Factors of Employee Motivatio1

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DEPARTMENT OF BUSINESS MANAGEMENT, NATIONAL INSTITUTE OF TECHNOLOGY,DURGAPUR

Factors of Employee

Motivation: Research Methodology

By-Pramita Samanta-11/MBA/03

Sameer Ranjan Singh-11/MBA/05Somedatta Banerjee-11/MBA/08

Hemanga Pathak-11/MBA/30

Granularity

[Pick the date]


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ABSTRACT

The present study focus that internal and external locus of control of an employee has

influenced the motivation level of an individual. However, few studies exist that investigatethe relationship between locus of control and job satisfaction managers. This studyinvestigates the relation between job satisfaction level and personality trait of 40 Indianofficers. The results indicate that officers having internal locus of control are more satisfiedthan officers having external locus of control.

The retention of human resources has been shown to be momentous to the development andthe accomplishment of the organiza tions goals and objectives. The primary aim of this studyis to explore the main factors that affect employee motivation in the organization. The survey

questions designed to determine the impact of motivational factors on employee in theorganization. The eight motivational factors rated most important to workforce environmentare Appreciation For Job Done, Job Security, Responsibility, Pay & Benefits, Promotion &growth, Working Conditions, Competition among employees and company policies.. Whilework at home, voluntary reduction in Work schedule and alternative work schedule wererated as least important. The culture of organizations are based on openness and trusts,effectively communication and good deal of time spent from supervisor listening toemployees ideas and suggestions.

In our research, we have estimated the relationship between Employee Motivation and someother important motivational factors and got some interesting results related to this. We havetaken into consideration the multicollinearity problem among different independent variablesand attempted to eliminate it. We have used statistical methods to do the analysis based onthe survey of the impact of different motivational factors. Finally we got some relationshipsof those factors with Employee Motivation . In our analysis we found that Factor 1i.e;External locus of motivation and Factor score 2i.e; Internal locus of motivation aresignificantly affecting Employee motivation.


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INTRODUCTIONThis study analyses the impact of locus of motivation on job motivation. The researcherattempts to assess whether internal or external locus of motivation relates to job motivation of Indian officers. By determining that internal locus of motivation relates to job motivation,

management efforts might focus on job factors such as supervision and empowerment withhopes of enhancing motivation. An employee with an internal locus of motivation wouldprefer less direct supervision and believe in narrow span of management. Conversely, anemployee with an external locus of motivation would prefer more direct supervision andbroad span of management. Management strategies that empower individuals with an internallocus of motivation might increase an individual's job motivation because empowerment iscongruent with the employee's internal locusbeliefs that they have motivation over their own actions. The opposing strategy would be tooffer limiting empowering opportunities for those employees with external locus of motivation beliefs. Nonetheless, once locus of motivation is determined to be external orinternal then management strategies that best address locus of motivation behaviour can bedeveloped.

LITERATURE REVIEW

LOCUS OF MOTIVATION

Research has shown that a person's internal-external locus of motivation impacts his/herperformance and job motivation (Dailey, 1980; Brownell, 1981; Kasperson, 1982).Additionally, locus of motivation may relate to the amount of stress a person experiences as aresult of whether he/she has internal or external locus of motivation tendencies (Cummins,1989). Individuals with internal locus of motivation seem to better adapt to varying situationsin a more functional way than do people who have an external locus of motivation (Judge,Locke, Durham, and Klugar, 1998).

According to Spector (1982), internals look within themselves to determine a course of action, while externals focus on outside influences such as company policies, or supervisorsfor direction. He additionally asserted that an internal would be best suited for tasks involvingindependent actions and the creation of plans while an external would be better suited fortasks which involve following company procedures or policies. Spector (1982) furtherhypothesized that locus of motivation is related to a variety of variables regarding internalsand externals on different sets of criteria. He mentioned that internals are committed more totheir respective organizations and are more satisfied with their jobs than those with anexternal locus of motivation. Those with an internal locus of motivation are also likely to stayin their jobs longer, and they tend to perform better. Researchers have also argued thatexpectancies of an outcome are more important than the sense of personal motivation (Carver


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and Scheier, 1981; Carver and Scheier, 1994). Asserted was that people factor in the impactof external circumstances and their sense of personal motivation in determining theexpectancy of an outcome.

