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\textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt \textcolor{GJBlue}{\large\bfseries\abstractname\space} }{% \vskip8pt \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt } \usepackage[absolute,overlay]{textpos} \makeatother \usepackage{lineno} \linenumbers \begin{document} \author[1]{Mohammad Azizur Rahman} \affil[1]{ Begum Rokeya University} \renewcommand\Authands{ and } \date{\small \em Received: 13 December 2019 Accepted: 3 January 2020 Published: 15 January 2020} \maketitle \begin{abstract} The paper attempted to explore the most effective factors of demographic outlined affecting employees? motivation. With a view to empirical analysis data were collected from 400 bank employees. In the demographic issues gender, age, experience, pay, type and name of the bank, present and prior designations etc. were considered. For inferential statistics there were five regression models with ANOVA and coefficients models developed in the study \end{abstract} \keywords{age, gender, work experience, initial salary, gross salary, motivation.} \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 and Review Context}]{Introduction and Review Context}\par uman Resource Management (HRM) is regarded as the significant department for talent management.\par The talented employees compulsorily considered as assets in the organization which can be effective through acquisition, development, motivation and maintenance integrated from all units of the bank. HRD selects the right kind of personnel for the committed posts. In addition to this, HRD maintains talent human resources for the bank by applying various motivational techniques and HRs considered as soft assets (www.ventureline.com). Since bank is a service industry, this is why, its sustainability and competitive opportunities depends on how HR Department utilize their potentialities and competencies. The need of talent employees is well recognized in all events of global financial crisis {\ref (2007)} {\ref (2008)} that was mostly connected with ambitious and ineffectual investment decision by the banks \hyperref[b7]{(Islam et al, 2017)}. Bangladesh Bank (BB) and Ministry of Finance (MoF) emphasized to ensure better HRM in the banking sector of Bangladesh through circulars {\ref (BB \& MoF, 2015)}. The circulars are associated with rules and regulations, maintaining banking hours, salaries and incentives, leave and performance appraisal of female employees.\par Motivation means inspiring people to work continuation. Motivated employees do have best quality of performance. Employees' motivation is affected by various factors. The total environment of the banking industry has become changed due to face the competitive sustainability. Basic pay, gross pay, designation, promotion, scoring of banking diploma for promotion, experiences, location or posting, types of bank etc. are the issues of demographic factors. Designation and gross pay are highly correlated for motivation.\par Different emerging theories of motivation support that age, experience, pay, designation etc. affect the motivation. Work experience relates the holding position through up gradation in the organization. Maslow's need hierarchy theory proves the said issues of demographic \hyperref[b9]{(Maslow, 1954)}. On the other supportive part of the Two-Factor Model namely Hygiene and Motivating factors. Employees can enjoy these factors related to opportunities over a long-term service the organization {\ref (Herzberg, 1959}\hyperref[b6]{(Herzberg, , 1987))}. The Two Factor Theory states that human needs are ordered in a series of levels of the hierarchy accordance to importance which creates motivation. \hyperref[b0]{Alderfer's (1969)} ERG theory chains Need-Hierarchy and Two-Factor Theory of motivation. Existence (E) chains the basic need and job security, relatedness (R) actually related to social needs and growth (G) is supportive with status and highest level achievement. If a higher order need constants or unfulfilled, an individual can degenerate to lower order needs which cause satisfy easily. Mc Clellend's Three Needs {\ref Model (1961)} is also supportive to Needs Theory of Maslow whereas it can be seen that need for affiliation (social need), need for power related to ego/status and remaining one that is need for achievement connected to challenging achievement. An employer does have the ability to understand the needs so as to meet these needs having a better chance of involving and retaining talent employees. A survey conducted by Watson Wyatt Worldwide showed that 71\% people deliberate money as the main reason to stay as followed by promotion prospect (33\%) \hyperref[b3]{(Endress, 2007)}. Abraham Harold Maslow suggested a theory that delineated five hierarchical needs which could also be functional to an organization and its employees' performance \hyperref[b4]{(Gordon, 