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\title{A Comparative Study of Motivating Potential Score of Employees of Public and Private Commercial Banks: An Assessment of Demographics Influence}
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             \author[1]{Md. Hasebur  Rahman}

             \author[2]{Sheikh M.  Nurullah}

             \affil[1]{  Pabna University of Science and Technology, Rajshahi University}

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\date{\small \em Received: 16 December 2013 Accepted: 2 January 2014 Published: 15 January 2014}

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\begin{abstract}
        


The article presents a theoretical-empirical reflection about concepts, models, and practices of Job Diagnostic Survey Model for computing Motivating Potential Score (MPS) of employees of public and private commercial banks in Bangladesh with demographics influence. Empirical part of this study comprises 130 samples, have been collected through a structured questionnaire. The study reveals that demographic factors such as education, marital status, gender, experience, age, salary and designation have insignificant influence on Motivating Potential Score. The study also reveals that Motivating Potential Score of private commercial bank is slightly higher than public commercial bank but mean difference is statistically insignificant on one way ANOVA statistics at the five percent level of significance. The study concludes that MPS properties closer to a particular job irrespective to the demographics and nature of the organization.

\end{abstract}


\keywords{MPS; skill variety; task identity; task significance; autonomy feedback from job; commercial bank.}

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\section[{Introduction}]{Introduction}\par
anking job is now one of the challenging and dynamic professions in Bangladesh \hyperref[b10]{(Rahman, 2013a)}. Banking employees organize financial system and works as a core of an economy by mobilizing savings of households, public and private sectors of the economy. Thus banks can play a vital role in fostering the economic and social condition of a country  {\ref (Islam \& Husain, 2001)}. According to Scheduled Banks Statistics (Bangladesh Bank, 2013) fifty two banks currently operate in Bangladesh including four state owned commercial bank and thirty five private commercial banks including eight Islamic commercial banks. Banking sector employs a significant number of employees in Bangladesh. Both public and private commercial banks are operating in the country with a highly competitive pressure with customized service through competent and motivated employees. Commercial banks as the most important functionary of the financial system play a dynamic role in the economic development of a nation through mobilization of savings and allocation of credit to productive sectors. Motivation agenda become a driving force for employees of commercial banks to serve internal and external customer with customer demand and satisfaction \hyperref[b11]{(Rahman, 2013b)}.\par
The success of any organization falls back upon its competent and motivated human resources \hyperref[b8]{(Mohiuddin, 2008)}. Human resources regarded as the most valuable assets and sometimes irreplaceable assets in the organization. It is simply impossible on the part of an organization to get these activities performed efficiently and effectively unless the people of the organization extend their sincere and voluntary cooperation. Motivated employees are sincere, dutiful, and laborious; therefore, need less supervision of expert best performance out of them \hyperref[b11]{(Rahman, 2013b)}. Motivated employees are productive they exert their efficiency and effectiveness on organizational success.\par
The content theories of work motivation attempt to determine what it is that motivates people at work. Process theories concerned with the cognitive antecedents that go into the motivation or efforts \hyperref[b7]{(Luthans, 2011)}. In the year 1975, Hackman and Oldham, developed a theory called job characteristics theory of motivation. Job Characteristics Model, a motivational based model describing those job characteristics thought to motivate work behaviors and performance \hyperref[b3]{(Hackman and Oldham, 1975)}. The Job Diagnostic Survey tool measures Motivational Potential Score (MPS), which provides the basis for quantifying the theoretical nexus among the three critical psychological components and the five dimensions of the central work.\par
However, this study is an attempt to compare Motivational Potential Score (MPS) in terms of demographics by Job Diagnostic Survey of Public and Private Commercial Bank in Bangladesh. Changing demographic character is an important reason for emergence of diversity issues in organizational interfaces \hyperref[b12]{(Rahman, 2013c}). An individual may differ in their motivation in terms of demographics (age, sex, experience etc.), MPS be also impacted in terms of demographic. Due to this differentiation of challenges and environmental pressures of employees of public and private commercial banks, their environments for work tasks as well as motivational factors relating to the performance of these tasks may be impacted, MPS may differ. 
\section[{II.}]{II.} 
\section[{Research Objective}]{Research Objective}\par
