INFLUENCE OF PORTER’S FIVE FORCES ON THE PERFORMANCE OF OIL INDUSTRY IN SOUTH SUDAN

BY

BENJAMIN BOL MEL KUOL

UNITED STATES INTERNATIONAL UNIVERSITY AFRICA

FALL 2015

INFLUENCE OF PORTER’S FIVE FORCES ON THE PERFORMANCE OF OIL INDUSTRY IN SOUTH SUDAN

BY

BENJAMIN BOL MEL KUOL

A Dissertation Report Submitted to the Chandaria School of Business in partial fulfillment of the Requirement for the Degree of Doctor of Business Administration (DBA)

UNITED STATES INTERNATIONAL UNIVERSITYAFRICA

FALL 2015

DECLARATION PAGE I, the undersigned, declare that this is my original work and has not been submitted to any other institution, or university other than the United States International UniversityAfrica in Nairobi for academic credit.

Signed: ________________________

Date: _____________________

Benjamin Bol Mel Kuol (ID 609231)

This dissertation has been presented for examination with our approval as the appointed supervisors.

Signed: ________________________

Date: _____________________

Dr. Juliana Namada

Signed: ________________________

Date: _____________________

Prof. Paul Katuse

Signed: ________________________

Date: _____________________

Dean, Chandaria School of Business

Signed: ________________________

Date: _____________________

Deputy Vice Chancellor, Academic Affairs

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COPYRIGHT All rights reserved. No part of this dissertation report may be photocopied, recorded or otherwise reproduced, stored in retrieval system or transmitted in any electronic or mechanical means without prior permission of USIU-Africa or the author. Benjamin Bol Mel Kuol © 2016.

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ABSTRACT The purpose of this study was to examine the influence of Porter’s five forces effect on the performance of the oil industry in South Sudan. To cover the information gap, policy gap, and practice gap of oil industry in south Sudan, the research questions guiding the study were: What level of influence does threat of new entrants have on performance of oil industry in South Sudan?, What extent of bargaining power do suppliers have relative to performance of oil industry in South Sudan?, What is the influence of substitute products on performance of oil industry in South Sudan?, What extent of bargaining power do buyers have relative to performance of oil industry in South Sudan? Lastly; what is the effect of rivalry among firms on performance of oil industry in South Sudan? The research design used was descriptive research and analytic research; descriptive research described the phenomena of oil industry performance while analytic design established relationship among these phenomena. The population of study was all oil industry managers in South Sudan and sample population was obtained through a census of all oil industry managers in South Sudan. Data was collected through 84selfadministered questionnaires distributed to the middle and top management of all 21 oil firms operating in South Sudan. Only 66 questionnaires were filled and returned representing a 78.6% response rate. Multi liner regression was used for analysis and the results adopted the alternate hypothesis for all the research questions. On the first research question, the findings indicate: threat of new entrants have positive significant influence on performance of oil industry in South Sudan which rejects the null hypothesis; on the second research question, rivalry between firms have positive significant effects on performance of oil industry in South Sudan which rejects the null hypothesis; on the third research question, the bargaining power of suppliers have positive significant effects on performance of the oil industry in South Sudan which also rejects the null hypothesis. The forth research question found out that substitute products have positive significant influence on performance of the oil industry in South Sudan which rejects the null hypothesis; and the last research question found out that bargaining power of buyers has positive affect on performance of oil the industry in South Sudan hence accepts the alternate hypothesis. The time of entry in the business had no statistical significant effect on each of the five forces.

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Based on the findings, the study recommends that the government of South Sudan should develop or update existing policies and regulation of the oil industry in South Sudan based on the Porter’s five forces model. Also oil firms operating in South Sudan should consider Porter’s five forces which have significant influence on their performance regardless of the type of oil business they are involved in. Lastly, oil firms preparing to enter South Sudan oil fields should review the Porter’s five forces before investing in the South Sudan oil industry. Future studies need to focus on government regulations and peace in south Sudan as moderating variables. Similar studies should be conducted with the financial performance from government revenues and books of account as dependent variables. Lastly, further studies should concentrate on a specific type of business such as oil piping for in-depth results that can inform specific in-depth policy changes or development.

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ACKNOWLEDGEMENT This work has been tough and I would like to acknowledge a number of people for the success. First my sincere gratitude goes to my supervisors Prof. Paul Katuse and Dr. Juliana Namada for their intellectual contribution, guidance and many hours spent on the development on this research. Secondly, I would like to deliver appreciation to the DBA class for the support and encouragement even when the going was tough due to my many meetings and travelling. To my many friends in Kenya who stood with me, thank you for you are part of my family.

I highly acknowledge my country South Sudan. The many men and women who fought and continue to campaign for peace in our nation, the development in business and other sectors, I acknowledge you. It’s a journey for development that I conducted this research for fair competition of oil industry in South Sudan. All the oil firm operating in South Sudan, I thank you for sharing the information and to my research assistants who persevered to get the right information, I greatly appreciate you. Without you, I would not have received credible information.

Last paragraph, I acknowledge the greatest: my family and God. I appreciate my family for their moral support and understanding for many hours I have been away in order to finish this project. Above all, I thank the Almighty God for the gift of life and strength granted to me throughout this period and I pray for his continuous blessings.

Thank you all and God bless you.

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DEDICATION I dedicate this work to my family and the people of South Sudan.

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TABLE OF CONTENTS DECLARATION PAGE ................................................................................................ ii COPYRIGHT ................................................................................................................iii ABSTRACT ................................................................................................................... iv ACKNOWLEDGEMENT ............................................................................................ vi DEDICATION .............................................................................................................. vii LIST OF TABLES ........................................................................................................ xii ABBREVIATIONS AND ACRONYMS ................................................................... xvi CHAPTER ONE ............................................................................................................. 1 1.0. INTRODUCTION ................................................................................................... 1 1.1 Background of the Study ........................................................................................ 1 1.2 Statement of the Problem ...................................................................................... 11 1.3 Purpose of the Study ............................................................................................. 13 1.4 Research Questions ............................................................................................... 13 1.5 Hypotheses ............................................................................................................ 14 1.6 Justification of the Study ...................................................................................... 14 1.7 Scope of Study ...................................................................................................... 15 1.8 Definition of Terms .............................................................................................. 16 1.9 Chapter Summary ................................................................................................. 19

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CHAPTER TWO .......................................................................................................... 20 2.0. LITERATURE REVIEW ..................................................................................... 20 2.1 Introduction ........................................................................................................... 20 2.2 Theoretical Review ............................................................................................... 20 2.3. Conceptual Framework ........................................................................................ 24 2.4. Porter’s Five Forces Model on performance ....................................................... 36 2.5 Chapter Summary ................................................................................................. 50 CHAPTER THREE...................................................................................................... 51 3.0. RESEARCH METHODOLOGY ......................................................................... 51 3.1 Introduction ........................................................................................................... 51 3.2 Research Philosophy ............................................................................................. 51 3.3 Research Design ................................................................................................... 53 3.4 Population ............................................................................................................. 54 3.5 Sampling Design ................................................................................................... 54 3.6 Data Collection Methods ...................................................................................... 56 3.7 Research Procedures ............................................................................................. 57 3.8 Data Analysis Method .......................................................................................... 61 3.9 Chapter Summary ................................................................................................. 64

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CHAPTER FOUR ........................................................................................................ 65 4.0. DATA ANALYSIS AND PRESENTATION ...................................................... 65 4.1 Introduction ........................................................................................................... 65 4.2 Demography.......................................................................................................... 65 4.3. Threat of New Entrants on Performance of Oil Industry in South Sudan ........... 75 4.4. Suppliers Bargaining Power on Performance of Oil Industry in South Sudan .... 85 4.5. Threat of Substitution on Performance of Oil Industry in South Sudan .............. 94 4.6. Bargaining Power of Buyers on Performance of Oil Industry in South Sudan . 104 4.7. Competitive Rivalry on Performance of Oil Industry in South Sudan .............. 112 4.8. Influence of Five forces on Performance of Oil Industry in South Sudan ........ 121 4.9. Chapter Summary .............................................................................................. 125 CHAPTER FIVE ........................................................................................................ 126 5.0 SUMMARY, DISCUSSION, CONCLUSION AND RECOMMENDATIONS ........................................................................................... 126 5.1 Introduction ......................................................................................................... 126 5.2 Summary of the Study ........................................................................................ 126 5.3 Discussion of Results .......................................................................................... 129 5.4 Conclusions ......................................................................................................... 144 5.5 Recommendations ............................................................................................... 147

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REFERENCES ........................................................................................................... 152 APPENDICIES ........................................................................................................... 156 Appendix 1: Questionnaire ....................................................................................... 156 Appendix II: Data Collection Authorization Letter .................................................. 164 Appendix III: Oil Firms In South Sudan .................................................................. 165 Appendix IV: Item – Total Statistics ........................................................................ 166 Appendix V: Combined Variable Analysis Output .................................................. 167

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LIST OF TABLES Table 3. 1 Sample Characteristic of Oil firms in South Sudan. ...................................... 55 Table 3. 2 Summary of Reliability .................................................................................. 57 Table 3. 3: Reliability Output .......................................................................................... 58 Table 4. 1: Comparison of Age and Gender .................................................................... 66 Table 4. 2: Correlation between Age, Length of Service and Role ................................. 68 Table 4. 3: Comparison of Firm Ownership and Branches outside South Sudan. .......... 70 Table 4. 4: Business Type and Ownership of the Firm. .................................................. 72 Table 4. 5: Length of Service and Number of Employees .............................................. 73 Table 4. 6: Cluster of Employees Based on Employer. ................................................... 74 Table 4. 7: General Perception on Threat of New Entrants (percentage) ....................... 76 Table 4. 8: Time of Operation and Type of Business...................................................... 77 Table 4. 9: Relationship between Type of Business and New Entrants .......................... 79 Table 4. 10: Relationship between Ownership of the Firm and Threat of New Entrants 80 Table 4. 11: Number of Branches of the Firm and Threat of New Entrants ................... 81 Table 4. 12: Relationship; Duration of Operation and Threat of New Entrants.............. 82 Table 4. 13a: Model Summary on New Entrants Influence of Performance .................. 83 Table 4. 14b: ANOVA on New Entrants Influence of Performance ............................... 84 Table 4. 15c: Coefficients on New Entrants Influence of Performance .......................... 84 Table 4. 16: Suppliers Bargaining Power (in percentage) ............................................... 86 Table 4. 17: Type of Business and Suppliers Bargaining Power. ................................... 88

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Table 4. 18: Correlation between Ownership and Suppliers Bargaining Power. ............ 89 Table 4. 19: Correlation between Duration of Operation and Suppliers Bargaining Power. .............................................................................................................................. 91 Table 4. 20a: Model Summary on Suppliers Bargaining Power on Performance ........... 92 Table 4. 21b: ANOVA on Suppliers Bargaining Power on Performance ....................... 92 Table 4. 22c: Coefficients on Suppliers Bargaining Power on Performance .................. 93 Table 4. 23: Frequency of Substitute Products ................................................................ 95 Table 4. 24: Relationship between Type of Business and Threat of Substitution. .......... 97 Table 4. 25: Relationship between Ownership and Threat of Substitution. .................... 98 Table 4. 26: Mean Comparison of Threat of Substitution and Firm Branches. .............. 99 Table 4. 27: Correlation between Moderating and Threat of Substitution Variables. .. 101 Table 4. 28a: Model Summary on Substitution Influence of Performance ................... 102 Table 4. 29b: ANOVA on Substitution Influence of Performance ............................... 102 Table 4. 30c: Coefficients on Substitution Influence of Performance .......................... 103 Table 4. 31d: Excluded Variables on Substitution Influence of Performance .............. 103 Table 4. 32: Buyers Bargaining Power Perception. ...................................................... 105 Table 4. 33: Relationship between Buyers Power and Threat of Substitution. ............. 106 Table 4. 34: Mean Comparison of Buyers Power and Firm Branches. ......................... 108 Table 4. 35: Correlation between Moderating and Threat of Substitution Variables. .. 109 Table 4. 36a: Model Summary on Bargaining Power of Buyers on Performance ........ 110 Table 4. 37b: ANOVA on Bargaining Power of Buyers on Performance .................... 111 Table 4. 38c: Coefficients on Bargaining Power of Buyers Influence on Performance 111 xiii

Table 4. 39d: Excluded Variableson Bargaining Power of Buyers Influence on Performance ................................................................................................................... 112 Table 4. 40: Competitive Rivalry Perception. ............................................................... 114 Table 4. 41: Mean Comparison of Competitive Rivalry and Oil Business ................... 115 Table 4. 42: Mean Comparison of Competitive Rivalry and Firm Branches. ............... 117 Table 4. 43: Correlation between Moderating and Competitive Rivalry. ..................... 118 Table 4. 44a: Model Summary on Competitive Rivalry on Performance. .................... 119 Table 4. 45b: ANOVA on Competitive Rivalry on Performance. ................................ 119 Table 4. 46c: Coefficients on Competitive Rivalry on Performance. ........................... 120 Table 4. 47d: Excluded Variable on Competitive Rivalry on Performance. ................. 120 Table 4. 48a: Model Summary on Influence of Five Forces on Performance. ............. 122 Table 4. 49b: ANOVA on Influence of Five Forces on Performance. .......................... 123 Table 4. 50c: Coefficients on Influence of Five Forces on Performance. ..................... 124

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LIST OF FIGURES Figure 2.1: Porter’s Five Forces of Industry Competition .............................................. 21 Figure 2.2: Conceptual Framework Source ..................................................................... 25 Figure 4.1: Gender of Respondents ................................................................................. 65 Figure 4.2: Management Position of Respondents. ......................................................... 67 Figure 4.3: Ownership of Oil Firms in South Sudan. ...................................................... 69 Figure 4.4: Specific Type of Business. ............................................................................ 71

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ABBREVIATIONS AND ACRONYMS B/d

Barrels per day

BSC

Balance Score Card

BP

British Petroleum Company Ltd

CGMA

Chartered Global Management Accountant

CNPC

China National Petroleum Corporation

CNOOC

China National Offshore Oil Corporation

DF

Degree of freedom

EU

European Union

FME

Free Management E-book.

NOC

National Oil Companies

PESTLE

Political, Economic, Social, Technological, Legal and Environmental

PWC

PricewaterhouseCoopers

SE

Standard Mean Error

Sdvt

Standard Deviation

SKW

Skewness

SS

South Sudan

SWOT

Strength, Weakness, Opportunities and Threat

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CHAPTER ONE 1.0. INTRODUCTION 1.1 Background of the Study According to Porter, organization strategy requires an industry to identify its market forces, which industry it is competing in, the structure and possibly the conduct of those industries in order to improve its performance (Porter, 2013, 2008).Ehlers and Lazenby (2010), Grundy(2003), Porter (2013) and Lopez-Claros, Porter, Sala-i-Martin, and Schwab (2008) stated that an industry also needs to understand the determinants of competition and which organizations produce substitute products as potential competitors for relevance. Further, with various forces that affects competition both at micro and macro levels, industries also need to strategically place themselves for relevance. An industry constitutes the internal variables such as leadership and management issues (governance), the resources or inputs that go into the productive process and the business functions such as finance, marketing and logistics that are performed by the companies (Pearce and Robinson, 2008). According to Pearce and Robinson (2010) the external or macro environment variables include Political, Economic, Socio-cultural, Technological and Legal actors (PESTEL). These factors have been studied widely especially in the discipline of strategic management (Wit and Meyer, 2010; Pearce and Robinson, 2010; Hunger, 2001). Different organizations have implemented different internal strategies as a control mechanism on factors that influence performance such as strategic coherence, performance management style, social norms and how they influence behavior, standards at work, communication, supportive supervision, leadership functions, resources, work ethics, structures and work culture. However, strategies for developing and emerging economies face unique challenges and as a result, the assumptions that internal strategies for developed countries will work in developing ones need to be challenged through empirical research. Arguably, markets in developing countries, provide a new context in which to understand issues of strategy, competition and institutions among others (Hough et al., 2008). Strategy scholars such as Khanna et al. (2005) and Wright et al. (2005) appear to agree that strategies for emerging markets face unique challenges, and that what 1

works in the developed nations is likely not to work in other contexts and especially those of developing nations. According to Hough, et al. (2008), research on the practice of organization strategic management in emerging economies has grown and coverage of countries and regions in emerging economies has been uneven. South America has had a large share of the research based on different models and theories which clearly depicts strategies used in business. Similar studies are required for new and developing markets as observed by Wright et al. (2005) as quoted in Hough, et al. (2008:38): “for the same reason that strategy practice in emerging economies pushes the frontier in strategic thinking, strategy with a focus on these emerging economies, both as an opportunity and as a necessity, is challenging conventional wisdom in academic thinking and theories in significant ways. To the extent that emerging economies are fertile grounds not only for testing existing theories but also for developing newer ones, these endeavors are likely to greatly enrich the strategy enterprise globally.” The top managers of the players in the oil industry have to understand their micro and macro environments well in order to come up with good strategies. On the external factors, research shows Political-legal forces allocates power and provide constraining and protecting laws and regulations (Fisher, Schoenfeldt and Shaw, 2003; Harrison, 2002; Hunger and Wheelen, 2001; Hill and Jones, 2004). Some important variables here include tax laws, special incentives, setting of wages, laws on hiring and promotion, and stability of government (Hunger and Wheelen, 2001). In the context of the oil industry in different countries, these factors result in government policy and regulations on such issues as wage, remuneration and employment levels, incentives relating to work and other factors such as leave allowances, the tools to be provided for work such as uniforms and protective gear, skills levels and training, laws on hiring and promotion and taxation. Such strategies can only be implemented if there are performance measures in place. According to Kaplan (2009), any organization cannot manage what cannot be measured hence the need for any organization to measure its activities to determine the strengths and the weakness in its operations. Though different measures are used to measure performance, the traditional management systems is dependent of financial indicators 2

which reports past action. However, Kaplan and Norton in 1992 introduced the Balance Score Card (BSC) based on the integration of measurement of intangible assets into organizations management systems which enable organizations to also improve the management of their intangible assets based on performance indicators and not only financial indicators (Kaplan & Norton, 2007). Different factors affect the performance indicators but the five forces that affect performance based on competition are rivalry between firms, threat of entrant into an industry, substitute products, bargaining power of buyers and bargaining power of sellers (Porter, 2008).

Kaplan and Norton (1996, as cited in Kaplan, 2009) believed the four perspectives of the BSC provides a balance between hard objectives and more subjective measures on an institution strategy and market environment; and between outcomes desired and the performance drivers of those outcomes based on the environment. In measure of performance, the greatest tool integrates financial and non-financial measures together by measuring both strategic and business performance across four interrelated perspectives; financial, customers, internal processes, and learning and growth activities (Abdelnabi, Hasnan, and Osman, 2012). Further, Giannopoulos, Holt, Khansalar and Cleanthous (2013) stated the cause-and-effect relationship that exists among the financial and nonfinancial measures cannot be separated in any business when measuring performance.

These four measures of performance are: the firm profitability measure performance based on past actions on finance; firms’ growth in market share internal or learning and growth measure performance based on market size; customer satisfaction measures performance based on the outside reflections of the buyers; and lastly business efficiency level based on the internal processes measure performance based on the strategy and implementation of such strategies (Collis, Holt and Hussey, 2012 as cited by Giannopoulos, Holt, Khansalar and Cleanthous, 2013). The four focuses also inform specific improvement in the internal business processes for an improved learning based on the growth performance (Kaplan, 2009). Porter’s five forces: rivalry between firms, threat of entrant into an industry, substitute products, bargaining power of buyers and bargaining power of sellers (Porter, 2008) greatly affect the financial, market share, customer satisfaction and business efficiency of any business.

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Competition affects performance differently. For any complete growth, customer perspective will be improved which will improve customer satisfaction and in turn, customers will significantly improve the market share of the products and lastly, increase the financial returns (Giannopoulos, Holt, Khansalar and Cleanthous, 2013). The Porter five forces of competition determine the intensity of competition of firms in particular industries which affects profitability and attractiveness of an industry hence determines an industry performance in the short and long run (Porter,2013). This shows duration of operation as a factor that affects performance regardless of the measurement matrxi used (Meena, 2009).

According to Meena (2009), performance in any organization is measured over a sustained period of operation. Meena outlined time dimension affects performance as the results of the current measure of performance is largely a consequence of an action applied last quarter or last year. He further outlined that, if new skills are added now, it should have consequences for the next years measure of performance after a sustained period of implementation. Sahu and Parekh (2012) and Porter, Lorsch, and Nohria (2004) also discussed that performance of an organization based on the market share can be determined by time of entry or the duration that one has been operating in an industry. Duration of operation determines the market share based on economies of scale and area of operation. If the area of operation allows a firm to expound, it captures more market share over a period of time based on the economic competitions and grows to be multinationals such as ExxonMobil, Royal Dutch Shell or BP, Total, TexacoChevron or TotalFin aElf (Grünig and Best, 2007).

