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}{% \vskip8pt \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt } \usepackage[absolute,overlay]{textpos} \makeatother \usepackage{lineno} \linenumbers \begin{document} \author[1]{Md. Hasebur Rahman} \author[2]{Dr. Md. Mushfiqur Rahman} \affil[1]{ Pabna University of Science and Technology} \renewcommand\Authands{ and } \date{\small \em Received: 16 December 2013 Accepted: 5 January 2014 Published: 15 January 2014} \maketitle \begin{abstract} Employees are regarded as a most valuable resource and the main driver for organizational success. In order to be successful, an organization needs to consistently motivate employees so that they can act toward the goals of the organization and have a strong desire to remain in the organization. This study attempted to draw influences of different motivational factors such as salary adequacy, future security, social dignity/status, career ambition, training and development, comfortable physical environment, \end{abstract} \keywords{motivation, employee, commercial bank, job interesting, motivational factors} \begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) \textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark} \end{textblock*} \begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) \textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}} \end{textblock*} \let\tabcellsep& \section[{Introduction}]{Introduction}\par otivation in simple terms may be understood as the set of forces that cause people to behave in certain ways. A motivated employee generally is more quality oriented. Highly motivated employees are more productive than apathetic employee, one reason why motivation is a difficult task is that the workforce is changing. Employees join institutions with different needs and expectations. Their values, beliefs, background, lifestyles, perceptions and attitudes are different. Not many institutions have understood these and not many HR experts are clear about the ways of motivating such diverse workforce (Goswami G.T. and Harsh Dwivedi H., 2011).\par Motivation has been recognized as a dilemma that managers must face because what motivates one individual may not motivate another \hyperref[b1]{(Geren B., 2011)}. Motivation is a very interesting topic for research, though numerous research studies have been conducted by organizational behavior experts at many times. Now a day's employees have been hired, trained and remunerated and also need to be motivated for better performance. Motivation agenda become a driving force for managing a diverse workforce in organizational interfaces. Motivated Employees are laborious and ambitious for organizational success. Employees and their behavior represent strong forces that can enhance or diminish the effectiveness of the organization (Hasebur Rahman, M., 2013).\par According to theory Y employees are ambitious and self-motivated, exercise self-control; enjoy their mental and physical work duties. According to Douglas M. \hyperref[b3]{McGregor (1957)} to their work is as natural as play. Employees possess the ability for creative problem solving, but their talents are underused in most organizations. In light theory Y this study is initiated to explore how motivational factors in commercial banks in bangladesh influence in making job interesting. Employees regarded as a distinctive resource is a strategic advantage of an organization. so organizations need to constantly motivate their employees for exerting maximum efforts from them. It becomes obvious when employees feel an interest in doing their jobs. On the basis of previous research indicated by Hasebur Rahman, M. (2013) motivational factors such as salary adequacy, future security, social dignity/status, career ambition, training and development, comfortable physical environment, mutual cooperation and management relation influence on making job interesting have been taken for that study. \section[{Literature Review and Hypotheses Development a) Concept of Motivation}]{Literature Review and Hypotheses Development a) Concept of Motivation}\par Motivation becomes an important agenda for managers and management scholars nowadays and it will remain in the future (Hasebur Rahman, M., 2013). Motivation is defined as the force or forces that arouse enthusiasm and persistence to pursue a certain course of action \hyperref[b4]{(Daft \& Marcic, 2008)}. Motivation, derived from the Latin word meaning "to move" represents those psychological goals directed processes \hyperref[b5]{(Kreitner \& Kinicki, 2007)}. Motivation is a general term applying to the entire class of drives, desires, needs, wishes, wants, aims, goals, motives, and incentives. It is a basic psychological process that includes the need-driveincentive sequence or cycle. Motivation is a process that starts with a physiological or psychological deficiency or need that activates behavior or a drive that is aimed at a goal or incentive \hyperref[b6]{(Luthans, 1998)}. It also refers to the processes that account for an individual's willingness to exert a high level of effort to reach organizational goals, conditioned by the effort's ability to satisfy needs \hyperref[b7]{(Robbins \& Coulter 2006)}.\par The motivation remains a key secret of managing people at organizational interfaces. Different people from differing background come together within an organization having different aims incompatible to organizational aims. Motivation acts as key forces to drive diversified workforce to meet organizational objectives (Hasebur Rahman, M., 2013). 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 human resources who set organization's objectives and strategies, design and produce goods and services, quality control and market goods and services. 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 depending upon the level of motivation an individual has with his or her job, to put forth his or her best to the organization (Hasebur Rahman, M., 2013). Motivated employees in the workplace can be termed as those who willingly and voluntarily extend their best efforts in order to help the organization attaining its goal. Motivated employees are sincere, dutiful, and laborious; therefore, need less supervision of expert best performance out of them (Hasebur Rahman, M., 2013).