Determinants of profitability. A case of flour factories in Wert Arsi Zone, Oromia Regional State, Ethiopia


Master's Thesis, 2020

89 Pages, Grade: 4


Free online reading

TABLE OF CONTENTS

ABSTRACT

ACKNOWLEDGEMENTS

TABLE OF CONTENTS

LIST OF TABLES

LIST OF FIGURES

LIST OF ABBREVIATIONS AND ACRONYMS

DEDICATION

CHAPTER ONE INTRODUCTION
1.1. Background of the Research
1.2. Statement of the Problem
1.3. Objectives of the Study
1.4. Research Hypothesis
1.5. Significance of the study
1.6. Scope and limitations of the study
1.7. Organization of the thesis

CHAPTER TWO REVIEW OF LITERATURES
2.1. Theoretical Review
2.1.1. Evolution of Manufacturing Industry in Ethiopia
2.1.2. Overviews of Wheat Production
2.1.3. Theories of Profitability
2.1.4. Financial analysis
2.1.5. Measurement Tools of Profitability
2.2. Empirical Review on Firm Specific Determinant of Profitability
2.3. Empirical Studies on Determinant of Profitability
2.4. Conceptual Framework

CHAPTER THREE RESEARCH METHODOLOGY
3.1. Description of the Study Area
3.2. Research Design
3.2.1. Research Approach Adopted
3.2.2. Types and Sources of Data
3.2.3. Population and Sample Size of the Study
3.2.4. Sampling Technique
3.2.5. Data collection Methods
3.2.6. Methods of Data Analysis
3.2.7. Model specification
3.2.8. Model Assumptions
3.3. Operational Definitions of Variables

CHAPTER FOUR DATA ANALYSIS AND PRESENTATION
4.1. Introduction
4.2. Descriptive Statistics
4.4. Test Results for the Classical Linear Regression Model (CLRM) Assumptions
4.5. Correlation Matrix between Return on Asset and Independent Variables
4.6. Correlation Matrix between Return on Equity and Independent Variables
4.7. Random Effect versus Fixed Effect Models
4.8. Analysis and Interpretation of Fixed Effect Models Result
4.8.1. Fixed Effect Model Result of Return on Asset (ROA)
4.8.2. Fixed Effect Model Result of Return on Asset (ROE)

CHAPTER FIVE CONCLUSIONS AND RECOMMENDATIONS
5.1. Summary of Findings
5.2. Suggestions

References

Appendixes

ABSTRACT

This study was conducted to investigate the determinants of profitability of firms engaged in the flour manufacturing companies in West Arsi zone of Oromia Regional state, Ethiopia. A total of samples of 13 flour companies were taken for this study by using census method and secondary data were collected from audited financial statements of the companies for the period of seven years (2004 to 2010 E.C). Six firm specific independent variables (age, size, managerial efficiency, expense management, leverage management and sales growth) on profitability proxied by Return on asset (ROA) and Return on equity (ROE) were used. And two dependent variables namely return on asset and return on equity have been tested using ordinary least square (OLS) fixed effect regression analysis by using statistical package Eview 11. Descriptive statistics, correlations coefficient, and OLS fixed effect regression statistics were used to analyze and interpret the results. The descriptive statistics statistical summary inferred that, nonexistence of variation in ROA and ROE since the standard deviation statistics for return for asset 3.4% and return on equity 5.5%) which were below their respective means 7.4% and 12%. Farther, the OLS fixed effect regression analysis findings show that age, size, leverage management and sales growth are significant determinants of profitability. Moreover, except size all explanatory variables have positive significant relationship with profitability as measured by ROA and ROE. However, no clear and statistically proved relations are obtained for the variables namely managerial efficiency, expense management and leverage management (only on ROE statistically significant) with profitability. Thus, the flour companies' managers should give due attention to the significant variables (i.e. age, size, leverage management and sales growth) that determine the profitability of flour producing firms.

Key words: Firm specific factors, profitability, ROA, ROE

ACKNOWLEDGEMENTS

First I would like to praise the almighty God for all his unspeakable gifts throughout my life and for his immense gift of health, compassionate, source of knowledge and wisdom to accomplish this thesis and in keeping me safe from COVD-19 during my research work.

Next, it is my pleasure to convey my gratitude and sincerely thanks to my advisor Mr. Abdulansir Abdulmelike (Ass. Professor) for his constructive and critical comments, valuable suggestions, unfailing support, advices, for his quick and proactive response and encouragement starting from proposal write-up to the realization of this thesis work.

My appreciation is also extending to friends especially to Dawit Tsegaye (PhD) for his critical reading and comments on the whole manuscript and Ato Misrak Ayalew (MBA candidate) who was beside me in providing technical comments on my manuscript.

Great appreciation is also extended to staff members of Oromia Region state, West Arsi zone Revenue and custom authority, sampled flour factories and Zonal Investment offices for their valuable cooperation during data collection and in providing valuable data for the study.

I am also very grateful for my family specially my dearest wife, Ayinelam worku and my kids who were besides me in their priceless love, encouragement and in understanding me during my research work.

Last, but not least, my appreciation goes to friends and family whose names are not mentioned but directly or indirectly who always gave me a great support and encouragement during this research work.

LIST OF TABLES

Table 1 List of Flour Manufacturing Companies

Table 2 Determinants of Profitability, Indicator/ Proxy and Research Hypothesis

Table 3 Summary of Descriptive Statistics

Table 4 Test Results for Multi-Collinearity

Table 5 Correlation Matrix between Return on Asset and Independent Variables

Table 6 Correlation Matrix between Return on Equity and Independent Variables

Table 7 Hausman Test Results

Table 8 Summary of Regression output for ROA

Table 9 Summary of Regression output for ROE

Table 10 Summary of Fixed Effect Regression Result

LIST OF FIGURES

Figure 1 Conceptual Frame work

Figure 2 Map of study area

Figure 3 Normality test for ROA residuals (results from Eviews 11)

Figure 4 Normality test for ROE residuals (results from Eviews 11)

LIST OF ABBREVIATIONS AND ACRONYMS

Abbildung in dieser Leseprobe nicht enthalten

DEDICATION

This thesis is dedicated to my beloved wife Ayinalem Worku and my Kids Aron and Eyuel!!

CHAPTER ONE INTRODUCTION

1.1. Background of the Research

In most developed countries, manufacturing industry is the backbone of their economy and contributing the lion share of their gross domestic product (GDP) compared to agriculture and services sector in their economic growth (Bhayani 2010). However, in most Sub-Sahara Africa countries, their economy, export and huge labor force is largely dependent on agriculture. Contributions of manufacturing sector to the gross domestic product (GDP) of most SSA countries are less compared to services and agricultural sectors (Bhorat et al. 2017). The contribution of manufacturing companies to the economic growth is so minimal in developing countries as compared to agriculture and services sectors (NBE 2010/11).

As indicated by African Development Bank (AfDB 2014), manufacturing sectors become due policy interest and cornerstone of economic development since the sector is a key driver of productivity growth, formal employment growth, innovation and technological advance and export performance. Like most SSA countries, Ethiopia's economy predominately dependent on agriculture (45.2%), while services sector (44.0%), manufacturing (3.8%) and other Industries (7.0%) in terms of their contributions to the GDP of the country (AfDB 2014). Manufacturing sector is among the key productive sectors of the economy identified under growth and transformational plan (GTP II 2015/16-2019/20) which can spur economic growth and development because of its immense potential for wealth creation, employment generation and poverty alleviation (National Planning Commission 2016).

Manufacturing firms in Ethiopia are experiencing low return, which is an indicator of poor financial performance. As a result, the development of other sectors is suffered by lack of modernization in manufacturing sector (Andualem 2011). Therefore, for the sake of bringing significant development in the overall economy, the manufacturing sector needs to be transformed to contribute its expected role for the economy of the country.

The sector contribution to the gross domestic product in 2012/2013 was 4.8 percent. The performance of the arena has been plagued by low productivity of employees and use of obsolete technologies that is attributed to the poor state of physical infrastructure, limited access to finance, limited research and development, poor institutional framework, and inadequate managerial technical skills (AACCSA 2014)

The top two manufacturing subsectors in Ethiopia are - food and beverage and metal and engineering industries. These two account for 51 percent of the sector's gross domestic products (AACCSA 2014). The food and beverage sub-sector alone accounts 38 percent of the employment in the manufacturing sector. The sector contribution to the gross domestic product in 2012/2013 was 4.8 percent. The performance of the arena has been plagued by low productivity of employees and use of obsolete technologies that is attributed to the poor state of physical infrastructure, limited access to finance, limited research and development, poor institutional framework, and inadequate managerial technical skills (AACCSA 2014).

Due to several internal and external factors, the performance of this sub sector (food and beverage) in Ethiopia is under low status. Internal factors are factors that most of the time controlled/managed by firms themselves; whereas for external factors firms do have little or no control over them. Firms should clearly understand the key determining factors for its performance (profitability, return on investment, customer satisfaction, healthy financing etc). One of key driving and motivating factors or ind icator of healthy business for the sub-sector to be competitive and viable is, its profitability which can be indicated by the wealth maximizations and return on investments generated from its business transactions. Thus, sub sector need to understand the key determinants of profitability to be viable in the business environment.

The study of determinants of profitability of firms has bonded special attention overtime by different fields of knowledge base. Firms' profitability and ways of improving it are hotly debated issues among managers and scholars. Id entification of the sources of variation in firm level profitability is an important research theme in economics, strategic management as well as accounting and finance (Goddard et al. 2005). According to Marshall (2009), sound profitability rewards the shareholders for their investment, which in turn, encourages additional investment and brings about economic growth. On the contrary, poor financial performance can lead to failure, which has negative consequences on the economic growth. Thus, assessing the firm specific determinants of profitability of flour factories will have paramount roles to get an insight about their financial situations.

Profitability of any firms that are operational within the business environment can be assessed in two ways i.e. through financial measurement and non-financial measurement. Mostly the financial measures focus mainly on figures, which may not explicitly indicate the whole story of the firm and the specific reasons behind the reported figures. However, financial measures are the most commonly used methods used to evaluate the financial performances of firms (Yodit 2017). Financial performance of firms can be different but the most common method is financial ratio analysis (Lin et al. 2005). As indicted by Lin et al., 2005 , the basic reasons behind this is, financial ratio analysis provides a simple description about the financial status of the firm in comparison with its formers periods and helps to improve its performance. Among the most commonly used measurement of firm's financial performance of assets, equity, investment and sales that the firms achieved are profitability ratio. Specifically, return on assets (ROA) and return on equity (ROE) are among the most commonly used measures of financial performance (Meseret and Getahun 2017).

In the existing strong competitive business environment, ensuring viable financial inflow and growth are highly imperative to be competitive in the market. Thus, assessing the determinants of profitability could enable the business organizations to tackle the problems and be competitive within the industry. As result, the aggregate efforts will bring changes in the economic situations, which might result in, to structural change in the economy of the country. Accordingly, this research meant to investigate major firm-specific determinants of profitability of flour companies in West Arsi Zone by employing different statistical analysis.

1.2. Statement of the Problem

The growth of the manufacturing sector is indispensable to build national technological capacity, industrial capability, create wide range job opportunity, and improve income. Besides, the growth of the manufacturing industry will enhance the total factor of productivity and competitiveness of the overall economy of the country (Tekeba 2018).

Promotion of agricultural-led industrialization, export led development, and expansion of labour intensive industries are the principal strategic directions put in place in the Industry development Strategy of Ethiopia. Moreover, the industry development Strategy of Ethiopia points out as an agrarian country, the building up of industrialized Ethiopia can be true only through the implementation of agricultural and rural centered economy. To realize this strategic direction, the government has been implementing some efforts to exploit the number of comparative advantage the country has and to realize structural transformation the manufacturing sector to contribute significant share to the economy of the country. However, still the sector is handicapped by various factors (Ministry of Industry 2002).

As Tekeba (2018) indicated, unskilled workforce with limited expertise and infrastructure, external pressure from global market, shallow industrial research and development activities, underdeveloped marketing system, issues associated with trade logistics, and inadequate promotion made on the resources and other opportunities are some of the foremost bottlenecks for the manufacturing sector. Besides, as reported by Ethiopian Central Statistics Authority (CSA 2017) shortage of supply of raw materials, shortage of electricity and water supply are mentioned among major obstacles of food products and beverage manufacturing companies for not being operational throughout the year.

According to CSA (2017), the number of persons engaged in manufacturing industries between 2005 and 2009 E.C. were over 298,510 persons. Of the total employed workforce, 21% was reported from the manufacture of food products and beverages. Besides, about 30% of the value added by manufacturing industries to the national account was contributed by food and beverages manufacturing industry, which indicate significant contributions, compare to others.

This manufacturing sector contribution in the country's economy is huge as compared to other sub sectors of the manufacturing sectors. However, the sector does not show consistent profitability trend and some are not that much profitable (ERCA 2016, as cited in Yodit 2017).