While Strength, Weakness, Opportunities and Threat (SWOT) and Political, Economic, Social, Technological, Legal and Environmental (PESTLE) analysis measures performance as strategic analysis based on past performance and environmental scanning (CGMA, 2013), Porter’s five forces are inter-twinned and look at industrial structure as well as the outside forces that affect competitors in specific industry and analyses different forces in the market rather than strategies only (Porter, 2008). With none usage of Porter’s five forces model such as SWOT and PESTLE analysis only, strategic management excludes intensity of competition in a particular industry which affects micro-economics and in the long run, macro-economics due to the tripple down effect (Grundy, 2003; 4

Porter, Lorsch, and Nohria, 2004; Porter, 2013)thus the importance of analyzing the industry using the five forces. The Porter’s five forces are: rivalry between firms, threat of entrant into an industry, substitute products, bargaining power of buyers and bargaining power of sellers. These forces determine the intensity of competition of firms in a particular industry which affects profitability and attractiveness of an industry hence determines an industry performance in the short and long run (Porter,2013; Grundy, 2003;Lopez-Claros et al., 2008). Rivalry between firms is determined by an industry Concentration Ratio (CR) which measures the market share of large industries in a particular sector. The higher the concentration ratio, the higher the market share is controlled by few firms hence less competition and approach to monopoly in an industry. While, the lower the concentration ratio, the higher the market share is controlled by many firms hence high competition in an industry. In an industry where concentration ratio is high, performance is low due to monopoly as compared to a low concentration industry where many industries compete to deliver products to the same market hence high performance (Free Management Ebook (FME), 2013; Porter, 2008). Globally, the number of industries in the oil sector differs with oil production. According to Grünig and Best (2007), in 25 EU members states, there were 839 firms involved in oil and gas and incidental services. Out of this, 322 companies were involved in direct extraction with 106 in UK, 104 in France and less than ten in other countries. Majority of companies provide incidental services to exploration of oil and gas, 517; 286 in UK, 60 in Netherlands, and 25 each in France and Germany. Such multinationals involved in extraction, transport, refinement to wholesalers and retail trade are ExxonMobil, Royal Dutch Shell or BP, Total, Texaco Chevron or Total FinaElf. Majority of oil industries were in UK and France where the industry concentration ratio is low. Based on this statistics, it’s clear that the oil industry globally is controlled by few firms creating monopoly. In 2013, Africa produced nearly nine million barrels of crude oil per day with more than 84% from Nigeria, Libya, Algeria, Egypt and Angola. According to 32 National Oil Companies (NOC) in Africa, more international companies are increasingly investing in 5

the oil industry in Africa with Sonangol (Angola), Sonatrach (Algeria), Statoil (Norway), ONGC (India), PetroSA (Ghana), CNPC and Sinopec (China), Statoil, Gazprom (Russia) and CNOOC (China) eyeing Tanzanian. (PWC, 2014; African Development Bank and Africa Union, 2009). This trend is similar to the global trend as the multinationals are involved in extraction, transportation and wholesale hence creating a monopoly in the oil industry which lowers performance of the industry (Porter, 2008). Further, African government implements new policies that attempt to increase their share in proceeds thus increasing rivalry between government and investing firms (PWC, 2013). In any industry, products substitution affects performance of an industry. This occurs when there are changes in any products for expansion of customer size such as price, branding, and market share. When a substitute product is favored by clients, the performance of the other product is affected (FME, 2013; Porter, Lorsch, and Nohria, 2004). Globally, threat posed by alternate fuels particularly bio-fuel are minimal with less than 2% usage at EU level (Grünig and Best, 2007). Although oil remains largely used primary as a source of energy, it has continuously lost its market share for the last 14 years in a row due to the increase in other forms of energy with hydroelectric and renewable forms of energy attaining a high growth rate of 6.7% and 2.2% respectively in 2013 (Asian Oilfield Services, 2013; Brown, 2013). The increase in alternate source of energy can be attributed to green energy campaigns and the use of natural resources for production of energy. The BP Statistical Review of World Energy, (2014) supports the above findings as it shows emerging economies have increased the energy consumption to 80% with diversification of primary sources of energy to oil, coal, nuclear power, natural gas, hydroelectricity, and renewable energy. In 2014, oil consumption stood at 32.9% globally with the market share loss due to other forms of energy; in 2013, consumption was at 1.4 million barrels per day (b/d) (1.4%) compared to a production rate of 560,000 b/d (0.6%), the lowest since 1965 (BP, 2014). Africa is no different; the research conducted by African Development Bank and Africa Union (2009) and PWC (2014) found out that, alternate automotive fuel such as natural gas, electricity and solar is continuously being exploited in Africa though it has minimal competition as substitute for oil. This leaves the oil industry with minimal competition 6

posed by substitute products from alternate sources of energy. Interestingly, the research found out that in the oil industry, low quality and high quality oil products form a substitute of themselves especially where quality of oil product is very low. With the increase in technology, high quality oil products are produced at lower prices while in some instance, lack of quality products makes the low quality products to be sold at high prices (PWC, 2014). Another force in the market is the power of buyers which affects performance in an industry. According to Porter (2008), when a buyer has a stronger power in purchasing, the industry tends to be monopolized by the buyer which increases the oil industry performance. Similar characteristics of a stronger buyer is when the product is standardized and has to meet the standard set by buyers to purchase or when the buyers can purchase producing firms. When the buyer is weak, the industry performance is poor since the industry manipulates the buyers. According to African Development Bank and Africa Union, (2009) report on Oil in Africa, the power of buyers in the oil industry is mainly price driven. In any market, buyers have little interest in products that exceed basic standards for they are unwilling to pay more for such products. On the other hand, buyers are constrained to adhere to the market price due to lack of oil substitute products in the oil industry (BP, 2014). According to PWC (2014) gas study in South Africa, buyers have low bargaining power which can be enhanced by introduction of additional supply and new infrastructure. Oppositely, buyers are willing to spend less for more oil product benefit. This has seen the introduction of competitive high-performance fuel such as shell fuel saver offered by Shell Ltd which trades at the same market rate with other fuel products but with anticipated high value to the vehicle (Munyua, 2014). Similar to the power of buyers, when the suppliers have high power, the raw materials can be monopolized. Such a situation forces the industry to abide by the condition of the supplier which lowers the general industry performance. When the suppliers’ power is competitive based on market forces, industry performance will be competitive as well. However, imbalance of supplier causes poor performance due to monopoly of favored industries that will intern, control the market. Similarly, with a low bargaining power of the buyers, suppliers are left to monopolize the oil industry (Porter, 2008). 7

According to PWC (2014), in 2013 alone, six of the top 10 global discoveries by size were made in Africa. A report by African Development Bank and Africa Union (2009) shows that the capacity of oil-rich African countries have not been fully exploited. In a bid to increase performance and tap the large oil discoveries in Africa, there has been competition on companies investing in the oil industry in Africa. Major companies mentioned are Sonangol (Angola), Sonatrach (Algeria), Statoil (Norway), ONGC (India), PetroSA (Ghana), CNPC and Sinopec (China), Statoil, Gazprom (Russia) and CNOOC (China) having bid recently in Tanzanian (PWC, 2014; African Development Bank and Africa Union, 2009). With majority of these firms being multinational companies, they are involved in extraction, transport, processing and refining of crude oil as well as in wholesale and retail (PWC, 2012). This monopoly of suppliers locks out majority of middle and small size suppliers hence lower competition which affects performance of oil industries in comparison to free market economy. African countries are yet to benefit from such business. For example, Nigeria which has been exploiting oil resources for the last 55years has 400% lower physical capital development due to a number of factors including low power of buyers and minimal involvement of local suppliers. This lowers the extraction rate and in the long run, the performance of oil industries due to monopoly by few farms in the industry. Different studies show that oil industry is capital intensive businesses especially on oil extraction and refining which limits the number of new entrants in the industry. Despite this, PWC (2012) study revealed most African governments are also a source of barriers for new entrants with strict rules on the license for operation, legal requirement, availability of infrastructure and environmental factors. This forces most oil and gas companies to contract local companies, procure local suppliers and labor, most of whom lack required knowledge (PWC, 2014; Brown, 2013). The oil firm that enters such markets dominates as others are limited to entry which lowers the performance of oil industries in comparison to free market economy. According to BP, (2014), Brown, (2013) and PWC (2013), the shortage of trained oil and gas workers is a concern globally. PWC (2013) further urges donors like the World Bank to invest in capacity building which in turn will prevent suppliers’ monopoly due to lack of professional labor required for production. This will lead to expansion on partnership, 8

transportation, refinery and distribution. Such a partnership is the one signed between Kenya and South Sudan in January 2012 ‘to build a port to the Kenyan city of Lamu and sought in February 2012 to strike a deal with Ethiopia to build a pipeline via Ethiopia to Djibouti’s port’ (Brown, 2013; P. 858). In micro-economics, the forces of demand and supply determine the cost of goods and services which determines the profit level in a normal competitive environment. However, the possibility of new firms entering an industry may affect competition and profit. For example, in a monopoly market, the performance is poor due to lack of competition in comparison to free market where entry is determined by the market forces. Porter (2004) sum up the threat to entry as follows: ‘Barriers to entry are more than the normal equilibrium adjustments that markets typically make. For example, when industry profits increase, we would expect additional firms to enter the market to take advantage of the high profit level, over time driving down profits for all firms in the industry. When profits decrease, we would expect some firms to exit the market thus restoring market equilibrium. Falling prices, or the expectation that future prices will fall, deters rivals from entering a market. Firms also may be reluctant to enter markets that are extremely uncertain, especially if entering involves expensive start-up costs’ (Porter, Lorsch, and Nohria, 2004, p63). Based on the discussions above, Porter’s five forces significantly influence performance of oil industry in different ways. The following paragraphs looks at the background of the oil industry. According to the Global Oil statistics, production of oil in thousand barrels daily (b/d) in 2013 shows that the major oil production was in the Middle East, 32.2% (28,358 b/d), followed by Europe and Eurasia, 20.2% (17,226 b/d), North America, 18.9% (16,826 b/d), South and Central America, 9.1% (7,293 b/d), Africa, 9.5% (8,232 b/d), and Asia Pacific 9.5% (8,232 b/d). The Middle East was the biggest contributor, though North America had a higher growth rate of 8.7% between 2012 -2013 followed by Europe and Eurasia at 0.3%, and South and Central America at 0.2%. Others region recorded a drop including but not limited to Middle East (-0.7%), Asia Pacific (-1.7%) and Africa (-5.7%) (BP Statistical Review of World Energy, June 2014). Africa had oil reserves of 132.4 billion barrels at the end of 2011, which was an increase of 154% compared to the 1980 figure of 53.4 billion barrels (Brown, 2013). PWC (2014) 9

revealed Africa produces nearly nine million barrels of crude oil per day with more than 84% from Nigeria, Libya, Algeria, Egypt and Angola. Though a large production, researchers argue that this underestimates Africa performance as it does not include current and future unexploited ‘proven reserves’ in Mauritania basin, Uganda, Kenya, South Sudan, Congo and Tanzania (Brown, 2013; PWC, 2014; African Development Bank and Africa Union, 2009). Further, in 2013 alone, six of the top 10 global discoveries by size were made in Africa (PWC, 2014). Though African nations are rich in the oil capacity and exploring the increase in production, most countries are still underdeveloped. It is the view of the researcher that without any proper literature on competition that informs strategic plan and management, such nations continue to languish in poverty as the product of oil industry is not seen as what happens in Nigeria. Nigeria has been exploiting oil resources for the last 55years but has 400% lower physical capital development due to a number of factors including low power of buyers and minimal involvement of local suppliers (Africa Economic Outlook, 2012). This lowers the extraction rate and in the long run, the performance of oil industries due to monopoly by few farms in the industry. South Sudan independence came with occupation of 75% of oil reserve of Sudan. According to Africa Business initiative (2011), South Sudan sits on the third-largest oil reserves in Africa, with 98% of the government’s revenue and GDP from oil production. However, with lack of resources, South Sudan remains dependent on the north for processing, refinement, and export (Africa Business Initiative, 2011; Brown, 2013; PWC, 2014). Further, South Sudan has continuously been affected by decline in production; in 2011, production was 425,000 b/d, a significant decline from 470,000b/d in January 2011, mainly due to professional labor shortages. In 2012, there was little production due to pipes being short down in Sudan from January 23rd 2012 to early 2013 (Africa Business Initiative, 2011; Brown, 2013). According to the Oil and Gas in African report by Kantai (2004) titled young nation divided, South Sudans’ oil reservoir coverage is largely in the Upper Nile and all of Unity State. Major investors in the oil industry includes Indian firm ONGC Videsh which holds equity with Greater Pioneer Operating Company (GPOC) and SUDD petroleum operating company (SPOC). Also, China National Petroleum Corporation (CNPC) and Malaysian 10

firm Petron as are the largest investors in block 3 and block 7. Despite the monopoly, oil fields in South Sudan are ravaged by war between the government and the rebel groups which is majorly two tribes; Dinka and Nuer groups. This continuously affects oil exploration, production and attraction of other investors. The high dependence by government GDP on Oil largely affects the operation of South Sudan especially in the capital city, Juba. In December 2014 at the brink of war, Badreld in Mohmaoud Abbes, the South Sudan finance minister was quoted saying ‘even if the oil production in South Sudan stops, we have prepared first aid arrangement to compensate for the loss. We will deal with the issue without imposing taxes or increasing in price, but we will reduce government spending’ (Kantai, 2004. P. 41). At the time, a litter and a half of fuel was selling for SS pound 40 ($4.8), six times the market price (Ford, 2014; Kantai, 2014) “Every morning in Africa, a gazelle wakes up. It knows it must run faster than the fastest lion, or it will be killed. Every morning a lion wakes up. It knows it must outrun the slowest gazelle, or it will starve to death. It does not matter whether you are a lion or a gazelle...when the sun comes up, you’d better be running”. The African oil and gas business is no different inclusive of South Sudan (PWC, 2014. P. 56) 1.2 Statement of the Problem Different researches have been conducted that show how a variety of factors affect performance of oil industries. Africa Business Initiative, (2011) and Brown, (2013) did studies on Oil exploration in Africa. These studies dwelt on how government barriers are affecting market competition and performance of oil industries. On the other part, BP (2014), and Grünig and Best (2007) looked at the state of oil the industry on the continent of Africa. Key results indicated how lack of knowledge and skills of local professionals, as well as the economies of scale operations have enhanced monopoly of supply in oil industries hence affecting performance. Based on PWC (2012, 2013, and 2014) studies, annual indicators on performance of Oil and Gas in Africa have been identified; the indicators are on challenges, exploration, oil market trend, investors and potential area of improvement. A number of factors in the indicators includes: political interference, war, shift from oil to gas, and human capital. Lastly, research on Oil and Gas in Africa by 11

Africa Development Bank and African Union (2012) dwelt on government policies, oil price, suppliers, economies of scale and diversification. From the above studies we see that the different angles of Porter’s five forces have been used in relation to performance. However, none of these mentioned studies used a combination of all the five forces. A number of studies on performance of oil industries have been done based on SWOT and PESTLE analysis measures(CGMA, 2013). According to BP (2014), and Grünig and Best (2007), studies on SWOT and PESTLE analysis of the oil industries in Africa, low suppliers power have enabled most multinational companies to monopolize supply of oil industry due to economies of scale, specialized knowledge, controls of oil industry revenue and acquiring of small scale oil companies that are forced to shut due to unfavorable business environment. A report by PWC (2013) found out that the threat of entrants was attributed to war in Mozambique, and the weak local suppliers’ power was attributed to lack of professionals as well as lack of competent knowledge and increase in expense. Based on the above studies, only components of substitute products, power of buyer and threat of entrants have been discussed in brief while other arms of Porter’s are not used. This shows that there is need to conduct performance analysis on the oil industry using the entire Porter’s five forces. A report by African Development Bank and Africa Union (2009) shows that the capacity of oil-rich African countries has not been fully exploited. Africa Oil and Gas Review Annual report titled ‘From Promise to Performance’ by PWC (2013) analyzed the oil industry competition by looking at the reserves and production, growth and development, and the challenges. On reserves and production, the research found out that 9 million of crude oil b/d was produced in Africa with 81% of the production from Nigeria, Libya, Algeria, Egypt and Angola in 2011. Further, the study established that oil companies have potential to grow. Sasol is a South African company that produces oil from coal since 1950. Having operated for more than 60 years, Salol has competitive advantage on technology and experience hence low threat of entrants. Despite the huge potential to mine

and process oil, African countries are still lagging behind due to poverty and war as experienced in South Sudan while it’s the land of ‘black gold’ Beukes (2012). From all these studies we see that the aspect of oil in Africa is discussed. However we also note that none of the discussions above uses Porter’s five forces with specific linkage to performance in the oil industry in South Sudan.

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South Sudan alone has an oil reservoir capacity of 3.5 thousand million barrels with the highest reserves to production ratio of 96.9% R/P in Africa (BP, 2014). This is estimated to be the third-largest oil reserves in Africa with 98% of the government’s revenue and GDP from oil production. However, current trends show decrease in oil production especially in oil fields which hamper oil performance and development in South Sudan. Being an emerging nation, South Sudan lacks reliable industrial data or information on oil (Africa Economic Outlook, 2012; World Bank, 2011). Similarly, South Sudan lacks literature that explain the market forces in relation to oil industry performance which is characterized by corruption, politics, oil siphoning, instability and war (BP, 2014; Africa Economic Outlook, 2012). From the statements above, we see that South Sudan is experiencing an economic problem associated with decrease in oil production and lack of information on oil. Based on the above information this study addresses the issues raised. It is also the first, to the best of the researchers’ knowledge, to examine the South Sudan oil industry using Porter’s five forces in the context of the practices of strategic management in emerging economies. It applies the structural analysis of oil industry performance using Porter’s five forces of industry competition. The findings offer probable solution to the oil industry challenges in South Sudan based on Porter’s five forces for practice, literature and policy adjustments. 1.3 Purpose of the Study The purpose of this study was to evaluate the influence of Porter’s five forces effect on the performance of the oil industry in South Sudan. 1.4 Research Questions The following research questions guided this research. i.

What level of influence does threat of new entrants have on performance of oil industry in South Sudan?

ii.

What extent of bargaining power do suppliers have relative to performance of oil industry in South Sudan? 13

iii.

What is the influence of substitute products on performance of oil industry in South Sudan?

iv.

What extent of bargaining power do buyers have relative to performance of oil industry in South Sudan?

v.

What is the effect of rivalry between firms on performance of oil industry in South Sudan?

