4-Data Analysis D - INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/30563/11/11_chapter...

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4-Data Analysis

ata Analysis of study chapter will present the data that has

been collected through quantitative survey. At first we give an

overview of the data that means the sample population

(Characteristics and descriptive statistics of survey) and after that data

will be checked assumption through normality, linearity, multicollinearity,

outlier, homoscedasticity, and Independence of Residual tests. Next part

will present Reliability test and hypothesis testing.

D

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4.1 Characteristics Survey Data

Table No.9 Characteristics Survey Data

Frequency Percent

GENDER

Male 209 92.5

Female 17 7.5

AGE

Less than 30 54 23.9

30 to 40 82 36.3

41-50 54 23.9

51-60 31 13.7

More than 60 5 2.2

EDUCATION

Bachelor 113 50

Post Graduate 36 15.9

Master 67 29.6

Others 10 4.4

POSITION

Entrepreneur 71 31.4

Employees 62 27.4

Supervisor 9 4

Manager 67 29.6

President & Vice President 17 7.5

EMPLOYEES

Less than 50 107 47.3

51-100 54 23.9

101-150 10 4.4

151-200 13 5.8

More than 200 42 18.6

CLASS OF FIRM

Local 180 79.6

Foreign/Export Oriented 25 11.1

Joint Venture 19 8.4

Others 2 0.9

YEAR OF OPERATION

Less than 5 22 9.7

5-10 46 20.4

11-15 49 21.7

16-20 26 11.5

More than 20 83 36.7

INDUSTRY TYPE

Manufacturing 226 100

TOTAL 226

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4.2 Normality of Data:

Kline (1998) suggested that all variables in the analysis for univariate

skewness and kurtosis were satisfactory within conventional criteria for

normality i.e. -3 to 3 for skewness and -10 to 10 for kurtosis. Multivariate

normality (the combination of two or more variables) means that the

individual variable is normal in a univariate sense and that their

combinations are also normal (Hair et al. 2010).

4.2.1 HRM Practices:

Table No.10 Descriptive Statistics of HRMP

N

Skewness Kurtosis

Statistic Std. Error Statistic Std. Error

V1 226 -.892 .162 .569 .322

V2 226 -.121 .162 -.481 .322

V3 226 .129 .162 -.850 .322

V4 226 -.684 .162 .107 .322

V5 226 -.826 .162 .315 .322

V6 226 -.676 .162 .672 .322

V7 226 -.927 .162 .680 .322

V8 226 -.942 .162 .907 .322

V9 226 -1.017 .162 1.161 .322

V10 226 -.732 .162 .712 .322

V11 226 -.488 .162 .147 .322

V12 226 -.783 .162 .627 .322

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V13 226 -.019 .162 -1.153 .322

V14 226 -.742 .162 .341 .322

V15 226 -.491 .162 -.787 .322

V16 226 -.310 .162 -.783 .322

V17 226 -.600 .162 -.332 .322

V18 226 -.707 .162 .212 .322

V19 226 -.629 .162 -.058 .322

V20 226 -.959 .162 .975 .322

V21 226 -.418 .162 -.291 .322

V22 226 -.725 .162 .511 .322

V23 226 -1.175 .162 1.348 .322

V24 226 -.323 .162 .069 .322

V25 226 -.634 .162 -.709 .322

V26 226 -.230 .162 -.650 .322

V27 226 .062 .162 -.929 .322

V28 226 -.269 .162 -.483 .322

V29 226 -.826 .162 .014 .322

V30 226 -.434 .162 -.227 .322

V31 226 -.777 .162 -.209 .322

V32 226 -.124 .162 -.496 .322

V33 226 .042 .162 -1.021 .322

V34 226 -.540 .162 -.610 .322

V35 226 -.463 .162 -.811 .322

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V36 226 -.610 .162 .124 .322

Valid N

(listwise) 226

All skewness value is from -0.019 to -1.175 and kurtosis value is from

0.014 to 1.348. According to the guideline suggested by Kline (1998), all

variables are univariate normal and the individual variable is normal in a

univariate sense and that their combinations are also normal. So

researcher can conclude that HRM data is multivariate normal and

should be used for further multivariate analysis.

4.2.2 Operation Performance:

Table No.11 Descriptive Statistics- Operation Performance

N Skewness Kurtosis

Statist

ic

Statist

ic

Std.

Error

Statist

ic

Std.

Error

V37 226 -.899 .162 .948 .322

V38 226 .009 .162 -1.280 .322

V39 226 -1.038 .162 .889 .322

V40 226 .062 .162 .075 .322

V41 226 -.064 .162 -.221 .322

V42 226 .167 .162 .508 .322

V43 226 -.943 .162 .953 .322

V44 226 -.134 .162 -.689 .322

V45 226 -.023 .162 -.744 .322

V46 226 -.645 .162 -.073 .322

V47 226 -.492 .162 -.369 .322

V48 226 -.835 .162 .400 .322

V49 226 -.700 .162 .111 .322

Valid N (list

wise)

226

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All skewness value is from .009 to 1.035 and kurtosis value is from .073

to 1.280. According to the guideline suggested by Kline (1998), all



researcher can conclude that Operation Performance data is multivariate

normal and should be used for further multivariate analysis.

4.2.3 Firm Performance:

Non Financial Performance:

Table No. 12 Descriptive Statistics- Non Financial Performance

N Skewness Kurtosis

Statistic Statistic Std. Error Statistic Std. Error

V50 226 -1.055 .162 1.116 .322

V51 226 -.046 .162 -.677 .322

V52 226 -.449 .162 -.272 .322

V53 226 -.560 .162 -.449 .322

V54 226 -.279 .162 -.984 .322

Valid N

(listwise)

226

All skewness value is from -.279 to -1.055 and kurtosis value is from -

.272 to 1.116. According to the guideline suggested by Kline (1998), all



researcher can conclude that Firm’s Non Financial Performance data is

multivariate normal and should be used for further multivariate analysis.

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Financial Performance:

Table No.13 Descriptive Statistics- Financial Performance

N Skewness Kurtosis

Statist

ic

Statist

ic

Std.

Error

Statist

ic

Std.

Error

V55 226 -.402 .162 .115 .322

V56 226 -.560 .162 .572 .322

V57 226 -.415 .162 -.125 .322

V58 226 -.477 .162 .367 .322

V59 226 -.390 .162 .227 .322

V60 226 -.564 .162 -.359 .322

V61 226 -.558 .162 .413 .322

V62 226 -.972 .162 1.387 .322

Valid N (list

wise)

226

All skewness value is from -.390 to -.972 and kurtosis value is from .115

to .572. According to the guideline suggested by Kline (1998), all



researcher can conclude that Firm’s Financial Performance data is


4.2.4 Business Operation Modes

Table No.14 Descriptive Statistics- Business Operation Modes

N Skewness Kurtosis

Statist

ic

Statist

ic

Std.