As Nelson and Winter (1982) indicated, profitable firms are more provoked to grow, since they had the financial means to expand, but their ongoing profit creation will also make it possible to sustain growth. As several studies reported that, profitability of companies can be affected by so many firm specific and macroeconomic determinants. Firm specific factors appear to be the major determinants of profitability and the main gears of competitive advantage (Athanasoglou et al 2006; Gemechu 2013). Determinants of profitability are forces that directly influence the profitability of a firm, and are useful tools for relevant firms to understand what to do and where they should focus in order to improve on the profit venture of their business.

A range of studies were conducted by considering both firm specific and macroeconomic factors on banks, insurance companies, beverage industry and in some manufacturing industries to identify the key determinants of financial performance (e.g. Pratheepan 2014; Yodit 2017; Aster and Mesert, 2019). Chi et al. (2018) discussed the internal factors affecting bank profitability by employing return on assets return on assets (ROA) as tools to measure profitability of banks in Vietnam. They found the firm specific factor significantly affected the profitability of the bank industry in Vietnam. In addition, they also suggested that banks should improve their profitability by increasing capital size and loan, remaining asset size, deposits, liquidity risk and bad debts reasonably.

The food and beverage industries are highly dependent on agricultural raw materials. For beverage industries, wine, different chemicals, different agricultural products etc are important raw materials. For food industries, grains of crops are the key raw materials. Flour industries aggressively require the supply of grains from various crops depending on the food products they are producing. For instance, wheat is the main agricultural raw materials for wheat flour factories.

Wheat is an important industrial and food grain, which ranks second among the most important cereal crops in the world together with rice and maize. These three crops in total contribute more than half of all calories consumed by human beings. Looking the specific case of Ethiopia, wheat is one of the most important cereal food crops grown and the second in terms of total outputs following maize in Ethiopia (Gethahun 2006). Ethiopia is the second wheat producing country in Africa following Egypt with a total of 4.83 million tons in 2018/19 (CSA 2019). Of the main wheat growing areas of Ethiopia in terms of regional contributions; Oromia accounts (57.4%), Amhara (27%), SNNP (8.7%) and Tigray (6.2%) respectively. Of these, more than 41% of the annual wheat production comes from only three zones in Oromia and one in Amhara regions (CSA 2019). Arsi Zone in Oromia regional state is known as one of the top surplus wheat producing areas of the region as well as the country.

Several wheat flour-manufacturing companies have established in west Arsi zone taking in to considerations the geographical advantages, access to raw materials, cheap labor forces and other business factors. In specific west Arsi zone of the study areas, there are about fifteen (15) wheat flour manufacturing were operational in the areas due to the geographic advantage and other investments incentives factors. As several internal and external factors can significantly contribute to the performance of these factories, it is paramount important to investigate the financial viability and determinants of profitability of the companies. Most of earlier studies focused on the performance of these factories in other study areas. To the best of the researcher knowledge, reports regarding in the identification of key determinants of profitability is scarce (A shenafi 2015; Meseret and Getahun 2017) who conducted study on determinants of financial performance of Wheat flour-producing companies and on determinants of capital structure of food Manufacturing Industry in Ethiopia respectively. Determinants of profitability in flour factories in West Arsi Zone have not yet sufficiently investigated.

Therefore, this study devoted to investigating the key determinants associated of with the profitability of wheat flour factories in Arsi zone. Unlike to the previous limited attempts by other researchers, this study focused on firm specific factors and used both return on assets (ROA) and return on equity (ROE) as important measuring tools to evaluate profitability of flour companies. Moreover, both descriptive statistical tools and multiple regression analysis specifically Ordinary Least Square ( OLS) estimation methods were employed to identify the determinants of profitability to have a broader spectrum and objective based assessment. Thus, making empirical investigations on determinants of profitability have paramount role to come up with valuable information to help concerned bodies to focus on relevant factors affecting profitability of flour firms and to fill the gap of literature in the study areas.

1.3. Objectives of the Study

1.3.1. General objective

The general objective of this study is to investigate determinants of profitability in wheat flour manufacturing factories in West Arsi zone Oromia Regional State, Ethiopia

1.3.2. Specific objectives

In relation to the above general objective, the study had the following specific objectives.

1. To identify the effect of age on profitability of flour factories;
2. To explore the effect of firm size on profitability of flour factories;
3. To examine the impact of managerial efficiency on profitability of flour factories;
4. To describe the effect of expense management on profitability of flour factories;
5. To investigate the impact of leverage management on profitability of flour factories;
6. To examine the relationship between sales growth and profitability of flour factories

1.4. Research Hypothesis

In this study, it was hypothesized that age of flour factories, size of flour factories, managerial efficiency, expense management, leverage management and sales growth are expected to affect profitability of flour factories' as measured by Return on Assets (ROA) and Return on Equity (ROE). Accordingly, to achieve the objectives of the study, the following hypotheses were formulated in this study:

H1: Age has negative significant relationship with profitability of flour factories.
H1a: Age has portative significant relationship with profitability of flour factories.
H 2: Size has a positive significant effect on profitability of flour factories.
H2a: Size has a negative significant effect on profitability of flour factories.
H 3: Managerial efficiency has negative and significant effect on profitability of flour factories. H3a: Managerial efficiency has positive and significant effect on profitability of flour factories.
H 4: Expense management has a positive significant effect on profitability of flour factories.
H4a: Expense management has a negative significant effect on profitability of flour factories.
H 5: Leverage management has a negative significant effect on profitability of flour factories.
H5a: Leverage management has a positive significant effect on profitability of flour factories.
H 6: Sales growth has negative and significant effect on profitability of flour factories. H6a: Sales growth has positive and significant effect on profitability of flour factories.

1.5. Significance of the study

Most literatures shows research on profitability revolves around banks, insurance, stock market, beverage industry etc. Unfortunately, there are only few empirical researches in Ethiopia concerning flour factories profitability.

In general, the results of this study are significant in various respects for the following beneficiaries:-

- First, it is helpful specifically for flour factories in the study areas to get deep insights about their overall business situations and measures to be taken in to consideration.
- Second, it would be an input for financing organizations and investors who want to invest in the areas and taken as policy inputs.
- Third, it would contribute to dearth of literature to broadening the understanding of determinants of profitability in specific context.
- In addition, it would be a useful reference for researchers and other persons interested.

Therefore, it is hoped that, results from this study would have practical use mainly to this area and similar other areas and can serve as a base for any further studies to be conducted in the study and other areas in this line of study subject.

1.6. Scope and limitations of the study

1.6.1. Scope of the study

The research is delimited geographically, conceptually, methodologically and in terms of time. This study was conducted on flour factories in west Arsi Zone, Oromia regional state, Ethiopia. The study was only considering factory specific determinants namely age of flour factories, size of flour factories, managerial efficiency, expense managements, leverage management and sales growth as measured by Return on Assets (ROA) and Return on Equity (ROE), in west Arsi Zone. The source of data for this study was collected from on secondary data specifically from audit reports of the factories and supportive data was collected from individual senior manager, finance managers and operational managers of the flour factories through a semi-structured interview questionnaire.

The researcher employed both quantitative and qualitative research approaches to deal with the analysis of the data. Therefore, any of the analysis and finding of this research confined only to the selected case study area and other geographic areas having same attributes. Besides, this study considered data of 2004-2010 E.C from audited financial statements of flour manufacturing companies in the study areas.

1.6.2. Limitations of the study

This study has the following limitations;

- This study considered factories specific determinants of profitability, it did not consider macro level determinants of profitability like GDP, inflations and other external factors that may have a role in determining of profitability. For the purpose of credibility of the study, researcher was used only sample factories having and submitted audit reports with research period to concerned government authority.
- Because of financial constraints, the area coverage of this study was delimited to West Arsi Zone of Oromia Regional State.
- On top of this, the conclusions and recommendations drawn from this study are applicable on manufacturing firms in the study areas and firms having same attributes in the country.

1.7. Organization of the thesis

This study mainly comprises of five chapters. The first chapter is an introductory part which consists of introduction, background of the research, statement of the problem, research objectives, and research hypotheses, significance of the study and scope and limitation of the study. Chapter two presents theoretical and empirical review of the literature related to the issue of determinants of profitability. The third chapter focuses on the components of the research methodology including description of the study area, data type and source, research design, sampling design and sample determination, analysis methods and operational definition of variables. The fourth chapter is devoted to the empirical results and discussion on the findings of the research work. The fifth chapter portrays the conclusions and recommendations drawn based on the findings of this study. Finally, the references and appendices are attached on separate part towards the end of this thesis.

CHAPTER TWO REVIEW OF LITERATURES

This chapter of the research portrayed review of the related literature and establishes theoretical and empirical foundations on which the study had leaned. Specifically, literature review covered theoretical analysis composed of the concept of profitability, firm specify determinants of profitability, profitability measurements, Ratio analysis, empirical study related to determinants of profitability, other theoretical and empirical review relevant to the study subject.

2.1. Theoretical Review

2.1.1. Evolution of Manufacturing Industry in Ethiopia

According to Getnet and Admit (2001), the history of Ethiopian manufacturing industry more or less is connected to the post Ethio- Italy war. In the second half of 1940s, there was very few manufacturing industry, which accounted for only 1% of the national income. Industrialization really begun in the 1950s and was categorized in to the following three successive five-year developments plans.

After the collapse of the Imperial regime, the Derg nationalized enterprises involved in major economic activities and the private sector was allowed only to participate in small-scale industries and handicraft activities. With regard to industrialization, there were not any economic plans for the first four years (1975-1978), with all sectors of the economy becoming run down as the period was characterized by intense political confrontation, fierce power struggles within the Derg itself and the Ethio-Somali war. At the end of 1978, the Central Planning Supreme Council was set up as an instrument to control and allocate resources. Following its establishment, Six Annual Development Campaign Plans were successively launched, between 1979 and 1984 with the aim of rehabilitating the war-ravaged economy of the country. It should be noted, however, that these were annual programmes, short-term in nature, intended to meet the immediate challenges of food shortages, low capacity utilization in industry and the like, and could by no means be construed as comprehensive development plans (Getnet and Admit 2001).

According Getnet and Admit (2001), in September 1984, the regime issued a comprehensive and long-term development plan, which came to be known as the Ten-Year Perspective Plan, covering the period from 1985 to 1994. The development strategy was the same, import substitution industrialization. The major difference was that during the socialist regime, the strategy was state-led

The current Government is pursuing agricultural development led industrialization as opposed to the previous regimes. It is thought that main concern to agriculture in the short and medium term will produce a big domestic market for industry and supply food and raw material to industry. This is anticipated to strengthen the inter-sectoral linkages between agriculture and industry and will lead the economy to the development of industry. The problem, however, is that the urban sector of the economy is somehow ignored and the focus on agriculture has not even emancipated peasants from the havoc of periodic famine. There have been long years of adverse policies and economic management in which the private sector remained inactive and where the state sector lacked the dynamism required to foster industrial growth (Getnet and Admit 2001).

Manufacturing sector in Ethiopia can be divided into various subsectors namely food and beverage products, textiles and apparel products, leather and leather products, wood nd pulp products, chemicals and chemical products, rubber and plastic products, other non-metallic minerals products and metal and engineering products industries. The food and beverage sector is one of the main components of Ethiopia's manufacturing sector accounted the highest percentage 29.46% distribution of large and medium scale manufacturing industries by the regional state public and private industrial group. Among the key productivity sectors of the economy in Ethiopian, Manufacturing sector is one that can speedup economic growth and development because of its immense potential for wealth creation, employment generation and poverty alleviation (CSA, 2017).

Currently, the total number of large and medium scale manufacturing industries reported in CSA 2017 (2009 E.C) was 3627. About 39% of the manufacturing industries were located in Addis Ababa followed by Oromiya with more than 29% and Amhara with about 14% of the industries.

The number of manufacturing industries by industrial classification in the same year indicates 26% fell in the category of food products and beverages followed by non-metallic mineral products with about 18% and the furniture industry with more than 13%. According to CSA 2017 (2009 E.C) survey reports number of persons engaged in manufacturing industries between 2005 and 2009 E.C. were Over 298,510 persons. Of the total employed workforce 21%, persons engaged in the manufacturing industries were reported to be in the manufacture of food products and beverages. Besides, about 30% of the value added by manufacturing industries to the national account was contributed by food and beverages manufacturing industry, which indicate significant contributions, compare to others.

2.1.2. Overviews of Wheat Production

Agriculture is an important economic sector for sustaining growth and reducing poverty in developing countries. For the majority of the worlds' population who lives in the rural areas, agriculture is the key economic activity being as the major source of food, employment and income. Agricultural sector dominates most developing countries' economies in terms of its contribution to GDP, employment and income. Hence, the sector growth and development are essential for the overall process of socio-economic development of developing countries (Andizo et al, 2004).

Wheat is one of the most important cereal food crops grown in Ethiopia. In terms of wheat production Ethiopia is the second wheat producing country, which is 4.54 million tons, in 2016 in Africa next to Egypt (CSA, 2017). Beside, Wheat is one of the most important cereal food crops grown in Ethiopia. It is the fourth dominant crop in area coverage and in the total output wheat ranks second following maize (CSA 2017). The main wheat growing areas of Ethiopia are the highlands of the central, south-eastern and northwest parts of the country. In terms of regional contribution, the production of wheat originates from Oromia (57.4%), Amhara (27%), SNNP (8.7%) and Tigray (6.2%); and more than 41% of the annual wheat production comes from only three zones in Oromia and one in Amhara regions (CSA, 2011-2013). Of the current total wheat production area, 75.5% is located in Arsi, Bale and Shewa districts. Particularly west Arsi districts are surplus wheat producing areas in the country.