1.5 Hypotheses Hypotheses predict relationships between variables. According to Creswell (2005) they can be categorized into the null hypotheses and alternative hypotheses. The null hypothesis predicts that no relationship exists between variables, and the alternative hypothesis is a true statement if the results of statistical analyses are used to reject the null hypothesis. Based on the research questions, the following null hypotheses guided this study: H01: Threat of new entrants does not have significant influence on performance of oil industry in South Sudan. H02: Bargaining power of supplier does not affect performance of oil industry in South Sudan. H03: Substitute products do not have significant influence on performance of oil industry in South Sudan. H04: Bargaining power of buyers does not affect performance of oil industry in South Sudan. H05: Rivalry between firms does not have significant effects on performance of oil industry in South Sudan. 1.6 Justification of the Study This study is significant to; academicians and researchers, government and policy makers, and oil industries in South Sudan. This is outlined below:

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1.6.1 Academicians and Researchers: The findings contribute to the literature on how Porter’s five forces affect performance. From each variable on the five forces, performance, the oil industry in South Sudan and time of operation, the findings contributes to empirical literature on the oil industry in South Sudan. The literature fills the knowledge gap on the oil industry in South Sudan and either supports or critiques the applicability of Porter’s five forces in the oil industry in South Sudan. Further, the findings of the study form the baseline for reference for future researchers interested in the dynamics of high performance of the oil industry and other related areas to be used as reference materials. 1.6.2 Government Policy Makers: South Sudan has a new economy that is struggling. Based on the findings from this study, the researcher plans to present a policy brief to the concerned government bodies and oil industries. With the large oil capacity, South Sudan requires policy documents that will create an even business environment for investors, for government to collect revenue and for oil firms to be involved in the business competitively. The policy variables can also be used to set the variable for follow up research in the oil industry and add to knowledge in terms of the gaps that are being addressed. 1.6.3 Oil Companies: Based on the findings, the study suggests strategies that players in the oil industry and government can use to monitor and manage as well as improve the performance of the oil industry based on Porter’s five forces model. The study also suggests possible interventions to be implemented for improvements. The results of this research can assist oil firms in South Sudan to strategically plan for their development. Further, new oil firms can use the result to strategically know the environment in which they invest in environmental scanning results. 1.7 Scope of Study Data was collected in South Sudan which is the main focus of study as outlined in the problem statement. Questionnaire was the main tool for data collection based on the research questions. The study focused on oil players and not individual firms as the model is unsuitable for this (Chartered Global Management Accountant (CGMA, 2013). Middle and Top managers in the oil industry were involved in the study as key oil players who understood the forces of competition and oil performance. This was a good entry point in 15

trying to shed light on the industry. The middle and top managers had the relevant experience with credible information required to respond adequately to the research questions. Fear of giving out information was handled by the researcher who assured the respondents of confidentiality and some respondents participated in the study outside their work environment due to fear. Also, data was only collected from every player or actor in the oil industry in South Sudan. Due to insecurity and doubt among rival communities and competitors, it was dangerous to collect data in the oil production fields. Further major oil companies in the fields have their head offices in Juba, the capital city of South Sudan. Hence a census of all oil companies in accessible geographical region was adequate representation of all players in the oil industry in South Sudan and the study findings are reliable to draw conclusion. 1.8 Definition of Terms 1.8.1 Environment In the context of this study, it includes internal as well as external elements affecting an industry (Pearce and Robinson, 2008). 1.8.2 Levels of analysis Level of analysis indicates the different steps of analysis that can be used in a study. The major level used are descriptive statistics (Level 1 of analysis) and inferential statistics (Level 2 of analysis) (Creswell, 2005). 1.8.3 Industry Industry is the group of firms producing products that are close substitutes for each other in terms of product, process, or geographic market boundaries (Porter, Lorsch, and Nohria, 2004).

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1.8.4 Competitive Advantage A firm is said to have a competitive advantage when it is implementing a value creating strategy not simultaneously being implemented by any current or potential competitors (Barney, Wright, and Ketchen, 2001). 1.8.4. Competitive Advantage Over Rivals A firm is said to have competitive advantage over its rivals when its profitability is greater than the average profitability of all firms in its industry (Pearce and Robinson, 2011). 1.8.5 Sustained Competitive Advantage A firm is said to have a sustained competitive advantage when it is implementing a value creating strategy not simultaneously being implemented by any current or potential competitors, when other firms are unable to duplicate the benefits of this strategy and the company is able to maintain above-average profitability for a number of years (Barney, Wright, and Ketchen, 2001). 1.8.6 Strategy Strategy is the actions and moves in the marketplace that managers take to improve the company’s financial performance, strengthen its long term competitive position and gain a competitive edge over its rivals (Brandenburger, 2002). 1.8.7 Strategic Management Strategic Management is the art and science of formulating, implementing and evaluating cross-functional decisions that enable an organization to achieve its objectives. The art and science encompasses the selection of policies, development of capacity, and interpretation of the environment by managers to focus organizational efforts toward the achievement of preset objectives (David, 2009). 1.8.8 Porter’s five forces This is a framework used to study competition in industries based on the competitive forces that shows how attractive or unattractive an industry is. The Five forces are: rivalry 17

between firms, threat of entrants into an industry, substitute products, bargaining power of buyers, and bargaining power of sellers (Porter, 2013). 1.8.9 Superior performance Generally described as one company’s profitability relative to that of other companies in the same or similar kind of business or industry (Pearce and Robinson, 2011). 1.8.10 Competitors These are companies or organizations that produce goods and services similar to a particular company’s or organization’s goods and services and compete for the patronage of the same customers (Ehlers and Lazenby, 2010). 1.8.11 Region distribution, as defined by BP (2014). North America: US (excluding Puerto Rico), Canada, Mexico. South & Central America Caribbean (including Puerto Rico), Central and South America. Europe & Eurasia: European members of the OECD plus Albania, Bosnia-Herzegovina, Bulgaria, Croatia, Cyprus, Former Yugoslav Republic of Macedonia, Gibraltar, Malta, Romania, Serbia and Montenegro. Former Soviet Union; Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, Uzbekistan. Middle East; Arabian Peninsula, Iran, Iraq, Israel, Jordan, Lebanon, Syria. Africa: North Africa -Territories on the north coast of Africa from Egypt to Western Sahara. West Africa - Territories on the west coast of Africa from Mauritania, to Angola, including Cape Verde, Chad. East and Southern Africa - Territories on the east coast of Africa from Sudan to Republic of South Africa. Also Botswana, Madagascar, Malawi, Namibia, Uganda, Zambia, Zimbabwe. Asia Pacific: Brunei, Cambodia, China, China Hong Kong SAR*,, Indonesia, Japan, Laos, Acau, Malaysia, Mongolia, North Korea, Philippines, Singapore, South Asia(Afghanistan, Bangladesh, India, Myanmar, Nepal, Pakistan, Sri Lanka), South 18

Korea, Taiwan, Thailand, Vietnam, Australia, New Zealand, Papua New Guinea, and Oceania.

1.9 Chapter Summary

Chapter one introduces the proposed study topic and provides the background of oil industry. The statement of the problem is clearly articulated based on knowledge and context gap. The purpose of study is to examine the extent of Porter’s five forces effect on the performance of the oil industry in South Sudan. Clear research questions and hypothesis are outlined in the chapter based on the Porter’s five forces. The study findings are significant to the policy, knowledge and Porter’s theory.

Chapter two discusses the theoretical, conceptual and empirical literature relevant to the variables of the study. Chapter three outlines the methodology used in this study in details, and chapter four presents the research findings. Lastly, chapter five concludes the research project with the discussion of research findings, conclusion and recommendations for further research.

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CHAPTER TWO 2.0. LITERATURE REVIEW 2.1 Introduction This chapter reviews relevant theoretical and empirical literature to this study and presents the conceptual and theoretical frameworks. The flow of the chapter is presented as follows: first the theoretical framework inclusive of Porter’s Five Forces Model, Transaction Cost Theory (TCT). Conceptual framework is the second section outlining the dependent, and independent variables and their interactions. Lastly, the chapter presents the empirical research on Porter’s five forces thematically, guided by the objectives. 2.2 Theoretical Review This study was guided by two theories: Porter’s Five Forces Model, which outlines all the five forces and Transaction Cost Theory (TCT). The conceptual framework variables are also derived from the theory. The outline and discussions of the theories are given below: 2.2.1 Porter’s Five Forces Model of Competitive Analysis Developed in 1979, Porter’s five forces model was developed by Prof. Michael Porter to enable organizations to analyze their competitors’ activities in a specific industry of operation (Porter, 2004). Several researchers have attributed the development of the five forces to fill the gap between SWOT (Strengths, Weaknesses, Opportunities and Threats) and PESTLE (Political, Economic, Social, Technological, Legal and Environmental) analysis that did not include the intensity of competition of firms in specific industry analysis. The Porter’s five forces are; rivalry between firms, threat of entrants into an industry, substitute products, bargaining power of buyers and bargaining power of sellers. Competition affects profitability and attractiveness of an industry hence determines an industry’s performance in the short and long run which are key in strategy (Grundy, 2003; Lopez-Claros et al., 2008; and Porter, 2013). The outlay of the forces are as presented in figure 2.1.

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Figure 2.1: Porter’s Five Forces of Industry Competition(Porter; 2008. P27) According to Porter (2008), the threat of entrant into an industry depends on the barriers to entry that are present, coupled with the reaction from the existing competitors in the industry that the new entrant can expect to meet. If barriers are high and existing companies give a sharp retaliation, the threat of entry is low. High barriers diminish the competitive advantage the new entrants could pose. According to Porter, the six major sources of barriers to entry are, economies of scale, product differentiation, capital requirements, switching costs, access to distribution channels and cost disadvantages independent of scale (Porter,2013; Lopez-Claros et al., 2008). Based on African environment and research conducted by PWC (2012, 2014), government policy was also mentioned as a key barrier in Africa though this is not covered in the original Porter’s five forces.

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It is almost universally agreed and a truism that the intensity of rivalry among existing competitors in an industry is the strongest of all the five forces proposed by Michael Porter in his seminal works. Rivalry is usually based on visible aspects such as price, quality and innovation (David, 2009; Ehlers and Lazenby, 2010). Rivalry between firms is determined by an industry Concentration Ratio (CR) which measures the market share of large industries in a particular sector. The higher the concentration ratio, the higher the market share is controlled by few firms hence less competition and approach to monopoly in an industry, while the lower the concentration ratio, the higher the market share is controlled by many firms hence competition in an industry(Lopez-Claros et al., 2008; Porter, Lorsch, and Nohria, 2004). In an industry where the concentration ratio is high there is monopoly compared to low concentration industries where many industries compete to deliver product to the same market (FME, 2013; Porter, 2004). Intense rivalry in an industry is the result of a number of interacting structural factors. These are conditions that influence the intensity of rivalry between competitors in an industry. They have been identified by Porter as; numerous or equally balanced competitors in the industry, slow industry growth, high fixed or storage costs, lack of differentiation or switching costs, capacity augmented in large increments, diverse competitors, high strategic stakes and high exit barriers(Porter,2013; Grundy, 2003; Ehlers and Lazenby, 2010). Substitute products or services limit the potential returns on an industry by placing a ceiling on the prices firms in an industry can charge comfortably above breakeven or can charge profitably. Porter, Lorsch, and Nohria, (2004: P. 63) observes that “the more attractive the price performance alternative offered by substitute products or services, the firmer the lid on industry profits”. Further, substitutes pose a strong threat to an organization when the switching costs for customers (if any) are low. Actions of buyers can force prices in an industry down; their power to bargain for higher quality or more services, and their ability to play competitors against each other can affect profitability levels in an industry. In effect, buyers’ power exercised through the actions above reduces profitability in an industry. Buyers in effect compete with their industry. According to Porter (2004), a buyer group is powerful if the following circumstances are true: purchases large volumes relative to sellers’ sales, products purchases from the 22

industry represent a significant fraction of the buyer’s costs or purchases, products purchases from the industry are standard or undifferentiated, faces few switching costs, and earns low profits. Also, buyers pose a credible threat of backward integration if the industry’s product is unimportant to the quality of the buyer’s products or services, and when the buyer has full information. According to Porter’s (Grundy, 2003; Lopez-Claros et al., 2008; Porter, 2013) a supplier group is powerful if the following conditions apply: dominates a few companies and more concentrated than the industry it sells to, not obliged to contend with other substitute products for sale to the industry, the industry is not an important customer of the supplier group, the supplier’s product is an important input to the buyer’s business, the supplier group’s products are differentiated or it has built up switching costs, and the supplier group poses a credible threat of forward integration. 2.2.2. Transaction Cost Theory (TCT) Emerging markets have come to get lots of attention as having future potential for pushing world development forward. However, as the emerging markets gets this kind of attention, it comes with a number of costs associated with new firms, industry, expansion, policy and a number of micro-economics. The TCT studies the influence of transaction costs on whether market, hierarchy or hybrid forms are the most appropriate governance mode (Hough, et al., 2008; Williamson, 1975). TCT expounds on the growth and existence of an industry based on its expansion and external environment (Vannoni, 2013). The expansion environment determines the cost of transaction every time a product or service is being transferred from one stage to another (Willianson, 1981). According to Willianson (1981), companies minimize their cost associated with expenses and bureaucracy within the company itself or with the environment. This determines whether a company will hire a supplier or perform the work internally. ‘The theory sees institutions and market as different possible forms of organizing and coordinating economic transactions. When external transaction costs are higher than the company's internal bureaucratic costs, the company will grow, because the company is able to perform its activities more cheaply, than if the 23

activities were performed in the market. However, if the bureaucratic costs for coordinating the activity are higher than the external transaction costs, the company will be downsized.’ (Vannoni, 2013. P.3). According to Williamson (1981), transaction cost is the costs of acquiring and handling the information about the quality of inputs, the relevant prices, the supplier’s reputation, buyers’ involvement, and government cost. Firms emerge as a way of economizing on transaction costs in a world of uncertainty, where contractual arrangements are too expensive. The basic framework was enriched by Williamson (1981) with the introduction of two concepts: bounded rationality and opportunism. Bounded rationality explains the limit of human competency while opportunism looks at self-interest. This theory affects the five forces of market competition from the power of supplier, buyer, rivalry and entrants to new market. 2.3. Conceptual Framework Conceptual frameworks are used in research to outline possible courses of action or to present a preferred approach to an idea or thought. Figure 2.2 shows the conceptual framework that was used in this study. The framework shows how applications of Porter’s Five Forces influence performance of oil the industry in South Sudan as explained in Porter’s Five Forces Model of competition in Figure 2.2.

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Independent variable

Dependent variable H3 H2 H1

PORTERS’ FORCES Threat of New Entrants

Power of Supplier

Performance of Oil Industry H6

Threat of Substitute

   

Power of Buyer

Market share Customer satisfaction Efficiency level Industry profit.

Rivalry between firms

H4 H5

Figure 2.2: Conceptual Framework Source (Author, 2015)

2.3.1. Development of Dependent Variable; Performance (Y) This study adopted the Balance Score Card (BSC) measure of performance from four perspectives; financial, customers, internal processes, and learning and growth activities (Kaplan, 2009). Performance measures are used as a matrix for improvement which is the aim of any performance measurement system. As Kaplan outlined, the company cannot 25

manage what cannot be measured hence the need for any organization to measure its activities to determine the strengths and the weakness in its operations. BSC was introduced by Kaplan and Norton in 1992 to cub the gap of the traditional management systems, especially the dependence of financial indicators which reports past action. Norton and Kaplan, who introduced this concept, based it on the integration of measurement of intangible assets into organizations management systems. This helps organizations to also improve the management of their intangible assets based on performance indicators and not only financial indicators (Kaplan & Norton, 1992 as cited by Kaplan, 2009). Based on the four perspectives: financial, customers, internal processes, and learning and growth activities, each provides feedback based on the performance of an organization. The feedback gives direction on specific areas that needs to be improved (Abdelnabi, Hasnan, and Osman, 2012). Further, Abdelnabi, Hasnan, and Osman stated, BSC is a performance measure that can be applied for all type of business and business size strategies. Kaplan and Norton (1996, as cited in Kaplan, 2009) believe that the four perspectives of the BSC provide a balance between hard objectives and more subjective measures on an institution strategy and market environment; and between outcomes desired and the performance drivers of those outcomes based on the environment. BSC was selected in this study based on its greatest strength of performance measurement, its innate ability to integrate financial and non-financial measures together by measuring both strategic and business performance across four interrelated perspectives (Abdelnabi, Hasnan, and Osman, 2012). Giannopoulos, Holt, Khansalar and Cleanthous (2013) in their research on use of BSC in companies’ performance stated that the cause-and-effect relationship that exists among the financial and non-financial measures cannot be separated in any business. Kaplan and Norton (1996, as cited in Kapla, 2009) confirm this when they stated that the cause-andeffect of financial and none-financial involves a connection from the formulation of the strategy to financial outcomes. The four measure of performance has been used in various studies; the firm profitability measure performance based on past actions on finance; firms’ growth in market share internal or learning and growth measure performance based on market size; customer satisfaction measures performance based on the outside 26

reflections of the buyers; and lastly business efficiency level based on the internal processes measure performance based on the strategy and implementation of such strategies (Collis, Holt and Hussey, 2012 as cited by Giannopoulos, Holt, Khansalar and Cleanthous, 2013). Based on the above, the study adopted BSC which is key in measuring all round performance of an organization despite the type of business involved in and the size of business performed (Kapla, 2009) which does not limit the oil industry. The four focuses also inform specific improvement in the internal business processes for an improved learning based on the growth performance. For any complete growth, customer perspective will be improved which will improve customer satisfaction and in turn, customers will significantly improve the market share of the products and lastly, increase the financial returns (Collis, Holt and Hussey, 2012 as cited by Giannopoulos, Holt, Khansalar and Cleanthous, 2013). The effect of the Porters five forces on the BSC is discussed as follows. 2.3.2. Effect of Threat of New Entrants on Performance According to Porter (2008), the threat of entrant into an industry depends on the barriers to entry that are present, coupled with the reaction from the existing competitors in the industry that the new entrant can expect to meet. If barriers are high and existing companies given a sharp retaliation, the threat of entry is low. High barriers diminish the competitive advantage the new entrants could pose. Porter further outlined high threat of entry means competitors are likely to be attracted to enter into the market due to high profit they are likely to enjoy. This creates a platform where companies are likely to enter with ease which decreases the market share and profitability of existing competitors. In the long run, this may lead to change in existing product quality or price level for firm sustainability due to competition. High threat of entrants affects the competitive environment for oil firms already in the market. The high threat increases competitors which in turn influences the existing firms’ performance through profitability, market share, and efficiency. The increase in number of industry increases competition of the same market and resources. On the other hand, low threat of entry makes an industry less competitive and the possibility to increase its 27

profit level especially in a growing market due to expansion (Lopez-Claros et al., 2008; Porter, 2008). Economies of scale are a key factor in the oil industry which affects oil industry performance. According to Sahu and Parekh (2012), oil and gas industry depends on economies of scale due to its commercial viability and feasibility of projects. Such economies of scale helps companies to achieve competitive advantage thought high market share, due to cost optimization, time reduction and commercial viability of a project. Firms with high economies of scale are likely to have a high market share in production, exploration or transportation. With high market share, the firms are likely to leave a monopoly hence high profit level and profitability (Porter, 2008). Despite this, the internal process and customer satisfaction differ with the firms market share; with monopoly, the buyers have lower power hence the satisfaction have minimal significance similar to the firms internal processes. Similar to economies of scale, a number of research found out, new entrants to the extraction and refining industry is highly capital intensive. This has minimized the number of refineries to one or two in smaller EU member states (Grünig and Best, 2007). Further, the high capital intensive aspect has been attributed to specialized labor required at different stages of processing, and intensive equipment due to intense processes and procedure at different stages. Industries involved in the business have only been made stronger as research shows due to the capital intensive. Due to this, multinational companies have evolved and largely been involved in extraction, transport, refinement to wholesalers and retail trade due to what Porter (2008) attributed to economies of scale. Similar to the economies of scale, the cost of entry affects performance by increase in profit due to monopoly which directly increases the market share but indirectly affects the customer satisfaction and internal processes of the industries. Sahu and Parekh (2012) and Porter (2008) posit it that the markets shares can be determined by time of entry or the duration that one has been operating in an industry. The duration of operation determines the market share based on economies of scale and area of operation. If the area of operation allows a firm to expand, it captures more market share over a period of time based on the economic competitions. Such companies are in many countries and in some is a complete monopoly (BP, 2014). Examples of such 28

companies are ExxonMobil, Royal Dutch Shell or BP, Total, TexacoChevron or TotalFinaElf (Grünig and Best, 2007). Based on the above, duration of operation can affect the profit level and firm growth in the market share based on their growth strategy and business structure (Sahu, and Parekh, 2012). However, this may not directly impact customer satisfaction and efficiency level/internal business process especially if the firm has monopoly power which affects the buyers’ power in the market (Porter, 2008). Technology has evolved over time and the required technology for operation of the oil industry is key for its performance. According to Grünig and Best (2007) and LopezClaros et al., (2008), technology in the oil industry is key in reducing the capital for investment, economies of scale, it ensure quality oil products, and saves costs in exploration. Without appropriate technology, high loss can be experienced in the oil industry. Technology is key for functions such as converting natural gas and coal to liquid fuel, minimize pipeline failure, remote monitoring, visualization, security, environmental monitoring (SRI, international, 2015). Technology can lead to invention which increases the profit level, market share, customer satisfaction of an oil firm due to quality produce and efficiency of the oil industry. This is also supported by David (2009), Ehlers and Lazenby (2010) who stated that price, quality and innovation as a strategy determines the growth in market share hence increase in profit in the long run. This study adopts the hypothesis; H01: Threat of new entrants does not have significant influence on performance of the oil industry in South Sudan which is tested based on correlation and regression between the threat of new entrants and performance of oil industry. 2.3.3. Effect of Bargaining Power of Suppliers on Performance According to Porter a supplier group is powerful if the following conditions apply: dominates a few companies and is more concentrated than the industry it sells to; not obliged to contend with other substitute products for sale to the industry; the industry is not an important customer of the supplier group; the supplier’s product is an important input to the buyer’s business; the supplier group’s products are differentiated or it has 29

built up switching costs; and the supplier group poses a credible threat of forward integration (Grundy, 2003;Lopez-Claros et al., 2008; Porter,2013).