Error

Statist

ic

Std.

Error

V63 226 .090 .162 -1.400 .322

V64 226 -.273 .162 -1.579 .322

V65 226 -.829 .162 -.812 .322

V66 226 -.685 .162 -1.034 .322

V67 226 -.624 .162 -1.087 .322

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V68 226 -.588 .162 -1.151 .322

Valid N

(listwise)

226

All skewness value is from .090 to -.892 and kurtosis value is from -.812

to -1.579. According to the guideline suggested by Kline (1998), all



researcher can conclude that Firm’s Business Operation Modes data is


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4.3 Multicollinearity:

The correlations between the variables in your model are provided in the

table labeled Correlations. Check that your independent variables show

at least some relationship with your dependent variable (above .3

preferably). (Pallant, 2005)


Table No.15 Correlation Matrix- HRM Practices

HR Planni

ng

Staffi

ng Practic

es

Incen

tives Practi

ces

Perfor

mance Appra

isal

Training Prog

ram

Team wo

rk

Empl

oyee Particip

ation

CSR t

owards emp

loyees

HR

Planning

1

Staffing Pra

ctices

.353** 1

Incentives

Practices

.450** .463** 1

Performan

ce Appraisal

.576** .416** .484** 1

Training P

rogram

.451** .402** .353** .486** 1

Team work .280** .473** .464** .345** .347** 1

Employee

Participatio

n

.229** .435** .263** .285** .306** .480** 1

CSR towar

ds employe

es

.440** .333** .303** .461** .369** .215** .262** 1

**. Correlation is significant at the 0.01 level (2-tailed).

In HRM construct, the correlation between all independent variables are

less than 0.9, therefore, as per guideline suggested by Pallant (2005) all

variables will be retained. It indicates that data is free from

multicollinearity problem and need not to remove any variable form

further analysis. All HRMP dimensions are correlated with each other

and it’s statistically significant as p-value is less than 0.01.

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4.3.2 Operation Performance

Table No. 16 Correlation Matrix - Operation Performance

Product Quality Product Cost Delivery Flexibility

Product Quality Pearson Correlation

1

Product Cost Pearson

Correlation

.256** 1

Delivery Pearson

Correlation

.448** .167** 1

Flexibility Pearson

Correlation

.398** 0.047 .285** 1

**. Correlation is significant at the 0.01 level (2-tailed)

In Operation Performance construct, the correlation between all

independent variables are less than 0.9, therefore, as per guideline

suggested by Pallant (2005) all variables will be retained. It indicates that

data is free from multicollinearity problem and need not to remove any

variable form further analysis. All Operation Performance dimensions are

correlated with each other and it’s statistically significant as p-value is

less than 0.01.

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Table No.17 Multicollinearity- HRM Practices

Model

Standardized

Coefficients

t Sig.

Collinearity

Statistics

Beta Tolerance VIF

1 (Constant) 7.536 0

HR Planning 0.134 1.773 0.078 0.578 1.729

Staffing Practices 0.152 2.056 0.041 0.609 1.642

Incentives Practices 0.118 1.599 0.111 0.606 1.649

Performance Appraisal 0.098 1.233 0.219 0.527 1.896

Training Program -0.035 -

0.492

0.623 0.661 1.513

Team work -0.115 -

1.559

0.12 0.611 1.637

Employee Participation 0.17 2.465 0.014 0.697 1.435

CSR towards employees 0.195 2.85 0.005 0.711 1.407

According to Juie Pallant (2005) have quoted commonly used cut-off

points for determining the presence of multicollinearity (tolerance value

of less than .10, or a VIF value of above 10). These values, however, still

allow for quite high correlations between independent variables (above

.9), so we should take them only as a warning sign, and check the

correlation matrix.

The tolerance value for each independent variable is ranged from 0.527

to 0.711, which is not less than .10; therefore, data have not violated the

multicollinearity assumption. This is also supported by the VIF value,

which is ranged from 1.407 to 1.896, which is well below the cut-off of

10.

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4.4 Outlier, Normality, Homoscedasticity, Independence of

Residual:

One of the ways that these assumptions can be checked is by inspecting

the residuals scatterplot and the Normal Probability Plot of the regression

standardized residuals that were requested as part of the analysis.

Figure No. 5 P-P Plot

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Figure No. 6 Scatterplot

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In the Normal Probability Plot (Figure No. 5), we observed that our points

have lie in a reasonably straight diagonal line from bottom left to top

right. This would no major deviations from normality.

In the Scatter plot of the standardized residuals (Figure No. 6) we

observed that the residuals were roughly rectangular distributed, with

most of the scores concentrated in the centre (along the 0 point).

Standardized residual (as displayed in the scatter plot) concentrated of

more than 3.3 or less than -3.3.

The other information in the output concerning unusual cases is in the

Table titled Case wise Diagnostics. This presents information about cases

that have standardized residual values above 3.0 or below -3.0. In a

normally distributed sample we would expect only 1 per cent of cases to

fall outside this range.

Table No.18 Case wise Diagnostics

Case

Number

Std.

Residual

Firm

Performance

Predicted

Value

Residua

l

5 4.160 65.00 41.2424 23.7576

2

142 3.091 58.00 40.3467 17.6533

2

a. Dependent Variable: FIRM PERFORMANCE

To check whether this strange case is having any undue influence on the

results for this model as a whole, researcher can check the value for

Cook’s Distance given towards the bottom of the Residuals Statistics

table.

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Table No.19 Residuals Statistics

Minimum Maximum Mean

Std.

Deviation N

Predicted Value 34.6856 53.6396 45.8009 3.49702 226

Std. Predicted Value -3.178 2.242 .000 1.000 226

Standard Error of

Predicted Value

.564 2.436 1.103 .287 226

Adjusted Predicted

Value

34.6191 53.8061 45.7750 3.54069 226

Residual -16.10012 23.75762 .00000 5.60812 226

Std. Residual -2.819 4.160 .000 .982 226

Stud. Residual -2.880 4.225 .002 1.005 226

Deleted Residual -16.79870 24.49747 .02585 5.87797 226

Stud. Deleted Residual -2.930 4.400 .003 1.013 226

Mahal. Distance 1.202 39.939 7.965 5.072 226

Cook's Distance .000 .127 .005 .013 226

Centered Leverage Value .005 .178 .035 .023 226

a. Dependent Variable: FIRM PERFORMANCE

According to Tabachnick and Fidell (2001, p. 69), cases with values

larger than 1, are a potential problem. In data, the maximum value for

Cook’s Distance is .005, suggesting no major problems.

In nutshell, data are not violating of assumption of Normality, Linearity,

Multicollinearity, Outlier, Homoscedasticity, and Independence of

Residual and fit for multivariate analysis.

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4.5 Reliability Analysis

The coefficient alpha or Cronbach’s alpha is the average of all possible

split-half coefficients resulting from different ways of splitting the scale

items. This coefficient varies from 0 to 1, and a value of 0.6 or less

generally indicate unsatisfactory internal consistency reliability

(Malhotra & Dash, 2011).

