Describe about the Research Paper For Business and Engineering Fields.
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 
25 610 1115 1620 2125 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 interitem Covariance mean is 0.378 while the interitem correlation is 0.414 for the 24 items analyzed.
Table 6: InterItem test statistics
Summary Item Statistics 

Mean 
Minimum 
Maximum 
Range 
Maximum / Minimum 
Variance 
N of Items 

InterItem Covariance 
.378 
.078 
.834 
.756 
10.739 
.019 
24 
InterItem 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 pvalue 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 

KaiserMeyerOlkin Measure of Sampling Adequacy. 
.894 

Bartlett's Test of Sphericity 
Approx. ChiSquare 
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 pvalue for both KologomorovSmirnoff and ShapiroWilk 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 Fstatistic to test the null hypothesis that the variances are significantly equal to zero is 7.037 with a pvalue 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 Statistics^{a} 

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}: HighPerformance HR Practices is positively related to Innovative Work Behaviors
H_{ a}: HighPerformance HR Practices is not positively related to Innovative Work Behaviors
The ChiSquare 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}: PersonOrganization (PO) Fit mediates the relationship between HighPerformance HR Practices and Innovative Work Behaviors
H_{ a}: PersonOrganization (PO) Fit does not mediate the relationship between HighPerformance HR Practices and Innovative Work Behaviors
The ChiSquare value is 0.000 which is greater than 0.005, we therefore fail to reject the null hypothesis and conclude that PersonOrganization (PO) Fit mediates the relationship between HighPerformance 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 TuckerLewis Index (TLI) is 0.458
The model has a high chisquare statistic of 536.047 indicating that the model is a good fit, which according to David (2016) “a model with a chisquare of more than 500” the chisquare 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 PersonOrganization (PO) 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: HighPerformance 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, PersonOrganization (PO) Fit mediates the relationship between HighPerformance 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, HighPerformance 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), 918930.
Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A fivestep 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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