2.1.3. Theories of Profitability

There are a number of theories propagated by various scholars and theoreticians concerning profits. To mention, dynamic theory of profits, which was propounded by Prof. J.B. Clark in the year 1900; wage theory of profit, which was propounded by Taussig, the American economist; and Rent theory of profitability that was first propounded by the American Economist, Walker based on the ideas of Senior and J.S. Mill were the dominant one (Racheal 2017). On the other hand, in this study profitability is the ability of the company to make a profit in relation to sales, total assets and own capital. According to Brigham and Houston (2009), profitability is the net result of a number of policies and decisions done by companies. Profitability is measured by return on equity, return on assets, and return on investment. Asset management is measured by receivable turnover, total asset turnover, and inventory turnover (Asheghian, 2012) .

The ultimate aim of any economic activities is to generate profit. By doing so, it motivates the investors to invest more and accumulate wealth. Profitability and return on equity (ROE) determine the long-term growth scenario of a business organization. A high return on equity (ROE) can create a capacity to invest which leads to accelerated growth. Although it is not necessary for a firm to reinvest all of its profits, we assume that all firms will at least reinvest a minimum proportion of their profits. Profitability is a leading indicator, as such, measures ultimate performance of industries and is an important area of review by the regulatory bodies. Apart from assessments made by investors, creditors, and other stakeholders to ensure its sustainability, it helps the industries to understand and scale and scope of their activity and enabling them to position and take appropriate actions to stay competitive in the market (Brigham and Houston 2004).

Profitability is essential for any firm from both shareho lders and economic point of view because as the firm grows or performs well in terms of profitability, it will have strengthening dividend payment to owners, improve capital structure, safety and soundness of the financial operation, increase employment opportunity, tax payment and other positive impact on shareholders and other stakeholders. Performance at microeconomic level is that the direct results of managing diverse economic resources and of their proficient use within operational, investment and financing activities. To optimize economic results, a special attention ought to be given to the proper grounding of managerial decisions. These ought to be lied on complex information regarding the evolution of all types of activities within the company. A synthetic picture of the company's financial position and its performance is found in the annual financial statements, which thus become the main information sources that allow the qualitative analysis of how resources are used during the process of creating value. In order one business to run on a long­term performance way, it is needed to develop, implement and maintain the strategies, measures and coherent policies from economic and financial point of view. What is said so far can result from a good understanding of internal and external specific conditions in which the firm acts. The qualities of managerial options depend by the ability of identifying those elements that productively used could lead to increasing of the results and performance (Burja, 2011).

2.1.4. Financial analysis

Financial analysis consists of the evaluation of the financial condition and operating performance of a business firm, an industry, or even the economy, and the forecasting of its future condition and performance. In other ways, it is a means of examining risk and anticipated return. According to David and Sylvia (2012) profitability ratios show firm's overall efficiency and measure both the profit margin, that the firm is able to generate plus the return it provides on the physical facilities and its investments. Ratio analysis refers to selection, evaluation and interpretation of financial data, along with other pertinent information to assist investment and financial decision making (David 2010). According to Kieso (2011), profitability ratio is a ratio that illustrates the business organizations capability to generate a profit through all the existing capabilities and resources such as sales activities, from its overall assets, number of branches and so on.

For any business organizations to be viable and be within the business environment, it ought to be able to produce sufficient revenue to cover its operating cost and make enough profit as reimbursement to the providers of capital. Every firm is most concerned with its profitability. The most widely used financial gauge for performance evaluation is profitability measures. The reason behind this is the majority business concerns function to earn ample profit to remain as a going business concern. To determine firm's profitability, one of the most frequently used tools is financial ratio analysis, which includes profitability ratios (Brigham and Houston 2004).

2.1.5. Measurement Tools of Profitability

In today's economy, where strong competition dominates and all processes are highly dependent on information and timely effective d ecision-making, the success of an enterprise requires specific measurement and management systems. To comply with the principle of rational economics, an enterprise must systematically analyze its financial result. Besides, in this globalized business environment, business to have a sound financial performance and be efficient and effective in order to compete and stay in the business, financial performance analysis is utmost essential (Mehran and Izah 2012). As the business organizations, performances become good, it rewards the shareholders for their investment, which in turn, encourages additional investment and brings about economic growth. On the contrary, poor performance can lead to failure, which has negative consequences on the economic growth (Marshall 2009). The ultimate aim of any economic activities is profit generating. By doing so, it motivates the investors to invest more and accumulate wealth.

Different scholars define financial performance d ifferently. Brigham and Houston (2004), defined financial performance analysis is a process of examining, interpreting and converting historic records of company's financial operation into meaningful input for decision making. Financial performance analysis is key indicators of how the companies' resources have been managed in the past as well as in the current financial condition. Financial performance examination conjointly encourages business organizations to realize a better level of performance by ind icating its current financial position compare to other firms and creating a competitive environment ( Hawawini et al. 2003 ).

As indicate Lin et al. (2005), the analysis of a firm's financial performance often employs the financial ratio method, since it provides a simple description regarding the firm's financial performance as compared with previous periods and helps to improve its performance. Profitability ratios are among the most commonly used measure of business organizations financial performance in using their assets, equity, investment, and sales that the firms can achieve. In particular, return on asset and return on equity are among the most widely used measure of financial performance. According to Fabozzi and Peterson (2003 ), the higher to these ratios indicate the more the efficiency and effectiveness of the business originations in utilizing their assets and equity invested.

In general, the overall financial performance of the company is determined by indicators like profit or value added; sales, fees, budget; costs or expenditure; stock market indicators like share price and return on equity and return on asset (Gichaaga 2014). Furthermore, as stated by Asheghian (2012), profitability is measured by return on equity, return on assets, and return on investment. Asset management is measured by receivable turnover, total asset turnover, and inventory turnover.

2.1.5.1. Return on Assets (ROA)

As Lawrence J. Gitman (2009) indicated, understanding Return on Asset (ROA) is a measure of the overall effectiveness of management in generating profits with available assets. This Ratio (ROA) is an indicator of the success of the company for the management of wealth (assets) owned by the company. Thus, an increase in this ratio (ROA) reflects the company's performance in managing assets held, so that it can generate profits or earnings. Besides, this ratio is used to measure the soundness of a company to generate earnings of all assets owned by the company.

2.1.5.2. Return on Equity (ROE)

Hansen and Mowen (2012) describes Return on equity is the ratio between the net profit after tax and total equity. Return on equity is a measure of earnings (income) available to owners of the company (both common shareholders and preferred shareholders) on the capital that they invested in the company. The Return on Equity is a company's ability to earn a net profit seen from the use of equity. The higher this ratio is the better the company condition. Profitability and return on equity (ROE) determine the long-term growth scenarios of a company. A high return on equity (ROE) creates a span to invest and good investments lead to accelerated growth. Although it is not necessary for a firm to reinvest all of its profits, at least they reinvest a minimum proportion of their profits. Profitable firms will be further motivated to grow, given that they will not only have the financial means to expand, but their ongoing profit creation will also make it possible to sustain growth (Nelson & Winter, 1982).

2.2. Empirical Review on Firm Specific Determinant of Profitability

As literature indicates, the financial performance of firm's can be affected by several firm specific and macroeconomic factors. Nevertheless, the degree of impact that these factors have can differ. According to Gemechu (2013) and Athanasoglou et al. (2006), firm specific factors seem to be the major determinants of firm's financial performance, and are the main drivers for competitive advantage, which is crucial for surviving economic downturns. On the other hand, Hawawini et al. (2003) stated that macro economic factors play a more important role in dictating the influence of firm performance. Here under, the researcher tries to review different researches conducted formerly in different perspectives and theory by considering only firm specific determinants of profitability.

2.2.1. Age and Profitability

As study shows, the relationship between firm age and profitability is arguable. This can be evidenced by researcher's study result reveled in (Halil & Hasan 2012; Ofuan and Izien 2016; Papadogonas, 2007) in which there is positive and significant relationship between age and profitability of companies. While contrary to the above study, (Majumdar 1997; Dogan 2013; and Coad et al. 2007) studies showed the existence of negative relationship between age and profitability.

In addition, Majundar (1997) in his study investigated the impact of size and age on firm- level performance of 1020 Indian firms. It was discovered that Indian older firms are more productive but less profitable. In the same vein, Dogan (2013) focused on 200 companies listed on the Istanbul Stock Exchange from 2008 to 2011. The study found a negative relationship between age and profitability. Coad et al. (2007) in their study by using a sample of Spanish firms from 1998 to 2006 found that firm performance improves with the age of the firm and that older firms have a lower level of productivity and profitability.

Besides, Maja et al. (2017) on their study by using panel analysis based on a sample of 956 firms operating in Croatian food industry during the 2005-2014 period analysis result showed that age negatively affects firm's performance. While, as indicated on the study conducted by Ofuan and Izien (2016) with objective to investigate the relationship between company age, company size and profitability against the background of the learning by doing and structural inertia hypotheses. Their study result reveled that, there is significant positive relationship between company age and profitability.

Thus, this mixed controversial issue attracted the attentions of the researcher to consider age as one of the firm specific determinants of profitability to verify its effects on the profitability of flour factories in the study areas.

2.2.2. Firm size and Profitability

Firm size has been recognized as an important variable in illustrating organizational profitability and a Varity of studies tried to explore the effect of firm size on profitability. As various study indicate firm size has been determined as the most important factors affecting profitability and found to have a positive and significant influence on performance. As the study conducted by (Pratheepan 2014; Yuvaraj and Abate 2013; Yodit 2017; Aster and Meseret 2019), depicted that, larger firm easily enjoy economies of scale, are less risk and thus, can achieve lower cost of production and capital. In general, size has positive and significant relations with profitability i.e. the larger companies achieve a higher ROA and ROE than smaller ones. Specifically, when the companies' asset goes up the profitability also will increase.

Besides, John & Adebayo (2013) in their study, they examined the effect of firm size on the profitability of Nigerian manufacturing sector. To do so, Panel data and Return on assets (ROA) was used as a proxy for profitability while log of total assets and log of turnover were used as proxies for firm size. Furthermore, liquidity, leverage and the ratio of inventories to total assets were used as the control variables. Accordingly, the study revealed that firm size, both in terms of total assets and in terms of total sales, has a positive effect on the profitability of Nigerian manufacturing companies. Meanwhile, on the controlled variables, a negative correlation with inventory was revealed while others have positive relationship.

Contrary to the above study results, the study conducted by Niresh, A. and Thirunavukkarasu, V. (2014), to explore the effects of firm size on profitability of quoted manufacturing firms in Sri Lanka; their finding revealed that there is no indicative relationship between firm size and profitability of sampled manufacturing firms. In addition, their results showed that firm size has no profound impact on profitability of the listed manufacturing firms in Sri Lanka. During their study, they employed as proxy of firm profitability, return on assets and net profit were used. While; total assets and total sales were used as indicators of firm size. In the empirical analysis, Correlation and regression methods were employed to verify the stated hypothesis.

Thus, this mixed controversial issue attracted the attentions of the researcher to consider firm size as one of the firm specific determinants of profitability to verify its effects on the profitability of flour factories in the study areas.

2.2.3. Managerial Efficiency and profitability

Management efficiency is an important component of corporate financial management because it directly affects the profitability of the firms and its survival. According to Jariya (2013), Management efficiency is an integral part of the overall corporate Strategy to create shareholder value and for the survival of a business as it has direct impact of firm's profitability. He investigated the relationship between management efficiency and profitabil ity for a sample of 20 manufacturing companies listed on the Colombo Stock Exchange for the period of 5 years from 2007 to 2011. Descriptive and simple linear regression analyses were used to study the relationship between management efficiency and profitability. The results of the statistical test of the hypothesis indicated that the relationship between Fixed Assets Turnover has significant impact on Return on Assets and it is positive. Besides, the relationship between Fixed Assets Turnover and Net Profit is positive but it is in significant. The relationship between Total Assets Turnover and Return on assets is positive and significant while the relationship between Total Assets Turnover and Net Profit is positive and insignificant while Working capital turnover is insignificant in the study. He concluded that, managers could use the implication of the study to improve their financial performance and formulate policies that will promote effective assets management system.

Likewise, to investigate the relationship between management efficiency and firms' profitability study was conducted by Jamali and Asadi (2012) by using sample of 13 auto-manufacturing companies listed on the Bombay Stock Exchange, located in Pune for the period of 5 years from 2006 to 2010. In this study, analysis was carried out using Minitab 14 and Pearson Coefficient correlation test was conducted on variables of the study including Gross Profit Ratio (GPR) and Assets Turnover Ratio (ATR). The fundamental conclusion of their study was that, profitability and management efficiency are highly correlated to each other.

On the other hand, study conducted by Yodit (2017) to examine the determinants of food and beverages profitability specifically focused on large manufacturing companies of the sector during the period 2011-2015 in Addis Ababa the result depicted that, Managerial efficiency has a positive and statistically strong significant impact on manufacturing food and beverage companies' profitability.