The objectives of the study are -1. To overview Job Characteristics Model in work motivation. 2. To investigate respondent's demographic. 3. To investigate the respondent's demographics influence on MPS. 4. To investigate MPS differences in public and private commercial banks in Bangladesh. 
\section[{III.}]{III.}\par
Theoretical Background and Conceptual Model a) Job Characteristics Model Job Characteristics Model (JCM) is one of the influential theories ever accepted and adopted in the field of Organizational Behavior (OB). \hyperref[b4]{Hackman and Oldham (1980)} developed a job characteristics approach to job enrichment. The model is based on the assumption that jobs can be designed not only to help workers get enjoyment from their jobs but also to help workers feel that they are doing meaningful and valuable work. Specifically, the model identifies five core job characteristics that help create three critical psychological states, leading, in turn, to several personal and work outcomes \hyperref[b6]{(Lunenburg, 2011)}. This model recognizes that certain job characteristics contribute to certain psychological states and that the strength of employees' need for growth has an important moderating effect \hyperref[b7]{(Luthans, 2011)}. ? Skill Variety: Skill variety refers to the extent to which job requires the employee to draw from a number of different skills and abilities as well as on a range of knowledge \hyperref[b7]{(Luthans, 2011)}. Jobs that require high skills variety gives employees a greater sense of competence and skills.\par
? Task Identity: Task Identity is defined as the extent to which an individual can complete a whole and identifiable piece of work. Employees who work on a tiny part of whole work are unable to identify their contribution to the work. However, when tasks are broadened to produce a whole product or an identifiable part of it, then task identity has been established  {\ref (Lunenburg, 2013)}. \hyperref[b3]{Hackman and Oldham (1975)} indicate that this characteristic creates a sense of responsibility for completion that acts as a motivational driver.\par
? Task Significance: Task significance is the degree to which the job has a substantial impact on the lives of other people, whether those people are in the immediate organization or in the external environment  {\ref (Lunenburg, 2013)}. High task significance intensifies employee's mindset that they are doing worthwhile in their organization or society, or both.\par
? Autonomy: Autonomy is the degree to which the job provides substantial freedom, independence, and discretion to the individual in scheduling the work and in determining the procedures to be used in doing the work  {\ref (Lunenburg, 2013)}. Autonomy is essential for building a sense of responsibility and a certain degree of freedom of employees.\par
? Job Feedback from Job: Feedback from the job itself is defined as the degree to which carrying out the activities inherent in the job provide clear information to the employee about the effectiveness of their performance  {\ref (Hackman \& Oldham, 1974)}. The only way employees can make adjustments in their performance is to know how they are performing now, not later. Feedback can come directly from the job itself, or it can be provided verbally by management or other employees  {\ref (Lunenburg, 2013)}.\par
Figure \hyperref[fig_0]{1} indicates core job characteristics have important effects on various critical psychological states. According to  {\ref Hackman and Oldham (1974)} skill variety, task identity, and task significance together contribute to a person's experienced meaningfulness of the work. Jobs that provide a great deal of autonomy are said to contribute to a person's experienced responsibility for the outcomes of the work  {\ref (Lunenburg, 2013)}. When employees have the freedom to decide what is to be done than employees feel more responsible for the results of work. Finally, feedback from job gives employees' information regarding the actual results of work activities. When a job is designed to provide employees with information about the effects of their actions in the workplace, they are better able to develop an understanding of how well they have performed-and such knowledge improves their effectiveness. The job characteristics model indicates that the three critical psychological states affect various personal and work outcomes-namely, people's internal work motivation, growth satisfaction, general job satisfaction, and work effectiveness.\par
The higher the experienced meaningfulness of work; experienced responsibility for the outcomes of the work; and knowledge of the actual results of work activities, the more positive the personal and work outcomes will be. When employees perform jobs that incorporate high levels of the five core job characteristics, they should feel highly motivated, be highly satisfied with their jobs, and perform work effectively  {\ref (Lunenburg, 2013)}. IV. 
\section[{Methodology}]{Methodology}\par
This methodology section defines the research design, hypothesis development, population samples, data collection procedures and the techniques of data analysis for examining the demographics influence on MPS of employees of public and private commercial bank in Bangladesh. The said factors are education, marital status, gender, experience, age, salary designation and public/ private commercial banks. 
\section[{a) Research Design}]{a) Research Design}\par
