the impact of employee engagement and gross output on productivity in different industry groups
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THE IMPACT OF EMPLOYEE ENGAGEMENT AND GROSS OUTPUT ON PRODUCTIVITY IN DIFFERENT INDUSTRY GROUPS
Outline of Presentation
Introduction Literature Review Research Methodology Analysis Conclusion
Presented By
APPADU GANGAMAH DEVIBAGHA KESHIKADIXIT CHANDREEKADOMUN NEERAJALUCKYRAM URVASHEE
CHAPTER ONE INTRODUCTION
INTRODUCTION
Industries in Mauritius Faced increased competition due to: Globalisation Changes in Technology Political and economic environment Must train employees to develop them to face competition Enhance the contribution as a means of sustaining effective performance and ensure output Training of employees can increase productivity and the output will be managed efficiently
INTRODUCTION
Overview of different industry in Mauritius Largest sector in Mauritian economy is Manufacturing industry , it contributed towards 20% to the GDP Construction industry make use of core values to contribute to economic growth Cohesiveness Integrity Developing people Building trust Responsibility Excellence Quality Financial and Insurance activities boost up the GDP contribution from Rs 31,263 millions in 2012 to Rs 32,799 Million in
2013 Transport and Storage increased GDP from Rs 17,797 million in 2012 to Rs 18,784 million in 2013 Mauritius remain the Information and Technology leader in the African region.
Problem Statement
Productivity is affected due to Poor Supervision and management Poor communication Insufficient budgeting and staffing It has become a challenge to maintain a good workforce Moreover, preferences towards some employees might affect production thus resulting in
gender inequalities
Research Objectives
To assess whether productivity, employee engagement and gross output are related to each other
To assess the link between productivity and employee engagement To measure the impact of gross output and productivity
CHAPTER TWO LITERATURE REVIEW
Overview of this chapter
Definition of employee engagement, productivity and output Constraints limiting output Productivity measurement Relationship between employment, output and productivity
Employee Engagement
Extent to which employees feel passionate about their jobs Are committed to the organisation Put discretionary effort into their work Employ and express themselves physically, cognitively and emotionally during
role performances
Employee Engagement
According to Gibbins work, there are 8 drivers of employee engagement which include: Trust and Integrity Nature of the job Line of sight between employees performance and company performance Career growth opportunities Pride about the company Co workers/ Team member Employee Development Relationship with one’s manager
Output
Amount of goods and services produced in a system Constraints limiting output include: Quality of machinery Availability of workers Demand from consumers
Productivity
Rate of efficiency by which a company produces goods and services
Productivity Measurement
Technology Efficiency Real Cost savings Benchmarking Production Processes Living Standards
Relationship between Productivity, Employment and Output
Employment , productivity and output are not independently determined variables that is , the 3 variables are linked
It is expressed as Output = Employment * Productivity
CHAPTER THREE
RESEARCH METHODOLOGY
Research Methodology
This chapter relates to the different methods used in this study and includes a review of research design which consists of :
Simple Linear Regression Multiple Linear Regression Hypothesis Development
Research Design
Simple Linear Regression
The Dependent variable would be Production Units The Independent Variable would be Persons engaged The Equation is as follows: Y= B0+ B1X +
Where Y would be the dependent variable, implying Production units
X would be the independent variable, implying the Persons engaged
Would be the Random error component
Multiple Linear Regression
In this case, the Independent variables would be Persons engaged and Gross output The Dependent variable would be Production Units The Equation is as follows:
Y= B0 + B1X1+B2X2+
Where Y = dependent variable, implying Production Units
X1= independent variable, implying Persons engaged
X2= independent variable, implying Gross Output
= Random error term
Sampling
The sample size would be 12, comprising of the different sectors namely:
Manufacturing
Construction
Wholesale and retail trade
Transportation and storage
Accommodation and Food services
Financial and Insurance activities
Professional, scientific and technical activities
Administrative and support service
Education
Human Health and social work
Art and entertainment
Other services
Research Tool
The data would be mostly of secondary nature It was evaluated using the Integrated Statistical Software Stata 13.0
Research Questions
What is the relationship between productivity, employee engagement and gross output?
What is the link between productivity and employee engagement ?
To measure the impact of gross output and productivity.
Hypothesis Development
Hypothesis #1 : Productivity and Employee engagement
H0: There is no relationship between productivity and employee engagement
H1: There is a relationship between productivity and employee engagement
Hypothesis #2: Gross Output , Productivity and Employee engagement
H0: There is no relationship between gross output, productivity and employee engagement
H1: There is a relationship between gross output, productivity and employee engagement
CHAPTER FOUR ANALYSIS
Contents
Simple Linear Regression Model Multiple Linear Regression Model Post Estimation tests
Simple Linear Regression Model
General equation: Y = B0 + B1X + Epsinote
Where B0 is the Y-intercept and B1 is the gradient or slope
Reg production person
Interpretation of Outcomes
Equation of the Model: Production = 4.56+0.22 person engaged R2 is the proportion of variance in the dependent variable For this model, the value of R2 = 0.984
Hypothesis Testing
Hypothesis #1
H0: There is no relationship between productivity and employee engagement
H1: There is a relationship between productivity and employee engagement
Since the p -value of the model is 0.000 and less than the level of significance (0.05), the
null hypothesis is rejected. Hence, there is a relationship between person’s engagement
and production.
Multiple Linear Regression Model
Reg production person output
Interpretation of Results
General equation : Y = B0 + B1X+B2X+……BNX+ Epsinote
Equation of this Model Production = 4.63 + 0.254 Person Engaged – 0.0000269 Gross Output
Hypothesis Testing
Hypothesis #2
H0: There is no relationship between gross output, productivity and employee engagement
H1: There is a relationship between gross output, productivity and employee engagement
Since the p -value of the model is 0.946 and greater than the level of significance (0.05), the
null hypothesis is accepted. Hence, there is no relationship between gross output and
productivity.
Comparative Analysis between Simple and Multiple Regression Model
Production = 4.56 + 0.255 person engaged Production = 4.63 + 0.254 person engaged – 0.0000269 Gross output
Post-Estimation Tests
Statistical tests carried out for checking: Multicollinearity Homoskedasticity Heteroskedasticity Specification
Multicollinearity
Multicollinearity refers when two variables are highly correlated, which may create biased results.
In order to detect for multicollinearity, the Variance Inflation Factors (VIF) test is used.
However, in case the VIF value > 10, there exists multicollinearity in the model.
VIF Test
Upon using the VIF command, the VIF value was 1.01,which indicates that there is no multicollinearity in this study.
Homoskedasticity
One of the main assumptions for the ordinary least squares regression is the homogeneity of variance of the residuals.
Constant variance The command ‘robust’ controls for Homoskedasticity in the regression model
Homoskedasticity
reg production person output, robust
Heteroskedasticity
The Breusch-Pagan test is used to check the linear form of Heteroskedasticity and it represents the error variance.
A larger Chi-square and a smaller p-value implies that there exists heteroskedasticity in the model.
Link Test
No other independent variables should be significant above chance in case a regression equation is suitably specified.
The hatsq is normally used as the p-value. If p-value < 0.05,Reject H0
Link Test Specification
Link Test output
Conclusion
In this research, we have shown employee’s engagement and output affect productivity of the different industry groups in Mauritius.
To have a clearer view of the impact, we had to make use of the simple regression and multiple regression.
The simple regression showed that the more people involve the more the production would be. As concerned for multiple regression, productivity affects the gross output thus decreasing it.
Thank You for your attention
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