\par Individuals differ not only in their ability to do, but also in their determinations to do, or motivates managers who are successful in motivating employees are often providing an environment in which appropriate goals are available for needs satisfaction. Retaining and motivating workers require special attention and the responsibility falls squarely on all levels of management. Management have to create a work environment where people enjoy what they do, feel like they have a purpose and have pride in the mission of the organization. It requires more time, more skill, and managers who care about people. It takes true leadership. By giving employees with special tasks, you make them feel more important. When your employees feel like they are being trusted with added responsibilities, they are motivated to work even harder so they won't let the company down. Motivation is essential for any institution because employees are the pertinent intellectual assets of the company. Motivation is important for the growth of employees as well as for contributing organizational productivity (Goswami G.T. and Harsh Dwivedi H., 2011). \section[{b) Motivational Factors for making Job Interesting}]{b) Motivational Factors for making Job Interesting}\par To keep the people working efficiently, they need to consistently motivate. Money is not a sole motivating factor. Besides money, there are many other financial and non-financial factors that can keep people happy. Good interpersonal relations, prestige and social dignity, open communication, training and development, job security, reward and recognition, security for the future, growth/promotion are perceived as keys motivating factors in commercial banks in Bangladesh (Hasebur Rahman, M., 2013). Research suggests that as employees' income increases, money becomes less of a motivator \hyperref[b9]{(Kovach, 1987)}. Also, as employees get older, interesting work becomes more of a motivator (James R. Lindner, 1998). \section[{i. Salary/Pay}]{i. Salary/Pay}\par Salary plays a significant role in motivation level of employees, but motivation is determined by a number of contributing variables and salary is one of them \hyperref[b19]{(Arshad M. et al., 2012)} as Bown, Cattel, Michell and Edwards (2008) conducted research on the quantity surveying profession in South Africa and found that salary, promotion prospects, personal satisfaction and recognition etc. are motivating factors for employees in that particular case. The amount of money a person receives monthly can be best predictor of his/her motivation level. The employees who are efficient and effective in achieving tasks and goals deserve a good salary package \hyperref[b21]{(Igalens and Roussel, 1999)}. One of the major criteria for the quality of work life is adequate and fair compensation. Compensation broadly refers to all the ways in which an organization may reward employees for the services that they render \hyperref[b22]{(Sethi \& Pinzon, 1998)}. For maintaining a higher level of motivation, it is very important to maintain a reasonable \section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par Volume XIV Issue I Version I Year ( ) A level of salaries. If an organization combines few other positive factors with better salary levels, then it can produce very highly motivated work force which can guarantee a glorious future for workers and the organization \hyperref[b19]{(Arshad M. et al., 2012)}. Therefore, the 1st hypothesis of this study is: H 1 : There is a positive/significant relationship between the salary adequacy and the interesting job. \section[{ii. Social dignity/status}]{ii. Social dignity/status}\par Social status can then be considered an ultimate motive for human action. Since people are social beings, they need to belong, to be accepted by others (A. H. {\ref Maslow, 1943)}. Employees' perception of their own socioeconomic status depends on their employment status. The literature review section shows that social distinction and status are among the strongest motivations of human behavior. Therefore the 2nd hypothesis of the study is:H 2 :\par There is a positive/significant relationship between the social dignity/status and the interesting job. \section[{iii. Career ambition}]{iii. Career ambition}\par Research evidence (Amy Wrzesniewski et. al., 1997)) suggested that most people see their work as either a Job (focus on financial rewards and necessity rather than pleasure or fulfillment; not a major positive part of life), a Career (focus on advancement), or a Calling (focus on enjoyment of fulfilling, socially useful work). The work is not an end in itself, but instead is a means that allows individuals to acquire the resources needed to enjoy their time away from the Job. The major interests and ambitions of Job holders are not expressed through their work \hyperref[b12]{(Bellah et al., 1985)}. Therefore the 3rd hypothesis of the study is:H 3 :\par There is a positive/significant relationship between the career ambition and the interesting job. \section[{iv. Training and development}]{iv. Training and development}\par One key factor in employee motivation and retention is the opportunity employees want to continue to grow and develop job and career enhancing skills. In fact, this opportunity for employees to continue to grow and develop through training is one of the most important factors in employee motivation (Susan M. \hyperref[b13]{Heathfield, 2013)}. Therefore the 4th hypothesis of the study is:H 4 :\par There is a positive/significant relationship between the training and development and the interesting job. \section[{Comfort}]{Comfort}\par The workplace environment plays a crucial role for the employees. Nowadays employees may have a large number working alternatives, then the environment in the workplace becomes a critical factor for accepting and/or keeping the jobs. The quality of the environment in the workplace may simply determine the level of employee motivation, subsequent performance and productivity. A widely accepted assumption is that better workplace environment motivates employees and produces better results (Demet \hyperref[b14]{Leblebici, 2012)}. The physical environment is a tool that can be leveraged both to improve business results \hyperref[b15]{(Mohr, 1996)} and employee well-being \hyperref[b16]{(Huang, Robertson and Chang, 2004)}. Therefore the 5th hypothesis of the study