A MIXED STRATEGY FOR VEHICLE VALUATION

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Abstract

The number of vehicles in Kenya grows at a rate of 12% annually, with the national registered fleet standing at 4 million as of 2018. All these vehicles have to be valued regularly for a variety of reasons not limited to insurance, resale, leasing and accounting. As such, it is important to have an easy to use, reliable, readily available system that can determine the value of a vehicle given some properties about the said vehicle. The variation of values obtained from different valuers for identical vehicles exposes irregularities in the contemporary automobile valuation systems. When in need of quick car valuation services, the lack of consistent, accurate and readily available tools to perform the required valuation is glaring, as the primary way to get an automobile valued is through contacting an expert from a licensed evaluation firm or an insurance agent. The existing car valuation mechanisms rely chiefly on expert opinions and the use of the formulae to calculate a used car’s compound annual depreciation which is subtracted from the price at 0 mileage, adjusted for inflation over the years. There have been attempts to automate vehicle valuation by use of machine learning, which yielded promising results. Multiple regression analysis has been employed to identify vehicle properties that have the greatest bearing on the value of the vehicle, as well as predict the price given values of the different parameters. This approach has also been applied successfully in other domains for valuation of assets such as land and FMCGs. For this study, a multi-agent systems architecture was employed to encapsulate three regression models for vehicle value prediction, as well as a natural language processing model to extract vehicle features from vehicle descriptions in unstructured text. The three models were built and trained to generate predictions, each leveraging either of the SVM-based regression and Neural Networks (ANNs) implementation in WEKA, or the Deep Learning regression provided by WekaDeeplearning4j version 3.8.5. The best performing model provided a reliable option for vehicle valuation, with 11% relative mean error, having been trained on only 1000 rows of data, out of a possible 200,000 records, and thus was used in the design of the functional prototype. Given the temporal, budgetary and computational resource restrictions on this study, there is great potential for improving the performance of the prediction models given more time, data and computing power.



 
List of important Abbreviations

AMS – JADE platform's in-built Agent Management System
DF – JADE platform’s Directory Facilitator
RMS – JADE platform's in-built Remote Management System
ANNs – Artificial Neural Networks
CUDA – NVIDIA's Compute Unified Device Architecture
GPU – Graphical processing unit
JADE – Open-source Java Agent DEvelopment Framework.
JDK – Java Development Kit, Standard edition
MAS – Multi agent system
MLP – Multi-layer Perceptron
NLU – Natural language understanding
OpenNLP – Apache's open-source OpenNLP project for natural language processing
RSE – Relative Squared Error
SMOreg/SVMreg – SVM-based regression implementation
WEKA - Waikato Environment for Knowledge Analysis




 
Table of contents

Declaration 2
Acknowledgement 3
Abstract 3
List of important Abbreviations 5
Table of contents 6
List of Tables and Diagrams 8
Definition of Important Terms 10

Chapter 1: Introduction
1.1 Background of the study 11
1.2 Statement of the problem 12
1.3 Study Objectives 12
1.3.1 General Objective 12
1.3.2 Specific Objectives 13
1.4 Goals 13
1.5 Limitations of the study 13
1.6 Scope of the study 13
1.7 Expected Contributions 13
1.8 Proposal Organisation 14

Chapter 2. Literature Review
2.1 Theoretical literature review 15
2.1.1 Introduction 15
2.1.2 Linear Regression 15
2.1.3 Natural Language Processing 15
2.1.4 Agents 16
2.1.4.1 Multi-Agent Systems 17
2.1.5 Machine Learning 17
2.1.6 Feature Extraction from Natural Language 17
2.1.6.1 Natural language understanding 17
2.1.6.2 Named-entity recognition 18
2.1.7 Artificial Neural Networks 18
2.1.7.1 ANNs and Deep Neural Networks 18
2.1.8 WEKA, Waikato Environment for Knowledge Analysis 19
2.1.9 Asset Valuation 19
2.1.9.1 Machine Learning for Car Valuation 19
2.1.9 Web Scraping 19
2.2 Empirical literature review 20
2.2.1 Raphael Kieti M. 20
2.2.2 Hammad Hai & Haydn Ramanna Sonnad 20
2.2.3 Kaneeka Vidanage & Amjadh Ifthikar 20
2.2.4 Zhang Yuquan & Chang Jiangxue 21
2.2.5 Sandbhor & Chaphalkar 22
2.3 Opportunities for improvement 22
2.4 Conceptual model 23
2.5 Chapter Summary: Literature Review 23