1965)}. Without one of the lower ones the second need could be achieved or satisfied. Maslow's needs theory is static imperative and applicable in today's business organizations, for every organization that seek to obtain success and excellence reflects positively the organizational culture, HRM and the employee's productivity, to attain organizational excellence and create good environment, better and enjoyable work environment and achieve goal at the right time then a effort and application of the theory is supreme \hyperref[b11]{(Jerome, 2013)}. When an organization tries to know what drives personnel to work more, it is in a better position to influence them to perform well \hyperref[b8]{(Kovach, 1987)}. Again it clarifies that employees performance can be examined by three indicators namely ability, workplace environment, and motivation \hyperref[b5]{(Griffin, 1990)}. It is critical job for the managers in terms of motivating subordinates since it is noticed that there globally changes in demographic factors, as well as highly advanced technology \hyperref[b12]{(Wiley, 1997)}. This only emphasized the need to explore what motivates employees in order to get better performance. The author highlighted the most and less important factors of motivation in workplace from the earlier management. The most important factors in 1946 (appreciation), 1980 (interesting work), 1986 (interesting work) and 1992 (good wages) and less important factors were in 1946 (discipline), 1980 (discipline), 1986 (personal problems), and 1992 (personal problems). The study received the most striking findings that money and job security are the clear indicators of motivation. The effective pay program, a primary motivating factor is critical due to individuals and psychological possessions. It is does have immaterial value of the reward, but the increase in self-regard that public recognition accompanying with monetary compensation affords {\ref (Dawson and Dawson, 1990}). In the past, huge numbers of research studies on employee motivation in the different perspectives already conducted. Very few of them were conducted relating to demographic composition with motivation. The present paper stressed to observe the influential factors of motivation with the sources variables of demographic issues among the employees in the sampled banks. The study also designed to explore the most effective factors in motivation.\par Research questions: Does demographic composition affect employee motivation in the banking sector?\par Objective of the study: The main objective of the study was to scan effective factors in demographic and motivating focuses among the employees of the banking sector in Bangladesh. \section[{II.}]{II.} \section[{Research Framework}]{Research Framework} \section[{Results and Discussion}]{Results and Discussion} \section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par Volume XX Issue XIII Version I Year 2020 ( ) Remarks: The fitted linear regression model for working experience (year) and other independent variables listed. The model is good fit for this dataset and the coefficient of multiple determinations R 2 is 0.942 (Table \hyperref[tab_2]{2}). Since the R 2 \textasciitilde 0.94 then the dependent variable work experience 94\% explained by the independent variables/predictors. Hypothesis: The null hypothesis, H 0 :B 0 = B 1 = B 2 =???..= B 13 =0\par The alternative hypothesis, H 1 :B 0 = B 1 = B 2 =???..= B 13 ?0\par Remarks: From the Table \hyperref[tab_3]{3} ANOVA table the fitted regression model F-test statistic value is 483.944 and the significance value (p value) 0.000 which is less than 0.05 (p value50 years\tabcellsep 26.8\tabcellsep \tabcellsep \tabcellsep \\ Marital status\tabcellsep Married Single\tabcellsep 99.0 1.0\tabcellsep 1.010\tabcellsep 0.100\tabcellsep 9.86\%\\ Religion\tabcellsep Islam Hindu\tabcellsep 89.3 10.75\tabcellsep 1.110\tabcellsep 0.321\tabcellsep 18.94\%\\ Type of bank\tabcellsep Public Private\tabcellsep 80.0 20.0\tabcellsep 1.200\tabcellsep 0.401\tabcellsep 33.38\%\\ \tabcellsep Rangpur\tabcellsep 41.5\tabcellsep \tabcellsep \tabcellsep \\ Location (District)\tabcellsep Dinajpur\tabcellsep 42.8\tabcellsep 1.743\tabcellsep 0.712\tabcellsep 40.88\%\\ \tabcellsep Nilphamari\tabcellsep 15.8\tabcellsep \tabcellsep \tabcellsep \\ Educational Qualification\tabcellsep Bachelor Master\tabcellsep 17 83\tabcellsep 2.660\tabcellsep 0.749\tabcellsep 28.15\%\\ \tabcellsep Science\tabcellsep 31.8\tabcellsep \tabcellsep \tabcellsep \\ Discipline/ Group\tabcellsep Humanities\tabcellsep 50\tabcellsep 1.865\tabcellsep 0.695\tabcellsep 37.26\%\\ \tabcellsep Business\tabcellsep 18.3\tabcellsep \tabcellsep \tabcellsep \\ \tabcellsep Nil\tabcellsep 62.3\tabcellsep \tabcellsep \tabcellsep \\ Bank Diploma\tabcellsep Part-1\tabcellsep 21.3\tabcellsep 0.543\tabcellsep 0.761\tabcellsep 140.34\%\\ \tabcellsep Part-2\tabcellsep 16.5\tabcellsep \tabcellsep \tabcellsep \\ \tabcellsep 1-5\tabcellsep 11.3\tabcellsep \tabcellsep \tabcellsep \\ Job Experience (Year)\tabcellsep 6-10 Years 11-15 Years 16-20 Years\tabcellsep 45.5 8.0 4.0\tabcellsep 2.985\tabcellsep 1.483\tabcellsep 49.70\%\\ \tabcellsep 20 Years (more)\tabcellsep 31.3\tabcellsep \tabcellsep \tabcellsep \\ Present Designation/ Post\tabcellsep Entry Level Officer Executive Level Officer\tabcellsep 77.4 22.9\tabcellsep 5.505\tabcellsep 1.319\tabcellsep 23.96\%\\ Present Basic Pay (Tk.)