4.5.1 Split-Half Reliability

Table No.20 Split-Half Reliability

Reliability Statistics HRMP OP FP

Cronbach's

Alpha

Part

1

Value .861 .601 .713

N of

Items

18 7 7

Part

2

Value .861 .694 .721

N of

Items

18 6 6

Total N of

Items

36 13 13

Correlation Between

Forms

.603 .494 .428

Spearman-

Brown

Coefficient

Equal Length .752 .661 .600

Unequal

Length

.752 .662 .601

Guttman Split-Half

Coefficient

.752 .661 .590

In above table, the items on the scales are divided into two halves and

the resulting half score are correlated. High correlations between the

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halves indicate high internal consistency. The measurement scales have

good internal consistency, with a correlation between forms is more than

0.4, instruments are reliable for measuring HRMP, FP and OP.

4.5.2 Cronbach Reliability

Table No.21 Cronbach Reliability

Reliability

Statistics

HRM

Practices OP FP

Cronbach's

Alpha

N of

Items

Cronbach's

Alpha N of Items

Cronbach's

Alpha

N of

Items

.908 36 .755 13 .783 13

Table No.22 Cronbach Reliability- HRMP Dimensions

Dimensions item

Cronbach’s

alpha

Human Resource Planning

(HR Planning) 4 0.682

Staffing Practices 4 0.598

Incentives Practices 3 0.74

Performance Appraisal 3 0.496

Training Program 3 0.734

Team work 3 0.697

Employee Participation 3 0.585

CSR towards employees 13 0.877

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Table No.23 Cronbach Reliability- Operation Dimensions

Dimensions item

Cronbach’s

alpha

Product Quality 3 0.329

Product Cost 3 0.712

Delivery 3 0.666

Flexibility 4 0.782

Table No.24 Cronbach Reliability- Firm Performance Dimensions

Dimensions item

Cronbach’s

alpha

Non Financial Performance 5 0.733

Financial Performance 8 0.797

According to Malhotra & Dash (2011), the scale has good internal

consistency, with a Cronbach’s alpha coefficient reported of more than

0.60. In the current study the Cronbach’s alpha coefficient were >0.75.

Hence, all the scales can be considered reliable for measuring the

construct.

4.5.3 Item-Total Reliability Statistics

Correlated Item-Total Correlation gives us an indication of the degree to

which each item correlated with total score. Low values (less than 0.3)

indicate the item is measuring something different from the scale as a

whole. If the Cronbach’s alpha is too low (e.g. less than 0.7), it may need

to consider removing items with low-total correlation.

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In the column headed Alpha if Item Deleted, the impact of removing each

item from the scale is given. Compare these values with the final alpha

value obtained. If any of the values in this column are higher than the

final alpha value, we may want to consider removing this item from the

scale. For established, well validated scales, we would normally consider

doing this only if the alpha value was low (less than .7).

HRM Practices

Table No.25 Item-Total Statistics-HRMP

Scale Mean if

Item Deleted

Scale

Variance if

Item Deleted

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

V1 123.59 328.208 .473 .906

V2 123.96 324.488 .550 .905

V3 124.23 328.243 .389 .907

V4 123.54 329.476 .392 .907

V5 123.59 331.985 .341 .907

V6 123.18 331.100 .433 .906

V7 122.97 337.617 .318 .909

V8 123.16 324.342 .569 .904

V9 123.33 331.128 .374 .907

V10 123.40 326.792 .534 .905

V11 123.40 328.019 .478 .906

V12 123.37 328.092 .474 .906

V13 123.95 321.744 .484 .906

V14 123.32 329.829 .427 .906

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V15 123.59 324.589 .462 .906

V16 123.68 321.969 .532 .905

V17 123.49 327.575 .426 .906

V18 123.41 330.198 .402 .907

V19 123.54 331.343 .342 .908

V20 123.38 329.339 .437 .906

V21 123.66 333.086 .325 .908

V22 123.38 330.975 .403 .907

V23 123.00 335.013 .284 .908

V24 123.64 325.902 .567 .905

V25 123.56 317.305 .560 .904

V26 123.57 321.953 .581 .904

V27 123.89 326.543 .421 .907

V28 123.65 329.661 .386 .907

V29 123.45 321.999 .535 .905

V30 123.70 326.725 .440 .906

V31 123.59 323.968 .465 .906

V32 123.54 334.232 .280 .908

V33 123.90 322.533 .543 .905

V34 123.90 320.207 .576 .904

V35 123.35 323.750 .535 .905

V36 123.28 334.931 .280 .908

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In HRMP Scale, Correlated Item-Total Correlation values are higher than

0.30, means all the items are measuring scale as a whole. So there is no

need to remove any item from the scale.

“Cronbach’s Alpha if Item Deleted” Colum shows that overall reliability of

scale would increase if some of the item will be removed from the scale.

But from above table, we found that none of the item of HRMP, can be

deleted.

Operational Performance

Table No.26 Item-Total Statistics-OP

Scale

Mean if

Item

Deleted

Scale

Variance

if Item

Deleted

Corrected

Item-Total

Correlation

Cronbach's

Alpha if

Item

Deleted

V37 42.48 36.224 .437 .734

V38 43.74 35.972 .353 .760

V39 42.27 36.091 .449 .733

V40 43.17 37.361 .352 .743

V41 43.36 37.858 .341 .754

V42 43.35 38.387 .318 .755

V43 42.49 35.220 .488 .728

V44 42.86 35.452 .452 .732

V45 43.04 36.808 .302 .748

V46 42.83 35.412 .396 .738

V47 42.79 35.768 .383 .739

V48 42.47 34.597 .528 .723

V49 42.58 35.054 .490 .728

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In Operation Performance Scale, Correlated Item-Total Correlation values

are higher than 0.30, means all the items are measuring scale as a

whole. So there is no need to remove any item from the scale.

“Cronbach’s Alpha if Item Deleted” Colum shows that overall reliability of

scale would increase if some of the item will removed from the scale. But

from above table, we found that none of the item of Operation

Performance Scale can be deleted.

Firm Performance

Table No.27 Item-Total Statistics-FP

Scale

Mean if

Item

Deleted

Scale

Variance

if Item

Deleted

Corrected

Item-Total

Correlation

Cronbach's

Alpha if

Item

Deleted

V50 42.16 37.524 .475 .763

V51 42.77 37.058 .427 .768

V52 42.26 38.896 .379 .772

V53 42.31 37.733 .313 .782

V54 42.39 36.701 .373 .776

V55 42.41 37.176 .545 .757

V56 42.10 38.865 .442 .767

V57 42.33 37.387 .539 .758

V58 42.21 39.368 .393 .771

V59 42.33 38.187 .396 .771

V60 42.23 37.982 .408 .769

V61 42.27 40.303 .268 .781

V62 42.00 37.716 .471 .764

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In Firm Performance Scale, Correlated Item-Total Correlation values are

higher than 0.30, means all the items are measuring scale as a whole. So

there is no need to remove any item from the scale.