2.2.4. Expenses management and profitability

Expenses management is another important variable which negatively and significantly affected flour companies' profitability as measured by ROA and ROE. The negative coefficient of cost to income ratio shows the existence of inefficient cost management system in flour companies. As the study conducted by Meseret Getahun (2017), the regression result indicates the sample flour companies are affected by poor cost management system which arises from high level of operating, administrative and personnel expenses during the study period. This implies the poor expenses management is one of the main contributors for poor performance of flour companies. This finding is consistent with the finding of Aburime (2008) and Jiang et al (2003) who noted that expenses management appear to be an important determinant of financial performance.

As a result, researcher interested to consider this variable to quantify the expanse management ability of flour manufacturing companies in particular to verify its effects on the profitability.

2.2.5. Leverage Management and Profitability

Financing mix is a key factor that affects the liquidity and the going concern of a business enterprise. After an idea has been conceived by an entrepreneur, there is need to also analyze the capital required for startup and means of financing the project. A good mixture of sources of finance is likely to improve the profitability of an organization, but if not properly mixed, could have a negative effect on the profitability of the organization.

Ahmad and Alghusin (2010) on their study to examine the impact of financial Leverage, business organization growth, noncurrent / total assets ratio, and firm's size as independent variables on profitability in proxy of Return on Assets ratio as dependent variable. By using a sample of 25 Jordanian Industrial companies listed on Amman Stock Exchange(ASE) for selected period of 10 years (from 1995- 2005). Results of their research showed that there is a significant effect of the Financial Leverage, and Growth on profitability of industrial companies. Therefore, industrial companies may improve the profitability of their firms by diminishing the debt, and rising financial assets compared with total assets.

Obigbemi et al (2016) conducted with objective of evaluating the effects of financial structure on the profitability of manufacturing companies in Nigeria. Their study employed secondary data. The Spearman' s Rank correlation and regression techniques were used for analysis, using the STATA Package. A sample of 25 manufacturing companies quoted on the Nigerian Stock Exchange for the period 2008-2012 were used. Their study results showed that, equity has a significant positive relationship with the profitability of manufacturing companies in Nigeria. Based on their results, they recommended that managers should put greater stress on the facilitation of equity capital and policy makers should encourage manufacturing business organization by reducing the cost of debt.

Corresponding to the above study, the study was conducted by Yodit (2017) with objective of examining the determinants of food and beverages profitability the case of large manufacturing companies of the sector during the period 2011-2015 in Addis Ababa. Her results of panel least square regression analysis showed that: Firm size, Leverage and capital intensity, have statistically significant and negative impact on profitability. The study concluded that size may have no or negative impact on profitability. Beside the she suggested that more attention should be given on the major variables of the sectors such as firm size and capital intensity.

2.2.6. Sales Growth and Profitability

Hansen and Mowen (2012) argues sales growth is an increase in sales from year to year or from time to time. In simple theoretical views, Companies that have high sales growth rates will require more investment in the various elements of assets, either fixed assets or asset lancer, by knowing how big the sales growth, the company can predict how much profit they will get. Besides, as Suriadi (2013) indicated, when sales increase, the sales growth ratio will increase. This affects the operational performance of the company, where the higher the sales growth ratio, increasing the level of profits the company. Whereas, based on the studies conducted by Hansen et al. (2014) result revealed that, sales growth had no effect on profitability, but the results of Suriadi (2013) showed that, sales growth has positive effect on profitability. According to Yazdanfar (2013); Asimakopoulos et al. (2009) , sales growth has a positive influence on the performance of a firm. The increase in sales growth leads to an increase in profitability. Thus, majority of the studies found growth opportunities influence performance positively.

2.3. Empirical Studies on Determinant of Profitability

Study was conducted by Yuvaraj and Abate (2013), with the objective of examining the effects of firm specific factors (age of company, volume of capital, leverage ratio, liquidity ratio, growth and tangibility of assets) on profitability measured by return on assets by using multiple regression analysis on 9 insurance companies for nine years (2003 -2011). As study result depicted, growth, Leverage, volume of capital, size and liquidity are identified as most important determinant factors of profitability. Hence, growth, size, volume of capital is positively related. However, liquidity ratio, and Leverage ratio are negatively but significantly related with profitability. At the same time, age of the company and tangibility of assets are not significantly related with profitability of insurance companies.

S imilarly, as the study conducted by Pratheepan (2014) by using a balanced panel data and static panel models to identify determinants of profitability of manufacturing companies set of Sri Lankan listed manufacturing companies. In this study, 550 observations of 55 listed manufacturing companies over the period of 2003 - 2012 were included. Return on assets was taken as a measure for profitability as a dependent variable. While size, leverage, liquidity and tangibility were considered as independent variables. Consequently, the empirical study results disclosed that, size is significant and has positive correlation with profitability whereas tangibility shows significant of inverse correlation with profitability for designated listed manufacturing companies in Sri Lanka. Leverage and liquidity point out insignificant impacts on profitability.

Moreover, the Study conducted by Eljelly (2003) with the aim of investigating the determinants of profitability of Islamic banks in Sudan, the result revealed that, only the internal factors to these banks have a significant impact on banks' profitability, as measured by return on assets (ROA) and return on equity (ROE).

Besides, Aster and Meseret (2019) has conducted a study with objective of identifying the determinants of financial performance of Ethiopian Insurance Companies over the period of 2010 to 2015. In their study, profitability ratios were used as proxy of financial performance measurement; return of asset (ROA) and return of equity (ROE). Besides, Panel data set from nine insurance companies over the period of six years were employed. As result of their descriptive statistics showed, nonexistence of variation in ROA and ROE since the standard deviation statistics for ROA (34%) and ROE (11%) were below the respective means (63% and 19%). To identify the determinants of financial performance, Ordinary least squire (OLS) estimation method was employed. The estimation result showed that capital adequacy, liquidity, size, age, loss, Leverage were the key determinants of financial performance. Based on their study results, they concluded that financial performance mainly driven by firm specific factors and attention should be given to firm specific variables to have a sound financial performance.

While, Habtamu (2012) in his study with objective of investigating determinants of private commercial banks profitability in Ethiopia employed panel data of seven private commercial banks from year 2002 to 2011. The study employed quantitative research approach. Besides, secondary financial data was used and analyzed by using multiple linear regressions models to measure the profitability of the three banks by considering Return on Asset, Return on Equity (ROE), and Net Interest Margin (NIM). Fixed effect regression model was applied to investigate the impact of capital adequacy, asset quality, managerial efficiency, liquidly, bank size, and real gross domestic product growth rate on major bank profitability measures such as Return on Asset , Return on Equity, and Net Interest Margin separately. Beside this the study used primary data analysis to solicit mangers perception towards the determinants of private commercial banks profitability. The empirical study revealed that; bank specific factors; capital adequacy, managerial efficiency, bank size and macro-economic factors; level of GDP, and regulation have a strong influence on the profitability of private commercial banks in Ethiopia.

Furthermore, as the study conducted Yodit (2017), by using quantitative research approach and panel least square regression analysis shows Firm size, Leverage and capital intensity, have statistically significant and negative impact on profitability. On the other hand, Managerial efficiency has a positive and statistically significant impact on manufacturing food and beverage companies' profitability.

Besides, study was conducted by Meseret and Getahun (2017) with objectives of examining the effect of firm-specific and macroeconomic determinants of financial performance of wheat flour producing companies' in Hawassa city. They used panel data from eight flour manufacturing companies over the period of 2008 to 2012. They aimed to measure financial performance by using profitability ratios; return of asset (ROA) and return of equity (ROE). As result, their study results revealed the, though the average ROA and ROE for flour companies were accounted for 6.5 % and 23 %, their financial performance was affected by several factors. Thus, to identify the factors that affect the performance of wheat flour producing companies a multiple linear regression model was employed. Thus, the estimation result shows that firm-specific variables namely capital adequacy (CAR), asset utilization (ASU), age (AGE), expense management (EXM) and leverage (LEV) have significantly affected companies' financial performance. And they concluded that, attention should be given to firm specific variables to have a sound financial performance.

Thus, researcher keen to conducted study on firm specific determinants of on the profitability of the flour factories to come up with valuable inputs to in the study areas.

2.4. Conceptual Framework

Conceptual framework means the concept that related to one another and used to explain the research problem. Since the growth and profitability is influenced by various factors, wheat flour companies' need to understand what influences the industry businesses hit the highest point in terms of profitability. The factors include from firm specific that is, age of flour factories in the market, size of flour factories, managerial efficiency, expense managements, leverage management and sales growth. Understanding the influences of these factors to the firm profitability is very important for the firms to take appropriate measures maintain the firms' profitability ventures and sustain in the existing competitive market. To align the conceptual framework with the research objectives, profitability is the dependent variable measured in terms of ROA and ROE whereas the firm specific factors are the independent variables.

Figure 1 Conceptual Frame work

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CHAPTER THREE RESEARCH METHODOLOGY

3.1. Description of the Study Area

The study area is located in west Arsi zone, Oromia National Regional State, Ethiopia. Arsi is among the twelve administrative Zones of Oromia National Region State, bordered in south by Bale, on the South West by Southern Nations, Nationalities and people's region (SNNPR), North West Shewa, on North by Afar Regional State and west Hararghe. The capital of the Zone is Shashemene, which is 250 kms from Addis Ababa. It is the second largest Zone of the Region having a total area of 12,410 square kilometers. Cultivated land, grazing land, forest and parks, water coverage and others (residential areas, roads, ...) account for 45.3 %, 11.4 %, 22 %, 4.9 % and 16.3 % of the total area of the Zone, respectively. The rainfall is bimodal in most of the areas with 1,500 mm in low lands and 2,500mm in highlands parts of the zone. The area is divided in to three major climatic zones, highland, lowland and semi-highlands. It has two main cropping seasons. Meher season in the area extends from August to December and is locally called ‘ bona ' while the Belg season extends from March to July and is locally called ‘ ganna '. In terms of total cultivated area, both seasons are almost equally important while there are some variations in terms of area allocated to individual crops.

The total population of the Zone was estimated 2,450,480 in July 2014. More than 90.2 percent of the population , who live in rural areas is reliant on agriculture. Crop and livestock production are the main economic activities. The farming system in the area is characterized by mixed cropping system. The major food crops grow in the area include cereals, pulses, oil crops, vegetables and root crops, with area coverage 61 percent, 28 percent, 7 percent, 2.1 percent and 1.9 percent, respectively. The Zone is known for its extensive wheat production (56.68 percent production of crops) and is sometimes called ‘ 'the wheat belt of Ethiopia ''. Livestock production is also a major component, though the rapid expansion of crop cultivation and increasing population pressure are reducing the grazing land (West Arsi Zone Agricultural Office 2019).

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Figure 2 map of study area

3.2. Research Design

Research design is a complete master plan of the research study to be commenced. It is a framework, a blue print for the research study, which guides the collection and analysis of the data (Kothari 2004). In this study, of the Conclusive research design specifically descriptive research and explanatory design were used to address the objectives of the study. Besides, the detail research designs that followed in conducting this study is presented as follows.

3.2.1. Research Approach Adopted

According to Creswell (2014), there are three approaches of research; qualitative, quantitative, and mixed. To attain the objectives of the study, in consideration of the nature of research problem and the research perspective, this study applied mixed methods (both quantitative and qualitative) research approach to investigate and assess the determinants of profitability of flour manufacturing companies over the period of 2004-2010 E.C. The mixed approach provides a more complete understanding of a research problem than either approach alone (Creswell 2014). To achieve the research objective and to examine the relationship of the stated variables the researcher applied panel data ordinary least square method which combines the attributes of cross sectional (inter-firm) and time series data (inter-period). The data for this study were collected from 13 subjected flour companies; financial statement and reports (i.e. income statements and balance sheet) over the period of 2004 to 2010 E.C.

According to Gujarati (2004), panel data analysis has the advantage to make more reliable estimate of the parameters in the model. Panel data comprise data sets consisting of multiple observations for each sampling unit. By using panel data, we can get better estimations as well as it will enable the researcher to test more complicated behavioral models with less restrictive assumptions (Aster and Meseret, 2019). Working with panel data allows using various techniques to estimate models with specific effects. Panel data allows controlling for variables, which we cannot observe or measure, or for variable that change over time and not across entities (Baltagi 1995). Thus, this approach has advantages compared to the cross sectional approach often used in financial research. In addition, by using panel data, it is possible to include time effects as well as to control for individual heterogeneity, which is captured by firm specific fixed or random effects components, that leads to biased results when neglected in cross section or time series estimations. Panel data could better detect and measure effects that simply cannot be observed in pure cross-section or pure time series data, better to suited to study the dynamics of change (Baltagi 1995).

As it is indicated in source of data of this study below, data were gathered from secondary sources including annual financial statement and audit reports. Furthermore, the variables were analyzed through descriptive statistics, and the different relationships among the variables were examined through the correlation matrix. Afterward, before regressions analysis was performed, the researcher conducted various specification tests and correlation matrixes. At this point correlation matrix was conducted to identify the relationship between independent variables as well as with dependent variables.