When industry suppliers have significant power, it can directly affect profitability, market share and customer service. This can be reflected through the product prices, quality and quantity in the market. This reduces the power of the buyers as they are being controlled by the suppliers. A stronger supplier can increase profit by increasing the selling price of the product as the buyers with lower power will abide by the new cost. This may lead to loss of customers to available substitute products. Similarly, the supplier may reduce the cost of production and hence increases the profit level (Porter, 2008; Lopez-Claros et al., 2008). Shortage of products supplied also affects the profit level of the supplier, the company efficiency and customer satisfaction as this leads to increase in price due to high demand. Similarly, when the supplier compromises on quality of the product produced in order to bring down the cost of production, it may create a negative impact with the end consumers and affect customer satisfaction. Such customers may complain, return the product or turn to alternate products hence reduce profit of the product (CGMA, 2013).

A large supplier dominates the market due to monopoly and can drive other companies outside the business due to economies of scale (Porter, 2008; PWC, 2014). If the product is fully manufactured by a supplier, they may also choose to sell it directly to the customer, often at a lower price, while still making profit and expanding the market share. Such also creates a strong product design which will highly be consumed by consumer hence high profit and expansion of market share while satisfying the customers. Oppositely, when products are readily available from many suppliers at the different market places, the profit of the firm reduces as buyers’ power increases due to availability of various choices. Buyers can choose from different market place. Also, this increases customer satisfaction as customers choose from markets that they like while oil firms increase their internal efficiencies to satisfy the customers.

When suppliers provide items that account for a sizeable fraction of the industry products, the forces of market will determine the profitability of the oil firm due to the balance of power of buyer and supplier at equilibrium (CGMA, 2013). Unlike this market where many suppliers exist, the presence of few large suppliers in the industry who dominate 30

the market share of the oil industry increases monopoly hence the presence of few suppliers who make large profit in an industry (Lopez-Claros et al., 2008). According to Porter (2008), monopoly increases the market share and profit of the firm but indirectly affects the customer satisfaction and internal processes of the industries. This is similar to economies of scale and cost of entry. According to the micro-economics principles, if a firm has monopoly power then it has little competition, therefore demand will be more inelastic. This enables the firm to increase profits by increasing the price (CGMA, 2013; Lopez-Claros et al., 2008). The study hypothesis was H02: Bargaining power of a supplier does not affect performance of the oil industry in South Sudan. This was tested by perception of each measure of bargaining power, the existing relationship between bargaining power and performance and lastly the prediction of the bargaining power on performance. 2.3.4. Effect of Substitute Products on Performance Porter, Lorsch, and Nohria, (2004:63) observes that “the more attractive the price performance alternative offered by substitute products or services, the firmer the lid on industry profits”. Substitutes pose a strong threat to an organization when the switching costs for customers (if any) are low, meaning that it costs nothing in financial terms or effort to switch from one’s preferred brand to a competing brand that has a lower price or that is better in terms of quality and performance (Porter, 2008). If the switching cost is low, it lowers the profit level and market share of a firm abruptly as customers have power to change and determine the products to purchase. Substitute products or services limit the potential returns on an industry by placing a ceiling on the prices that firms in an industry can charge comfortably above breakeven or can charge profitably. Such ceilings limit potential of an industry which can be overcome by increase in quality offered to the market or product differentiation (Lopez-Claros et al., 2008). Porters (2008) states that, such price ceiling affects an industry earnings and possibly it’s growth. An oil firm operating where there is a price ceiling, substitute products are likely to affects its performance though reduction of profit, competitive growth which determines its market share, customer satisfaction and efficient internal process. 31

In any industry, products substitution affects its performance. This occurs when there are changes in any products for expansion of customer size such as price, branding, and market share. When a substitute product is favored by clients, the performance of the other product is affected (Porter, 2013, 2008). Alternate, automotive fuel such as natural gas, electricity and solar is continuously being exploited in Africa though it has minimal competition as substitute of oil. This leaves the oil industry with minimal competition posed by substitute products from alternate source of energy (PWC, 2014). An oil firm that operates where there are minimal substitute products dominates the market hence increases its profit level which in turn influences the existing firms’ performance through profitability, market share, and efficiency. Interestingly, PWC (2014), and Sahu and Parekh (2012) found out that in the oil industry, low quality and high quality oil products form a substitute of themselves especially where quality of the oil product is very low. With the increase in technology, high quality oil products are produced at lower prices while in some instance, lack of quality products makes the low quality products to be sold at high prices (PWC, 2014). Oil firms operating where there are a number of high and low quality may affect its profit as the low quality acts as the substitute of the higher quality product. Such happens when the low quality oil products are readily availability in the oil industry, hence reducing profits, keeps changing, determining market share, and when they are attractively priced and therefore increase competition which affects profit (Porter, 2008). The presence of any of these factors reduces oil profit, market share and affects the customer satisfaction which in turn affects the products efficiency or internal process. The study hypothesis was H03: Substitute products do not have significant influence on performance of the oil industry in South Sudan. This was tested by perception of each measure of substitute products, the existing relationship between substitute products and performance and lastly the prediction of the substitute products on performance. 2.3.5. Effect of Buyers Bargaining Power on Performance Actions of buyers can force prices down and affect profitability levels in an industry when they have the power to bargain for higher quality or more services, and when they have the ability to play competitors against each other. Buyers’ power exercised through the actions above reduces profitability in an industry as buyers can access similar products or 32

services with other firms or industries. According to Porter (2008), a buyer group is powerful and affects oil products performance if the following circumstances are true: purchases large volumes relative to sellers’ sales, products purchases from the industry represent a significant fraction of the buyer’s costs or purchases, products purchases from the industry are standard or undifferentiated, faces few switching costs, and earns low profits. Also, buyers pose a credible threat of backward integration if the industry’s product is unimportant to the quality of the buyer’s products or services, and when the buyer has full information. Based on Porters outline, buyers can increase the profit of a firm and its market share when they purchases large volumes relative to sellers’ sales frequently which reduces the turnaround period of products in the market hence a firm can increase its production and expand the market share. Alternate, buyers reduce the profit of a firm when products purchases from the industry represent a significant fraction of the buyer’s costs or purchases, products purchases from the industry are standard or undifferentiated, if the buyer faces few switching costs, and earns low profits. This reduces the profit of firms as they operate based on the power of the buyer and not the power of the seller. In any market, buyers have little interest in products that exceed basic standards for they are unwilling to pay more for such products. However, in the oil industry, buyers are constrained to adhere to the market price mainly due to lack of oil substitute products (BP, 2014). According to PWC (2014) studies on oil and gas, buyers have low bargaining power which can be enhanced by introduction of additional supply and new infrastructure. Oppositely, buyers are willing to spend less for more oil product benefit which reduces profit of oil firms. Buyers knowledge of alternate products have increased the market share of high-performance fuel such as shell fuel saver offered by Shell Ltd which trades at the same market rate with other fuel products but with anticipated high return (Ford, 2014; Munyua, 2014). When buyers have lower bargaining power, oil firms are left to monopolize the industry. Such forces the buyers to abide by the condition of the supplier which lowers the general industry performance unlike when the industry performance is competitive based on the market forces. The balance between the buyers’ power and suppliers’ power can operate as equilibrium market forces (Economist Intelligence Unit, 2011). Such balance affects 33

the profit of oil firms as they operate on market competition which determines their profit level. Similarly, the oil firms marker share will be determined by the customer satisfaction and firms efficiency level for cost minimization, market expansion and profit growth. However, imbalance of supplier causes poor performance due to monopoly of favored industry that will intern, control the market (Porter, 2008). The study hypothesis was H04: Bargaining power of buyers does not affect performance of oil industry in South Sudan. This was tested by perception of each measure of buyers’ power, the existing relationship between buyer’s power and performance and lastly the prediction of the buyers’ power on performance. 2.3.6. Effect of Rivalry between Firms on Performance Rivalry between firms is usually based on visible aspects such as price, quality and innovation (David, 2009; Ehlers and Lazenby, 2010). Rivalry between firms is determined by an industry Concentration Ratio (CR) which measures the market share of large industries in a particular sector. The higher the concentration ratio, the higher the market share is controlled by few firms hence less competition and approach to monopoly in an industry. Oil firms that operates in an environment with higher concentration ratio, makes profit and controls high market share which increases its internal efficiency. Oppositely, oil firms that operate in the lower concentration ratio environment experience high competition due to many oil firms in the market hence the profit level and market share is determined by strength of competition in an industry; many industries compete to deliver products to the same market hence high performance (Porter, 2013). Further, the profit level will be determined by internal process efficiency which reduces the cost of production and increases profit. Similarly, good customer satisfaction is a factor in determining the market growth hence determines the profit of a firm (Lopez-Claros et al., 2008; Porter, Lorsch, and Nohria, 2004). Intense rivalry in an industry is the result of a number of interacting structural factors. These are conditions that influence the intensity of rivalry between competitors in an industry. They have been identified by Porter as: numerous or equally balanced competitors in the industry;slow industry growth; high fixed or storage costs; lack of differentiation or switching costs; capacity augmented in large increments; diverse 34

competitors; high strategic stakes and high exit barriers (Grundy, 2003; Lopez-Claros et al., 2008; Porter, 2013). The study hypothesis was H05: Rivalry between firms does not have significant effects on performance of oil industry in South Sudan. This was tested by perception of each measure of rivalry between firms, the existing relationship between rivalry between firms and performance, and lastly the prediction of the performance based on rivalry between firms. 2.3.7. Effect of Duration of Operation According to Meena (2009), performance in any organization is measured over a sustained period of operation. Meena whose research is based on a new look at BSC, outlined a number of ways that time of operation affects performance. According to Kaplan (2009), the four perspective that measure performance are finance, customer satisfaction, internal efficiency and market share. Meena stated that time dimension affects performance as the results of the current measure of performance is largely a consequence of an action applied last quarter or last year. He further stated, if new skills are added now, it should have consequences for the next years measure of performance after a sustained period of implementation. According to Giannopoulos, Holt, Khansalar and Cleanthous (2013) in their research on the use of BSC in companies’ performance stated the cause-and-effect relationship that exists among the financial and non-financial measures cannot be separated in any business. Kaplan and Norton (1996, as cited in Kaplan, 2009) confirm this when they stated that the cause-and-effect of financial and none-financial involves a connection from the formulation of the strategy to financial outcomes over a period of time. Meena (2009) stated that it is important for any organization to reflect the length of operation which strengthens the four perspective linkages. Further, Collis, Holt and Hussey (2012, as cited by Giannopoulos, Holt, Khansalar and Cleanthous, 2013) outlined the firm profitability measure performance based on past actions on finance; firms’ growth in market share internal or learning and growth measure performance based on market size over a period of time; customer satisfaction measures performance based on the outside reflections of the buyers; and lastly business efficiency 35

level based on the internal processes measure performance based on the strategy and implementation of such strategies. Such can only be measured over a period of time that an action has been implemented. Sahu and Parekh (2012) and Porter (2008) also argue that the market share can be determined by the time of entry or the duration that one has been operating in an industry. Duration of operation determines the market share based on economies of scale and area of operation. If the area of operation allows a firm to expand, it captures more market share over a period of time based on the economic competitions. Such companies are in many countries and in some completely being monopoly (BP, 2014). Examples of such companies that have been operational over a period of time and capture a large market size are ExxonMobil, Royal Dutch Shell or BP, Total, TexacoChevron or TotalFinaElf (Grünig and Best, 2007). Meena (2009) gives an example when Kaplan and Norton introduced BSC in 1992, it took time for its effective results to be collected from organization. BSC seemed hard to implement, seemed tedious and effort was required but after duration of time when results were achieved, it became effective. Based on the above, duration of operation can affect the profit level and firm growth in market share based on their growth strategy and business structure (Sahu, and Parekh, 2012). However, this may not directly impact customer satisfaction and efficiency level/internal business process especially if the firm has monopoly power which affects the buyers’ power in the market (Porter, 2008). The duration of time that oil firms have been operating in South Sudan was proposed to be used in this study as a moderating variable at proposal level but later dropped during analysis due to poor response rate on duration of operation in South Sudan. 2.4. Porter’s Five Forces Model on performance Studies on Porter’s Five Forces model on performance of the oil industry are limited in the world. According to Hough, et al, (2008), emerging markets get lots of attention as having future potential for pushing world development forward. On the other hand, academics and practitioners start to pay more attention to research in the developed countries which explains the shortage of material.

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2.4.1. Influence of the Threat of New Entrants on Performance Grünig and Best (2007) conducted a research on 25 EU countries using Porter’s Five Forces. They found out that17 countries had more oil industries with 839 firms involved in oil and gas and incidental services. Out of this, 322 (38.4%) of the companies were involved in direct extraction of oil with 106 companies operating in UK, 104 in France. Further, majority 517 (61.6%) of the companies provide incidental services to exploration of oil and gas such as transport, piping, refinery, distribution, retail, and waste management. The findings clearly show the disparity in the number of companies involved in extraction and other incidental services. According to BP (2014) oil production is high in UK which can be attributed to the number of companies involved in extraction. However, despite the number, their research found out that new entrants to the extraction and refining industry is highly capital intensive. This has minimized the number of refineries to one or two in smaller EU member states (Grünig and Best, 2007). Further, with the high capital intensive, companies involved in the business have only been made stronger as research shows, multinational companies have evolved and largely been involved in extraction, transport, refinement to wholesalers and retail trade due to what Porter (2008) attributed to economies of scale. Such companies are in many countries and in some completely being monopoly (BP, 2014). Examples of such companies are ExxonMobil, Royal Dutch Shell or BP, Total, TexacoChevron or TotalFinaElf (Grünig and Best, 2007). Despite the capital requirement and machinery, the study pointed the opportunity for emerging nations through mergers and acquisitions (Grünig and Best, 2007) which will highly be dependable on the area of specialization between the mergers, knowledge and equipment availability. Further, new entrance can capitalize on existing gaps in most countries and in recycling the waste. Deloitte research supports the recycling of waste for special oil product as shown below: ‘Waste oil blenders as a possible threat to the incumbent refiners: such producers buy low-cost ‘out-of-specification’ oil from established refiners, who would usually consider this oil as waste. Waste oil blenders use this to produce a special gasoline for (vintage) cars that run on now-banned leaded gasoline. Their market 37

share is still very small, yet the growth rate of such producers is considerable’ (Deloitte Research, 2006, p.6; as cited by Grünig and Best, 2007)

Although emerging studies identify the opportunity in oil recycling, new entrants can capitalize on this existing gaps in most countries if they focus on provision of incidental services. However, Porter (2008) and PWC (2012) support Grünig and Best (2007) through in the phase of mergers and acquisitions with intend to build market position that can cause major shake-ups. The limitation of such is only the companies as can expand to exploration or incidentail of oil indictry services.Further, PWC (2012) study on Gas in South Africa revealed, legal regulatory environment were not supportive of new entrants despite the attractiveness of the deal to investors. Also, the capital requirement and lack of infrastructure required to start gas production was pointed out as a significant threat to new entrants. With the gas production process being similar to oil, or as a substitute of oil in some commodities, the threat of new entrants in gas industry can be perched as similar in the oil industry. It is also important to note, threats to new entrants encourage speculative approach to market entry which in the long run is harmful to the development of the industry to all the stakeholders; industry, investors and government.

Whenever new firms easily enter a particular industry the intensity of competitiveness increases as the market remains the same. Such new entrants can threaten the market share of existing competitors by bringing additional production capacity to the industry (David, 2009). The threat of entry is a force therefore that refers to the possibility that profits of established companies in the industry may be reduced by the entrants of new competitor organizations (David, 2009; Ehlers and Lazenby, 2010; Pearce & Robinson, 2011; Porter, 2008). These findings are in line with Porter’s factors on threat of new entrants: Economies of scale, product differentiation, capital requirements, switching costs, access to distribution channels and Government Policy (Lopez-Claros et al., 2008; Porter,2013; PWC, 2012; 2014).Another study by PWC in 2014 on Africa titled ‘On the Brink of a Boom Africa Oil & Gas Review’ showed that, South Sudan has an investment promotional act which provides incentive to the community and investor. Investors are given capital allowance of 20% to 100%, deductible annual allowance of 20% to 40%, and depreciation allowance of 8% to 10%. In return, investors in the oil industry are required to construct infrastructure such as roads, schools and hospitals in their area of 38

operation. The impact of such encouragement by the government to the new entrants remains unknown and is further explored in this study.

Economies of scale are achieved when production is increased during a given period, and this result in lower production costs because of the spreading of costs over a larger number of units (Ehlers and Lazenby, 2010). This means that the per unit cost of production becomes lower and lower over time. Companies enjoying economies of scale can afford to keep prices constant and increase profits. Sometimes they can lower their prices to a point where other companies cannot match. Economies of scale can also arise from effectiveness and efficiency in distribution, utilization of sales force or even technology, financing and from other functions of business (David, 2009). New entrants do not usually have the advantage of economies of scale. However, companies diversifying through acqusition into an industry from other markets with intend to build market position can cause major shake-ups (Porter, 2008; PWC, 2012). They should also be viewed as new entrants.

Product differentiation relates to the belief by customers that a company offers a unique product. Customers’ perception is that the product is different from competing products and close substitutes. The customers are loyal to the organization and do not easily shift their loyalty (Pearce & Robinson, 2011). Product differentiation means that established organizations have brand identification and customer loyalties, which stem from past advertising, customer service, product differences, or simply being the first in the industry and benefiting from first movers advantage (Pearce & Robinson, 2011; Porter, Lorsch, and Nohria, 2004; Ehlers and Lazenby, 2010). New entrants have to spend heavily to overcome existing customer loyalties. The process is likely to take a long time.

New entrants require considerable resources to compete in a new industry. Capital is required to buy physical facilities, production facilities, inventories, to meet start-up costs, to carry out marketing activities, and to bridge customer credit among other costs (Ehlers and Lazenby, 2010). The need to spend huge capital resources in order to compete in a new industry creates a barrier to entry. Sometimes there may be need to spend resources on risky or unrecoverable up-front advertising or research and development. These are barriers to entry for new comers and can be discouraging in themselves. Even if capital is 39

available on the capital markets, entry represents a risky use of that capital. Existing or going firms have a cushion or advantage in this regard (Porter, Lorsch, and Nohria, 2004).

Switching Costs are once-off costs which customers incur when they switch from one supplier’s product or service to another (David, 2009; Ehlers and Lazenby, 2010). Switching costs create a barrier to entry. Switching costs may include employee retraining costs, cost of new ancillary equipment, cost and time in testing or qualifying a new source, product redesign, or even psychic costs of severing a relationship (Porter, Lorsch, and Nohria, 2004). New entrants can overcome these high swtiching costs by offering either a substantially lower price or a much better product or service to attract customers. This engenders improvements in cost or performance, is an expensive affair.

Access to distribution channels is important as new entrants must get their products to customers. New entrants have to create their own distribution channels for a product, especially where wholesale and retail channels are limited, or have to persuade distributors to carry their products (Ehlers and Lazenby, 2010). Existing competitors may have close ties with channels based on long term relationships, high quality service, exclusive relationships and even some form of control. This creates a high barrier that is not easy to overcome. When this is the case the option is usually to create own distribution channel. Creating own channels of distribution is expensive and takes a long time. In most cases, new entrants have to convince existing distributors to carry their products through price breaks, cooperative advertising allowances and other benefits, all of which reduce profits (Porter, 2008).

Cost Disadvantages independent of Scale arise from the fact that established firms in an industry may have cost advantages independent of their size or economies of scale, that are not easily replicable by potential entrants. These cost advantages may include favorable access to raw materials, favorable locations, Government subsidies, proprietary product technology (product now-how or design characteristics) kept proprietary through patents or secrecy, and favourable effects arising from learning or experience curve. As a result of learning or experience, there is an observed tendency for unit costs to decline as the firm gains more cumulative experience in producing a product (Porter, 2004; Ehlers and Lazenby, 2010).