Chapter 3: Research Methodology
3.1 Introduction 25
3.2 Feasibility study 25
3.2.1 Time feasibility 25
3.2.2 Technical feasibility 25
3.2.3 Financial feasibility 25
3.2.4 Functional feasibility 25
3.3 Prometheus Design Methodology for Multi-Agent Systems 25
3.3.1 Iterative Development Process 26
3.3.2 Phase 1: specification of the system 27
3.3.3 Phase 2: Agent specification and architectural design 28
3.3.3.1 Agent descriptor for the User interaction agent 28
3.2.3.2 Monitor and Restoration agent’s descriptor 29
3.2.3.3 Descriptor for structured dataset pre-processing agents: 29
3.2.3.4 Descriptor for the text pre-processing agent: 30
3.2.3.5 Agent descriptor for Model trainer agents 30
3.2.3.6 Agent descriptor for Instance Prediction Agents 31
3.2.4 Phase 3: The detailed design 31
3.2.4.1 Common Agent-Actions and Percepts 32
3.2.4.2 Behaviours common to all agents 32
3.2.4.3 Common functionality 32
3.2.4.4 System Event Descriptors 33
event descriptor: Performance evaluation 33
GUI Action 33
Instance valuation event descriptor 33
event descriptor: Raw Text Preprocessing 34
Model training Action 34
3.4 Research procedure 35
3.4.1 Resource aggregation stage 35
3.4.2 Methods Used to Collect Data 36
3.4.2.1 Considerations for web scraping 36
3.4.3 Data Pre-processing 36
3.5 Unprocessed data repository 37
3.6 Algorithm training and comparison 38

Chapter 4: Data Analysis, Prototype Design and Implementation
4.1 Overview 39
4.2 Current vehicle valuation practice analysis 39
4.2.1 Contemporary used car valuation methods 39
4.2.2 Inspection of unprocessed data repository 39
4.3 Selection of attributes 41
4.4 Data pre-processing   43
4.5 Splitting the dataset    44
4.6 Algorithm testing and selection   44
4.7 Implementation     46
4.7.1 Tools                     46
4.7.2 Graphical User Interface Design                  47
4.7.3 Agent Platform                           49

Chapter 5: Results and Discussion
5.1 Results   51
5.2 Discussion         52
5.2.1 Quality of data         52
5.2.2 Nature of data           52
5.2.3 Dimensionality reduction              53
5.2.4 CPU vs GPU                     53
5.2.5 Suitability of the Multi-agent Architecture          53
5.2.6 Ethical Considerations for Collecting Data by Web Scraping          53

Chapter 6: Conclusion and Recommendations     
6.1 Conclusion           55
6.2 Contributions             55
6.3 Future Work                 55
References                      57
Appendices                     60
Flow chart of machine learning centred research  60
Sample SQL queries deployed to cleanse unwanted data from the repository      60
Approximate project schedule         61
Requirements                 61
Financial plan              62
List of Tables and Diagrams






List of Figures
Figure 1. A Deep Neural Network 19

Figure 2. A high-level diagram of the conceptual design.   24

Figure 3. Common notation used for Prometheus design elements 27

Figure 4. A stratified depiction of the 3 phases of Prometheus methodology 28

Figure 5. General flow of the research process 36

Figure 6. design of the repository database 38

Figure 7. screenshot of algorithm parameters on WEKA workbench 39

Figure 8. PhPMyadmin Visualization of Raw Data Table 41

Figure 9. WEKA pre-processor visualization window for all attributes 42

Figure 10. Attribute-Relation File Format supported by W.E.K..A. 44

Figure 11. SMOreg algorithm performance after preliminary testing 45

Figure 12. Visualization of ANN layers during preliminary testing 46

Figure 13: Out of memory error caused by training Weka on the full training dataset. 47

