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Describe about the Research Paper For Business and Engineering Fields.

Overview of the Dataset

The dataset for analysis contains 7 variables with 216 entries. The value for the 7 variables is as given in the table below.

Table 1

Variable

Sum

Value

Gender

216

Male

Female

Nationality

216

United Arab Emirates-

Jordan

Pakistan

South Africa

India

Lebanon

Andorra

Greece

United Kingdom

Nepal

Sudan

United States

Yemen

Russia

Morocco

Current Age

216

Less than 25

25 – 35

36 – 45

46 – 55

56 – 65

More than 65 years

Educational Level

216

High Diploma

Masters

Bachelors

High School or less

Pree Degree

Job Level

216

Lower Management

Middle Management

Junior Employee

Junior Employee

Outsource

Accountant

Top Management

Inspector

Sales coordinator

Officer

Office Administrator

Grade 8

Field of Work

216

Business

Medicine

Maintenance

Information  Technology

Engineering

Office Work

Aviation

Laboratory

Logistics

Corporate Affairs

Tourism and Hospitality

Shift Supervisor

HSSE

Legal

Media, Communications or Public Relations

Years Of Experience

216

2-5

6-10

11-15

16-20

21-25

More than 25

The total number of respondents for the questions was 216.

From table 1 above, it is noted that majority of the respondents are male with a percentage of 74.1% while females have a percentage of 25.1. Additionally, the United Arab Emirates have a 59.3% respondent score which is the highest, while Argentina, Yemen, United States of America and Nigeria record among the lowest entries at 0.5% each. From the statistics, majority of the workers are of the age between 25 and 48 making it a total of 48.6%. 

Additionally, most of the workers assessed in the research project are from the engineering and business faculties at 22.2 and 19.0% respectively. Whereas, the level of education with the most entries is the bachelors degree followed by masters. Such may explain the relatively big percentage of the entrants in the business and engineering fields.

Table 4: Descriptive statistics (Mean, Variance...)

Descriptive Statistics

N

Minimum

Maximum

Sum

Mean

Variance

Statistic

Statistic

Statistic

Statistic

Statistic

Std. Error

Statistic

HPHRP 4

216

1

5

654

3.03

.078

1.311

HPHRP 5

216

1

5

665

3.08

.073

1.143

HPHRP 3

216

1

5

675

3.12

.072

1.124

HPHRP 2

216

1

5

685

3.17

.074

1.194

HPHRP 1

216

1

5

695

3.22

.069

1.036

POF 3

216

1

5

756

3.50

.061

.791

POF 5

216

1

5

761

3.52

.068

.986

POF 2

216

1

5

762

3.53

.071

1.078

JBT 3

216

1

5

778

3.60

.066

.929

JBT 2

216

1

5

794

3.68

.061

.797

POF 4

216

1

5

808

3.74

.067

.974

POF 1

216

1

5

809

3.75

.062

.833

JBT 1

216

1

5

809

3.75

.059

.758

POF 6

216

1

5

810

3.75

.068

1.007

IWB 2

216

1

5

832

3.85

.062

.843

IWB 1

216

1

5

834

3.86

.063

.864

IWB 5

216

1

5

842

3.90

.068

.985

IWB 6

216

1

5

844

3.91

.059

.745

JBE 1

216

1

5

847

3.92

.066

.929

IWB 4

216

1

5

851

3.94

.058

.727

IWB 3

216

1

5

857

3.97

.065

.915

HPHRP 6

216

1

5

867

4.01

.062

.842

JBE 2

216

1

5

876

4.06

.059

.760

JBE 3

216

1

5

911

4.22

.053

.618

From table 3, the average response from the respondents was that of above average opinion, i.e. they mostly have an average of 3, however, majority of respondents reported better HR in their work places. The variance of the dataset variable range from 0.618 to 1.311

Scale Statistics

Mean

Variance

Std. Deviation

N of Items

88.06

230.917

15.196

24

The Overall Variance is 230.917 and the mean is 88.06.

The Cronchbach’s Alpha statistic for the dataset is 0.943, while the Cronchbach’s Alpha based on Standardized Items is 0.944 for 24 items which according to  Gilem and Gilem(2013), the closer the Cronchbach’s statistic is closer to 1 the greater the internal consistency of the model. Therefore the model used for analysis is consistent.

Table 5: Reliability tests

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha Based on Standardized Items

N of Items

.943

.944

24

The inter-item Covariance mean is 0.378 while the inter-item correlation is 0.414 for the 24 items analyzed.

Table 6: Inter-Item test statistics

Summary Item Statistics

Mean

Minimum

Maximum

Range

Maximum / Minimum

Variance

N of Items

Inter-Item Covariance

.378

.078

.834

.756

10.739

.019

24

Inter-Item Correlations

.414

.086

.934

.848

10.827

.020

24

Construct validity of the questionnaire is conducted by use of the Pearson correlation coefficient test. From table 7, the Pearson correlation coefficient of the variables is 0.000 which is less than 0.05 at 95% level of significance, indicating that all the questionnaire variable are valid for analysis. 