\tabcellsep <10000-300000/-30000/-(more)\tabcellsep 49.0 511.0\tabcellsep 35512\tabcellsep 14246\tabcellsep 40.12\%\\ Present Gross Pay (Tk.)\tabcellsep <20000-40000/->40000-60000/-60000/-(more)\tabcellsep 19.0 38.0 43.0\tabcellsep 60945\tabcellsep 26809\tabcellsep 44\%\\ First Joining Designation\tabcellsep Entry Level Officer Executive Level Officer\tabcellsep 99.5 0.5\tabcellsep 4.110\tabcellsep 1.925\tabcellsep 46.85\%\\ Initial Basic Pay (Tk.)\tabcellsep <10000-20000/->20000-30000/-\tabcellsep 96.6 3.5\tabcellsep 35512\tabcellsep 1424 6\tabcellsep 40.12\%\\ Initial Gross Pay (Tk.)\tabcellsep <20000-40000/-40000-60000/-(more)\tabcellsep 98.8 1.2\tabcellsep 13401\tabcellsep 16433\tabcellsep 122.63\%\end{longtable} \par {\small\itshape [Note: Regression Models (Table 2,\hyperref[b4]{5,}\hyperref[b7]{8,}\hyperref[b10]{11,} \& 14) ]} \caption{\label{tab_1}Table 1 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{2} \par \begin{longtable}{P{0.03695652173913043\textwidth}P{0.04927536231884058\textwidth}P{0.08007246376811594\textwidth}P{0.13550724637681158\textwidth}P{0.16630434782608697\textwidth}P{0.08623188405797101\textwidth}P{0.17862318840579708\textwidth}P{0.012318840579710146\textwidth}P{0.03695652173913043\textwidth}P{0.06775362318840579\textwidth}} Model\tabcellsep R\tabcellsep R Square\tabcellsep Adjusted R Square\tabcellsep Std. Err. of Estimates\tabcellsep R 2 Change\tabcellsep \multicolumn{2}{l}{Change Statistics F df1}\tabcellsep df2\tabcellsep Sig. F\\ 1\tabcellsep 0.971 a\tabcellsep 0.942\tabcellsep 0.940\tabcellsep 2.741\tabcellsep 0.94\tabcellsep 483.94\tabcellsep 13\tabcellsep 386\tabcellsep 0.000\end{longtable} \par {\small\itshape [Note: a. Predictors: (Constant), Banking Diploma, Group/ Subject, Gender, Religion, Educational Qualification, Marital Status, Location (District), Type of Bank, Present Designation, First joining designation/post, Name of the Bank, Age, Location (Upazilla). b. Dependent Variable: Working experience (year).]} \caption{\label{tab_2}Table 2 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{3} \par \begin{longtable}{P{0.0069672131147540985\textwidth}P{0.19508196721311474\textwidth}P{0.2786885245901639\textwidth}P{0.06967213114754098\textwidth}P{0.16721311475409836\textwidth}P{0.05573770491803279\textwidth}P{0.07663934426229509\textwidth}} \tabcellsep Model\tabcellsep Sum of Squares\tabcellsep df\tabcellsep Mean Square\tabcellsep F\tabcellsep Sig.\\ \tabcellsep Regression\tabcellsep 47261.760\tabcellsep 13\tabcellsep 3635.520\tabcellsep 483.944\tabcellsep 0.000 b\\ 1\tabcellsep Residual\tabcellsep 2899.737\tabcellsep 386\tabcellsep 7.512\tabcellsep \\ \tabcellsep Total\tabcellsep 50161.498\tabcellsep 399\tabcellsep \tabcellsep \end{longtable} \par {\small\itshape [Note: a. Dependent Variable: Working experience (year) b. Predictors: (Constant), Banking Diploma, Group/ Subject, Gender, Religion, Educational Qualification, Marital Status, Location (District), Type of Bank, Present Designation, First joining designation/post, Name of the Bank, Age, Location (Upazilla).]} \caption{\label{tab_3}Table 3 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{4} \par \begin{longtable}{P{0.19103960396039604\textwidth}P{0.32064356435643565\textwidth}P{0.08794554455445544\textwidth}P{0.03408415841584159\textwidth}P{0.10814356435643564\textwidth}P{0.0425\textwidth}P{0.03408415841584159\textwidth}P{0.03155940594059406\textwidth}} \tabcellsep Model\tabcellsep \multicolumn{3}{l}{Unstandardized Coefficients}\tabcellsep Standardized Coefficients\tabcellsep t\tabcellsep Sig.\\ \tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep \tabcellsep Beta\tabcellsep \\ \tabcellsep (Constant: Experience)\tabcellsep -16.110 (B 0 )\tabcellsep 2.519\tabcellsep \tabcellsep \tabcellsep -6.395\tabcellsep 0.000\\ \tabcellsep Gender\tabcellsep 0.664 (B 1 )\tabcellsep 0.402\tabcellsep \tabcellsep 0.022\tabcellsep 1.652\tabcellsep 0.099\\ \tabcellsep Age\tabcellsep 0.800 (B 2 )\tabcellsep 0.034\tabcellsep \tabcellsep 0.663\tabcellsep 23.649\tabcellsep 0.000\\ \tabcellsep Marital status\tabcellsep 0.697 (B 3 )\tabcellsep 1.417\tabcellsep \tabcellsep 0.006\tabcellsep 0.492\tabcellsep 0.623\\ \tabcellsep Religion\tabcellsep -0.535 (B 4 )\tabcellsep 0.439\tabcellsep \tabcellsep -0.015\tabcellsep -1.220\tabcellsep 0.223\\ \tabcellsep Present designation\tabcellsep 1.294 (B 5 )\tabcellsep 0.181\tabcellsep \tabcellsep 0.152\tabcellsep 7.150\tabcellsep 0.000\\ 1\tabcellsep First joining post Type of Bank\tabcellsep -1.663 (B 6 ) -0.567 (B 7 )\tabcellsep 0.147 0.636\tabcellsep \tabcellsep -0.286 -0.020\tabcellsep -11.334 -0.891\tabcellsep 0.000 0.374\\ \tabcellsep Name of the Bank\tabcellsep -0.192 (B 8 )\tabcellsep 0.113\tabcellsep \tabcellsep -0.039\tabcellsep -1.697\tabcellsep 0.091\\ \tabcellsep Location (District)\tabcellsep 0.341 (B 9 )\tabcellsep 0.665\tabcellsep \tabcellsep 0.020\tabcellsep 0.512\tabcellsep 0.609\\ \tabcellsep Location (Upazilla)\tabcellsep -0.055 (B 10 )\tabcellsep 0.098\tabcellsep \tabcellsep -0.023\tabcellsep -0.555\tabcellsep 0.579\\ \tabcellsep Educational qualif.