms are most successful at maximizing the effectiveness of their training programs (Huang, 2001). Organizations that commit effort and finances to training programmers’ and employee development do so with the objective of a pay-off in terms of increased skill-sets, increased motivation, increased knowledge transfer (Pate, Martin et al., 2000), more positive psychological and organizational dynamics, as well as a measurable aggressive edge.
The use of training courses future outstrips what is known of their usefulness (Foxon, 1989; Schonewille, 2001). Mann (1996) maintains that despite heavy investment in training, organizations can frequently fail to evaluate adequately the value or success of their training programmes. Organizations that devote considerable resources to training also understand the value of evaluating the training process (Motswana, Frahm et al., 1994; Mann, 1996).
Such evaluation is a key phase in any proposed training and development process (Al-Khayyat and Elgamal, 1997). While such appraisal is desirable in principle it is difficult in practice (Morris, 1984). Even those companies who do carry out evaluations often use measures later considered ineffective (Schonewille, 2001). The most common metric of evaluation is trainee perceptions .Such assessments are random, informal, and unstructured evaluations of training programmers, which tend to be post training appraisals rather than approaching the evaluation of training programmers from their design stages (James and Rolfe, 2000).
Many forms of training exist ( Switzer and Kleiner, 1996; Huang, 2001). The range of training techniques has been expanded by the application of technology in its "hard" (for example through computing technology) and "soft" (for example through instructional design) (Sadler-Smith, Down et al., 2000).
In relation to IT training, many methodologies for the approach to and delivery of training can be used: forms of training include instructor console training in a classroom situation, stand-alone terminals with remote instruction, computer based training (CBT) without instructor, hypermedia training (a computer based method of non-sequential reading and writing, a technique with which chunks of information can be arranged and rearranged according to an individual's needs, previous knowledge, and curiosities (Higginbotham-Wheat, 1992; Murray, 1998)), self-paced training using a variety of delivery methods (Compeau, 1995), distance learning (whether by videoconferencing, email, or other method), learning networks, simulations, groupware communication, use of mentors or coaches, job rotation, management games, role playing and behavior modeling (Williams, 2001), or Internet based training.
While many new training approaches based on new technology exist, these modern training methods have been subjected to comparatively little empirical or critical study (Sadler-Smith, Down et al., 2000). The literature suggests that that some of the most effective training techniques are not new, but are merely the application of old-fashioned common sense to the assessment of training needs (Switzer and Kleiner, 1996; Sadler-Smith, Down et al., 2000; Smith, 2002). Sadler-Smith et al. (2000) believe that flexibility of delivery is a fundamental issue for smaller firms, to which open/distance/technology-based learning may present a workable solution; however, the modernity of some delivery methods may in itself lead to assumption of applicability and efficiency.
Bostrom at all (1988) argue that the delivery method can directly influence the effectiveness of, and the benefits accrued from training. Read and Kleiner (1996) present the most commonly used training methods across non-industry specific
U.S Companies. They found that the top ten training methods used in business, listed in order from highest to lowest use, were: videotapes, lectures, one-on-one instruction, role plays, games/simulation, case studies, slides, computer-based training, audio tapes, and films.
In a survey carried out by 450 respondents, Mathews et al. (2001) studied the incidence of training delivery methods across non-industry specific organizations in the U.K., Portugal, and Finland within the context of benefits accrued. They found that training methods most commonly used tended to be traditional, with little impact evident of more HITECH methods. Traditional methods included external short courses, internal lectures and seminars, issuing of training manuals and materials to be self-taught, using training videos, short demonstrations, and the delegation of training responsibilities to training consultants.
This study found that in-house participative seminars were the preferred training delivery method in the UK, whereas external short courses were the preferred method in Finland and Portugal. Impersonal methods such as training videos, and internet or Computer-based training, were viewed across the UK, Finland, and Portugal as poor methods. In contrast, highly personal methods of training such as participative courses and seminars were viewed as the most effective and highly regarded methods.
From a company perspective, training and development of company employees are essential for organizational operation, and organizational development. From an employee perspective, these same factors are both vital and critical for skill development and for career advancement. Retention of employees, and the retention of valued skill sets, is important for continued business achievements (Mak and Sockel, 1999).
The successful retention of employees leads to knowledge conservation within the organization (Cappelli, 2000). Employee turnover may lead to a loss of human resources weakening competitive positions. At a company level, mechanisms that allow for and promote knowledge transfer amongst employees can help minimize the effect of the loss of skilled staff to other companies (Cappelli, 2000). Training employees leads to increased employee satisfaction, facilitates the updating of skills, leads to an increased sense of belonging and benefit, increased employee commitment to the organization (Bushardt, Fretwell et al., 1994), and strengthens the organization’s competitiveness (Hughey and Mussnug, 1997; Burden and Proctor, 2000). Job-related training increases an employee's ability to perform job-related tasks.
Job satisfaction is an important motivator for employee performance and is negatively related to turnover (Mak and Sockel, 1999). Company commitment to the training needs of its employees positively influences employee satisfaction, leading to an increase in employee motivation and an increase in retention (Mak and Sockel, 1999; Ranft and Lord, 2000). Such commitment culminates in employee exposure to quality job-related training, leading to better employee morale, an increased sense of employee achievement and accomplishment (Elizur, 1996), and ultimately to an increase in organizational competitiveness.
Whilst company commitment to training for its employees positively affects retention and leads to desirable outputs, there are many different categories and types of training (Switzer and Kleiner, 1996; Huang, 2001; Mathews, Ueno et al., 2001). To have positive results, organizational commitment to training must tie closely to appropriate effective training methods and training delivery mechanisms. In terms of training methodologies, what may be appropriate for one company (or employee) may not be for another.