Finally, the panel data Ordinary Least Squares (OLS) regression analysis technique was used to analyze the panel data, which basically combined the attributes of cross sectional (inter-firm) and time series data (inter-period). E-Views 11 was employed to analyze the data to make inferences of each of the explanatory variables on dependent variable.

3.2.2. Types and Sources of Data

For the purpose of this study, secondary data were collected from internal and external sources. The internal sources were the balance sheet and income statement of 13 flour companies; - whereas, the external sources were the annual reports taken from each year audit reports of the flour companies from concerned Government body (specifically shashamena, Revenue and Custom Authority Zonal Branch). The secondary data for the empirical analysis derived mainly from the audited financial statements (yearly income statements and balance sheets) of respective sampled flour companies.

To address the objectives of this study panel data was employed to examine the effect of firm specific factors on profitability of flour companies. Panel data is favored over pure time-series or cross-sectional data because it can control for individual heterogeneity and there is a less degree of multicollinearity between variables (Altai 2005). Only audited financial reports were included in this study. Besides, Tobias and Themba (2011) also indicated, the advantage of using panel data is that it controls for individual heterogeneity, less collinearity of variables and tracks trends in the data something which simple time-series and cross-sectional data cannot provide

Besides, secondary data were also collected from periodicals including books, journals, scientific articles, various manuals, regional and federal level reports, CSA, government policy documents and other relevant publications and documents. Such information was useful to gain an insight about the manufacturing sectors in general and flour manufacturing in particular. Semi-structured interview questionnaire was employed to the respective managers, financial managers and operational managers of flour manufacturing factories in order to have first hand information about overall insights of flour factories.

3.2.3. Population and Sample Size of the Study

There were 15 flour-manufacturing companies are identified under operational in west Arsi zone.

The names of these companies with their year of establishment are presented in table 1.

Table 1 List of Flour Manufacturing Companies

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Source: West Arsi Zone Investment Office, 2020

3.2.4. Sampling Technique

Within the study area there were 15 (Fifteen) manufacturing factories producing wheat flour as the investment office indicates. All the 15 (Fifteen) manufacturing factories were the target population for the present study. For this reason, census technique was employed and all factories were approached for this study as long as they have audited financial statements (information) for the required period and whose year of service lies within 2004 to 2010 E.C. This allowed to make the panel data model structured, i.e. every cross section follows the same regular frequency with the same start and end dates. Besides, Census method will reveal more reliable and accurate results compare to sampling techniques (Gujarati 2004). The researcher consulted all the 15 companies and 13 of them were fulfilled the requirements to be considered for this study. These companies were, Eshet, Dibora, Durresa, Ahmed, Umer, Kubssa, Edate, Awll, Langano, Fitsum, Haji Mustefa, Minyahel and Bale Arsi, the remaining two companies namely, Robedu and Raya Farmers' Cooperative union food complex flour companies were not selected because, they did not have information for the required period; i.e their year of service was below seven, and thus they were excluded from the study. Therefore, a total of 91 observations were collected from 13 flour manufacturing factories for 7 consecutive years i.e. 2004-2010 E.C.

Number of observation = Number of Year X sample selection

= 7 X 13

= 91 (pooled) observations

3.2.5. Data collection Methods

For the purpose of this study, secondary data were collected from internal and external sources. Basically data were collected from all concerned Government body (specifically shashamena, Revenue and Custom Authority Zonal Branch). Financial data were extracted from the audited financial statements of the companies under examination i.e. audited financial statements (yearly income statements and balance sheets) of respective sampled flour companies. The internal sources were the balance sheet and income statement of 13 flour companies; - whereas, the external sources were the annual audit reports taken from each year audit reports of the flour companies Besides, to get additional information and overall pictures of the flour manufacturing factories secondary semi-structured questioner were used.

3.2.6. Methods of Data Analysis

For the purpose of data analysis, data have been moved through different stages before being imported and used in the data analysis program. Firstly, secondary data collected from financial statements (Income statements and Balance sheets) were entered manually in the Microsoft Excel sheet to calculate different financial ratios. Then the independent variables were calculated using Excel. In this stage, the data was ready for inferential analysis i.e. correlation and regression and moved to the data analysis. Then the data were analyzed using Econometric View (Eviews 11) software.

To test the proposed hypotheses, statistical analyses were carried out using the following methods: First, descriptive statistics of the independent variables and dependent variable (profitability measured by ROA and ROE) i.e. standard deviations, mean, minimum and maximum were calculated over the sample period. Descriptive statistics for dependent variable and all independent variables was used to check whether there is a substantial variation in the data. This method gives guarantee for variation of data. Gujarati (2004) and Malhotra (1997) reported that; using descriptive statistics methods help the researcher in picturing the existing situation.

Second, before going to perform OLS regressions, researcher accompanied various specification tests and correlation matrixes. Correlation matrix was employed to identify the relationship among variables both dependent and independent variables. Before running inferential statistics, to determine which model to use in OLS the Hausman test was conducted to select the right model among the fixed and random effects model. Finally, OLS regressions have been carried out and the proposed hypotheses were tested statistically to arrive at the conclusion. To conclude, in this research, the collected panel data was analyzed by using descriptive statistics, correlations coefficient, and regression statistics.

3.2.7. Model specification

As stated earlier, the main purpose of this study was to identify the determinants of profitability, using the annual balanced panel data, where all the variables are observed for each cross-section and each time-period. According to Gujarati (2004), if each cross-sectional unit has the same number of time series observations then such panel (data) is called a balanced panel. Thus, in this study, a balanced panel was employed and analysis was conducted by using techniques for fixed effects panel model. This indicates panel data comprises both cross-sectional elements and time­series elements. The cross sectional element was reflected by the different flour companies and the time-series element was reflected in the period of study (2004 to 2010E.C). To achieve the main objective of the study, two multiple regression models were specified and estimated. In the first model ROA was used as the dependent variable and in the second model ROE was used as dependent variable.

According to Gujarati (2004) for pooling data, the general regression model (mathematical equation) can be stated as:

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Where, i stands for the i th cross-sectional unit and t for the t th time period; ß0 is a constant term; ß1 ... Bn is estimated coefficient; X1it ... Xn it are the vector of explanatory variables and uit is the combined cross-section and time series error component.

Based on the general regression equation two multiple regression models were specified and employed to examine the relationship between the dependent variable and independent variables. The models are precisely stated as below: -

Model 1

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Model 2

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Where

i = indicate company index

t = indicate year index

Profitability i t = (ROA and ROE) i t

ROA i t represents the return on assets for flour company i in year t

ROE i t represents the return on equity for flour company i in year t

ß0 is constant, ßi are co-efficient where i =1, 2,3,4,5,6. which represent the proportionate change in dependent variable due to independent variables

AGE i t represents age of flour company i in year t

SIZ i t represents size of flour Company i in year t

ME i t represents managerial efficiency of Flour Company i in year t

Exp Mgt i t represents expense management ability of flour company i in year t

LEM i t represents leverage management of flour company i in year t

SG it represents sales growth of flour company i in year t

e i t represents unobservable factors of company i in year t (error term)

This model was employed by different researchers under wider contexts (e.g. Yuvaraj and Abate, 2013; Pratheepan, 2014; Meseret and Getahun, 2017; Yodit, 2017; Aster and Meseret, 2019). The chosen model is strongly believed to capture the essence of the subject under study. The above two models are specified based on the relation outlined in the hypothesis.

The impact of independent variables on profitability are assessed in terms of the statistical significance of the coefficients ‘ßs' by employing OLS equation with fixed effects under the two models presented above. Using a 1%, 5%, and 10% level of significance, and estimated coefficient was statistically significant: at 1%, if p -value < 0.01, at 5%, if p -value < 0.05 and at 10%, if p -value < 0.1. It is conventional to use a 5% significance level, but 10% and 1% are also commonly used (Brooks 2008).

3.2.8. Model Assumptions

It is recommended to check the assumptions of the model before running the model (Brooks 2008). This is important to check whether the data collected fit to required standards in order to avoid misleading results and conclusions. For the same, normality, collinearity, multi-collinearity and other assumptions of classical linear regression model (CLRM) underlying the OLS diagnostic tests were conducted to make sure that the suitability of the data to the basic assumptions.

3.3. Operational Definitions of Variables

This section is explained the definition and operational meaning of both dependent and independent variables that addressed in the present study.

Dependent variables

Profitability (PR): It is a continuous variable representing dependent variables. As Meseret and Getahun (2017) indicated in their study on identifying the determinants of financial performance of wheat flour manufacturing companies in Hawassa City, South Ethiopia, return of asset (ROA) and return of equity (ROE) were used as profitability ratio to measure financial performance. Besides, a multiple linear regression model was also employed to identify the factors that affect the performance of wheat flour producing companies in their study. Moreover, the study conducted by Yuvaraj et al (2013) on the performance of insurance companies in Ethiopia was used multiple regression analysis, to identify and measure the determinants of profitability. Furthermore, Aster and Meseret (2019) in their study, profitability ratios were also used as proxy of financial performance measurement; return of asset (ROA) and return of equity (ROE) and panel data was used.

Thus, to address the objectives of this study, profitability ratio analysis i.e. return of asset (ROA) and return of equity (ROE) was employed to identify the key determinants of profitability of wheat flour factories in the study area. Besides, a multiple linear regression OLS approach was employed to identify the factors that affect the performance of wheat flour producing companies.

Return on Assets (ROA): It is used as proxy of profitability. It indicates that the amount of return obtained in unit of investment on asset of flour manufacturing companies. It is computed as by dividing net profit to total assets.

Return on Equity (ROE): It is also used as proxy of profitability and it measured the rate of return on the investment made by shareholders', and computed by dividing net income by equity capital.

Independent (explanatory) variables

The choice of independent variables is based on their theoretical relationship with the dependent Variables. In this paper, firm specific variables affecting the profitability of wheat flour companies are accounted based on empirical evidence and semi-structured interview conducted. However, it was found that one independent variables access to raw materials i.e wheat it is found difficult to obtain from their financial statements and too general, ambiguous, and difficult measure. The selected independent variables and their measurement are discussed as follows.

1. Age of the flour factories in the market (AGE): It is a continuous variable as the flour factories exist for longer years in the industry. The assumption as a firm has long year experience; it would have the capacity to increase its sales level which eventually has direct contribution for the increment of the profitability. However, the reports from earlier study show that, the relationship between firm age and profitability is arguable. There are two basic augments to magnify the debate whether age does have direct relationship with profitability or not. The first argument is that, as the number of years increases the manufacturing companies might not change their technology, no/very limited shift in market preference, which resulted in low profitability and difficult to cope-up the existing market competitions. The second argument is that presence of newly established but highly competent companies in the existing market. As the younger manufacturing firms come with new technology and dynamic market; they can boost their competitiveness and profitability. This can be evidenced by study result reveled in (Halil & Hasan 2012; Ofuan and Izien 2016; Papadogonas 2007) in which there is positive and significant relationship between age and profitability of companies. While contrary to the above study, others study showed the existence of negative relationship between age and profitability (e.g. Majumdar 1997; Dogan 2013 and Coad et al. 2007). This mixed reaction has made the debate inconclusive and attracted the attention of the researcher to consider age of the firm as one of the firm specific determinants of profitability. Thus, this variable is expected to influence profitability of flour factories negatively. It is measured in terms years.

2. Size of company (SIZ): Firm size is one of the most acknowledged determinants of profitability (Beard and Dess, 1981). In this study, total asset is used as a measure for company size. It is hypothesized that firm size, both in terms of total assets and in terms of total sales, has a positive effect on the profitability flour factories. It is measured as, Firm Size = log of total assets

3. Managerial Efficiency (ME): - The ratio of asset turnover (efficiency ratio) was used to measure managerial efficiency and the higher the ratio the higher the managerial efficiency. The total asset turnover ratio is a general efficiency ratio that measures how efficiently a company uses all its assets. This gives investors and creditors an idea of how the flour factory is managed and uses its assets to produce products and sales. Managerial efficiency= Total revenue /Total asset.

4. Expense Management (EXPMGT): Expenses management (EXM) is another important variable which negatively and significantly affected flour companies' profitability as measured by ROA and ROE. The negative coefficient of cost to income ratio shows the existence of inefficient cost management system in flour companies. As the study conducted by Meseret and Getahun (2017), the regression result indicates the sample flour companies are affected by poor cost management system which arises from high level of operating, administrative and personnel expenses. This implies the poor expenses management is one of the main contributors for poor performance of flour companies. This finding is consistent with the finding of Aburime (2008) and Jiang et al (2003) who noted that expenses management appears to be an important determinant of financial performance. Thus, expense management is measured by the ratio of operating expense to income.

5. Leverage Management (LEM): A good combination of sources of finance is expected to boost the profitability of an organization, but if not properly mixed, could have a negative effect on the profitability of the organization. It is a financial ratio that indicates the percentage of a firm's assets that are financed with debt. Thus, the more the flour factories used equity financing the growth and profitability of the firms expected to have negative impacts on flour factories profitability. The Leverage (Debt) Ratio is measured as: Leverage Ratio= Total Liabilities/Total Asset.

