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non-‐oil & gas THE CRITICAL SUCCESS FACTORS FOR FOREIGN DIRECT INVESTMENT IN NIGERIA AND THE RELATIONSHIPS TO
CHOICE OF ENTRY MODE.
Non-Oil and Gas 2014
A mixed quantitative and qualitative research By
Dr. Anthony Chibo – Christopher Development Economist – International Business, Trade and investment strategist
ii
ABSTRACT International businesses are increasingly seeking out international-foreign
markets and they need to decide on their most beneficial market entry modes,
as they seek opportunities for growth. The International Monetary Fund
(IMF) tells us that Africa will continue to provide business growth
opportunities, at least for the next eight decades, at a time where there are
currently little or no growth opportunities in many other regions of the world.
This research contributes and betters knowledge and practice in international
business by revealing the critical success factors (CSFs) and entry mode
recommendations to be considered in order to achieve a successful non-oil &
gas Foreign Direct Investment (FDI) project and entry mode into Africa’s
largest economy Nigeria, with its 165 million people is also Africa’s most
populous nation. This research reveals the statistical relationships between
factors in Nigeria, such as infrastructure, political stability, size of market,
government support services and the choice of entry mode into the Nigerian
market. The relationship between political stability in Nigeria and the choice
of market entry mode is found to be statistically significant, on account of the
probability of the model chi-square = 0.001, Sig p < 0.05. The relationship
between the critical success factors for FDI in Nigeria’s non-oil & gas sectors
and the choice of entry mode is also found to be statistically significant on
account of the probability of the model chi-square = 0.000, Sig p < 0.05. This
research then determines, presents and demonstrates the use of a set of
significant statistical probabilities of outcome (or statistical predictions) for
choice of entry mode as determined by each and every one of the critical
success factors. These statistical predictions for the outcome of the choice of
entry mode serve as a new guide set for recommending the best entry mode
on a case-by-case basis to future potential foreign direct investors for the
Nigerian market.
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Table of Contents
Chapter One: Introduction p.1 1.1 Research Area - Definitions p.1 1.2 Research problem - Problem statement p.3 1.3 Research hypotheses p.5 1.4 Relevant background economic data for Nigeria p.5 1.5 Why the non-oil & gas sector of the Nigerian economy p.6 1.6 Research significance, justification, and benefits & uniqueness p.7 1.7 Research design, method, variables & analysis techniques p.8 1.8 Validity and reliability p.8 Chapter Two: Literature Review p.9 2.1 Introduction (Literature Review) p.9 2.2 The Nigerian economy and market p.12 2.3 Recent trends on the Nigerian economy: KPMG p.23 2.4 Recent trends on the Nigerian economy: Deloitte p.24 2.5 Recent trends on the Nigerian economy: (PwC) p.24 2.6 Recent trends on the Nigerian economy: Ernst Young p.25 2.7 Current administrators of the Nigerian economy p.26 2.8 Foreign direct investment in Nigeria p.27 2.9 Success factors for FDI p.33 2.10 Choice of market entry modes p.41 2.11 Identifying & explaining the gap in literature p.47 2.12 Explaining the literature gap and its reasons p.49 2.13 Conclusions (Literature Review) p.50 Chapter Three: Research Methods, Procedures, Design p.51 3.1 Introduction (Methods-Procedures-Design) p.51 3.2 Research paradigm p.52 3.3 Variables p.52 3.4 Hypotheses p.53 3.5 Research design, techniques, strategy p.54 3.6 validity and reliability p.56 3.7 Populations and sample p.57
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3.8 Data collection p.58 Chapter Four: Analysis – Results - Findings p.58 4.1 Reliability p.58 4.2 Correlation p.59 4.3 Factor analysis p.60 4.3.1 BTS test & KMO sampling adequacy p.61 4.3.2 Factor extraction p.63 4.3.3 Identification, labeling of factors p.64 4.3.4 Inter factor correlation p.66 4.4 The critical success factors p.67 4.5 Testing the hypotheses p.68 4.5.1 The multinomial logistic regression 1 p.68 4.5.2 Statistical software and model p.69 4.5.3 Testing hypothesis 1 p.70 4.5.4 Testing hypothesis 2 p.73 4.5.5 Testing hypothesis 3 p.74 4.5.6 Testing hypothesis 4 p.75 4.6 CSFs relationship with entry mode p.77 4.6.1 Testing hypothesis 5 p.77 4.6.2 The multinomial logistic regression 2 p.78 4.7 The parameters estimates table p.80 4.8 Probabilities of outcome for entry mode p.83 Chapter Five: Discussions and Interpretations for Results p.87 5.1 Discussions factor analysis results p.87 5.2 Discussions hypotheses testing results H1 – H4 p.92 5.3 Discussions hypotheses testing results H5 p.95 5.4 Discussions probabilities of outcome for entry mode p.96 Chapter Six: Further research and recommendations p.102 Chapter Seven: Summary and conclusions p.104 Chapter Eight: Applying the research findings – Case study p.106 Chapter Nine: Ethics p.113 Chapter Ten: Appendices p.114 Chapter Eleven: References p.132
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Lists Of Figures, Tables & Abbreviations Figures Figure 1: Formula for multinomial logistic regression
Figure 2: Formula for standardized Cronbach’s alpha
Figure 3: Formula for calculating the correlation coefficient “r”
Figure 4: Formula for multinomial logistics regression
Figure 5: Africa: Top 5 recipients of FDI inflow 2011 and 2012
Tables
Table 1: Reliability Statistics
Table 2: Variables with High Inter Correlations
Table 3: KMO and Bartlett’s Test
Table 4: Communalities
Table 5: Total Variance Explained
Table 6: Pattern Matrix
Table 7: Inter-factor Correlation Matrix
Table 8: Case Processing Summary
Table 9: Model Fitting Information (INFRASTR) H1
Table 10: Classification (INFRASTR) H1
Table 11: Goodness of Fit H1
Table 12 Likelihood Ratio Tests (INFRASTR) H1
Tables 13: Model Fitting Information (GOVSUPP) H2
Table 14: Goodness of fit (GOVSUPP) H2
Table 15: Classification (GUVSUPP) H2
Table 16: Likelihood Ratio Tests (GUVSUPP) H2
Table 17: Model Fitting Information (POLSTAB) H3
Table 18: Goodness of fit (POLSTAB) H3
Table 19: Classification (POLSTAB) H3
Table 20: Likelihood Ratio Tests (POLSTAB) H3
Table 21: Model Fitting Interaction (SIZEOMACK) H4
Table 22: Classification (SIZEOMACK) H4
Table 23: Goodness of Fit (SIZEOMACK) H4
Table 24: Likelihood Ratio Test (SIZEOMACK) H4
Table 25: Descriptive Statistics CSFs
Table 26: Model Fitting Interaction CSFs
Table 27: Classification CSFs
Table 28: Likelihood Ratio Tests CSFs
Table 29: Correlations
Table 30: Parameter Estimates
Table 31: Entry Mode Frequencies
Abbreviations
AEO African Economic Outlook
AFDB Africa Development Bank Group
AU African Union
BRICS Brazil Russia India China South Africa.
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BTS Bartlett’s Test of Sphericity
CBN Central Bank of Nigeria
CCA Corporate Council on Africa
CEO Chief Executive Officer
CSF Critical Success Factor
COMESA Common Market For East and Southern Africa
EAC East African Community
ECOWAS Economic Community of West African States
EU European Union
FAO Food and Agriculture Organization
FDI Foreign Direct Investment
GDP Gross Domestic Product
IBM International Business Machines
IEF Index of Economic Freedom
IMF International Monetary Fund
KMO Kaiser – Meyer – Olkin
MIGA Multilateral Investment Guarantee Agency
MINT Malaysia Indonesia Nigeria Turkey
NBS National Bureau of Statistics
NEEDS National Economic Empowerment and Development Strategy
NERC Nigerian Electricity Regulatory Commission
NIPC Nigerian Investment Promotion Commission
NTH Nigeria Trade Hub
OECD Organization for Economic Cooperation and Development
OSIC One Stop Investment Center
PPP Purchasing Power Parity
PSRC Puget Sound Regional Council
PwC PricewaterhouseCoopers
SADC Southern Africa Development Community
SPSS Statistical Package For Social Sciences
TCN Transmission Company of Nigeria
UBA United Bank for Africa
UNECA United Nations Economic Commission for Africa
UNIDO United Nations Industrial Development Organization
UK United Kingdom
WTO World Trade Organization
1
1.0 INTRODUCTION 1.1 Research Area - Definitions This research is in the field of international business - strategic management.
It provides new research findings in international business strategy. The
findings from this research are important to Business practice as businesses
increasingly seek out international-foreign markets and decide on their most
beneficial market entry modes, as they seek opportunities for growth. Most
relevant published work such as IMF (2014) express that Africa continues to
provide Foreign Direct Investment (FDI) - business growth opportunities, at a
time where there is little or no growth in many other regions of the world.
KPMG (2013) report agrees that, businesses internationally have become more
focused on where in Africa to invest, as opposed to whether to invest or not.
The United Bank for Africa (UBA) (2014) asserts that investors serious about
Africa must have a big presence in Nigeria. This is because Nigeria is Africa’s
largest market and economy, with Africa’s largest population at 165 million
people, and has 47 percent of the Gross Domestic Product (GDP) of West
Africa. According to Mediafacts (2013), Nigeria is Africa’s largest
international business investors market, according to them; investors serious
about Africa must have a big presence in Nigeria. The author defines Critical
success factors as variables or circumstances, attributes, idiosyncrasies that
have a straight and major impact on the efficiency, effectiveness, and
practicability of a project, program or organization. This research determines
the critical success factors for locating and operating a non-oil and gas FDI
business in Nigeria. In other words, it determines what critical factors to be
considered in order to achieve a successful non-oil & gas FDI project and
entry mode into Nigeria. This research reveals the statistical predictable
relationships between factors in Nigeria, such as infrastructure, political
stability, size of market, government support services and the choice of entry
mode into the Nigerian market. This research then goes on to determine and
present a set of significant statistical probabilities of outcome (or statistical
predictions) for choice of entry mode as determined by each of the critical
success factors. The statistical predictions for the outcome of the choice of
entry mode serve as a new guide set for recommending the best entry mode
on a case-by-case basis to future potential foreign direct investors for the
Nigerian market.
2
Extant researches such as Tsang (1999) and Lou and Peng (1999) have
explained Foreign Direct Investment (FDI) as the transfer of the
organizational knowledge of a firm from one country to another. According
to OECD (2013) FDI is the category of investment that reflects the aim of set
up a lasting interest by a local enterprise in one economy in an enterprise or
venture that is local in an economy other than that of the direct investor.
Likewise, this researcher defines FDI as a tangible business investment made
in a certain country by a company or a person from another country. Dunning
(2002) explains that FDI provides businesses the means to take advantage of
new markets in order to achieve more or higher profits and growth. On the
other hand, as Obida and Abu (2010) explains, FDI not only provides
developing countries such as Nigeria with the much-needed capital for
investment, it also enhances job creation, managerial skills as well as transfer
of technology, which all contribute to economic growth and infrastructural
development. These investments are done through different entry modes.
This can mean buying up an existing company, setting up a new company, or
expanding company operations from one country into the new country of
investment. Root (1994) explains a foreign market entry mode to be an
institutional arrangement that a firm uses to market its product in a foreign
market in the first three to five years, which is generally the length of time it
takes a firm to completely enter a foreign market. There are various known
entry modes such as licensing, franchising, exporting, countertrade, strategic
alliances, joint ventures, sole ventures, Greenfield investments and
acquisitions. Agarwal and Ramaswami (1992) explain that the four most
common modes of foreign market entry are licensing, joint ventures,
exporting and sole ventures. Zhang et al. (2007) explains that the choice of
entry mode is regarded as a very serious strategic decision in international
business. Root (1987) expresses that all modes of entry involve considerable
resource commitments and an initial entry mode choice is difficult to change
without a considerable loss of time and money. Therefore, the selection of an
entry mode into a foreign market such as Nigeria is a critical strategic
decision. Dunning (1988) developed a framework for explaining choice
among exporting, licensing, joint venture, and sole venture modes and serves
in part as validity for this research.