Figure 14. Prototype UI: Model Training Tab 48

Figure 15. Prototype UI: Prediction Tab 49

Figure 16. Prototype UI: Text Feature Extraction and Prediction Window 49

Figure 17. Prototype UI: System Logs Tab 50

Figure 18. Remote Monitoring Agent URI showing the main-container 50

Figure 19. Communication between agents shown by a Sniffer Agent 51

Figure 20. Research process, highlighting machine learning steps 61

Figure 21. Expected project duration 62





List of Tables
Table 1. Expected input data and corresponding actions.
29

Table 2. Sample agent-descriptor: User-interaction agent 30

Table 3. Sample agent-descriptor: System Runner 30

Table 4. Sample agent-descriptor: pre-processing agent 31

Table 5. sample agent-descriptor: Text pre-processing agents 31

Table 6. sample agent-descriptors: Model training agents
32

Table 7. sample agent-descriptor: Instance valuation 32

Table 8. event descriptor: Model evaluation 34

Table 9. event descriptor: User interface event   34

Table 10. event descriptor: Vehicle instance valuation    35

Table 11. event descriptor: NLP event     35

Table 12. event descriptor: Model training      35

Table 13. Sample features for Text based model     43

Table 14. Sample features used in main model     43

Table 15. The ignored attributes        44

Table 16. Algorithm performance: 4433 rows, 3 features.    52

Table 17. Algorithm performance; 500 rows, 1000 training cycles       52

Table 18. Algorithm performance; all attributes, 10 training cycles         52

Table 19. Expected costs        63

 



Definition of Important Terms

Autonomous: Capable of acting independently and of exercising control over an internal state.

Semantics: Using NLP techniques to extract meaning from text

Social agent: An agent with the ability to interact with other agents

Software agent: In the context of this research, a software agent (or an agent) is an intelligent autonomous social agent existing within a software multi-agent environment tasked with a particular function.

SMOreg: Regression algorithm based on the scalable vector machine algorithm.




 
Chapter 1
Introduction

1.1 Background of the study
A report published by the United Nations Environment Programme in 2020, the Used Vehicles and Environment report, stated that second-hand vehicle sales account for nearly 95% of vehicles imported into Kenya annually. As of 2018, there were 3,280,934 registered units as reported by the Kenya National Bureau of Statistics. This accounted for an annual growth rate of 12% as cited in a 2016 report.
These figures suggest that hundreds of thousands of cars are added annually to the existing millions of other old cars operating within Kenya. Prospective buyers, sellers, insurance firms, property valuers, and numerous stakeholders must estimate the value of these cars with the utmost precision.

According to the Automobile Association of Kenya reasons for vehicle valuation include:

Pre-insurance valuation: this valuation is conducted to ensure that vehicular owners pay precise premiums when subscribing to insurance policies for their vehicle and that Insurers generate concise premium rates and minimise revenue loss as a result of value miscalculation.

Technical Brief Valuation: This refers to the precise determination of the price of a car or a machine when purchasing, vending and or disposing of antiquated equipment or in a scenario where an automobile is presented as a guarantee during loan acquisition.

Full Mechanical Valuation: It is an evaluation of a preventative nature and done to identify possible mechanical faults and reduce the chance of a vehicle breakdown.

Pre-Theft/Pre-fire valuation: when a vehicle is stolen this procedure must be conducted by the insuring body. To correctly compensate the owner, the value of the vehicle before it was stolen has to be calculated.

Accident Assessment: This inspection is carried out upon the loss of the vehicle after the loss and a claim settlement have been issued. In circumstances whereby it is difficult to fix the vehicle due to high replacement costs or extensive damage; advice is given on the restorative and salvage cost values.

Examining of automotive components: This is performed when an unbiased perspective is consorted regarding automotive failure. Failure could encompass accidents or automotive damage occurring during a repair.

According to (Bennett, 2016), the globally accepted method used to evaluate the value of an old car consists of pinpointing the vehicle’s value when it was new - and deducting the amortised cost by the cumulative years the vehicle has been used.

The resale value of a vehicle is affected by other factors such as:

Mileage: This refers to the distance covered by a vehicle in miles; the more miles covered lower the car’s worth as it increases its depreciation.

Vehicle Condition: Refers to any sort of damage to both the exterior and interior of a used vehicle; this naturally has a negative impact on the vehicle’s value.

Make and Model: Understandably, vehicles that offer lower fuel consumption over the distance covered fetch higher resale prices are generally popular.

The pricing and availability of spare parts, servicing and models, which are no longer produced, are factors that determine the resale value.
 
Vehicle Age: Most automobile’s age is proportional to its sale price, the older the vehicle the lower the resale price. This is because vehicles have a lifespan and their functional usage is expected to erode with time.

Ownership chain: The more times an automobile is sold the more its sale price keeps depreciating as a consequence of increasing maintenance costs by latter owners.

After-Sale Service: The quality of post-sale services varies from brand, thus affecting the condition of an automobile over time.

Features, and Options: The availability of options in the market features a car has, the likelier the price is to deteriorate; however, it has been noted that additional safety features increase a car’s value.

Colour of the car: Basic colours such as silver, black, or white have a preference among potential purchasers. Selling a uniquely coloured car increases the difficulty of finding potential customers thus the value of the automobile could be negatively impacted.