In order to explore the underlying theoretical structure of the dataset, an exploratory factor analysis is conducted to examine the relationship between the variable chosen and the respondent.

The sample is from a normal distribution

The sample size is of size n=200 or more

From the component table, it is clear that only four components are extracted using the principle component analysis (Williams and Brown, 2010). 

Component Matrix

Component

1

2

3

4

HPHRP 1

.555

.539

.209

.049

HPHRP 2

.561

.570

.311

.021

HPHRP 3

.593

.489

.281

.033

HPHRP 4

.552

.525

.274

.089

HPHRP 5

.663

.257

.191

.143

HPHRP 6

.675

-.050

.136

-.197

POF 1

.709

.261

-.214

.138

POF 2

.755

.279

-.257

.080

POF 3

.695

.269

-.267

.167

POF 4

.730

.186

-.359

.103

POF 5

.471

.064

-.396

.177

POF 6

.753

.284

-.309

.137

IWB 1

.632

-.391

-.062

.302

IWB 2

.679

-.366

-.007

.061

IWB 3

.730

-.409

.257

.161

IWB 4

.661

-.553

.165

.230

IWB 5

.748

-.353

.310

.146

IWB 6

.647

-.532

.234

.295

JBE 1

.705

.065

-.161

-.302

JBE 2

.600

-.301

-.235

-.354

JBE 3

.639

-.358

-.367

-.085

JBT 1

.712

-.183

.094

-.446

JBT 2

.709

-.044

.095

-.475

JBT 3

.705

-.005

.150

-.388

Extraction Method: Principal Component Analysis.

a. 4 components extracted.

Therefore the number of components extracted are 4

From the table below the Kaiser Meyer sampling statistic for adequacy of the questionnaire is 0.894 which is less than 0.9 and greater than 0.5, hence indicating the questionnaire is adequate for analysis since it is above the recommended value of adequacy (Pett et al., 2010). Whereas the Bartlett’s test of sphericity has a sigma value of 0.000, which is less p-value of 0.05, therefore the significance level is small enough to reject the null hypothesis (Williams et al., 2010). Therefore, the correlation matrix in table 5 is not an identity matrix. Hence the sampling sufficiency of the variables is accepted.

Descriptive Statistics

Table 7: KMO output results

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.894

Bartlett's Test of Sphericity

Approx. Chi-Square

4045.398

df

276

Sig.

.000

Table 8: Total variance explained

From the scree plot given below, four factors were extracted, i.e. the point of inflection of the scree plot produced is 4 at Eigen value >1 (Ghodsi and Zhu, 2006).

The extraction method of the rotated component matrix is principle component matrix with 6 iterations and a Varimax with Kaiser Normalization rotation. Factors with a loading of more than 0.5 are assumed to contribute more to the given variable; hence from the rotated component matrix the factors are bold to indicate their contribution to a factor.

Rotated Component Matrix

Component

1

2

3

4

HPHRP 1

.026

.754

.238

.136

HPHRP 2

.030

.828

.159

.155

HPHRP 3

.095

.772

.185

.176

HPHRP 4

.072

.781

.193

.102

HPHRP 5

.304

.607

.276

.159

HPHRP 6

.347

.327

.172

.508

POF 1

.193

.421

.620

.193

POF 2

.166

.426

.665

.263

POF 3

.175

.396

.664

.161

POF 4

.191

.298

.720

.249

POF 5

.155

.080

.614

.080

POF 6

.170

.408

.722

.213

IWB 1

.701

.023

.375

.125

IWB 2

.622

.063

.294

.348

IWB 3

.805

.205

.130

.291

IWB 4

.864

.031

.158

.225

IWB 5

.788

.278

.104

.302

IWB 6

.893

.082

.118

.157

JBE 1

.146

.256

.416

.598

JBE 2

.285

-.096

.335

.655

JBE 3

.416

-.155

.521

.458

JBT 1

.332

.200

.130

.762

JBT 2

.227

.294

.147

.762

JBT 3

.254

.358

.133

.678

Extraction Method: Principal Component Analysis.

 Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 6 iterations.

Therefore the selected factors are:

Component

1

2

3

4

IWB 6

HPHRP 2

POF 6

JBT 1

JBT 2

Figure 3: Scatter plot matrix 

In order to determine the variables that pass the reliability test, the Cronchbach’s test is conducted for both when the item is deleted and when it is kept. 

From the statistics, there is a high percentage of consistency between the variables since the Cronchbach statistic ranges from 0.939 to 0.942 which is relatively higher than the o.5 minimum that indicate reliability. 

From the tables below, the number of items used in the Cronchbach test are six and the alpha statistic is greater than 0.7, where the Innovative work behavior has 0.922, the High performance HR practices has 0.865, the Person- Organization fit has 0.899 and lastly the employee performance has a Cronchbach alpha of 0.922.