\tabcellsep -0.860 (B 11 )\tabcellsep 0.257\tabcellsep \tabcellsep -0.057\tabcellsep -3.343\tabcellsep 0.001\\ \tabcellsep Group/ Subject\tabcellsep 0.001 (B 12 )\tabcellsep 0.206\tabcellsep \tabcellsep 0.000\tabcellsep 0.005\tabcellsep 0.996\\ \tabcellsep Banking Diploma\tabcellsep 0.013 (B 13 )\tabcellsep 0.223\tabcellsep \tabcellsep 0.001\tabcellsep 0.059\tabcellsep 0.953\\ \multicolumn{3}{l}{a. Dependent Variable: Working experience (year).}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{3}{l}{The fitted regression model can be defined as,}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{2}{l}{??????????????????????????????????(????????)}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \tabcellsep \multicolumn{7}{l}{= ?16.110 * (????????????????) + 0.664 * ???????????? + 0.800 * ?????? + 0.697 * ?????????????????????????? ? 0.535}\\ \tabcellsep \multicolumn{6}{l}{* ???????????????? + 1.294 * ???????????????????????????????????? ? 1.663 * ?????????????????????????????????????????????? ? 0.567}\\ \tabcellsep \multicolumn{6}{l}{* ???????????????????? ? 0.192 * ????????????????????????? + 0.341 * ????????????????(????????????????) ? 0.055}\\ \tabcellsep \multicolumn{7}{l}{* ????????????????(????????????????) ? 0.860 * ???????????????????????????????????????????????? + 0.001 * ???????????????????????????? + 0.013}\\ \tabcellsep \multicolumn{2}{l}{* ????????????????????????????}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{4}{l}{Remarks: From the fitted (Table 4) the age, present}\tabcellsep \multicolumn{4}{l}{or major discipline does not directly affect motivation}\\ \multicolumn{4}{l}{designation, first joining designation, educational}\tabcellsep \multicolumn{4}{l}{(? 12 ) =0.001 and p value is 0.996. Among the significant}\\ \multicolumn{4}{l}{qualification were statistically significance at 5\% level of}\tabcellsep \multicolumn{4}{l}{independent predictors age (0.800) and present}\\ \multicolumn{4}{l}{significance. Therefore, employees' motivation is}\tabcellsep \multicolumn{4}{l}{designation (1.294) were positive contribution on the}\\ \multicolumn{4}{l}{exaggerated by the control variable namely age,}\tabcellsep \multicolumn{3}{l}{dependent variable working experience (year).}\\ \multicolumn{4}{l}{designation, and educational qualification. The subject}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_4}Table 4 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{} \par \begin{longtable}{P{0.12808219178082192\textwidth}P{0.04657534246575342\textwidth}P{0.04657534246575342\textwidth}P{0.09897260273972602\textwidth}P{0.18630136986301368\textwidth}P{0.04657534246575342\textwidth}P{0.16883561643835615\textwidth}P{0.02910958904109589\textwidth}P{0.034931506849315064\textwidth}P{0.06404109589041096\textwidth}} \tabcellsep \tabcellsep \tabcellsep \tabcellsep Std. Err. of\tabcellsep \tabcellsep \multicolumn{2}{l}{Change Statistics}\tabcellsep \tabcellsep \\ Model\tabcellsep R\tabcellsep R 2\tabcellsep Adjusted R 2\tabcellsep the Estimates\tabcellsep R 2\tabcellsep F change\tabcellsep df1\tabcellsep df2\tabcellsep Sig. F\\ 1\tabcellsep 0.877 a\tabcellsep 0.769\tabcellsep 0.762\tabcellsep 6987.86\tabcellsep 0.769\tabcellsep 99.1\tabcellsep 13\tabcellsep 386\tabcellsep 0.000\\ \multicolumn{2}{l}{a. Predictors: (}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par {\small\itshape [Note: (District), Type of Bank, Present Designation, First joining designation/post, Name of the Bank, Age, Location (Upazilla). a.Dependent Variable: Present Basic Salary.]} \caption{\label{tab_5}}\end{figure} \begin{figure}[htbp] \noindent\textbf{6} \par \begin{longtable}{P{0.0688095238095238\textwidth}P{0.11333333333333333\textwidth}P{0.4047619047619047\textwidth}P{0.04047619047619047\textwidth}P{0.14976190476190476\textwidth}P{0.028333333333333332\textwidth}P{0.04452380952380952\textwidth}} \tabcellsep Model\tabcellsep Sum of Squares\tabcellsep df\tabcellsep Mean Square\tabcellsep F\tabcellsep Sig.