Despite numerous pre-existing vehicle valuation mechanisms, value determination is still a major challenge in Kenya. Similar car models can fetch exceedingly different prices from valuers. Concise mechanisms are yet to be installed to ensure that there are standardised vehicular valuation tools to minimise inconsistencies from being experienced, Stakeholders use different methods and data to inform their valuation processes hence yielding different results, making it hard to get a standard market price for a vehicle. Thus, the importance of having a readily available accurate and consistent vehicle valuation mechanism that ensures the automobile’s worth can be accurately determined.

Natural language processing, artificial neural network algorithms and multi-agent systems are some of the proposed tools that will be used to accurately generate the value of a car.

1.2 Statement of the problem

The existence of irregularities in the contemporary vehicle valuation processes is proven in the variation of values provided by different valuers, for similar vehicles. When you need to determine an acceptable value for a vehicle, the lack of an easily accessible and accurate instrument for vehicle valuation is clear, since the only method to acquire a vehicle assessment is to contact a valuer, an insurance broker or a valuation firm. And without a method to cross-validate the data presented, this could result in incorrect vehicle valuation and potentially inflated vehicle costs and insurance premiums being paid by unwary individuals. Again, the majority of people lack the technical expertise required to construct, grasp, and apply valuation formulae to arrive at car value estimations. (Kieti, 2005) .

1.3 Study Objectives
1.3.1 General Objective
Creating a mixed strategy vehicle valuation prototype that is simple to use, multi-agent-based, and fully functional, and that uses neural networks for numerical regression and natural language processing for feature extraction to forecast the value of a vehicle given precisely defined parameters or textual descriptions.
 
1.3.2 Specific Objectives
To aggregate and analyse data using the existing vehicle valuation models from varied domains, and identifying areas with inconsistencies and discrepancies within the current vehicle valuation procedures.

To design a mixed strategy prototype that considers the findings of the analysis and the deduction on related topics by other researchers.

To implement a functional mixed-strategy vehicle valuation prototype.

To amass data and examine the reliability of the evaluation prototype by establishing its performance on real vehicle data.

1.4 Goals
The study aims to produce an accurate easy-to-use vehicle valuation tool for public use, as well as industrial and professional functions such as insurance and car sales.

1.5 Limitations of the study
The accuracy of the models created in this research are subject to inconsistency taking into consideration the varying prices from different car dealers will not be similar for identical cars. Car dealers have different profit and cost considerations and hence produce prices that are expected to differ and compromise the predictions of the real value.

Automobile valuation statistics provided by different dealers are based on different valuation techniques leading to different degrees of accuracy.

Supply and demand, monetary inflation and other financial and market factors have an impact on the valuation of a vehicle. When the market preference is in high demand for a specific model, its value tends to be greater than that of a less preferred model, and these dependent factors will be reflected in the dataset available for this research.

Import taxes on foreign vehicles affect the buying and selling prices, which proves challenging when it comes to accurately identifying the intrinsic value of the vehicle.

1.6 Scope of the study
The scope of this report is confined to the value estimation of cars in Kenya. This study aims to produce a correct vehicle and machinery valuation model that could be implemented in different fields of vehicle valuation such as inspection of motor vehicles, pre-insurance valuation, accident assessment, pre-fire and pre-theft assessments, full mechanical assessment and technical brief assessment.

1.7 Expected Contributions
1) At the closing of this study, it is expected to have derived in an operational automobile valuation framework based on natural language processing, regression algorithms and multi- agent systems to determine the value of a vehicle.
 
The framework will be expected to serve as a readily available instrument providing a closely accurate market value for an automobile taking into account various characteristics of the said vehicle.


2) Finally, this study aims to direct future research by laying a solid platform for future researchers to build on.

1.8 Proposal Organisation
Chapter 1: Introduction – Describes the definition of the problem, scope, the study's objectives, research questions, and constraints.

Chapter 2: Literature review - This section contains general facts about the relevant work, suggested solution's design and the problem domain.

Chapter 3: Methodology - This chapter covers the whole study process, as well as specific methods to be used, as well as a well-planned timeline, budget, and resource requirements.

Chapter 4: Data Analysis, Prototype Design and Implementation - This segment presents the analysis, design, and execution of the proposed solution.

Chapter 5: Results and Discussion – In this segment, the outcomes of this research are presented and analysed.

Chapter 6: Conclusion and Recommendation - This chapter offers the opinions of the researcher and provides suggestions for the preceding work. 