3
1.2 Research problem - Problem statement The purpose of this proposed quantitative research is to identify factors that
are crucial to determining successful non-oil & gas FDI and entry mode into
the Nigerian market. This research also reveals a statistical predictable
relationship or non-between factors in Nigeria, such as Infrastructure,
Political Stability, Size of Market, Government Support Services and the
choice of entry mode into the Nigerian market. This research then goes on to
determine and present a set of significant statistical probabilities of outcome
(or statistical predictions) for choice of entry mode as determined by each and
every one of the critical success factors. These statistical predictions for the
outcome of the choice of entry mode serve as a new guide set for
recommending the best entry mode on a case-by-case basis to future potential
foreign direct investors for the Nigerian market. Nigeria has the largest
population in Sub Saharan Africa at about 165 million people and accounts
for 18 percent of the continent's total population. Nigeria’s and South Africa’s
GDP in nominal prices comprised over 50 percent of total Sub Saharan
Africa’s GDP. Rebasing of Nigeria’s GDP in 2014 has meant that Nigeria has
overtaken South Africa as Africa’s largest economy. According to Mediafacts
(2013) Nigeria’s economic market size now is in the region of $509 billion and
about 58 percent of the Gross Domestic Product (GDP) of the entire nations in
West Africa. According to Nigeria’s current minister of national planning,
Shamsuddeen Usman, Nigeria is by a large margin Africa’s largest
international business investors market, recording $7.01 billion of FDI in 2012
this confirms Nigeria’s status as receiving the largest amount of FDI in Africa.
Owing to the above reasons, The United Bank for Africa (UBA) (2013) asserts
that Nigeria is one of the key markets behind the African growth story. This is
because Nigeria is Africa’s largest market and economy, with Africa’s largest
population at 165 million people, and has 58 percent of the Gross Domestic
Product (GDP) of West Africa. Furthermore, according to the United Nations
Conference on Trade and Development (UNCTAD) (2013), Nigeria with $8.92
billion of FDI in 2012, has also been ranked Africa’s largest international
business investors market, they insist that any investor serious about Africa
must have a big presence in Nigeria. However, the negative perceptive news
about Nigeria in many international media and the dearth of empirical study
into the critical success factors for locating and operating a non-oil & gas FDI
4
company in Nigeria has meant that, Many international businesses consider
Nigeria a high-risk investment environment where FDI should not be
attempted. In spite of this Nigeria receives the most FDI in Africa, which
according to the IMF (2012) is arguably the most attractive market for
investment today. This has only increased the need for the research and its
findings. According to Thisday (2014), Michael Andrew the Global Chairman,
KPMG International, explained that offers to invest in Nigeria are enormous
and intense. According to him investors want to know how to do business in
Nigeria and people also want to know how to access the markets in the most
beneficial ways for them. The testimony of the KPMG boss and investment
trends towards Nigeria surely emphasizes the need and importance for this
research. Research such as Asiedu (2003) and Games (2004) shows that in
spite of Nigeria attracting the most FDI in Africa, there are still much more
growth opportunities untapped. The United Bank for Africa (UBA) (2013)
reveals that Nigeria is known for its oil and gas, however, oil and gas
accounts for only 14 percent of Nigeria’s GDP, with agriculture accounting for
44 percent. The mining sector could earn double the income that the oil sector
generates and provide even more growth opportunities for international
businesses. Reports of very large success and profits by FDI companies in
Nigeria such as MTN, Julius Berger, Standard Bank, Procter & Gamble for
example in the non-oil & gas sectors show that there is indeed much profit
and growth to be attained in the non-oil & gas sectors, however as mentioned
previously, the dearth of empirical study into the critical success factors for
locating and operating a non-oil & gas FDI company in Nigeria and the
dearth of empirical research producing models for the choice of entry mode
into the Nigeria market, leaves an important gap which this research fills.
This research determines and presents a set of significant statistical
probabilities of outcome (or statistical predictions) for choice of entry mode to
serve as a new guide set for recommending the best entry mode on a case-by-
case basis into the Nigeria market.
5
1.3 Research Hypotheses
The hypotheses are:
H1 = There is a predictable relationship between the quality of infrastructure
in Nigeria and choice of entry mode for FDI into its non-oil & gas sectors.
H2 = There is a significant relationship between the active government
support services for FDI in Nigeria and choice of entry mode for FDI into its
non-oil & gas sectors.
H3 = There is a significant relationship between the state of political stability
in Nigeria and the choice of entry mode for FDI into its non-oil & gas sectors.
H4 = There is a significant relationship between the size of the market in
Nigeria and the choice of entry mode for FDI into its non-oil & gas sectors.
H5 = There is a significant relationship between the extracted critical success
factors (CSFs) for FDI in Nigeria’s non-oil & gas sectors collectively, and the
choice of entry mode.
1.4 Relevant Background Economic Data for Nigeria.
The United Bank for Africa (UBA) (2013) reveals that Nigeria is known for its
oil and gas, however, oil and gas accounts for only 14 percent of Nigeria’s
GDP, with agriculture accounting for 44 percent. The non-oil and gas sectors
account for 86 percent of GDP sector contribution. The telecom sector alone
accounts for 18.29 percent. The index of economic freedom IEF (2013) states
that Nigeria’s GDP as at September 2012 was $413.4 billion and recorded 7.2
percent growth in 2011 with a 5-year compound annual growth of 7.0 percent.
The corporate council on Africa (CCA) (2013) explained that the Rebasing of
Nigeria’s GDP in 2014 has meant that Nigeria has overtaken South Africa as
Africa’s largest economy. They remark that this comes as little surprise. They
argue that Nigeria is a booming consumer market, and with a population of
160.3 million people which is nearly one-fourth of the population of Sub-
Saharan Africa. South Africa has only one forth the population of Nigeria.
Significantly CCA (2013) remarks that Nigeria’s market is more open to new
6
investment than the South African market. IEF (2013) states Nigeria has
experienced a compound annual growth of 7.0 percent. For five years leading
up to 2013, publications such as Ndikumana (2013) tell us that the South
African economy has been growing at only about 2 percent for more than the
same period, and the other countries in the top five in Africa had grown at
lower rates of about 5.5 percent on average, therefore it is not surprising that
according to IEF (2013) Nigeria’s purchasing power parity (PPP) as at 2011
was $413.4 billion, which is the largest in Africa. Also noteworthy is the fact
that according to UNCTAD (2013), Nigeria has been ranked Africa’s biggest
destination for FDI, with total inflows of US$8.92 billion. South Africa
followed with US$5.81 billion, while Ghana received US$3.22 billion in 2012.
This background economic information about Nigeria makes it arguably the
most attractive place to invest in Africa. Which is even more significant
bearing in mind that according to the IMF (2012) Africa is one of the only
regions providing the world economy with significant growth opportunities?
1.5 Why the Non-oil & gas Sector of the Nigerian Economy.
The African Development Bank Group (AFDB) (2013) and the African
economic outlook organization (AEO) (2013) explain that Nigeria’s economic
growth has not been accompanied by a structural change of the Nigerian
economy. They express that the Nigerian economy lacks diversification and
its agriculture sector needs even more modern methods of production. To
address this, the government is encouraging the diversification of the
Nigerian economy away from the oil and gas sector. It is addressing the
infrastructure deficit in the country and the development of the agricultural
sector through modernization and the establishment of staple-crop processing
zones, with the value chain model to provide linkages to the manufacturing
sector. Punch (2013) quotes the President of Nigeria, Dr. Goodluck Jonathan
reaffirming his administration’s determination to do all within its powers to
facilitate and encourage the rapid diversification of Nigeria’s economy away
from oil and gas dependency. This researcher recognizes that petroleum has
been Nigeria’s mainstay, and Nigeria has somewhat allowed revenues from
oil to slow the rapid growth of other sectors like agriculture, manufacturing
solid minerals, tourism and many others. There is indeed a call to action for
all Nigerians to contribute to the economic diversification process. Should
7
Nigeria attract FDI in other sectors, including manufacturing, tourism,
consumer products, and construction, these new FDI projects could generate
greater employment and create more balanced economic growth. Therefore,
this research is relevant and useful to intending FDI companies, the
international business community and the Nigerian government as it
determines the critical success factors for FDI and entry mode for the non-oil
& gas sectors of Nigeria.
1.6 Research Significance, Justification, Benefits and Uniqueness
According to the IMF (2012) Africa is providing significant growth
opportunities to businesses internationally at a time where most other global
regions are experiencing or offering little or no growth. Nigeria is by a large
margin Africa’s largest international business investors market. Its economy
has been growing at about 6.9 percent for the last eight years. Its financially
empowered and educated middle-class has a big appetite for almost all
consumer goods and services. Doing successful business in Nigeria would
make significant sense to any international business organization seeking
growth. Therefore the findings on what factors to be considered in order to
achieve a successful non-oil & gas FDI project and entry mode into Nigeria,
Makes this proposed research very significant and justified.
This proposed research is unique because it identifies statistically the critical
success factors for foreign direct investment specifically for the Nigeria
market and specifically for the non-oil & gas sectors of Nigeria’s economy. It
does this by uniquely measuring and analyzing data obtained also from CEOs
and top managers of currently successful non-oil & gas FDI businesses in
Nigeria. It goes on uniquely to analyze the critical success factors in a
multinomial logistic regression, creating statistical predictions for choice of
entry mode that serve as a guide set for recommending or deciding on
successful entry modes into the Nigeria market. While other research such as
Obida and Abu (2010) have been conducted in to the determinants of FDI in
Nigeria, and the impact of FDI on the Nigerian economy such as Osinubi and
Amaghionyeodiwe (2010), as well as research on entry modes such as Ekeledo
Swakumar (2004), that investigate international market entry mode strategies
of manufacturing firms and service firms from a resource-based perspective,
8
an extensive literature review reveals this proposed research has not been
done before.
1.7 Research Design, Method, Variables, and Analysis Techniques.
A Quasi – experimental research design is employed where non-random
sampling is executed and a quantitative questionnaire method is used to
obtain data. An extensive review of established literature is employed here to
obtain the initial FDI success factors measured as variables in a scaled
questionnaire. Other variables such as “industry sector” and “entry mode”
are also measured in the questionnaire. A correlation test and analysis is
carried out to establish the variables that correlate and imply validity. Only
variables that pass the correlation test undergo the statistical factor analysis to
determine the Critical Success Factors. All variables in the questionnaire pass
Cronbach's alpha test for measure of internal consistency or reliability. A
multi-nominal logistic regression is used to test the hypotheses and determine
the relationship between the CSFs, and the dependent variable, which is
“choice of entry mode” in order to reveal statistical predictions for choice of
entry mode that serves as a guide set for recommending or deciding on
successful entry modes into the Nigeria market. The business practice
application of this guide set in recommending or deciding on successful entry
modes in business practice, is demonstrated in a case study of the
CEO/Managing Director and managers in an FDI company not already used
as part of the sample for this research. A semi – structured interview is carried
out with each of them, and the interview determines which of the critical
success factors they consider most critical. An understanding of hierarchical
importance for the other seven critical success factors is also determined
according to the interviewed. In consulting and applying the statistical
predictions for choice of entry mode as a guide, the most suitable, fitting and
corresponding statistical prediction is chosen, and an entry mode into Nigeria
for the company is recommended. This recommended entry mode is then
compared to the actual entry mode used by the company into Nigeria.
1.8 Validity and Reliability
The researcher has adopted measures to ensure proper and valid conclusions
can be made from this research’s data and findings. All variables obtained in
9
this research are obtained from an extensive peer reviewed literature review
and therefore would have been used or named in previous peer reviewed
research as FDI success factors. Furthermore, only the variables that pass the
research correlation test are used further in the research. A number of
researches such as Agarwal and Ramaswami (1992) Korey (1995), Dunning
(1980), Dunning (1988), Hambrick and Mason (1984), theory. Thomas et al.