However, studies had also been conducted revealing contradicting results thatinvestigate the relationship between locus of motivation and job performance. Broedling(1975) and Majumder, McDonald and Greever (1977) found that internals have higher levelsof performance than externals while Johnson, Luthans and Hennessey (1984) did not re-port arelationship between locus of motivation and performance.

Locus of motivation is grounded in both the Social Learning Theory (1954), developed byRotter and the Expectancy-Value Theory (1970), developed by Martin Fishbein. Boththeories purport that reinforcements act to strengthen the expectancy that a particularbehavior or event will be followed by that same reinforcement in the future (Mearns,2005).Conversely, once a relationship is established between a behavior and reinforcement, theabsence of the reinforcement will reduce or extinguish the expectancy.

Locus of motivation refers to the extent of the belief of a person in terms of whether or notthe individual believes that actions taken can affect outcomes. If someone feels that he/she isin motivation of what happens, then he/she has an internal locus of motivation.

Generally stated, persons with a strong belief in internal motivation are more confident andassertive, are active searchers for information that will help them to achieve their own

objectives, and are attracted to situations that offer opportunities of achievement (Bush,1988). In contrast, if someone feels that fate, luck, or chance affects what happens to him orher then he/she has an external locus of motivation. Externally motivationled persons see thatreinforcement does not come from their own behaviors but from events that are beyond theirreach. They see themselves as pawns, possible victims of circumstances beyond theirmotivation, and feel that success and failure in a job depends on outside forces (Bush, 1988).Conversely, someone with an internal locus of motivation will likely have a greaterconfidence level concerning outcomes.

Many studies have been conducted regarding locus of motivation (Dailey, 1980; Kasperson,1982; Knoop, 1981). Dailey's (1980) study of 281 scientists addressed the relationshipbetween locus of motivation and task variability, task difficulty, and job performance. Hefound that persons with an internal locus of motivation were more satisfied, motivated andhad a high level of participation within their jobs. Kasperson (1982) completed a study of hospital employees, which revealed a high positive correlation between negative attitudes andexternal locus of motivation. This resulted in a low motivation level with the job. Those withpositive attitudes are generally more satisfied with outcomes because of the amount of motivation they have to make things happen. Knoop (1981) discovered a relationshipbetween persons with an internal locus of motivation and how they looked at

their jobs in terms of skill variety, task uniqueness and consequence, self-sufficiency,


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and feedback from the job. Persons with an internal locus of motivation felt that they weregiven more opportunities to engage in positive work outcomes. Overall, they felt moreinvolved and felt that they had the power to make decisions.

An individual's locus of motivation can have far-reaching impact on work and life.Someone with an internal locus of motivation would see challenges as opportunities forlearning and professional growth. In contrast, someone with an external locus of motivation would ignore these challenges due to their sense that learning will not have animpact on him/her. Findings of a study by Judge et al. (1998) determined that locus of motivation is highly correlated with self-efficacy. They define self-efficacy as one's estimateof one's capabilities to mobilize the motivation, cognitive resources, and courses of actionneeded to exercise general motivation over events in one's life.

OBJECTIVEThis assignment attempts to investigate the relative influence of the factors affectingmotivation and thereby categorizing them. We considered the following determinants work challenges, feedback, flexible work hours, job autonomy, Dollar price, F D I, ForeignPortfolio Investment and Foreign Exchange Reserve (Forex). With the help of multipleregression model and applying Factor analysis the primary factors are traced out.

In our project, we have estimated the relationship between Motivation and some otherimportant behavioural factors and got some results related to this. We have taken into

consideration the Multicollinearity problem among different independent variables andattempted to eliminate it.