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    ABSTRACT This study analyzed the effects of labour turnover on productivity in Nigerian Bottling Company Plc and 7up Bottling Company Plc, Aba, Nigeria. Specifically, the study determined the effect of workers retention (pay and allowance) on quantity of sales in Nigerian Bottling Company Plc and 7up Bottling Company Plc Aba; determined the effect of training of workers on profit in Nigerian Bottling Company Plc, and 7up Bottling Company Plc Aba; investigated the effect of promotion on output in Nigerian Bottling Company Plc, and 7up Bottling Company Plc, Aba; and determined the effect of skills of workers on customers satisfaction in Nigerian Bottling Company Plc, and 7up Bottling Company Plc, Aba. Primary and secondary data were used for the study. The secondary data covered between 2010 and 2019. A total of 345 respondents consisting of 190 respondents from Nigerian Bottling Company Plc and 155 respondents from 7up Bottling Company Plc were used for the study after retrieving the questionnaire. Also, a total of 345 customers consisting of 190 customers that patronizes the Nigerian Bottling Company Plc and 155 customers that patronizes the 7up Bottling Company Plc were used for the study. Data obtained were analyzed using simple regression technique and mean score. Pearson product moment correlation coefficient (r) and simple regression were used to test the various formulated hypotheses for the study. Findings shows that retention (pay and allowance) of workers has significant positive effect on quantity of sales in both Nigerian Bottling Company Plc and 7up Bottling Company Plc, Aba. Training of workers have significant positive effect on profit in both Nigerian Bottling Company Plc and 7up Bottling Company Plc, Aba. Promotion of workers have significant positive effect on output in both Nigerian Bottling Company Plc and 7up Bottling Company Plc, Aba. Skills of workers have significant positive effect on customers’ satisfaction in both Nigerian Bottling Company Plc and 7up Bottling Company Plc, Aba. The study recommends that efforts aimed at tackling labour turnover in Nigerian Bottling Company Plc and 7up Bottling Company Plc should focus more on developing the proficiency of workers through a need-identified training. Prompt promotion of workers and the use of other compensation incentives that should increase the willingness of employees to remain at work is strongly advocated. Many bottling industries such as the Nigerian Bottling Company Plc and 7up Bottling Company Plc, Aba require a formidable workforce to have a competitive edge amidst her competitors. CHAPTER 1 INTRODUCTION 1.1 BACKGROUND TO THE STUDY In today's global environment, each business must have a strong labour turnover avoidance policy in place to guarantee that the finest minds and well-experienced employees contributing to the organization's overall growth and development are kept. Employer turnover should be reduced as a result of this. This is because labour turnover is one element that may impact employee retention, organizational profit, production, and customer satisfaction with the organization's products and services in a positive or negative way. The sort of labour turnover prevention program that will encourage employees to perform well will be determined by how well it meets their needs for status, job security, and survival, as defined by Maslow's hierarchy of needs (1943 and 1954). Managerial and supervisory turnover has long been a key human relations issue, and its importance in any particular company cannot be overstated. Almost all employers of labour confront a big problem with labour turnover nowadays, all around the world (Barmase and Shukla, 2013). This is due to the fact that it creates a significant financial strain on businesses and has a negative impact on productivity. Labour turnover is a serious workplace problem that cannot be overlooked by any meaningful and target driven organisation. Organizations all around the globe must endeavor to regulate and reduce labour turnover since it has both economic and psychological implications on production. In terms of psychological consequences, labour turnover has been associated with a number of negative job attributes such as low level of job satisfaction, low esteem for promotion opportunities, mental stress on the part of management on how best to sort and replace exited experienced workers etc. As a result, when a person departs abruptly, it throws the entire organization's production strategy into disarray. This might have a significant impact on the organization's production and, as a result, its effectiveness. If the company provides a service, employee turnover may have an impact on the quality and/or quantity of service provided, especially if one person's output is the input of another (Blau, 2014). Hill and Twist (2015) define labor turnover as withdrawal behaviors that lead psychologists to believe that it is the result of unfavorable workplace attitudes affected by factors such as income, job security, recognition and appreciation, working hours, and physical conditions, among others. There are also psychological withdrawal behaviors such as a lack of creativity or putting in little effort on a work, which frequently show as laziness and an unwillingness to think and enhance creativity (Pinder, 2018). There is also an attempt to comprehend managerial turnover and determine why employees leave their jobs. Carbery, Garavan, Brien, and McDomel (2013) believe that, all other things being equal, management turnover is likely to be lower than operational turnover, which might be due to the fact that they are more devoted and have a stake in the company. Labour turnover also has