(1991) Ross (1973), Kogut and Singh (1988), L, Brouthers, Brouthers and
Werner (1999) all give validity to research such as this that reveals
relationship between factors and choice of market entry mode. They also help
expose joint ventures, sole ventures, licensing and exporting as the most
common and basic entry modes considered by FDI companies. The sample
used in this research is a more than adequate sample of carefully selected
participants for what the participants represent, which are CEOs and
managers in 30 FDI companies that are located and have been operating for a
minimum of 20 years in the non-oil & gas sectors of Nigeria. The objective of
such a sample is to sample a body of people that have been and still are
successful non-oil & gas FDI managers in Nigeria. With a minimum
experience of 20 years of doing business in the Nigerian environment, such
companies have succeeded through different governments, political and
economic changes. For additional reliability the whole data in this research
passes a Cronbach’s reliability test in IBMs Statistical Package for Social
Sciences (SPSS).
2.0 Literature Review
2.1 Introduction
The Multilateral Investment Guarantee Agency (MIGA) stated in their world
investment trends and corporate perspectives report for the year 2010, that
owing to confident domestic demand, many developing economies in Africa,
were expected to grow by a minimum of six percent a year in 2010, 2011, and
2012—over twice as fast as in high income countries. MIGA (2010) also
expected such developing economies such as those in Africa to significantly
contribute to generating half the annual increase in global demand between
2010 and 2012, even as their rising imports would correspond to 30 percent of
the increase in global exports. In 2014, observers will agree that these past
10
expectations are indeed the fact today. As a result KPMG (2012) explain that
increasingly, investors have become aware of the risk of not investing in the
continent Africa. They have become more focused on where in Africa to
invest, as opposed to whether to invest or not. Increased awareness of the
potential size of the African consumer market, which is $2.6 trillion in 2020
according to McKinsey (2010), has contributed to bringing continually
increasing FDI to the continent. Also significant positive reforms in the
political, business, and economic conditions in Africa make African
economies more attractive than ever before. However, the author points that
while KPMG (2013) and MIGA (2010) like many others point out the benefits
to be gained by investing in Africa, they stop short of informing from the
practical business point of view of how best to enter the African market and
what factors are crucial for success. Nigeria has the largest population in Sub
Saharan Africa at about 160 million people, and accounts for 18 percent of the
continent's total population. Nigeria has just overtaken South Africa as
Africa’s largest economy. According to Mediafacts (2013) Nigeria’s economic
market size is in the region of $509 billion and about 58 percent of the Gross
Domestic Product (GDP) of the entire nations in West Africa. It is therefore
not surprising that Nigeria recorded is by a large margin Africa’s largest
foreign direct investment in 2012. Recording US$8.92 billion of FDI in 2012.
Confirming Nigeria’s status as receiving the largest amount of FDI in Africa
in 2012. Owing to the reasons outlined above, The United Bank for Africa
(UBA) (2013) asserts that Nigeria is one of the key markets behind the African
growth story. They insist that any investor serious about Africa must have a
big presence in Nigeria. However, Asiedu (2003) contends that the level of
FDI attracted by Nigeria is lower than its resource base and potential. Games,
(2004) reveals sectors such as mining could earn double the income that the
oil sector is generating and provide even more growth opportunities for
international businesses. Consequently, there is a genuine and steadily
increasing interest and demand for business practice intelligence on Nigeria.
UBA (2013), Mediafacts (2013), Asiedu (2003) and Games (2014) all provide
important information on Foreign Direct Investment in Nigeria and the
Nigeria economy, but their purpose are not set out to provide empirical
findings that guide potential FDI businesses on the best practices and
consideration factors suited for investment success and market entry modes
11
into the Nigerian market. Neither do they set out to provide findings that
guide the practical international business on what factors must be taken into
account in order to successfully locate and operate their business in Nigeria.
Research has been done on the determinants of FDI location in Nigeria, and
others on the effects of FDI on the economy of Nigeria. Extant literatures such
as Hambrick and Mason (1984) and Thomas et al. (1991) have investigated the
relationship between CEO characteristics and the choice of foreign market
entry mode. Kogut and Singh (1988) investigate the relationship between
national characteristics and country culture on the choice of foreign market
entry mode. However, the author wishes to stress that before this research no
other research has been published on the critical success factors for locating
and operating non-Oil and Gas sector FDI in Nigeria. Furthermore, no other
research before this paper has investigated from the CEO and manager
perspective the relationship between the critical success factors for FDI in
Nigeria and the choice of entry mode into the Nigerian market. This research
produces a set of statistical probabilities of outcome for choice of entry mode,
a guide for recommending the best choice for an entry mode into the Nigeria
market; the focus on the non-Oil & Gas sectors further establishes its
uniqueness, appeal and importance in the context of Nigeria diversifying its
economy away from Oil & Gas. The existence of the theories and models
found in extant research such as Dunning (1980), Dunning (1988), Hambrick
and Mason (1984), Theory. Thomas et al. (1991) Ross (1973), Kogut and Singh
(1988) to name a few, establishes the importance and validity of this research.
The following paragraphs are a critical review of literature pertaining to FDI
in Nigeria, entry modes into the Nigeria market and the Nigerian Economy in
general. The collection of literature available to be reviewed reveals the dearth
of research on Nigeria concerning the Critical Success Factors (CSFs) for
locating and operating non-oil and gas FDI businesses in Nigeria, and also a
non-existences of research providing models for potential FDI investors in
Nigeria’s non-oil and gas sector to choose the most appropriate market entry
mode for their businesses into the Nigeria market. This clear gap in business
research literature is now filled by this research. This critical literature review
will begin with reviewing literature revealing the Nigerian economy today.
12
2.2 The Nigerian Economy and Market.
According to the latest Nigerian economic report from the Central Bank of
Nigeria (CBN) (2013) and available data from the National Bureau of Statistics
(NBS) (2013), Nigeria’s Gross Domestic Product (GDP) was estimated to have
grown by 6.9 percent in the third quarter of 2013, compared with 6.2 percent
in the preceding quarter. The growth mentioned above was attributed to the
contribution of the non-oil sector. Ogunkeye (2014) explains that although
much is known about Nigeria being a major exporter of oil, oil revenues
account for about 11 percent of official GDP figures and these drops to 8
percent when the informal economy is considered. The oil & gas sector is an
important but small part of Nigeria’s economy. Ogunkeye (2014) goes on to
explain Nigeria as a middle income, mixed economy and emerging market,
with fast growing communications, financial, service, technology and
entertainment sectors. It is ranked number one in Africa in terms of
Purchasing Power Parity (PPP) as of 2013, and as mentioned before has just
become the largest economy in Africa in 2014. According to The World Bank
(2010) and Ogunkeye (2014) Nigeria is poised to become one of the 20 largest
economies in the world by 2020. The Nigerian economy produces most of the
goods and services for the West African region. Ogunkeye (2014) says
Nigeria’s manufacturing sector is re emergent but currently underperforming.
However it’s still the third largest on the continent. This author attributes this
to the economic mismanagement and lack of direction Nigeria faced mostly
during its time of military government. However, Index Mundi (2014) and
Ogunkeye (2014) explain that the present economic reforms of the past decade
have put Nigeria back on track towards achieving its full economic potential.
Ogunkeye (2014) gives even more data such as revealing that Nigerian GDP
at purchasing power parity (PPP) has almost tripled from US$170 billion in
2000 to US$451 billion in 2012, although estimates of the size of the informal
sector (which is not included in official figures) put the actual numbers closer
to US$630 billion. Correspondingly, the GDP per capita doubled from
US$1400 per person in 2000 to an estimated US$2,800 per person in 2012
(again, with the inclusion of the informal sector, it is estimated that GDP per
capita hovers around US$3,900 per person). They go on to note that
population increased from 120 million in 2000 to 160 million in 2010. The non-
13
oil and gas sectors account for 86 percent of Nigeria’s GDP. The telecoms
sector alone accounts for 18.29 percent. The Index of Economic Freedom (IEF)
(2013) states that Nigeria’s GDP as at September 2012 was US$413.4 billion
and recorded 7.2 percent growth in 2011 with a five year compound annual
growth of 7 percent. Nigeria’s economic profile on Index Mundi (2014)
reports similar to Ogunkeye (2014) on Nigeria. It reports past political
instability, corruption, inadequate infrastructure, and poor macroeconomic
management. However it also states that in 2008 the Nigerian economy began
undergoing economic reforms the government started executing market-
oriented reforms such as modernizing the banking system, removing
subsidies, and resolving regional disputes over the distribution of earnings
from the oil industry. GDP continues to rise strongly since 2008, also because
of growth in non-oil & gas sectors together with robust global crude oil prices.
The present government administration continues its efforts at diversifying
Nigeria’s economy and its growth.
The author asserts that intra-African trade is a booster for the diversification
and growth of the Nigerian economy that should be explored vigorously.
African Union (AU) (2014) expresses that not much trade takes place between
African countries. As a result African nations such as Nigeria have not tackled
the positive combined effect, improvement and emphasis on their different
qualities and capabilities, to bring about economic growth to their respective
economies. African Union (2014) explains that it is currently the practice for
an African country to procure products and services from Asia, America or
Europe, when such could have been procured more beneficially from another
African country. African Union (2014) assets that Africa is especially
unprotected from external macroeconomic shocks and protectionist trade
policies from countries and organizations outside Africa. African countries
such as Nigeria must learn to protect themselves from such by promoting and
implementing intra-African trade.
Africa Growth Initiative (2012) points out that intra-African trade will help
Nigerian and the African industries improve their competitiveness via
economies of scale as well as improve product quality and distribution.
Technology transfer and the improvement of infrastructure could also be one
14
of the benefits from intra-Africa trade. For the reasons stated above, Africa
Growth Initiative (2012) assert that rigorously implementing intra-African
trade is critical to speeding up economic growth in all African countries such
as Nigeria.
The author will also point out that there are many obstacles for African
countries to overcome in establishing beneficial intra-African trade. Adetunji,
and Gbadebo (2012) express that although the importance of promoting intra-
African trade has led to the formation of several African regional economic
communities such as SADC, EAC and COMESA, (Southern Africa
Development Community, East African Community and Common Market for
Eastern and Southern Africa). All of such currently have obstacles and
hindrances to overcome before the African countries involved, can reap the
benefits of such regional economic communities. Adetunji and Gbadebo
(2012) explain that an example of such obstacles and hindrances are the
overlapping memberships many African countries have with regional
economic communities. Most African countries belong to two or more
regional economic communities. This according to the authors of Adetunji
and Gbadebo (2012) makes the enforcement of agreements almost impossible
for agreements across regional economic communities often conflict with one
another. Another hindrance is the fact that most African countries do not
have internationally competitive services and industries. They do not produce
the mutually beneficial goods or services to exchange with one another to
benefit from comparative advantages.
It is important to know as Tafirenyika (2014) explains, that not everybody
agrees intra-Africa trade is inadequate. According to Tafirenyika (2014), many
experts such as the Mr. Carlos Lopez, the executive secretary of the UN
Economic Commission for Africa, argues that most of Africa’s internal trade
takes place informally and often across country borders that are permeable.
Such experts assert that borders in Africa are not managed adequately and
data on informal trade across borders are non-existent. Customs officials
simply do not record such trade officially. According Tafirenyika (2014) Mr.
Carlos Lopez explains that UN Economic Commission for Africa is in the
process of trying to document and record such informal trade activity across
15
borders in Africa. However, Adetunji and Gbadebo (2012) tell us that intra-
Africa trade is low at between 10 percent and 12 percent of total trade in
Africa. In comparison this figure is 40 percent in North America and
approximately 60 percent in Western Europe. Tafirenyika (2014) explain that
the level of intra-Africa trade has hardly increased in the last 40 years.
Tafirenyika (2014) concludes with explaining the opinion of Trudi
Hartzenberg, the head of the trade law center for Southern Africa, which is
that intra-African trade that will lead to more economic growth for Nigeria
and the other African countries can only thrive at the time when countries in
Africa produce what other African countries are keen to purchase. This
scenario does not yet exist in Africa. As at now, Nigeria and Africa as a whole,
utilize what it does not produce, and produces what it does not utilize.