\\ \tabcellsep Regression\tabcellsep 62872948092.258\tabcellsep 13\tabcellsep 4836380622.481\tabcellsep 99.045\tabcellsep 0.000 b\\ 1\tabcellsep Residual\tabcellsep 18848472031.68\tabcellsep 386\tabcellsep 48830238.424\tabcellsep \\ \tabcellsep Total\tabcellsep 81721420123.94\tabcellsep 399\tabcellsep \tabcellsep \\ \tabcellsep \tabcellsep \multicolumn{3}{l}{a. Dependent Variable: Present Basic Salary}\tabcellsep \\ \multicolumn{2}{l}{b. Predictors: (}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_6}Table 6 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{6} \par \begin{longtable}{P{0.5331010452961672\textwidth}P{0.3168989547038327\textwidth}} ) ANOVA (Analysis of\tabcellsep statistically strongly significant at 5\% level of\\ Variance) table the fitted regression model F-test\tabcellsep significance. That is alternative hypothesis is accepted.\\ statistic value is 99.045 and the significance value (p\tabcellsep \\ value) 0.000. Then all the regression coefficients were\tabcellsep \end{longtable} \par \caption{\label{tab_7}Table 6}\end{figure} \begin{figure}[htbp] \noindent\textbf{7} \par \begin{longtable}{P{0.0038636363636363634\textwidth}P{0.1506818181818182\textwidth}P{0.28204545454545454\textwidth}P{0.12363636363636363\textwidth}P{0.1506818181818182\textwidth}P{0.06568181818181817\textwidth}P{0.07340909090909091\textwidth}} \tabcellsep Model\tabcellsep \multicolumn{2}{l}{Unstandardized Coefficients}\tabcellsep Standardized Coefficients\tabcellsep t\tabcellsep Sig.\\ \tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep \tabcellsep \\ \tabcellsep (Cons: Present basic pay)\tabcellsep -38670.051 (B 0 )\tabcellsep 6422.814\tabcellsep \tabcellsep -6.021\tabcellsep 0.000\\ 1\tabcellsep Gender\tabcellsep 554.461 (B 1 )\tabcellsep 1024.262\tabcellsep 0.014\tabcellsep 0.541\tabcellsep 0.589\\ \tabcellsep Age\tabcellsep 709.891 (B 2 )\tabcellsep 86.209\tabcellsep 0.461\tabcellsep 8.235\tabcellsep 0.000\end{longtable} \par {\small\itshape [Note: © 2020 Global Journals]} \caption{\label{tab_8}Table 7 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{5} \par \begin{longtable}{P{0.30482758620689654\textwidth}P{0.24855172413793103\textwidth}P{0.0949655172413793\textwidth}P{0.068\textwidth}P{0.06917241379310345\textwidth}P{0.06448275862068965\textwidth}} Marital Status\tabcellsep -5124.611(B 3 )\tabcellsep 3613.823\tabcellsep -0.036\tabcellsep -1.418\tabcellsep 0.157\\ Religion\tabcellsep 1360.314 (B 4 )\tabcellsep 1118.681\tabcellsep 0.031\tabcellsep 1.216\tabcellsep 0.225\\ Present designation\tabcellsep 6201.641(B 5 )\tabcellsep 461.322\tabcellsep 0.571\tabcellsep 13.443\tabcellsep 0.000\\ First joining post\tabcellsep 1902.867 (B 6 )\tabcellsep 374.125\tabcellsep 0.256\tabcellsep 5.086\tabcellsep 0.000\\ Type of Bank\tabcellsep 1834.212 (B 7 )\tabcellsep 1621.950\tabcellsep 0.051\tabcellsep 1.131\tabcellsep 0.259\\ Name of the Bank\tabcellsep 282.551 (B 8 )\tabcellsep 289.017\tabcellsep 0.045\tabcellsep 0.978\tabcellsep 0.329\\ Location (District)\tabcellsep -1672.287 (B 9 )\tabcellsep 1695.606\tabcellsep -0.078\tabcellsep -0.986\tabcellsep 0.325\\ Location (Upazilla)\tabcellsep 74.986 (B 10 )\tabcellsep 250.856\tabcellsep 0.024\tabcellsep 0.299\tabcellsep 0.765\\ Educational qualifi.\tabcellsep 1623.504 (B 11 )\tabcellsep 656.077\tabcellsep 0.085\tabcellsep 2.475\tabcellsep 0.014\\ Group/ Subject\tabcellsep -323.377 (B 12 )\tabcellsep 525.967\tabcellsep -0.016\tabcellsep -0.615\tabcellsep 0.539\\ Banking Diploma\tabcellsep 878.569 (B 13 )\tabcellsep 569.666\tabcellsep 0.047\tabcellsep 1.542\tabcellsep 0.124\\ a.\tabcellsep \multicolumn{3}{l}{Dependent Variable: Present Basic Salary (B 0 )}\tabcellsep \tabcellsep \\ \multicolumn{2}{l}{The fitted regression model can be defined as,Model:}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Present Basic Salary =\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ ?38670.051\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par {\small\itshape [Note: * (????????????????) + 554.461 * ???????????? + 709.891 * ?????? ? 5124.611 * ?????????????????????????? + 1360.314 * ???????????????? + 6201.641 * ???????????????????????????????????? + 1902.867 * ?????????????????????????????????????????????? + 1834.212 * ???????????????????? + 282.551 * ????????????????????????? ? 1672.287 * ????????????????(????????????????) + 74.986 * ????????????????(????????????????) + 1623.504 * ???????????????????????????????????????????????? ? 323.377 * ???????????????????????????? + 878.569 * ???????????????????????????? Remarks: From the fitted (Table 7) the age, present designation, first joins designation, educational qualification were statistically significance at 5\% level of significance. Among the significant independent predictors age (709.891), present designation (6201.641) and first joining designation/post (1902.867) were positive contribution to the dependent variable present basic salary.]