the effect of impeding the attainment of larger corporate objectives since it necessitates a significant investment in training, induction, growth, and skills development to replace personnel who leave the company. Controlling labour turnover, on the other hand, is critical for businesses and must be handled well due to the impact it has on organizational productivity (Adewole, 2017). In Nigeria, the issue of labour turnover cannot be neglected by many firms operating in the country. This is because ineffective labour turnover management in any Nigerian organization would have a significant negative impact on not just that organization's performance and output, but also on the economy as a whole. For example, in the late 1980s and early 1990s, Nigeria experienced a turning point in her history when Nigerian universities lost a slew of well-trained teachers in what became known as the "Brain-drain." Perhaps the situation that occurred in our universities had an impact on some businesses, such as the Nigerian Bottling Company Plc. and the 7up Bottling Company Plc., where some of these academics serve as consultants. Terrible pay rates, a lack of advancement, a lack of sufficient training of trained and competent labour force, and a poor work environment may have all contributed to such a choice to quit a company (Adewole, 2017). This is likely to have an impact on the manufacturing line in terms of profit maximization. The situation hasn't altered much since then, and many businesses are calculating their losses (Orji, 2018). According to a Mercer report on the total financial impact of employee turnover, the cost of labour turnover is sometimes misunderstood, seen as incalculable, or disregarded as a minor expense, yet the total cost of labour turnover is considerable, accounting for 36 percent of payroll. The actual cost of employing someone to cover absentee employees is a significant but frequently ignored expense. In Nigeria bottling firm and 7up Bottling Company Plc. Aba, Nigeria, this is a typical practice in enterprises that leads to a certain level of turnover and its probable impacts on productivity. Organizational Productivity is defined as an organization's, institution's, or business's ability to achieve desired outcomes with the least amount of energy, time, money, staff, material, and so on. It is a measure of an organization's ability to meet its output targets via the use of its labour, authority strategies, machinery, equipment, and assets (Adewole, 2017). Productivity increase is crucial for organizations since delivering more goods and services to customers equates to better profitability. As productivity rises, an organization's resources may be converted into revenues, allowing it to pay stakeholders while reserving cash flows for future development and expansion. With increased productivity, an economy may create and consume more products and services for the same amount of effort. Individuals (workers and customers), company executives, and analysts all value productivity (such as policymakers and government statisticians). Labour turnover is inextricably linked to an organization's productivity and is frequently a sign of other issues confronting both the organization and its personnel. A variety of strategies have been proposed by management scholars in order to overcome high rates of labour turnover among employees and enhance employee retention. According to Ibrahim, Usman, and Bagudu (2013), employees who resigned their employment did so due to bad working circumstances that required them to execute their tasks. Poor working circumstances owing to physical factors may result in reduced productivity and general job unhappiness. Nigerian bottling firms, such as Nigerian Bottling Company (NBC) and 7up Bottling Company Plc. (7UP), are not immune to the effects of high labor turnover. The capacity of these businesses to fulfill rising demand for their goods and services is heavily reliant on the efficiency of their skilled employees, who assure optimal production, sales, and profit margins. Labour turnover, particularly among experienced employees, is a major and continuous issue that employers of labor in these organizations are concerned about. This is due to the high expense of finding a substitute for such high quality, which is sometimes difficult to come by. Most new employees are more prone to accidents since there are more breakages and they make more mistakes than experienced workers, resulting in the expense of replacing a man exceeding the recruiting projections by a significant margin (Stessin, 2011). When a company's labor turnover is a problem, management must identify the root reasons, monitor the turnover rate, calculate the cost of turnover, and solve the issue. Given the reality of unemployment and economic hardship in Nigeria, knowing the impact of labor turnover on productivity at Nigerian Bottling Company (NBC) and 7up Bottling Company Plc. is crucial. Such knowledge will aid these businesses in developing effective labor turnover prevention plans that will allow them to function sustainably and adequately satisfy consumer needs as well as corporate objectives. As a result, the purpose of this study was to examine in depth how labor turnover management affects organizational productivity of Nigerian Bottling Company (NBC) and 7up Bottling Company Plc in Aba, Nigeria. 