This scenario thwarts and confuses efforts towards beneficial intra-African
trade. As a result Adetunji and Gbadebo (2012) explain that Nigerian and
African economic growth has not included boosts from intra-African trade.
Which remains an opportunity for economic growth that can be exploited
rigorously?
Nevertheless, in explaining the forecasts for the Nigerian economy The
National Bureau of Statistics (NBS) (2013) employed macro-econometric
modeling techniques and explained that the Nigerian economy will expand
significantly within the periods between 2013-2016, partly because the
Nigerian government endeavors to ensure macroeconomic stability. The NBS
(2013) goes on to tell us that exports from Nigeria are expected to increase and
contribute in a proportionate manner to the total merchandise trade in the
country therefore bringing a beneficial balance of trade status for the Nigerian
economy. On the whole, the economy is projected to follow a steady growth
pattern in the next four years with real GDP growth expected at 6.74 percent
in 2013 and inflation rate of 9.74 percent, coupled with rising exports and
imports. Rising imports and exports are expected to lead to higher trade
merchandise trade values over the forecast period. Chete et al. (2014) reveals
the view that the Nigerian government seems to understand, that
productivity and performance in very increased measures is crucial for the
rapid industrialization and economic growth it seeks for the Nigerian
economy. Consequently, greater emphasis on productivity has been put since
16
Nigeria’s adoption of its economic reform. One of the important features in its
reforms is the creation of the industrial sector special economic zones. Which
are set up with the important purpose of encouraging and developing
industrialization? Economic activity in these zones is clustered in an enclosed
and controlled environment with a mind to compensate for deficits in
infrastructure outside the enclosed environment. Chete et al. (2014) explains
that this enables the prioritization in the provision of infrastructure to be
effective and therefore enhancing the effect of the whole economic reform
process. However, Chete et al. (2014) expresses that the economic zones have
not been given the necessary attentions to enable them contribute their full
potential to the Nigerian economy. Chete et al. (2014) observes that many of
these economic zones are basically left to turn into specialized markets, and
that while they have brought some positive consequences for the Nigerian
economy, if the original visions for these economic special zones are
implemented, there would be of very much more benefit to the Nigerian
economy. This is a fact that in the author’s opinion should have always be
pointed out by any research paper mentioning these special economic zones
as part of Nigeria’s economic reform. Within the Nigeria economic reforms
started in 2003, Economy watch (2010) expresses that the government laid
huge emphasis on improving the telecommunications sector. The Nigeria
Communications Commission has the responsibility to develop mobile and
Internet communication facilities in the country. The telecommunications
sector in Nigeria remains in popular view for investors and consumers, and is
considered by Nigeria’s government as one of Nigeria’s economic success
stories. However the author must point out in agreement with the authors in
Economy watch (2010) that there is very much more potential in the non-oil
mining sector, which is yet to be, developed to even a minute fraction of its
potential to contribute towards national production. Economy watch (2010)
points out that indeed the country Nigeria has very notable reserves of coal,
iron, gold, uranium and tantalum.
Okonjo-Iweala and Osafo-kwaako, (2007) explains a history of the Nigerian
economy and its reforms. The authors explain that after a period of economic
stagnation, Nigeria embarked in 2003 on what is understood as thorough
economic reform program known as the National Economic Empowerment
17
and Development Strategy (NEEDS). With major aims and strategies at its
center, as bettering Nigeria’s macro-economic environment, going after
structural reforms, solidifying and improving public expenditure
management, and executing governance and institutional reform changes. In
Okonjo-Iweala and Osafo-kwaako (2007) it is explained that challenges
existed from the very beginning; especially when trying to convert the
advantages of the reforms into beneficial and tangible improvements for
Nigerian citizens. They went on to explain that even more challenging was
trying to improve the internal business environment, and in spreading the
benefits of the reform process and its policies to states and local governments
throughout the Federation of Nigeria. For all the above factors, the authors
pointed out that the economic reforms Nigeria begun in 2003 must see as only
the beginning of the very long but stable journey of Nigeria’s economic,
stabilization and sustained growth. The aim of the reforms was to stabilize
the economy, to improve budgetary planning and execution, and to enable for
diversification of the Nigerian economy for non-oil sector growth. A task
among many was to separate public expenditures from oil revenue earnings,
and to ensure that this policy resulted in public savings. According to the
authors, success was achieved regarding this task for in some measure
government expenditures were separated from oil revenue earnings,
therefore acting as a buffer and easing any external shocks into the domestic
economy. Okonjo-Iweala and Osafo-kwaako (2007) noted that significant
progress in the government’s fiscal balance was achieved, with the previous
deficit of 3.5 percent of GDP in 2003 becoming surpluses of about 10 percent
of GDP in 2004 and 11 percent of GDP in 2005. Savings from crude oil
excesses increased from $6.35 billion in 2004 to $17.68 in 2005. Nigeria’s
foreign reserves went up greater than 5 times between 2003 and 2006. More
good news was that inflation dropped from 21.8 percent to about 10 percent
through the central bank executing several reform monetary policies.
Nigeria adopted the wholesale Dutch Auction System in regulation of its
foreign exchange market. This speeded up the merging of foreign exchange
markets and removed the existence of the black market. Prime-lending rates
declined from 21.3 percent in 2003 to 17.6 percent in 2005. Chete et al. (2014)
express that in general the economic reforms of Nigeria begun from 2003
18
resulted in the stable macroeconomic environment desired by 2006.
According to them, also notable is that private sector credit grew by 30.8
percent to N2.01 trillion (US$15.1 billion), and increased vastly to averages of
7.1 in 2006 from 2.3 before the reforms in 2003. Very importantly is the fact
that the growth achieved was driven by the non-oil sectors. GDP for the non-
oil sector was 8.26 percent as at 2006. Constantly growing from 2.2 as at 2003.
At this point, the author will point out that one of the authors of Okonjo-
Iweala and Osafo-kwaako, (2007) was Finance minister for Nigeria for the
period reviewed by the paper. Incidentally Professor Okonjo-Iweala is again
in 2014 currently the Finance minister and minister for the Economy of
Nigeria. However, Chete et al. (2014) tells us that “vision 2020” Nigeria’s
current economic transformation agenda, leads the direction Nigeria’s
industrial and economic policy follow as at 2014. Global competiveness in the
manufacturing sector is the priority on the policy agenda. The policy for
industrialization includes linking industrial activity with other crucial
activities within the Nigerian economy, being the primary sector, domestic
and foreign trade, and services activities. For export manufacturing and
processing, Nigeria is also pursuing a cluster development strategy whereas
mentioned before more strategic creations and development of industrial
parks, industrial clusters and enterprise zones and incubator facilities are
being implemented.
Akinlo (2004) is in agreement with many of today’s economic reforms in
Nigeria. The results of his research indicate that extractive FDI particularly oil
sector based may not be as growth promoting as much as manufacturing FDI
would. His research findings also suggested instead that export; labor and
human capital are more positively related to growth. In addition, interestingly
his results claim that private capital has a positive effect on growth in Nigeria
but only an insignificant positive effect. Akinlo (2004) asserts that the policy
implications of his research findings for the Nigerian economy are firstly that
FDI into the non- oil and gas sectors would enhance growth. He goes on to
assert that the Nigerian Government will have to provide or foster an
enabling environment to attract manufacturing FDI for this will further
enhance growth in the now already fast growing economy of Nigeria.
Therefore, it is suffice to note that Akinlo (2004) like many other research
19
publications agrees with and advocates policies such as the relaxing or the
elimination of restrictions on profits and capital remittances in FDI ventures,
the opening of previously closed areas for FDI. However, Akinlo (2004) warns
that government must make sure that these efforts to ensure or secure FDI
through these incentives must produce the positive advantages to the local
people and the Nigerian economy in general. He also emphasizes the need to
integrate Nigeria’s oil sector into its economy by creatively utilizing inflows
from the oil sector to enhance the attractiveness of other non- oil and gas
sectors and therefore leading to increased private participation in the non-
oil& gas sectors. In his research, he is emphasizing that the Nigerian
economy will benefit from increased FDI inflows into the oil sector if the
sector is integrated into the economy as explained above. Furthermore on the
other hand, both Chete et al. (2014) and Akinlo (2004) suggest that
government rolling back its participation in many other sectors of the
economy. They recommend privatization as the means or vehicle to do this. It
must also be noted that Ogunkeye (2014), points out that currently in 2014,
most of all the above mentioned policies recommended by many research
publications are in the process of implementation by the Nigerian
government, and are in various stages of success. Most publications warn that
privatization in the Nigerian economy should avoid the reasons for the failure
experienced in an initial attempt at the privatization exercise that took place
in 1988.
Many extant related researches such as Chete et al. (2014), Akinlo (2004) and
Ogunkeye (2014) expresses that the Nigerian Government must always
provide a transparent administration and legal structure for its privatization
process at all times. Many in extant research also suggest the important need
to increase exports in order to achieve significantly increased growth
performance in the Nigeria economy. In the author’s view, care must be
taking to ensure that a much greater percentage of the increase in exports
must come from the currently underdeveloped but fast improving non-oil &
gas sectors. Even though it has been stressed in a number of extant research
and publications, Ogunkeye (2014) still echoes that developmental economic
policies in Nigeria must always be aimed at enhancing increased private
(domestic and foreign) participation in Nigeria’s economy, and this will in
20
turn result in much increase in exports. In other words, Ogunkeye, (2014) as
many others, suggest that Nigeria’s economy be opened up, via much more
privatizations. The author will point out that indeed from 2003 this is already
being implemented with some success. However and quite importantly,
Ogunkeye (2014) also stresses the need to stem capital flight from the country.
It goes on to suggest that a level of legal and administrative playing field for
domestic investors as well as a stable macroeconomic environment should
stem capital flight. Ogunkeye (2014) explains that encouraging Nigerian
private holders of externally located money and assets to invest in Nigeria is
very important. Ajakaiye and Fakiyesi (2009) also tell us that the Nigerian
economy experienced growth even during a time period corresponding to the
global financial crisis from 2007 to 2011. It explains that data from the NBS
showed GDP grew to 6.1 percent in the first half of 2008, from the 5.5 percent
recorded in 2007. Collective growth was spurred almost entirely by the non-
oil sector, which grew by 8.7 percent and contributed 80.7 percent of GDP, as
oil sector output declined. Furthermore according to Ajakaiye and Fakiyesi
(2009) the non-oil & Gas GDP recorded was broad based, as building and
construction grew by 13 percent, wholesale and retail trade 12.0 percent,
services 10.3 percent and agriculture 6.3 percent. They go on to explain that
agriculture sector of the Nigerian economy remained dominant in terms of
sectorial contributions to GDP, accounting for 39.8 percent of GDP; industry,
services, wholesale and retail trade and building and construction followed it,
with contributions of 22.1, 18, 17.9 and 2.1 percent, respectively. They echo the
NBS as the explain the balance of payments for 2008 as against 2007 as having
remained impressive, with an increase of 8.2 percent in the current account
surplus and a reduction by 61.1 percent in the capital and financial account
deficit in 2007. Ajakaiye Fakiyesi (2009) goes into some detail explaining
Nigeria’s external sector remained relatively viable in the three years of its
study, 2006,2007,and 2008. They explain that an impressive balance of
payments surplus of $999.0 billion in 2008 compared with $41.6 billion in the
corresponding period and in 2007, respectively. These figures reflected a good
trade balance and high crude oil prices as well as capital repatriations such as
Diaspora in bound transfers as well as foreign direct and portfolio
investments. Ajakaiye and Fakiyesi, (2009) goes into even more detail by
referencing The Central Bank of Nigeria CBN (2008) explaining “current
21
account surplus represented 17.3 percent of GDP, while the deficit in the
capital and financial account narrowed from 2.4 percent and 4.6 percent of
GDP in the first and second half of 2007 to 1.1 percent in 2008”.