RESEARCH MODELWe fit a Multiple Regression Model to the data set and carryout the analysis to examine theimpact of the determinants affecting Motivation and at the same time computing the degreeof association among the determinants. Further Factor Analysis is carried out to categorizethe determinants into groups. Eventually the crucial factors ..8factors..are traced out using the above methodology. Our analysis is based on somestrong statistical methods like correlation analysis, regression analysis, multiple regressionanalysis and Factor Analysis.

Regression AnalysisThen we have done the regression analysis to justify the relations strength or weakness likeas we have found that there is a significant relation between motivation and other specificfactors but we dont know how strong the relationship are. So by this analysis we have triedto figure it out. Regression analysis is a method of modeling the relationships among two ormore variables. It is used to predict the value of one variable given the values of the others.


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Multiple regression analysisMultiple regression can establish that a set of independent variables explains a proportion of the variance in a dependent variable at a significant level (through a significance test of R2).

In general, multiple regression procedures will estimate a linear equation of the form:Y = a + b 1*x1 + b 2*x2 +. +b n*xn In the above the regression coefficients (or B coefficients) represent the independentcontributions of each independent variable to the prediction of the dependent variable. HereY = Motivation, x 1 = work challenges,x 2 = feedback, x 3= flexible working hours and so onaccording to our analysis.

Multicollinearity:A state of very high intercorrelations among independent variables. We have checkedmulticollinearity in order to check wether there exists any correlations among the 8 factorsconsidered by us. Multicollinearity is one of the important problems in multiple regressionanalysis. It is usually regarded as a problem arising out of the violation of the assumption thatexplanatory variations are linearly independent.In our project this is considered to beimportant in order to carry out factor analysis.

Factor analysis:The main goal of factor analysis is data reduction , that is ,to simplify some number of measurements by identifying a smaller number of underlying traits , attitudes , or beliefs

Factor analysis is a collection of methods used to examine how underlying factors ordeterminants influences the responses on a number of measured variables. Factor analyses areperformed by examining the pattern of correlations (or co variances) between the observedmeasures. Measures that are highly correlated (either positively or negatively) are likelyinfluenced by the same factors, while those that are relatively uncorrelated are likelyinfluenced by different factors.

Data:Our sample size is 50 .The survey has been conducted with 50 different employees , givingtheir feedback on the possible reasons that increase their motivation based on certain 8factors. The data in spss data view has been entered from the responses in the questionnaireby various employees .

Dependant variable Motivation

Independent variables- 1. Appreciation for Job Done

2. Job Security

3. Responsibility

4. Pay and Benefits


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5. Promotion and Growth

6. Working Conditions

7. Competition among Employees

8. Company Policies

REGRESSION ANALYSIS

Correlation Matrix

Appreciation for

JobDone

JobSecurit

yResponsi

bility

Payand

Benef its

Promotion andGrowth

Working

Conditions

Competition

amongEmploye

es

Company

Policies

Correlation

Appreciation forJob Done

1.000 .534 .639 .399 .505 .447 .234 .416

Job Security .534 1.000 .795 .356 .331 .273 .359 .419Responsibility .639 .795 1.000 .319 .341 .440 .457 .425Pay andBenefits .399 .356 .319 1.000 .604 .623 .162 .632Promotion andGrowth

.505 .331 .341 .604 1.000 .419 .125 .263

WorkingConditions

.447 .273 .440 .623 .419 1.000 .331 .434

CompetitionamongEmployees

.234 .359 .457 .162 .125 .331 1.000 .095

Company

Policies.416 .419 .425 .632 .263 .434 .095 1.000

The correlation matrix shows the Pearsons correlation coefficients which is a symmetricmatrix. The matrix is tested to check whether the values lie between 0.5 to -0.5. Somevariables did not show R values above the given limit and hence were eliminated, since theydid not showed high multi-collinearity and inappropriate for factor analysis. Also thedeterminant of the matrix is 0.017 which is quite low and can be considered tending to zero.

Thus, on eliminating the variable competition among employees the correlation matrixrevealed the suitability for factor analysis.