6. Sale Growth (SG): One of the parameters used to measure the growth is the sales growth, which shows the percentage increase in sales the current year compared to the previous year. The larger the sales growth, the better the profitability of the flour factories and growth. It is assumed that, sales growth would have negative influence on growth and profitability of flour factories. It is measured in terms of the percentage increase in sales the current year compared to the previous year.

CHAPTER FOUR DATA ANALYSIS AND PRESENTATION

4.1. Introduction

This part of the thesis presents the findings and discussion of the study under different parts. The first part presents the results of d escriptive statistics and econometric analysis using OLS regression analysis to determine factors influencing profitability of flour factories. Descriptive statistics were used to quantitatively describe the important features of the variables using mean, median, maximum, minimum and standard deviations. In each of the sub-sections, brief interpretations presentation was given to explain the results obtained. . This chapter of the thesis basically describes the results of the following main sub contents i.e. explained in separate sections. Firstly, it presents the results based on discussion of the summary of descriptive statistics to understand the variations, the average, minimum and maximum values of the variables. Secondly, the illustration and discussion of the correlation analysis among basic variables by using correlation matrix was presented. Thirdly, the tests for fulfillment of basic OLS assumptions and it present the results of OLS and discusses against with the prior assumptions. Fourthly, it gives brief discussions on the results of regression in order to measure the how variables affect the profitability.

Table 2 Determinants of profitability, indicator/ proxy and research Hypothesis

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Source: Self-developed based on the empirical literature, 2020.

4.2. Descriptive Statistics

The descriptive statistics explores and presents a statistical description of flour manufacturing companies' profitability as conveyed by both return on asset (ROA) and return on equity (ROE) from 2004 to 2010 E.C. Thus, table 3 presents the result of descriptive statistics for independent variables (age, size, managerial efficiency, expense management and leverage management) and dependent variables (ROA and ROE). The table summarizes descriptive statistics of all dependent and independent variables given the general distribution of the data set. It measures the mean, the standard deviations, minimum and maximum of the wide range of profitability measured in ROA and ROE. The analysis was based on six explanatory variables for the 13 flour manufacturing companies over a period of seven years (2004 to 2010 E.C). Descriptive statistics can be performed by different software. Among them, STATA, SAS, SPSS and Eviews are very common among the economists. This study has used Eviews software version 11 to undertake the analysis and present the descriptive statistics.

As one can depict from table 3, the mean values of ROA and ROE for flour companies were 7.4% (0.074237) and 12% (0.121291) with standard divisions of 0.034 and 0.055 respectively. This implies that; flour manufacturing companies in the study areas have an average positive profit over the study. The minimum value of ROA was 2.1% (0.020743) and the maximum was 17.75% (0.177531). This is to indicate, the most profitable flour companies earned 0.18 ETB (17.75%) of net income from a single one-birr investment on asset. On the other hand, the mean of ROE equals 12% (0.121291) with a minimum of 3% (0.031445) and a maximum of 26% (0.257599). That means, the most profitable flour company of the sampled companies in the study areas earned 0.26 ETB (26%) of net income from one-birr equity investment.

In general, the descriptive statistics statistical summary inferred that, nonexistence of variation in ROA and ROE since the standard deviation values for return for asset 0.034 and return on equity 0.055 which were below their respective means 7.4% (0.074237) and 12% (0.121291) of the sampled flour companies during the period 2004-2010 E.C in the study areas.

Table 3 Summary of Descriptive statistics

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Source: Eviews s ummery of descriptive statistics result, 2020

Age (AGE) is one of the firm specific determinant variables of profitability considered in this research which is indicated by operating years of the flour companies from date of establishment to the date of observation. As indicated in the table 3 above, most of the companies selected for this study have been in operation for an average of 7.5 years; with minimum 1 year and maximum 16 years, and standard deviations of 3.73. This shows firms have been operating in the business for reasonable period. Concerning size (SIZE) of flour companies which is measured by natural Logarithm of total asset is one of firm specific variables showed a mean value of 7.05 (12,664,138 ETB). The maximum and minimum values were 7.39 (24,479,708 ETB) and 6.51 (3,221,098 ETB), respectively, with standard deviation of 0.23 (5,735,961 million ETB). It implies the existence of disparity on size of flour companies as measured by total asset across companies during the study period.

On the other hand, managerial efficiency (ME) which is measured in terms of total revenue to total asset is used to measure the ability of the flour companies to generate sales with unit of asset. As results, the mean of asset utilization is 2.45 ETB; and the maximum and the minimum values of 6.37 ETB and 0.95 ETB respectively with standard deviation of 1.18 ETB. It implies that with 1 ETB asset the companies in the study areas with the study period can generate 2.45 ETB sales revenues. Concerning the expense management (EXP_MGT), as indicated in the table 3 the mean value is 0.066 ETB and the maximum and minimum values were 0.18 ETB and 0.01 ETB, respectively with standard deviation of 0.035 ETB. The mean value of 0.066 ETB implies that the companies incurred 0.066 ETB expenses from unit sales. In another words, to obtain 1 ETB sales revenue the companies incurred an average of 0.66 ETB expenses. Conversely, the most efficient companies incurred 0.01 ETB of expenses and the inefficient companies incurred 0.18 ETB expenses. This infers that; the efficient flour companies have cost management advantage over the inefficient flour companies. In other ways, this is to mean that, when we see the minimum and maximum values; it implies that the most efficient companies have a relatively considerable cost advantage as compared to the least efficient flour companies.

Table 3 above also portrays the results of leverage management (LEV_MGT), which is a specific variable measured by debt ratio. Result show (LEV_MGT) has a mean value of 0.37 ETB (37%). Its maximum and minimum values were 0.65 ETB and 0.11 ETB, respectively with standard deviation of 0.14. This implies, the flour companies included in the study area and period, were financed their total assets by 37% with debt and the remaining 63% was from equity.

While another firm specific determinant factor considered in this study was sales growth (SG) which is measured as percentage change in total sales. As result on table 3 above shows, the mean value of SG was 13.18 percent; and maximum and minimum values were 42.31% and - 34.83 %., with a standard deviation of 13.16%. This infers that, there was slight instability in sales growth among flour factories during the study periods.

4.3. Discussion of Semi-Structure Interview Results

To support the secondary data of the study collected from financial statements to get the overall pictures of sample flour factories and to find out the major determinants affecting profitability of the firms', semi-structure interviews were conducted with general managers, financial managers and operational mangers of the flour factories. Of the total 13 sampled flour factories 8 (61.54%) of them were included in the interview.

The results revealed that, electricity supply, workers' skills and experience, lack of market linkage, access and consistence supply of raw materials (specifically wheat), market fluctuation, cost of machineries spare-parts, down time of operations, financial capacity of the factories and tax rate etc were mentioned as determinates factors affecting the performance of flour factories. Previous studies indicated that these factors potentially contribute in determining the performance of firms in terms of profitability, customer satisfaction, return on investment (RoI) and other performance dimensions. The interview argued that these and other unmentioned firm specific and external factors hampered their profitability. In addition, most of the flour manufacturing companies indicated that, they financed their firms in last seven years through debt and equity financing, though there are variations in their capital structures. Besides, it was identified that, most of them have poorly planned growth strategy and lacks proper marketing strategy that enable them to penetrate in to the market even for existing produces.

4.4. Test Results for the Classical Linear Regression Model (CLRM) Assumptions

As noted in Brooks (2008), the classical linear regression model (CLRM) assumptions, which were required to show that the estimation technique of OLS. It has several desirable properties, and also that hypothesis tests regarding the coefficient estimates could validly be conducted. Confirming whether the data collected fit to the CLRM, prior test was carried out. The sections below present the results of CLRM in addressing the assumptions proposed in this study.

Assumption 1: The error term has zero mean E (eit) = 0

The first assumption required is that the average value of the errors is zero. Indeed, if a constant term is included in the regression equation, this assumption will never be violated. If the regression did not include an intercept, and the average value of the errors was non-zero, several undesirable consequences could arise (Brooks 2008). First, and the worst effect is, a regression with no intercept parameter could lead to potentially sever biases in the slope coefficient estimates. This will result that, the estimated line in this case is forced through the origin. Thus, the estimate of the slope coefficient ('ß) is biased. Besides, R2 defined as ESS/TSS can be negative, implying that the sample average, y, ‘explains' more of the variation in y than the explanatory variables. Therefore, when this happened, R 2 and R2 are usually become meaningless in such a context. However, based on the results of OLS fixed effect model regression analysis of this study the constant term is included in the regression. Thus, in this case the first assumption of classical linear regression model (CLRM) is not violated.

Assumption 2: Test for Autocorrelation, Cov (eit, ej) =0 i / j Assumption 2 that is made of the CLRM's disturbance terms is that the covariance between the error terms over time (or cross-sectionals, for that type of data) is zero. In other words, it is assumed that the errors are uncorrelated with one another. If the errors are not uncorrelated with one another, it would be stated that they are ‘auto correlated' or that they are ‘serially correlated'. A test of this assumption is, therefore, required. (Brooks 2008)

If there is no serial correlation, the Durbin-Watson (DW) statistic will be around 2. The DW statistic will fall below 2 if there is positive serial correlation (in the worst case, it will be near zero). If there is negative correlation, the statistic will lie somewhere between 2 and 4 (Brooks, 2008).

Positive serial correlation is the most commonly observed form of dependence. As a rule of thumb, with 50 or more observations and only a few independent variables, a DW statistic below about 1.5 is a strong indication of positive first order serial correlation (Brooks, 2008). Thus, from the result of fixed effect regression analysis (see table 8 and 9) the Durbin-Watson statistics (DW) are 1.86 and 1.97 for ROA and ROE models respectively indicating that there is no serial correlation in the residuals.

Assumption 3: Test for Multi-collinearity

Multi-collinearity means that there is linear relationship between explanatory variables which may cause the regression model biased (Gujarati, 2004). This problem occurs when the explanatory variables are very highly correlated with each other. In order to examine the possible degree of multi-collinearity among the explanatory variables, correlation matrixes of the selected explanatory variables were presented in table 4. The results show that the absence of strong pair­wise correlation between the explanatory variables (AGE, SIZE, ME, EXP_MGT, LEV_MGT and SG). As a rule of thumb, inter-correlation among the independent variables above 0.80 signals a possible multi-collinearity problem (Gujatati, 2004). As it appears in the correlation matrix table 4, there were no such high correlation between the explanatory variables.

Table 4 Test Results for Multi-Collinearity

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Source: Eviews Output, 2020

Assumption 4: Test for Normality

One of the most commonly applied tests for normality is the Bera-Jarque (BJ) test. BJ uses the property of a normally distributed random variable that the entire distribution is characterized by the first two moments the mean and the variance. The standardized third and fourth moments of a distribution are known as its skewness and kurtosis. Skewness measures the extent to which a distribution is not symmetric about its mean value and kurtosis measures how fat the tails of the distribution are. A normal distribution is not skewed and is defined to have a coefficient of kurtosis of 3. It is possible to define a coefficient of excess kurtosis, equal to the coefficient of kurtosis minus 3 a normal d istribution will thus have a coefficient of excess kurtosis of zero. A normal distribution is symmetric and said to be mesocratic. If the residuals are normally distributed, the histogram should be bell-shaped and the Bera-Jarque statistic would not be significant. This means that the p -value given at the bottom of the normality test screen should be bigger than 0.05 in order not to reject the null of normality at the 5% level (Brooks, 2008).

The result of normality tests for this study as shown in figure 2 and 3 below where the coefficient of kurtosis is around 3 (i.e. for ROA and ROE 3.21 and 2.86, respectively), and the Bera-Jarque statistic had a ^ -value of 0.3137 and 0.5507 for ROA and ROE respectively. This implies that, the residual of this study is normally distributed and the data were consistent with a normal distribution assumption. Therefore, the analysis confirms that the residuals have a normal distribution pattern.

Figure 3 Normality test for ROA residuals (results from Eviews 11)

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4.5. Correlation Matrix between Return on Asset and Independent Variables

The ROA reflects the amount of return obtained in unit of investment on asset of flour manufacturing companies and this profitability measure is correlated with other independent variables either positively or negatively. Table 5 below shows, the correlation between profitability measure (ROA) and independent variables (age, size, managerial efficiency, expense management, leverages management and sales growth). Results reveal that, ROA had negative and significant association with age, size and expense management, but negative and non-significant association with leverage management. Results also show that ROA was positively and significantly correlated with managerial efficiency; and positively but negatively correlated with sales growth.

As table 5 below disclose, age, size and expense management have negative correlation with statistical significant level of 1% on profitability as measured by ROA. While, managerial efficiency and sales growth have positive correlations at 1% and 10% statistically significant level respectively. However, leverage management has statistically negative insignificant correlation with ROA.

Table 5 Correlation Matrix between Return on Asset and Independent Variables

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Source: Eviews Output, 2020

4.6. Correlation Matrix between Return on Equity and Independent Variables

The ROE reflects the amount return on the investment made by shareholder's, and computed by dividing net income by equity capital and this profitability measure is correlated with other independent variables either positively or negatively. In table 6 below, the same correlation analysis was undertaken like that of ROA between profitability measure i.e. ROE and independent variables; age, size, managerial efficiency, expense management, leverage management and sales growth. As it can be seen from the table below, there was a negative correlation between ROE and age, size and expense management. While, sales growth, leverage management and managerial efficiency have a positive correlation with ROE.