This paper describes a descriptive study, which assesses the impact of training on employee retention, and examines the relationship between organizational commitment to training and benefits accrued. Results of the study demonstrate that organizational attitudes and provision for training relate positively to employee expectations and requirements. Findings indicate that well-engineered training initiatives lead to increased organizational strength, job-related employee competencies, and job satisfaction.
The study finds that training helps in retaining knowledge within the organization, but may not help in retaining employees. The main method of training delivery is by instructor-led formal sessions, followed by self-training and workshops. Findings show that more modern methods such as web-based and computer based training are not pervasive. Almost one third of respondents believe that training received has not helped to reduce job-related stress. More than one quarter of respondents indicate that their organization does not structure training based on employee feedback on requirements. There are many cases where the training needs of employees have not been sufficiently addressed and cases where organizations have not evaluated the quality or effectiveness of training programmers, making return on investment hard to measure. To succeed, an organization must create an environment that not only attracts people to join and give their best every day, but one that also strives to retain existing staff. The retention of talented experienced, productive and knowledgeable employees can be a source of competitive advantage for companies (King, 1997; Cheng and Brown, 1998; Roepke, Agarwal et al., 2000).
The maintenance of employees provides staff stability, which aids organizational knowledge retention (Cappelli, 2000), offers the opportunity to raise quality standards through continuous improvement practices (Motwani, Frahm et al., 1994) and facilitates the achievement of more reliable customer care (Rowley and Purcell, 2001). It is important for employers to identify and to understand their employees' viewpoints on what the employees consider to be the most important aspects of their jobs, if employees are to be more content (Ventakesh, 1999; Mulder, 2001).
While staff retention in general is important, the retention of IT employees is vital for business success (Mak and Sockel, 1999; MacDonald, Gabriel et al., 2000). understanding IT leaders recognize that the greatest impediments to success are often related to people rather than to information, technology, and systems (Roepke, Agarwal et al., 2000). Considering the high costs associated with replacing IT staff and their experience, it makes sense for companies to invest in mechanisms designed to keep IT staff longer (Mak and Sockel, 1999; Moore, 2000).
One such staff retention mechanism is the use of employee training programmes for existing members of staff (Mulder, 2001). The use of such programmes in recent times by employers may have more to do with securing employee commitment in uncertain times than about transforming skill levels (Hallier and Butts, 1999). As such, for some organizations the key objective of training is to increase employee commitment to the organization and to create a culture that underlines the value of long-term employment.
Mak and Sockel (1999) found that most employees consider career development a priority motivational tool; and once motivated, they are more likely to be devoted to their job and the company's retention rate should improve. As such, management commitment to the development of the employee can significantly affect retention, even in situations where economic incentives such as incremental salary increases do not (Ranft and Lord, 2000).
Specific training initiatives have specific goals. These include the improvement of employee job performance, employee development (Burden and Proctor, 2000), the development of skills, knowledge, and attitudes (Al-Khayyat and Elgamal, 1997), and a means of achieving a competitive edge (Hughey and Mussnug, 1997; Hallier and Butts, 2000). Given the fast obsolescence of IT specific skills there is a repeated need to provide opportunities for employees to update their technical skill sets. The failure to provide such training increase the chance of failure and such companies may pay more in the long run (Auer, 1995). Organizations must respond to demands for change while at the same time realizing that advances in technology and knowledge are rendering many traditional employee skills obsolete, while simultaneously developing needs for new ones (Read and Kleiner, 1996).