Adenikinju (2008) gives us necessary information on Nigeria’s reforms in the
energy sector of its economy. It explains that while there are big hurdles to
clear in reforming the energy sector in order to achieve attainable and reliant
energy for the economy, good and hope bringing progress is being made. The
creation of the Nigerian Electricity Regulatory Commission (NERC)
government was to enable structure and competitiveness in the energy sector.
New energy tariffs, gas meters as well as a new gas pricing and allocation
policy. Gives hope for future progress. This includes the fact that has a new
electricity master plan. Nigeria now also has a National Electricity Master
Plan, together with an electricity reform act. Nevertheless, Adenikinju (2008)
warns that several issues such as funding, adequate coordination of activities
among various stakeholders in the energy sector, expansion of transmission
and distribution networks, and enlightenment of the public on issues of
energy use efficiency must be addressed. The author will point out that
recently in February 2014 there are strong revelations that these issues and
much more a being progressively tackled, this is evidenced in a recent
publication such as Amadi (2014). The author of Amadi (2014) is the head of
Nigeria’s electricity regulatory commission. He states Finally the structure of
electricity supply in Nigeria has changed. In one day, the country moved from public
ownership of most electricity utilities to almost complete private sector ownership of the
utilities. By the time the Nigerian Integrated Power Plants (NIPPs) are sold to preferred
private sector bidders late this year, it would be a complete restructuring of the electricity
industry from a vertically integrated monopoly industry to a privatized competitive
electricity market. At that stage, the only asset that would be fully owned by government
would be the Transmission Company of Nigeria (TCN). Even that would be privately
managed. The just concluded privatization is historic not just because it is the largest single
sale of utilities in recent time. It is historic also because it effectively marks the beginning of
an electricity market in Nigeria. Any person who left Nigeria in 2000 and suddenly re-
emerged on November 2, 2013 would not recognize the structure of the electricity industry.
Things have changed significantly. We have moved from a mere industry to an electricity
market, even if the fundamentals of that market are still rudimentary. (Amadi 2014,p.1).
22
It is only proper to continue in this “Nigerian economy” part of this literature
review, by mentioning what the Central bank of Nigeria has published
recently on the economy. Available economic information on Nigeria clearly
shows that Nigeria’s economy in 2012 and 2013 performed at least as well as
the CBN’s (Central bank of Nigeria) 2011 economic outlook/forecast for 2012
and 2013. In CBN (2011) the outlook for the domestic economy in 2012/2013
was cautiously optimistic. The global demand for crude oil is projected to
remain sluggish as the US and Euro zone economies recover slowly. The
agricultural sector is expected to lead growth and remain robust if recent
trends in the increased public sector funding of the sector are sustained. With
bumper harvest, food prices would trend downwards, thus moderating
inflationary pressures. In mid-2014, one can say that the CBN’s forecasts were
good. However, while reports such as Ogunkeye (2014) and Index Mundi
(2014) give a fair perspective on the economy of Nigeria today, they never set
out to include much information important and useful to the practical FDI
businessman wishing to invest in Nigeria, such as the size and importance of
Nigeria’s informal economy and the importance of local intelligence and
partnership. As mentioned before, this author’s research will provide
empirical findings crucial to the practicing international business FDI
organization for Nigeria. This research will provide knowledge on how best
to enter the Nigerian market on a business-to-business basis and what factors
must be taken into consideration in order to achieve success in Nigeria.
However there is still more important information to be found in publications
such as IEF (2013).
IEF (2013) Express that Nigeria is a booming consumer market, and with a
population of 160.3 million people which is nearly one-fourth of the
population of Sub-Saharan Africa. South Africa has only one forth the
population of Nigeria. According to Mediafacts (2013) Nigeria’s economic
market size is in the region of (US$347 billion) and about 47 percent of the
Gross Domestic Product (GDP) of the entire nations in West Africa.
Significantly the corporate council on Africa CCA (2013) remarks that
Nigeria’s market is more open to new investment than the South African
market. IEF (2013) states Nigeria has experienced compound annual growth
of 7.0 percent. For five years leading up to 2013, and publications such as
23
Ndikumana, (2013) tells us that the South African economy has been growing
at only about 2 percent for more than the same period, and the other countries
in the top five had grown at lesser rates of about 5.5 percent averagely.
Therefore it is not surprising that according to IEF (2013) Nigeria’s GDP (PPP)
purchasing power parity as at 2011 was US$413.4 billion, which is the largest
in Africa. Also noteworthy is the fact that according to UNCTAD (2013),
Nigeria has been ranked Africa’s biggest destination for FDI, with total
inflows of US$8.92 billion, South Africa followed with US$5.81 billion, while
Ghana received US$3.22 billion in 2012. This background economic
information about Nigeria makes it arguably the most attractive place to
invest in Africa. Which is even more significant when we bear in mind that
according to the IMF (2012) Africa is one of the only regions providing the
world economy with significant growth opportunities? An extensive search
reveals that there is no published empirical research guiding the potential
investor into Nigeria, with what the best entry mode for his or her business is
into the Nigerian market. Neither is there published empirical research
guiding the international business with the factors that must be taken into
account in order to successfully locate and operate of their business in the
Nigerian market. This research provides the international business with such
crucial empirical findings. The literature review will continue by reviewing
current and up to date literature published on the Nigerian economy by the
world’s foremost / big four audit and professional business services firms:
KPMG, Deloitte, Ernst & Young and PricewaterhouseCoopers.
2.3 Recent Trends on the Nigerian Economy: KPMG
According to Thisday (2014) KPMG, asserts that offers to invest in Nigeria
have been enormous and intense and their records place Nigeria among the
four major investment destinations and growth areas in the world. KPMG
explains that Nigeria’s new repute is as a result of the discouraging
performances by the BRICS, with the exception of China. The“BRICS” are
Brazil, Russia, India China and South Africa known as emerging global
economic powerhouses. KPMG explains in their report that discouraging
performances of the BRICS countries is working in favors of Mexico,
Indonesia, Nigeria and Turkey, which have been termed the MINTs which
KPMG explain to be the new destinations for global capital and investors.
24
Michael Andrew the global chairman, KPMG International, asserts that
Nigeria and the other countries among the MINTs have attracted increasing
investment offers and enquiries through the services of KPMG with a view to
taking advantage of the high rates of return on investment. Mr. Michael
Andrew explained that offers to invest in Nigeria are enormous and intense.
According to him, investors want to know how to do business in Nigeria;
investors also want to know how to access the markets in the most beneficial
ways for them.
2.4 Recent Trends on the Nigerian Economy: Deloitte.
According to The Vanguard (2014) Deloitte demonstrated its trust in the
Nigerian economy, market and its future growth, by recently investing
considerably in Nigeria. It announced the formation of an integrated practice
across Africa with Nigeria playing the pivotal role in the new growth process.
With the establishment of the integrated Deloitte Africa with dedicated
investment in Nigeria, Deloitte has designated Nigeria a Priority Market.
Deloitte Nigeria will therefore receive substantial financial investments aimed
at enhancing the quality and breadth of services provided to its local and
cross border clients.
2.5 Recent Trends on the Nigerian Economy: PricewaterhouseCoopers (PwC) Polity (2014) tells us about PwC’s report on ten African countries where
Nigeria is named as Africa’s number one rising stars because of her likelihood
for very vibrant and flourishing transportation and logistics industry.
PricewaterhouseCoopers (PwC) (2014) tells us that seaports, airports and
railways in Nigeria have received significant investment over the past few
years, resulting in good and promising international and local transport
portals. They explain that owing to Nigeria’s petroleum revenues; Nigeria is
in a much better position compared with many of its African neighbors to
allocate the necessary funds needed to improve its infrastructure. PwC (2014)
asserts that Nigeria raising its infrastructure level to that of South Africa,
would spur its annual real GDP growth by about five percentage points. Mr.
Andrew Shaw PwC’s projects and infrastructure solutions associate director
asserted that Nigeria should see its economy double in ten years, Nigeria had
only to manage 6.8 percent gross domestic product (GDP) growth forecast for
25
2012 to 2017. PwC ranked Nigeria as the world’s fourth-fastest growing
economy, Shaw reported of the Nigerian government’s plans to expand its
infrastructure, primarily to grow its economy further. PwC also mentioned
the new US$1.6-billion deep-sea port Nigeria plans, and that US$2-billion is
being invested to reconstruct 2000 km of rail across the Nation. Once again
the author will point out that PwC presents very information on the Nigerian
economy, which further highlights the importance and the dearth of empirical
findings on how to enter the Nigerian market and how to successfully locate
and operate an FDI project in Nigeria.
2.6 Recent Trends on the Nigerian Economy: Ernst & Young
Channelstv (2014) reports that Ernst & Young attributes the increased private
equity investment in Nigeria to the positive government reforms, which has
in turn lead to increased Direct Foreign Investment (FDI). Furthermore Ernst
& Young ranked Nigerian banks at the top in there the latest barometer of
banking in emerging markets. Channelstv (2014) explained the top ranking is
based the Gross Domestic Product (GDP) of banks in emerging countries.
“Nigeria’s GDP as at 2011 stood at US$1,200, indicating room for growth,
hence explaining the global interest of investors in Nigerian banks. He noted
that the banking barometer report discovered that Nigerian banks have a
massive opportunity for credit expansion and a huge number of Nigerians are
still unbanked. According to the report Ernst and Young’s Regional Managing
Partner, West Africa added that by 2017, Nigeria’s GDP would have increased
to US$2,000 and there will be an increase in demand for finance and product
financing. This represents opportunities to global banks with bigger balance
sheets to increase Nigeria banks capitalization, which will enhance their
capacity to make more regional impact beyond the domestic focus. Again
such economic outlook for Nigeria highlights the necessity for the author’s
research. Empirical findings guiding potential investors of the importance of
local partnerships, intelligence and where in the banking sector of Nigeria to
invest is now crucial to the discerning international FDI investor to Nigeria. In
the author’s view, it is also important to most current and potential FDI
investors to know about the administrators of the economies in which their
investments have been made. The next paragraph explains about the current
administrators of the Nigerian economy.
26
2.7 Current Administrators Of The Nigerian Economy
The current government of Nigeria has been publicizing their major
achievements. It included Standard and poor (The internationally respected
and independent economic and financial ratings agency), revising Nigeria's
ratings from stable to Positive. 2. Being the fourth fastest growing economy in
the world as attested by the UK government. 3 Non-oil & gas exports from
2010 (standing at US$2.3 Billion) and subsequent years are ten times what
they were in 2000 (which were US$200 Million) as a direct result of this
administration’s intervention in the textile industry and real sector. From an
FDI point of view, the present Nigeria government is proud that for the
second year running, the United Nations Conference on Trade and
Development, (UNCTAD), has named Nigeria as the No.1 destination for
investments in Africa. The author will point out that the current Nigerian
administration has certainly made Nigeria’s attractiveness for FDI one of its
priorities. The Nigerian government started executing market-oriented
reforms such as modernizing the banking system, removing subsidies, and
resolving regional disputes over the distribution of earnings from the oil
industry. If these achievements are good then credit should be given to
Nigeria’s current March (2014) Dr. Ngozi Okonjo-Iweala, Finance Minister
and Coordinator of the Nigerian economy. It must be said that she was the
managing director of the World Bank, but now coordinates Nigeria’s
economic team, which is chaired by the President of Nigeria. Recently, the
Financial Times FT.COM (2014) published an article commenting on the
recent suspension of the Nigerian Central Bank Governor. It eluded that some
analysts and advisers raised alarm at the potential damage that removing the
outspoken governor will do to investor confidence given the role he played in
establishing Nigeria’s credibility as a frontier market. The article is a typical
example for the fact that while many investors in fast increasing numbers are
taking advantage of Africa’s opportunities for business profit and growth,
some others see reasons not to invest in Africa. Strictly from a FDI point of
view, such articles reinforce the urgent need for the publication of research
findings from this research. According to UNCTAD (2013), Nigeria has been
ranked Africa’s biggest destination for FDI, with total inflows of US$8.92
billion. South Africa followed with US$5.81 billion, while Ghana received
27
US$3.22 billion in 2012. Nigeria attracting ever increasing FDI and being
Africa’s top destination for FDI, has happened together with the ex-central
bank’s governor’s standings against corruption in certain institutions.