} \caption{\label{tab_9}Table 5 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{8} \par \begin{longtable}{P{0.4854312354312354\textwidth}P{0.01585081585081585\textwidth}P{0.01585081585081585\textwidth}P{0.11888111888111888\textwidth}P{0.08717948717948718\textwidth}P{0.01585081585081585\textwidth}P{0.07925407925407926\textwidth}P{0.003962703962703962\textwidth}P{0.005944055944055944\textwidth}P{0.021794871794871794\textwidth}} Model\tabcellsep R\tabcellsep R 2\tabcellsep Adjusted R 2\tabcellsep Std. Err. of the Estimates\tabcellsep R 2\tabcellsep \multicolumn{3}{l}{Change Statistics F change df1 df2}\tabcellsep Sig. F\\ 1\tabcellsep 0.826 a\tabcellsep 0.682\tabcellsep 0.672\tabcellsep 15365.25\tabcellsep 0.682\tabcellsep 63.742\tabcellsep 13\tabcellsep 386\tabcellsep 0.000\\ \multicolumn{10}{l}{a. Predictors: (Constant), Banking Diploma, Group/ Subject, Gender, Religion, Educational Qualification, Marital Status,}\\ \multicolumn{10}{l}{Location (District), Type of Bank, Present Designation, First joining designation/post, Name of the Bank, Age, Location}\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep (Upazilla)\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{b. Dependent Variable: Present gross salary}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_10}Table 8 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{9} \par \begin{longtable}{P{0.637032967032967\textwidth}P{0.09153846153846154\textwidth}P{0.035494505494505495\textwidth}P{0.07659340659340659\textwidth}P{0.0018681318681318681\textwidth}P{0.0074725274725274725\textwidth}} Model\tabcellsep Sum of Squares\tabcellsep df\tabcellsep Mean Square\tabcellsep F\tabcellsep Sig.\\ \multicolumn{3}{l}{1 Regression 195635872639.298 13}\tabcellsep \multicolumn{3}{l}{15048913279.950 63.742 0.000 b}\\ Residual\tabcellsep 91131097360.703\tabcellsep \multicolumn{2}{l}{386 236090925.805}\tabcellsep \tabcellsep \\ Total\tabcellsep \multicolumn{2}{l}{286766970000.000 399}\tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{3}{l}{a. Dependent Variable: Present gross salary}\tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{6}{l}{b. Predictors: (Constant), Banking Diploma, Group/ Subject, Gender, Religion,}\\ \multicolumn{6}{l}{Educational Qualification, Marital Status, Location (District), Type of Bank, Present}\\ \multicolumn{6}{l}{Designation, First joining designation/post, Name of the Bank, Age, Location}\\ (Upazilla)\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_11}Table 9 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{10} \par \begin{longtable}{P{0.16118963486454652\textwidth}P{0.4094817432273262\textwidth}P{0.09411071849234393\textwidth}P{0.060070671378091876\textwidth}P{0.04905771495877503\textwidth}P{0.03854534746760895\textwidth}P{0.037544169611307424\textwidth}} \tabcellsep Model\tabcellsep \multicolumn{2}{l}{Unstandardized Coefficients}\tabcellsep Standardized Coefficients\tabcellsep t\tabcellsep Sig.\\ \tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep \tabcellsep \\ \tabcellsep (Constant: Present gross)\tabcellsep -76402.124\tabcellsep 14122.791\tabcellsep \tabcellsep -5.410\tabcellsep 0.000\\ \tabcellsep Gender\tabcellsep 1172.807\tabcellsep 2252.196\tabcellsep 0.016\tabcellsep 0.521\tabcellsep 0.603\\ \tabcellsep Age\tabcellsep 1244.412\tabcellsep 189.561\tabcellsep 0.432\tabcellsep 6.565\tabcellsep 0.000\\ \tabcellsep Marital Status\tabcellsep -9286.091\tabcellsep 7946.247\tabcellsep -0.035\tabcellsep -1.169\tabcellsep 0.243\\ \tabcellsep Religion\tabcellsep 1028.259\tabcellsep 2459.809\tabcellsep 0.012\tabcellsep 0.418\tabcellsep 0.676\\ \tabcellsep Present designation\tabcellsep 10226.033\tabcellsep 1014.378\tabcellsep 0.503\tabcellsep 10.081\tabcellsep 0.000\\ 1\tabcellsep First joining design. Type of Bank\tabcellsep 3129.422 15523.420\tabcellsep 822.645 3566.423\tabcellsep 0.225 0.232\tabcellsep 3.804 4.353\tabcellsep 0.000 0.000\\ \tabcellsep Name of the Bank\tabcellsep 420.502\tabcellsep 635.505\tabcellsep 0.036\tabcellsep 0.662\tabcellsep 0.509\\ \tabcellsep Location (District)\tabcellsep -4565.112\tabcellsep 3728.379\tabcellsep -0.113\tabcellsep -1.224\tabcellsep 0.222\\ \tabcellsep Location (Upazilla)\tabcellsep 326.900\tabcellsep 551.594\tabcellsep 0.057\tabcellsep 0.593\tabcellsep 0.554\\ \tabcellsep Educational qualification\tabcellsep 2791.159\tabcellsep 1442.615\tabcellsep 0.078\tabcellsep 1.935\tabcellsep 0.054\\ \tabcellsep Group/ Subject\tabcellsep -282.423\tabcellsep 1156.521\tabcellsep -0.007\tabcellsep -0.244\tabcellsep 0.807\\ \tabcellsep Banking Diploma\tabcellsep 1700.733\tabcellsep 1252.610\tabcellsep 0.048\tabcellsep 1.358\tabcellsep 0.175\\ \tabcellsep \tabcellsep \multicolumn{3}{l}{a. Dependent Variable: Present gross salary}\tabcellsep \tabcellsep \\ \multicolumn{3}{l}{The fitted regression model can be defined as,}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{2}{l}{Present Gross Salary}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \tabcellsep \multicolumn{6}{l}{= ?76402.124 * (????????????????) + 1172.807 * ???????????? + 1244.412 * ?????? ? 9286.091 * ??????????????????????????}\\ \tabcellsep \multicolumn{6}{l}{+ 1028.259 * ???????????????? + 10226.033 * ???????????????????????????????????? + 3129.422 * ??????????????????????????????????????????????