Download effects of working capital management on financial

EFFECTS OF WORKING CAPITAL MANAGEMENT ON
FINANCIAL PERFORMANCE OF ENERGY AND PETROLEUM
COMPANIES LISTED AT NAIROBI SECURITIES EXCHANGE
BY
PAUL MASINDE SIMIDI
A RESEARCH SUBMITTED IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS OF MASTER OF BUSINESS ADMINISTRATION
(MBA), SCHOOL OF BUSINESS, UNIVERSITY OF NAIROBI
OCTOBER, 2015
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DECLARATION
This research project is my original work and has not been submitted for a degree in any
other university.
Signed……………………………………….…………………..…Date…………………
Paul Masinde Simidi
Reg. No. D61/68512/2013
This research project has been submitted for examination with my approval as the
University supervisor.
Signed……………………………………….………Date………………………
Dr. Duncan Elly Ochieng, PhD, CIFA
Lecturer, Department of Finance and Accounting
University of Nairobi
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DEDICATION
This research project is dedicated to my family Caroline, Breana, Ryan and Prince.
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ACKNOWLEDGEMENT
I want to pass my gratitude to my supervisors Dr. Duncan Elly Ochieng, for having
provided valuable guidance during the research period. I also owe gratitude to University
of Nairobi campus for great atmosphere, competent personnel and interesting courses
offered during my study.
I want to acknowledge the support of my family Caroline, Breana, Ryan and Prince. My
sincere hope is that the findings of this research project will stimulate and benefit the
research about working capital management in the future.
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TABLE OF CONTENTS
DECLARATION .......................................................................................................... ii
DEDICATION ............................................................................................................. iii
ACKNOWLEDGEMENT ........................................................................................... iv
TABLE OF CONTENTS ..............................................................................................v
LIST OF TABLES ..................................................................................................... viii
LIST OF ABBREVIATIONS ...................................................................................... ix
DEFINITION OF KEY TERMS ..................................................................................x
ABSTRACT……..………………………………………………………………………xi
CHAPTER ONE – INTRODUCTION .........................................................................1
1.1. Background of the study ...........................................................................................1
1.1.1 Working Capital Management .........................................................................2
1.1.2 Firm’s Performance .........................................................................................5
1.1.3 Relationship between WCM and Firm’s Performance ......................................6
1.1.4 Energy and Petroleum Sector in Kenya ...........................................................7
1.2. The Research Problem ..............................................................................................8
1.3. Research Objective ................................................................................................. 10
1.4. Value of the Study .................................................................................................. 10
CHAPTER TWO- LITERATURE REVIEW ............................................................ 13
2.1. Introduction ............................................................................................................ 13
2.2. Theoretical Review ................................................................................................. 13
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2.3. Determinants of firm’s Performance ....................................................................... 16
2.3.1. Corporate Governance .............................................................................. 17
2.3.2. Economic Conditions ............................................................................... 17
2.3.3. Capital Structure....................................................................................... 18
2.3.4. Risk Appetite............................................................................................ 18
2.4. Empirical Review ................................................................................................... 18
CHAPTER THREE – RESEARCH METHODOLOGY ........................................... 24
3.1 Introduction ............................................................................................................. 24
3.2 Research Design ...................................................................................................... 24
3.4. Data Collection Method .......................................................................................... 25
3.4.1. Data Collection Tool ................................................................................ 25
3.5. Data Analysis.......................................................................................................... 26
3.5.1. Analytical Model ...................................................................................... 26
3.5.2. Operationalization of Study Variables ...................................................... 27
3.5.3. Test of Significance .................................................................................. 28
CHAPTER FOUR - DATA ANALYSIS, RESULTS AND DISCUSSION................ 29
4.1. Introduction ............................................................................................................ 29
4.2. Descriptive Statistics ............................................................................................... 29
4.3. Diagnostic Tests ..................................................................................................... 30
4.4. Correlation Analysis ............................................................................................... 33
4.5. Effects of WCM on Financial Performance ............................................................. 38
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CHAPTER FIVE – SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
...................................................................................................................................... 42
5.1. Introduction ............................................................................................................ 42
5.2. Summary of Findings .............................................................................................. 43
5.3. Conclusions ............................................................................................................ 45
5.4. Limitation of the study ............................................................................................ 46
5.5. Recommendations................................................................................................... 47
5.6. Suggestions for further research .............................................................................. 49
REFERENCES ............................................................................................................ 51
APPENDICES ............................................................................................................. 57
APPENDIX I: DATA COLLECTION TOOL .............................................................. 57
APPENDIX II: ENERGY AND PETROLEUM COMPANIES LISTED AT NSE ....... 58
APPENDIX III: NORMALITY DISTRIBUTION OF ROA ........................................ 59
APPENDIX IV: NORMALITY DISTRIBUTION OF ICP ........................................... 60
APPENDIX V: NORMALITY DISTRIBUTION OF ACP ........................................... 61
APPENDIX VI: NORMALITY DISTRIBUTION OF APP .......................................... 62
APPENDIX VII: NORMALITY DISTRIBUTION OF CCC ........................................ 63
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LIST OF TABLES
Table 4.1: Descriptive Statistics ..................................................................................... 29
Table 4.2a: Tests of Normality ....................................................................................... 31
Table: 4.2b Durbin-Watson test ..................................................................................... 32
Table 4.3: Correlations .................................................................................................. 33
Table 4.4a: Combined Model Summary ......................................................................... 38
Table 4.4b: Combined ANOVAa ................................................................................... 39
Table 4.4c: Coefficientsa ................................................................................................ 39
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LIST OF ABBREVIATIONS
ACP -Accounts Collection Period
APP - Account Payable Period
CCC - Cash Conversion Cycle
EAC- East African Community
EBIT - Earnings before Interest & Tax
EPS - Earnings per Share
ERC - Energy Regulatory Commission
GPM - Gross Profit Margin
ICP - Inventory Conversion Period
KENGEN - Kenya Electricity Generating Company Limited
KIPRA – Kenya Institute for Public Policy Research And Analysis
KPLC - Kenya Power and Lighting Company
MOE - Ministry of Energy
NPM - Net Profit Margin
NPV - Net Present Value
NSE - Nairobi Securities Exchange
NTC - Net-Trade Cycle
NYSE - New York Stock Exchange
OPEC - Organization of the Petroleum Exporting Companies
OPM - Operating Profit Margin
PIEA - Petroleum Institute of East Africa
RCP - Receivable Collection Period
ROCE - Return on Capital Employed
ROI - Return on Investment
RONW - Return on Net Worth
SMEs - Small Medium Enterprises
WCM - Working Capital Management
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DEFINITION OF KEY TERMS
Account Collection Period (ACP) - is the average length of time required by a firm to
convert the firm’s receivables into cash.
Accounts Payable Period (APP) - is the average length of time between the purchase of
materials and labor from suppliers and the settlement (payment) of payable accounts.
Cash Conversion cycles (CCC) - Is the length of time between when the company makes
payments for inventories and when it receives cash inflows from the sales.
Inventory Conversion Period – Number of days it takes to convert inventories into sales.
Profitability - Is the income available to common shareholders which is revenues less
expenses, taxes and preferred dividends but before paying common dividend.
ABSTRACT
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The Kenya’s Vision 2030 plan has identified Energy and Petroleum sector as a key
foundation and one of the infrastructural enablers upon which the economic, social and
political pillars of its long-term development strategy will be built. However, energy and
petroleum sector in Kenya faces challenges such as low access to energy, high costs,
irregular supply as well as heavy capital investment requirements. Such challenges have
made Energy and Petroleum companies to maintain either excessive or inadequate working
capital levels. The objective of the study was to establish the effect of working capital
management on the financial performance of Energy and Petroleum Companies listed at
the Nairobi Securities Exchange. The census research adopted a correlation design. A data
collection sheet was used to collect secondary data from the published financial statements
of all Energy and Petroleum companies listed at Nairobi Securities Exchange for a period
of eight years between 2007 and 2014. Both descriptive and quantitative analyses were
adopted. Pearson correlation, regression and ANOVA analysis were conducted. The
research indicated that WCM influence the ROA significantly. 17.8% of the variations in
profitability was influenced by variations in WCM. The study established that the influence
of WCM on profitability is statistically significant. It is therefore incumbent upon the
Finance Managers of Energy and Petroleum companies listed at NSE to understand the
business operations, and put in place robust WCM policy framework because of its
implications on the financial performance of these companies. Adoption of these
recommendations will lead to a vibrant and profitable Energy and Petroleum sector which
is key to the achievement of Kenya’s Vision 2030.
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CHAPTER ONE – INTRODUCTION
1.1.Background of the study
Working Capital Management (WCM) occupies the major portion of a finance Manager’s
time and attention (Richard & Laughlin, 1980). This is because of its implication on both
liquidity and performance of a firm. Managers are coming to realization that incorrect
evaluation of the liquidity implications of the firm’s working capital needs may, in turn
subject creditors and investors to an unanticipated risk of default (Richard &Laughlin,
1980).WCM remains one of the most important issues in the organization where many
financial executives are struggling to identify the basic working capital drivers and the
appropriate level of working capital (Lamberson, 1995). The main objectives of working
capital management are therefore to minimize these risks by ensuring seamless business
operations and at the same time ensuring the business is in a better position to meet its
short-term obligations.