This study is descriptive and hypotheses testing in natural. This study aimed to examine the factors that affect of demographics on the properties of MPS. The hypothesis tested was explaining the relationship between mean differences of MPS on the basis of core job characteristics (Skill variety, task identity, task significance, autonomy and feedback from job) in respect to demographics. Finally, data was collected using the survey method where questionnaires were used to collect information. 
\section[{b) Hypotheses of the Study}]{b) Hypotheses of the Study}\par
? H 1 : Mean values of MPS differ due to designation factor.\par
? H 2 : Mean values of MPS differ due to salary factor. ? H 3 : Mean values of MPS differ due to age factor. ? H 4 : Mean values of MPS differ due to work experience factor.\par
? H 5 : Mean values of MPS differ due to gender factor.\par
? H 6 : Mean values of MPS differ due to the marital status factor.\par
? H 7 : Mean values of MPS differ due to education factor.\par
? H 8 : Mean values of MPS differ due to bank factor. 
\section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par
Volume XIV Issue IV Version I ( )\par
A  {\ref Oldham (1974, 1975}) prescribe a method for computing an overall summary score based on the individual job characteristics measured by the Job Diagnostic Survey, termed the Motivating Potential Score (MPS), reflecting the motivational potential of a job. The five major model variables can be viewed as either task-related (skill variety, task identity, and task significance) or job management related (autonomy and feedback). The model posits a multiplicative relationship between the major components. In computing an overall motivation scores the task-related three core variables are averaged and then multiplied by autonomy and then by feedback scores (See Figure \hyperref[fig_0]{1}). This type of relationship means that when any of the components are low, there is a significant impact on the MPS score. Hence, maximal outcomes can only be achieved when all characteristics are maximized (Weaver, 2006). For questionnaire survey convenient method of sampling was used. There is no available source for the address of employees. Therefore, friends, relatives, and other informal reference group were used to locate the potential respondents in Bangladesh. Questionnaires were sent by email, postal mail and directed to 200 respondents. The number of initial replies received was 110. After a screening first round replies a second round personal contract conducted by a researcher and finally 130 respondents were taken for this study. 
\section[{d) Data Collection Technique}]{d) Data Collection Technique}\par
This study mainly based on primary data originating from a survey. For this purpose a constructed questionnaire was developed. Excepting the questions regarding demographic characteristics of skill variety, task identity, task significance, autonomy and feedback from the job were constructed, measured and investigated through 7 point Scale. The scale consists of 5 statements, for each statement has seven options/ points such as strongly agree/ 7, agree/ 6, slightly agree/ 5, undecided/ 4, slightly disagree/ 3, disagree/ 2, and strongly disagree/ 1. e) Data Analysis Technique SPSS Statistics software package was used for statistical analysis. Reliability of data was measured by using the Chronbach's Alpha  {\ref (Cornbach, 1951)}. Chronbach Alpha was .546. Alpha is higher than that is suggested by \hyperref[b9]{Nunnally (1978)} and therefore data collected can be considered reliable. Descriptive statistical techniques such as mean and standard deviation were used to measure the mean scores and their variability. One way ANOVA is used to test mean differences of core job characteristics by factors* lead (*education, *marital status, *gender, *experience, *age, *salary designation and *public/ private commercial banks).\par
V. 
\section[{Analysis and Findings a) Demographics Influence on Motivating Potential}]{Analysis and Findings a) Demographics Influence on Motivating Potential}\par
Score (MPS)\par
Hackman and Oldham's JCM  {\ref (1976,} {\ref 1980}) identified a set of job characteristics that are proposed to motivate employees intrinsically. The model further states that the five core job characteristics can be combined into a single index of motivating potential score (MPS) that reflects the overall potential of a job to influence an individual's feelings and behaviors \hyperref[b2]{(Fried \& Ferris, 1987)}. Many empirical studies have been done to test the job characteristics model in a variety of work settings, including banks, insurance companies, dentist offices, corrections departments, telephone companies, manufacturing firms, government agencies, and other service organizations \hyperref[b6]{(Lunenburg, 2011)}. However this empirical study investigates motivational differences in a single work setting like employees of commercial bank in terms of employee's designation, salary or pay, age, work experience, gender, marital status, level of educational attainment of respondents and employee's public/private commercial bank in Bangladesh. According to the of human resource management; employees differ in respect of demographic variables.\par
The assumption of this study is for every employee is unique; the properties of MPS may also differ within the single work setting due to demographics.\par