It is this continuous risk of knowledge obsolescence that makes training and retraining necessary, not only for individual growth but also for organizational growth (Read and Kleiner, 1996). Within the IT sector, training can be considered to encompass organized, structured, formal events and sessions offered to IT employees as a company initiative. This paper does not consider on-the-job daily experiences to be classed as formal training, although such experiences can aid the development of skills related to job functions (Sadler-Smith, Down et al., 2000; Smallbone, Supri et al., 2000).
The beginning of the modern concern about skills and economic competitiveness in the United States came perhaps with the government report, A Nation at Risk (National Commission on Excellence in Education, 1983), which documented the poor academic performance of U.S. students compared to those of major competitor nations. Studies such as Baumol, Blackman, and Wolff (1989) focused attention on the long-run and comparative performance of the U.S. economy. Piore and Sabel (1984), Cohen and Zysman (1987), and others drew attention to the importance of production work to an economy and to the fact that work organization and employee skills influenced the competitiveness of manufacturing firms and their ability to adapt to changing markets.
Dertouzos, Lester, Solow, and the Industrial Productivity (1989) developed these views into an argument about declining U.S. competitiveness that became almost a standard for future studies. The work organization and management structures of U.S. firms rely too much on outdated scientific management approaches. They are hierarchical, based on narrow job titles and unskilled workers, and, as a result, are not as flexible in adjusting to changing markets as the competitor firms in other countries. The more flexible techniques of Japanese management in particular demand higher skills from the labor force.
Other studies soon pounced on the connection between skills, productivity, and economic performance. Both America’s Choice (1990) and the Office of Technology Assessment’s report (1990) argued that higher levels of skills in the workforce were necessary in order to develop the new, more productive systems of work organization and compete successfully with other nations.
With these reports as a backdrop, the Secretary of Labor’s Commission on Achieving Necessary Skills (SCANS) was established in 1990 to identify the skills that the workplace was demanding. In its various reports, the Commission has argued forcefully that new types of organizations and new arrangements for organizing work—employee empowerment, teams, and new work technologies—require new skills and a higher level of existing skills from workers. Furthermore, the skills that are required are at least in part general work skills that translate across employers and industries. Both employers and individual workers are seen as benefiting from those higher skills (SCANS, 1992).
Arguments like these have in large measure been responsible for a new thrust in public policy toward raising skill levels, especially through schooling.
The National Goals for Education, for example, is an effort to raise educational standards in the country at least in part to improve competitiveness. The list of skills identified by SCANS as reasonably generic to the U.S. economy has been used to drive the curriculum in high schools and in training programs such as the Job Corps and those funded by the Job Training Partnership. The School-to-Work Opportunities Act, passed by Congress to establish school-to-work change programs like youth apprenticeships, is also designed to raise work-related skills.
Given the speed with which these arguments have moved forward, it is indeed surprising to find so little experiential research that examines the relationship between skills, worker productivity, and economic performance.
It is not obvious, in the absence of empirical evidence; those higher levels of skills will necessarily lead to better economic performance. Unless jobs require or allow workers to make use of higher skills, for example, one should not expect performance to improve when skills increase. Further, jobs that require higher levels of skills now than in the past still may not tax the skills that employees already have. In assembly jobs, for example, the initial skill requirements are so low that they could rise substantially and still be within the set that virtually all workers possess.
Loewenstein and Spletzer (1999), Booth and Bryan (2002) find, that most or all the training that is sponsored by employers result in workers acquiring general skills that can be used at other firms. Such recent findings have sparked a renewed interest in the following question: do workers who acquire transferable skills from employer-sponsored training continue working in the same job or do they seek better returns for their newly acquired skills from other employers? While this question has been explored in many recent studies, most of this research (possibly due to the nature of the survey data available) has focused on younger workers or a cross-section of workers.