It may be difficult for new entrants to duplicate these cost 40

advantages enjoyed by incumbent firms. Reducing the relevance of these advantages by new entrants may be a difficult and expensive undertaking. Government Policy is a major source of entry barriers. Government can limit or even foreclose entry into industries through such control mechanisms as licensing requirements and limits to access to raw materials (Porter, 2008). Other government controls may include requirements for standards for goods and services and also regulations that may affect industries e.g. liquor retailing, trucking, banking, and railroads (PWC, 2014). 2.4.2. Suppliers Bargaining Power on Performance Based on Porter’s Five Force Model, the power of supplier in the oil industry is divided into smaller suppliers and the major suppliers due to capital intensive of the business. Small suppliers have significant power over small drilling and support companies while large suppliers conduct the major supply of rigs and pipeline from the drilling area (World Oil, 2012). Major companies such as ExxonMobil, Royal Dutch Shell, BP, Total, TexacoChevron or TotalFinaElf control the suppliers’ power in labor production by supplying, refining and distributing oil in their own. Unfortunately, these multi-national companies control the production and their own supply hence lower monopoly in the market which limits other suppliers and companies. This has leverage the companies over consumers making supernormal profit (Brown, 2013; Grünig & Best, 2007). Consumers have on the other hand to choose few companies that determine the performance of oil industry. In Kenya, only Tullow has been allowed to explore oil production which gives it a competitive power on extraction, refinery, transportation and even selling. PWC (2012) report indicates that, investors in the oil industry in Africa are on the increase though they are still few who control the market. Porter (2008) clearly outlined monopoly in the market as a factor that increases suppliers’ power and affects competition. Other factors that increases suppliers power that affect competition in the market according to Porter are: supplier is not obliged to contend with other substitute products for sale to the industry, the industry is not an important customer of the supplier group, the supplier’s product is an important input to the buyer’s business, the supplier group’s 41

products are differentiated or it has built up switching costs and the supplier group poses a credible threat of forward integration(Grundy, 2003; Lopez-Claros et al., 2008; Porter, 2013). Further to these studies, suppliers are also known as sellers. They supply inputs to the producers of goods. Their bargaining power can be exerted in an industry by threatening to raise prices or reduce the quality of the goods and/or services they supply. An organization must have a good relationship with its suppliers in order to enhance its operations. It must ensure that it is supplied with the right quantity, right quality, at the right price, at the right time and at the place the inputs are expected to be delivered. Operators in logistics, and in Purchasing & Supply discipline, popularly call these rights the “Rights” of Purchasing and Supply (David, 2009; Porter, 2013). When there is dominance of the supply group by a few companies and the supply group is more concentrated than the industry it sells to, the supplier group can exert its strong power and affect the competitiveness in the industry as a result. If suppliers sell to more fragmented buyers, they are able to exert a lot of influence in regard to business terms such as prices, discounts, quality, and can also the quantity they may determine from time to time (Porter, 2008; CGMA, 2013). Also, where there is no obligation to contend with other substitute products for sale to the industry the suppliers have considerable power. If there are competing substitutes then the suppliers’ power is minimal. When suppliers do business with several industries and a particular industry does not buy substantial inputs or supplies from the suppliers, the particular industry is not an important customer of the supplier group (Porter, Lorsch, and Nohria, 2004). In this case, the supplier group will exert considerable power in negotiating prices, quality and other terms with the particular industry. However, if the industry is an important customer of the supplier group, suppliers’ fortunes will be closely tied to the success of the industry and they will be amenable to giving the industry good terms including good pricing and high quality. When the supplier’s product is an important input to the buyer’s business, then the supplier has considerable power. The buyer has to endeavor to maintain a good relationship with the supplier as the buyer’s manufacturing activities are closely tied to a reliable supply regime (CGMA, 2013). The situation is much more pronounced in the case where the input is not storable, thus forcing the buyer to build up stocks of inventory 42

or engage in strategies such as “Just-In-Time” supply to ensure regular supply as per the manufacturing schedules and plans (Porter, Lorsch, and Nohria, 2004). In situations where the supplier group’s products are differentiated or it has built up switching costs the options of the buyer to play one supplier against another are minimized quite considerably or completely cut off. If the supplier faces switching costs, the effect is the reverse. Under the conditions where the supplier group poses a credible threat of forward integration the industry’s ability to improve on the purchasing and supply terms is minimized and remains under check until the situation changes (Porter, Lorsch, and Nohria, 2004). 2.4.3. Threat of Substitution on Performance PWC (2012, 2014), Brown (2013), World Oil (2012) and Grünig and Best, (2007) reports indicates there is a major substitute of oil in the world. With vehicles being the main instrument of transport, oil is largely in demand and intimated to remain so for a number of years as technology try to capture new ventures such as gas, electricity, hydrogen, coal, wind power, and even nuclear. Based on studies reviewed, this is the least influential factor among the five factors that determine competition of oil industry as substitute products in the market are controlled by a number of market factors as discussed. Competitive pressures arising from substitute products or services increase as the relative price of substitute products or services declines and as buyers’ or consumers’ switching costs decrease (David, 2009). As David (ibid) observes, the competitive strength of substitutes is best measured by the inroads into the market share those substitutes obtain, as well as those firms’ plans for increased capacity and market penetration. Such are determined by the market forces of demand and supply and in the event where the supply of the original and quality products are available in the market, the substitute products have minimal influence. However, when the products are not in the market, it increases the demand, the price increases and demand of substitute products increase. Firms have to be aware of the substitutes that exist in other industries as the substitutes are a major determinant of competitiveness in an industry. Identifying substitute products is a matter of searching for other products or services that perform the same function as the product of the industry (Porter, Lorsch, and Nohria, 2004). If there are no substitute 43

products or services, the threat will be low or nonexistent, but if there are substitutes, firms in the industry must worry because of the threat of competition the presence of the substitutes will pose (David, 2009; Porter,2013; Grundy, 2003). David further states the substitute products can defy the market forces depending on the type of the products. BP (2014) and PWC (2014) also outlined oil from the ‘black’ market can be purchased in the market in the presence of the original products hence affect the performance of oil products in the market based on the number of gallons, workers and market price in the industry. Such products that are mandatory regardless of the quality offered in the market defy the market forces of demand and supply. 2.4.4. Effect of Bargaining Power of Buyers on Performance The balance of power in the oil industry is shifting toward buyers. According to World Oil report (2012), this has been necessitated by the fact that oil as a commodity from one company to another is the same. This leaves the buyer with the power to seek for lower prices and better contract terms with the oil industry. Similarly, Deloitte research (2006 as cited by Grünig and Best, 2007), buyers competition is mainly price driven as long as the standards in the oil industry have been maintained. Further, with lack of substitutes, buyers tend to go for the cheapest product hence the industry performance is forced to abide by the power of the buyer. According to PWC (2012), many consumers of oil products have little interest in products that exceed basic standards for they are unwilling to pay more for such products and are constrained by lack of oil substitute products. Most industries have realized this and are working on increasing the quantity at a lower or same cost because buyers have power in the industry and are willing to spend less for more product benefit. An example is shell Kenya which introduced shell fuel saver which is a competitive high-performance fuel at the same cost (Munyua, 2014). Oppositely, buyers have low bargaining power in gas industry in South Sudan which can be enhanced by introduction of additional supply and new infrastructure (PWC, 2012). According to Grundy (2003), Ehlers and Lazenby (2010), a buyer group is powerful if the following circumstances are true: they purchases large volumes relative to seller sales, products purchases from the industry represent a significant fraction of the buyer’s costs 44

or purchases, products purchases from the industry are standard or undifferentiated, faces few switching costs, and earns low profits, buyers also pose a credible threat of backward integration if the industry’s product is unimportant to the quality of the buyer’s products or services, and when the buyer has full information. When consumers are concentrated or are large or buy in volume, their bargaining power is a major force that affects the intensity of competition in the industry of their operation. If a large portion of what is produced is purchased by a given buyer this raises the importance of the buyer’s business in results (PWC, 2012; Porter, 2008). Under the circumstances the buyer has a very powerful negotiating position compared to buyers who buy in small quantities. Such buyers also determine the quality of products purchased due to their buying power, if the buyer prefers high quality, the industry will produce such quality and if large scale buyers prefers lower quality, then the industry will produce low quality based on the demand of such buyers. Similarly, if the products a buyer purchases from the industry represent a significant fraction of the buyer’s costs or purchases, the buyer will look for favorable prices from the seller and is most likely to purchase selectively based on the price offered in sellers (Grundy, 2003). This gives the buyer a vantage point in bargaining for better quality, discounts and delivery. However, it the bargaining power of buyers is less, they constitutes a small proportion of the industry production hence minimal bargaining, quality and delivery power. This also applies when the buyers are fewer prices sensitive and what they buy from the industry constitutes a small proportion of their costs. Small scale buyers have minimal effect on the power of buyer. In situations where the product buyers purchase from an industry is standard or undifferentiated edit means that buyers can obtain their needs from different sources of supply. Under the circumstances, buyers also have the power to play one supplier against another in their bid to get the best deals possible. Switching costs are costs incurred in switching from one buyer to another. Switching costs lock a buyer to a particular seller or sellers. If the seller faces switching costs then the buyer’s position to bargain for better deals is enhanced (Ehlers and Lazenby, 2010; Porter, Lorsch, and Nohria, 2004). Low profits create great incentives to lower purchasing costs. If the customer or the buyer earns low profits then the buyer is powerful. If buyers either pose a credible threat of 45

backward integration or are partially integrated, they are in a powerful position to demand bargaining concessions (Ehlers and Lazenby, 2010; Porter, 2013, 2008). This power of buyers can be partially reduced or neutralized when firms in the industry offer a threat of forward integration into the buyer’s industry. When the industry’s product is unimportant to the quality of the buyer’s products or services, then the buyer is powerful. When the quality of the buyer’s products or services is very much affected by the industry’s product, buyers are generally less price sensitive. Porter (2008) observes that industries in which this situation exists include oil-field equipment, where a malfunction can lead to large losses. Further, where the buyer has full information about demand, actual prices, supplier costs and possibly about the planned strategic moves of suppliers, this usually yields the buyer greater bargaining leverage than when information is poor. With full information, the buyer is able to negotiate the best purchase terms possible based on the market offer or competitive market forces. 2.4.5. Impact of Rivalry between Firms on Performance According to World Oil Report (2012), the slow growth rate in the oil industry is the biggest challenge facing some rivalry firms in USA. Using the Porter’s Five Forces, Grünig and Best (2007) shows the degree of rivalry depends on product differentiation which is limited in the oil industry. Automotive has been homogenous for decades though there is considerable overcapacity in some refining industry. Since 2005, there have been a rise in the refining margins in Europe; from less than 3 US$ in 1990s, to more than 7 US$ in mid-2005. With the many industry firms and the strict competition in EU countries, the concentration ratio is low hence high competition which increases performance. In Africa, the current demand in the oil industry is high characterized by high demand, continues exploration and investment by multi-nationals. However, rivalry between firms is on the minimal due to few players in the industry and entry of a number of potential rivals could create more competition and reduce the current high level of industry concentration (PWC, 2012). Porter’s five forces on the study of Gas in South Africa revealed the rivalry was neutral but could be revived by entrants of more industry. Similarly, in most African countries, only one or two firms are involved in oil production which leaves high level of industry concentration that can create monopolization. 46

According to African Development Bank and Africa Union report in oil production in Africa (2009) and also mentioned by PWC in a 2014 report on the oil trend in Africa, the major multi-national companies entering Africa since 2014 are: Sonangol (Angola), Sonatrach (Algeria), Statoil (Norway), ONGC (India), PetroSA (Ghana), CNPC and Sinopec (China), Statoil, Gazprom (Russia) and CNOOC (China) eyeing Tanzanian. Such companies are also mentioned in National Oil Corporation report on 32 multi-national companies in Africa. With high capacity for production, there is need for more firms in the industry which will increase competition and production of oil products. Further, World Oil, (2012) cluster oil firms into two based on their market share, capital investment and capability; smaller and major largely due to capital intensive of the business. Small companies compete with other small companies in over small drilling and support companies while large companies (multi-nationals) compete for the major supply of rigs and pipeline from drilling area. This explains rivalry in different specs. Porters’ explains this by outlining a number of factors that influence rivalry between firms as it emerged in the above studies. These factors are numerous or equally balanced competitors in the industry, slow industry growth, high fixed or storage costs, lack of differentiation or switching costs, Capacity Augmented in large increments, diverse competitors, high strategic stakes and high exit barriers (Grundy, 2003; Ehlers and Lazenby, 2010; Lopez-Claros et al., 2008; Porter, 2013). Numerous or equally balanced competitors in an industry can bring about intensity in competition. Intense competition is common in industries with many players or actors. Where an industry has numerous firms, the likelihood of mavericks is great and some firms may habitually carry the belief that they can make moves without being noticed by their competitors (Porter, 2008). Rivalries and intense competition are also common in industries with a few actors of equivalent size and economic power as they constantly jockey for position (Porter,2013;Ehlers and Lazenby, 2010). Under such conditions instability is likely to be created as the competitors fight each other. Where an industry is dominated by one or a few players the leaders can impose some discipline in issues such as pricing. Slow industry growth turns competition into a market share game for firms seeking to expand in the industry (Porter, 2008). If industries are still growing, there is reduced 47

pressure to attract customers away from competitors. Competition in static or slow growing markets is, however severe, because companies battle to increase their market share by attracting customers from competing firms (Ehlers and Lazenby, 2010). Competition is a great deal more volatile in this case as indistries fight to outweigh each other for survival. The more the market share an industry command in mining, processing, distribution, oil waste menagement or oil poping, the stronger the company in each sector or in all the sector as depictred in most mulit-national companies. High fixed or storage costs create strong pressures for the firms affected to fill capacity. Organizations try to maximize the use of their productive capacity when their fixed costs are high. This often leads to rapidly escalating price cutting by firms in an industry (Ehlers and Lazenby, 2010; Lopez-Claros et al., 2008; Porter, Lorsch, and Nohria, 2004). Firms cut the price of their products and offer rebates and other special discounts and offers in order to reduce their inventories and thus lower storage costs. Also, lack of differentiation or switching costs influences the nature of competition in an industry. If companies in an industry successfully differentiate their products there is less rivalry between competitors (Porter, Lorsch, and Nohria, 2004). Product differentiation creates layers of protection or insulation and loyalties to particular sellers. If buyers view products as similar and therefore as undifferentiated, competition increases (Ehlers and Lazenby, 2010). World Oil report (2012), supports this when they stated oil as a commodity from one company to another are the same hence competition based on customer loyalty, quantity and quality of production and the market intensity in the oil industry. Capacity Augmented in large increments is an important factor in the intensity of rivalry. In some instances economies of scale dictate that capacity must be added in large increments. This can be chronically disruptive to the industry in terms of supply/demand balance (Porter, 2008). This is particularly the case where there is the risk of bunching capacity additions. Where this happens the industry may experience recurring periods of over-capacity and price cutting. Where there is over capacity, competitors try to lower prices in order to get rid of the excess capacity. This competition on the basis of price does hurt the firms even though it may be a boon to buyers.

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Diverse competitors often have diverse strategies or game plans in terms of how to approach competition in their industry. They may also be diverse in terms of origins, personalities, networks, partnerships, sources of supply of raw materials, and in terms of relationships with their parent companies. These differences mean that the companies have different goals and strategies on how they compete in their industry. Under the circumstances the companies may not agree on the “rules of the game” in the industry, a good thing for competitors and the health of the industry, and will often run head on into each other in their operations (Porter, Lorsch, and Nohria, 2004). Where this happens to be the case, intensity of rivalry increases. High strategic stakes that some competitors may have in an industry can cause high volatility in terms of rivalry. For example, a diversified firm may place great importance on achieving success in a particular industry in order to further its overall corporate strategy (Porter, Lorsch, and Nohria, 2004). Achieving success in the industry may be achieved through the firm solidifying its position in the industry through expansion. Expansion requires financing and this may be achieved through sacrificing overall profitability in order to achieve the desired expansion and the positioning required. The result is industry destabilization. With high strategic stake, exit is a problem, meaning inability to exit or move away from an industry easily. According to Ehlers and Lazenby (2010) and also outlined by Porter (2008), exit barriers are economic, strategic and emotional factors that keep companies competing in businesses even though they may be experiencing low or even negative returns on their investment and their assurance of long term profit potential in the industry may be in doubt. Common exit barriers include highly specialized assets to the particular business or industry or location; fixed costs of exit such as labor agreements; relocation/resettlement costs; strategic interrelationship costs such as where facilities or markets may be shared; emotional barriers such as loss of pride and prestige, loyalty to employees, fear for one’s own career and other factors, and government or social restrictions such as government’s concern for job losses and the effect on the regional economic situation (Ehlers and Lazenby, 2010; Porter, Lorsch, and Nohria, 2004). When exit barriers are high and the above factors are at play, competitors resort to extreme tactics trying to remain afloat and rivalry is intense. As a result the profitability in the industry can be persistently low.

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2.5 Chapter Summary

Chapter two reviewed literature regarding the study as follows: presentation of the Porter’s five forces, strategic management theory, and further, other studies conducted on oil industry performance using Porter’s five forces has been presented with South Sudan oil industry as a structure for analysis and lastly the conceptual framework. The next chapter looks at the methodology used for the study.

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CHAPTER THREE 3.0. RESEARCH METHODOLOGY 3.1 Introduction This chapter describes the methods and procedures used in the study to satisfy the research objectives. It includes the research philosophy, research design, population of study, sampling design, data collection method, and data collection instruments. Research procedure followed during the study and how collected data was analyzed is also presented. 3.2 Research Philosophy Over the years, several philosophical world views and paradigms have emerged due to the growth in social science research. The difference in ideology has resulted in different paradigm that one can use to solve research problem (Cohen, Lawrence, & Morrison, 2000). In choosing the paradigm, one must look at the content and process of creating the knowledge through research as well as the type of study conducted and nature of data. This is called epistemology which informs the various philosophy as Positivism, Interpretivism and Realism as applied in a number of researches (Creswell, 2013; Polit and Beck, 2004; Saunders et al., 2003). Positivism reflects the philosophical stance of the natural scientist. The end product of such observable social reality can be law-like generalizations similar to those produced by the physical and natural scientist (Creswell, 2013; Saunders et al., 2003). Further, positivism confines its output to data collected over a long period of time based on metaanalysis; big data analysis. Those critical of this philosophy argue that rich insights into this complex world are lost if such complexity is reduced entirely to a series of law-like generalizations of the output or low data (Cooper and Schindler, 2005). On the other hand, Interpretivism looks at what Remenyi et al., (1998) call “the details of the situation to understand the reality or perhaps a reality working behind them.” This follows from interpretivists’ position that, it is necessary to explore the subjective meanings motivating people’s actions in order to be able to understand these. Social science methodology opposes positivists and states that there is a difference between sociology and natural 51

science. It applies the principle that understanding human is key before studying them based on the social reality. Further, validity and reliability of representativeness is key in interpretivists (Cooper and Schindler, 2005; Saunders et al., 2003). Many interpretivists consider generalization to be of less value due to different nature and environment of study. On the other hand, realism is based on the belief that a reality that is independent of human thoughts exists. Social objects and phenomena that are external to, or independent of, individuals will therefore affect the way in which these people perceive their world, whether they are aware or not such as language, culture, beliefs, schemes and practices. Realism recognizes that people are not objects to be studied in the style of natural science but human who behave the way they do because of the environmental influence consciously or unconsciously (Saunders et al., 2003; Remenyi et al., 1998). It also recognizes the importance of understanding people’s socially constructed interpretations and meanings, or subjective reality, within the context of seeking to understand broader social forces, structures or processes that influence, and perhaps constrain the nature of people’s views and behaviors (Cooper and Schindler, 2005). The orientation used in this study assumes positivist philosophical paradigm. The positivists tend to assume that a single, objective reality exists independent of what individuals perceive; they share the fundamental belief that the material world of tangible objects does not exist unperceived. They place a high priority on identifying causal linkages between and amongst variables. The positivists view involves: the observation of real world facts or phenomena, the formulation of explanations for such facts or phenomena using inductive processes, the generation of predictions about real world phenomena using the previously formulated explanations and deductive processes, and lastly the attempted verification of these predictions through systematic, controlled experimentation or observation (Polit and Beck, 2004; Remenyi et al., 1998; Saunders et al., 2003). In view of the philosophical orientation adopted for this study, ‘influence of Porter’s five forces on performance of oil industry’ survey method was used to obtain the relevant data used to identify relationship between Porter’s five forces variable and oil industry performance. This approach presents the observation using descriptive statistics, identify 52

and explain linkages using correlation, test the effect of the linkage using regression, and provide an opportunity to develop a broad-based understanding using previous studies and secondary data on performance of oil industry in South Sudan using five market forces by Porter’s. 3.3 Research Design A research design is a framework that constitutes the blue print for collection, measurement and analysis of data (Cooper and Schindler, 2005; Oladipo et al., 2015; Saunders et al., 2003). The above authors further argue that research design describes the procedure for conducting the study, including when, from whom and under what conditions the data would be obtained. On the other hand Seal (2004), describes research design as an intergraded map of the research project that determines the most suitable method of investigation, the nature of the instruments, the sampling plan and the types of data. According to Cooper and Schindler (2005), a number of research designs exist but the main categories are: exploratory, descriptive, causal and correlation design. Descriptive design describes phenomena associated with a subject population according to Cooper and Schindler (2005). In this study, the phenomena were the competition at micro and macro-economic levels that affects performance based on the five Porter’s forces model. This study employed the two approaches of descriptive design namely, descriptive and analytic research. Descriptive research aims at describing phenomena or narrating how various behavior and events occur. On the other hand, the analytic research seeks to establish relationships among phenomena or variables by asking “what” and “why” certain behaviors occur and “how” these behaviors relate to other types of behaviors and other variables. This study employed the two approaches or forms of survey namely: the descriptive survey research and the analytic survey research. Descriptive survey research aims at describing phenomena or narrating how various behaviors and events occur. On the other hand, the analytic survey research seeks to establish relationships among phenomena or variables by asking “what” and “why” certain behaviors occur and “how” these behaviors relate to other types of behaviors and other variables. Further, the market structure was divided into five forces as stated by Porter. The combined form creates descriptive design 53

and structural analysis as proposed by Porter (2008) and applied in a number of researchers. 3.4 Population In a research, population refers to the total collection of elements about which the researcher wishes to make inference. It is the universe of people, place or things to be investigated (Kombo & Tromp, 2006). The total population of interest in this study was all the middle and top managers in the oil companies in South Sudan who are members of the Chamber of Commerce and Industry. According to the chamber of commerce and industry there are more than 54 large oil industry players in South Sudan but the number of oil industries involved in extraction and with offices in Juba were identified physically to be 21. All the oil firms in South Sudan constituted the population of study. However, firms operating in Juba constituted the sample due to three reasons: Juba has the head offices of major oil firms in South Sudan where middle and top managers worked; secondly, these firms were located within the geographical area of study and lastly; oil firm offices in Juba were selected due to accessibility as oil firms in South Sudan were inaccessible and some had closed down due to ongoing war at the time of this study. The middle and top managers were selected because they understood the operation of the companies, were involved in company strategy and actualization of such strategies hence the best in giving the required information in this study. 3.5 Sampling Design A research sampling design is that part of the research plan that indicates how cases are to be selected for observation (Cooper and Schindler, 2005; Saunders et al., 2003). The design therefore maps out the procedure to be followed to draw the study’s sample based on sample size. In this study, sampling design was divided into sample frame and sampling technique.