Keynesians Theory, Operating Cycle Theory and Cash Conversion Cycle Theory are some
of the theories that provide guidance on effective WCM. These theories emphasize the
need to establish an optimal level of working capital. This need was arrived after realization
that working capital elements including cash, inventories, accounts receivables, accounts
payables are key to the smooth operation of the business but on the other hand bear a cost
to the business. Baumal (1952) and Tobin (1956) noted that optimal cash balances, just like
inventory models have cost associated with sourcing, maintenance, beside the benefits that
firms derive from optimal cash levels.
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Baumal (1952) developed the Economic Order Quantity (EOQ) of inventory management.
The model strives to balance marginal cost associated with ordering and holding the
inventory to minimum. Brigham & Ehrhardt (2012) held similar views where they
observed that the twin goals of inventory management are to ensure sustainable operations
as well as hold cost of ordering and carrying inventories to the lowest possible level. WCM
management is therefore, a tradeoff between risk and profitability of the firm (Pandey,
1995). It is therefore incumbent upon Finance Managers to critically review working
capital components to establish optimal levels.
Oduor (2013) noted that the main goal for the energy sector in Kenya is to provide
affordable, sustainable and reliable supply of energy that will stimulate high and sustained
economic growth leading to higher income, increased employment and reduced poverty.
Kenya National Bureau of Statistics (2015) noted that in 2014, Kenya spent a total of
Kshs.333.1 billion in importation of petroleum products by various Oil and Petroleum
companies which was 11.7% increment over the previous year. This is a huge investment
and consequently working capital management is critical to meet the desired goals of the
sector.
1.1.1 Working Capital Management
Brigham and Ehrhardt (2012) noted that working capital refers to the current assets used
in the operations of a firm. The net operating working capital is the current assets minus
the current liabilities. A firm can operate either a relaxed or restricted WCM Policy.
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Brigham and Ehrhardt (2012) noted that in a relaxed WCM policy, a firm would hold
relatively large amounts of each type of current asset while in a restricted WCM policy; a
firm would hold minimum amounts of each type of the current assets. Raheman and Nasr
(1998) noted that WCM is the management of current assets to meet short term obligations
of the company. Kallberg and Parkinson (1993) observed that in short-term financial
management, a great deal of emphasis is placed on the levels and changes in current assets
and liabilities.
Horngren, Datar and Foster (2013) described inventory managements as the planning,
coordinating and controlling activities related to the flow of inventory through and out of
an organization. Brigham and Houston (2002) observed that inventory may be classified
as Supplies, Raw materials, Work in progress and finished goods. Brigham and Ehrhardt
(2012) noted that the twin goals of inventory management are to ensure the inventories
needed to sustain the operations are available and that the firm should hold the cost of
ordering and carrying inventories are held at lowest level possible. Pandey (1995) observed
three motives why firms hold inventories: transactional, precautionary and speculative
motives. A firm cam measure inventory management by analysis of the Inventory
Conversion Period (ICP).
Firms generally sell on cash basis. However, competitive pressure forces firms to offer
credit. Brigham and Ehrhardt (2012) noted that carrying receivables has both direct and
indirect cost, but also has an important benefit of increased sales. Martin, Petty, Keownand
Scott (1991) observed that Accounts Receivables (AR) makes up a very large portion of
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the firm’s assets; they actually composed of 25.97 % of a typical firm asset. Brigham and
Ehrhardt (2012) added that receivables management begins with credit policy which
summarizes credit period, discounts, and credit standards and collections terms. Kallberg
and Parkinson (1993) noted that monitoring on both the aggregate and the individual level
is commonly based on ageing schedule and on measuring Accounts Collection Period
(ACP). Brigham and Ehrhardt (2012) observed that ACP is the average length of time
required by a firm to convert the firm’s receivables into cash.
Brigham and Ehrhardt (2012) explained that firms hold cash for transaction, precautionary
purposes and compensation to the banks for providing loan and services. Brigham and
Ehrhardt (2012) explained that Cash conversion cycles (CCC) focuses on the length of time
between when the company makes payments and when it receives cash inflows. Berk and
Demerzo (2011) observed that an important consideration for all firms is the ability to
finance the transition from cash to inventories to receivables and back to cash. Pandey
(1995) further noted that a firm should develop strategies regarding cash management
which involves cash planning, managing the cash flow, optimal cash levels and investing
surplus cash. Pandey (1995) noted that the cost of excess cash and the dangers of cash
deficiency should be matched to determine the optimal level of cash balances.
Firms generally make purchases from other firms on credit, recording debts as an account
payable. Brigham and Ehrhardt (2012) noted that trade credit (Accounts payables) is the
largest single category of operating current liability representing about 40% of the current
liabilities of the average nonfinancial firm. Brigham and Ehrhardt (2012) further noted that
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Accounts Payable Period (APP) is the average length of time between the purchase of
materials and labor and the payment of cash for them.
1.1.2 Firm’s Performance
Performance is the process of measuring the results of the firm’s policies and operations in
monetary form (Metcalf & Titard, 1976). Brigham and Ehrhardt (2012) defined
profitability as the income available to common shareholders which is revenues less
expenses, taxes and preferred dividends but before paying common dividend. Shareholder
value creation is the primary goal of all firms. In the absence of profitability the business
will not survive in the long run. Measuring current and past profitability and projecting
future profitability is very important.
Measuring firm’s profitability is a strategic part of any successful business entity because
the long term survival depends on its performance. Profitability is measured with a
statement of comprehensive income. Ochieng (2012) noted that there exist an array of
measures of firm performance, though there is yet to be a consensus on a universally
acceptable measure of performance. Some of the performance measures of profitability
include Net Income (NI), Return on Capital Employed (ROCE), Return on Investment
(ROI), Operating Margins (OM) and Net Profit (NP). Brigham and Ehrhardt (2012)
considers the measurement of Return on Capital Employed (ROCE) as a more
comprehensive profitability measures because it gauges management’s ability to generate
earnings from a capital investment.
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1.1.3 Relationship between WCM and Firm’s Performance
Peel and Wilson (1996) noted that there is a negative relationship between profitability and
the cash conversion cycle, inventory receivable days, accounts payable days and accounts
receivable days which were used as measures of working capital management efficacy.
Peel and Wilson (1996) further observed that efficient WCM and more recently good credit
management practice are pivotal to the health and performance of the small firm sector.
Therefore it seems that operational profitability will determine how managers or owners
will behave in terms of managing the working capital of the firm.
Gul, Khan, Rehman, Khan, Khan and Khan (2013) noted that APP, growth and size have
positive association with Profitability whereas ACP, ICP, CCC and DR have inverse
relation with profitability. Gakure, Cheluget, Onyango and Keraro (2012) found that there
is a negative coefficient relationship between ACP, APP, ICP and profitability but CCC
was found to be positively correlated with profitability. Sharma and Kumar (2011)
concluded that ICP and APP negatively correlated with a firm’s profitability, whereas
number of ACP and cash CCC exhibit a positive relationship with profitability of the
company.
Gill, Biger and Mathur (2010) established significant relationship between the CCC and
profitability, measured using GPM. The findings implied that firm’s management can
create profits for their companies by handling correctly the CCC and by keeping accounts
receivables at an optimal level. Overall from these studies done in the past, there is a
relationship between working capital management and firm profitability in various
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markets. There are various conclusions, but a majority of studies conclude a negative
relationship between working capital management and firm profitability.
1.1.4 Energy and Petroleum Sector in Kenya
The Kenyan Economy, population and industry are all expanding at such a rate that there
is currently a 13.5% annual increase in demand for electricity demand in the country with
demand expected to reach 15 GW in year 2030 (Ministry of Energy, 2013). Energy is
essential to economic and social development and to improve the quality of life of the
people and also an important development indicator (MOE, 2013). In Kenya, the main
sources of energy are wood fuel, petroleum and electricity accounting for 69%, 22%, and
9% of the total energy respectively (Ministry of Energy, 2013).
More precisely, 67.5% of electricity is generated using renewable energy sources which
are predominantly Hydro with 47.8% and Geothermal with 12.4% respectively, while
32.5% is from fossil fuels (Ministry of Energy, 2013). The total electricity which is
generated is shared by 20% of the population while the rest of the population remains
without electricity (Ministry of Energy, 2013). Ministry of Planning and National
Development (2007) reported that Kenya Vision 2030 has identified Energy sector as a key
foundation and one of the infrastructural enablers upon which the economic, social and
political pillars of its long-term development strategy will be built. The report further
explained that the growth agenda is pegged on the Kenya Vision 2030, which aims at
creating a globally competitive and prosperous country with a high quality of life by 2030.