i. Respondent's Demographic   Table \hyperref[tab_7]{8} shows Motivating Potential Score (MPS) of employees whose experience below 10 years is 132.693, within 10-20 years is 127.292 and above 20 years is 148.950. It reveals that MPS is slightly higher of experienced respondent's. Table \hyperref[tab_0]{10} shows Motivating Potential Score (MPS) of male respondent's is 133.535 and female respondent's is 144.773. It reveals that MPS of female respondent's is slightly higher than male respondents'.    
\section[{Result and Discussion}]{Result and Discussion}\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-3.png}
\caption{\label{fig_2}Figure 2 :}\end{figure}
  \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.225\textwidth}P{0.175\textwidth}P{0.45\textwidth}}
Frequency\tabcellsep Percent\tabcellsep Cumulative Percent\end{longtable} \par
  {\small\itshape [Note: A]} 
\caption{\label{tab_0}Table 1 ;}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.36833333333333335\textwidth}P{0.056666666666666664\textwidth}P{0.2833333333333333\textwidth}P{0.028333333333333332\textwidth}P{0.11333333333333333\textwidth}}
Sum of\tabcellsep df\tabcellsep Mean\tabcellsep F\tabcellsep Sig.\\
Squares\tabcellsep \tabcellsep Square\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.2987220447284345\textwidth}P{0.08690095846645367\textwidth}P{0.062460063897763575\textwidth}P{0.06381789137380192\textwidth}P{0.172444089456869\textwidth}P{0.06924920127795527\textwidth}P{0.054313099041533544\textwidth}P{0.042092651757188494\textwidth}}
\multicolumn{2}{l}{Salary}\tabcellsep Skill\tabcellsep Task\tabcellsep Task\tabcellsep \multicolumn{2}{l}{Autonomy Feedback}\tabcellsep \\
\tabcellsep \tabcellsep Variety\tabcellsep Identity\tabcellsep Significance\tabcellsep \tabcellsep from Job\tabcellsep MPS\\
Below Tk 30,000\tabcellsep Mean\tabcellsep 5.11\tabcellsep 4.97\tabcellsep 5.80\tabcellsep 5.09\tabcellsep 4.97\tabcellsep 133.907\\
\tabcellsep Std. Deviation\tabcellsep 1.129\tabcellsep .975\tabcellsep .858\tabcellsep .886\tabcellsep .992\tabcellsep \\
\multicolumn{2}{l}{Tk 30,000-50,000 Mean}\tabcellsep 5.14\tabcellsep 4.93\tabcellsep 5.75\tabcellsep 4.95\tabcellsep 5.18\tabcellsep 135.313\\
\tabcellsep Std. Deviation\tabcellsep .979\tabcellsep 1.021\tabcellsep .719\tabcellsep 1.077\tabcellsep .815\tabcellsep \\
\multicolumn{2}{l}{Above Tk 50,000 Mean}\tabcellsep 5.09\tabcellsep 4.59\tabcellsep 5.45\tabcellsep 4.91\tabcellsep 5.41\tabcellsep 133.966\\
\tabcellsep Std. Deviation\tabcellsep .921\tabcellsep 1.141\tabcellsep 1.101\tabcellsep 1.019\tabcellsep .734\tabcellsep \\
Total\tabcellsep Mean\tabcellsep 5.12\tabcellsep 4.89\tabcellsep 5.72\tabcellsep 5.02\tabcellsep 5.12\tabcellsep 134.766\\
\tabcellsep Std. Deviation\tabcellsep 1.039\tabcellsep 1.021\tabcellsep .863\tabcellsep .972\tabcellsep .903\tabcellsep \\
\multicolumn{4}{l}{Table 4 shows Motivating Potential Score (MPS)}\tabcellsep \multicolumn{4}{l}{50,000 is 133.966. It reveals that MPS is alike}\\
\multicolumn{4}{l}{of employees whose salary below Tk 30,000 is 133.907,}\tabcellsep \multicolumn{2}{l}{irrespective to rage of salary.}\tabcellsep \tabcellsep \\
\multicolumn{4}{l}{salary within Tk 30,000-50,000 is 135.313 and above Tk}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.36833333333333335\textwidth}P{0.056666666666666664\textwidth}P{0.2833333333333333\textwidth}P{0.028333333333333332\textwidth}P{0.11333333333333333\textwidth}}
Sum of\tabcellsep df\tabcellsep Mean\tabcellsep F\tabcellsep Sig.\\
Squares\tabcellsep \tabcellsep Square\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.1414018691588785\textwidth}P{0.12710280373831775\textwidth}P{0.05242990654205607\textwidth}P{0.0794392523364486\textwidth}P{0.08261682242990653\textwidth}P{0.0794392523364486\textwidth}P{0.08261682242990653\textwidth}P{0.06990654205607477\textwidth}P{0.08261682242990653\textwidth}P{0.05242990654205607\textwidth}}
\tabcellsep \multicolumn{4}{l}{Source: Field Survey (July-December, 2013)}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Volume XIV Issue IV Version I\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Global Journal of Management and Business Research A ( ) ( )\tabcellsep Below years 30-45 years\tabcellsep \multicolumn{2}{l}{Age 30 Mean Std. Deviation Mean}\tabcellsep Skill Variety 5.03 1.028 5.07\tabcellsep Task Identity 4.92 .967 4.86\tabcellsep Task Significance 5.89 .785 5.61\tabcellsep Autonomy 4.94 1.040 4.90\tabcellsep Feedback from Job 5.14 .931 5.16\tabcellsep MPS 134.067 130.971\\
\tabcellsep \tabcellsep \tabcellsep Std. Deviation\tabcellsep 1.054\tabcellsep .997\tabcellsep .952\tabcellsep .950\tabcellsep .879\\
\tabcellsep Above\tabcellsep 45\tabcellsep Mean\tabcellsep 5.38\tabcellsep 4.96\tabcellsep 5.79\tabcellsep 5.46\tabcellsep 4.96\tabcellsep 145.608\\
\tabcellsep years\tabcellsep \tabcellsep Std. Deviation\tabcellsep 1.013\tabcellsep 1.197\tabcellsep .658\tabcellsep .833\tabcellsep .955\\
\tabcellsep Total\tabcellsep \tabcellsep Mean\tabcellsep 5.12\tabcellsep 4.89\tabcellsep 5.72\tabcellsep 5.02\tabcellsep 5.12\tabcellsep 134.766\\
\tabcellsep \tabcellsep \tabcellsep Std. Deviation\tabcellsep 1.039\tabcellsep 1.021\tabcellsep .863\tabcellsep .972\tabcellsep .903\end{longtable} \par
  {\small\itshape [Note: Source: Field Survey (July-December, 2013)]} 
\caption{\label{tab_4}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.85\textwidth}}
Year 2014\end{longtable} \par
 