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3.5.1 Sampling Frame According to Oladipo et al. (2015) a sample frame is the detailed presentation of population of study outlined in a table or figure. It also guides the outline of sampling. In this study, though there were 54 oil companies registered and operating in South Sudan, there was no data on their characteristics inclusive of the locations at the time of the proposal development for this study. However, the physical counting and confirmation of the number of oil companies in Juba shows there were 21 oil firms operating in Juba as indicated in table 3.1. Table 3.1: Sample Characteristic of Oil firms in South Sudan. Ownership

No.

Locally owned

3

Internationally owned

6

Joint venture

4

Government owned

8

Total

21

3.5.2 Sampling Technique Literature shows that sampling can be divided into two broad categories: probability or representative sampling and non-probability sampling (Saunders et al., 2003; Kothari; 2003; Kombo and Tromp, 2009). Non-probability sampling is used in large-scale surveys where the subjects are not known and thus non-random selection is used (Kothari, 2003; Kombo and Tromp, 2009).According to Saunders et al., (2003), there are four types of non-probability sampling: convenient, snowball, quota and purposive or judgmental sampling. On the other hand, probability sampling allows for the calculation of the desired sample size for the margin of error the researcher will agree to (Polit and Beck, 2004). Similarly, there are four types of probability sampling: systematic, simple random, stratified random and cluster sampling (Cooper and Schindler, 2005; Saunders et al., 2003; Polit and Beck 2004). This study employed census design. According to Kombo and Tromp (2009) and Oladipo et al. (2015), census involved including all the population into the study (100%) 55

and is convenient when the total population is less than 100. Further, census gives equal opportunity for participation, and yields representative results from all the oil industry sectors involved in the oil industry, mining, exploration, transportation, and piping. Lastly, census is easy to conduct as it involved giving every person in the population a questionnaire (Kombo and Tromp, 2009). Top managers were involved in previous studies by Brown (2013), and PWC(2012). 3.5.3 Sample Size It is the number of items to be selected from the universe to constitute a sample (Kothari, 2003; Kombo and Tromp, 2009; Oladipo et al., 2015). As indicated in table 3.1, all (census) 21 oil firms operating South Sudan were also involved in the study. These companies were identified physically and counter checked with the Chamber of Commerce and Industry for authenticity. All the middle and top managers of these companies were involved in the survey. The areas of focus were on the buyers, suppliers, new entry, rivalry and firms producing substitute products. These areas attracted the involvement of section/departmental managers in oil firms where only the general manager existed. All the 84 middle and top managers in the 21 oil firms were involved in the study. 3.6 Data Collection Methods According to Saunders, Lewis and Thornhill (2012) triangulation method involves the use of more than one form of data collection to test the same hypotheses within a unified research plan. Lisle (2011) posits that mixed methods research represents a unique way of seeing and investigating the world, an approach that is congruent with positivism philosophies. Moreover, Kothari, (2003) supports mixed method research in the study of complex and multiplex social issues as this because it enables the researcher to achieve important legitimating goals, such as greater transferability and overcome the deficiencies of employing one method. Additionally the use of analytic triangulation provides stronger evidence to support. The researcher used questionnaire with both qualitative and quantitative questionnaire that covered all the five clusters based on Porter’s model. Further, observation of oil industries in South Sudan and secondary data from chamber of commerce was used. 56

Because of poorly answered questions on qualitative, and lack of secondary data in Southern Sudan, the researcher heavily relied on primary data, and secondary data outside South Sudan as a nation.

3.7 Research Procedures

The researcher obtained an introduction letter from United States International University – Africa to conduct this research. The letter was presented to the government of South Sudan specifically the National Oil and Chamber of Commerce Offices which regulate the oil industry. The researcher received another acceptance letter from the industry regulators to collect data based on South Sudan regulation. It’s based on these two letters that the researcher approached 15 people for pilot of the questionnaire, 5 from friends working in the oil industries, 5 from managers working in oil industries in the outskirts of South Sudan and 5 from oil processing firms.

The researcher approached each of the target population for pilot, explained the purpose of the study and requested for their participation. A weekly follow up was done for a month; 10 questionnaires were returned from two organizations representing 67% response rate. This warned the researcher of the possible slow rate of return at the main study. Out of the 10 returned, 8 were fully completed representing 80% of returned questionnaires (table 1). However, the researcher noticed some questions were poorly answered across especially on financial information. The returned questionnaires were analyzed using SPSS v20 to test for reliability using Cronbach’s alpha test; inter-item correlation, and description for item, scale and the scale it some items were to be deleted.

Table 3.2: Summary of Reliability N Cases

%

Valid

8

80.0

Excludeda

2

20.0

10

100.0

Total a. Listwise deletion based on all variables in the procedure. 57

3.7.2 Reliability of the Instrument Reliability and validity are critical elements of good measurement practices (Salkind, 2003). Reliability refers to the consistency and stability of scores obtained from an instrument, (Creswell, 2005; Kothari, 2003). Structured questionnaire was used to obtain information. Cronbach’s reliability alpha was used to test for reliability of the constructs used in the analysis. For a construct to be considered reliable, the Cronbach’s alpha should have a value ranging between 0.8 and 1.00; for it to be considered acceptable its value should range between 0.70 and 0.80 while Cronbach’s alpha with value less than 0.70 is considered unacceptable (Nunnally, 1978). The reliability (table 3.3) shows an internal consistency of .858 (85.8%) which is highly reliable as many researchers have attributed reliability of above, 70 (70%) as high. Table 3.3: Reliability Output Cronbach's Alpha Based on Cronbach's Alpha

Standardized Items .858

N of Items .856

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A further analysis on inter-reliability of items on scale if some items were to be deleted shows there will be minimal effect on reliability if a question is to be deleted: Cronbach’s highest bond at .875 and lowest bound at .834. This result as shown in table 3 means, the questions were well answered hence highly reliable to give further information as the difference of overall Cronbach’s (.858) with the highest Cronbach’s bond (.875) and lowest bond (.834) were minimal. Table 3.3on item statistic of each item is indicated in appendix III. 3.7.3 Validity of the Instrument Validity refers to the ability to gain meaning and sense from the scores obtained from an instrument or the degree to which an instrument measures what it purports to measure (Creswell, 2005; Saunders et al., 2003; Oladipo et.al. 2015; Polit and Beck, 2004). Validity is also important in the sense that it helps in drawing accurate conclusions from scores collected from instruments (Creswell, 2005). Validity can be internal or external.

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The experiences of participants can threaten internal validity and can affect conclusions drawn about a study (Creswell, 2005; Saunders et al., 2003, Kothari, 2003). The correct cause and effect can be drawn if correct error term is minimized on the design such as selection biased, poor sampling, unresponsive respondents and missing data are accounted for in the design (Oladipo et.al. 2015). The process of selecting participants for a study is another internal threat to validity. To reduce the threat of selection bias to internal validity, this study used census which is has systematic structure hence reduced biasness. External validity involves generalizing the results of the study to other areas or populations. To ensure external validity in this study, all oil operators in the oil industry were involved in the study. This is based on correct inferences and sampling size (Creswell, 2005; Kothari, 2003; Oladipo et.al. 2015). Further, in this study content and face validity was used to assess the validity of the instruments by assessing the adequacy, appropriateness, inclusiveness, and relevancy of the questions to the subject under study. After data entry of the pre-test questionnaire, the researcher realized part three of the questionnaire that measures performance was poorly filled, out of the 10 returned, only one (10%) indicated the previous annual revenue of their company. Based on the feedback received during the pilot, this was attributed to fear and lack of publicity of annual financial statement as required by law in other developing countries like Kenya. The researcher planned to ascertain confidentiality during the data collection and also try to obtain financial information from other sources. 3.7.4 Administration of the Instrument The researcher obtained an introduction letter from United States International University – Africa to conduct this research. The letter was presented to the government of South Sudan specifically the National oil and chamber of commerce offices which regulate oil industry. The researcher received another acceptance letter from the industry regulators to collect data based on South Sudan regulation. After the approval, the researcher obtained the list and contact of all CEOs from the Chamber of Commerce. It’s from this list that the researcher counterchecked the authenticity of 21 oil firms operating in Juba. The researcher then selected 3 oil supporting 59

firms with offices in the outskirts of Juba to be involved in pilot study only as they were not included in the final data collection or analysis. Data collected in the pilot study was analyzed and reliability checked using alpha Cronbach. This helped the researcher to identify any ambiguous and unclear questions which were restructured and some dropped. During the pilot, the researcher was available to clarify any questions that were not clear and received recommendation from the respondents. After the pretest and correction of questionnaire, the researcher sent the introduction letter to all the CEOs prior to data collection. The researcher then made a formal contact with all the CEOs, explaining the purpose of the research, and agreed on the best way that the researcher would collect the data from the middle and top managers who were willing to participate. The involvement of the CEOs was necessitated by their length of service in the oil industry hence rich knowledge required in this study. Further, the managers only accepted to be involved in the study with the approval of the CEO. At the same time, a group of research assistants were trained on how to collect data, the importance of the survey and how to avoid conflict which is key factor for successful data collection. Each questionnaire had a covering letter stressing the anonymity of respondents as high confidentiality was maintained. The researcher worked closely with research assistants who delivered the questionnaire to the industries under the researchers guide and helped the researcher to collect them. However, all the Chinese firms denied entry and accessibility of their industries hence they were not involved in this study. The research assistants were also threatened severally but the researcher bargained for their lives. In general, the data collected was viewed as suspicious despite the ethical principles applied: privacy of participants, voluntary participation, consent and freedom to withdraw from the process. Participants were also informed on the aim of the study, methods used, the benefit and confidentiality of information provided after data collection as proposed by different researchers like Creswell (2005), and Kothari (2003). In addition, Babbie (2005) stresses the importance of protection against any physical or physiological harm. Despite these principles being followed, the researcher experienced hostility. Although this study proposed focus group discussion among managers, it was dropped because of two reasons: the CEOs were skeptical of discussing any company matters with the competitors, and when the researchers organized focused group discussion secretly, 60

individual managers were hostile and resistance to each other due to war at the mining fields at the time of data collection hence withdrawal of focused group discussion as a tool for data collection due to people’s lives in danger. 3.7.5. Ethical Considerations In research, ethics refer to the appropriateness of behavior and conduct in relation to the rights of those who become the subjects of the study or are affected by it (Creswell, 2005; Saunders et al., 2003, Kothari, 2003, Saunders et al., 2007). Wells (1994) defines ethics in terms of a code of behavior appropriate to academics and the conduct of research. In this study, efforts was made to stick to the ethical principles including privacy of participants, voluntary participation, consent and freedom to withdraw from the process, confidentiality of information provided and anonymity, and participants being fully informed about the aims, methods, and benefits of the research (Creswell, 2005; Kothari, 2003). In addition, Babbie (2005) stresses the importance of protection against any physical or physiological harm. The researcher followed these principles. On voluntary participation, no respondent was coerced into giving out information either by force, by blackmail or by reward. Participants were allowed to decide whether to participate in the study or not. The information received was specifically for academic research purposes. It will therefore not be used in any manner detrimental to the wellbeing of the operators in the oil industry or people in the oil industry. An introduction letter from United States International University – Africa was given to each operator participating in the research explaining the purpose of the research and introducing the researcher. A cover letter signed by the researcher also accompanied each questionnaire, stressing that anonymity of participants and confidentiality will be maintained. 3.8 Data Analysis Method Quantitative Data – data from the questionnaire was cleaned before entry. This ensured completeness and accuracy of the questionnaires as minor errors such as missing pages of questionnaire was identified before entry. Coding for data entry was done in SPSS followed by data entry. After data entry, data was also cleaned before analysis. Data 61

cleaning is the process of ensuring correct data has been entered from the questionnaire based on the screen. Poorly entered data was corrected and variables transformed such as missing data to minimize error during analysis (Cooper & Schindler, 2005; Kothari, 2003). Cleaned data was analyzed thematically using multi-linear regression modeling. According to Kothari, (2003), Multi-linear model identifies relationship of variables based on clustered dependent variables. In this research, the researcher employs this model to analyze the influence of Porter’s five forces on Oil performance. Basic descriptive analysis was performed for demographic data and beginning data followed by correlation of five forces variables to identify their relationship and oil performance. Lastly, Regression tested the magnitude of change of oil performance in relation to each of the five market forces. The findings are presented thematically in graphs and charts. Further, secondary data collected supports the findings. The model that guided the study was: y = β0 + βi x i + ε Where y = dependent variable β0 = Constant i = 1,2,3,4,5 are the research objectives (each of Porters five forces). Hence Performance = β0 + β1 × (objective) + ε The output of each objective accepted or rejected the null hypothesis. To test the hypotheses; H01: Threat of new entrants does not have significant influence on performance of oil industry in South Sudan. The regression model used was: 62

Model 1, Performance = β0 + β1 × Threat of new entrants + ε To test the second hypothesis; H02: Bargaining power of supplier does not affect performance of oil industry in South Sudan. The regression model used was: Model 1, Performance = β0 + β1 × Bargaining power of supplier + ε To test the third hypothesis; H03: Substitute products do not have significant influence on performance of oil industry in South Sudan. The regression model used was: Model 1, Performance = β0 + β1 × Substitute products + ε To test the fourth hypothesis; H04: Bargaining power of buyers does not affect performance of oil industry in South Sudan. The regression model used was: Model 1, Performance = β0 + β1 × Bargaining power of buyers + ε To test the fifth hypothesis; H05: Rivalry between firms does not have a significant effect on performance of oil industry in South Sudan. Model 1, Performance = β0 + β1 × Rivalry between firms + ε A combined model was developed using step method on the effect of adding additional variables of each of the models to form: Model 1, Y1 = β0 + β1 X1 + β2 X2 + β3 X3 + β4 X 4 + β5 X5 + ε Key; y is the performance; β0 is the constant; β1 is the coefficient of the first variable; x1 is the first variable (Threat of new entrants); β2 is the coefficient of the second variable; x2 is the second variable (Bargaining power of suppliers); β3 is the coefficient of the third variable; x3 is the third variable (Substitute products); β4 is the coefficient of the fourth 63

variable; x4 is the fourth variable (Bargaining power of buyers); β5 is the coefficient of the fifth variable; x5 is the fifth variable (Rivalry between firms); and ε is the error term.

3.9 Chapter Summary

This chapter denotes the research methods to be used to in this study. A review of various research philosophies, research design and data analysis approaches, and ethical considerations has been presented. The research design used was descriptive research and the study sample was census with the main data collection instrument being questionnaire. A pilot study was conducted and ethics was followed to the latter. Multi-linear regression was used for analysis of the data in this study. The next chapter outlines the findings of the research and presentations done in descriptive, tables and figures. Chapter five covers the findings summary, discussions, conclusions and recommendations.

64

CHAPTER FOUR 4.0. DATA ANALYSIS AND PRESENTATION 4.1 Introduction This chapter presents the findings of the study based on analysis of the data collected through self-administered questionnaires. The questionnaires were distributed to the middle and top managers of 21 oil firms. Using survey method, a total of 84 questionnaires were given out but only 66 were filled and returned representing 78.6% response rate. The research findings of the study are presented in tables and figures in two phases: phase one has demographic data of respondents and oil companies operating in South Sudan; while phase two presents the thematic analysis based on objectives. The presentations are as follow:

4.2 Demography 4.2.1 Gender and Age of Respondents Of the 66 respondents who participated in the study, 93.9% (62) were male, and 6.1% (4) were female. This shows a great disparity on gender portraying oil in South Sudan as a male dominated field as shown in figure 4.1.

Female 6%

Male 94%

Figure 4.1: Gender of Respondents

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In terms of age, majority of the respondents 36.4% (24) were aged 33-37 years, followed by those aged 28-32 years at 27.3% (18), 38 years and above at 24.2% (16) and 23-27 years as the least age bracket at 12.1% (8). This shows more than half (63.7%) of those in management positions were above 28 years old while 36.3% were below 28 years old. Based on this, the older one becomes, the more likely he ascends to management position.

The comparison of age based on gender shows 75% (3) of the females were 23-27 years while the remaining 25% (1) were 28-32 years. For male respondents, those aged 33-37 years were 38.7% (24) of the respondents followed by 28-32 years at 27.4% (17) and 38 years and above at 25.8% (16). This comparison of age based on gender shows female respondents were at the entry level of management; three quota (75%) aged 23-27 years and none was more than 32 years old. Age of male respondents spread across all the age brackets from youngest (23-27 years) to highest (38 and above years). This portrays male dominance at management level of the oil industry at all age levels as further summarized in the following cross tabulation (Table 4.1).The disparity is based on the health and oil environment factor as explained further in chapter 5.

Table 4. 1: Comparison of Age and Gender

Female

Male

Total

Count % within Gender Count % within Gender Count % within Gender

Age 23-27 years 3 75.0%

28-32 years 1 25.0%

33-37 years 0 0.0%

38 years and above 0 0.0%

Total 4 100.0%

5 8.1%

17 27.4%

24 38.7%

16 25.8%

62 100.0%

8 12.1%

18 27.3%

24 36.4%

16 24.2%

66 100.0%

On the length of service in the industry, the minimum period one had worked was 1.5 years, the maximum was 15 years with a mean of 4 years, median of 3 years, mode of 2 years and SD of 2.71 years. Further comparison of the length of service in the industry based on gender shows, men had worked for longer period than women with a mean of 3, compared to mean of 2. However, the SD of women respondents was high (4) compared 66

to men respondents (2.65) due to disparity of a women who had worked for more than 10 years. This shows majority of employees at management positions have been in those position for less than 4 years hence at the entry level of management position. Despite the gender difference, men at entry level had worked for lesser years compared to women based on the SD comparison.

4.2.2 Role and Working Duration The study targeted middle and top managers. Of the respondents, 63.6% (42) were in middle level management and 36.4% (24) were in top management. All the women respondents (100%) involved in the study were in middle level management which support that women were at the entry level of management as attributed based on age (in table 4.1) and working duration in the industry. The management level of respondents is shown in figure 4.2.

70.00%

63.60%

60.00% 50.00% 40.00%

36.40%

30.00% 20.00% 10.00% 0.00% Top management

Middle level management

Figure 4.2: Management Position of Respondents. Correlation between age and management level shows a positive correlation (P=.005, r=.345) indicating as one advances in age the likelihood of advancing from middle management to top management because of his/her age is 11.9% (r2 of .119). Similarly, there is positive correlation between management position and duration that one has served in the company (P=.0001, r=.534) indicating 28.5% (r2 of .285) likelihood of one 67

ascending from middle management to top management based on the year of service in an oil company operating in South Sudan. This supports the earlier finding indicating that most respondents were older in higher level management compared to those in middle level management. Table that follows summarizes the correlation discussed.