1.2 STATEMENT OF THE PROBLEM Despite the fact that there appear to be no permanent solutions, attempts have been made to reduce the problem of labour turnover. Many individuals have left their jobs due to factors such as professional progress, more promising positions, and external incentives such as higher pay scales, promotion in other companies, and pleasant working circumstances. High labour turnover can have a negative influence on a company's production. However, because of the restricted resources available for staff recruiting, the negative impacts on firms might be extremely severe. Employees who are happy in their jobs are less likely to leave. High employee turnover is typically a sign of a longer-term issue, such as a lack of improved pay structures, training or career opportunities, or promotion, to name a few. Workers who are dissatisfied with their occupations are inclined to depart (Mobly, 2017). Mobly (2017) goes on to say that being dissatisfied with a job isn't the only reason why individuals switch jobs; it may also be because the talents and competencies they possess are in high demand. They may be enticed to leave for greater salary, perks, or career advancement opportunities. Because enterprises have little influence over what happens in other firms, they may take efforts to boost employee morale in the workplace, making people who work for them happy and productive. For companies like Nigerian Bottling Company Plc. and 7up Bottling Company Plc., employee turnover is a major issue. The high rate of labor turnover in bottling businesses, which has risen to about 15% in Nigerian Bottling Company Plc. in 2019 (NBC, 2019) and 22% in 7up Bottling Company Plc. in 2019 (NBC, 2019), is one of the issues that inspired this study (7up, 2019). It is important to remember that a high labour turnover rate reduces an organization's revenue and profitability through lowering productivity. Another issue is that labour turnover increases hiring costs and training expenses, which is especially problematic in organizations that need to replace individuals with specialized skills and a high educational level to fill complicated job responsibilities. Recruiting new employees to replace those who have left the company might be a positive start in the right direction. However, their ability to match the unique abilities necessary for complicated activities previously performed by top executives, as well as highly paid vocations, is subject to cost impacts, making their replacement extremely challenging for the organization. This is likely to have a noticeable impact on the productivity of the company. This is not to suggest that every employee who leaves a company is dissatisfied with their work. Some people will retire, leave town, or abandon their jobs due to family obligations, a desire to change careers, or even the urge to start their own business (Kiunsi,2014). In terms of labour turnover management, there is a knowledge vacuum and a point of departure for prior studies on labour turnover and organizational productivity. There is a knowledge gap in understanding the effect of worker retention (pay and allowance) on sales quantity, the effect of worker training on profit, the effect of promotion on output and effect of workers skills on customers satisfaction in Nigerian Bottling Company Plc. and 7up Bottling Company Plc. Aba. Against this backdrop, this research work investigates labour turnover management and organisational productivity of Nigerian Bottling Company Plc. and 7up Bottling Company Plc in Aba, Nigeria. 1.3 OBJECTIVES OF THE STUDY The major aim of this study is to analyze the effects of labour turnover on productivity in Nigerian Bottling Company Plc., and 7up Bottling Company Plc., Aba, Abia state, Nigeria. Specifically, the study sought to examine the following objectives: (1) determine the effect of workers retention (pay and allowance) on sales quantity in Nigerian Bottling Company Plc. and 7up Bottling Company Plc. Aba; (2) determine the effect of workers training on profit in Nigerian Bottling Company Plc., and 7up Bottling Company Plc. Aba; (3) investigate the effect of promotion on output in Nigerian Bottling Company Plc., and 7up Bottling Company Plc., Aba; (4) determine the effect of workers skills on customers’ satisfaction in Nigerian Bottling Company Plc., and 7up Bottling Company Plc., Aba. 1.4 RESEARCH QUESTIONS Based on the specific objectives, the following research questions were raised. 1) What effect has workers’ retention (pay and allowance) on sales quantity in Nigerian Bottling Company Plc. and 7up Bottling Company Plc. Aba? 2) What effect has workers training on profit in Nigerian Bottling Company Plc., and 7up Bottling Company Plc. Aba? 3) What effect has promotion of workers on output in Nigerian Bottling Company Plc., and 7up Bottling Company Plc., Aba? 4) What effect has workers skills on customers satisfaction in Nigerian Bottling Company Plc., and 7up Bottling Company Plc., Aba? 1.5 RESEARCH HYPOTHESES From the above research questions, the following null hypotheses were formulated to guide the study. H01: There is no significant effect of workers’ retention (pay and allowance) on sales quantity in Nigerian Bottling Company Plc. and 7up Bottling Company Plc. Aba. H02: There is no significant effect of workers training on profit in Nigerian Bottling Company Plc., and 7up Bottling Company Plc. Aba. H03: Promotion of workers does not significantly correlate with output in Nigerian Bottling Company Plc., and 7up Bottling Company Plc., Aba, Nigeria. H04: Workers skills have no significant effect on customers’ satisfaction in Nigerian Bottling Company Plc., and 7up Bottling Company Plc., Aba, Nigeria. 