“Cronbach’s Alpha if Item Deleted” Column shows that overall reliability

of scale would increase if some of the items will be removed from the

scale. But from above table, we found that none of the item of Firm

Performance Scale can be deleted.

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4.6 Hypothesis Testing

4.6.1 H1: HRM practices are positively related to firm’s

performance.

Table No.28 Correlations between HRMP and FP

HRMP

FIRM

PERFORMANCE

HRM Practices Pearson

Correlation

1 .494**

Sig. (2-tailed) .000

N 226 226

FIRM

PERFORMANCE

Pearson

Correlation

.494** 1


N 226 226


The relationship between HRM Practices and Firm Performance was

investigated using Pearson product-moment correlation coefficient.

Preliminary analyses were performed to ensure no violation of the

assumption of normality, linearity and homoscedasticity. There

was a positive correlation between HRM Practices and Firm

Performance.

There is linear positive correlation between HRM Practices and Firm

Performance. The correlation coefficient is 0.494 and is statistically

significant as the p-value is less than 0.01. In other words,

researcher failed to accept the Ho and leads to rejection of Ho.

It means, HRM practices are positively related to firm’s performance.

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ii. Correlation between HRM Dimensions and

Financial & Non-Financial Performance

Table No.29 Correlations between Nonfinancial and Financial

Performance

Nonfinancial

Performance

Financial

Performance

HR Planning Pearson

Correlation

.499* .130

Sig. (2-tailed) .000 .050

N 226 226

Staffing

Practices

Pearson

Correlation

.294* .287*

Sig. (2-tailed) .000 .000

N 226 226

Incentives

Practices

Pearson

Correlation

.329* .214*

Sig. (2-tailed) .000 .001

N 226 226

Performance

Appraisal

Pearson

Correlation

.464* .163*

Sig. (2-tailed) .000 .014

N 226 226

Training

Program

Pearson

Correlation

.271* .153*

Sig. (2-tailed) .000 .022

N 226 226

Teamwork Pearson

Correlation

.121 .182*

Sig. (2-tailed) .068 .006

N 226 226

Employee

Participation

Pearson

Correlation

.189* .295**

Sig. (2-tailed) .004 .000

N 226 226

CSR towards

employees

Pearson

Correlation

.414* .227*

Sig. (2-tailed) .000 .001

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N 226 226

*. Correlation is significant at the 0.05 level (2-tailed).

The relationship between HRM Dimensions and Financial & Non-

Financial Performance was investigated using Pearson product-

moment correlation coefficient. Preliminary analyses were

performed to ensure no violation of the assumption of normality,

linearity and homoscedasticity. There was a positive correlation

between HRM Dimensions and Financial & Non-Financial

Performance.

There is linear positive correlation between HRM Dimensions and

Financial & Non-Financial Performance.The correlation coefficients

are positive and are statistically significant as the p-value is less

than 0.05.

There is no statistically significant correlation between team work

and Non-financial performance of firm, as p-value is greater than

0.05.

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4.6.2 H2: HRM practices are positively related to Operation performance.

Table No.30 Correlations between HRMP and OP

HRM

OPERATION

PERFORMANCE

HRM Practices Pearson

Correlation

1 .399**


N 226 226


The relationship between HRM Practices and Operation Performance

was investigated using Pearson product-moment correlation

coefficient. Preliminary analyses were performed to ensure no

violation of the assumption of normality, linearity and

homoscedasticity. There was a positive correlation between HRM

Practices and Operation Performance.

There is linear positive correlation between HRM Practices and

Operation Performance. The correlation coefficient is 0.399 and is

statistically significant as the p-value is less than 0.01. In other

words, researcher failed to accept the Ho and leads to rejection of

Ho.

It means, HRM practices are positively related to of Operation

performance.

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Correlation between HRM Dimensions and Operation

Performance

Table No.31 Correlations between HRMP Dimensions and OP

Operation

Performance

HR Planning Pearson

Correlation

.275**


N 226

Staffing Practices Pearson

Correlation

.282**


N 226

Incentives Practices Pearson

Correlation

.343**


N 226

Performance Apprai

sal

Pearson

Correlation

.280**


N 226

Training Program Pearson Correlation

.202**


N 226

Team work Pearson

Correlation

.209**


N 226

Employee Participat

ion

Pearson

Correlation

.241**


N 226

CSR towards emplo

yees

Pearson

Correlation

.309**


N 226

**. Correlation is significant at the 0.01 level (2-

tailed).

The relationship between HRM Dimensions and Operation

Performance was investigated using Pearson product-moment

correlation coefficient. Preliminary analyses were performed to

ensure no violation of the assumption of normality, linearity and

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homoscedasticity. There is a positive correlation between HRM

Dimensions and Operation Performance. The correlation

coefficients are positive and are statistically significant as the p-

value is less than 0.01 between HRM Dimensions and Operation

Performance.

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4.6.3 H3: HRM Practices has impact on firm’s non financial performance.

Table No.32 Model Summary HRM Practices & financial performance

Mode

l R

R

Square

Adjusted R

Square

Std. Error of

the

Estimate

1 .503a .253 .250 3.268

a. Predictors: (Constant), HRMP

From above table, the value of R-Square is .250, which means that about

25 per cent variation in the dependent variable-non-financial

performance is explained by the independent variable- HRM Practices.

Table No.33 ANOVA- HRM Practices & financial performance

Model

Sum of

Squares df

Mean

Square F Sig.

1 Regression 810.929 1 810.929 75.920 .000a

Residual 2392.633 224 10.681

Total 3203.562 225


b. Dependent Variable: Non Financial Performance

The F-value is the Mean Square regression dived by the Mean Square

Residual, yielding F=75.920. The p-value associated with the F value is

very small (.000). These values are used to answer the questions “Do the

independent variable reliably explain the variations in the dependent

variables?” The p-value is compared to chosen alpha level (0.05) and, if

smaller, one can conclude that the independent variable explain

151 | P a g e

variations in the dependent variable. If the p-value was greater than

0.05, then the group of independent variables does not show a

statistically significant relationship with the dependent variables nor

does it explain the variation in the dependent variables. Here we can say

that HRM practices explain the significant amount of variation in the

Non-Financial performance of the firm.

Table No.34 Coefficients-HRM Practices & financial performance

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 4.196 1.506 2.786 .006

HRMP .102 .012 .503 8.713 .000

a. Dependent Variable: Nonfinancial Performance

From above table, the beta of HRMP variable is .503 and it’s significant

(p<.05), it means HRMP have strong impact on Non-financial

performance of firm.

152 | P a g e

4.6.4 H4: HRM practices have impact on firm’s financial

performance.