As table 6 below indicate, age, size and expense management have negative correlation with profitability as measured by ROE at statistical significant level of 1% but expense management statistical significant at 10%. Furthermore, leverage management and managerial efficiency and showed positive correlation at 1% however; sales growth has positive correlation with ROE at statistical significant level of 10%.

In general, from both correlation matrix tables of ROA and ROE results, it is found that expiatory variables i.e. age and leverage management confirm the null hypothesis; while, the remaining explanatory variables contrasted the null hypothesis in relation to ROA. Whereas, in the case of ROE except age, all the remaining explanatory variables contrasted the null hypothesis of the researcher.

Table 6 Correlation Matrix between Return on Equity and Independent Variables

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Source: Eviews Output, 2020

4.7. Random Effect versus Fixed Effect Models

Fixed or random effect, situation often arises in financial model where we have data comprising both time series and cross-sectional elements. Such a data-set would be known as a panel data or longitudinal data. A panel of data would embody information across both time and space. Importantly, a panel keeps the same individuals or objects and measures some quantity about them over time (Brooks 2008).

The simplest way to deal with such data would be to estimate a pooled regression, which would involve estimating a single equation on all the data together, so that the dataset for “Y'' the dependent variable is stacked up into a single column containing all the cross-sectional and time­series observations. And with similar fashion, all of the observations on each explanatory variable would be stacked up into single columns in the “X” matrix. Then this equation would be estimated in the usual fashion using OLS (Brooks 2008).

We could, of course, estimate separate time-series regressions for each of objects or entities, but this is likely to be a sub-optimal way to proceed since this approach would not consider any common structure present in the series of interest. Alternatively, we could estimate separate cross-sectional regressions for each of the time periods, but again this may not be wise if there is some common variation in the series over time (Brooks 2008),

There are broadly two classes of panel estimator approaches that can be employed in financial research: fixed effects models and random effects models. The simplest types of fixed effects models allow the intercept in the regression model to differ cross-sectional but not over time, while all of the slope estimates are fixed both cross-sectional and over time (Brooks 2008).

A simple fixed effects panel estimator would robust the findings of OLS regression, because the dummy variables included to control for the individual effect automatically control for any time­invariant variable. This constitutes a compelling reason to employ panel estimators wherever possible. It also makes a strong argument to use fixed effects rather than random effects estimators, because random effects require that the regression's other explanatory variables are uncorrelated with the individual effects. Thus, what distinguishes the two approaches is the structure of the correlations between the observed variables and the unobserved variables. In a random effects model, the unobserved variables are assumed to be uncorrelated with all the observed variables. In a fixed effects model, the unobserved variables are allowed to have any correlations whatever with the observed. This is what makes the fixed effects approach so attractive (Brooks 2008).

As a result, considering the above justifications, to select which model is appropriate the researcher decided to conduct Hausman chi square test. Thus, to determine the right model between the fixed and random effects model the following hypothesis is hypothesized.

Hypothesis: Null hypothesis (H0) Random effects model is appropriate

Alternative hypothesis (HA) Fixed effects model is appropriate

Decision Criterion: Rej e ct H0 if probability value is less than 5%, Accept H0 if probability value is greater than 5% (Brooks C., 2008).

Therefore, based on the above hypostatical assumptions the Hausman test is conducted and the result is presented on table 7 below.

Table 7 Hausman Test Results

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Source: Eviews Output, 2020

Thus, as depicted in table 7 above, that the Hausman test is statically significant at 0.001 both for ROA and ROE models. The implication of this is that rejecting the null hypothesis of random effect model is appropriate, and reaching to a conclusion that the fixed effect estimate is preferable than the random effect estimate in order to make robust the OLS regression results. Thus, the analysis is made based on the fixed effects estimates.

4.8. Analysis and Interpretation of Fixed Effect Models Result

This parts of the study presents the empirical findings from the econometric point of view regarding the effect of determinants on the profitability of flour companies'. Table 8 and 9 below illustrate regression results between the dependent variable (i.e. profitability as measured by ROA and ROE) and independent variables (i.e. age, size, managerial efficiency, expense management, leverage management and sales growth). As can be seen from the results, the beta coefficient value may be negative or positive. Beta coefficient indicates that each variable's level of influence on the dependent variable. P- value indicates at what percentage or precession level of each variable is significant. The R-squared value measures how well the regression model explains the actual variations in the dependent variable (Brooks 2008).

To realize the main objective of this study, two OLS regression models were specified and estimated: ROA used as the dependent variable in the first model, whereas ROE used as dependent variable in the second model. The characteristics of the model and proposed variables in equation, likely not violate the classical assumptions underlying the OLS model. In the same way, to verify the fitness of this model (Prob > F) value checked, the result signifies a strong statistical significance (Prob > F = 0.0000). This shows the models are statistically significant at 1% (p =0.001), which enhanced the reliability and validity of the model. Thus, based on the regression results for each of the models, the following two sub-sections present the detail interpretations of the results and discussions.

4.8.1. Fixed Effect Model Result of Return on Asset (ROA)

The adjusted- R squared statistics of the ROA was 74.84% indicating the explanatory power of the model. This indicate, the independent variables in this study explain at about 75 percent of the variation in the profitability of flour manufacturing companies' as measured by ROA. While, the remaining 25 percent of the variation in the profitability of flour manufacturing companies explained by other variables which are not included in the model in this research as measured by return on assets. Whereas, the R-square values of the return on asset (ROA) of the model was 0.798 (79.88%). This clearly shows that those determinant variables that included in this study are the key factors that significantly contribute for the performance of flour factories in the study area. Managers should consider these key factors that have high contribution for performance/success of their companies in their strategies and operational plans. Moreover, they should clearly aware employees that maximum exert should be invested on these variables for improving the performance of the companies

The operational panel least square regression analysis above was used to estimate by the following modes: -

Model 1

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Accordingly, when the above panel least squares model is converted into specified variables with their coefficient it becomes:

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The F-statistics (15.8767) and significant level p-value (0.0000) shows that the explanatory variables such as age and size are significant at 1 percent, sales growth of the firm is significant at 10 percent. In other words, the regression analysis reveals that, age, size of the firm and sales growth are significantly affect the ROA of the companies; but the effect of managerial efficiency, leverage management and expense management is not statistically significant for the performance of the companies' ROA.

Table 8 Summary of Regression output for ROA

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Source: Eview- 11 output based on financial statements of flour companies

As one can depict from table 8 above, the independent variables age, size and sales growth except size which is negatively significant had positive significant impact on profitability. Whereas, managerial efficiency, expense management and leverage management becomes statistically insignificant in this model. With regard to coefficient of the independent variables, age and sales growth had positive relationship with profitability with coefficient of 0.009178 and 0.032355 respectively. On the other hand, size has a negative relationship with profitability with its respective coefficient of -0.148333. The discussion and implication of the results concerning these independent variables are presented below.

Age

Age is one of the firm specific variables which have a significant and positive effect on ROA. As it can be inferred from the panel OLS regression results of this study on table 8, there exist a significant and positive relationship among age and profitability of flour manufacturing companies with beta coefficient of 0.009178 at 1 percent significance level. It has the second highest t-statistic of 3.808227 with the second p value of (p =0.0003). Thus, the results do not fully support the first hypothesis. In other words, the hypothesis age has negative relation with profitability is rejected. It is, therefore, the researcher accepted the alternative hypothesis by the statistically significant of positive relationship between age and profitability as measured by ROA of the flour companies.

The finding of this study is consistence with what previous studies have reported (e.g., Yodit (2017); Ofunn and Izin (2016); Hail and Husun (2012); and with that of Majunder (1997). However, the result of this study is inconsistence with finding of Majent et al., (2017) and Meseret and Getahun (2017) concerning the effect of age on ROA of the companies. In general, results indicate that aged firm has advantage to have more market shares and their product brand positioning is built in the customer mind than that of the newly emerging ones. Besides, the aged firm has also public images; they can have more customers and enable to penetrate in to the new market than that of the new ones.

In view of that, the coefficient result of age (0.0092) it implies that keeping other thing constant as the age of firms changes by one year a flour manufacturing companies' profitability increased by 0.9 percent.

Size

Size is one of the firm specific variables which has a significant and negative effect on profitability. As it can be inferred from the panel OLS regression results of this study, there exist a significant and negative relationship among size and profitability of flour manufacturing companies with a regression beta coefficient of -0.1483, t-statistics of -4.1394 and p -value of (p =0.0001). Size has been considered as a major variable in explaining firm profitability at 1 percent significance level. It has the first highest t-statistic and with the first in p value of 0.0001. Hence, based on the results, the hypothesis that size has positive relation with profitability is rejected. In other words, the alternative hypothesis about the significant of negative relationship between size and profitability as measured by ROA is accepted.

Similar results were reported by Yodit (2017) which is consistence the results of this study. However, other studies reported for the positive effect of size on ROA. For example, Adeayo (2013) reported the positive effect of size on the manufacturing industries in Nigeria which is inconsistence with the result of this study. Several studies also reported the absence of relationship between size of the firm and ROA (e.g., Niresh and Thirunavuk 2014).

A meta-analysis study conducted by Yisau (2013) shows that several studies tried to investigate the effects of size on firm profitability (ROA). He summarized those studies' report on the contribution of size of the firm on ROA. The results are mixed. In some of the studies, size found positively contribute; while in others negatively. He stated that according to the conclusions of various studies the impacts of size on profitability can be negative or positive. For as much as some authors argued that larger firms have some advantages such as a greater possibility of taking advantage of scale of economies which can enable more efficient production, a greater bargaining power over both suppliers and distributors or clients, exploiting experience curve effects and setting prices above the competitive level. However, in this study size has significant and negative impact on the profitability. This might be the sample flour manufacturing companies which have large assets are not able to utilize their assets properly.

As a result, the beta coefficient result of size (-0.148) infers that keeping other thing constant as the asset of firms change by one unit a flour manufacturing companies' profitability changed by opposite direction of change by 14.8 percent.

Sales growth

With a regression beta coefficient sales growth of 0.0323, t-statistics of 1.9715 and p -value of 0.052 (p =0.052) the regression results of the study showed that there is a statistically significant positive relationship between sales growth of flour manufacturing companies and their profitability at 10% significant level. Sales growth has been considered as a third major variable in explaining firm profitability at 10 percent significance level. It has the third and lowest t- statistic and with the third in p value of 0.052. The findings of the study do not support the sixth hypothesis (H6). Hence, the six hypotheses that sales growth has negative relation with profitability is rejected. In other words, the alternative hypothesis statistically significant of positive relationship between sale growth and profitability as measured by ROA is accepted by at statistical significant level of 10 percent.

As Suriadi (2013) indicated, when sales increase, the sales growth ratio will increase. This affects the operational performance of the company, where the higher the sales growth ratio, increasing the level of profits the company. Whereas, based on the studies conducted by Hansen et al. (2014), sales growth had no effect on profitability, but the results of Suriadi (2013) showed that, sales growth has positive effect on profitability. According to Yazdanfar (2013) and Asimakopoulos et al. (2009) , sales growth has a positive influence on the performance of a firm. Thus, the result of this research is in line with Suriadi (2013) and Yazdanfar (2013). However, the study result is inconsistence with research conducted by Meseret and Getahun (2017). The difference in result might arise due to experience of the firms, the specific economic development of the countries where there studies conducted, market, technology, access to market; financial capacity and other factors might contribute to these effects.

The result implies the beta coefficient result of sales growth (0.0323), it indicates that keeping other thing constant as the sales increases by one unit the flour manufacturing companies' profitability increased by 3.23 percent. In general, the increase in sales growth leads to an increase in profitability.

4.8.2. Fixed Effect Model Result of Return on Asset (ROE)

As it can be from table 9 seen that, the adjusted- R squared statistics of the ROE was 77.24% (0.772430). This designates, the independent variables in this study explain at about 77 percent of the variation in the profitability of flour manufacturing companies' as measured by ROE. Whereas, the remaining 23 percent of the variation in the profitability of flour manufacturing companies explained by other variables which are not included in this model. While, The return on asset (ROE) model R-square values was 81.79%. The operational panel least square regression analysis above was used to estimate by the following modes: -

Model 2

Abbildung in dieser Leseprobe nicht enthalten

Accordingly, when the above panel least squares model is converted into specified variables with their coefficient it becomes:

Abbildung in dieser Leseprobe nicht enthalten

Table 9 Summary of Regression output for ROE

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Source: Eview- 11 output based on financial statements of flour companies

As it is presented on table 9 above, the F-statistics (17.97126) and significant level p-value (0.0000) shows that, the explanatory variables such as age, size and leverage management are significant at 1 percent, sales growth of the firm is significant at 5 percent.

As one can depict from table 9 above, all statistically significant explanatory variables (i.e. age, size, leverage management and sales growth) except size had positive significant impact on profitability. On the contrary to the above, size has negative significance impact on profitability. Whereas, managerial efficiency and expense management were statistically insignificant in this model also like that of ROA. Furthermore, age, leverage management, sales growth had positive relationship with profitability with coefficient of 0.014007, 0.189597 and 0.059360 respectively.