The petroleum sector boasts of over 30 oil importing and marketing companies comprising
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of five major companies namely Shell, Total Kenya, KenolKobil, Oil Libya, Chevron and
other emerging oil companies which include Government of Kenya (GoK) owned National
Oil Corporation of Kenya (NOCK). At Nairobi Securities Exchange (NSE) only five
Energy and Petroleum companies have been listed and they include: Total Kenya,
KenolKobil, Kenya Electricity Generating Company Limited (KenGen), Umeme Ltd and
the Kenya Power &Lighting Company ltd (KPLC). In Kenya, the price of petroleum
products is regulated by the Energy regulatory Commission (ERC) that set the prices for
various petroleum products. Globally Oil and Gas sectors has experienced price fall
attributed to sharp growth in Non-Organization of the Petroleum Exporting Companies
(OPEC) oil suppliers, sluggish oil demand brought about by 2008-9 financial crisis and
subsequent global recession (OPEC, 2015).
1.2.The Research Problem
Peel and Wilson (1996) observed that efficient WCM and more recently good credit
management practice is pivotal to the health and performance of the small firms. WCM
requires businesses to be run both efficiently as well as profitable. WCM management is
therefore, a tradeoff between risk and profitability. Risk and profitability mismatch may
increase firm’s profitability in the short run but at a risk of insolvency (Pandachi, 2006).
Managers are therefore coming to realization that incorrect evaluation of the liquidity
implications of the firm’s working capital needs may, in turn subject creditors and investors
to an unanticipated risk of default (Richard &Laughlin, 1980). Contrary to the relative
importance of WCM, the practice of WCM has been neglected (Smith, 1980).
Energy is consumed in all sectors of economy including transport, manufacturing,
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commercial, and residential. Energy and petroleum sector remain an engine for
development globally as well as locally. Ministry of Planning and National Development
(2007) has noted that Kenya is expected to be a middle income economy by year 2003. The
ministry has further identified Energy sector as a key foundation and one of the
infrastructural enablers upon which the economic, social and political pillars of its longterm development strategy will be built. However, energy sector in Kenya faces challenges
such as low access to energy, high cost of energy, irregular supply and high cost of energy
investments (KIPRA, 2013).
It will therefore be in the interest of Kenya as a nation, to reduce the cost of production
which includes cost of energy. Globally, the petroleum energy supply has outpaced demand
resulting in oil prices tumbling by approximately 42% from the June 2014 peak (PIEA,
2015). The considerable low crude Oil prices are delivering a short term stimulus, not only
to the East Africa Community (EAC) countries but also for many other countries around
the work (PIEA, 2015). It is anticipated that the historic decline in crude Oil prices, will
reduce inflation, the oil import bill, fiscal deficit and interest rates (PIEA, 2015). Price is a
component of revenue and therefore international price changes will have an impact on the
working capital component particularly inventories, receivables as well as cash for Energy
and Petroleum companies.
Various studies have been done internationally as well as locally on the effect of WCM on
firm’s performance. Internationally, Deloof (2003), Falope and Ajilore (2009), Sharma and
Kumar (2011), Garcia and Martinez (2007), Gill, Biger and Mathur (2010), Gul, Khan,
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Rehman, Khan, Khan and Khan (2013),Shin and Soenen (1998) have all researched on
WCM and its impact on performance of companies. Similarly, local studies by Gakure,
Cheluget, Onyango and Keraro (2012), Mathura (2009), Akoto, Awunyo and Angmor
(2013), Nyambwaga et al. (2012) have been conducted on Kenya firms. All these studies
have established that WCM impacts on the profitability of a firm. However, these studies
do not establish the impact of WCM on profitability of Energy and Petroleum companies.
This is because minimal studies that have been conducted in the Kenyan economy
particularly the Energy and Petroleum sector on the impact of working capital management
and company’s performance. This research therefore aimed at addressing this gap.
Secondly, previous studies have given conflicting findings. Nyambwaga et al. (2012)
established a positive relationship between WCM and firm’s performance. Deloof (2003)
on the other hand, noted a negative relationship between WCM and firms performance.
This study therefore, sought to iron out these differences. The research therefore sought
answers to the following research question: What are the effect of WCM practices on the
performance of Energy and petroleum Companies listed at NSE?
1.3. Research Objective
Objective of the study was to establish the effect of WCM on the financial performance of
Energy and Petroleum firms listed on the Nairobi Securities Exchange.
1.4. Value of the Study
There are various stakeholders and other parties such as regulators, scholars, practitioners
of Finance and providers of capital who are more informed by this study as follows:
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Regulators: Institutions such as Energy Regulatory Commission (ERC) are more informed
on the impact of price controls on working capital components such as fuel inventories and
reserves particularly its implication of the performance of Energy and Petroleum
companies. Regulator can now make informed and prudent price control decisions.
Ministry of Planning and National Development (2007) in its Vision 2030, has identified
Energy sector as a key foundation and one of the infrastructural enablers upon which the
economic, social and political pillars of its long-term development strategy will be built.
Corporate WCM policy changes are more informed by the outcome of this research which
will lead to a vibrant and well managed Energy and Petroleum sector, which will be a key
milestone in the achievement of Kenya’s Vision 2030.
Scholars and Researchers: There are still minimal studies that have been conducted in
the Kenyan economy particularly the Energy and Petroleum sector on the impact of
working capital management and company’s performance. This study was a contribution
to fill this gap by estimating the relationship between working capital management
variables and firm profitability of Energy and Petroleum companies in Kenya.
Practice of Corporate Finance: This study enlightened the management of Energy and
Petroleum companies firms to come up with working capital management policies such as
credit policies, Treasury Policies, Inventory Management policies, Accounts Payable
policies which will ensure improved company profitability through prudent WCM
practices.
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Providers of Capital: The findings of this study informed the providers of funds to better
understand the implication of WCM practices on the performance of a company. The study
provided more information for making informed decision to extend credit facilities to
companies in the Energy and Petroleum sector.
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CHAPTER TWO- LITERATURE REVIEW
2.1. Introduction
Chapter two examined the literature relating to the objectives of the study. This chapter
covered the key theories that guided the study as well as WCM practices and measurement.
The chapter further reviewed the various determinants and measurements of the company
performance. Lastly, this chapter concluded on the relevant empirical studies
internationally and locally on WCM in order to outline the research gap.
2.2. Theoretical Review
This study was guided by various theories of Working Capital Management (WCM). They
include Keynesian Theory of Money, Operating Cycle Theory and Cash Conversion Cycle
Theory.
2.2.1. Keynesian Theory of Money
Keynes (1935) developed the General Theory of Employment, Interest and Money in
which he argues three motives for holding cash namely transactional, precautionary and
speculative motives. Transactional motive is the need for cash for the current transactions
of personal or business exchanges (Keynes, 1935). Precautionary motive is the desire for
security as to the future equivalent of a certain proportion of total resources (Keynes, 1935).
The firm can also hold money for speculative motive which is the object of securing profits
from knowing better than the market what the future will bring forth.
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Brigham and Ehrhardt (2012) held similar views and explained that firms hold cash for two
reasons namely for transactional reasons and for compensations to banks for providing
loans and services. Brigham and Ehrhardt (2012) identified techniques such as
synchronizing cash flows, speeding up cheque-clearing process, use of float and speeding
up receipts as Cash as some of the WCM techniques. On synchronizing cash flows, firms
can improve their forecast and by timing cash receipts to coincide with cash requirements,
such firms can hold transactional balance to minimum (Brigham & Ehrhardt, 2012).
Lockbox plans and payment by wire (automatic debts) are two major techniques largely
used both to speed up collection and get funds where they are needed. A lockbox system
is where the cheques are sent to post office boxes for direct banking, rather than to
corporate headquarters (Brigham & Ehrhardt, 2012). An automatic debit is where payments
are deducted from one account (customer) and added to other (recipient) account thereby
speeding up the collection process.
The Keynesian Theory of Money was key to this research because as noted by Baumal
(1952) and Tobin (1956), the optimal cash balances, just like inventory models have cost
associated with sourcing, maintenance, beside the benefits that firms derive from optimal
cash levels. Therefore the levels of cash as WCM component will have an impact of the
performance of the firm.
2.2.2. Operating Cycle Theory
This theory was developed by Richard and Laughlin (1980). Incorporating accounts
receivable and inventory turnover measures into the operating cycle concept provides a
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more appropriate view of liquidity measurement than does reliance on current and acidtest ratios indicators of solvency (Richard & Laughlin, 1980). This additional liquidity
measures explicitly recognizes that the life expectancies of some working capital
component depends upon the extent to which three basis activities namely production,
distribution (sales) and collection are non-instantaneous and unsynchronized (Richard &
Laughlin, 1980). The theory explains that the cumulative per turnover for accounts
receivable and inventory investment approximates the length of the firm’s operating cycle.
Richard and Laughlin (1980) noted that operating cycle concept is deficient as a cash flow
measures in that it fails to consider the liquidity requirements imposed on a firm by the
time dimension of the current liability commitments. Liquidity is the ability of the firm to
meet its obligation as and when they fall due and therefore cannot be ignored in studying
the performance of a firm. In-depth understanding of Operating Cycle Theory is therefore
key to this research. By analyzing the various levels of WCM component, the researcher
was able to establish how liquidity contributes to the performance of Energy and Petroleum
Companies.