Table No.35 Model Summary- HRM practices and financial performance

Model R

R

Square

Adjusted R

Square

Std. Error of the

Estimate

1 .300a .090 .086 4.344



9 per cent variation in the dependent variable-financial performance is

explained by the independent variable- HRM Practices.

Table No.36 ANOVA- HRM practices and financial performance

Model

Sum of

Squares df

Mean

Square F Sig.

1 Regressi

on

419.268 1 419.268 22.223 .000a

Residual 4226.007 224 18.866

Total 4645.274 225


b. Dependent Variable: Financial Performance



very small (.000). These values are used to answer the questions “Do the

independent variable reliably explain the variations in the dependent

variables?” The p-value is compared to chosen alpha level (0.05) and, if

smaller, one can conclude that the independent variable explain



153 | P a g e



that HRM practices explain the minor significant amount of variation in

the financial performance of the firm.

Table No. 37 Coefficients HRM practices and financial performance

Coefficients

Model

Unstandardized

Coefficients

Standardize

d

Coefficients

t Sig. B

Std.

Error Beta

(Constant) 19.282 2.002 9.633 .000

HRM .073 .016 .300 4.714 .000

a. Dependent Variable: Financial Performance

From above table, the beta of HRMP variable is .300 and it’s significant

(p<.05), it means HRMP have impact on Financial Performance of firm.

154 | P a g e

How to measure mediating effect??

Figure No.7 Introduction of B as mediator variable between A and C

In the first regression, the significance of the path from A to B is

examined. In the second regression, the significance of the path A to the

dependent variable C is examined. Finally, the significance of the path B

to C is examined in the third regression by using A and B as predictors of

C. In the third equation simultaneous entry, rather that hierarchical

entry is used. Simultaneously energy allows for controlling the effect of A

while the effect on B on C examined, and controlling the effect of B while

the effect of A on C is examined (Baron & Kenny, 1986; Holmbeck, 1997).

The results are then compared, that is, the relative effect of A to C (when

B is controlled in the third equation) to the effect of A on C (when B is

not controlled in the second equation). The sequence of these regression

is summarized below.

155 | P a g e

Table No.38 Sequence of Regression Analysis, to establish the Mediating

Effect.

Equation

No.

Description

1 B regressed on A

2 C regressed on A

3 C regressed on A and B

simultaneously

4 Compare equation 3 with Equation 2

5 Mediating established if A to C is

non-significant in the third equation

effect

156 | P a g e

4.6.5 H5: Management Style (Decentralization Vs

Centralization) moderately affects the relationship

between HRM practices and firm’s Operational

performance.

Figure No.8 X (a) significant direct relationship between HRMP and

Firm’s Operation Performance.

Figure No.8 (b) Introduction of Management style a mediator show that

the relationship between HRMP and Firm’s Operation Performances

In above model, it assumes a three-variable system. First, a direct and

significant relationship between HRMP and Firm’s Operation

Performances is established after introducing the mediator variable

Management Style the path between HRMP and Firm’s Operation

Performances become significant or non-significant.

157 | P a g e

Table No.39 Sequence of Regression Analyses to establish the Mediating

Effect of Management Style on Operation Performance

R

Square F Sig. Beta sig.***

a HRMPMS .017 3.904 0.049 0.131 0.049

b HRMPOP .160 42.53 0.000 .399 0.000

c

HRMP and

MS OP .160 21.18 0.000 .401* .000

-

.008**

.894

*beta of HRMP , **beta of MS,***Sig. at 95% Confidence Level

To establish mediation effect of Decentralization (management style), the

following condition must be hold; First, independent variable (HRM

Practices) must affect mediator (Management style) in the first equation;

second, the independent variable HRM Practices must affect outcome

variable (Firm’s Operation Performance) in the second equation and

third, the mediator (Management style) must affect the outcome the

outcome variable (Firm’s Operation Performance) in the third equation. If

all these conditions hold in predicted direction then effect of independent

variable on outcome variable will be certainly lessened in the third

equation than in second equation.

If the effect of independent variables on outcome variable in presence of

mediator variable is reduced (in terms of regression coefficient but still

significant), the model is consistent with partial mediation.

Here, the mediator variable Management style has no significant effect

(p>.05,in equation no.3) on the relationship between HRM practices and


158 | P a g e

Table No.40 Partial Correlations

Control Variables HRMP

Operation

Performance

Management

style

HRMP Correlation 1.000 .397*

Significance (2-

tailed)

. .000

df 0 223

Operation

Performance

Correlation .397* 1.000

Significance (2-

tailed)

.000 .

df 223 0


The relationship between HRM Practices and Operation Performance





Practices and Operation Performance.


Operation Performance. The correlation coefficient is 0.399 and is

statistically significant as the p-value is less than 0.05.

We are correlating HRM Practices with Operation Performance while

controlling for management style. Thus, we have measure of the

association between HRM Practices with Operation Performance,

while removing the association between management style and the

two variables are correlating.

The changes in the correlation is very small, correlation coefficient is

.397 and is statistically significant as the p-value is less than 0.05.

159 | P a g e

It means, if we control mediator variable decentralization, then

there is a slight effect on the relationship between HRM practices

and firm’s Operational performance.

160 | P a g e

4.5.6 H6: Management Style (Decentralization Vs

Centralization) moderately affects the relationship

between HRM practices and firm’s performance.

Figure No.9 X (a) significant direct relationship between HRMP and Firm

Performance.

Figure No. 9. (b) Introduction of Management style a mediator show that

the relationship between HRMP and Firm Performances

The model assumes a three-variable system. First, a direct and

significant relationship between HRMP and Firm Performance is

established. After introducing the mediator variable Management Style,

the path between HRMP and Firm Performance become significant or

non-significant.

161 | P a g e


Effect of Management Style on Firm Performance

R


a HRMPMS 0.017 3.904 0.049 0.131 0.049

b HRMPFP 0.224 72.23 0.000 0.494 0.000

c

HRMP and

MS FP 0.237 36.01 0.000 0.492* 0.000

0.292** 0.771

*beta of HRMP, **beta of MS, ,***Sig. at 95% Confidence Level

To establish mediation effect of Decentralization (management style), the

following condition must be hold; First, independent variable (HRM

Practices) must affect mediator (Management style) in the first equation;

second, the independent variable HRM Practices must affect outcome

variable (Firm Performance) in the second equation and third, the

mediator (Management style) must affect the outcome the outcome

variable (Firm Performance) in the third equation. If all these conditions

hold in predicted direction then effect of independent variable on

outcome variable will be certainly lessened in the third equation than in

second equation.




Here, the mediator variable management style has no significant effect

(p>.05, in equation no.3) on the relationship between HRM practices and

firm’s Operation performance.