is:H 5 :\par There is a positive/significant relationship between the comfortable physical environment and the interesting job. \section[{vi. Mutual cooperation}]{vi. Mutual cooperation}\par According to McClelland's Theory of Needs (1961) The Need for affiliation (nAff) is the desire for friendship and close and close interpersonal relationships. There for the 6th hypothesis of the study is:H 6 :\par There is a positive/significant relationship between the mutual cooperation and the interesting job. \section[{vii. Management relation:}]{vii. Management relation:}\par Managers use motivation in the workplace to inspire people to work, both individually and in groups, to produce the best results for business in the most efficient and effective manner. The manager must identify what actually motivates associates. People tend to do their best work when they are in an environment that makes them feel valued for a job well done. These courtesies may seem simple that can have a great impact on organizational morale to motivate associates to "go the extra mile" (Ian Bessel et. al., 2012). There for the 7th hypothesis of the study is:H 7 :\par There is a positive/significant relationship between the management relation and the interesting job. \section[{IV.}]{IV.} \section[{Research Methodology}]{Research Methodology}\par This study is initiated for measuring motivational impact on employees of commercial bank in Bangladesh for making their job interesting. The said factors responsible for making job interesting are salary adequacy, future security, social dignity/ status, career ambition, training and development, comfortable physical environment, mutual cooperation and management relation. This study is hypotheses testing in nature. The hypothesis testing is explaining the relationship between the independent and dependent variables. In this study, the hypothesis have been selected based on the literature review mentioned above to describe the relationship among those variables that salary adequacy, future security, social dignity / status, career ambition, training and development, comfortable physical environment, mutual cooperation and management relation influence on making job interesting.\par For questionnaire survey, convenient method of sampling have used. There is no available source for the \section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par Volume XIV Issue I Version I Year ( ) address of employees of a commercial bank. 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 100 respondents. The number of initial replies received was 70. After a screening first round replies a second round personal contract conducted by a researcher and finally 80 respondents were taken for this study.\par This study mainly based on primary data originating from a survey during the period of July-November, 2013. For this purpose a constructed questionnaire was developed. The questionnaire was constructed, measured and investigated through 2 point Scale.The scale consists two options/ points such as strongly yes/ 2, no/ 1. The 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 0.648. Alpha is higher than that is suggested by \hyperref[b24]{Nunnally (1978)} and therefore data collected can be considered reliable. Pearson Correlation is used to indicate correlations among the variables, Linear Regression analysis is used to test the hypothesis.\par V. \section[{Findings and Data Analysis a) Respondent's Demographic}]{Findings and Data Analysis a) Respondent's Demographic} \section[{b) Correlations among Variables}]{b) Correlations among Variables}\par The Pearson's correlation is used to measure the significance of linear bivariate correlation between the independent and dependent variables. Variable association refers to a wide variety of coefficients which measure the strength of a relationship. Theoretically, the higher value of the correlation between two variables, the more related these variables are to each other (these values show the strength of relationships among variables). The direction of relationships among variables is another issue that should be considered in analyzing the correlations between variables. A positive correlation indicates that the direction of the relationship is positive (if one increases, the other one increases). A negative correlation indicates an inverse relationship between variables (if one increases, the other one decreases). Bivariate Correlations are used to know the nature, direction and significance of the bivariate relationship of the variables of this study. Therefore, the Bivariate Correlations procedures have used to compute Pearson's correlation coefficient. A rule of thumb is that multicollinearity may be a problem if a correlation is >. 90,in the correlation matrix formed by all the independent variables (Coakes, S. J. and L. G. {\ref Steed, 2000)}. Based on the analysis presented in Table \hyperref[tab_1]{02} the result shows correlation between the variables, the V1 and V2, r =.390 at p<.000 level, V1 and V3, r =.224 at p<.046 level, V1 and V4, r =-.044 at p< .696 level, V1 and V5, r =.153 at p< .177 level, V1 and V6, r =.054 at p<.637 level, V1 and V7, r =.379 at p<.001 level, V1 and V8, r =-.161 at p<.154 level, V1 and V9, r =.032 at p<.776 level, V2 and V3, r =.132 at p< .242 level, V2 and V4, r =.198 at p<.078 level, V2 and V5, r =.356 at p<.001 level, V2 and V6, r =.306 at p<.006 level, V2 and V7, r =.198 at p<.078 level, V2 and V8, r =.131at p<.247 level, V2 and V9, r =.328 at p< .003 level, V3 and V4, r =.079 at p<.487 level, V3 and V5, r =-.013 at p< .910 level, V3 and V6, r =.145 at p< .201 level, V3 and V7, r =.321 at p<.004 level, V3 and V8, r =.087 at p<.444 level, V3 and V9, r =-.059 at p<.602 level, V4 \section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par Volume XIV Issue I Version I Year ( ) and V5, r =.093 at p<.411 level, V4 and V6, r =.330 at p<.003 level, V4 and V7, r =.050 at p<.662 level, V4 and V8, r =.130 at p<.251 level, V4 and V9, r =.212 at p<.060 level, V5 and V6, r =.303 at p<.006 level, V5 and V7, r =.152 at p<.177 level, V5 and V8, r =.021at p<.852 level, V5 and V9, r =.275 at p<.014 level, V6 and V7, r =.276 at p< .013 level, V6 and V8, r =.124 at p< .274 level, V6 and V9, r =.358 at p<.001 level, V7 and V8, r =.050 at p<.660 level, V7 and V9, r =.102 at p<.366 level and V8 and V9, r =.254 