2.2.3. Cash Conversion Cycle Theory
Richard and Laughlin (1980) noted that by reflecting the net interval between actual cash
expenditure of a firm’s purchases of productive resources and the ultimate recovery of cash
receipts from product sales establishes the period of time to convert a dollar of cash
disbursement back into a dollar of cash inflow from a firm’s regular course of operations.
Richard and Laughlin (1980) explained that a movement towards a longer cash conversion
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cycle will produce a larger required commitment to cash, as well as non-cash, currents
assets, investment in less extensive relative ability to finance these investments with current
liabilities.
Understanding the cash conversion cycle of the firm is an effective way if evaluating the
efficiency of the firm operations. Berk and Demerzo (2011) observed that an important
consideration for all firms is the ability to finance the transition from cash to inventories,
to receivable and back to cash. Pandey (1995) added that firms should develop strategies
regarding cash management which involves cash planning, optimal cash levels and
investing surplus cash. It is through the understanding of this Cash Conversion Cycle
Theory that the researcher was able to evaluate the efficiency in the management of WCM
components and how it impact on the performance of the Energy and petroleum companies
In summary, the WCM theories emphasized on the need to ensure optimal levels of
working capital for a smooth operation of the firm as well minimize the cost to the firm.
Incorrect evaluation of the liquidity implications of the firm’s working capital needs may,
in turn subject creditors and investors to an unanticipated risk of default (Richard &
Laughlin, 1980). It is therefore incumbent upon the Finance Management practitioners to
understand the business operations, and put in place robust WCM policy framework
because of its implications on the performance of the business.
2.3. Determinants of firm’s Performance
There are several factors internally and externally that can affect the firm’s performance.
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The success of the firm performance is how well these factors are incorporated in strategic
decision of the firm. Johnson, Scholes and Whittington (2008) observed that a business
strategy is the direction and scope of an organization over the long term, which achieves
advantage in a changing environment through its configuration of resources and
competences with the aim of fulfilling stakeholder expectations. Some of these factors are
discussed below.
2.3.1. Corporate Governance
Chugh, Meador and Kumar (2009) noted that good corporate governance practices enhance
the performance of a firm. Corporate governance practices here include structure and
behaviors adopted by a firm in pursuit of its objectives. Shareholder model and stakeholder
model are the two models of corporate governance stricture (Chugh et al, 2009).
Shareholder model focuses on shareholder wealth creation whereas the stakeholder model
focuses on welfare of the numerous stakeholders in the firm.
2.3.2. Economic Conditions
Economic conditions particularly the cost of borrowing can negatively influence
profitability (Ntim, 2009). Very high inflation, cost of imports, exorbitant utility costs, low
incomes level for consumers can reduce the demand for goods and services thereby
adversely affecting firm’s performance (Forbes, 2002). According to the agency there of
the firm, if managers also have ownership stake, they are likely to maximize shareholders’
wealth (Dutta, 1999). Based on this theory, it can therefore be concluded that ownership
structure has an impact on the forms performance.
17
2.3.3. Capital Structure
Capital structure is another factor that has a bearing on the performance of a firm. To
finance operations, a firm can generate funds internally or source the required capital
externally through loans. Lewellen and Lewellen (2004) observed that capital generated
internally has highest opportunity cost because retained profits can affect shareholder trust
since it could have been paid out as dividend. Pandey (1995) on the other had observed
that externally generated capital such as loans are cheaper because of the tax shield.
Therefore capital structure will have an impact on the profitability of a firm.
2.3.4. Risk Appetite
Mirza and Javed (2013) noted that risk management of a firm may also impact on its
performance. The relationship of risk and return has to be managed so that the investors do
get the return associated with the risk they are bearing (Mirza & Javed, 2013). Other
determinants of firm’s performance include size (Love & Rachinsky, 2007) and sales
(Forbes, 2002). In addition, other factors include liquidity, dividend, and growth rate
(Gurbuz et al., 2010).
2.4. Empirical Review
Various researches have been conducted by different scholars locally and internationally
to test the relationship between WCM and company profitability. Akoto, Awunyo and
Angmor (2013) analyzed the relationship between WCM practices and profitability of
listed manufacturing firms in Ghana. The study used data collected from annual reports of
18
all the 13 listed manufacturing firms in Ghana covering the period from 2005-2009. Using
panel data methodology and regression analysis, the study found a significant negative
relationship between Profitability and ACP. However, the firms’ CCC, Current Asset
Ratio, Size, and Current Asset Turnover significantly positively influence profitability. The
study suggests that managers can create value for their shareholders by creating incentives
to reduce their ACP to 30 days. The research further recommended that, enactments of
local laws that protect indigenous firms and restrict the activities of importers are eminent
to promote increase demand for locally manufactured goods both in the short and long runs
in Ghana.
Gul, Khan, Rehman, Khan, Khan and Khan (2013) investigated the influence of WCM on
performance of small medium enterprises (SMEs) in Pakistan. The duration of the study
was seven years from 2006 to 2012. The data used in this study was taken from Karachi
Stock Exchange, tax offices and individual companies. The dependent variable of the study
was Return on Assets (ROA) which was used as a proxy for profitability. Independent
variables were ACP, ICP, CCC and APP. In addition to these variables some other
variables were used which included Firm Size (SIZE), Debit Ratio (DR) and Growth
(GROWTH). Regression analysis was used to determine the relationship between WCM
and performance of SMEs in Pakistan. Results suggested that APP, GROWTH and SIZE
have positive association with Profitability whereas ACP, ICP, CCC and DR have inverse
relation with profitability.
Gakure, Cheluget, Onyango and Keraro (2012) analyzed the relationship between WCM
and performance of 15 manufacturing firms listed at the NSE from 2006 to 2010. They
19
used secondary data from a sample of 18 companies at the NSE. A regression model was
used to establish the relationship between the dependent variable and the independent
variables. Pearson’s correlation and regression analysis were used for the analysis. The
results indicated that there is a strong negative relationship between firm’s performance
and liquidity of the firm. The study found that there is a negative coefficient relationship
between ACP, APP, ICP and profitability. CCC was found to be positively correlated with
profitability.
Nyambwaga et al. (2012) researched on the effects of WCM on SME’s in Kisii District,
Kenya. They focused on 159 SME’s including 101 trading and 58 manufacturing. Crosssectional design was used, based on primary quantitative data obtained through structured
questionnaire. A sample of 113 SMEs was used through stratified random sampling.
Descriptive and inferential statistics was used to analyze data. The research revealed that
performance was positively related CCC, ACP, ICP at 0.01 significance level.
Sharma and Kumar (2011) examined the effect of WCM on profitability of Indian firms.
They collected data about a sample of 263 non-financial firms out of 500 firms listed at the
Bombay Stock Exchange (BSE) from years 2000 to 2008 and evaluated the data using OLS
multiple regression. The results revealed that WCM and profitability are positively
correlated. ICP and APP were negatively correlated with a firm’s profitability, whereas
ACP and CCC exhibit a positive relationship with profitability of the company.
Gill, Biger and Mathur (2010) studied the relationship between WCM and profitability. A
total of 88 American firms listed on New York Stock Exchange (NYSE) were selected for
20
a period of 3 years from 2005 to 2007. The data was analyzed using Pearson Bivariate
Correlation Analysis and Weighted Least Squares (WLS) Regression techniques. The
research established significant relationship between the CCC and profitability, measured
using GPM. The findings implied that firm’s management can create profits for their
companies by handling correctly the CCC and by keeping accounts receivables at an
optimal level.
Falope and Ajilore (2009) researched on relationship WCM and corporate profitability.
They used a sample of 50 Nigerian quoted non-financial firms for the period 1996 -2005.
Their study utilized panel data econometrics in a pooled regression, where time-series and
cross-sectional observations were combined and estimated. The research established a
significant negative relationship between net operating profitability and the ACP, ICP, APP
and CCC. Furthermore, the research noted no significant variations in the effects of WCM
between large and small firms.
Mathura (2009) examined the influence of WCM on corporate profitability by using a
sample of 30 firms listed on the NSE for the periods 1993 to 2008. The researchers used
Pearson and Spearman’s correlations, the pooled ordinary least square (OLS), and the fixed
effects regression models to conduct data analysis. The research found out that there exist
a significant negative relationship between ACP and profitability. The research also
established a highly significant positive relationship between ICP and profitability. Further,
the research revealed a highly significant positive relationship between APP and
profitability.
21
Garciaand Martinez (2007) collected a panel of 8,872 small to medium-sized enterprises
(SMEs) from Spain covering the period 1996 - 2002. They tested the effects of WCM on
SME profitability using the panel data methodology. The results demonstrated that
managers could create value by reducing their inventories and ACP. Moreover, shortening
the cash conversion cycle also improves the firm's profitability.
Deloof (2003) tested the relationship between WCM and corporate profitability. A sample
of 1,009 large Belgian non-financial firms for a period of 1992-1996 was used. By using
correlation and regression tests, he found significant negative relationship between gross
operating income and the ACP, ICP, and APP of Belgian firms. Based on the study results,
the researcher suggested that managers can increase corporate profitability by reducing the
ACP and inventories.