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Coefficients

a Dependent Variable: Employee Motivation

The Coefficients box gives information about the independent variable(s). First, the "B"column under "Unstandardized Coefficients" in the "Coefficients" box provides the value of the Y-intercept [labeled "(Constant")] and the slope representing the effect of different factorson the dependent variable employee motivation. This least squares regression line is thestraight line for which the sum of the squared prediction errors are minimized. The Y-intercept tells us that the predicted value of motivation for someone 0 percent affected bymotivational factors is 1.092 . The slope for the different variable tells us that the predictedvalue of respondents' motivation increases by for every one-unit increase in CompanyPolicies, Competition among Employees, Promotion and Growth, Job Security, Working

Conditions, Appreciation for Job Done, Pay and Benefits and Responsibility.

The next column of the "coefficients" box displays the "Standardized coefficient" for theeffect of "different motivational factors" on "Employee Motivation." The standardizedcoefficient (called Beta or b*) expresses the impact of the independent variable in terms of standard deviation units. It tells us the number of standard deviations the dependent variableincreases or decreases with a one standard deviation increase in the independent variable. Thestandardized coefficient is calculated by multiplying the unstandardized coefficient, B, by theratio of the standard deviations for the independent and dependent variables. Because theyexpress all coefficients in terms of the same units (standard deviations), standardized

Model

Unstandardized Coefficients

Standardized

Coefficients Collinearity Statistics

BStd.

Error Beta t Sig. Tolerance VIF1 (Constant) -

1.092.296 -3.692 .001

Appreciation for JobDone

.097 .080 .076 1.209 .233 .473 2.116

Job Security .972 .086 .865 11.303 .000 .322 3.104Responsibility -.128 .107 -.105 -1.199 .237 .244 4.098Pay and Benefits .129 .100 .102 1.287 .205 .302 3.310Promotion and Growth .038 .073 .032 .527 .601 .496 2.014Working Conditions .034 .074 .029 .455 .652 .471 2.124Competition amongEmployees

.093 .065 .072 1.423 .162 .729 1.372

Company Policies .107 .085 .080 1.271 .211 .475 2.105


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coefficients become especially handy in multivariate models where we want to directlycompare the size of the impacts of different independent variables.

Model Summary

Model R R Square

AdjustedR Square

Std. Errorof the

Estimate1 .961(a) .923 .908 .261

a Predictors: (Constant), Company Policies, Competition among Employees, Promotion andGrowth, Job Security, Working Conditions, Appreciation for Job Done, Pay and Benefits,

Responsibility

ANOVA b

ModelSum of Squares Df Mean Square F Sig.

1 Regression 33.214 8 4.152 61.093 .000 a

Residual 2.786 41 .068

Total 36.000 49

a. Predictors: (Constant), Company Policies, Competition among Employees,Promotion and Growth, Job Security, Working Conditions, Appreciation for JobDone, Pay and Benefits, Responsibility

b. Dependent Variable: MotivationLevel


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Multicollinearity Test

Since the VIF of the factor Competition among employees is greater than two and we alsoknow the condition of multicolinearity where r value lies between -5 to +5 so it needs to beremoved.

CORRELATION AFTER DATA REDUCTION

Correlation Matrix

Appreciation for

JobDone

JobSecuri

tyResponsib

ility

Payand

Benefits

Promotion andGrowth

WorkingConditio

nsCompanyPolicies

Correlation

Appreciation forJob Done

1.000 .534 .639 .399 .505 .447 .416

Job Security .534 1.000 .795 .356 .331 .273 .419Responsibility .639 .795 1.000 .319 .341 .440 .425Pay and Benefits .399 .356 .319 1.000 .604 .623 .632

Promotion andGrowth

.505 .331 .341 .604 1.000 .419 .263

WorkingConditions

.447 .273 .440 .623 .419 1.000 .434

CompanyPolicies

.416 .419 .425 .632 .263 .434 1.000

a. Determinant = .023

The correlation matrix shows the Pearsons correlation coefficients which is a symmetric

matrix. The matrix is tested to check whether the values lie between 0.5 to -0.5. Somevariables did not show R values above the given limit and hence were eliminated, since they