at p<.023 level. \section[{c) Regression Analysis}]{c) Regression Analysis}\par The multiple regression analysis determines which variables (independent variables) explain variability in the outcome, how much variability in the dependent variables is explained by the independent variable(s), and which variables are significant (over other variables) in explaining the variability of the dependent variable. Multiple regression estimates the coefficients of the linear equation, involving one or more independent variables that best predict the value of the dependent variable (Hashed Ahmed Nasser M. et al., 2012). H 1 : Result shows (Table \hyperref[tab_2]{03}) the value of R is (.054), the value of R square is (.003) and the standard error of the estimate was (.502). In this case multiple regressions were used to study the effect of the independent variables salary adequacy (V1) to dependent variable interesting job (V6). \hyperref[tab_3]{4} shows analysis of variance test statistics (ANOVA) indicates that the model is insignificant at ? =. 637. Findings that the independent variable has insignificant relationships with interesting job (F=. 224) (Sig.637). \hyperref[tab_4]{05} shows the value of the T-statistic is insignificant for salary adequacy. The hypothesis is rejected because the result of insignificance is more than 0.05 (Significance requirement standard < 0.05).\par H 2 : Result shows (Table \hyperref[tab_18]{06}) the value of R is (.306), the value of R square is (.093) and the standard error of the estimate was (.478). In this case multiple regressions were used to study the effect of the independent variables future security (V2) to dependent variable job interesting (V6). Table \hyperref[tab_13]{07} shows analysis of variance test statistics (ANOVA) indicates that the model is significant at ? =.006. Findings that the independent variable has significant relationships with interesting job (F=8.029) (Sig.006). In this case multiple regressions were used to study the effect of the independent variables Dignity and Status (V3) to dependent variable Interesting job (V6). \hyperref[tab_11]{11} shows the value of the T-statistic is insignificant for social dignity/status. The hypothesis is rejected because the result of insignificance is more than 0.05 (Significance requirement standard < 0.05). H 4 : Result shows (Table \hyperref[tab_12]{12}) the value of R is (.330), the value of R square is (.109) and the standard error of the estimate was (.474). Table \hyperref[tab_13]{07} shows analysis of variance test statistics (ANOVA) indicates that the model is significant at ? =.006. Findings that the independent variable has significant relationships with interesting job (F=8.029) (Sig.006). \hyperref[tab_14]{13} shows analysis of variance test statistics (ANOVA) indicates that the model is significant at ? =. 003. Findings that the independent variable has significant relationships with interesting job (F=9.514) (Sig.003). \hyperref[tab_15]{14} shows the value of the T-statistic is significant for Career ambition. The hypothesis is accepted because the result of significance is less than 0.05 (Significance requirement standard < 0.05).\par H 5 : Result shows (Table \hyperref[tab_16]{15}) the value of R is (.303), the value of R square is (.092) and the standard error of the estimate was (.479). In this case multiple regressions were used to study the effect of the independent variables Comfortable physical environment (V7) to dependent variable job interesting (V6). \hyperref[tab_24]{20} shows the value of the T-statistic is significant for Comfortable physical environment. The hypothesis is accepted because the result of significance is less than 0.05 (Significance requirement standard < 0.05).\par H 7 : Result shows (Table \hyperref[tab_25]{21}) the value of R is (.124), the value of R square is (.015) and the standard error of the estimate was (.449). In this case multiple regressions were used to study the effect of the independent variables Mutual cooperation (V8) to dependent variable interesting job (V6). \begin{figure}[htbp] \noindent\textbf{}\includegraphics[]{image-2.png} \caption{\label{fig_0}}\end{figure} \begin{figure}[htbp] \noindent\textbf{1} \par \begin{longtable}{P{0.25144131777625256\textwidth}P{0.19076870281400135\textwidth}P{0.27361015785861353\textwidth}P{0.07467398764584762\textwidth}P{0.05950583390528483\textwidth}} \multicolumn{2}{l}{Respondent's Demographic}\tabcellsep \multicolumn{3}{l}{Frequency Percent Cumulative Percent}\\ Sample\tabcellsep Public Commercial Bank\tabcellsep 40\tabcellsep 50\tabcellsep 50\\ \tabcellsep Private Commercial Bank\tabcellsep 40\tabcellsep 50\tabcellsep 100\\ \tabcellsep Total\tabcellsep 80\tabcellsep 100\tabcellsep \\ Income Level\tabcellsep 15000-30000\tabcellsep 28\tabcellsep 35.0\tabcellsep 35.0\\ \tabcellsep 31000-50000\tabcellsep 29\tabcellsep 36.3\tabcellsep 71.3\\ \tabcellsep 51000 and above\tabcellsep 23\tabcellsep 28.8\tabcellsep 100.0\\ \tabcellsep Total\tabcellsep 80\tabcellsep 100.0\tabcellsep \\ Designation\tabcellsep Junior Officer\tabcellsep 14\tabcellsep 17.5\tabcellsep 17.5\\ \tabcellsep Officer\tabcellsep 29\tabcellsep 36.3\tabcellsep 53.8\\ \tabcellsep Officer\tabcellsep 25\tabcellsep 31.3\tabcellsep 85.0\\ \tabcellsep Principal Officer\tabcellsep 7\tabcellsep 8.8\tabcellsep 93.8\\ \tabcellsep Senior Principal Officer\tabcellsep 4\tabcellsep 5.0\tabcellsep 98.8\\ \tabcellsep Senior Principal Officer\tabcellsep 1\tabcellsep 1.3\tabcellsep 100.0\\ \tabcellsep Total\tabcellsep 80\tabcellsep 100.0\tabcellsep \\ Age\tabcellsep 20-30 years\tabcellsep 50\tabcellsep 62.5\tabcellsep 62.5\\ \tabcellsep 31-40 years\tabcellsep 12\tabcellsep 15.0\tabcellsep 77.5\\ \tabcellsep 41-50 years\tabcellsep 18\tabcellsep 22.5\tabcellsep 100.0\\ \tabcellsep Total\tabcellsep 80\tabcellsep 100.0\tabcellsep \\ Experience\tabcellsep 1-5 years\tabcellsep 43\tabcellsep 53.8\tabcellsep 53.8\\ \tabcellsep 6-10 years\tabcellsep 11\tabcellsep 13.8\tabcellsep 67.5\\ \tabcellsep Above 11 years\tabcellsep 26\tabcellsep 32.5\tabcellsep 100.0\\ \tabcellsep Total\tabcellsep 80\tabcellsep 100.0\tabcellsep \\ Gender\tabcellsep Male\tabcellsep 73\tabcellsep 91.3\tabcellsep 91.3\\ \tabcellsep Female\tabcellsep 7\tabcellsep 8.8\tabcellsep 100.0\\ \tabcellsep Total\tabcellsep 80\tabcellsep 100.0\tabcellsep \\ Marital Status\tabcellsep Married\tabcellsep 