\caption{\label{tab_5}Table 6 shows}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7} \par 
\begin{longtable}{P{0.371875\textwidth}P{0.053125\textwidth}P{0.2921875\textwidth}P{0.0265625\textwidth}P{0.10625\textwidth}}
Sum of Squares\tabcellsep df\tabcellsep Mean Square\tabcellsep F\tabcellsep Sig.\end{longtable} \par
 
\caption{\label{tab_6}Table 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8} \par 
\begin{longtable}{P{0.11829896907216494\textwidth}P{0.1577319587628866\textwidth}P{0.10296391752577319\textwidth}P{0.10296391752577319\textwidth}P{0.10515463917525773\textwidth}P{0.08762886597938144\textwidth}P{0.10734536082474226\textwidth}P{0.06791237113402061\textwidth}}
\multicolumn{2}{l}{Experience}\tabcellsep Skill\tabcellsep Task\tabcellsep Task\tabcellsep Autonomy\tabcellsep Feedback\tabcellsep MPS\\
\tabcellsep \tabcellsep Variety\tabcellsep Identity\tabcellsep Significance\tabcellsep \tabcellsep from Job\tabcellsep \\
Below 10 years\tabcellsep Mean\tabcellsep 5.08\tabcellsep 4.84\tabcellsep 5.73\tabcellsep 4.92\tabcellsep 5.17\tabcellsep 132.693\\
\tabcellsep Std. Deviation\tabcellsep 1.002\tabcellsep .966\tabcellsep .912\tabcellsep .991\tabcellsep .914\tabcellsep \\
10-20 years\tabcellsep Mean\tabcellsep 4.71\tabcellsep 4.93\tabcellsep 5.43\tabcellsep 5.14\tabcellsep 4.93\tabcellsep 127.292\\
\tabcellsep Std. Deviation\tabcellsep 1.267\tabcellsep 1.269\tabcellsep .852\tabcellsep .864\tabcellsep 1.072\tabcellsep \\
Above 20 years\tabcellsep Mean\tabcellsep 5.55\tabcellsep 5.10\tabcellsep 5.90\tabcellsep 5.40\tabcellsep 5.00\tabcellsep 148.950\\
\tabcellsep Std. Deviation\tabcellsep .945\tabcellsep 1.119\tabcellsep .553\tabcellsep .883\tabcellsep .725\tabcellsep \\
Total\tabcellsep Mean\tabcellsep 5.12\tabcellsep 4.89\tabcellsep 5.72\tabcellsep 5.02\tabcellsep 5.12\tabcellsep 134.766\\
\tabcellsep Std. Deviation\tabcellsep 1.039\tabcellsep 1.021\tabcellsep .863\tabcellsep .972\tabcellsep .903\tabcellsep \end{longtable} \par
 