Finally, where skills are in shortage, the relevant skills may be job-specific ones that are typically seen as being the responsibility of the employer to provide. Perhaps the main reason for the lack of research on skills and performance is the difficulty in obtaining direct measures of an employee’s skill. What are typically available are aggregate measures of the amount of education and training workers receive. These are the inputs that should produce skill and that are related to indirect measures of performance.
The body of research on the economic returns on education is particularly wide and may have some relevance for these questions.
Human capital research clearly finds that employees with more education earn more, suggesting that the skills they have are valued in the market. Whether education is simply a alternate or screen for some other desirable characteristic, such as resolve, is a complicating factor in the argument. The fact that the return on education appears to be rising over the past decade—rising rapidly for college graduates and falling sharply for high school dropouts— suggests that such education is increasingly valuable in the labor market (cf. Levy & Murnane, 1992).
The fact that both initial and further education and training earn a higher return suggests that some of the skills associated with education are increasingly valuable (see Tuijnman, 1992, for references to research in Colombia, the Netherlands, Sweden, Norway, and the United States). But for which specific skills is the return being earned? Research on the relationship between vocational course work and subsequent job performance may shed some light on this question.
Vocational education programs typically provide training for specific occupations, and research on the labor market outcomes for students in these programs can help in understanding the effects of general or vocational skills on the economy. Altonji (1992) found that students who took more vocational courses earned higher wages, other things being equal. Other studies find that enrollment in vocational education programs improves participants’ labor market experience but only for those who find jobs in the field for which they received training (e.g., Campbell, Eliot, Laughlin, & Suesy, 1987). High school students who participate in vocationally oriented programs like workstudy and co-op substitute on-the-job training for academic classes, andstudies suggest that they do not necessarily do better in the labor market than those who did not participate in such programs (Bishop, Blakemore, & Low, 1985). Hollenbeck (cited in Stern, Stone, Finkelstein, Latting, & Martinez, 1993) found that students enrolled in occupationally based technical training following high school did better in the labor market than did those who pursued a baccalaureate program.
It is difficult to draw reliable conclusions from these studies about the skills needed to improve economic performance (Berryman, 1994; Stern & Tuijnman, in press). The fact that vocational skills pay off when graduates find jobs in their field of training but not otherwise may indicate, for example, that the programs help simply by giving access to a well-paying job market. In one of the few studies that attempts to sort out the source of higher wages, Grubb (1991) concludes that the return on a two-year college degree comes mainly from access to better paying occupations than are available to non-degree workers and not from obtaining higher paying jobs within the same occupation.
The latter measures the extent to which education produces higher performance for the economy as a whole. The complication noted above about interpreting evidence on returns from education is that education may function as a screen for some other desirable characteristic, such as persistence, that covaries with educational attainment and drives success. One way around this problem is to examine individuals’ skills directly, as opposed to their educational attainment. Bishop’s (1991) comparison of workers’ wages with their scores on the Armed Services Vocational Aptitude Battery is one example of this approach. (Al-Khayyat and Elgamal, 1997)