On the other hand, size has a negative relationship with profitability with its respective coefficient of -0.24587348333. Thus, let us see the details and implication of these variables one by one under the following sections.

Age

As results of this study on table 9 indicate, there exist a significant and positive relationship among age and profitability with beta coefficient of 0.014007 at 1 percent statistically significance level. It has the third highest t-statistic of 3.822099 with the third p value of 0.0003 (p =0.0003). Thus, the researcher's first hypothesis that age has negative relation with profitability is rejected. The researcher accepts the alternative hypothesis by the statistically significance of positive relationship between age and profitability as measured by ROE.

In view of that, the coefficient result of age (0.0140) it implies that keeping other thing constant as the age of firms changes by one year a flour manufacturing companies' profitability changes in the same direction of changes by 0.9 percent. In other words, aged companies, can be earn large return on equity than the younger.

Size

Size has a significant and negative relationship among size and profitability of flour manufacturing companies with beta coefficient of -0.245873, t-statistics of -4.511897 and p- value of 0.0000. This variable is also statistically significant determinant of profitability as measured by ROA. Size has been considered as a major variable in explaining firm profitability at 1 percent significance level. It has the second highest t-statistic and with the second in p value of 0.0000. Hence, the researcher's second hypothesis that size has positive relationship with profitability is rejected. Thus, the alternative hypothesis accepted by 1 percent statistically significant negative relationship between size and profitability.

As a result, the beta coefficient result of size (-0.245873) infers that keeping other thing constant as the asset of firms change by one unit a flour manufacturing companies' profitability changed by opposite direction of change by 24.6 percent.

Leverage Management

Leverage had a positive and significant impact on profitability of flour manufacturing companies with beta coefficient of 0.189597, t-statistics of 6.642581 and p-value of 0.0000. it is one of the basic determinant factor in explaining firm profitability at 1 percent statistically significance level. It has the first highest t-statistic and with the first in p value of 0.0000. Henceforth, the researcher's fifth hypothesis that leverage has negative relationship with profitability is rejected. As a result, the alternative hypothesis accepted by 1 percent statistically significant of positive relationship between leverage management and profitability. Literatures in capital structure confirmed that a good combination of sources of finance is expected to boost the profitability of an organization, but if not properly mixed, could have a negative effect on the profitability of the organization (Rajan and Zingales, 1995).

As a result, the beta coefficient result of leverage management (0.1895) infers that keeping other thing constant, as the debt financing of firms change by one unit a flour manufacturing companies' profitability changed by same direction of change by 18.95 percent or vise verse.

Sales Growth

Likewise, with beta coefficient of 0.0593, t-statistics of 2.3800 and p-value of 0.0200, there is a statistically significant positive relationship between sales growth of flour manufacturing companies and their profitability at 5% statistically significant level as measured by return on equity. Sales growth has been considered as a fourth major variable in explaining firm profitability at 5 percent statistical significance level. It has the fourth and lowest t-statistic (2.380063) and with the fourth in p value of 0.0200. Hence, the researcher's the six hypothesis that sales growth has negative relation with profitability is rejected.

Correspondingly, the alternative hypothesis, there is positive relationship between sale growth and profitability as measured by ROE 5 percent statistically significant level is accepted.

The result implies the beta coefficient result of sales growth (0.059360), it indicates that remaining other thing constant as the sales increases by one unit the flour manufacturing companies' profitability increased by 5.94 percent. In general, the increase in sales growth leads to an increase in profitability as measured by return on equity.

Table 10 Summary of Fixed Effect Regression Results

Abbildung in dieser Leseprobe nicht enthalten

Source: Eviews Output, 2020

CHAPTER FIVE CONCLUSIONS AND RECOMMENDATIONS

Introduction

The former chapter presented the analysis of the findings, while this chapter deals with the conclusions and recommendations provided based on the findings of the study. In view of this, this chapter is organized into two subsections. The first section presents the main conclusions and the second section presents the key recommendations that might be taken in to consideration to improve the performance of factories.

5.1. Summary of Findings

This study aimed to identify the main factors that determine flour manufacturing companies' profitability and the extent to which these determinants exert impact on profitability for the case of Oromia Regional state, West Arsi Zone flour manufacturing firms. In doing so, previous studies have been reviewed and it is summarized that the profitability of flour manufacturing companies are affected by both micro and macro factors. The internal determinants refer to the factors originating from flour manufacturing companies' financial statements (income statement and balance sheets) and termed as specific determinants of profitability. As empirical results from previous studies concluded that, internal factors have a profound ability to explain a large proportion of flour profitability; than the macro factors. Thus, for this study only firm specific determinant or the internal determinants factors affecting profitability were considered. The formula for each ratio was presented. The dependent variables were profitability as measured by ROA and ROE.

Six explanatory variables have been proposed i.e. age, size, managerial efficiency, expense management, leverage management and sales growth based on empirical review and semi­structure interview on preliminary study. The dependent variable in this study was profitability proxy by ROA and ROE. The yearly financial data has been used and collected from the audited financial statements of flour manufacturing companies from concerned body of the government and flour companies. To find the best empirical results, the financial data were collected through survey of document reviews from a sample of thirteen flour manufacturing companies over the time period from 2004-2010 E.C. The collected data were analyzed by employing OLS fixed effect model regression using statistical package ‘EVIEW 11'.

To comply with the objective of this research, quantitative research method was employed. Panel fixed effect model, multiple regression analysis is adopted to measure the determinants of flour manufacturing companies' profitability. From the empirical findings on the impact of flour manufacturing companies' profitability in Oromia Regional state, West Arsi Zone for the sample suggest the following conclusions.

From the descriptive statistics, it was revealed that the dependent variable mean as measured by ROA and ROE are 0.07423 and 0.1212 respectively. This indicates that, on average the flour companies can earn 7.4 percent and 12.1 percent on the investment on assets and shareholders' equity. Regarding the correlation between dependent and independent variables in both models employed similar positive and negative correlation is maintained with ROA and ROE except leverage management that has negative insignificant correlation with return on assets. While, leverage management has positive significant correlation with return on equity.

The results of profitability as measured in ROA verified that at about 75 percent of the change in the dependent variable is explained by the explanatory variables that are selected and entered in the model. While the remaining, can be explained by other variables which are not taken in to account in this research. This shows that the variables under consideration in this study (age, size, sales growth, management efficiency, expense management and leverage management) are the key determinant factors for the success of flour factories in the study area. Age of the firm, size of the firm and sales growth found the only determinant factors affecting profitability when measured in ROA. The remaining explanatory variables, i.e. management efficiency, expanse management and leverage management found for their insignificant effect on the profitability of flour manufacturing companies (i.e. ROA) West Arsi zone of Oromia regional state, Ethiopia.

Furthermore, the dependent variable return on equity of fixed effect regression model result indicate that, explanatory variables i.e. age, size, leverage management and sales growth were found to have a statically significant relationship with profitability (ROE). However, the remaining variables i.e. managerial efficiency and expense management were found statistically insignificant.

To conclude, the fixed effect regression results of ROA and ROE, the explanatory variables age and sales growth found statistically significant and positive relationship with profitability. In additions, size becomes statistically significant and has negative relationship on both models. However, leverage management was found statistically significant and has positive relationship with profitability only on profitability as measured by return on equity or ROE model. Rajan and Zingales (1995), indicate that debt suppliers should be more willing to lend to profitable firms. Accordingly, a positive dependence is expected to be observed between leverage and profitability. Thus, the result of the study show that firms financed their assets by debt can generate higher profit than that of firms financed their assets by equity.

In general, age, size and sales growth are a significant determinant of flour manufacturing companies affecting profitability in either of both directions i.e. positively and negatively in both (ROA and ROE) models of profitability employed in the study. Furthermore, leverage management become statistically significant only on ROE model. While, no clear and statistically proven relationships were obtained for explanatory variables, that are managerial efficiency and expense management. As a result, managers should consider these key factors that have high contribution for performance/success of their companies in their strategies and operational plans. Moreover, they should clearly aware employees that maximum exert should be invested on these variables for improving the performance of the companies.

5.2. Suggestions

The ultimate aim of any economic activities is to generate profit. By doing so, it motivates the investors to invest more and accumulate wealth. Return on asset (ROA) and return on equity (ROE) determine the long-term growth scenario of a business organization. In order to hold up risky surprises and maintaining financial stability, it is vital to identify the determinants that mostly influence the overall performance of flour manufacturing companies. Based on the findings of this study and the conclusions drawn above by the researcher, the researcher recommended the following.

The analysis indicates that, the variables age, size leverage management and sales growth were significantly determining the profitability as measured by return on asset and return on equity. The researcher recommended that, aged firms should able to sustain their market shares, product brand positioning and should develop their public image by developing and implementing proper research and development and innovative activities to boost up market competitive advantage than the newly emerging ones to be profitable and contribute in the economy of the country.

In relation to size the negative relationship between flour manufacturing factories profitability and size indicate that, the management inefficiency of in utilizing assets. Thus, the researcher strongly recommends to the management of the flour companies, they should have to take care of not losing their economies of scale by utilizing their assets.

With respect to leverage management the positive significant relationship between profitability and leverage indicate that, the flour manufacturing companies manage properly their capital structure. Thus, the companies to maintain sound financial decisions by controlling their mix of finance and they should regularly revise their optimum capital structure to viable and profitable in business environments.

Furthermore, the variable that determines profitability is sales growth. In this study, it was found that significant positive relationship between both on return on asset and return on equity. Accordingly, the management of flour manufacturing companies should give attention to their growth strategy. It is clearly observed that ccurrently, bread & bakery products segment has witnessed large-scale adoption of flour, owing to increase in demand for bread. Moreover, rise in government support both in the investment and supply of raw materials for flour manufacturing firms shows the need for flour products in Ethiopia. Besides, the shouting demand of flour and flour product like bread & bakery products, macaroni, pasta, biscuits, animal feed, and others are other popular segments in Ethiopia in particular and in the global flour market in general. The rapid growth for flour products and bread & bakery products is expected to grow in subsequent years. Thus, the researcher strongly recommends to develop and implement growth strategies like market penetration, product development, market expansion and diversification to sustain the sales growth that have significant contribution on profitability.

Firms should revise strategies regarding leverage management, their marketing strategy, sales strategy etc to enhance profitability of their business i.e . by reduce cost to increase profit and by enhancing their asset utilizations and modernizing their technology.

Therefore, the flour companies' managers should give due attention to the significant variables (i.e. age, size, leverage management and sales growth) that determine the profitability of flour producing firms. In general, managers should consider these key factors that have high contribution for performance/success of their companies in their strategies and operational plans. Moreover, they should clearly aware employees that maximum exert should be invested on these variables for improving the performance of the companies.

Policy makers also should consider financing system in this sector (i.e.flour manufacturing companies) as they are engaged in value additions activities of farmers produce and strategic directions in which the vast majority unemployed workforce and investors would have a chance to engage in this investment. Beside, oother firms (non-flour producing factories) can consider the key determining factors in their business to improve the performances.

Finally, further research should be done on large sample and by considering those variables which were not included in this study like macro and micro determinants of profitability.

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Appendixes

Table 1.1. Result of Descriptive statistics

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Table 1.2. Test Results for Multi-Collinearity

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Table 1.3. Correlation Matrix between Return on Asset and Independent Variables Covariance Analysis: Ordinary

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Table 1.4. Correlation Matrix between Return on Equity and Independent Variables

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Table 1.5. Summary of Regression output for ROA Dependent Variable: ROA

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Table 1.6. Summary of Regression output for ROE

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Appendix II:

Table 2.1. Summary of Raw Data

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Appendix III: Interview Schedule

Madda Walabu University School of Business and Economics Department of Management

Interview questions for selected Flour companies' managers

1. What are the overall factors, which can affect your flour factories profitability?
2. How do those identified factors affect/influence your flour factories profitability in general?
3. Among the identified factors that can influence your flour factory profitability, which of them are the major determinants of profitability.
4. What types of measures are taken by your flour factory in order to reduce the influence of factors that affects profitability negatively?
5. Do you think that the identified factors contribute to the existence of poor or good performance of flour factory when measured in terms of profitability? If they have, how do they contributed.
6. Does your company obtain consistent supply of inputs/ raw materials? If not what are the major problems related to this.
7. How your flour factory does financed in the past 7 years
8. Do your flour factories operate in full capacity? If your flour factory company is not operating in full capacity, what are the major reasons for not working at full capacity? Please indicate based on the their influence
9. Any comments and additional point you want to add.

Thank you

[...]

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Title
Determinants of profitability. A case of flour factories in Wert Arsi Zone, Oromia Regional State, Ethiopia
Grade
4
Author
Year
2020
Pages
89
Catalog Number
V1188630
Language
English
Tags
determinants, wert, arsi, zone, oromia, regional, state, ethiopia
Quote paper
Addisu Wodimu (Author), 2020, Determinants of profitability. A case of flour factories in Wert Arsi Zone, Oromia Regional State, Ethiopia, Munich, GRIN Verlag, https://www.grin.com/document/1188630

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