\caption{\label{tab_7}Table 8 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{9} \par 
\begin{longtable}{P{0.36833333333333335\textwidth}P{0.056666666666666664\textwidth}P{0.2833333333333333\textwidth}P{0.028333333333333332\textwidth}P{0.11333333333333333\textwidth}}
Sum of\tabcellsep df\tabcellsep Mean\tabcellsep F\tabcellsep Sig.\\
Squares\tabcellsep \tabcellsep Square\tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: ASource: Field Survey (July-December, 2013)]} 
\caption{\label{tab_8}Table 9 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{11} \par 
\begin{longtable}{P{0.36833333333333335\textwidth}P{0.056666666666666664\textwidth}P{0.2833333333333333\textwidth}P{0.028333333333333332\textwidth}P{0.11333333333333333\textwidth}}
Sum of\tabcellsep df\tabcellsep Mean\tabcellsep F\tabcellsep Sig.\\
Squares\tabcellsep \tabcellsep Square\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_9}Table 11 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{12} \par 
\begin{longtable}{P{0.07269736842105264\textwidth}P{0.1789473684210526\textwidth}P{0.10625\textwidth}P{0.10904605263157895\textwidth}P{0.11184210526315788\textwidth}P{0.09226973684210527\textwidth}P{0.11184210526315788\textwidth}P{0.06710526315789474\textwidth}}
\tabcellsep Marital Status\tabcellsep Skill\tabcellsep Task\tabcellsep Task\tabcellsep Autonomy\tabcellsep Feedback\tabcellsep MPS\\
\tabcellsep \tabcellsep Variety\tabcellsep Identity\tabcellsep Significance\tabcellsep \tabcellsep from Job\tabcellsep \\
Married\tabcellsep Mean\tabcellsep 5.13\tabcellsep 4.94\tabcellsep 5.71\tabcellsep 5.06\tabcellsep 5.05\tabcellsep 134.408\\
\tabcellsep Std. Deviation\tabcellsep 1.069\tabcellsep 1.013\tabcellsep .874\tabcellsep .910\tabcellsep .905\tabcellsep \\
\multicolumn{2}{l}{Unmarried Mean}\tabcellsep 5.07\tabcellsep 4.71\tabcellsep 5.79\tabcellsep 4.86\tabcellsep 5.36\tabcellsep 135.197\\
\tabcellsep Std. Deviation\tabcellsep .940\tabcellsep 1.049\tabcellsep .833\tabcellsep 1.177\tabcellsep .870\tabcellsep \\
Total\tabcellsep Mean\tabcellsep 5.12\tabcellsep 4.89\tabcellsep 5.72\tabcellsep 5.02\tabcellsep 5.12\tabcellsep 134.766\\
\tabcellsep Std. Deviation\tabcellsep 1.039\tabcellsep 1.021\tabcellsep .863\tabcellsep .972\tabcellsep .903\tabcellsep \end{longtable} \par
 
