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