1.6 SIGNIFICANCE OF THE STUDY The significance of this study is divided into empirical and theoretical significance. Empirical significance: This research will serve as a resource for all organizational management, particularly the management and employees of Nigerian Bottling Company Plc. and 7Up Bottling Company Plc. in Aba, Nigeria, in understanding labour turnover management and organizational productivity. The research will assist both commercial and public organizations, including the government, in limiting their human resource capabilities by implementing methods to minimize labour turnover through worker retention, training, rapid promotion, and skill development. It would give important information to Nigerian businesses' management and staff on employee retention and limiting the negative influence of labour turnover on organizational productivity. Theoretical significance: This study has contributed to the current body of information on labour turnover and organizational productivity. This study will be useful to scholars and postgraduate students in the Departments of Industrial Relations and Personnel Management, Business Administration, and Entrepreneurship because it will serve as a reference material for future researchers on the effects of labour turnover on organizational productivity. It may also pique the interest of other academies in conducting more study on the reasons and constraints of labour turnover in a company. The study will also help the Nigerian public and people in other disciplines understand the impact of labour turnover on the productivity of Nigerian Bottling Company Plc. and 7Up Bottling Company Plc., Aba. 1.7 SCOPE OF THE STUDY The scope of the study is divided into unit scope, content scope, and geographical scope. Unit scope: This study is on individual level of analysis of selected bottling companies in Aba. Content scope: This study covers only labour turnover management on organizational productivity between 2010 and 2019. Geographical scope: This study covered the Nigerian Bottling Company Plc. and 7Up Bottling Company Plc., Aba, Nigeria. 1.8 LIMITATION OF THE STUDY The most significant restriction of the study is having access to the office since the setting was extremely limited for security reasons, and entry into the business was mostly by invitation. As a result, obtaining an invitation to share the questionnaire and conduct interviews was extremely difficult, and there were limits on the number of times the researcher was authorized to enter the offices where the necessary information was obtained. As a result, the researcher had to devote many months to data gathering during the research process. Furthermore, there was a constraint on the number of years of information the researcher could be given by the organisations, since the selected bottling businesses only granted the researcher access to ten (10) years of data on different labour turnover management indicators and organisational productivity. Another difficulty encountered in performing this study was the inability to express the dependent variable "productivity" as well as the independent variable "labour turnover" with appropriate indicators for each specific aim. For this study, it took the intervention of the supervisory committee to resort to quantity of sales, profit, output, and customer satisfaction as appropriate indicators of organisational productivity, as well as worker retention (pay, allowances), worker training, promotion, and worker skills as appropriate indicators of labour turnover management. Generally, eliciting the required information from the various issues of the annual reports of Nigerian Bottling Company Plc. and 7Up Bottling Company Plc., Aba were the major constraint encountered in completing the study. The researcher was put through rigorous methods of transforming existing information to fit the necessary variables for the investigation. 1.9 OPERATIONAL DEFINITION OF TERMS Labour turnover: - This is the overall change in the number of people employed in a business entity during a particular period. It takes into consideration the number of exiting personnel, new joinees and the total number of workers as listed in the payroll at the end of a given period. Productivity: - is a phenomenon, which is concerned with the utilization of resources to produce a given output, the resources could be labour materials and capital. Incentives: - Something, which encourages you to work harder, start new activities. Remuneration: - An amount of money paid to someone for work done. Promotion: - is the Vertical movement of employees in the organization to a position of higher authority. Profit: - This is the financial benefit realized when revenue generated from a given business activity or numerous business activities exceeds the expenses, cost and taxes involved in sustaining the business activity in question. It calculated as the naira difference between total revenue and total expenses Output: - This is the number of units of goods produced in a specific time period. The period could be monthly or yearly. Retention: Retention refers to employees’ abilities to not only absorb and retain training or specialized skills, but to apply the learned skills to their job. Worker/Employee retention: Refers to the ability of an organization to retain its employees Sales quantity: This is the number of units of goods sold in a specific time period. The period could be daily, weekly, monthly, quarterly, biannually or yearly. Consumer satisfaction: Consumer satisfaction is a term that measures how products or services supplied by a company meet or surpass a customers’ expectation. Customer satisfaction is important because it provides marketers and business owners with the metric that they can use to manage and improve their businesses as well as shows how productively relevant the organisation is to its business environment.   CHAPTER 2 REVIEW OF RELATED LITERATURE 2.1 CONCEPTUAL REVIEW 2.1.1 Labour turnover Labor turnover, also known as staffing turnover, is defined as the ratio of employees who leave a firm due to attrition, dism

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