Therefore logically, the suspension of the governor should bring any negative
difference in the volume of FDI flow into Nigeria. With the huge effort being
made to attract even more FDI into Nigeria. The author’s research is the only
one that presents potential FDI businesses globally, with a scientific model,
which determines the business’s best entry mode into the Nigeria market. It is
also the only research that presents the potential FDI business with scientific
findings on what factors must be considered in order to achieve FDI success
in Nigeria. At this point it is only proper for this literature review, to
continue by reviewing extant literature pertaining to FDI in Nigeria.
2.8 Foreign Direct Investment in Nigeria
The International Monetary (IMF) (2012) expresses that Africa is arguably the
most attractive market for investment today. It has been established in UBA
(2013) that Nigeria is the crucial part of Africa’s FDI attractiveness. Research
such as Asiedu (2003) and Games (2004) shows that in spite of Nigeria
attracting the most FDI in Africa, there are still much more growth
opportunities untapped. The United Bank for Africa UBA (2013) reveals that
Nigeria though known for its oil and gas, in fact oil and gas accounts for only
14 percent of Nigeria’s GDP, with agriculture accounting for 44 percent. Its
mining sector could earn double the income that the oil sector generates and
provide even more growth opportunities for international businesses. Reports
of very large success and profits by FDI companies in Nigeria such as MTN,
Julius Berger, Standard Bank, Procter & Gamble for example in the non-oil &
gas sectors show that there is indeed much profit and growth to be attained in
the non-oil & gas sectors, Media publications in the heart of Western Europe
such as the Tagesanzeiger (2014) from Switzerland, report of the international
consulting firm McKinsey’s praise and optimistic regard of Africa’s economic
growth and rise. In their article titled “the new scramble for Africa”, they tell
of the huge investments in Africa from international corporations such as
General electric, Coca-Cola and Nestle in Africa and Nigeria in particular.
However, the authors do not share in the optimism for Africa. According to
the publication, Mr. Jean Louis Arcand, an economist with the Geneva
28
Graduate institute says that these investments, do little good for Africa itself,
and based on his research findings, Mr. Arcand concludes that in spite of the
huge investments, there would be no significant economic prosperity in
Africa if strong political institutions, legal structures and much attention to
agriculture are not implemented. The author agrees that such mentioned are
important for the socio-economic growth of all nations, including those in
Africa and Nigeria. The author will point out here that corruption is often
named as one negative factor that impedes the speed at which the Nigeria
grows economically. However, it is interesting to note that Nigeria’s economy
has grown into Africa’s largest economy attracting the largest amount of FDI
in Africa, in spite of this perception by many. Punch (2013) quotes the
President of Nigeria reaffirming his administration’s determination to do all
within its powers to facilitate and encourage the rapid diversification of
Nigeria’s economy away from oil and gas dependency. Should Nigeria attract
FDI in other sectors, including manufacturing, tourism, consumer products,
and construction, these new FDI projects could generate greater employment
and create more balanced economic growth. However, it should be noted
that in the past FDI was not cherished in Nigeria. Ekperiware, (2011) reveals
and interesting point about this.
Ekperiware (2011) tells us that not too long ago in Nigeria, FDI was seen as
parasitic and was detested until the 1990s. From the late 1990s, a globalization
conscious agenda by government and the private sector encouraged cross-
border investment especially by multinational corporations and firms. Today
Nigeria, now sees attracting FDI as an important part in its strategy for
economic development. African Growth Initiative (2012) tell us that Nigeria
in its economic reforms, is beginning to harness its prominent role in regional
economic bodies such as the Economic Community Of West African States
(ECOWAS), in its quest for continued increase in FDI and economic growth.
Nigeria Trade Hub (NTH) (2014), revealed that according to the European
Union (EU), the world sees Nigeria as the economic and business gateway to
Africa, it quotes the EU as explaining that Nigeria, as the largest economy in
Africa and the industrial hub of West Africa, the West African market was in
fact an extension of Nigeria’s domestic economy, Nigeria must always take
the leadership role and drive the further integration of West Africa. Aribisala
29
(2014) expresses that Nigeria has begun as part of economic reforms to lead
and support the ECOWAS initiative, for Nigeria knows that the regional
structure and market is beneficial for its attracting FDI and economic growth.
However, Nigeria’s efforts toward more economic growth through global
treaties and agreements within the World Trade Organization (WTO), are
impeded by what Fleshman (2003) explains to be an impasse within the WTO
where developed countries have been strongly in disagreement over
protectionism and subsidies, as African countries such as Nigeria have been
calling for improved and fair market access into developed countries in
industries such as agriculture. According to the United Nations Economic
Commission for Africa (UNECA) (2004), the WTO was created to bring about
free and unimpeded trade among countries of the world, thereby fostering
mutual peace, development and economic growth in all member nations.
Cline (2004) explains that the WTO adopts traditional trade theories where
mutual economic gain is achieved among trading countries owing to the
complementarity between them in terms of the different industrial,
technological capabilities and natural resources they each possess. Duruji et
al. (2014) contends that the WTO system has only profited industrial societies
who are the innermost assembly of the WTO. Duruji et al. (2014) Assert that
Nigeria and African countries do not benefit from the current WTO system,
the authors explain that discussions within the WTO to remove restrictions on
free and fair trade in Agriculture for example, have failed consistently,
because developed industrial countries will not stop spending huge amounts
of money on subsides and protectionism towards farmers in their countries.
Nigeria’s opportunities at attracting beneficial FDI and more economic
growth through the WTO system has much to be improved on, for as Duruji
et al. (2014) stress, The world capitalist system places Nigeria and African
countries, being less or non-industrialized countries at the periphery of the
global economy, without a significant voice.
Obida and Abu, (2010) tells us Foreign direct investment (FDI) supplies
Nigeria and other developing countries with the much needed money for
investment, it also creates jobs, managerial skills and transfer of technology,
30
all contributing to economic growth and development. Therefore, the
Nigerian government tries to attract FDI through many different reforms. The
reforms included the much publicized deregulation of the economy, the new
industrial policies of 1989, the Nigeria Investment Promotion Commission
(NIPC) in early the 1990s, and many Bilateral Investment Treaties (BITs).
According to UNCTAD (2013) Nigeria has been ranked Africa’s biggest
destination for FDI, with total inflows of US$8.92bn, (see figure 5 appendix 6)
South Africa followed with US$5.81billion, while Ghana received US$3.22
billion in 2012. The report noted that FDI inflows to African countries went up
by 5 percent to US$50 billion in 2012, though global FDI declined by 18
percent. According to the report, most of the FDI into Africa were mainly
driven by the extractive industry however non-extractive industries also
experienced growth. The FDI friendly policies in Nigeria have increased
research into FDI such as Obida Abu, (2010) in which the determinants of
foreign direct investment in Nigeria is investigated. They recommended the
expansion of the country’s GDP via production incentives; further
deregulation of the economy through privatization and reduction of
government interference in economic activities among others. Osinubi and
Amaghionyeodiwe (2010) focused on analyzing the direction and significance
of the effect of foreign direct investment on the GDP of Nigeria. Their
research finds that that Foreign Private Investment, Domestic Investment
growth and Net Export growth are positively related to economic growth in
Nigeria. Asiedu (2003) asserts that the level of FDI attracted by Nigeria is
lower than its resource base and potential. Games (2004) reveals sectors such
as mining could earn double the income that the oil sector is generating and
provide even more growth opportunities for international businesses. Ekpo,
(1995) remarks that the extent of public investment is directly proportional to
private investment, and therefore strongly recommends the high investing in
infrastructure by government. This according to them will attract foreign
direct investment to Nigeria. The recommendations from papers such as Egbo
(2008) saying the necessary policy measures to bring foreign capital should be
formulated and implemented to uplift increased economic growth in Nigeria,
are in this authors opinion vague and do not share the characteristics of
recommendations from good quality research. Interestingly Olokoyo (2012)
concludes that foreign direct investment regardless of its size may not
31
guarantee the relative impact on the growth of the Nigerian economy. It
advises the Nigeria government to juxtapose foreign investment with
domestic investment in order to maintain high levels of income and
employment. Foreign investment can be effective if it is directed at improving
and expanding managerial and labor skills. In other words, foreign direct
investments into Nigeria will not on its own lead to sustainable economic
growth except it is combined with the right structures and infrastructures that
could facilitate fruitful results. Similarly Abass (2006) contends that the
strategy government must adopt in order to further improve the economic
climate for foreign direct investments in Nigeria, should be the
encouragement of domestic investors first prior to going after FDI. Abass
(2006) also asserts that the purpose of foreign direct investment is supposed to
serve as means of increasing Nigeria’s resources in order to effectively
execute her development strategies and raise the standard of living of its
people.
Interestingly Akinlabi et al. (2011) interpret their findings as showing a
significant relationship between the level of corruption prevalent and the
inflow of Foreign Direct Investment into Nigeria. Within the period of 1990-
2010, Akinlabi et al. (2011) claims in that corruption had a negative impact on
the amount of FDI coming into Nigeria. The author explains that the
implication is Nigeria can only attract large FDI when corruption in all
government manifestations is greatly reduced. On this point the author has to
cautiously disagree. This is simply because there is no measure showing
anywhere that corruption in government in Nigeria has dropped, however it
is clear that FDI inflow into Nigeria has increased many fold especially in
recent years. Furthermore the author will point out that there is currently no
measure if possible of FDI project brought or not into Nigeria owing to
corruption in the first place. Instead moving on to measurable relationships
for FDI, in Okon et al. (2012) single and simultaneous equation models gives
us evidence that point to a two way mutual relationship between economic
growth and FDI inflows to Nigeria. Through this they express that as FDI
encourages growth and more growth also encourages more FDI. They explain
a positive returning relationship between FDI and economic growth in
Nigeria.
32
Another interesting point is from Osinubi and Amaghionyeodiwe (2010),
which tells us that the Nigeria’s experience concerning foreign private direct
investment is different if not contradictory to reports or information from
other developing countries. Osinubi and Amaghionyeodiwe (2010) tells us
that the Nigerian case is a bit different in that clearly FDI continues to grow,
while there is no evidence that corruption has reduced. Ogunleye (2014) adds
that encouraging Nigerian private holders of externally located money and
assets to invest in Nigeria is very important and will result in an increase in
GDP. Osinubi and Amaghionyeodiwe (2010) likewise assert that Foreign
Private Investment has a positive significant effect on GDP growth rate of
Nigeria. They therefore advised the government, recommending that issues
on Foreign Private Investment should feature in policy geared towards
economic development in Nigeria. They again echo what many other research
have asserted and indeed what the government is implementing with some
success, which is that one of the ways Nigeria can boosts its economy is by
implementing innovative strategies for attracting more and more foreign
private Investment.
The author agrees with Olokoyo (2012) in the most part. Enhancing or
encouraging domestic entrepreneurship and investment is noble and would
make the Nigerian market attractive to FDI, however the author will contend
that the federal government of Nigeria must do this concurrently with strong
efforts to encourage FDI because, on the other hand as Obida and Abu (2010)
points out Foreign Direct Investment (FDI) not only provides developing
countries such as Nigeria with the much needed capital for investment, it also
enhances job creation, managerial skills as well as transfer of technology. All
of these contribute to economic growth and development and in turn
domestic entrepreneurship and investment. Keeping the findings of this
research in view, the fact remains that extant publications concerning FDI and
Nigeria have focused on recommending measures to attract FDI, or its
significance to the economy. The importance and significance of this research
is its focus on empirically researched findings for the international business
practitioner to ensure the success of FDI in Nigeria, while harnessing the
viewpoints and real life experience of already successful managers of FDI
33
businesses in Nigeria. Knowledge of the FDI experiences and history is
important.
According to Corporate Guides (2011) Nigeria’s significant source of FDI had
been the home countries of the major multi-national oil-petroleum companies.