Shin and Soenen (1998) researched the relationship between WCM and value creation for
shareholders. The standard measure for WCM was CCC. The CCC period reflects the time
span between disbursement and collection of cash. It is measured by estimating the ICP
and ACP less the APP. The study used Net-Trade Cycle (NTC) as a measure of WCM.
NTC is basically equal to the CCC where all three components are expressed as a
percentage of sales. NTC may be a proxy for additional working capital needs as a function
of the projected sales growth. They examined this relationship by using correlation and
regression analysis, by industry, and working capital intensity. Using a COMPUSTAT
sample of 58,985 firm years covering the period 1975-1994, the research noted a strong
22
negative relationship between the length of the firm's net-trade cycle and its profitability.
Based on the findings, they suggest that one possible way to create shareholder value is to
reduce firm’s NTC.
2.5. Literature Review Summary
International studies by Deloof (2003), Falope and Ajilore (2009), Sharma and Kumar
(2011), Garcia and Martinez (2007),Gill, Biger and Mathur (2010), Gul, Khan, Rehman,
Khan, Khan and Khan (2013),Shin and Soenen (1998) have extensively focused WCM and
its impact on performance of companies. Locally, studies by Gakure, Cheluget, Onyango
and Keraro (2012), Mathura (2009), Akoto, Awunyo and Angmor (2013) have also
researched on WCM impact on the profitability of a firm
From these empirical reviews, it was established that WCM impacts on the performance of
a firm. Some findings have been contradictory. Nyambwaga et al. (2012) established a
positive relationship between WCM and firm’s performance. Deloof (2003) on the other
hand, noted a negative relationship between WCM and firms performance. This study
therefore, seeks to iron out these differences. Beside differences in the research outcomes,
previous researches concentrated either on Manufacturing Sector, SME’s sectors or all
firms listed on NSE. However, there were minimal studies that have been done focusing
on Energy and Petroleum companies listed at NSE. This study therefore aimed at
addressing this gap as well.
23
CHAPTER THREE – RESEARCH METHODOLOGY
3.1 Introduction
This chapter presents the research methodology. Section 3.2 discusses the research design.
Section 3.3 presents the population and sample. Section 3.4 presents the data and data
collection instruments. Section 3.5 presents the data analysis model. Section 3.6 presents
the variables in the analysis model.
3.2 Research Design
The study adopted a quantitative approach which involved collection and analysis of
numerical data. Using a correlation design, the researcher investigated the effect of WCM
on the performance of the Energy and Petroleum companies listed on NSE. Correlation
design investigates a range of factors including the nature of relationships between two or
more variables and the theoretical model that might be developed and tested to explain
these resultant correlations (Grimm and Yarnold, 2000). The purpose of correlation design
was therefore to determine the relations between two variables two or more variables.
Correlation design was found to be suitable for this study since it will be used to test impact
and relationship direction between WCM components and financial performance. There
researcher will therefore be able to establish changes in one variable as a result of changes
in another variable. Gakure, Cheluget, Onyango and Keraro (2012), Gill, Biger and Mathur
(2010) used correlative analysis in their studies on the effect of WCM on performance.
Therefore, this design helped the researcher compare the research findings with those from
previous studies.
24
3.3. Population of the study
This was a census study. The study population therefore included all the five Energy and
Petroleum companies listed on Nairobi Securities Exchange. These companies include
KenolKobil ltd, Total Kenya Ltd, KenGen Ltd, Umeme Ltd and the KPLC. Companies
listed on the NSE were preferred for this study because of data availability as well as
reliability of the data since they are subject to statutory audits.
3.4. Data Collection Method
To investigate the effects of WCM on performance of Energy and Petroleum companies
listed on NSE, the researcher used secondary data. Secondary sources of data specifically
published Financial Statements for eight years between 2007 and 2014 were used. It is
from these financial statements that variables such as ROCE, ACP, APP, ICP and CCC
were generated. The published financial statements were obtained from individual
company websites, Ministry of Energy (MOE) and the Petroleum Institute of East Africa
(PIEA).
3.4.1. Data Collection Tool
Table (Appendix I) was used to collect data from the published financial statements
specifically of WCM component and performance. The data in the tables was then fed into
the Statistical Package for Social Science (SPSS) for analysis.
25
3.5. Data Analysis
The researcher sought to establish the impact of WCM on the performance of the five
Energy and Petroleum companies listed at NSE for the period between 2007 and 2014. To
be consistent with previous researches, the firm’s performance was modeled as a function
of the four core WCM measures.
3.5.1. Analytical Model
The effects of WCM on the firm’s performance were modeled using the following
equations to obtain the estimates:
ROAt = β1 (ACPt) + β2 (ICPt) +β3 (APPt) +β4 (CCCt) +ε
Where:

ROAt= Return on Assets of the firm o at time t: =1, 2, 3

β1... β4 =coefficient of the variables

t = time years 1, 2, 3

ACP = Accounts Collection Period

ICP= Inventory Conversion Period

APP= Average Payable Period

CCC = Cash Conversion Cycle

ε = Error Term
26
3.5.2. Operationalization of Study Variables
For comparison with other researches on the effects of WCM on the firm’s performance,
the researcher used the Return on Assets (ROA) as the dependent variable to measure the
firm’s performance. ROA will be measured as follows:
Return on Assets (ROA) = Earnings before Interest and Tax (EBIT)
Capital Employed
x 100
On independent variables, the researcher used Inventory Conversion Period (ICP), Account
Payable Period (APP), Accounts Collection Period (ACP) and Cash Conversion Cycle
(CCC) as a comprehensive measure of WCM. These measures were determined as follows:
Inventory Conversion period (ICP) = Inventory
x 365
Cost of Goods Sold
Account Payable Period (APP)
= Payables x 365
Purchases Per Day
Accounts Collection Period (ACP) =
Receivables x
Sales Per Day
365
Cash Conversion Cycle (CCC) = ICP + ACP - APP
To determine direction and relationship between WCM and Performance, the researcher
used both descriptive and quantitative analysis. Mean, Median and Standard Deviation are
examples of descriptive statistic that were used to explain various variables.
The descriptive and quantitative analyses were done using Statistical Package for Social
Science (SPSS).
27
3.5.3. Test of Significance
The significance was tested at 95% confidence level. This was established by standard error
test, t-statistics, F-statistics, Durbin-Watson statistics and Prob (F-statistic).
28
CHAPTER FOUR - DATA ANALYSIS, RESULTS AND
DISCUSSION
4.1. Introduction
Chapter four presents the analysis of the study findings on the impact of WCM on the
financial performance of the Energy and Petroleum companies listed at NSE. It analyzed
the variables involved as well as the estimate of the model. The data used for the study was
obtained from the financial statements of five companies listed under the category Energy
and Petroleum sector at the NSE. The data covered eight year period between 2007 and
2014. The data collected was analyzed using different statistical tools including financial
ratios, Pearson correlation analysis and regression analysis.
4.2. Descriptive Statistics
Table 4.1 below shows the descriptive statistics for all the variables. It shows the numbers
of observation for all variables, their average values and their standard deviation. It shows
the minimum and maximum values.
Table 4.1: Descriptive Statistics
N
Minimum Maximum
Statistic
Statistic
Statistic
Mean
Statistic
Std. Deviation Skewness
Statistic
Statistic
Lnroa
38
.00
3.02
1.8285
.65006
-.574
Lnicp
38
1.70
5.07
3.5696
.74061
-.523
Lnacp
38
1.49
5.29
3.9641
.86595
-.773
Lnapp
38
.00
6.04
4.0051
1.43874
-1.123
Lnccc
38
-.51
4.94
2.3502
1.91357
-.376
Valid N (listwise)
38
Source: Researcher (2015)
29
The descriptive statistics shows that there were 38 observations for all variables. The
skewness measured the degree and direction of a symmetry. A distribution that is skewed
to the left, for example when the mean is less than the median, has a negative skewness.
The dependent variable (ROA) had an average value of 1.8285 with a minimum value of
0.00 and a maximum value of 3.02. The standard deviation of ROA was 0.65006. The
independent variable, Inventory Conversion Period (ICP) had an average value of 3.5696,
a minimum value of 1.7 and a maximum value of 5.07 with a standard deviation of 0.74061.
Accounts Collection Period (ACP) had an average value of 3.9641, minimum value of
1.49, and maximum value of 5.29 with a standard deviation of 0.86595. The Accounts
Payable Period (APP) had a mean value of 4.0051, a minimum value of 0.00, and maximum
value of 6.04 with a standard deviation of 1.43874. The Cash Conversion Cycle (CCC) had
a mean value of 2.3502, a minimum value of -0.51, and a maximum value of 4.94 with a
standard deviation of 1.91357.
4.3. Diagnostic Tests
The statistical methods applied assumed that variables were normally distributed.
Multivariate statistics were adopted with the assumption that the combination of variables
follows a multivariate normal distribution. Since there was direct test for multivariate
normality, the study tested each variable individually and assumed that they are
multivariate normal if they are individually normal. Normality test were undertaken and
the results were as shown in the table 4.2a below.
30
Table 4.2a: Tests of Normality
Inroad
Lnicp
Lnacp
Lnapp
Lnccc
Kolmogorov-Smirnova
Shapiro-Wilk
Statist
Df
Sig.