Correlation Matrix(a)AppreciatiJob SecuritResponsibiPay and B Promotion Working C Competiti Company Policies

Correlatio Appreciation for Job Done 1 0.534064 0.638569 0.399109 0.505307 0.447183 0.234139 0.415575

Job Security 0.534064 1 0.795044 0.355567 0.330623 0.272908 0.359425 0.418854Responsibility 0.638569 0.795044 1 0.318858 0.340884 0.439733 0.456761 0.424822Pay and Benefits 0.399109 0.355567 0.318858 1 0.603904 0.622763 0.16188 0.631963Promotion and Growth 0.505307 0.330623 0.340884 0.603904 1 0.419067 0.125436 0.263117Working Conditions 0.447183 0.272908 0.439733 0.622763 0.419067 1 0.331326 0.434372Competition among Employees 0.234139 0.359425 0.456761 0.16188 0.125436 0.331326 1 0.095346Company Policies 0.415575 0.418854 0.424822 0.631963 0.263117 0.434372 0.095346 1

a Determinant = .017


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did not showed high multi-collinearity and inappropriate for factor analysis. Also thedeterminant of the matrix is 0.023 which is quite low and can be considered tending to zero.

MULTICOLINEARITY TEST AFTER DATA REDUCTION

Appreciation for Job Done Job Security Responsibility Pay and BenefitsWorking Conditions Company Policies1 0.534064283 0.638569369 0.399109488 0.447183486 0.415575437

Appreciation for Job Done 0.534064283 1 0.795043988 0.355567392 0.272907767 0.418853908

Job Security 0.638569369 0.795043988 1 0.318858308 0.439733324 0.424822147

Responsibility 0.399109488 0.355567392 0.318858308 1 0.622762563 0.631962993

Pay and Benefits 0.505307044 0.330623261 0.340883571 0.60390417 0.419066625 0.263117406Promotion and Growth 0.447183486 0.272907767 0.439733324 0.622762563 1 0.434372243

Working Conditions 0.415575437 0.418853908 0.424822147 0.631962993 0.434372243 1

Company Policies

Determinant = .023


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NORMALITY TEST

One-Sample Kolmogorov-Smirnov Test

a. Test distribution is Normal.

b. Calculated from data.

FACTOR ANALYSIS

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy..704

Bartlett's Test of Sphericity

Approx. Chi-Square 172.203Df 21Sig. .000

Appreciation for

JobDone

JobSecurit

yResponsi

bility

Payand

Benefits

Promotion andGrowth

Working

Conditions

Competition

amongEmploy

ees

Company

Policies

N 50 50 50 50 50 50 50 50NormalParameters(a,b)

Mean3.54 3.50 3.52 3.52 3.40 3.30 3.40 3.60

Std.Deviation

.676 .763 .707 .677 .728 .735 .670 .639

MostExtremeDifferences

Absolute.312 .364 .351 .279 .255 .249 .315 .306

Positive .228 .256 .249 .279 .249 .238 .225 .306Negative -.312 -.364 -.351 -.261 -.255 -.249 -.315 -.254

Kolmogorov-Smirnov Z

2.204 2.574 2.485

1.97

1 1.803 1.764 2.225 2.165Asymp. Sig. (2-tailed) .089 .064 .076 .062 .094 .081 .086 .074