162 | P a g e

Table No.42 Partial Correlations between HRMP and FP

Correlations

Control Variables HRM FIRM PERFORMANCE

Management

style

HRM Correlation 1.000 .489*

Significance (2-

tailed)

. .000

df 0 223

FIRM

PERFORMANCE


Significance (2-

tailed)

.000 .

df 223 0


The relationship between HRM Practices and Firm’s Performance





Practices and Firm’s Performance.


Firm’s Performance. The correlation coefficient is 0.494 and is

statistically significant as the p-value is less than 0.05.

We are correlating HRM Practices with Firm’s Performance while

controlling for management style. Thus, we have measure of the

association between HRM Practices with Firm’s Performance, while

removing the association between management style and the two

variables are correlating.



163 | P a g e

It means, if we control mediator variable decentralization, then

there is a slight effect on the relationship between HRM practices and

Firm’s Performance

164 | P a g e

4.6.7 H7: Social Capital (Trust Vs Distrust) moderately

affects the relationship between HRM practices and firm’s

Operation performance.

Figure No.10 (a) significant direct relationship between HRMP and Firm’s

Operation Performance.

Figure No.10 (b) Introduction of Social Capital a mediator show that the

relationship between HRMP and Firm’s Operation Performances


significant relationship between HRMP and Operation Performance is

established after introducing the mediator variable Social Capital, the

path between HRMP and Operation Performance become significant or

non-significant.

165 | P a g e


Effect of Social Capital on Operation Performance

R


a HRMPSC .100 24.79 0.000 -.316 0.000

b HRMPOP .160 42.53 0.000 .399 0.000

c

HRMP and

SC OP .160 21.18 0.000 .397 .000

-.007 .908 *beta of HRMP , **beta of SC, ,***Sig. at 95% Confidence Level

To establish mediation effect of Social Capital (Tust), the following

condition must be hold; First, independent variable (HRM Practices)

must affect mediator (Social Capital) in the first equation; second, the

independent variable HRM Practices must affect outcome variable

(Operation Performance) in the second equation and third, the mediator

(Social Capital) must affect the outcome the outcome variable (Operation

Performance) in the third equation. If all these conditions hold in

predicted direction then effect of independent variable on outcome

variable will be certainly lessened in the third equation than in second

equation.




Here, the mediator variable Social Capital has no significant effect



166 | P a g e

Table No.44 Partial Correlation between HRMP and OP

Correlations

Control Variables HRM

OPERATION

PERFORMANCE

Social

Capital

HRM Correlation 1.000 .380*

Significance (2-

tailed)

. .000

df 0 223

OPERATION

PERFORMANCE


Significance (2-

tailed)

.000 .

df 223 0


The relationship between HRM Practices and Firm’s Operation





Practices and Firm’s Operation Performance.


Firm’s Operation Performance. The correlation coefficient is 0.399

and is statistically significant as the p-value is less than 0.05.

We are correlating HRM Practices with Firm’s Operation

Performance while controlling for Social Capital. Thus, we have

measure of the association between HRM Practices with Firm’s

Operation Performance, while removing the association between

Social capital and the two variables are correlating.



167 | P a g e

It means, if we control mediator variable Social Capital, then there

is a slight effect on the relationship between HRM practices and

Firm’s Operation Performance

168 | P a g e

4.6.8 H8: Social Capital (Trust Vs Distrust) moderately

affects the relationship between HRM practices and firm’s

performance.

Figure.No.11 (a): Significant direct relationships between HRMP and


Figure No.11. (b) Introduction of Social Capital a mediator show that the

relationship between HRMP and Firm Performances



established. After introducing the mediator variable Social Capital, the

path between HRMP and Firm Performance become significant or non-

significant.

169 | P a g e


Effect of Social Capital on Firm Performance

R


a HRMPSC .100 24.79 0.000 -.316 0.000

b HRMPFP .244 72.23 0.000 .494 0.000

c

HRMP and

SC FP .244 35.98 0.000 .489 .000

-.014 .824 *beta of HRMP , **beta of SC, ,***Sig. at 95% Confidence Level

To establish mediation effect of Social Capital (Tust), the following


must affect mediator (Social Capital) in the first equation; second, the

independent variable HRM Practices must affect outcome variable (Firm

Performance) in the second equation and third, the mediator (Social

Capital) must affect the outcome the outcome variable (Firm




equation.




Here, the mediator variable Social Capital has no significant effect


Firm Performance.

170 | P a g e

Table No.46 Partial Correlation between HRMP and FP

Correlations


FIRM

PERFORMANCE

Social

Capital


Significance (2-

tailed)

. .000

df 0 223

FIRM

PERFORMANCE


Significance (2-

tailed)

.000 .

df 223 0







Performance.



significant as the p-value is less than 0.05.

We are correlating HRM Practices with Firm Performance while

controlling for Social Capital. Thus, we have measure of the

association between HRM Practices with Firm Performance, while

removing the association between Social capital and the two




171 | P a g e

It means, if we control mediator variable Social Capital, then there

is a slight effect on the relationship between HRM practices and

Firm Performance

172 | P a g e

4.6.9 H9: Corporate culture (Proactive Vs Reactive)

moderately affects the relationship between HRM

practices and firm’s Operation performance.

Figure 12 (a) significant direct relationship between HRMP and Firm’s


Figure No. 12(b) Introduction of Corporate Culture a mediator show that

the relationship between HRMP and Firm’s Operation Performances


significant relationship between HRMP and Operation Performance is

established. After introducing the mediator variable Corporate Culture,

the path between HRMP and Operation Performance become significant

or non-significant.

173 | P a g e


Effect of Corporate culture on Operation Performance

R


a HRMPCC .105 26.37 0.000 -.325 0.000

b HRMPOP .160 42.53 0.000 .399 0.000

c

HRMP and

CC OP .160 21.30 0.000 .389 .000

-.031 .631 *beta of HRMP , **beta of CC, ,***Sig. at 95% Confidence Level

To establish mediation effect of Corporate Culture the following condition

must be hold; First, independent variable (HRM Practices) must affect

mediator (Corporate Culture) in the first equation; second, the

independent variable HRM Practices must affect outcome variable

(Operation Performance) in the second equation and third, the mediator

(Corporate Culture) must affect the outcome the outcome variable

(Operation Performance) in the third equation. If all these conditions hold

in predicted direction then effect of independent variable on outcome


equation.




Here, the mediator variable Corporate Culture has no significant effect



174 | P a g e

Table No.48 Partial Correlation between HRMP and OP

Correlations


OPERATION

PERFORMANCE

Corporate

Culture


Significance (2-

tailed)

. .000

df 0 223

OPERATION

PERFORMANC

E


Significance (2-

tailed)

.000 .

df 223 0

*. Correlation is significant at the 0.05 level (2-tailed). The relationship between HRM Practices and Firm’s Operation





Practices and Firm’s Operation Performance.


Firm’s Operation Performance. The correlation coefficient is 0.399

and is statistically significant as the p-value is less than 0.05.