Table 4.2: Correlation between Age, Length of Service and Role

Age

Age

Length of service

Role

r

Length of service

Role

1

p r

0.224

1

p

0.07

r

.339**

.534**

p

0.005

0

1

r = Pearson correlation. p = Sig. (2 tailed). **. Correlation is significant at the 0.01 level (2-tailed). 4.2.3. Industry Background When respondents were asked to state the ownership of the firm where they work as either government, internationally owned, joint venture or locally owned; 36.4% were government owned followed by 25.8% that were internationally owned, 21.2% as joint venture and 16.7% were locally owned. This reflects the ownership of operating oil companies as shown in Figure 4.3. However, based on the researchers’ knowledge and government policy in South Sudan, all the oil mines belong to the government of South Sudan who leases them to oil companies for operation hence the ownership is based on the shareholders of companies and not owners of the oil mines. Despite the ownership and leasing of oil mines, the findings shows government is actively involved in the business as the highest shareholder (36.4%) ownership of the oil companies.

68

40.00% 35.00%

36.40%

30.00% 25.00%

25.80%

20.00% 15.00%

21.20% 16.70%

10.00% 5.00% 0.00% Locally owned

Internationally owned

Joint venture

Government owned

Figure 4.3: Ownership of Oil Firms in South Sudan. Table 4.3 shows a comparison of firm ownership and branches outside South Sudan. More than half of these firms (57.6%) operating in South Sudan have no branches outside South Sudan while the remaining 42.4% have branches outside South Sudan. A further analysis on branches of oil firms in South Sudan based on ownership shows that, all (100%) locally owned firms have branches only in South Sudan while all (100%) internationally owned firms have branches outside South Sudan. As for the government owned firms, 12.5% have branches outside South Sudan with majority (87.5%) operates only in South Sudan. Among the Oil firms owned as joint ventures, 57.1% have branches outside South Sudan while 42.9% operates only in South Sudan. These values are statistically significant at (P=.0005, X2= 41.217, df (3)) meaning, there is a positive strong relationship (X2= 41.217) between ownership and oil firms with branches outside South Sudan; all locally owned firms have branches only in South Sudan; all internationally owned firms have branches outside South Sudan; more than three quota of government owned firms have branches in South Sudan while there is equal distribution for joint venture firms.

69

Table 4.3: Comparison of Firm Ownership and Branches outside South Sudan. Branches outside SS No

Yes

Total

Count

11

0

11

% within Ownership of the firm

100.0%

0.0%

100.0%

% of Total

16.7%

0.0%

16.7%

Internationally

Count

0

17

17

owned

% within Ownership of the firm

0.0%

100.0%

100.0%

% of Total

0.0%

25.8%

25.8%

Count

6

8

14

% within Ownership of the firm

42.9%

57.1%

100.0%

% of Total

9.1%

12.1%

21.2%

21

3

24

% within Ownership of the firm

87.5%

12.5%

100.0%

% of Total

31.8%

4.5%

36.4%

Count

38

28

66

% within Ownership of the firm

57.6%

42.4%

100.0%

% of Total

57.6%

42.4%

100.0%

Locally owned

Joint venture

Government owned Count

Total

Respondents were also asked to indicate the type of business that their firms are involved in. The major type of business that oil firms in South Sudan were involved in are oil mining, oil processor, oil distributors, oil waste management and oil piping. Being a multi-response question, 46.0% of firms were involved in more than one type of oil business and 54% concentrated in only one type of business as follows; 27.0% were in oil mining, 11.1% in oil processor, 9.5% in oil distributor and the least business type was oil waste management and oil piping evenly at 3.2% each. This indicates nearly half of the oil companies in South Sudan are involved in more than one type of oil business. Of the firms involved in more than one type of business, multi-responses analysis on the specific type of business (Figure 4.4) shows 33 firms (26.2%) were involved in oil mining, followed closely by 32 firms (25.4%) involved oil distribution, 28 firms (22.2%) involved in oil processor, 17 firms (13.5%) involved in oil waste management, and 16 firms (12.7%) involved in oil piping. Using non-parametric test, there was no statistical significance between specific business type and involvement in more than one type of 70

business. This means that, there is no specific trend in choosing the kind of business oil firms are involved in and the type of business to be combined. 30.00% 25.00%

26.20%

20.00%

25.40% 22.20%

15.00% 10.00%

13.50%

12.70%

Oil waste management

Oil piping

5.00%

0.00% Oil mining

Oil processor

Oil distributor

Figure 4.4: Specific Type of Business.

All (100%) the locally owned companies in South Sudan were involved in more than one business venture specifically as processor, distributors and waste management. Internationally owned companies core business was oil mining (62.5%) though they were also involved in other business ventures (37.5%) such as processing, distributing, waste management and piping. Joint venture owned companies were evenly distributed across all the business type without any specialization while government owned companies, were involved in all business ventures except on oil waste management and oil piping. On business specialization or the area of core business, the output shows major oil mining firms are government owned, 45.5% and internationally owned, 42.4% while 12.1% are owned jointly and none by locals. A similar trend of ownership is observed for oil distributors, oil processor and oil waste management. Oil processor are owned by government (46.4%), followed by locals (28.6%), joint ventures (14.3%), and lastly international firms (10.7%). For oil processor, 37.5% is owned by government, 28.1% by locals, 21.9% as joint venture and 12.5% by international firms. While for oil waste management, the government and locals own similar share of 29.4% evenly followed by joint ventures at 23.5%, and internationally owned at 17.6%. Oil piping had a different trend with dominance by joint venture firms at 37.5% followed closely by government and international ownership each at 31.3%. 71

As summarized in table 4.4, the government of South Sudan is the major stakeholder of all business ventures: oil processor (46.4%), oil mining (45.5%), oil distributor (37.5%), oil piping (31.3%), and oil waste management (29.4%). International firms have strong base on oil mining (42.4%) and oil piping (31.3%), while their other business venture are less than 20%. Joint ventures have invested in oil piping (37.5%), oil waste management (23.5%), and oil distribution (21.9%). Lastly, locally owned companies have a stake in oil waste management (29.4%), oil processor (28.6%), and oil distributor (28.1%) while they have no stake in oil mining and oil piping (0%). Oil mining has dominance by internationally owned firms and government owned firms while oil processor, oil distribution and oil waste management is dominated by government and locally owned firms. Table 4.4: Business Type and Ownership of the Firm. Ownership of the firm Locally owned 0 0.0%

Internation ally owned 14 42.4%

Joint venture 4 12.1%

Governme nt owned 15 45.5%

Total 33

Oil mining

Count % within $business_type

Oil processor

Count % within $business_type

8 28.6%

3 10.7%

4 14.3%

13 46.4%

28

Oil distributor

Count % within $business_type

9 28.1%

4 12.5%

7 21.9%

12 37.5%

32

Oil waste manageme nt

Count % within $business_type

5 29.4%

3 17.6%

4 23.5%

5 29.4%

17

Oil piping

Count % within $business_type

0 0.0%

5 31.3%

6 37.5%

5 31.3%

16

Count % of Total

9 14.3%

16 25.4%

14 22.2%

24 38.1%

63 100.0 %

Total

Generally, oil firms have more permanent than casual employees though the minimum and maximum number of casual employees were more than permanent employees. 72

Representation of permanent employee: mean = 227, median = 200, mode= 100/150/200, SD=150, minimum number of employee= 5 and maximum number of employee =500. While for casual employee; mean = 188, median = 100, mode= 100, SD=305, minimum = 25 and maximum number of employee =1500. Table 4.5 shows this.

Table 4.5: Length of Service and Number of Employees Statistics Length of

Length of

No. of

No. of casual

service

operation

permanent

employees

employees Valid

66

66

62

56

Missing

0

0

4

10

Mean

4.000

8.23

227.15

188.02

Median

3.000

7.00

200.00

100.00

Mode

2.0

7

100a

100

Std. Deviation

2.7075

4.140

149.948

305.004

Minimum

1.5

3

5

25

Maximum

15.0

22

500

1500

N

A mean comparison of the number of employees based on ownership of the firm shows that government owned firms were the biggest employer for both permanent and casual employees. For permanent employees, government owned firms had a mean of 314 employees followed by Joint ventures firms with a mean of 179 employees, internationally owned firms had a mean of 169 employees and lastly, locally owned had a mean of 168 employees. Similarly, for casual employees, government owned firms had a mean of 328 employees, Joint ventures firms had a mean of 140 employees, internationally owned firms had a mean of 65 employees while locally owned had a mean of 55 employees. The mean comparison of both permanent and casual employees is indicated in table 4.6.

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Table 4.6: Cluster of Employees Based on Employer.

Ownership of the firm Locally owned N Valid Mean Median Std. Deviation Minimum Maximum Internationally N Valid owned Mean Median Std. Deviation Minimum Maximum Joint venture N Valid Mean Median Std. Deviation Minimum Maximum Government N Valid owned Mean Median Std. Deviation Minimum Maximum

No. of permanent employees 11 167.73 115.00 156.594 5 500 14 169.00 150.00 107.479 76 500 13 179.38 150.00 164.511 11 500 24 314.17 350.00 124.032 110 500

No. of casual employees 11 55.64 50.00 33.395 25 120 12 65.17 65.00 21.328 30 100 9 140.00 105.00 128.038 30 450 24 328.13 125.00 422.387 40 1500

4.2.4. Duration of Operation Variable The duration on which a firm has been operating in South Sudan was the key moderating variable. As to the findings, the mean duration that oil firms had operated in South Sudan was 8.23 years, median and mode of 7 years, and SD of 4.14. The new firms in the market had operated for 3 years and maximum number of years a firm had operated in South Sudan was 22 years. Using cumulative frequency, only 24.2% of the firms had operated in South Sudan for a maximum of five years; since South Sudan attained independence on July 2011. This shows that, most of the firms (75.8%) had been in existence in South Sudan long before independence. Further description shows 22.7% of the firms have been operating for 7 years which is the median and mode of all firms, 13.6% had been operating for 10 years and similar number (13.6%) having been in operation for 5 years only. In conclusion, most firms have been operating in South Sudan for 5 to 7 years indicating 74

they started their operation towards or immediately after South Sudan independence in July 2011.The preceding presentations are descriptive and inferential of each research question.

4.3. Threat of New Entrants on Performance of Oil Industry in South Sudan This was the first research question of the study and the analysis tests the hypothesis ‘H01: Threat of new entrants does not have significant influence on performance of oil industry in South Sudan’. Threat of new entrants on Porter’s five forces of industrial competition discussed in this research were: economies of scale determines market share of a firm; market share is determined by the time of entry or the duration that one has been operating; the cost of entry determines the profit of a firm; the economy of scale determines the profit of a firm; and the technology required for operation can prevent a firm from operation. Using a five scale measure in all the five items, there was consensus on ‘agree’ among the respondents (median = 4; mode = 4). All the items were negatively skewed indicating ‘agreed’ and ‘strongly agreed’ as the highly selected scale. The skewness and summation of ‘agreed’ and ‘strongly agreed’ were: Economies of scale determine profit at 87.9%, skewness of -1.428; Operation technology required can prevent a firm from operation at 84.4%, skewness of -1.461; Cost of entry determines the profit of a firm at 81.8%, skewness of -1.291; Economies of scale determine market share at 86.2%, skewness of -1.031; and time entry/duration of operation at 77.2%, skewness of -.627. Only time of entry/duration of operation was moderately skewed (-.627 .05) between operating firms having branches in other countries and other variables under threat of entry as indicated in table 4.10. In summary, the findings of the relationship between: oil firm have branches outside South Sudan and economies of scale determine market share (p=.704, X2=1.406, df of 1); oil firm have branches outside South Sudan and time of entry/duration of operation determines profit (p=.142, X2=5.447, df of 3); oil firm have branches outside South Sudan and economies of scale determine profit of the firm (p=.423, X2=3.879, df of 4); oil firm 80

have branches outside South Sudan and operation technology required can prevent a firm from operation(p=.560, X2=2.990, df of 4). This shows among all the items under threat of new entrants, only ‘the cost of entry determines the profit of a firm’ has positive correlation with oil firm having branches outside South Sudan as indicated in table 4.11. Table 4.11: Number of Branches of the Firm and Threat of New Entrants

Economies of scale determine

Pearson

market share

Chi-Square

Time entry/duration of

Pearson

operation

Chi-Square

Cost of entry determines the

Pearson

profit of a firm

Chi-Square

Economies of scale determine

Pearson

profit

Chi-Square

Operation technology required can prevent a firm from operation

Pearson Chi-Square

Value

Df

P

1.406a

3

0.704

5.447a

3

0.142

11.450a

4

0.022

3.879a

4

0.423

2.990a

4

0.56

p= Asymp. Sig. (2-sided)

4.3.3. Duration of Operation in South Sudan and Threat of New Entrants The duration a firm has been operating in South Sudan has no significant effect on the threat of new entrants as indicated in table 4.12. The correlations are: duration of operation and economies of scale determine market share (p= .785, r= -.034); duration of operation and time of entry/duration of operation determines profit (p= .911, r= -.014); duration of operation and cost of entry determine profit of the firm (p= .164, r= .173); duration of operation and economies of scale determine profit of the firm (p= .393, r= -.107); duration of operation and operation technology required can prevent a firm from operation(p= .583, r= -.069). The correlation between other variables on threat of entry reveals a relationship between Economies of scale determine profit and Economy of scale determine market share 81

(p=.0005, r=.599); Economies of scale determine profit and Cost of Entry (p=.039, r=.255). This shows a relationship between economies of scale determine profit and economies of scale determine market share has median strength of r=.599 while the relationship between economies of scale determine profit and cost of entry is weak at r=.255. On the operational technology, there was a positive relationship between operational technology and economies of scale determine market share (p=.001, r=.396); Operation technology and Duration of operation (p=.002, r=.372) and Operation technology and Economies of scale determine profit (p=.002, r=.380). In all the correlations, the relationships were weak at r < .5. These are as indicated in table 4.11. Further the correlations are good for regression with no element of multicollinearity. Further, more tests were conducted on multicollinearity during regression analysis. Table 4.12: Relationship; Duration of Operation and Threat of New Entrants. Length of operation Economies of scale determine market share Time entry/duration Cost of entry Economies of scale determine profit Operation technology

r p r p r p r p r p r

1 -.034 .785 -.014 .911 .173 .164 -.107 .393 -.069

1 .132 .292 .068 .589 .599** .000 .396**

1 .218 .079 .151 .226 .372**

1 .255* .039 -.165

1 .380*

1

*

p .583 1

.001 2

.002 3

r = Pearson Correlation p = Sig. (2-tailed) **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

82

.185 4

.002 5

6

4.3.4. Threat of New Entrants Influence on Performance In order to determine the level of influence threat of new entrances have on performance of the oil industry in South Sudan, multiple regression was conducted. With performance as independent variable and threat of entrants as dependent variable, multiple regressions show how new entrants affects and predicts performance of oil firms. All the five variables on threat of new entrants were tested based on relationship and projection; how each affects the dependent variables depicting the nature of the relationship, direction, and strength of independent variable on dependent variable. The output of the regression is also discussed and equation presented after the model. The model summary below shows how the threat of entrants influences and predicts performance based on the regression outputs. The predictor variable (independent variable) is threat of entrants and dependent variables as performance. Table 4.13a: Model Summary on New Entrants Influence of Performance Model R

R Square

1

.596a .355

Adjusted R Std. Error of the Square

.345

Estimate

.53988

Change Statistics R Square

F

Change

Change

.355

df1 df2 Sig. F

35.281 1 64

Change .000

a. Predictors: (Constant), Threat_of_entrants As depicted in model summary, the model fits the data which means the strength of the correlation between threat of entrants and performance is r=.596 and coefficient of determination as R-square (r2) = .355 with Sig F Change p=.0005 of 35.281. Based on the model, 35.5% of performance outcome can be explained based on threat of new entrants in the oil industry in South Sudan. Therefore, the model summary explains the strength of the relationship (r=.596) and prediction of 35.3% firm performance while the remaining 64.7% of performance are caused by other variables.

83

Table 4.14b: ANOVA on New Entrants Influence of Performance Sum of Squares

Df

Mean Square

F

Sig.

Regression

10.283

1

10.283

35.281

.000b

Residual

18.654

64

0.291

Total

28.938

65

Model

1

a. Dependent Variable: performance b. Predictors: (Constant), Threat_of_entrants The ANOVA table shows whether or not the regression model explains a statistically significant proportion of variance. From the above table, it shows the regression model is better in predicting the outcome variable than the mean outcome (p= .0005 < p=.005). From model 1, (F=35.281, df=1, p.005); suppliers provide items that accounts for sizeable 88

fraction of the industry products (p=.207; >.005); there are few suppliers who make large profit in the oil industry in South Sudan (p=.056; >.005). This shows suppliers bargaining power does not depend on ownership of the firm. Similarly, the ownership of a firm does not influence suppliers bargaining power. Table 4.18: Correlation between Ownership and Suppliers Bargaining Power.

Products are readily available from many suppliers Few large suppliers who dominate market Suppliers provide items that account for industry products Few suppliers make large profit Ease of profit making by members on substitute products.

Pearson ChiSquare Pearson ChiSquare Pearson ChiSquare Pearson ChiSquare Pearson ChiSquare

Value

Df

P

19.184a

12

0.084

13.534a

9

0.14

12.111a

9

0.207

20.641a

12

0.056

22.482a

12

0.032

p=Asymp. Sig. (2-sided)

On the number of branches, the correlation between suppliers bargaining power and whether a firm operating in South Sudan had a branch outside South Sudan or not, had the following. Significant relationship between suppliers provide items that accounts for sizeable fraction of the industry products, p=.011, X2 = 11.116, df (3); and there are few suppliers who make large profit in the oil industry in South Sudan, p=.035, X2 = 10.342, df (4). This shows, the branches that an oil firm operating in South Sudan has, significantly determines the ability of a firm to provide items that accounts for sizeable fraction of the industry products and the ability of few suppliers to make large profit in the oil industry in South Sudan. However, whether a firm has branches outside South Sudan or not does not significantly affect the availability of products from many suppliers at the different market prices, p=.165, X2 = 6.496, df (4); presence of few large suppliers who dominate oil market industry in South Sudan, p=.250, X2 = 4.106, df (3); and the ease of industry members to make profit by getting substitutes products, p=.059, X2 = 9.091, df (4).This shows the 89

availability of products from many suppliers at different market price, presence of few large suppliers and ease of industry members to make profit are determined by other factors other than the oil firms’ presence in other countries. 4.4.3. Duration of Operation in South Sudan and Suppliers Bargaining Power The number of years that a firm has been operating in South Sudan has no significant relationship with the suppliers bargaining power as indicated in table 4.19. However, there is significant relationship between suppliers who make large profit in the oil industry in South Sudan and products availability from many suppliers at different market price (p=.043, r=.250); suppliers who make large profit in the oil industry in South Sudan and suppliers ability to provide items that accounts for sizable fraction of the industry product (p=.0005, r=.547). These shows, suppliers who make large profit in the Oil industry in South Sudan are influenced by product availability from many suppliers at different market prices and ability to provide items in large scale in the industry. The ease of a supplier to make profit by getting substitute products is statistically determined by readily available products from many suppliers at different market price (p=.0005, r=.731) and the availability of large suppliers in the industry who dominates the market share of the oil industry (p=.004, r=.352). The relationship on the availability of product from many suppliers is stronger (r=.731) than dominance of supplier on the market share (r=.352). As indicated in table 4.15, there was no other relationship between suppliers bargaining power variables and duration that a firm has been operating in South Sudan.

90

Table 4.19: Correlation of Duration of Operation and Suppliers Bargaining Power Length of service Product availability Large suppliers Suppliers provision of items Large profit making Ease of profit making

r p

1

r

-0.161

1

p r p

0.197 -0.157 0.209

0.216 0.081

1

r

0.029

0.132

0.088

p

0.819

0.292

0.483

*

1

0.095 .547**

r

0.043 .250

p

0.733

0.043

0.448

-0.148

**

.352

**

0.109 0.108

1

0 2

0.004 3

0.385 0.386 4 5

6

r p

0.236 1 . Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

.731

1

0

4.4.4. Influence of Suppliers Bargaining Power on Performance of Oil industry In order to determine the level of influence that suppliers bargaining power have on performance of the oil industry in South Sudan, multiple regressions was conducted since the measure of performance was in scale ranking. With performance as independent variable and suppliers bargaining power as dependent variable, multiple regressions show how a suppliers bargaining power affects and predicts performance of oil firms. All the five variables on suppliers bargaining power were tested based on relationship and projection; how each affects the dependent variables depicting the nature of the relationship, direction, and strength of independent variable on dependent variable. The output of the regression is also discussed and the equation presented after the model. The Model summary shows how suppliers bargaining power influences and predicts performance based on the regression output. The predictor variable (independent variables) is suppliers bargaining power and dependent variables as performance.