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It shows several very important parts of the output: the Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett's test of sphericity. The KMO statistic varies between 0 and1. A value of 0 indicates that the sum of partial correlations is large relative to the sum of correlations, indicating diffusion in the pattern of correlations (hence, factor analysis is likely

to be inappropriate). A value close to 1 indicates that patterns of correlations are relativelycompact and so factor analysis should yield distinct and reliable factors. Kaiser (1974)recommends accepting values greater than 0.5 as acceptable (values below this should lead usto either collect more data or rethink which variables to include). Furthermore, valuesbetween 0.5 and 0.7 are mediocre, values between 0.7 and 0.8 are good, values between 0.8and 0.9 are great and values above 0.9 are superb. For these data the value is 0.704, whichfalls into the range of being satisfactory: so, we should be confident that factor analysis isappropriate for these data. Bartlett's measure tests the null hypothesis that the originalcorrelation matrix is an identity matrix. For factor analysis to work we need somerelationships between variables and if the Rmatrix were an identity matrix then all correlationcoefficients would be zero. Therefore, we want this test to be significant (i.e. have asignificance value less than 0.05). A significant test tells us that the R-matrix is not anidentity matrix; therefore, there are some relationships between the variables we hope toinclude in the analysis. For these data, Bartlett's test is highly significant (p < 0.001), andtherefore factor analysis is appropriate.

Communalities

Initial Extraction

Appreciation for JobDone 1.000 .648

Job Security 1.000 .830Responsibility 1.000 .881Pay and Benefits 1.000 .850Promotion andGrowth 1.000 .538

Working Conditions 1.000 .630Company Policies 1.000 .517

Extraction Method: Principal Component Analysis.


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It shows the table of communalities before and after extraction. Principal component analysisworks on the initial assumption that all variance is common; therefore, before extraction thecommunalities are all 1. The communalities in the column labelled Extraction reflect thecommon variance in the data structure. So, for example, we can say that 64.8% of the

variance associated with Appreciation for job done is common, or shared variance. Anotherway to look at these communalities is in terms of the proportion of variance explained by theunderlying factors. After extraction some of the factors are discarded and so someinformation is lost. The amount of variance in each variable that can be explained by theretained factors is represented by the communalities after extraction.

Rotated Component Matrix

Component1 2

Pay and Benefits .912Working Conditions .765Promotion andGrowth

.697

Company Policies .631Responsibility .912Job Security .895Appreciation for JobDone

.685

Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization

Total Variance Explained

Component Initial Eigenvalues

Extraction Sums of SquaredLoadings

Rotation Sums of SquaredLoadings

Total% of

VarianceCumulati

ve % Total% of

VarianceCumulati

ve % Total% of

VarianceCumulati

ve %1 3.75

6 53.662 53.662 3.756 53.662 53.662 2.556 36.512 36.5122 1.13

716.247 69.909 1.137 16.247 69.909 2.338 33.397 69.909

3 .766 10.939 80.8484 .553 7.900 88.7485 .437 6.241 94.9886 .210 3.005 97.9937 .140 2.007 100.000

Extraction Method: Principal Component Analysis.


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It lists the Eigenvalues associated with each linear component (factor) beforeextraction, after extraction and after rotation. Before extraction, SPSS has identified 7 linearcomponents within the data set (we know that there should be as many eigenvectors as thereare variables and so there will be as many factors as variables). The Eigenvalues associated

with each factor represent the variance explained by that particular linear component andSPSS also displays the Eigenvalue in terms of the percentage of variance explained (so,factor 1 explains 53.662% of total variance). It should be clear that the first few factorsexplain relatively large amounts of variance (especially factor 1) whereas subsequent factorsexplain only small amounts of variance. SPSS then extracts all factors with Eigenvaluesgreater than 1, which leaves us with two factors. The Eigenvalues associated with thesefactors are again displayed (and the percentage of variance explained) in the columns labelledExtraction Sums of Squared Loadings. The values in this part of the table are the same as thevalues before extraction, except that the values for the discarded factors are ignored (hence,the table is blank after the second factor). In the final part of the table (labelled RotationSums of Squared Loadings), the Eigenvalues of the factors after rotation are displayed.


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The Eigenvalues for successive factors can be displayed in a simple line plot. Cattell(1966) proposed that this scree plot can be used to graphically determine the optimal numberof factors to retain.A scree plot shows the sorted Eigenvalues, from large to small, as afunction of the Eigenvalue index. Another rule of thumb is to plot all the Eigen values in theirdecreasing order. The plot looks like the side of a mountain, and "scree" refers to the debrisfallen from a mountain and lying at its base.