}\\ \tabcellsep \multicolumn{6}{l}{+ 15523.420 * ???????????????????? + 420.502 * ????????????????????????? ? 4565.112 * ????????????????(????????????????)}\\ \tabcellsep \multicolumn{5}{l}{+ 326.900 * ????????????????(????????????????) + 2791.159 * ???????????????????????????????????????????????? ? 282.423}\tabcellsep \\ \tabcellsep \multicolumn{3}{l}{* ???????????????????????????? + 1700.733 * ????????????????????????????}\tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{4}{l}{Remarks: From the (Table 10) fitted the age, present}\tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{4}{l}{designation, first joins designation, educational}\tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{4}{l}{qualification were statistically significance at 5\% level of}\tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{4}{l}{significance. Among the significant independent}\tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{4}{l}{predictors age (1244.412), present designation}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par {\small\itshape [Note: (10226.033), first joining designation/post (3129.422) and type of bank (15523.420) were positive contribution to the dependent variable Present Gross Salary. Therefore, there is a strong association among age,]} \caption{\label{tab_12}Table 10 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{11} \par \begin{longtable}{P{0.12064516129032257\textwidth}P{0.04387096774193548\textwidth}P{0.04387096774193548\textwidth}P{0.09322580645161291\textwidth}P{0.19741935483870968\textwidth}P{0.04387096774193548\textwidth}P{0.2193548387096774\textwidth}P{0.01096774193548387\textwidth}P{0.016451612903225808\textwidth}P{0.060322580645161286\textwidth}} Model\tabcellsep R\tabcellsep R 2\tabcellsep Adjusted R 2\tabcellsep Std. Err. of the Estimates\tabcellsep R 2\tabcellsep \multicolumn{3}{l}{Change Statistics F change df1 df2}\tabcellsep Sig. F\\ 1\tabcellsep 0.689 a\tabcellsep 0.475\tabcellsep 0.457\tabcellsep 5713.00316\tabcellsep 0.475\tabcellsep 26.857\tabcellsep 13\tabcellsep 386\tabcellsep 0.000\\ \multicolumn{2}{l}{a. Predictors: (}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par {\small\itshape [Note: b. Dependent Variable: Initial basic salaryRemarks:The fitted linear regression model (]} \caption{\label{tab_13}Table 11 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{11} \par \begin{longtable}{P{0.85\textwidth}} )\end{longtable} \par \caption{\label{tab_14}Table 11}\end{figure} \begin{figure}[htbp] \noindent\textbf{13} \par \begin{longtable}{P{0.26576819407008084\textwidth}P{0.13288409703504042\textwidth}P{0.13975741239892184\textwidth}P{0.09164420485175202\textwidth}P{0.10768194070080861\textwidth}P{0.05498652291105122\textwidth}P{0.057277628032345006\textwidth}} Year 2020\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ 8\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Volume XX Issue XIII Version I\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ )\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ A\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ (\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Global Journal of Management and Business Research\tabcellsep Model\tabcellsep \multicolumn{2}{l}{Unstandardized Coefficients}\tabcellsep Standardized Coefficients\tabcellsep t\tabcellsep Sig.\\ \tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep \tabcellsep \\ \tabcellsep (Constant: Initial Basic Pay)\tabcellsep 6234.435\tabcellsep 5244.365\tabcellsep \tabcellsep 1.189\tabcellsep 0.235\\ 1\tabcellsep Gender Age\tabcellsep -350.697 -121.912\tabcellsep 838.100 71.022\tabcellsep -0.017 -0.146\tabcellsep -0.418 -1.717\tabcellsep 0.676 0.087\\ \tabcellsep Marital Status\tabcellsep 1247.972\tabcellsep 2977.753\tabcellsep 0.016\tabcellsep 0.419\tabcellsep 0.675\\ \multicolumn{2}{l}{© 2020 Global Journals}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_15}Table 13 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{12} \par \begin{longtable}{P{0.17435897435897435\textwidth}P{0.15256410256410255\textwidth}P{0.15256410256410255\textwidth}P{0.13076923076923078\textwidth}P{0.13076923076923078\textwidth}P{0.10897435897435896\textwidth}} Religion\tabcellsep -21.884\tabcellsep 918.839\tabcellsep -0.001\tabcellsep -0.024\tabcellsep 0.981\end{longtable} \par {\small\itshape [Note: * ?????????? ???? ?????????????? ? 199.290 * ?????????????? ?????????????? Comment: From the fitted coefficients (Table13) the, first name of the bank were statistically significance at 5\% level of significance. Among the significant independent predictors first joining designation/post (2220.853) and name of bank (1064.391) were positive contribution to the dependent variable Initial Basic Salary.]