68\tabcellsep 85.0\tabcellsep 85.0\\ \tabcellsep Unmarried\tabcellsep 12\tabcellsep 15.0\tabcellsep 100.0\\ \tabcellsep Total\tabcellsep 80\tabcellsep 100.0\tabcellsep \\ Education\tabcellsep SSC\tabcellsep 1\tabcellsep 1.3\tabcellsep 1.3\\ \tabcellsep HSC\tabcellsep 5\tabcellsep 6.3\tabcellsep 7.5\\ \tabcellsep Bachelor\tabcellsep 11\tabcellsep 13.8\tabcellsep 21.3\\ \tabcellsep Master\tabcellsep 63\tabcellsep 78.8\tabcellsep 100.0\\ \tabcellsep Total\tabcellsep 80\tabcellsep 100.0\tabcellsep \\ \multicolumn{2}{l}{80 samples have been drawn from commercial}\tabcellsep \multicolumn{3}{l}{17.5\% junior officer, 36.3\% officer, 31.3\% officer, 8.8\%}\\ \multicolumn{2}{l}{bank of Bangladesh among them public commercial}\tabcellsep \multicolumn{3}{l}{principal officer, 5\% senior principal officer and 1.3\%}\\ \multicolumn{2}{l}{banks have 50\% and private commercial banks have}\tabcellsep \multicolumn{3}{l}{have AGM rank. 62.5\% respondents are 20-30 within}\\ \multicolumn{2}{l}{50\% sample. 35\% respondent's have a monthly salary}\tabcellsep \multicolumn{3}{l}{years, 15\% respondents are within 31-40 years and}\\ \multicolumn{2}{l}{within Tk. 15000-30000, 36.3\% respondent's have a}\tabcellsep \multicolumn{3}{l}{22.5\% are within 41-50 years. 53.8\% respondents have}\\ \multicolumn{2}{l}{monthly salary within Tk. 31000-50000, 28.8\%}\tabcellsep \multicolumn{3}{l}{1-5 years, 13.8\% respondents have 6-10 years and}\\ \multicolumn{2}{l}{respondent's have a monthly salary above Tk. 51000.}\tabcellsep \multicolumn{3}{l}{32.5\% respondents have above 11 years job experience}\end{longtable} \par \caption{\label{tab_0}Table 1 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{2} \par \begin{longtable}{P{0.5547681539807524\textwidth}P{0.03867016622922135\textwidth}P{0.06916010498687664\textwidth}P{0.04461942257217848\textwidth}P{0.04610673665791776\textwidth}P{0.031233595800524934\textwidth}P{0.03718285214348206\textwidth}P{0.020822397200349955\textwidth}P{0.004461942257217848\textwidth}P{0.0029746281714785653\textwidth}} \tabcellsep V1\tabcellsep V2\tabcellsep V3\tabcellsep V4\tabcellsep V5\tabcellsep V6\tabcellsep V7\tabcellsep V8\tabcellsep V9\\ \multicolumn{2}{l}{V1 Pearson Correlation 1}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Sig. (2-tailed)\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ N\tabcellsep 80\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{3}{l}{V2 Pearson Correlation .390** 1}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Sig. (2-tailed)\tabcellsep .000\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ N\tabcellsep 80\tabcellsep 80\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{2}{l}{V3 Pearson Correlation .224*}\tabcellsep .132\tabcellsep 1\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Sig. (2-tailed)\tabcellsep .046\tabcellsep .242\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ N\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{2}{l}{V4 Pearson Correlation -.044}\tabcellsep .198\tabcellsep .079\tabcellsep 1\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Sig. (2-tailed)\tabcellsep .696\tabcellsep .078\tabcellsep .487\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ N\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{2}{l}{V5 Pearson Correlation .153}\tabcellsep \multicolumn{2}{l}{.356** -.013}\tabcellsep .093\tabcellsep 1\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Sig. (2-tailed)\tabcellsep .177\tabcellsep .001\tabcellsep .910\tabcellsep .411\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ N\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{2}{l}{V6 Pearson Correlation .054}\tabcellsep \multicolumn{2}{l}{.306** .145}\tabcellsep \multicolumn{3}{l}{.330** .303** 1}\tabcellsep \tabcellsep \tabcellsep \\ Sig. (2-tailed)\tabcellsep .637\tabcellsep .006\tabcellsep .201\tabcellsep .003\tabcellsep .006\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ N\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{3}{l}{V7 Pearson Correlation .379** .198}\tabcellsep \multicolumn{2}{l}{.321** .050}\tabcellsep .152\tabcellsep .276*\tabcellsep 1\tabcellsep \tabcellsep \\ Sig. (2-tailed)\tabcellsep .001\tabcellsep .078\tabcellsep .004\tabcellsep .662\tabcellsep .177\tabcellsep .013\tabcellsep \tabcellsep \tabcellsep \\ N\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep \tabcellsep \\ \multicolumn{2}{l}{V8 Pearson Correlation -.161}\tabcellsep .131\tabcellsep .087\tabcellsep .130\tabcellsep .021\tabcellsep .124\tabcellsep \multicolumn{2}{l}{.050 1}\tabcellsep \\ Sig. (2-tailed)\tabcellsep .154\tabcellsep .247\tabcellsep .444\tabcellsep .251\tabcellsep .852\tabcellsep .274\tabcellsep .660\tabcellsep \tabcellsep \\ N\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep \\ \multicolumn{2}{l}{V9 Pearson Correlation .032}\tabcellsep \multicolumn{2}{l}{.328** -.059}\tabcellsep .212\tabcellsep .275*\tabcellsep \multicolumn{4}{l}{.358** .102 .254* 1}\\ Sig. (2-tailed)\tabcellsep .776\tabcellsep .003\tabcellsep .602\tabcellsep .060\tabcellsep .014\tabcellsep .001\tabcellsep \multicolumn{2}{l}{.366 .023}\tabcellsep \\ N\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\tabcellsep 80\\ \multicolumn{4}{l}{**. Correlation is significant at the 0.01 level (2-tailed).}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{4}{l}{* . Correlation is significant at the 0.05 level (2-tailed).}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{9}{l}{V1=Salary adequacy, V2= Future security, V3= Social dignity and Status, V4= Career Ambition,}\tabcellsep \\ \multicolumn{9}{l}{V5= Training and development, V6= Interesting job , V7=Comfortable physical environment,}\tabcellsep \\ \multicolumn{4}{l}{V8= Mutual cooperation, V9= Management relation.}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_1}Table 2 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{3} \par \begin{longtable}{P{0.04214876033057851\textwidth}P{0.049173553719008264\textwidth}P{0.6954545454545454\textwidth}P{0.03512396694214876\textwidth}P{0.02809917355371901\textwidth}} Model\tabcellsep R\tabcellsep \multicolumn{3}{l}{R Square Adjusted R Square Std. Error of the Estimate}\\ 1\tabcellsep . 