\caption{\label{tab_10}Table 12 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{12} \par 
\begin{longtable}{P{0.43728323699421967\textwidth}P{0.4127167630057803\textwidth}}
Volume XIV Issue IV Version I\tabcellsep Source: Field Survey (July-December, 2013)\\
Global Journal of Management and Business Research A ( ) ( )\tabcellsep \\
\tabcellsep Source: Field Survey (July-December, 2013)\end{longtable} \par
 
\caption{\label{tab_11}Table 12 show}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{13} \par 
\begin{longtable}{P{0.85\textwidth}}
Year 2014\end{longtable} \par
 
\caption{\label{tab_12}Table 13 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{14} \par 
\begin{longtable}{P{0.05165745856353591\textwidth}P{0.19019337016574586\textwidth}P{0.10801104972375691\textwidth}P{0.10801104972375691\textwidth}P{0.11270718232044198\textwidth}P{0.09392265193370165\textwidth}P{0.11270718232044198\textwidth}P{0.07279005524861878\textwidth}}
\tabcellsep Education\tabcellsep Skill\tabcellsep Task\tabcellsep Task\tabcellsep Autonomy\tabcellsep Feedback\tabcellsep MPS\\
\tabcellsep \tabcellsep Variety\tabcellsep Identity\tabcellsep Significance\tabcellsep \tabcellsep from Job\tabcellsep \\
HSC\tabcellsep Mean\tabcellsep 6.50\tabcellsep 6.00\tabcellsep 6.00\tabcellsep 4.50\tabcellsep 5.00\tabcellsep 138.750\\
\tabcellsep Std. Deviation\tabcellsep .707\tabcellsep .000\tabcellsep .000\tabcellsep .707\tabcellsep .000\tabcellsep \\
Bachelor\tabcellsep Mean\tabcellsep 5.27\tabcellsep 5.27\tabcellsep 5.73\tabcellsep 5.32\tabcellsep 5.23\tabcellsep 150.896\\
\tabcellsep Std. Deviation\tabcellsep .935\tabcellsep .985\tabcellsep .935\tabcellsep .995\tabcellsep .813\tabcellsep \\
Master\tabcellsep Mean\tabcellsep 5.06\tabcellsep 4.79\tabcellsep 5.72\tabcellsep 4.96\tabcellsep 5.09\tabcellsep 131.028\\
\tabcellsep Std. Deviation\tabcellsep 1.050\tabcellsep 1.012\tabcellsep .859\tabcellsep .965\tabcellsep .931\tabcellsep \\
Total\tabcellsep Mean\tabcellsep 5.12\tabcellsep 4.89\tabcellsep 5.72\tabcellsep 5.02\tabcellsep 5.12\tabcellsep 134.766\\
\tabcellsep Std. Deviation\tabcellsep 1.039\tabcellsep 1.021\tabcellsep .863\tabcellsep .972\tabcellsep .903\tabcellsep \end{longtable} \par
 