Statistic
df
Sig.
ic
.094
38
.200*
.973
38
.479
*
.089
38
.200
.963
38
.241
*
.102
38
.200
.949
38
.082
.235
38
.000
.874
38
.001
.259
38
.000
.794
38
.000
This is a lower bound of the true significance. *
Lilliefors Significance Correctiona
Source: Researcher (2015)
From the table 4.2a above, ROA was normally distributed. The sig value of the ShapiroWilk Test was 0.479 which is greater than 0.05. From the normal Q-Q Plot of ROA
(appendix iii) it was noted that the observed variable ROA was normally distributed and
close to the expected values. The study noted that ICP on other had was also normally
distributed. The sig value of the Shapiro-Wilk Test was 0.241 which is greater than 0.05.
From the normal Q-Q Plot of ICP (appendix IV) it was noted that the observed variable
ICP was normally distributed and close to the expected values. ACP was normally
distributed. The sig value of the Shapiro-Wilk Test was .082which is greater than 0.05.
From the normal Q-Q Plot of ACP (appendix V) it was noted that the observed variable
ACP was normally distributed and close to the expected values.
The APP was not normally distributed. The sig value of the Shapiro-Wilk test was
.001which is less than 0.05. From the normal Q-Q Plot of APP (appendix VI) it was noted
that the observed variable APP were not normally distributed and were not close to the
31
expected values. The CCC was equally not normally distributed. The sig value of the
Shapiro-Wilk test was .000 which is less than 0.05. From the normal Q-Q Plot of CCC
(appendix VII) it was noted that the observed variable CCC were not normally distributed
and not close to the expected values. In general from the normality test, it was concluded
that the variables are normally distributed that is, the ROA, ICP, ACP, APP and CCC of
the Energy and Petroleum companies listed at NSE are even distributed. Statistically, the
researcher was therefore able to draw accurate and reliable conclusions about reality of the
impact of WCM on the profitability of Energy and Petroleum companies listed at NSE.
The assumption of the regression model adopted was that the error term was independent
and normally distributed, with a mean zero and a constant variance. To test for the
independence of the variables, Durbin-Watson statistical analysis was undertaken. This
analysis was used to test for the presence of auto correlation among the residuals. Residual
was the difference between the observed value and the predicted value of the variables.
Table 4.3b below shows the results of Durbin-Watson analysis.
Table: 4.2b Durbin-Watson test
Mode
l
R
R Square
Adjusted R
Square
Std. Error of
the Estimate
1
.517a
.267
.178
.58920
a. Predictors: (Constant), lnccc, lnacp, lnicp, lnapp
b. Dependent Variable: inroad
Source: Researcher (2015)
DurbinWatson
1.528
From the table 4.2b above, the Durbin-Watson value was 1.528 meaning the residuals
32
values were uncorrelated since it falls within the acceptable range of 1.50 and 2.50. This
means the size of the residual for one variable has no impact on the size of the residual for
the next variable.
4.4. Correlation Analysis
The Correlation Analysis indicated the relationship between the variables in the model.
The correlation showed the direction of the relationship between the WCM as the
independent variables and the ROA as the dependent variable. The correlation further
indicated the strength of the linear relationship between the variables as shown in the table
4.3 below.
Table 4.3: Correlations
Lnroa
lnicp
Lnacp
Lnapp
Lnccc
Lnroa
1
Lnicp
-.269
1
Lnacp
-.350*
.539**
1
Lnapp
-.301
.470**
.617**
1
Lnccc
-.227
.212
.052
-.455**
1
*Correlation is significant at the 0.05 level (2-tailed),
**. Correlation is significant at the 0.01 level (2-tailed).
Source: Researcher (2015)
From the correlation in Table 4.3 above, the Pearson correlation between ROA and ICP
was analyzed and the results indicated a negative correlation of -.269. However, there was
a weak liner relationship between these two variables. This means that if there is an increase
in ICP then it will be associated with a reduction in the ROA and vice versa. Gul, Khan,
33
Rehman, Khan, Khan and Khan (2013), established similar findings with negative
correlation between ROA and ICP. This negative relationship was also established by
Sharma and Kumar (2011) who also noted that ICP negatively correlated with a firm’s
profitability. The inventory conversion period (ICP) is the number of days it takes to
convert the inventories into sales. This means that in the Energy and Petroleum sector, the
longer it takes for a company to convert the inventories into sales, the less is the firm’s
ROA. On the other hand, lesser ICP was associated with higher ROA. Since the aim of the
company is to increase the ROA, then it means such a company will target a reduction in
the ICP.
To reduce the ICP, a firm has to increase its sales. This is because the cost of sales is a
factor in the calculation of ICP. A firm can use different strategies to increase sales. One
of the strategies is market development. This is a strategy where the company expands its
operations into new markets which could be new geographical areas. Umeme ltd for
instance, is an Energy and Petroleum Company incorporated in the republic of Uganda but
registered on the NSE. Such company can increase its sales by extending its Energy and
Petroleum operations beyond the borders of Uganda to cover Kenya, Tanzania and other
countries as well. Market development will require changes to marketing strategy that is
the new distribution channels, different pricing policy, and a new promotion strategy, to
attract different types of customers in the new market. By developing and implementing a
robust marketing plan such companies can increase sales thereby reducing on the ICP. The
Kenyan market is semi-regulated with the pump or retail prices set by the Energy
Regulatory Commission (ERC) whereas other lines of business such as Liquefied
34
Petroleum Gas (LPG) and wholesale business is not regulated. To avoid such price controls
that can limit growth in sales, such companies can diversify its operation to other
deregulated markets where they can make enormous growth. In conclusion since the firm’s
target is to increase the ROA, it means the business strategies should aim at reducing the
ICP through increasing sales.
The ROA and ACP had a negative correlation value of -.350 which is a weak correlation.
This means that if there is an increase in ACP, then such a scenario will be associated with
a reduction in the ROA and vice versa. Gakure, Cheluget, Onyango and Keraro (2012)
established similar findings where they found that there is a negative coefficient
relationship between ACP, APP, ICP and profitability for 15 manufacturing companies
listed at NSE for the period between 2006 and 2010. Nyambwaga et al. (2012) researching
on the effects of WCM on SME’s in Kisii District came to a different conclusion that
performance of the SME’S was positively related CCC, ACP, ICP at 0.01 significance
level. The different conclusions could be attributed to the nature of the industry. The ACP
is the average length of time required by the firm to convert its receivables into cash. This
means the longer it takes to convert receivables into cash, then the lesser such a company
will achieve ROA. Since the bottom line is to increase the ROA, it means that the firm
will target a reduction in the ACP.
In a strictly controlled organization, the accounts receivable should be managed through a
Credit Policy framework. Such a framework will stipulate the conditions under which a
firm will grant a credit sale, the discounts as well as the credit period. Since the target is to
35
reduce the amount of credit sales, then the credit policy should be strict on the qualification
for credit sales. The longer the credit period, the higher is the amount of receivables that
will be held at the end of the period. The credit policy should have a shorter credit period,
meaning the customers will be required to settle their accounts in a shorter period. Debt
recovery strategies can enable a firm to minimize the amount of accounts receivables held
at the end of the period. Energy and Petroleum Companies will then be required to
established credit control departments tasked with debt recovery responsibilities. Such
departments should adequately manned and resourced to ensure debts are collected on time.
Putting in place a guarantee or a security to mitigate the credit default risk as another
strategy to minimize the amount receivables held at the end of the period. This is because
in the event of customer’s default to settle the debt, the firm will have a fallback position
to recover the debt by liquidating the security. At the end, accounts receivables will be
minimized, ACP reduced and the ROA increased.
The ROA and APP had a negative correlation of value of -.301, meaning that if there is an
increase in APP, then there will be reduction in the ROA and vice versa. However, this
was a weak correlation. Falope and Ajilore (2009) established similar findings where they
established a negative relationship between net operating profitability and the ACP, ICP,
APP and CCC for a sample of 50 Nigerian quoted non-financial firms for the period 1996
-2005. Deloof (2003) also came to the same conclusion where he found a negative
relationship between gross operating income and the ACP, ICP, and APP of Belgian firms.
The APP is the average length of time it takes a firm to pay its suppliers. The negative
coefficient means the longer it takes to pay the supplier the lesser is the ROA.
36
On the other hand a shorter APP is associated with higher ROA. Since a firm in the Energy
and Petroleum sector would target to increase the ROA, it then implies that measures have
to be put in place to pay the suppliers in the shortest time possible. There are several
measures that a firm can put in place to reduce the APP. A cash flow planning is one such
a tool that a firm can use to project its cash inflows and cash outflows to ensure cash is
available as and when suppliers need to be paid. This minimizes delay in supplier payments
occasioned by unavailability of funds. Liquidity is therefore critical to ensure faster
settlement of suppliers. Automation of the Procure-To-Pay process is another strategy a
firm can use to shorten the supplier payment period. Automation minimizes payment
errors, ensure accurate and complete recording of supplier invoices in the payment process.
Electronic payment systems are faster and less error prone than manual payments systems.