054a\tabcellsep .003\tabcellsep -.010\tabcellsep .502\\ \tabcellsep \tabcellsep \multicolumn{2}{l}{a. Predictors: (Constant), Salary adequacy}\tabcellsep \end{longtable} \par \caption{\label{tab_2}Table 3 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{4} \par \begin{longtable}{P{0.53125\textwidth}P{0.19036458333333334\textwidth}P{0.044270833333333336\textwidth}P{0.017708333333333333\textwidth}P{0.04869791666666666\textwidth}P{0.017708333333333333\textwidth}} Model\tabcellsep \multicolumn{4}{l}{Sum of Squares df Mean Square F}\tabcellsep Sig.\\ \multicolumn{2}{l}{1 Regression .056}\tabcellsep 1\tabcellsep .056\tabcellsep .224 . 637a\\ Residual\tabcellsep 19.631\tabcellsep \multicolumn{2}{l}{78 .252}\\ Total\tabcellsep 19.688\tabcellsep 79\tabcellsep \\ \multicolumn{3}{l}{a. Predictors: (Constant), Salary adequacy}\tabcellsep \\ \multicolumn{2}{l}{b. Dependent Variable: Interesting job}\tabcellsep \tabcellsep \\ Table\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_3}Table 4 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{5} \par \begin{longtable}{P{0.35978835978835977\textwidth}P{0.2743386243386243\textwidth}P{0.08095238095238094\textwidth}P{0.03597883597883598\textwidth}P{0.06296296296296296\textwidth}P{0.03597883597883598\textwidth}} Model\tabcellsep \multicolumn{4}{l}{Unstandardized Coefficients Standardized Coefficients t}\tabcellsep Sig.\\ \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep \\ 1 (Constant)\tabcellsep 1.483\tabcellsep .176\tabcellsep \tabcellsep \multicolumn{2}{l}{8.424 .000}\\ \multicolumn{2}{l}{Salary adequacy .053}\tabcellsep .112\tabcellsep .054\tabcellsep .474\tabcellsep .637\\ \multicolumn{3}{l}{a. Dependent Variable: Interesting job}\tabcellsep \tabcellsep \\ Table\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_4}Table 5 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{6} \par \begin{longtable}{P{0.3483606557377049\textwidth}P{0.07663934426229509\textwidth}P{0.3692622950819672\textwidth}P{0.027868852459016394\textwidth}P{0.027868852459016394\textwidth}} \multicolumn{2}{l}{Model R}\tabcellsep \multicolumn{3}{l}{R Square Adjusted R Square Std. Error of the Estimate}\\ 1\tabcellsep \multicolumn{2}{l}{. 306a .093}\tabcellsep .082\tabcellsep .478\\ \multicolumn{4}{l}{a. Predictors: (Constant), Future security}\end{longtable} \par \caption{\label{tab_5}Table 6 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{8} \par \begin{longtable}{P{0.3445945945945946\textwidth}P{0.2756756756756757\textwidth}P{0.0827027027027027\textwidth}P{0.036756756756756756\textwidth}P{0.0918918918918919\textwidth}P{0.018378378378378378\textwidth}} Model\tabcellsep \multicolumn{4}{l}{Unstandardized Coefficients Standardized Coefficients t}\tabcellsep Sig.\\ \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\\ 1 (Constant)\tabcellsep .950\tabcellsep .223\tabcellsep \tabcellsep 4.266 .000\\ \multicolumn{2}{l}{Future security .350}\tabcellsep .124\tabcellsep .306\tabcellsep 2.834 .006\\ \multicolumn{3}{l}{a. Dependent Variable: Interesting job}\tabcellsep \end{longtable} \par \caption{\label{tab_6}Table 8 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{08} \par \begin{longtable}{P{0.85\textwidth}} H 3 :\end{longtable} \par \caption{\label{tab_7}Table 08}\end{figure} \begin{figure}[htbp] \noindent\textbf{9} \par \begin{longtable}{P{0.3893129770992366\textwidth}P{0.0648854961832061\textwidth}P{0.34389312977099235\textwidth}P{0.025954198473282442\textwidth}P{0.025954198473282442\textwidth}} \multicolumn{2}{l}{Model R}\tabcellsep \multicolumn{3}{l}{R Square Adjusted R Square Std. Error of the Estimate}\\ 1\tabcellsep \multicolumn{2}{l}{.145a .021}\tabcellsep .008\tabcellsep .497\\ \multicolumn{4}{l}{a. Predictors: (Constant), Social dignity and Status}\end{longtable} \par \caption{\label{tab_8}Table 9 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{10} \par \begin{longtable}{P{0.49210526315789477\textwidth}P{0.2137426900584795\textwidth}P{0.049707602339181284\textwidth}P{0.019883040935672513\textwidth}P{0.05467836257309941\textwidth}P{0.019883040935672513\textwidth}} Model\tabcellsep \multicolumn{4}{l}{Sum of Squares df Mean Square F}\tabcellsep Sig.\\ \multicolumn{2}{l}{1 Regression .411}\tabcellsep 1\tabcellsep .411\tabcellsep 1.664 .201a\\ Residual\tabcellsep 19.276\tabcellsep \multicolumn{2}{l}{78 .247}\\ Total\tabcellsep 19.688\tabcellsep 79\tabcellsep \\ \multicolumn{2}{l}{a. Predictors: (Constant),}\tabcellsep \tabcellsep \\ \multicolumn{2}{l}{b. Dependent Variable: Interesting job}\tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_9}Table 10 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{10} \par \begin{longtable}{} \end{longtable} \par \caption{\label{tab_10}Table 10}\end{figure} \begin{figure}[htbp] \noindent\textbf{11} \par \begin{longtable}{P{0.3655440414507772\textwidth}P{0.26424870466321243\textwidth}P{0.07927461139896373\textwidth}P{0.035233160621761656\textwidth}P{0.08808290155440414\textwidth}P{0.017616580310880828\textwidth}} Model\tabcellsep \multicolumn{4}{l}{Unstandardized Coefficients Standardized Coefficients t}\tabcellsep Sig.\\ \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\\ 1 (Constant)\tabcellsep .921\tabcellsep .500\tabcellsep \tabcellsep 1.841 .069\\ \multicolumn{2}{l}{Dignity and Status .329}\tabcellsep .255\tabcellsep .145\tabcellsep 1.290 .201\\ \multicolumn{2}{l}{a. Dependent Variable: Interesting job}\tabcellsep \tabcellsep \\ Table\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_11}Table 11 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{12} \par \begin{longtable}{P{0.3512396694214876\textwidth}P{0.07024793388429752\textwidth}P{0.37231404958677683\textwidth}P{0.02809917355371901\textwidth}P{0.02809917355371901\textwidth}} \multicolumn{2}{l}{Model R}\tabcellsep \multicolumn{3}{l}{R Square Adjusted R Square Std. Error of the Estimate}\\ 1\tabcellsep \multicolumn{2}{l}{.330a .109}\tabcellsep .097\tabcellsep .474\\ \multicolumn{4}{l}{a. Predictors: (Constant), Career Ambition}\end{longtable} \par \caption{\label{tab_12}Table 12 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{7} \par \begin{longtable}{P{0.26066666666666666\textwidth}P{0.35133333333333333\textwidth}P{0.011333333333333334\textwidth}P{0.056666666666666664\textwidth}P{0.12466666666666666\textwidth}P{0.04533333333333334\textwidth}} Model\tabcellsep \multicolumn{4}{l}{Sum of Squares df Mean Square F}\tabcellsep Sig.\\ \multicolumn{2}{l}{1 Regression 1.838}\tabcellsep 1\tabcellsep 1.838\tabcellsep 8.029 .006a\end{longtable} \par {\small\itshape [Note: (V4) to dependent variable Interesting job (V6).]