The USA, present in Nigeria’s oil sector through Chevron Texaco and Exxon
Mobil, had investment stock of USD3.4 billion in Nigeria in 2008. Similarly the
UK, the part home of Shell petroleum was another key FDI partner. As China
seeks to expand its trade relationships with Africa, it too is becoming one of
Nigeria’s significant sources of FDI; according to the Nigerian Ministry of
industry, trade and investment (2014), a report by Oxford Business Group
(OBG) shows that investment from China in 2011 was approximately 25 per
cent of Nigeria’s incoming FDI for that year, equivalent to approximately
$6.1billion (N988.2 billion). Other significant sources of FDI include Italy,
Brazil, the Netherlands, France and South Africa. In March 2006, the Nigerian
Investment Promotion Commission (NIPC) was setup to facilitate and
promote investment in Nigeria. The NIPC in turn set up a One Stop
Investment Centre (OSIC) on its premises in Abuja. This brings together all
agencies relating to investment with the aim of streaming the process of
investing in the Nigeria. Corporate Guides (2011) reveals that Nigeria’s FDI
framework has successfully catapulted the nation to the top of the investment
table in Africa, but the government is committed to bringing in even more
investment. The author will point out that the more confident investors are of
success in Nigeria, the more FDI will come into Nigeria. One of the aims of
this author’s research is to provide empirical findings to the business practice
that ensures and gives confidence of success to FDI in Nigeria. Which is one
thing all the papers quoted above do not set out to do. At this point this
literature review will review some extant literature pertaining to the success
factors of FDI.
2.9 Success factors for FDI.
(Accenture, 2010) researched into the challenges and success factors for
foreign businesses investing in Africa. They present three critical success
factors, 1. Sourcing materials locally. 2. Being authentically local. 3. Follow
the example of the Coca Cola Corporation using local brand ambassadors to
34
build brand equity in local markets. Musila and Sigue (2006) qualitatively
identified seven factors that influence the success of FDI businesses in Africa,
namely, market size; labor costs; infrastructure quality; openness; political
instability; corruption; macroeconomic instability. Similarly, Palmade (2008)
express that farmers associations, irrigation, non-price restrictions and trade
agreements are some key success factors in agricultural FDI. They go on to
express corporate social responsibility, low utilities, flexible labor markets
and competitive tax as key success factors for light manufacturing FDI
businesses. Ajayi (2006) presents similar factors to Musila and Sigue (2006)
adding to the list: Availability of natural resources, Concentration of other
investors and Enforceability of contracts. However, interestingly Singh Jun,
(1995) tell us that although political risk is frequently thought to influence the
decision to invest in a foreign country and its success, empirical results do not
support such hypotheses. Indeed, the effect of political stability on the inflow
of FDI into a country is ambivalent in extant literature. Li (2008) showed that
war or conflict involving arms and the military force and FDI inflow have a
contrary or negative relationship. Dupasquier and Osakwe, (2006) explain as
many others, that political stability is a statistically significant factor
determining the inflow of FDI into a country. However, many other
researchers and research work reveal opposing findings to those of Li (2008)
and Dupasquier and Osakwe, (2006) for example, Bennett and Green (1972)
showed that political instability does not discourage FDI investments. In
support of this finding, Kobrin (1976) fails to establish any relationship
between FDI and variables based upon political event data. Similarly Asiedu
(2001) explains that political instability or risk, has an insignificant effect on
FDI when it comes to Nigeria, she asserts that political stability has not
discouraged FDI inflows into Nigeria, because even after adjusting for risk the
profits are very high. On infrastructure quality as an FDI success factor,
research such as Musila and Sigue (2006) and Dupasquier and Osakwe (2006)
explain that FDI in Africa depends on the development of its infrastructure.
Indeed many extant literature such as Mengistu and Adams (2007), Cotton
and Ramachandran and (2001) Zhang (2001) depict an important role that
infrastructure plays in the attraction of FDI. In contrast Nnadozie and Osili
(2004) disagree with the findings of such literature as Mengistu and Adams
(2007), Cotton and Ramachandran on infrastructure and FDI. Nnadozie and
35
Osili (2004) found no significant evidence on the role of infrastructure on
attracting or influencing FDI. Asiedu (2002), research work from a leading
researcher on FDI in sub Saharan Africa, tells us that determinants of FDI
have a different effect on the FDI inbound of Sub-Saharan countries than that
of other developing countries. Asiedu (2002) found that some determinants
that normally correlate with FDI have a different effect on Sub-Saharan
Africa. She asserts that infrastructure development and a higher return on
capital are important determinants for the other developing countries but not
for Sub-Saharan countries. According to her, the perceived risky nature of
Africa cancels these normally reasonable relationships. According to
Anyanwu (2011) change in domestic investment, change in domestic output
or market size, indigenization policy and change in openness of the economy
are major determinants of the FDI. Ayanwale, (2007) presents the
determinants of FDI in Nigeria as market size, infrastructure development
and stable macroeconomic policy. In a guiding manner, Puget Sound
Regional council (PSRC) (2009) expresses that for the intending FDI business,
building fresh capacity in a foreign country is the riskiest way to operate
internationally, the business would have to have a clear and very good reason
to choose this way over exporting or licensing. The strategic advantages
would be clearly identified through FDI and how FDI is in line with the
business’s long-term global strategy clear to all stakeholders. PSRC (2009)
asserts that for any community to attract direct investment it must offer
foreign firms something they cannot get operating at home or elsewhere. As
far back as 2002 Aseidu, (2002) asserted what many African countries have
accepted the importance of FDI to their economies, and strive to attract it
today, Aseidu (2002) goes on to explain that their asserted research results
have policy implications to enhance FDI flows, one is that African countries
will need to liberalize their trade policies, and perhaps most importantly, that
the full benefits of trade liberalization can only be realized when investors
perceive reform actions to be credible and not subject to repeal or change. The
author stresses credibility as the backbone mechanism in any economic
reform aimed at attracting FDI. The author also adds that policies seen to
have been successful in other regions cannot be unthoughtfully copied into an
African economy for such policies could have different and negative impact
in the African economy. Furthermore the results from Aseidu (2002) suggest
36
that African countries operate under the disadvantage of the perception that
they are very risky. As a consequence Africa receives less FDI than other
many other regions because of its geographic location. These perceptions may
be born out of lack of knowledge however the author will point out that in
more recent time leading up to 2014 FDI into Africa is increasing rapidly, this
suggests that there is less ignorance on Africa. Aseidu (2002) suggests that
International organizations such as the World Bank should play a positive
and important role in enlightening the world about the risky perceptions
about Africa are over blown. In 2014, the author is not sure that operations in
such international organizations have resulted in much positive for African
economies in this regard. However, concerning positive perceptions for
African economies, It is interesting and important to note the content of The
Korea herald (2013) published for a significant foreign market, reports that
Nigeria had effectively commenced the implementation of the Nigeria
National Industrial Revolution Plan in 2013. According to the publication in
the Korea herald (2013) the goal of this plan is to increase manufacturing
industries contribution to the GDP of Nigeria. The plan here also looks to
increase over time and enhance priority sectors to be pace setters in Africa
and in the leading 10 industries internationally, and therefore, reduce any
dependence on imports and effectively create jobs. The article goes on to
explain that the goal of the plan includes placing Nigerian industries in the
forefront of inclusive economic growth and development. According to the
authors of the article, Nigeria is conscientiously executing a well locally
publicized backward integration policy for its cement industry and other key
sectors. The article also reveals that a new sugar master Plan for Nigeria has
been created, which plans to effectively put forward or create approximately
117,000 jobs, as well as 1.79 million metric tons of sugar, 161.2 million liters of
ethanol and 411 megawatt-hours of electricity every year upon final
completion and beyond. The plan being implemented by Nigeria has also
created the Nigerian Automobile Industry Development Plan to set the
conditions for the structured development of the sector. These initiatives
currently create the base for adequate job opportunities for all those in the
working group/related job skills. The article expresses that according to
statistics by the Manufacturing Association of Nigeria; industrial capacity use
has gone up from 46.44 percent in 2010 to 48.24 percent to date. Ability
37
exploitation in the textiles, apparel and footwear sector has remarkably
increased from 29.14 percent to 52.01 percent. The Nigerian Federal
government’s involvement in the textile industry has obviously and
delightfully led to the reopening of “moribund” textile mills, it has saved
about 8,070 jobs and provided 5,000 new jobs through the disbursement of the
100 billion naira ($62 million) Intervention Fund by government efforts.
Indeed the Korea herald (2013) via this article has report in some detail
positive economic information on Nigeria. The CBN’s Q1 2013 economic
report for the Nigerian economy also reports that largely receipts drove the
federal government’s $1.13 billion non-oil sector earnings in the first quarter
of 2013 in the industrial sector.
In furtherance of the topic success factors for FDI, Ozturk (2007) explains
much like Olokoyo (2012) and Ogunkeye (2014) that macro-empirical work on
the FDI-growth relationship regarding developing countries, has shown that
several critical factors, such as the trade regime, the human capital base in the
host country, financial market regulations, banking system and the degree of
openness in the economy towards FDI have a positive impact on overall
economic growth. Research such as Alfaro et al. (2004) and Durham (2004)
researched into the connection between the regulation of financial markets,
economic growth and FDI. Like Alfaro et al. (2004) and Durham (2004),
Hermes and Lensink (2003), finds that having better financial systems and
regulation enables the exploitation of FDI more efficiently, therefore enabling
a much higher growth rate for an economy. Such studies are asserting that
both a strong banking system, and well operating financial market are
necessary. These researchers recommend that countries should reform their
domestic financial system before working on attracting FDI. As well
intentioned as the recommendations may be, Okon et al. (2012) point out
those reforming domestic financial systems before setting out to attract FDI is
not normal practice, for most cannot wait the time it takes for reforms to
complete and take hold before executing efforts towards attracting FDI.
Implementing both at the same time is much more practical and is what the
Nigerian government is seen to be doing. Furthermore, many publications on
the determinants of FDI in developing countries clearly show the positive
influence FDI can have on infrastructure, skills development, macroeconomic
38
stability and sound institutions, which in turn influences towards reforms in
internal financial systems and goes on to attract more FDI.
While many extant literatures have heeded the importance of FDI to growth
and development, it also realizes that economic growth could be an important
factor in attracting FDI flows. The importance of economic growth to
attracting FDI is closely linked to the fact that FDI tends to be an important
component of investing firms’ strategic decisions. Okon et al. (2012) echoes
and agrees with Ozturk (2007) and several other empirical studies, and best
describes the Nigerian economic FDI scenario where as the according market
size hypothesis suggests that the markets with large population size and/or
rapid economic growths (as measured by real GDP per capita or its growth)
tend to give multinational firms more opportunities to generate greater sales
and profits and thus become more attractive to their investments which in
turn bring about more growth for the economy for the nation. This could
very well describe Nigeria economy and FDI well. Ozturk (2007) concludes
what most studies of such now conclude that FDI has positive effect on
growth, a positive effect on economic growth via several channels such as
capital formation, technology transfer and spillover, human capital
(knowledge and skill) enhancement, and so on.
Athukorala (2009) asserts that because various reasons are behind why
different companies will make their decisions to invest in a foreign country,
issues pertaining to the determinants of FDI are multidimensional; some
examples of the various reasons are seeking large domestic markets or
seeking a good supply of a natural resource. Mottaleb and Kalirajan (2010)
explain that on the other hand, many multinational corporations (MNCs)
need to quickly and simply relocate their production bases in order to cut
their production cost and to strengthen their access to the international
market. Mateev (2008) reveals that the key determinants of FDI inflows in
central and eastern European countries include the labour costs in host
country. Shamina et al. (2010) also reveals that cheap labour cost is also
among the motivational determinants for attracting FDI to Asian nations such
as Bangladesh. However, Mottaleb and Kalirajan (2010) note that possible
contending factors for being the determinants of FDI are usually multiple. The
39
author agrees with Mottaleb and Kalirajan (2010) when they express that
literature on FDI is thickening every day. Indeed publications on the
identification of the determinants for FDI are always growing in number.