He finds that higher competencies were not associated with higher starting wages. Basic academic competencies such as mathematical ability actually received a negative premium from the labor market while vocational skills such as typing speed earned a considerable premium. , (Hughey and Mussnug, 1997; Hallier and Butts, 2000). These competencies were related, however, to performance on the job as measured by the reports of supervisors.

SCANS conducted its own, although indirect, test of the relationship between skills and performance by examining the current wages for a sample of jobs and the SCANS competencies associated with them (SCANS, 1992, p. 9). Not surprisingly, it was found that jobs requiring higher skills pay more. As noted above, however, it is not clear what to conclude from this. It does not indicate, for example, that workers with higher skills perform better in the same job or that the economy would be better off if skills levels rose.
A second complication about interpreting evidence from the economic returns on skills as measured by wages is that such skills raise wages in two ways. The first is by providing access to higher paying occupations, and the second is by helping improve performance within occupations. The policy interest associated with the arguments above is mainly with the second relationship. While jobs in medicine, for example, require higher skills and pay individuals more, the economy as a whole cannot grow by making more and more people into doctors. Even for individuals, the gains from expanding access to higher wage occupations face the well-known fallacy of composition.
If the supply of workers with the skills needed to fill a particular job rose, the wages associated with that job would fall, as would its desirability. Performance and wages can grow, however, if all workers become more productive at their current jobs. An alternative approach, therefore, is to examine the relationship between skills and job performance within one’s current job, using actual job productivity measures for the estimates. Most of these studies come from personnel psychology where they form the basis of attempts to validate selection procedures (see, e.g., American Psychological Association, American Educational Research Association, & National Council on Measurement in Education [Joint Committee], 1985).
Studies of skills that might generalize across settings concentrate mainly on academic material of the kind associated with classroom instruction. Academic skills as measured by aptitude tests can be reasonably good predictors of job performance (cf. Barrett & Depinet, 1991). The best known of these tests is the General Abilities Test Battery (GATB), which is used extensively by the employment service. The cognitive composite scale from GATB measures traditional academic skills such as verbal and numeric skill.
It is related to job performance at roughly the same level as vocational skills, which correlate at levels between .20 and .30 (see National Research Council, 1989). Academic performance as measured by grades in school, however, is a substantially worse predictor of job performance (cf. Hunter & Hunter, 1984; Schmitt, Goodling, Noe, & Kirsch, 1984).
Other studies use organizational performance measures to examine the relationship with skills. Bartel and Lichtenberg (1987) find, for example, that the rate of innovation is higher in industries that have more educated workers. Cohen and Levinthal (1990) also find that firms that have made a greater investment in learning experience greater innovations. Overall, the results surveyed above suggest that job performance—and ultimately economic performance—might be improved by raising academic skills in the workforce as a whole.2 With respect to the policy arguments above, however, it is not clear which skills are the important ones for performance or whether new work systems are creating higher demands for skills.
SCANS essentially performed a job analysis for the economy as a whole, producing a set of basic skills that are said to generalize across virtually all jobs in the workplace. While all job analyses are somewhat subjective, the SCANS skills are similar to those generated by other widely used job analyses such as the Position Analysis Questionnaire (McCormick & Jeanneret, 1988). SCANS identified two categories of these general skills: foundation skills associated with traditional academic education and interpersonal skills, and workplace competencies, which are more practical and vocational, applying skills to a workplace context.
The publication of the SCANS skills and the effort to have these skills institutionalized in curricula create a need to examine the general relationship between skills and performance as well as the specific arguments put forward in the policy debate. Specifically, are these skills related to actual performance, and what is the relative importance of the different skills; do the more academically based skills or the vocational skills have a bigger impact on performance? The results have powerful implications for the reform of education in the United States, particularly concerning curricula.
While it is certainly possible to argue about the merits of the SCANS skills—whether important skills have been left out or those that are included have been accurately classified—the fact that these skills have become a part of the public policy debate makes it important to examine them. A related question is, what factors (if any) are creating a need for higher skills? Are the new systems of work organization associated with high performance work creating a need for higher skills, and, if so, what are those skills? To our knowledge, there have been very few attempts to examine the relationship between general skills and actual performance and no efforts other than case studies to examine the relationships between new work systems and skills demands.
Loewenstein and Spletzer (1999), Booth and Bryan (2002) find, that most or all the training that is sponsored by employers result in workers acquiring general skills that can be used at other firms. Such recent findings have sparked a renewed interest in the following question: do workers who acquire transferable skills from employer-sponsored training continue working in the same job or do they seek better returns for their newly acquired skills from other employers? While this question has been explored in many recent studies, most of this research (possibly due to the nature of the survey data available) has focused on younger workers or a cross-section of workers.
Even those companies who do carry out evaluations often use measures later considered ineffective (Schonewille, 2001). The most common metric of evaluation is trainee perceptions (Bostrom, Olfman et al., 1988; Foxon, 1989; Mann, 1996; Huang, 2001). Such assessments are ad-hoc, unsystematic, informal, and unstructured evaluations of training programmers, which tend to be post training appraisals rather than approaching the evaluation of training programmers from their design stages (James and Roffe, 2000).