So the scree test proposes to stop analysis at the point the mountain ends and thedebris (error) begins. In this instance, that point coincides with the Eigen value criterion. Onerule is to consider only those with Eigen values over 1. The number of points above 1 are 2,so there are 2 factors.

Component Matrix

Component1 2

Responsibility .781 -.521Appreciation for JobDone .775

Pay and Benefits .763 .518Job Security .730 -.545Working Conditions .706Company Policies .698Promotion andGrowth .667

Extraction Method: Principal Component Analysis.a 2 components extracted.

Model Summary

Model R R Square

AdjustedR Square

Std. Errorof the

Estimate1 .886(a) .785 .776 .406

a Predictors: (Constant), REGR factor score 2 for analysis 1, REGR factor score 1 foranalysis 1


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Component 1:

Model Summary

Model RR

SquareAdjusted R

SquareStd. Error of the Estimate

1 .964(a) .930 .924 .27586750

a. Predictors: (Constant), Promotion and Growth, Company Policies, Working Conditions,Pay and Benefits (External locus of motivation)

Component 2:

Model Summary

Model R R SquareAdjusted R

Square

Std. Errorof the

Estimate1 .962(a) .925 .920 .28323296

a Predictors: (Constant), Responsibility, Appreciation for Job Done, Job Security ( InternalLocus of Motivation)

Reliability test for Component 1

Reliability Statistics

Cronbach's Alpha

N of Items

.795 4


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Reliability test for Component 2

Reliability Statistics

Cronbach's Alpha

N of Items

.851 3

Cronbachs alpha for all four factors for component 1 and for all the three factors for component 2are more than .70 .Thus the factor reliabilities have satisfied the criteria.

RESULTSThe impact of the determinants affecting Employee Motivation has been captured statisticallyby the multiple regression models. The regression output reveals that 92.3% of the variationcan be explained by the determinants taken under study. This is confirmed by the F statistic at5% level. The remaining 7.7% is left unexplained. The model has a better goodness of fit.However when we look at the individual significance of the determinants it provides adifferent picture. All the independent variables except competition among employees arestatistically insignificant at 5% level. So in spite of high R2, the OLS estimates may havelarge variance and covariance. This reflects the association among the independent variables.The presence of Multicollinearity problem within the data set is evident from the above

result. This calls for diagnostic tests to affirm the presence of the problem. There by we gofor correlation matrix.

The matrix provides an introspective view regarding the inter relationship among thevariables. That means the cell other than the shaded region explains strong correlations.Intuitively when the values of r are less than -0.5 or more than 0.5 then the independentvariables are highly correlated to each other. So there is no way of disentangling the separateinfluence of the variables. The determinant value close to 0.So we can say that factor analysiscan be solved. confirms high correlation between independent variables. Further, there are

certain diagnostic tests that affirm multicollinearity. The KMO Bartlett Test Statistic is equalto 0.704 which exceeds 0.5 and hence, the Null hypothesis of spherical matrix is rejected.This conclusion is further supported by Bartlett Test of sphericity where the 2 statistic issignificant at 5% level. Hence non spherical correlation matrix confirms the presence of multicollinearity.

From the correlation matrix it is evident that competition among employees do not have anyassociation with other independent variables. Hence it has multicollinearity problem. Furtheranalysis is carried out except this variable.

So to reduce the severity of the problem and to eliminate it we have to go for data reduction


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with the help of factor analysis. There by using the extraction method via Principalcomponent analysis the communalities are computed. According to total variance explainedmatrix the first two components explains 69.9% of the change in the independent variables.Since there exists a combination among independent variables within this data set as

confirmed by the previous tests, data reduction is necessary. In this regard the original dataset is converted to groups on the basis of principal components. This is done by extractionmethod under factor analysis.Our component matrix table shows Promotion and Growth, Company Policies, WorkingConditions, Pay and Benefits will clearly be included in Component 1 and Responsibility,Appreciation for Job Done, Job Security in component 2. Since Responsibility,Pay andbenefits and Job security belong to both the components with different Loadings we havedone rotation.