We are correlating HRM Practices with Firm’s Operation

Performance while controlling for Social Capital. Thus, we have

measure of the association between HRM Practices with Firm’s

Operation Performance, while removing the association between

Social capital and the two variables are correlating.



175 | P a g e

It means, if we control mediator variable Corporate Culture, then


and Firm’s Operation Performance

4.6.10 H10: Corporate culture (Proactive Vs Reactive) moderately

affects moderately affects the relationship between HRM practices

and firm’s performance.

Figure No. 13(a) significant direct relationship between HRMP and Firm’s


Figure No.13. (b) Introduction of Corporate Culture as mediator show

that the relationship between HRMP and Firm Performances



established. After introducing the mediator variable Corporate Culture,

176 | P a g e

the path between HRMP and Firm Performance become significant or

non-significant.


Effect of Corporate Culture on Firm Performance

R


a HRMPCC .105 26.37 0.000 -.325 0.000

b HRMPFP .244 72.23 0.000 .494 0.000

c

HRMP and

CC FP .254 38.04 0.000 .458 .000

-.109 .077 *beta of HRMP, **beta of CC, ,***Sig. at 95% Confidence Level

To establish mediation effect of Corporate Culture, the following


must affect mediator (Corporate Culture) in the first equation; second,

the independent variable HRM Practices must affect outcome variable

(Firm Performance) in the second equation and third, the mediator

(Corporate Culture) must affect the outcome the outcome variable (Firm




equation.




Here, the mediator variable Corporate Culture has no significant effect


Firm Performance.

177 | P a g e

Table No.50 Partial Correlation between HRMP and FP

Correlations


FIRM

PERFORMANCE

Corporate

Culture


Significance (2-

tailed)

. .000

df 0 223

FIRM

PERFORMA

NCE


Significance (2-

tailed)

.000 .

df 223 0







Performance.



significant as the p-value is less than 0.05.

We are correlating HRM Practices with Firm Performance while

controlling for Social Capital. Thus, we have measure of the

association between HRM Practices with Firm Performance, while

removing the association between Social capital and the two




178 | P a g e

It means, if we control mediator variable Corporate Culture, then


and Firm Performance.

179 | P a g e

4.6.11 H11: There is no difference in of HRM Practices,

Firm Performance, and Operation Performance across year

of Operation of the firm.

Year of operation of firm has been classified in five categories i.e. firm is

less than 5 years old, 5 to 10 years 11 to 15 years, 16 to 20 years,

more than 20 years old.

vii. There is no difference in HRM Practices of firm

belonging to its year of Operations.

Table No.51 Test of Homogeneity of Variances-HRMP

HRM PRACTICE

Levene

Statistic df1 df2 Sig.

.741 4 221 .565

Table no. 51 reports Homogeneity of Variance. It is Levene’s statistics

along with its significant level. Levene’s test is used to examine the

quality of variance. The null and alternative hypothesis for Levelen’s test

for Equality of Variance is as follows;

Ho: Variances of two groups are equal.

H1: Variances of two groups are unequal.

Since the F-value is .741 and its associated significance (0.565) is greater

than 0.05, we fail to reject the null hypothesis and say that the variance

are equal for the five group of year of Operation.

180 | P a g e

Table No.52 ANOVA-HRMP and Year of Operation

ANOVA

HRM PRACTICE

Sum of

Squares df

Mean

Square F Sig.

Between

Groups

2286.562 4 571.641 1.677 .156

Within

Groups

75326.079 221 340.842

Total 77612.642 225

F-value is the ratio between-groups mean squares and within-group

mean square. The F-ratio equals 1.677 and its associated p-value (sig.) is

reported as .156. It indicates the probability of observed value happening

by chance. The result shows that the difference between means of five

groups (categories) of years of Operation of firms is non-significant. Thus,

we fail to reject null hypothesis and say that there is no difference in of

HRM Practices across year of Operation of firm.

181 | P a g e

iii. There is no difference in Dimensions of HRM Practices of firm


Table No.53 ANOVA on Dimensions of HRM Practices of firm belonging to

its year of Operations.

Sum of

Squares df

Mean

Square F Sig.

HR Planning Between

Groups

71.184 4 17.796 2.002 .095

Within

Groups

1964.219 221 8.888

Total 2035.403 225

Incentives

Practices

Between

Groups

40.560 4 10.140 1.859 .119

Within

Groups

1205.693 221 5.456

Total 1246.252 225

Performance

Appraisal

Between

Groups

15.076 4 3.769 .711 .585

Within

Groups

1171.066 221 5.299

Total 1186.142 225

Employee

Participation

Between

Groups

27.655 4 6.914 1.544 .191

Within

Groups

989.814 221 4.479

Total 1017.469 225

CSR towards

employees

Between

Groups

352.331 4 88.083 1.041 .387

Within

Groups

18700.045 221 84.616

Total 19052.376 225

Staffing

Practices

Between

Groups

56.820 4 14.205 2.239 .066

182 | P a g e

Within

Groups

1402.242 221 6.345

Total 1459.062 225

Training

Program

Between

Groups

29.140 4 7.285 .933 .446

Within

Groups

1726.082 221 7.810

Total 1755.221 225

Teamwork Between

Groups

19.705 4 4.926 .857 .490

Within

Groups

1269.764 221 5.746

Total 1289.469 225

The result shows that the difference between means of five groups

(categories) of years of Operation of firms is non-significant in all the

dimension of HRM Practices. Thus, we fail to reject null hypothesis and

say that there is no difference in HR Planning, Incentives Practices,

Performance Appraisal, Employee Participation, CSR towards Employees,

Staffing Practices, Training Program, Team work across year of Operation

of firm.

viii. There is no difference in Firm Performance (Financial

and Non-financial Performance) belonging to its year of

Operations.

Table No.54 Test of Homogeneity of Variances FP

Levene


Financial Performa

nce

2.267 4 221 .063

183 | P a g e

Levene


Financial Performa

nce

2.267 4 221 .063

Nonfinancial Perfor

mance

.781 4 221 .539

The F-values are 2.267 in financial performance and .781 in non

financial performance and its associated significance values are greater

than 0.05, we fail to reject the null hypothesis and say that the variance

are equal for the five group of year of Operation.

Table No.55 ANOVA Firm Performance (Financial And Non-Financial

Performance) Belonging To Its Year Of Operation

ANOVA

Sum of

Squares df

Mean

Square F Sig.

Financial Performa

nce

Between

Groups

256.504 4 64.126 3.229 .013

Within

Groups

4388.771 221 19.859

Total 4645.274 225

NonFinancial Perf

ormance

Between

Groups

56.831 4 14.208 .998 .410

Within

Groups

3146.731 221 14.239

Total 3203.562 225

The F-ratio in financial performance equals 1.677 and its associated p-

value (sig.) is reported as .013. It indicates the probability of observed

value happening are not by chance. The result indicates that the

difference between groups (categories) of years of Operation of firms is

184 | P a g e

significant. Moreover, thus we reject null hypothesis and say that there is

significant difference in of financial across year of Operation of firm.