To achieve a shorter APP and a higher ROA will therefore mean that the Energy and
Petroleum companies will have to invest in automation of the payment process.
ROA and CCC had a negative correlation value of -.227. This is a weak correlation. Shin
and Soenen (1998) reached at similar conclusion where they noted a negative relationship
between the length of the firm's net-trade cycle and its profitability for a sample of 58,985
firm years covering the period 1975-1994. Gul, Khan, Rehman, Khan, Khan and Khan
(2013), also established similar findings of negative correlation between ROA and CCC.
Negative correlation means that if there is an increase in CCC, then there will be reduction
in the ROA and vice versa. Garciaand Martinez (2007) also reached at the same conclusion
that shortening the cash conversion cycle, it improved the firm's profitability for a panel of
37
8,872 small to medium-sized enterprises (SMEs) from Spain covering the period 1996 2002. The CCC is the length of time between when the company makes payments of stock
purchases to the period cash is received from the sales made. It is therefore a summation
of ICP plus ACP less APP. The firms target is to reduce the CCC in order to increase the
ROA. This means the components of CCC which are mainly the ICP, ACP and APP should
be dealt with as discussed above, with an objective of shortening CCC in order to increase
ROA.
4.5. Effects of WCM on Financial Performance
From the Table 4.4a below, using the Adjusted R Square, it can be noted that 17.8% of the
variability in the Return on Assets (ROA) can be explained by variability in the Inventory
Conversion Period (ICP), Cash Conversion Cycle (CCC), Accounts Collection Period
(ACP) and Accounts Payable period (APP). It also follows that 82.2% of the changes in
ROA cannot be explained by the changes in the model variables hence the error term.
Table 4.4a: Combined Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
1
.517a
.267
.178
.58920
Source: Researcher (2015)
ANOVA was used to test the weight of the linear model. Table 4.4b below, indicates a
good result for a multiple linear relationship between the WCM variables and the ROA. It
was noted that WCM influence the ROI significantly. The analysis indicated an F statistics
of 3.010 with a P value is 0.032 which is less than 0.05. This confirmed that the model’s
goodness is fit to explain the variations as well as validate the independent variables effect
on the dependent variable. It can be concluded that all variables ICP, ACP, APP and CCC
38
have a significant combined effect on ROA and therefore these independent variables can
be used to predict ROA.
Table 4.4b: Combined ANOVAa
Model
Sum of
df
Mean
Squares
Regression
1
F
Sig.
Square
4.179
4
1.045
Residual
11.456
33
.347
Total
15.635
37
3.010
.032b
Source: Researcher (2015)
The coefficients explained the relationships between variables as shown in the Table 4.3c
below. The coefficients looked at the change in the dependent (ROA) variable, when
independent variables (WCM) increases by one.
Table 4.4c: Coefficientsa
Model
Unstandardized
Standardized
Coefficients
Coefficients
B
(Constant)
1
Std. Error
2.985
.532
Lnicp
.114
.178
Lnacp
-.031
Lnapp
Lnccc
t
Sig.
Beta
5.614
.000
.130
.639
.527
.163
-.042
-.192
.849
-.257
.122
-.569
-2.109
.043
-.174
.072
-.512
-2.399
.022
Dependent Variable: lnroa
Source: Researcher (2015)
The relationship between the dependent and independent variables can therefore be
summarized with a linear equation as follows:
39
ROAt = 2.985 + 0.114 (ICPt) – 0.031 (ACPt) – 0.257 (APPt) – 0.174 (CCCt) +ε
The constant in the model was 2.985. This means that if there was no changes in the
independent variables (WCM), then the dependent variable (ROA) will have a value of
2.985. The Inventory Conversion Period (ICP) had a positive coefficient of 0.114. This
means that for every one unit change in ICP, then ROA will increase by 0.114. This also
means that that the more optimal level of ICP, the higher is the level of ROA although the
relationship is not statistically significant since p=0.527. Mathura (2009) held similar
findings of a positive relationship between ICP and profitability. The focus of the Energy
and Petroleum companies will therefore be to increase ICP.
The regression coefficient on Accounts Collection period (ACP) was negative (-0.031).
This means that one unit change in ACP will lead to a 0.031 reduction in the ROA. Peel
and Wilson (1996) observed that efficient WCM and more recently good credit
management practice are pivotal to the health and performance of the small firm sector.
Angmor (2013) suggested that managers can create value for their shareholders by creating
incentives to reduce their ACP to 30 days. The implication of this findings is that Finance
Managers of Energy and Petroleum companies listed at the NSE will be required to put in
place a robust Credit Policy which focuses on minimizing accounts collection period,
reduction in accounts receivable investment in order to improve the financial performance
of the companies. The relationship is statistically not significant since the P=0.849.
This means that ACP on its own does not have a major impact on the ROA. The results
40
also indicated that Accounts Payable Period (APP) coefficient was negative (-0.257). This
means that one unit change in the APP will lead to a 0.257 reduction in the ROA. The
relationship is statistically significant since the P=0.043 which is less than 0.05 significant
level. Falope and Ajilore (2009) established similar findings where they established a
negative relationship between net operating profitability and the APP. The Finance
Managers of Energy and Petroleum companies should therefore put in place measures to
reduce APP in order to increase ROA. Automation of procure to pay process, cash flow
planning, negotiations of better trading terms from suppliers are some of the measures such
companies can adopt to reduce APP in order to increase ROA.
The Cash Conversion Cycle (CCC) coefficient was negative (-0.174) meaning that for one
unit increase in the CCC, will lead to a 0.174 reduction in the ROA. Berk and Demerzo
(2011) observed that an important consideration for all firms is the ability to finance the
transition from cash to inventories to receivables and back to cash. Shin and Soenen (1998)
arrived at similar conclusion where they observed that one possible way to create
shareholder value is to reduce firm’s CCC. The relationship is statistically significant since
the P=0.022 which is less than 0.05 significant level. Finance Managers of Energy and
Petroleum companies should therefore put in place measures to reduce CCC in order to
increase ROA. Since CCC is a product of ICP, ACP and APP then it follows that reduction
in the CCC can be achieved by effective management of individual components as
described above. From the Coefficients Table 4.4c above, the t-values indicates that
Inventory Conversion Period (ICP) is the most useful predictor ROI (t=0.639) followed by
ACP (t=-0.192), Accounts Collection Period (t=-2.109) and last Cash Conversion Cycle
41
(t= -2.399).
CHAPTER FIVE – SUMMARY, CONCLUSIONS AND
RECOMMENDATIONS
5.1. Introduction
The main objective of this research was to establish the effects of WCM on financial
42
performance of Energy and Petroleum companies listed at the NSE. The research sought
to establish if the Inventory Conversion Period, Accounts Collection Period, Accounts
Payable Period and Cash Conversion Cycle had any effects on financial performance of
Energy and Petroleum companies listed at the NSE. This chapter therefore presents the
summary of the findings, conclusions, limitations as well as the recommendations.
5.2. Summary of Findings
The study sought to establish the combined effect of WCM on the financial performance
of the Energy and Petroleum companies listed on the NSE. Linear regression was used to
analyze the relationship between variables, out of which inferential statistics analysis was
made for every variable. Multiple linear regression was used to establish the combined
effect of all independent variables (WCM) on the dependent variable (ROA). Normality
tests, were undertaken which established that the variables (ROA, ICP, ACP, APP and
CCC) of the Energy and Petroleum companies listed at NSE were normally distributed.
Statistically, the researcher was therefore able to draw accurate and reliable conclusions
about reality of the impact of WCM on the profitability of Energy and Petroleum
companies listed at NSE. The study indicated that WCM influence the ROA significantly.
Using the Adjusted R Square, it can be noted that 17.8% of the variability on the Return of
Assets (ROA) can be explained by variations in the Inventory Conversion Period (ICP),
Cash Conversion Cycle (CCC), Accounts Collection Period (ACP) and Accounts Payable
period (APP). It can be concluded that all variables ICP, ACP, APP and CCC have a
combined significant impact on ROA and therefore these independent variables can be
used to predict ROA.
43
The Pearson correlation coefficient between ROA and ICP was negative and weak. This
means that if there is an increase in ICP then it will be associated with a reduction in the
ROA and vice versa. The ROA and ACP have a negative correlation. This means that if
there is an increase in ACP, then such a scenario will be associated with a reduction in the
ROA and vice versa. However the linear correlation between these two variables exhibited
a weak linear relationship. The ROA and APP also have a negative correlation even though
the linear relationship was weak. An increase in APP will be associated with a reduction
in the ROA and vice versa. Similarly, the ROA and CCC have a negative correlation even
though the linear relationship is weak. If there is an increase in CCC, then there will be
reduction in the ROA and vice versa. The CCC is the length of time between when the
company makes payments of stock purchases to the period cash is received from the sales
made. It is therefore a summation of ICP plus ACP less APP. The firms target is therefore
to reduce the CCC in order to increase the ROA. Based on the t-values, ICP is the most
useful predictor of ROA followed by ACP, APP and lastly CCC.