After rotation, loading of factors corresponding to different variables changes theircorresponding values. The corresponding matrix represents two components which includesthe variables on the basis of Varimax rotation method. According to this matrix, Promotionand Growth, Company Policies, Working Conditions, Pay and Benefits belongs toComponent1 and Responsibility, Appreciation for Job Done, Job Security belongs tocomponent 2. Further regression analysis is conducted between regression factor score1 withrespect to corresponding variables i.e; Promotion and Growth, Company Policies, WorkingConditions, Pay and Benefits. While considering Factor score1, the model yields bettergoodness of fit as measured by R2.The model summary shows that it is able to explain 93%of the variation in Factor score 1 due to the above mentioned variables. All the variables are

statistically significant at 5% level.

Further regression is carried out with respect to factor score 2 corresponding to variablesResponsibility, Appreciation for Job Done, Job Security. The model summary reflects that92.5% is explained by factor score 2, all the variables are statistically significant at 5% levelof significance.Final regression is carried out between factor score 1 and Factor score 2. In this case, theoverall model is statistically significant. It explains 78.5% of the variation in the dependentvariables by the independent variables. Further all the coefficients are statistically significantat 5% level.

Factor 1 can be named as External Locus of Motivation and

Factor 2 can be named as Internal locus of motivation .

CONCLUSIONIn our research, we tried to find out the relationship between Employee Motivation and someother important motivational factors and got some interesting results related to this. We have

used statistical methods to do the analysis based on monthly basis database of differenteconomical factors. Finally we got some relationships of those factors with Employee


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motivation. In our analysis we found that Factor 1i.e; Externa l locus of Motivation andFactor score 2i.e. Internal locus of motivation are significantly affecting Employeemotivation.In our study we found that Employee Motivation depends on both External Locus of

Motivation and Internal Locus of Motivation, nearly to the same extent.FUTURE STUDIESFor future research, the relation between the other workforce motivational factors and theirimpact need to be focused and studied. Our results revealed that although the internal andexternal locus of motivation tended to think that satisfaction and motivation are enhanced insimilar ways, there were wide differences between how individual employees perceived thetwo concepts. Questioners may be develop to give deep understanding of employees feelingtoward their organization s and their expectations when they cond ucted the work . Forexample, what were the expectations you had when you first came to work for the

organization that havent been met yet? Are t he reasons you are motivated in work differentthan the reasons why you first came to? Finally Demographic factors were among the mostcommon predictors in the turnover literature.(Jinnett and Alexander 1999; Miller andWheeler 1992). Further studies may need to classify the sample by employee position,income, nationality, gender and age.


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BIBLIOGRAPHY

BOOKS

Fisher shoenfelt Shaw-Human resource management-4 th edition-macmilan

Press limited.

Kothari C.R research Methodology-Methods and techniques (2 nd edition)-new

age International (pvt) Limited.

Bratton John and Gold Jeffery (1994)-HRM-Theroy and practice, 6 th edition-

Macmillan pres Limited.

Mamoria C.B-Human Resource Management-Himalaya publishin home, 11 th

edition 1993.

REFERENCES

Sasmita Polo-National Journal on Personnel Management-VOL-XXII, No.3,

Pg no.16-20

Parveen Ahmed Alan and Mr. Kaushik-Personnel Today-Indian Journal on

personnel management-VOL.XXIX,NO.2, Pg No.13-15.

Prof.Bata.K.Dey-Indian Journal on Personnel Management-VOL XXIX-NO.2,

pg no. 9-12.

Websites

WWW.HVS INTERNATIONAL JOURNALS.COM

WWW.Google.com

WWW.SLIDESHARE.COM

WWW.SCRIBD.COM

WWW.CITEHR.COM

WWW.MANAGEMENTPARADISE.COM
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