} \caption{\label{tab_13}Table 7 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{13} \par \begin{longtable}{P{0.5301546391752577\textwidth}P{0.1884020618556701\textwidth}P{0.04381443298969072\textwidth}P{0.02190721649484536\textwidth}P{0.04819587628865979\textwidth}P{0.01752577319587629\textwidth}} Model\tabcellsep \multicolumn{4}{l}{Sum of Squares df Mean Square F}\tabcellsep Sig.\\ \multicolumn{2}{l}{1 Regression 2.140}\tabcellsep 1\tabcellsep 2.140\tabcellsep 9.514 .003a\\ Residual\tabcellsep 17.547\tabcellsep \multicolumn{2}{l}{78 .225}\\ Total\tabcellsep 19.688\tabcellsep 79\tabcellsep \\ \multicolumn{3}{l}{a. Predictors: (Constant), Career ambition}\tabcellsep \\ \multicolumn{2}{l}{b. Dependent Variable: Interesting job}\tabcellsep \tabcellsep \\ Table\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_14}Table 13 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{14} \par \begin{longtable}{P{0.3578947368421052\textwidth}P{0.26842105263157895\textwidth}P{0.08052631578947368\textwidth}P{0.035789473684210524\textwidth}P{0.0894736842105263\textwidth}P{0.017894736842105262\textwidth}} Model\tabcellsep \multicolumn{4}{l}{Unstandardized Coefficients Standardized Coefficients t}\tabcellsep Sig.\\ \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\\ 1 (Constant)\tabcellsep .987\tabcellsep .194\tabcellsep \tabcellsep 5.094 .000\\ \multicolumn{2}{l}{Career ambition .346}\tabcellsep .112\tabcellsep .330\tabcellsep 3.084 .003\\ \multicolumn{3}{l}{a. Dependent Variable: Interesting job}\tabcellsep \\ Table\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_15}Table 14 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{15} \par \begin{longtable}{P{0.6454237288135594\textwidth}P{0.0288135593220339\textwidth}P{0.15271186440677967\textwidth}P{0.011525423728813558\textwidth}P{0.011525423728813558\textwidth}} \multicolumn{2}{l}{Model R}\tabcellsep \multicolumn{3}{l}{R Square Adjusted R Square Std. Error of the Estimate}\\ 1\tabcellsep \multicolumn{2}{l}{.303a .092}\tabcellsep .080\tabcellsep .479\\ \multicolumn{4}{l}{a. Predictors: (Constant), Training and development}\\ \multicolumn{5}{l}{In this case multiple regressions were used to study the effect of the independent variables Training and}\\ \multicolumn{4}{l}{development (V5) to dependent variable Interesting job (V6).}\end{longtable} \par \caption{\label{tab_16}Table 15 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{16} \par \begin{longtable}{P{0.5366161616161615\textwidth}P{0.1845959595959596\textwidth}P{0.04292929292929293\textwidth}P{0.021464646464646464\textwidth}P{0.04722222222222222\textwidth}P{0.01717171717171717\textwidth}} Model\tabcellsep \multicolumn{4}{l}{Sum of Squares df Mean Square F}\tabcellsep Sig.\\ \multicolumn{2}{l}{1 Regression 1.811}\tabcellsep 1\tabcellsep 1.811\tabcellsep 7.903 .006a\\ Residual\tabcellsep 17.876\tabcellsep \multicolumn{2}{l}{78 .229}\\ Total\tabcellsep 19.688\tabcellsep 79\tabcellsep \\ \multicolumn{4}{l}{a. Predictors: (Constant), Training and development}\\ \multicolumn{2}{l}{b. Dependent Variable: Interesting job}\tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_17}Table 16 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{06} \par \begin{longtable}{} \end{longtable} \par \caption{\label{tab_18}Table 06}\end{figure} \begin{figure}[htbp] \noindent\textbf{17} \par \begin{longtable}{P{0.3680412371134021\textwidth}P{0.26288659793814434\textwidth}P{0.0788659793814433\textwidth}P{0.03505154639175258\textwidth}P{0.08762886597938144\textwidth}P{0.01752577319587629\textwidth}} Model\tabcellsep \multicolumn{4}{l}{Unstandardized Coefficients Standardized Coefficients t}\tabcellsep Sig.\\ \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\\ 1 (Constant)\tabcellsep .981\tabcellsep .214\tabcellsep \tabcellsep 4.594 .000\\ \multicolumn{2}{l}{Training and development .337}\tabcellsep .120\tabcellsep .303\tabcellsep 2.811 .006\\ \multicolumn{2}{l}{a. Dependent Variable: Interesting job}\tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_19}Table 17 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{17} \par \begin{longtable}{P{0.85\textwidth}} Year\\ Volume XIV Issue I Version I\\ ( )\\ H 6 : Global Journal of Management and Business Research\end{longtable} \par {\small\itshape [Note: A]} \caption{\label{tab_20}Table 17}\end{figure} \begin{figure}[htbp] \noindent\textbf{18} \par \begin{longtable}{P{0.6538461538461539\textwidth}P{0.10897435897435896\textwidth}P{0.04358974358974359\textwidth}P{0.04358974358974359\textwidth}} 1\tabcellsep .276a .076\tabcellsep .065\tabcellsep .483\\ \multicolumn{4}{l}{a. Predictors: (Constant), Comfortable physical environment}\end{longtable} \par \caption{\label{tab_21}Table 18 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{19} \par \begin{longtable}{P{0.5487864077669903\textwidth}P{0.1774271844660194\textwidth}P{0.04126213592233009\textwidth}P{0.020631067961165046\textwidth}P{0.0453883495145631\textwidth}P{0.016504854368932037\textwidth}} Model\tabcellsep \multicolumn{4}{l}{Sum of Squares df Mean Square F}\tabcellsep Sig.\\ \multicolumn{2}{l}{1 Regression 1.504}\tabcellsep 1\tabcellsep 1.504\tabcellsep 6.453 .013a\\ Residual\tabcellsep 18.183\tabcellsep \multicolumn{2}{l}{78 .233}\\ Total\tabcellsep 19.688\tabcellsep 79\tabcellsep \\ \multicolumn{4}{l}{a. Predictors: (Constant), Comfortable physical environment}\\ \multicolumn{2}{l}{b. Dependent Variable: Interesting job}\tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_22}Table 19 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{19} \par \begin{longtable}{} \end{longtable} \par \caption{\label{tab_23}Table 19}\end{figure} \begin{figure}[htbp] \noindent\textbf{20} \par \begin{longtable}{P{0.4002427184466019\textwidth}P{0.15266990291262136\textwidth}P{0.07839805825242718\textwidth}P{0.11553398058252426\textwidth}P{0.0866504854368932\textwidth}P{0.016504854368932037\textwidth}} Model\tabcellsep \multicolumn{2}{l}{Unstandardized}\tabcellsep Standardized\tabcellsep t\tabcellsep Sig.