Moreover F-ratio in non-financial performance .998 and its associated p-

value (sig.) is reported as .410. It indicates the probability of observed

value happening by chance. The result shows that the difference between

groups (categories) of years of Operation of firms is non-significant.

iv. There is no difference in Operation Performance of firm belonging

to its year of Operations.

Table No.56 Test of Homogeneity of Variances-OP

OPERATION PERFORMANCE

Levene


1.496 4 221 .204

The F-values are 1.496 in Operation performance and its associated

significance values are greater than 0.05, we fail to reject the null

hypothesis and say that the variance are equal for the five group of year

of Operation.

ix. Table No.57 ANOVA- difference in Operation Performance of firm


OPERATION PERFORMANCE

Sum of

Squares df

Mean

Square F Sig.

Between

Groups

231.397 4 57.849 1.401 .235

Within

Groups

9128.568 221 41.306

Total 9359.965 225

185 | P a g e

F-value is the ratio between-groups mean squares and within-group

mean square. The F-ratio equals 1.496 and its associated p-value (sig.) is

reported as .204. It indicates the probability of observed value happening

by chance. The result shows that the difference between means of five

groups (categories) of years of Operation of firms is non-significant. Thus,

we fail to reject null hypothesis and say that there is no difference in of

Operation of Firm across year of Operation of firm.

186 | P a g e

4.6.12 Impact on HRMP, Management style, Social Capital

and Corporate Culture on Non Financial performance,

Financial Performance and Operation Performance

x. Impact on HRMP, Management style, Social Capital and

Corporate Culture on Non Financial performance

Table No.58 Model Summary

Mode

l R

R

Square

Adjusted R

Square

Std. Error of

the

Estimate

1 .595a .355 .343 3.059

a. Predictors: (Constant), HRM PRACTICE,

Management style, Social Capital, Corporate Culture


35.5% per cent variation in the dependent variable-non financial

performance is explained by the independent variables HRMP,

Management style, Social Capital and Corporate Culture.

Table No.59 ANOVA for Management style, Social Capital and Corporate

Culture on Non Financial performance

ANOVAb

Model

Sum of

Squares df

Mean

Square F Sig.

1 Regressi

on

1135.682 4 283.921 30.343 .000a

Residual 2067.880 221 9.357

Total 3203.562 225

a. Predictors: (Constant), HRM PRACTICE, Management style, Social

Capital, Corporate Culture

b. Dependent Variable: NonFinancial Performance

187 | P a g e



very small (.000). The p-value is compared to chosen alpha level (0.05)

and, if smaller, one can conclude that the independent variable explain





that HRMP, Management style, Social Capital and Corporate Culture

explain the significant amount of variation in the Non-Financial

performance of the firm.

Table No.60 Regression Analysis

Coefficients

Model

Unstandardized

Coefficients

Standardize

d

Coefficients

t Sig. B

Std.

Error Beta

1 (Constant) 10.173 1.811 5.618 .000

Management

style

-.022 .036 -.034 -.620 .536

Social

Capital

7.123E-5 .046 .000 .002 .999

Corporate

Culture

-.211 .046 -.334 -4.604 .000

HRM

PRACTICE

.081 .012 .399 6.866 .000

a. Dependent Variable: Nonfinancial Performance

From above table, the beta of HRMP and Corporate Culture variables are

-.399 and .334 respectively and it’s significant (p<.05), it means HRMP

and Corporate Culture have strong impact on Non-Financial Performance

of firm.

188 | P a g e

xi. Impact on HRMP, Management style, Social Capital and

Corporate Culture on Financial performance

Table No.61 Model Summary -HRMP, Management style, Social

Capital and Corporate Culture on Financial performance

Mode

l R

R

Square

Adjusted R

Square

Std. Error of

the

Estimate

1 .339a .115 .099 4.312




9 per cent variation in the dependent variable- financial performance is

explained by the independent variables HRMP, Management style, Social

Capital and Corporate Culture.

Table No.62 ANOVA- HRMP, Management style, Social Capital and

Corporate Culture on Financial performance

ANOVAb

Model

Sum of

Squares df

Mean

Square F Sig.

1 Regressi

on

535.232 4 133.808 7.195 .000a

Residual 4110.042 221 18.597

Total 4645.274 225

a. Predictors: (Constant), HRM PRACTICE, Management style, Social


b. Dependent Variable: Financial Performance






189 | P a g e





explain the significant amount of variation in the Financial performance

of the firm.

Table No.63 Coefficients- HRMP, Management style, Social Capital

and Corporate Culture on Financial performance

Coefficients

Model

Unstandardized

Coefficients

Standardize

d

Coefficients

t Sig. B

Std.

Error Beta

1 (Constant) 15.423 2.553 6.041 .000

Management

style

.050 .050 .064 1.004 .317

Social

Capital

.098 .065 .128 1.508 .133

Corporate

Culture

.030 .065 .040 .468 .640

HRM

PRACTICE

.084 .017 .345 5.074 .000

a. Dependent Variable: Financial Performance

From above table, the beta of HRMP variables is 0.345 and it’s significant

(p<.05), it means HRMP have strong impact on Financial Performance of

firm.

190 | P a g e

v. Impact on HRMP, Management style, Social Capital and

Corporate Culture on Operation performance

Table No.64 Model Summary- HRMP, Management style, Social

Capital and Corporate Culture on Operation performance

Mode

l R

R

Square

Adjusted R

Square

Std. Error of

the

Estimate

1 .401a .161 .145 5.96215




16 per cent variation in the dependent variable-Operation performance is

explained by the independent variables HRMP, Management style, Social

Capital and Corporate Culture.

Table No.65 ANOVA- HRMP, Management style, Social Capital and


ANOVAb

Model

Sum of

Squares df

Mean

Square F Sig.

1 Regressi

on

1504.027 4 376.007 10.578 .000a

Residual 7855.938 221 35.547

Total 9359.965 225

a. Predictors: (Constant), HRM Practice, Management style, Social


b. Dependent Variable: Operation Performance





191 | P a g e






explain the significant amount of variation in the Operation performance

of the firm.

Table No.66 Coefficients- HRMP, Management style, Social Capital and


Coefficients

Model

Unstandardized

Coefficients

Standardize

d

Coefficients

t Sig. B

Std.

Error Beta

1 (Constant) 29.607 3.529 8.389 .000

Management

style

-.007 .069 -.007 -.105 .917

Social

Capital

.019 .089 .018 .216 .829

Corporate

Culture

-.046 .089 -.042 -.509 .611

HRM

PRACTICE

.136 .023 .392 5.919 .000

a. Dependent Variable: OPERATION PERFORMANCE

From above table, the beta of HRMP variables is .392 and it’s significant

(p<.05), it means HRMP have strong impact on Operation Performance of

firm.

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