The main objective of WCM is to minimize risks by ensuring seamless business operations
and at the same time ensuring the business is in a better position to meet its short-term
obligations. Incorrect evaluation of the liquidity implications of the firm’s working capital
needs may, in turn subject creditors and investors to an unanticipated risk of default
(Richard & Laughlin, 1980). It is therefore requisite upon the Finance Managers of these
Energy and Petroleum companies listed at NSE to understand the business operations, and
put in place robust WCM policy framework because of its implications on the performance
of the business.
44
5.3. Conclusions
The variables (ROA, ICP, ACP, APP and CCC) of the Energy and Petroleum companies
listed at NSE are normally distributed. Statistically, the researcher was therefore able to
draw accurate and reliable conclusions about reality of the impact of WCM on the
profitability of Energy and Petroleum companies listed at NSE. The study has established
that WCM influenced the ROA significantly for the Energy and Petroleum companies
listed at NSE. It can be concluded that all variables ICP, ACP, APP and CCC have a
combined significant impact on ROA and therefore these independent variables can be
used to predict ROA. It was concluded that ROA and ICP have a negative correlation even
though the correlation was weak. This means that if there is an increase in ICP, then it will
be associated with a reduction in the ROA and vice versa. Since the objective of the Energy
and Petroleum Companies is to increase the financial performance (ROA), it can therefore
be concluded that Finance Managers of such companies will strive to minimize the ICP in
order to enhance the ROA.
It was concluded that ROA and ACP have a negative correlation. This means that if there
is an increase in ACP, then such a scenario will be associated with a reduction in the ROA
and vice versa. Similarly it can be concluded that Finance Managers of such companies
will strive to minimize the ACP in order to enhance the ROA. It was also concluded that
ROA and APP also have a negative correlation even though the linear relationship was
weak. An increase in APP will be reduction in the ROA and vice versa. It is therefore
incumbent upon the Finance Managers of Energy and Petroleum companies listed at NSE
45
to minimize the APP in order to enhance the ROA. It was also concluded that ROA and
CCC have a negative correlation even though the linear relationship is weak. If there is an
increase in CCC, then there will be reduction in the ROA and vice versa. Finance Managers
of these Energy and Petroleum companies listed at NSE will have to focus on minimizing
the CCC in order to enhance the ROA.
In relation to previous studies, it was concluded that the results of this study are similar to
the findings earlier international studies particularly Deloof (2003), Falope and Ajilore
(2009), Sharma and Kumar (2011), Garcia and Martinez (2007),Gill, Biger and Mathur
(2010), Gul, Khan, Rehman, Khan, Khan and Khan (2013),Shin and Soenen (1998) who
have all researched on WCM and its impact on performance of companies. Locally, the
study findings are similar to the conclusions arrived at by Gakure, Cheluget, Onyango and
Keraro (2012), Mathura (2009), Akoto, Awunyo and Angmor (2013) who have also
established that WCM impacts on the profitability of a firm.
5.4. Limitation of the study
The study was limited to WCM particularly Inventory Conversion Period (ICP), Accounts
Collection Period (ACP), Accounts Payable Period (APP) and Cash Conversion Cycle
(CCC) and its effect on the financial performance of Energy and Petroleum Companies
listed at the NSE. There are other factors such as capital structure, risk appetite, corporate
governance, economic conditions, government regulations, and volatility of international
oil prices which could have an impact on the financial performance (ROA) of the Energy
and Petroleum Companies listed at the NSE but were not factored in during this study.
Restriction to WCM components only was therefore limiting.
46
The petroleum sector in Kenya has over 30 oil importing and marketing companies. The
major five major companies are Shell, Total Kenya, KenolKobil, Oil Libya and Chevron.
Other emerging oil companies include Government of Kenya (GoK) owned National Oil
Corporation of Kenya (NOCK). This research was limited to only five companies listed at
the Nairobi Securities Exchange (NSE). These companies are Total Kenya, KenolKobil,
Kenya Electricity Generating Company Limited (KenGen), Umeme Ltd and the Kenya
Power &Lighting Company ltd (KPLC). The sample of 5 companies was limiting and
represents a small portion of the population of the Energy and Petroleum companies on
Kenya.
A multiple regression was used to study the effects of working capital management on the
financial performance of Energy and Petroleum companies listed at the NSE. The financial
performance was measured by the Return on Assets (ROA). There are other measure of
financial performance such as Gross Profit (GP), Net Profit (NP), Return on Equity (ROE),
Earning Per Share (EPS) among others that were not used in this study. The use of ROA
was therefore limiting.
5.5. Recommendations
In overall, the research has indicated that WCM influence the ROA significantly. The
following recommendation can be made regarding the effective WCM of Energy and
Petroleum companies listed on the NSE in order to enhance the financial performance
(ROA).
47
Practice of Corporate Finance: Financial Managers should review specific policies
regarding each component of WCM since they have a combined significant impact on
financial profitability of Energy and Petroleum companies. On credit policy, Finance
Managers should encourage credit sales to boost profitability but at the same time minimize
the risk of bad debts. Credit terms should be clearly spelled out in a credit Policy. Customer
should be vetted for credit qualification before extending credit facilities to the customer.
Credit control department should be established, adequately manned and provided with
budgetary resources to ensure timely collection of receivables from customers. Optimal
Accounts Collection Period (ACP) should be set as a performance target for the Financial
Managers.
The Finance Managers should have a documented Inventory Management Policy. This
should guide the effective inventory management practices. Inventory Management
techniques such as Economic Order Quantities should be deployed to ensure optimal level
of inventory. Financial Managers should also adopt technology for effective inventory
management practices. Vetting and prequalification of inventory suppliers will ensure
product quality, timely delivery of inventory to meet sales orders.
The Finance Managers should ensure an Accounts Payable Policy is in place. The policy
should stipulate the target Accounts payable Period (APP) which should be embedded in
the performance targets for the Finance Managers. Strained relationship with suppliers due
to late or non-payment of suppliers will negatively affect the ability of Energy and
Petroleum companies to maintain optimal inventories. Weekly and or monthly cash flow
projections practices should be encouraged to ensure suppliers are paid on time. Monthly
48
ageing of payable reports should be reviewed by Finance Managers to ensure Accounts
Payable Policies are being complied with.
Regulators: The Professional competence for the Finance Managers should be monitored
regularly. The Institute of Certified Public Accountant of Kenya (ICPAK) should ensure
Financial Managers meet Continuous Professional Education (CPE) requirements as a
means to ensure professional financial management practices are embedded in the
management of Energy and petroleum companies listed at the NSE. Kenya Government
regulates the price of petroleum products through Energy regulatory Commission. Such
price regulations will certainly impact on the valuation of inventories since price is a factor
in valuation of inventory. ERC should regularly review their pricing mechanism not to
negatively affect the performance of the Energy and Petroleum companies listed at the
NSE. Non-Organization of the Petroleum Exporting Companies (OPEC) should
continuously monitor the production and export of petroleum product based on demand
and supply. This is because inadequate production of petroleum products will have a direct
impact on the inventory availability for the petroleum trading companies.
5.6. Suggestions for further research
The study was limited to few components of WCM particularly Inventory Conversion
Period (ICP), Accounts Collection Period (ACP), Accounts Payable Period (APP) and
Cash Conversion Cycle (CCC). There are other factors such as capital structure, risk
appetite, corporate governance, economic conditions, government regulations, and
volatility of international oil prices which could have an impact on the financial
49
performance (ROA) of the Energy and Petroleum Companies listed at the NSE. These
factors could form a basis for future research.
The study was also limited to five Energy and Petroleum companies listed at the NSE. The
sample of 5 companies therefore represents a small portion of the population of the Energy
and Petroleum companies on Kenya. The petroleum sector in Kenya has over 30 oil
importing and marketing companies. The entire population could form the basis of future
research that can focus on all Energy and Petroleum companies in Kenya.
A multiple regression was used to establish the effects of WCM on the financial
performance of Energy and Petroleum companies listed at the NSE. Financial performance
was measured by the Return on Assets (ROA). Companies can also measure financial
performance using other measures such as Gross Profit (GP), Net Profit (NP), Return on
Equity (ROE), Earning Per Share (EPS) among others. The use of ROA was therefore
limiting. This can therefore form the basis of future research which can focus on other
measure of financial performance beside Return on Assets (ROA).
50
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APPENDICES
APPENDIX I: DATA COLLECTION TOOL
Firm Name
………………………………………………
Year
ROA
ACP
ICP
1
2
3
4
5
6
7
8
57
APP
CCC
APPENDIX II: ENERGY AND PETROLEUM COMPANIES LISTED AT NSE
1. Total Kenya
2. KenolKobil
3. Kenya Electricity Generating Company Limited (KenGen)
4. Umeme Ltd
5. Kenya Power &Lighting Company ltd (KPLC).
58
APPENDIX III: NORMALITY DISTRIBUTION OF ROA
59
APPENDIX IV: NORMALITY DISTRIBUTION OF ICP
60
APPENDIX V: NORMALITY DISTRIBUTION OF ACP
61
APPENDIX VI: NORMALITY DISTRIBUTION OF APP
62
APPENDIX VII: NORMALITY DISTRIBUTION OF CCC
63