} \caption{\label{tab_16}Table 12 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{14} \par \begin{longtable}{P{0.11987179487179486\textwidth}P{0.04358974358974359\textwidth}P{0.04358974358974359\textwidth}P{0.23974358974358972\textwidth}P{0.05993589743589743\textwidth}P{0.04358974358974359\textwidth}P{0.19615384615384615\textwidth}P{0.010897435897435897\textwidth}P{0.032692307692307694\textwidth}P{0.05993589743589743\textwidth}} Model\tabcellsep R\tabcellsep R 2\tabcellsep \multicolumn{2}{l}{Adjusted R 2 Std. Err. of the Estimates}\tabcellsep R 2\tabcellsep \multicolumn{2}{l}{Change Statistics F change df1}\tabcellsep df2\tabcellsep Sig. F\\ 1\tabcellsep 0.592 a\tabcellsep 0.351\tabcellsep 0.329\tabcellsep 13459.34450\tabcellsep 0.351\tabcellsep 16.058\tabcellsep 13\tabcellsep 386\tabcellsep 0.000\\ \multicolumn{2}{l}{a. Predictors: (}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_17}Table 14 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{15} \par \begin{longtable}{P{0.23831775700934577\textwidth}P{0.11121495327102802\textwidth}P{0.23831775700934577\textwidth}P{0.0397196261682243\textwidth}P{0.15093457943925231\textwidth}P{0.027803738317757006\textwidth}P{0.043691588785046725\textwidth}} \tabcellsep Model\tabcellsep Sum of Squares\tabcellsep df\tabcellsep Mean Square\tabcellsep F\tabcellsep Sig.\\ \tabcellsep Regression\tabcellsep 37815785084.065\tabcellsep 13\tabcellsep 2908906544.928\tabcellsep 16.058\tabcellsep 0.000 b\\ 1\tabcellsep Residual\tabcellsep 69925426394.726\tabcellsep 386\tabcellsep 181153954.390\tabcellsep \\ \tabcellsep Total\tabcellsep 107741211478.791\tabcellsep 399\tabcellsep \tabcellsep \\ \multicolumn{3}{l}{a. Dependent Variable: Initial gross salary}\tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{2}{l}{b. Predictors: (}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_18}Table 15 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{18} \par \begin{longtable}{P{0.11915887850467288\textwidth}P{0.037449933244325766\textwidth}P{0.4539385847797062\textwidth}P{0.09759679572763685\textwidth}P{0.04652870493991989\textwidth}P{0.029506008010680908\textwidth}P{0.041989319092122834\textwidth}P{0.023831775700934577\textwidth}} Dependent variable\tabcellsep R 2\tabcellsep Standardized coefficient\tabcellsep Effective factors\tabcellsep \multicolumn{4}{l}{Motivation}\\ Work experience Present basic pay Present gross pay Initial basic pay Initial gross pay\tabcellsep 94\% 77\% 68.2\% 47.5\% 35.1\%\tabcellsep Age (?=0.663), present designation (? = 0.152) Present designation (?=0.571), age (?=0.461), and first joining designation (?=0.256) Present designation (?=0.503), age (?=0.432), type of bank (?=0.232)and first joining designation (?=0.225) First joining designation (?=0.551), and Name of the bank (?=0.326) First joining designation (?=0.519), and Name of the bank (?=0.337)\tabcellsep Age Present designation Present designation First joining designation\tabcellsep Therefore, motivation is highly\tabcellsep associated and modified by\tabcellsep demographic effective factors already\tabcellsep explored in column 4.\end{longtable} \par \caption{\label{tab_19}Table 18 :}\end{figure} \footnote{© 2020 Global Journals} \footnote{Exploring Effective Factors in the Demographic and Motivation I ssues of the Banking Sector of Bangladesh} \backmatter \subsection[{Acknowledgement}]{Acknowledgement}\par The author of this article is grateful to the honorable Professor Dr. Md. Omar Faruk Sarker (PhD Supervisor of the author), Marketing Department, \& Dr. Md. Kamruzzaman (PhD Co-Supervisor of the author), Associate Professor (Statistics), IBS, University of Rajshahi, Bangladesh. \par Remarks: From the Table {\ref 15} (ANOVA) the fitted regression model F-test statistic value is 16.058 and the significance value (p value) 0.000. Then all the regression coefficients were statistically significant at 5\% level of significance. That is the null hypothesis is rejected. Therefore, all the regression coefficients were not zero (0). {\ref 16} the study observed that present designation, first joining and name of the bank were statistically significance at 5\% level of significance. Among the significant independent predictors first joining designation/post (4425.217)and name of bank (2449.083) were positively affected the dependent variable Initial Basic Salary.\par Remarks: From the fitted histogram (Figure {\ref 9} \& 10) showed the bell shape curve and Q-Q (quantilequantile) plot the fitted residual line passing through the origin. Therefore, the dependent variable initial gross salary is normally distributed and the linear regression model best fit for this dataset. \begin{bibitemlist}{1} \bibitem[Griffin ()]{b5}\label{b5} \textit{}, R W Griffin . \textit{Management} 1990. Houghton Mifflin Company. p. 437. (3rd ed.) \bibitem[ BRPD Circular Letter (2015)]{b1}\label{b1} \textit{}, \textit{BRPD Circular Letter} June 2015. (07) p. 17. \bibitem[Alderfer ()]{b0}\label{b0} ‘An empirical test of a new theory of human needs’. 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