91

Table 4.20a: Model Summary on Suppliers Bargaining Power on Performance Model

R

R

Adjusted Std. Error

Square R Square

of the

Change Statistics R Square

Estimate 1

.348a

.121

.107

F

df1

df2

Change Change

.63043

.121

8.810

Sig. F Change

1

64

.004

a. Predictors: (Constant), Power_of_supplier The model fits the data as depicted in model summary, which means the strength of the correlation between the power of supplier and performance is r=.348 and coefficient of determination as R-square (r2) = .121 with Sig F Change p=.004 of 8.810. Based on the model, only 12.1% of performance of oil firms can be explained based on power of supplier in the oil industry in South Sudan. Therefore, the model summary explains the strength of the relationship (r=.348) and prediction of 12.1% of firm performance based on power of supplier. The other 87.9% of performance are attributed to other factors other than bargaining power of suppliers. Table 4.21b: ANOVA on Suppliers Bargaining Power on Performance Model

1

Sum of Squares df

Mean Square F

Sig.

Regression

3.502

1

3.502

.004b

Residual

25.436

64

.397

Total

28.938

65

8.810

a. Dependent Variable: performance b. Predictors: (Constant), Power_of_supplier

The ANOVA shows whether or not the regression model explains a statistically significant proportion of variance. From the ANOVA table, it shows the regression model is better in predicting the outcome variable than the mean outcome (p= .004< p=.05). From model 1, (F=8.810, df=1,64 p= .004 -.80. The presentation of the variables in descending order based on the mean, summation of ‘agree’ and ‘strongly agree’ and skewedness of the findings: quality of products determine the profit margin of firms (M=4.29, 93.9%, skewness of 1.763); there are a number of customers who only buy from a specific firm therefore increases the firm profit (M=4.24, 87.9%, skewness of -.889); the number of competitors in the market affects the profit margin of firms (M=4.17, 86.4%, skewness of -1.256); many competitors in the market affects market share negatively (M=4.05, 86.4%, skewness of -1.639); and industrial competitors are involved in quality which determines product differentiation in the market and hence a positive effect on profit (M=4.02, 83.3%, skewness of -.846). Unlike the other five forces of competition, competitive rivalry had a mean of >4.0 in all the variables and cumulative percentage of >85%. This shows competitive rivalry is highly skewed on all the variables. The findings are in table 4.40.

113

Table 4.40: Competitive Rivalry Perception.

Many competitors Involvement of industry competitors Specific customers No. of competitors in the market Quality of products

SD D 4.5 3.0 6.1 6.1

N 6.1 10.6

A 56.1 59.1

SA 30.3 24.2

Mean 4.05 4.02

Sdvt .952 .774

Skewn -1.639 -.846

1.5

3.0 1.5

9.1 10.6

48.5 51.5

39.4 34.8

4.24 4.17

.745 .796

-.889 -1.256

1.5

3.0

1.5

53.0

40.9

4.29

.780

-1.763

SD (Strongly Disagree), D (Disagree), N (neutral), A (Agree), SA (Strongly Agree) and Sdvt (standard Deviation) 4.7.1. Type of Business and Competitive Rivalry The different types of business in the oil industry in South Sudan were; oil mining, oil processor, oil distribution, oil waste management, oil piping and combination of two or more business type. A mean comparison of the competitive rivalry based on the type of business shows that having many competitors in the market affect market share negatively was high in oil waste management business (M=5) and lower in oil piping business (M=1); industrial competitors are involved in quality which determines product differentiation in the market and hence a positive effect on profit was high in joint venture business where a firm operates more than one type of business (M=4.31) and lower in Oil waste management business (M=2). Business where there are a number of customers who only buy from a specific firm therefore increases in the firm profit was high in oil waste management (M=5) and lower in oil processing (M=3.71); where the number of competitors in the market affects the profit margin of firms was also high in oil waste management business (M=5) and lower in Oil piping (M=3). Lastly, the quality of products determine the profit margin of firms was highly applicable in Oil waste management (M=5), Oil piping (M=5) while least applicable in Oil mining (M=3.82). As presented in table 4.41, oil waste management as a type of business has the highest competitive rivalry except where competitors are involved in quality. This presents the picture of a black market where quality does not matter but its presence affects the market 114

share, there is loyalty of customers who buy from specific persons, and the number of competitors affect the profit margin. In the black market, the quality of product also determines the profit margin. Profitability of Oil piping is also determined by the quality of product produced. Table 4.41: Mean Comparison of Competitive Rivalry and Oil Business Type of

Many

Involvement of

business, Key competitors industry

Specific

customers competitors in

competitors Oil mining

No. of

Quality of products

the market

4.00

3.76

3.82

3.71

3.82

Oil processor 4.00

4.14

3.71

4.00

4.14

Oil distributor 4.50

4.00

4.67

4.33

4.83

5.00

2.00

5.00

5.00

5.00

1.00

4.00

4.00

3.00

5.00

More than one 4.17

4.31

4.48

4.41

4.41

Total

4.03

4.24

4.14

4.30

Oil waste management Oil piping

4.06

Using non-parametric equation, there was statistical significance between type of the business and: many competitors in the market affects market share negatively at p=.0005, X2 = 78.276, df (20); industrial competitors are involved in quality which determines product differentiation in the market and hence a positive effect on profit at p=.0005, X2 = 43.336, df (15); and the number of competitors in the market affects the profit margin of firms p=.042, X2 = 32.089, df (20). This show in the industry, many competitors highly affect market negatively (X2 = 78.276) and the profit margin at a lower strength (X2 = 32.089). Similarly, competition in the oil industry brings quality of products which inturn affect the profit margin of oil firms strongly at (X2 = 43.336). There was no significance between the type of business the oil firms were involved in and the number of customers who only buy from a specific firm therefore increases the firm profit at p=.059, X2 = 24.352, df (15); and quality of products determine the profit margin of firms at p=.255, X2 = 23.714, df (20). This variance in comparison with the mean also shows that the customer loyalty is not similar in all oil businesses and quality does not determine profit margin of the firm in all oil business types. 115

4.7.2. Ownership and Branches of oil Firms on Competitive Rivalry Oil firms in South Sudan are either locally owned, internationally owned, joint venture or government owned. There was no relationship between ownership of the firm with competitive rivalry in the oil industry. The negative (p>.05) chi-square results were: many competitors in the market affects market share negatively (p=.305, X2 = 13.936, df (12); industrial competitors are involved in quality which determines product differentiation in the market and hence a positive effect on profit (p=.166, X2 = 12.925, df (9); there are a number of customers who only buy from a specific firm therefore increases the firm profit (p=.231, X2 = 11.696, df (9); the number of competitors in the market affects the profit margin of firms (p=.625, X2 = 9.893, df (12); and quality of products determine the profit margin of firms (p=.338, X2 = 13.436, df (12). Mean comparison on whether oil firm has branches outside South Sudan and competitive rivalry revealed that, oil firms with no branches outside South Sudan had a mixture of mean comparison. Mean comparison of competitive rivalry where firms with no branches had higher mean than firms with branches outside South Sudan were: industrial competitors are involved in quality which determines product differentiation in the market and hence a positive effect on profit, M=4.26 for firms with no branches and M=3.68 for firms with branches; there are a number of customers who only buy from a specific firm and hence increase the firms profit, M=4.37 for firms with no branches and M=4.07 for firms with branches; and quality of products determine the profit margin of firms, M=4.42 for firms with no branches and M=4.11 for firms with branches. This shows that quality of products, customer loyalty and product differentiation determine profit level for firms with branches only in South Sudan more than firms with branches outside South Sudan. The variables where the mean for firms with branches outside South Sudan was higher than firms with no branches outside South Sudan, many competitors in the market affects market share negatively, M=4.03 for firms with no branches and M=4.07 for firms with branches; and the number of competitors in the market affects the profit margin of firms, M=4.16 for firms with no branches and M=4.18 for firms with branches. This shows that firms with branches outside South Sudan are affected by many competitors in the market which also affects their profit at a higher rate than firms with no branches outside South Sudan as shown in table 4.42. 116

Table 4.42: Mean Comparison of Competitive Rivalry and Firm Branches. Have branches outside South Sudan?

No

Yes

Total

Mean

Sdvt

Mean Sdvt

Mean Sdvt

Many competitors

4.03

.944

4.07

.979

4.05

.952

Involvement of industry competitors

4.26

.503

3.68

.945

4.02

.774

Specific customers

4.37

.633

4.07

.858

4.24

.745

No. of competitors in the market

4.16

.754

4.18

.863

4.17

.796

Quality of products

4.42

.500

4.11

1.031 4.29

.780

On the chi-square test, only ‘industrial competitors are involved in quality which determines product differentiation in the market and hence a positive effect on profit’ had positive relationship branches of the firm at (p=.005, X2 = 12.937, df (3)). Others with no significant relationship were; many competitors in the market affects market share negatively (p=.531, X2 = 3.161, df (4); there are a number of customers who only buy from a specific firm therefore increases the firm profit (p=.317, X2 = 3.527, df (3); the number of competitors in the market affects the profit margin of firms (p=.610, X2 = 2.694, df (4); and quality of products determine the profit margin of firms (p=.210, X2 = 5.860, df (4). Only industrial competitors are involved in quality which determines product differentiation in the market and hence a positive effect on profit had significant relationship with branches of oil industry. Other variables on competitive rivalry had not effect on oil firms based on branches outside south Sudan. 4.7.3. Duration of Operation in South Sudan and Competitive Rivalry The duration of operation in South Sudan had negative correlation on quality of products determine the profit margin of firms (p=.003, r= - .357). This explains the reduction in quality based on the duration that an oil firm operates in South Sudan; the longer a firm operates in South Sudan, the higher the chances of reduction in quality. As for the other variables, there was no correlation with the duration of operation indicating regardless of the number of years an oil firm operates in South Sudan, there is no statistical relationship on the competitive rivalry.

117

The correlation between other competitive rivalry variables shows weak correlation between customers who are likely to buy from specific firms with: many competitors in the market affect market share negatively (p=.031, r=.266) and involvement of industry competitors in quality which determines product differentiation (p=.020, r=.287). Also the positive correlation is between the number of competitors in the market affects the profit margin of the firm and, many competitors in the market affect market share negatively (p=.003, r=.356) and customers who are likely to buy from specific firms (p=.017, r=.294). Lastly, the quality of products determine the profit margin of firm has positive correlation with customers who are likely to buy from specific firms (p=.0005, r=.434). Table 4.43 shows the correlation. Table 4.43: Correlation between Competitive Rivalry. 1. Length of operation 2. Many competitors 3. Involvement of industry competitors 4. Specific customers 5. No. of competitors in the market 6. Quality of products

r p r p r p r p r p r p

1 -.018 .884 -.191 .125 -.191 .125 -.020 .871 -.357** .003 1

1 .103 .409 .266* .031 .356** .003 -.059 .636 2

1 .287* .020 -.079 .528 .196 .114 3

1 .294* .017 .434** .000 4

1 -.004 1 .974 5 6

r= Pearson correlation p=Sig. (2-tailed) **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). 4.7.4. Influence of Competitive Rivalry on Performance of Oil Industry In order to determine the level of influence competitive rivalry has on performance of the oil industry in South Sudan, multiple linear regressions was used since the measure of performance was in scale ranking. The independent variable was performance and competitive rivalry was the dependent variable; multiple regressions show how competitive rivalry affects and predicts performance of oil firms. All the variables on 118

competitive rivalry were tested based on relationship and projection; how each affects the dependent variables depicting the nature of the relationship, direction, and strength of independent variable on dependent variable. The output of the regression is also discussed and equation presented after the model. The model summary shows how competitive rivalry influences and predicts performance based on the regression output. The predictor variable (independent variable) as competitive rivalry, and dependent variable as performance. Table 4.44a: Model Summary on Competitive Rivalry on Performance. Model

R

R

Adjusted Std. Error

Square R Square

of the

Change Statistics R Square

Estimate 1

.414a

.171

.158

F

df1

df2

Change Change

.61210

.171

13.237

Sig. F Change

1

64

.001

a. Predictors: (Constant), competitive_rivalry As depicted in the model summary, the model fits the data, which means the strength of the correlation between competitive rivalry and performance is r=.414 and coefficient of determination as r2 = .171 with Sig F Change p=.001 of 13.237. Based on the model, 17.1% of performance of oil firms can be explained based on competitive rivalry in the oil industry in South Sudan. Therefore, the model summary explains the strength of the relationship (r=.414) and prediction of firm performance 17.1% based on competitive rivalry while the remaining 82.9% of performance are caused by other variables. Table 4.45b: ANOVA on Competitive Rivalry on Performance. Model

1

Sum of Squares Df

Mean Square

F

Sig.

Regression

4.959

1

4.959

13.237 .001b

Residual

23.978

64

.375

Total

28.938

65

a. Dependent Variable: performance b. Predictors: (Constant), competitive_rivalry The ANOVA shows whether or not the regression model explains a statistically significant proportion of variance. From the above table, it shows that the regression 119

model is better in predicting the outcome variable than the mean outcome (p= .0005 < p=.005). Model 1 shows, (F=13.237, df=1,64 p= .001 .05. The model presentation reveals threat of entrants’ (model 1) relationship with performance is r=.596 and the r2 = 35.5%. This means, 35.5% of performance can be explained by threat of entrants and the remaining 64.5% by other factors. Table 4.48a: Model Summary on Influence of Five Forces on Performance. Model Summaryg Model R

R

Adjusted Std. Error Change Statistics

Square R Square of the

R Square F

Estimate Change

Change

df1

df2

Sig. F Change

1

.596a .355

.345

.53988

.355

35.281 1

64

.000

2

.598b .357

.337

.54330

.002

.197

1

63

.659

3

.598c .358

.326

.54761

.000

.013

1

62

.910

4

.607d .368

.327

.54752

.011

1.019

1

61

.317

5

.608e .369

.317

.55148

.001

.128

1

60

.722

a. Predictors: (Constant), Threat_of_entrants b. Predictors: (Constant), Threat_of_entrants, Power_of_supplier c. Predictors: (Constant), Threat_of_entrants, Power_of_supplier, Power_of_substitution d. Predictors: (Constant), Threat_of_entrants, Power_of_supplier, Power_of_substitution, Power_of_buyer e. Predictors: (Constant), Threat_of_entrants, Power_of_supplier, Power_of_substitution, Power_of_buyer, competitive_rivalry g. Dependent Variable: performance Based on the ANOVA table, all the five forces regression model are better in predicting the outcome variable than the mean outcome (p= < .05). From model 1 to model 5, the regression model constructed is better in predicting the outcome variable than the mean outcome as the sum residual of each model is small.

122

Table 4.49b: ANOVA on Influence of Five Forces on Performance. Model

Sum of Squares df Mean Square F Regression 10.283 1 10.283 35.281 1 Residual 18.654 64 .291 Total 28.938 65 Regression 10.342 2 5.171 17.517 2 Residual 18.596 63 .295 Total 28.938 65 Regression 10.345 3 3.448 11.500 3 Residual 18.592 62 .300 Total 28.938 65 Regression 10.651 4 2.663 8.882 4 Residual 18.287 61 .300 Total 28.938 65 Regression 10.690 5 2.138 7.030 5 Residual 18.248 60 .304 Total 28.938 65 a. Dependent Variable: performance b. Predictors: (Constant), Threat_of_entrants c. Predictors: (Constant), Threat_of_entrants, Power_of_supplier d. Predictors: (Constant), Threat_of_entrants, Power_of_supplier, Power_of_substitution e. Predictors: (Constant), Threat_of_entrants, Power_of_supplier, Power_of_substitution, Power_of_buyer f. Predictors: (Constant), Threat_of_entrants, Power_of_supplier, Power_of_substitution, Power_of_buyer, competitive_rivalry

Sig. .000b

.000c

.000d

.000e

.000f

Based on the coefficient table that follows, the only threat of entrant is statistically significant in all the levels of model; from model 1 a model 5. As more variables are added on the equation, the Unstandardized Coefficients (B) reduces in number, and the error term increased which reduced significance of the equation. This calls for more research on the level of influence between the Porter’s five forces variables and form one variable to another.

123

Table 4.50c: Coefficients on Influence of Five Forces on Performance. Model

1

2

3

4

5

Unstandardized

Standardized t

Coefficients

Coefficients

B

Std. Error Beta

(Constant)

1.083

.525

Threat_of_entrants

.765

.129

(Constant)

1.194

.584

Threat_of_entrants

.813

.169

Power_of_supplier

-.074

.166

(Constant)

1.191

.590

Threat_of_entrants

.810

.172

Power_of_supplier

-.086

Sig.

2.064

.043

5.940

.000

2.043

.045

.634

4.814

.000

-.058

-.444

.659

2.021

.048

.631

4.710

.000

.200

-.068

-.430

.669

Power_of_substitution .016

.145

.016

.114

.910

(Constant)

1.045

.607

1.722

.090

Threat_of_entrants

.777

.175

.605

4.432

.000

Power_of_supplier

-.119

.203

-.094

-.587

.559

Power_of_substitution -.044

.156

-.044

-.281

.780

Power_of_buyer

.161

.159

.142

1.010

.317

(Constant)

.957

.660

1.449

.152

Threat_of_entrants

.749

.193

.583

3.869

.000

Power_of_supplier

-.136

.210

-.108

-.648

.519

Power_of_substitution -.052

.159

-.051

-.325

.746

Power_of_buyer

.159

.160

.140

.989

.327

competitive_rivalry

.075

.211

.053

.357

.722

.596

a. Dependent Variable: performance

With the general form of the regression model used being Y = β0 + βi xi + +βii xii + βiii xiii + βiv xiv + βv xv + ε β0 = Constant; βi 𝑡𝑜 βv = five Porter variables and ε= Error term.

124

From the dependent equation, there are positive prediction equations. However, the combined equation shows that the effect are not statistically significant hence the need for more step research on the variables.

4.9. Chapter Summary

This chapter presents the findings of the study. The demographic presentation covers the characteristics of the oil workers and oil firms in South Sudan. On each research question, the presentations are: the general perception of the respondents on the five forces; mean comparisons, chi-square test and correlation test of perception with firms’ characteristics; regression analysis on each of the five forces on performance; and lastly, multicollinearity test, normality, error term and distribution. The next chapter presents the summary of the findings, discussions, conclusions and recommendations.

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CHAPTER FIVE 5.0 SUMMARY, DISCUSSION, CONCLUSION AND RECOMMENDATIONS 5.1 Introduction This chapter covers the findings of the influence of Porter’s five forces on the performance of the oil Industry in South Sudan in four key sections. The first section is the summary of the study followed by discussion of the findings based on the result of study and key literature reviewed in chapter two. The third section presents the conclusion of the study and lastly, the recommendation for improvement and further studies. Presentations are arranged thematically based on the research objective and key findings from both descriptive and inferential statistics. 5.2 Summary of the Study The general objective of this study was to examine the extent of Porter’s five forces effect on the performance of the oil industry in South Sudan. The specific objectives were: To determine the level of influence threat of new entrants have on performance of oil industry in South Sudan; assess the effect of rivalry between firms on performance of oil industry in South Sudan, establish the extent of buyers bargaining power on performance of oil industry in South Sudan; establish the extent of bargaining power suppliers have on performance of the oil industry in South Sudan; and to evaluate the influence of substitute products on performance of oil industry in South Sudan. Using census method, data was collected using questionnaire as the main tool. The target population of the study was all middle and top managers of all oil industries operating in South Sudan. A total of 84 questionnaires were given to the middle and top management of all the 21 oil firms operating in Juba. Only 66 questionnaires were filled and returned representing 78.6% response rate. Collected data was cleaned, coded, keyed in to SPSS and analyzed thematically using descriptive and inferential statistics. On the first research question, threat of new entrants variables had significant relationship with type of business: economies of scale determine the market share (p=.0005, X2 = 53.108, df (15)), cost of entry determines the profit of a firm (p=.0005, X2= 57.613, df (20)) and the strongest significant with economies of scale determines the profit of the 126

firm (p=.0005, X2= 75.341, df (15)). Oil branches also had significant relationship with: cost of entry determines the profit of a firm at p=.022, X2=11.450, df of (4) and economies of scale determines the profit of a firm at p=.022, X2=11.450, df of (4).Ownership and duration of operation had no significant effect on threat of entrants. Based on regression model, 35.5% of performance can be explained by threat of new entrants and the model (F=35.281, df=1, p