Chapter # 3

METHODOLOGY

Tools of Data Collection:

In order to collect the data from the desired personnel, I decided to collect the data through questionnaires, as it is the most flexible, cheap and efficient source of data collection. Questionnaires were used because they are easy to analyze and almost all the statistical software for data analysis can support them and easily can experience certain procedures

The first benefit to use questionnaire technique is that • It is good for measuring attitude and eliciting other content from research participants. • It is in expensive. • It can manage to probability samples. • It can be quick turnaround. • It can be administered to groups. • The Perceived anonymity by respondents possibly high. • There is simplicity of data analysis for close-ended items.

SAMPLE SIZE OF QUESTIONNAIRE:
The sample size of my questioner is 50. I will develop 50 questioner copies.
Reasons to use questionnaires as the source of data collection:

• Questionnaires are the most flexible source of data collection it could be posted and it could be distributed face to face to the respondents. Its flexibility is high because if the respondent has any queries accept the questionnaire they could ask the respondent directly to the face of the respondent. Regarding that they could ask the research or study.

• In my questionnaire I have used close ended questionnaire. The reason behind this approach is that the respondent is bound to answer within the given options. It is easy to analyze such data instead of open ended questions. Open ended questions have many complications in them because it is much difficult to enter in the statistical software to apply the methods to generate results

• The major reason to use the questionnaire is that the data that I will get from the respondent will be consistent, so it is easy to understand the data and to analyze the data. It is then easy to enter into any statistical software to generate specific outputs and results and it will be easier to interpret those results

• The main reason to select questionnaire as the source of the data collection is that the data is gathered very quickly in this method and the person requiring the results could interpret the respondent about the study and could help in answering the questionnaire and if the respondent does not understand any of the questions the researcher can elaborate the meaning of the question and could tell about the association of that questions with the dependent variable or vice versa

PRIMARY DATA:
To conduct a research to know which training method, environment, and trainers play role to improve the performance of an employee in an organization. We developed a questionnaire to collect data about training programs, environment, trainers, and performance of employee. In this questionnaire we insert all possible questions about all variables.
UNPREDICTABLES:
There are many factors which are involved in organization development. But we have chosen common but important autonomous following variables: Independent Variable is: • On job Training • Off job Training Moderate Variable is: • Trainers