\caption{\label{tab_13}Table 14 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{14} \par 
\begin{longtable}{P{0.5721153846153847\textwidth}P{0.032692307692307694\textwidth}P{0.16346153846153846\textwidth}P{0.016346153846153847\textwidth}P{0.06538461538461539\textwidth}}
Table 15 : ANOVA Table\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Sum of\tabcellsep df\tabcellsep Mean\tabcellsep F\tabcellsep Sig.\\
Squares\tabcellsep \tabcellsep Square\tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: A]} 
\caption{\label{tab_14}Table 14 shows}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{18} \par 
\begin{longtable}{P{0.26045130641330166\textwidth}P{0.5249406175771971\textwidth}P{0.06460807600950119\textwidth}}
\tabcellsep H 4 : Mean values of MPS differ due to work\tabcellsep Rejected\\
\tabcellsep experience factor.\\
\tabcellsep H 5 : Mean values of MPS differ due to gender\tabcellsep Rejected\\
\tabcellsep factor.\\
\tabcellsep H 6 : Mean values of MPS differ due to the marital\tabcellsep Rejected\\
\tabcellsep status factor.\\
\tabcellsep H 7 : Mean values of MPS differ due to education\tabcellsep Rejected\\
\tabcellsep factor.\\
\tabcellsep H 8 :\\
Hypotheses\tabcellsep Results\\
H 1 : Mean values of MPS differ due to designation\tabcellsep Rejected\\
factor.\tabcellsep \\
H 2 : Mean values of MPS differ due to salary\tabcellsep Rejected\\
factor.\tabcellsep \\
\multicolumn{2}{l}{H Rejected}\end{longtable} \par
  {\small\itshape [Note: 3 : Mean values of MPS differ due to age factor.]} 
\caption{\label{tab_15}Table 18 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{16} \par 
\begin{longtable}{P{0.11350931677018633\textwidth}P{0.03695652173913043\textwidth}P{0.13198757763975155\textwidth}P{0.10031055900621118\textwidth}P{0.10031055900621118\textwidth}P{0.10559006211180123\textwidth}P{0.1108695652173913\textwidth}P{0.08711180124223603\textwidth}P{0.06335403726708075\textwidth}}
\tabcellsep Bank\tabcellsep \tabcellsep Skill\tabcellsep Task\tabcellsep Task\tabcellsep \multicolumn{2}{l}{Autonomy Feedback}\tabcellsep MPS\\
\tabcellsep \tabcellsep \tabcellsep Variety\tabcellsep Identity\tabcellsep Significance\tabcellsep \tabcellsep from Job\tabcellsep \\
\multicolumn{3}{l}{Public Commercial Bank Mean}\tabcellsep 5.02\tabcellsep 4.91\tabcellsep 5.75\tabcellsep 5.15\tabcellsep 4.88\tabcellsep 131.356\\
\tabcellsep \tabcellsep Std. Deviation\tabcellsep 1.125\tabcellsep 1.057\tabcellsep .848\tabcellsep .833\tabcellsep 1.008\tabcellsep \\
Private\tabcellsep Commercial\tabcellsep Mean\tabcellsep 5.22\tabcellsep 4.88\tabcellsep 5.69\tabcellsep 4.88\tabcellsep 5.35\tabcellsep 137.515\\
Bank\tabcellsep \tabcellsep Std. Deviation\tabcellsep .944\tabcellsep .992\tabcellsep .883\tabcellsep 1.083\tabcellsep .717\tabcellsep \\
Total\tabcellsep \tabcellsep Mean\tabcellsep 5.12\tabcellsep 4.89\tabcellsep 5.72\tabcellsep 5.02\tabcellsep 5.12\tabcellsep 134.766\\
\tabcellsep \tabcellsep Std. Deviation\tabcellsep 1.039\tabcellsep 1.021\tabcellsep .863\tabcellsep .972\tabcellsep .903\tabcellsep \end{longtable} \par
 
\caption{\label{tab_16}Table 16 :}\end{figure}
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\end{document}