Table 9

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha Based on Standardized Items

N of Items

.865

.863

6

Table 10

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha Based on Standardized Items

N of Items

.889

.890

6

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha Based on Standardized Items

N of Items

.922

.923

6

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha Based on Standardized Items

N of Items

.872

.873

6

In the test of normality, the p-value for both Kologomorov-Smirnoff and Shapiro-Wilk were 0.000 < 0.05 at a significance level of 95%. These statistics indicate normality of the dataset. The degrees of freedom for the test of normality is 216  

To prove that the dataset has a normal distribution, the fitted model residuals are not widely spread from the residual line. 

The F-statistic to test the null hypothesis that the variances are significantly equal to zero is 7.037 with a p-value of 0.000 <0.05 hence we fail to reject the null hypothesis and conclude that the variance are constant for the residual variables. 

Residuals Statisticsa

Minimum

Maximum

Mean

Std. Deviation

N

Predicted Value

2.24

6.08

4.06

.545

216

Residual

-2.462

2.761

.000

.680

216

Std. Predicted Value

-3.332

3.717

.000

1.000

216

Std. Residual

-3.466

3.887

.000

.957

216

a. Dependent Variable: JBE 2

The residual errors have a mean of zero. Therefore the error term is homoscedastic with equal variances and mean zero.

Some variables show a Variance inflation factor of above 5 up to 10 and a tolerance rate of less than 0.2 indicating presence of multicollinearity in variables such as IWB 6 

H 1: High-Performance HR Practices is positively related to Innovative Work Behaviors

H a: High-Performance HR Practices is not positively related to Innovative Work Behaviors 

The Chi-Square statistic to test the null hypothesis against the alternative hypothesis is 0.000 at 95% confidence interval which is less than 0.005. We therefore fail to reject the null hypothesis and conclude that Performance HR Practices are positively correlated with innovative work behaviors.

H 2: Person-Organization (P-O) Fit mediates the relationship between High-Performance HR Practices and Innovative Work Behaviors

H a: Person-Organization (P-O) Fit does not mediate the relationship between High-Performance HR Practices and Innovative Work Behaviors 

The Chi-Square value is 0.000 which is greater than 0.005, we therefore fail to reject the null hypothesis and conclude that Person-Organization (P-O) Fit mediates the relationship between High-Performance HR Practices and Innovative Work Behaviors.

H 3: IWB positively related to job performance

H a: IWB is not positively related to job performance 

Table 13  

Scalar Estimates (Group number 1 - SEM)

Maximum Likelihood Estimates 

For the confirmatory factor analysis there were no negative Eigen values, i.e. the factors are extracted for Eigen >1 

The goodness of fit (GFI) Index has a value of 0.738 which is near 1 hence indicating a good fit while the root mean square residual (RMR) is 0.440 for the SEM model which is greater than 0 indicating a not so perfect fit, whereas the

RMR, GFI 

The standardized RMR (SRMR) is 0.276 which near zero indicating a good model fit. Additionally, the Comparative fit index (CFI) is 0.432 while the Tucker-Lewis Index (TLI) is 0.458  

The model has a high chi-square statistic of 536.047 indicating that the model is a good fit, which according to David (2016) “a model with a chi-square of more than 500” the chi-square is always statistically significant.

CMIN 

Following the results and subsection discussion, it is noted that several factors are key in influencing employee job performance. Such factors may include Innovative Work Behaviors as well as Person-Organization (P-O) Fit. Pett and Sullivan (2003) in an article on management argue that the factors that influence job attitude and satisfaction are: Questions (HPHRP 1 to HPHRP 6) measures: High-Performance HR Practices

  • Probability of promotions
  • Working environment
  • Labor market behavior

Factors such as working environment, tend to affect the innovative nature of workers be it consciously or subconsciously. For instance, a worker in a company with adequate technological access may tend to be more engaged in innovative work behaviors than that with limited resources which ultimately affects the employee job performance. From the analysis, Person-Organization (P-O) Fit mediates the relationship between High-Performance HR Practices and Innovative Work Behaviors, i.e. Personal factors and organizational factors such as working environment and management tend to be the determining factor as to which extent a worker may engage in innovative worker behavior. Elsewhere, the results indicate that there is a relationship between, High-Performance HR Practices and Innovative Work Behaviors. High performance HR practices such as motivational incentives which may include prospects of promotion, good management- worker relationship etcetera, influence the workers motive to take part in innovative work practices. Ultimately, innovative work practices are therefore seen to be positively related to employee job performance, i.e. better engagement in innovative work leads to better employee performance.

References

Tavakol, M., &Dennick, R. (2011).Making sense of Cronbach's alpha. International journal of medical education, 2, 53.

Zhu, M., & Ghodsi, A. (2006).Automatic dimensionality selection from the scree plot via the use of profile likelihood. Computational Statistics & Data Analysis, 51(2), 918-930.

Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine, 8(3).

Gilem, K., & Gilem, V.(2013). Structural equation modeling. [online]. Available from: https//:www.statisyicalsolutions.com/sem 

Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research.Sage.

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