• Environment

Chapter #4

ANALYSIS

Reliability

Employee Perfomance

|Case Processing Summary |
| |N |% |
|Cases |Valid |50 |100.0 |
| |Excludeda |0 |.0 |
| |Total |50 |100.0 |
|a. Listwise deletion based on all variables in the procedure. |

|Reliability Statistics |
|Cronbach's Alpha |N of Items |
|.762 |5 |

ON JOB

|Case Processing Summary |
| |N |% |
|Cases |Valid |50 |100.0 |
| |Excludeda |0 |.0 |
| |Total |50 |100.0 |
|a. Listwise deletion based on all variables in the procedure. |

| |
| |
| |
| |
|Reliability Statistics |
|Cronbach's Alpha |N of Items |
|.776 |3 |

OFF JOB

|Case Processing Summary |
| |N |% |
|Cases |Valid |50 |100.0 |
| |Excludeda |0 |.0 |
| |Total |50 |100.0 |
|a. Listwise deletion based on all variables in the procedure. |

|Reliability Statistics |
|Cronbach's Alpha |N of Items |
|.692 |3 |

Enviornmental

|Case Processing Summary |
| |N |% |
|Cases |Valid |50 |100.0 |
| |Excludeda |0 |.0 |
| |Total |50 |100.0 |
|a. Listwise deletion based on all variables in the procedure. |

|Reliability Statistics |
|Cronbach's Alpha |N of Items |
|.764 |2 |

Trainer
|Case Processing Summary |
| |N |% |
|Cases |Valid |50 |100.0 |
| |Excludeda |0 |.0 |
| |Total |50 |100.0 |
|a. Listwise deletion based on all variables in the procedure. |

|Reliability Statistics |
|Cronbach's Alpha |N of Items |
|.830 |2 |

Interpretation

Above table shows that reliability test of all variables. It shows that the questioners were asked from respondents about the research of all variables were valid or not and the above results shows that all the questions asked from respondents were valid. In which reliability of Employee Performance is 0.762, ON Job 0.776, Off Job 0.692, Environmental 0.764, and Trainer 0.830.

Descriptive

|Descriptive Statistics |
| |
|Model |Variables Entered |Variables Removed |Method |
|1 |Trainer, Off job, |. |Enter |
| |Environmental, on job | | |
|a. Dependent Variable: Employee performance |
|b. All requested variables entered. |

| Model Summary |
|Model |R |R Square |Adjusted R Square |Std. Error of the |
| | | | |Estimate |
|1 |.542a |.294 |.231 |.17125 |
|a. Predictors: (Constant), Trainer, Off job, Environmental, on job |

|ANOVA |
|Model |
| b. Predictors: (Constant), Trainer, Off job, Environmental, on job |

| |
| |
|Coefficientsa |
|Model |Un standardized Coefficients |Standardized |t |Sig. |
| | |Coefficients | | |
| |

Regression Equation:

Employee Performance i = C + B1 (On job) + B2 (Off job) + B3 (Env) + B4 (Tra)

=1.325 + B1 (0.258) + B2 (0.063) + B3 (0.046) + B4 (0.002)

Interpretation

The above all tables related to the regression test. The most important table is coefficients table. This table shows the significance of all variables. According to this significance level we reject or accept the variable. My research study’s dependent variable’s significance is “0.00” its mean all variable influence on employee performance. When we try to compare than on job training method has significance level o.126 and off job training method has 0.712. Its means comparatively on job training method has strong relation with employee performance as compare to off job training method. The other factors which I selected as median variables like environment and trainer they have also strong affect on employee performance. However following is the detail of rejected and accepted hypothesis of my research study.

Hypothesis Rejection and Acceptance:

H0 = Null Hypothesis = reject when significance level less than 0.05 otherwise accepted.

H1 = Alternative hypothesis = accepted when significance level less than 0.05 otherwise rejected.

On-Job Training:

H0= null hypothesis is rejected because its significance level is less than 0.05 (o.oo