\\ \tabcellsep \multicolumn{2}{l}{Coefficients}\tabcellsep Coefficients\tabcellsep \\ \tabcellsep B\tabcellsep \multicolumn{2}{l}{Std. Error Beta}\tabcellsep \\ 1 (Constant)\tabcellsep \multicolumn{2}{l}{1.080 .197}\tabcellsep \tabcellsep \multicolumn{2}{l}{5.475 .000}\\ \multicolumn{2}{l}{Comfortable physical environment .290}\tabcellsep .114\tabcellsep .276\tabcellsep \multicolumn{2}{l}{2.540 .013}\\ a. Dependent Variable: Interesting job\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Table\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_24}Table 20 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{21} \par \begin{longtable}{P{0.3633064516129032\textwidth}P{0.06854838709677419\textwidth}P{0.3633064516129032\textwidth}P{0.027419354838709675\textwidth}P{0.027419354838709675\textwidth}} \multicolumn{2}{l}{Model R}\tabcellsep \multicolumn{3}{l}{R Square Adjusted R Square Std. Error of the Estimate}\\ 1\tabcellsep \multicolumn{2}{l}{.124a .015}\tabcellsep .003\tabcellsep .499\\ \multicolumn{4}{l}{a. Predictors: (Constant), Mutual cooperation}\end{longtable} \par \caption{\label{tab_25}Table 21 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{22} \par \begin{longtable}{P{0.5278947368421053\textwidth}P{0.19236842105263158\textwidth}P{0.04473684210526315\textwidth}P{0.017894736842105262\textwidth}P{0.049210526315789475\textwidth}P{0.017894736842105262\textwidth}} Model\tabcellsep \multicolumn{4}{l}{Sum of Squares df Mean Square F}\tabcellsep Sig.\\ \multicolumn{2}{l}{1 Regression .302}\tabcellsep 1\tabcellsep .302\tabcellsep 1.214 .274a\\ Residual\tabcellsep 19.386\tabcellsep \multicolumn{2}{l}{78 .249}\\ Total\tabcellsep 19.688\tabcellsep 79\tabcellsep \\ \multicolumn{4}{l}{a. Predictors: (Constant), Mutual cooperation}\\ \multicolumn{2}{l}{b. Dependent Variable: Interesting job}\tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_26}Table 22 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{22} \par \begin{longtable}{} \end{longtable} \par \caption{\label{tab_27}Table 22}\end{figure} \footnote{© 2014 Global Journals Inc. (US)} \footnote{© 2014 Global Journals Inc. (US) v.} \footnote{© 2014 Global Journals Inc. (US) estimate was (.483).} \backmatter {\ref 23} \par shows the value of the T-statistic is insignificant for Mutual cooperation. The hypothesis is rejected because the result of insignificance is more than 0.05 (Significance requirement standard < 0.05).\par H 8 : Result shows (Table {\ref 24}) the value of R is (.358), the value of R square is (.128) and the standard error of the estimate was (.469). {\ref 25} shows analysis of variance test statistics (ANOVA) indicates that the model is significant at ? =. 001. Findings that the independent variable has significant relationships with interesting job (F=11. 454) (Sig.001). {\ref 26} shows the value of the T-statistic is significant for Management relation. The hypothesis is accepted because the result of significance is less than 0.05 (Significance requirement standard < 0.05). \subsection[{Table 27 : The Summary of Hypotheses Results}]{Table 27 : The Summary of Hypotheses Results} \subsection[{Hypotheses Results}]{Hypotheses Results}\par H 1 : There is a positive/significant relationship between the salary adequacy and the interesting job. Rejected H 2 : There is a positive/significant relationship between the future security and the interesting job Accepted H 3 : There is a positive/significant relationship between the social dignity/status and the interesting job. Rejected H 4 : There is a positive/significant relationship between the career ambition and the interesting job. Accepted H 5 : There is a positive/significant relationship between the training/development and the interesting job. Accepted H 6 : There is a positive/significant relationship between the comfortable physical environment and the interesting job. Accepted H 7 : There is a positive/significant relationship between the mutual cooperation and the interesting job.\par Rejected H 8 : There is a positive/significant relationship between the management relation and the interesting job. Accepted The study is significant for future security in making job interesting; it indicates that for making job interesting to them security for the future is crucially important. The study is significant for career ambition in making job interesting; it indicates that they have joined in that organization have fulfilled their career so that they feel interested in the job. The study is significant for training/development in making job interesting; it indicates that training and development program can enable them to acquire knowledge in organizational problem solving which make their job interesting to them. The study is significant for comfortable physical environment in making job interesting; it indicates that the working environment has a significant impact on employee motivation for making job interesting. The study is significant for management relation to making job interesting; it indicates that management care on employees have significant impact on their work motivation which make the job interesting to them and they find they have strong ownership in the organization. The study is insignificant for salary adequacy for making job interesting; it indicates that their present salary does not meet their expectation for which they have joined.\par Here management should have to take responsibility for the redesigned salary structure for making them happy for which job is becoming interesting to them. The study is insignificant for social dignity/status for making them interesting in the job; it indicates that social dignity and status not related to making their job interesting. Off the job interesting. The study is insignificant for mutual cooperation for making job interesting; it indicates that this interpersonal relationship with colleagues have minimal impact on doing their job interesting. Here management should take care of conducting different social events on organizational interfaces for making job interesting for employees well being through interaction and cooperation. job motivational factor have minimal impact on making Motivated employees are ambitious and exercise self-control. Motivated employees enjoy their mental and physical work is as natural as play. Given the proper conditions, theory Y managers believe that VI. \subsection[{Conclusion and Managerial}]{Conclusion and Managerial}\par Implication \begin{bibitemlist}{1} \bibitem[Robbins and Coulter ()]{b7}\label{b7} \textit{}, Stephen P Robbins , Mary Coulter . \textit{Management} 2006. Prentice-Hall of India. \bibitem[Sethi and Pinzon ()]{b22}\label{b22} ‘A seven-step strategy to help retain your company's high-impact performers’. 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