Such examples include publications and research like Nunnenkamp and
Spatz (2002), Petrochilas (1989), Wheeler and Mody (1992), Jun and Singh
(1996). However, they continue by explaining that even though there seems to
be some agreement on a few economic variables being the major determinants
of FDI, such as the size of GDP and its growth, with regard to the other socio-
economic variables, for example, such as the role of a particular business
environment in attracting FDI, much is still largely unexplored. Mottaleb and
Kalirajan (2010) go on to assert that in some cases in publications these other
socio-economic variables have been very erroneously determined. Mottaleb
and Kalirajan state that as a result, empirical findings on the determinants of FDI are
quite chaotic and misleading sometimes. This necessitates undertaking more and more
empirical study with well-defined variables and new data sets to clearly understand the
determinants of FDI (Mottaleb and Kalirajan 2010, p.3).
Mottaleb and Kalirajan (2010) also state profit seeking foreign investors will prefer to
invest in the countries that welcome foreign investment. Schneider and Frey (1985) and
Kimura and Todo (2010) argued that developing countries that receive larger amount of
foreign aid might be more successful in attracting foreign investors compared to others for the
following two reasons. Firstly, inflow of a large volume of foreign aid might mitigate a
country’s internal macroeconomic problems, and it might help to enhance more business
friendly environment in the aid receiving countries due to conditions imposed by the donors.
Secondly, a high volume of aid inflow to a particular developing country might ensure foreign
investors that aid receiving host country may show more friendly gestures to the foreign
investors. Moreover, the aid dependent host countries might not dare to nationalize or
confiscate the property of the foreign investors without adequate compensation. It might also
be the case that the higher dependency on foreign aid might provide negative signal to the
foreign investors about the macroeconomic efficiency and the overall business environment of
a country. To see the effect of foreign aid on the determining inflow of FDI, the following
hypothesis has been formulated: (Mottaleb and Kalirajan 2010, p.8)
Zakari et al. (2010) tells us that according to their findings and analysis the
NIPC are a success factor in making Nigeria investment friendly, and
therefore attracting FDI into the country. According to them, evidence for this
is the increased FDI inflow to Nigeria for the 15 year period since its
40
conception. Their conclusions are clear that NIPC’s role has significantly and
positively influenced the growth of FDI in Nigeria. Most existing literature
provide important information on Foreign Direct Investment in Nigeria and
the Nigerian economy, but their purpose does not set out to provide empirical
findings that guide potential FDI businesses on the best-suited market entry
modes into the Nigerian market. Neither do they set out to provide findings
that guide the practical international business on what factors must be taken
into account in order to successfully locate and operate their business in
Nigeria. While research has been done on the determinants of FDI location in
Nigeria, and others on the effects of FDI on the economy of Nigeria, the
author wishes to stress that before this paper no research has been published
on the critical success factors for locating and operating FDI in Nigeria, let
alone for its non-Oil and Gas sectors. Furthermore no other research before
this paper has produced a model for recommending the choice of entry mode
in the Nigeria market, let alone into its non-Oil & Gas sectors. An example of
the many evidence of the importance for this research comes in the form of
Mr. Michael Andrew of KPMG who explained that offers to invest in Nigeria
are enormous and intense. According to him investors want to know how to
do business in Nigeria, people also want to know how to access the markets
in the most beneficial ways for them. Testimony such as that of the KPMG
boss surely emphasizes the need and importance for this research, which is to
identify factors that are crucial to determining successful non-oil & gas FDI
and entry mode into the Nigerian market. This research will determine what
factors to be considered in order to achieve a successful non-oil & gas FDI
project and entry mode into Nigeria. This research will also present a model
for recommending successful entry modes into the Nigeria market for
intending foreign direct investors. The United Bank for Africa (UBA) (2013)
asserts that Nigeria is one of the key markets behind the African growth story.
They insist that any investor serious about Africa must have a big presence in
Nigeria. The non - existence of empirical study into the critical success factors
for locating and operating a non-oil & gas FDI company in Nigeria and the
also the dearth of empirical research producing models for the choice of
Nigerian market entry mode leaves an important gap which this research will
fill adequately. This literature review will continue now by reviewing
literature pertaining to the choice of market entry modes.
41
2.10 Choice of Market entry modes
As mentioned previously a company’s implementation of an international
diversification or growth strategy will require the choice of a foreign market
entry mode. In Root (1987) foreign market entry mode is explained as
organized planning that enables a company’s goods or services entry into a
foreign market. As Porter (1980) explains the increased and continuous
international interaction of persons, businesses and nations through
globalization has led to resulted in businesses moving out of their domestic
markets to the international stage to achieve growth. Zhang et al. (2007)
explain that the choice of entry mode is viewed as a very important strategic
decision for multinational corporations in their quest for profits and growth
internationally. Similarly, Havnes (2002) tell us that a business’s decision to
grow beyond borders, is also called its internationalization decision, and is
one of the most important decisions facing majority of firms today. It is a very
prominent and important strategy in the growth process of a business today.
In Cyert and March (1963), it is presented that complex decisions such as the
choice of entry mode into a foreign market are largely the outcome of
behavioral factors rather than a mechanical quest for economic optimization.
The author will point out that a number of extant literatures on the choice of
market entry mode stress the influence of CEO’s characteristics on the
selection of an entry mode in to a new market. In Hambrick and Mason
(1984), the influence of CEO’s characteristics, for example age, gender, tenure
and international experience on the risk-taking attitude are explained by the
Upper Echelon Theory. It contends that strategic decisions are influenced by
the characteristics of CEO’s and it eventually leads to different organizational
decisions and performances. The risk-taking attitude influences the different
entry modes decision-making. As mentioned previously, other research such
as Thomas et al. (1991) equally found a very significant relationship between
the managers’ or CEO’s characteristics such as age, tenure and international
experience and the entry mode in host countries. Huang (2013) tells us that
results from his 328 entry events pertaining acquisition and joint-ventures in
his study, show that, CEO’s having long employment contracts tended to
prefer joint-ventures to acquisition investment. However, in contrast to
Hambrick and Mason (1984) Huang (2013) observed no significant influence
42
age and international experience, had on the choice of entry mode. Huang
(2013) still up holds the choice of entry mode into a foreign market
significantly depends on the risk taking attitude of CEOs, which is in turn
greatly determined by the individual characteristics. Additionally, the agency
theory from Ross (1973) explains that the choices and behaviors of CEO’s are
to a certain extent influenced by their personal characteristics yet also by the
principal-agent contract within the company. The agency theory explains that
the relationship between the principal who are the shareholders and the agent
who is the CEO is maintained by several factors, including the company’s
rules and regulations, board of directors and the payment of the CEO. The
nature of the relationship influences the choices and behavior of the CEO on
entry mode in to new and foreign markets.
In contrast Kogut and Singh (1988) investigate the influence of national
characteristics and cultures on the selection of entry modes. They employ a
multinomial logit model in analyzing data on 228 entries into the United
States market by acquisition, wholly owned green field, and joint-‐venture.
Kogut and Singh (1988) interpretation of their results find empirical support
for the effect of national culture on the choice of entry mode. Kogut and
Singh (1988) asserts that extant literature finds that uncertainty over the
foreign markets influences managers decisions when seeking to invest in
foreign markets, and that there are distinct un clarified country patterns in the
selection of entry modes, and that both firm-and industry-level variables are
related to the choice of entry mode. They contend that in spite of the
increasing international presence of multinational corporations, the
management of these firms is likely to be influenced by the dominant country
culture.
From a different perspective on choice of foreign market entry mode,
Dunning (2004) explains the eclectic paradigm also known as the OLI
paradigm. It expresses that a company (multinational) foreign market entry
mode choices can be explained by the OLI paradigm. ‘O’ as ownership
advantage representing the companies specific advantages, ‘L’ representing
location advantage which explains a major factor why multinational
companies expand their business to a particular country in the first place, and
43
‘I’ representing internalization advantages. These perceived advantages
influences how a firm chooses to enter and operate in a foreign country. In
contrast The Uppsala model from Johanson & Vahlne (1977) and Johanson &
Vahlne (2009) express that companies will choose entry modes in steps that
with time and experience reduce uncertainties in the foreign country. In the
model the strategy is to enter a new area step by step and retain the
knowledge from their experience. The acquired knowledge about country
specific markets leads to more certainties in future operations.
Root (1994) explains a foreign market entry mode to be an institutional
arrangement that a firm uses to market its product in a foreign market in the
first three to five years, which is generally the length of time it takes a firm to
completely enter a foreign market. Similarly this researcher defines FDI entry
mode as the manner by which an investing company makes is products and
or services available in its target new foreign country market. There are
various known entry modes such as licensing, franchising, exporting,
countertrade, strategic alliances, joint ventures, sole ventures, Greenfield
investments and acquisitions. Agarwal and Ramaswami (1992) explain that
the four most common modes of foreign market entry are licensing, joint-
ventures, exporting and sole-ventures. Zhang et al. (2007) explains that the
choice of entry mode is viewed as a very important strategic decision for
multinational corporations in their quest for profits and growth
internationally. Anderson and Gatignon (1986) and Herrmann and Datta
(2006) observe that international businesses are not only interested in which
foreign markets to enter and but equally important to them is knowing how
to enter such foreign markets. Ekeledo and Swakumar (2004) express that
today’s frameworks for entry mode strategy include many with roots in
neoclassical economics, and many based on organization behavior, but non
provide a complete explanation of entry mode choice by firms in today’s
business environment. They go on to explain three international entry mode
choice theories, The Internalization theory, The Eclectic theory and The
Resource based theory. Madhok (1997) and Ekeledo and swakumar (2004),
explain that the internalization theory and even the eclectic models do not
explain the choice of entry mode in today’s business environment entirely.
Ekeledo and swakumar (2004) present the resource-based theory as
44
appropriately explaining international entry mode choice by firms in today’s
business environment. Zekiri and Angelova (2011) explain that businesses
seeking to operate in foreign markets must carefully select the best mode of
entry choice into foreign markets. Understanding a country’s political,
economic, and social institutions and culture is crucial, for the wrong entry
mode could have irreversible effects. They explain four foreign market entry
mode mechanisms: exporting, licensing, joint venture and direct investment.
Top management on a country-to-country basis must consider their
advantages and disadvantages.
Anderson and Gatignon (1986) assert that the impact of entry modes on the
success of foreign operations is great, leading Wind and Perlmutter (1977) to
identify entry modes as a "'frontier issue" in international marketing. Entry
modes differ greatly in their mix of advantages and drawbacks. The tradeoffs
involved are difficult to evaluate and little understood. Several surveys of
how firms actually make the entry mode decision reviewed in (Robinson,
1978) indicate that few companies make a conscious, deliberate cost/benefit
analysis of the options. What is the best mode of entry for a given function in
a given situation? Despite the existence of relevant evidence, the literature
does not suggest how the manager should weigh tradeoffs to arrive at a
choice that maximizes risk-adjusted return on investment. Instead, much of
the literature contains many seemingly unrelated considerations, with no
identification of key constructs. Often, a consideration is mentioned as part of
a case study, with little indication of how the factor should affect other
situations. Further, relevant work is scattered across books and journals in
several disciplines, obscured by varying terminology, and separated by
differences in problem setup, theory, and method. Ogram, and Radebaugh
(1982) show that the traditional methods for to long-term strategic decisions,
like the choice of an entry mode into a new market, stress on selecting the
choice that offers the highest risk- adjusted return on investment in a
practicable setting. Anderson and Gatignon (1986) point out that in extant
literature on the choice of entry mode before 1986, very little is discussed on
risk or return. However, much is focused on the degree of control an entry
mode gives the foreign investor. Davidson (1982) explains that the focus on
control instead of risk or return is because control has a critical effect on the
45
future of a foreign business in a new market. Without control, it becomes hard
to organize and execute or revise strategies. The less control a business has in
a new market, the harder it is for disputes that could arise to be resolved with
whoever controlled is shared. Vernon (1983) argues that on the other hand,
though control is naturally attractive, it comes with its costs and
responsibilities. Control, while obviously desirable, carries a high price. To
have control responsibility for decision- making must be assumed and a much
higher commitment of resources, including high overhead is involved. This
means it becomes harder to change the direction of plans when they are seen
not to be working as expected. Therefore it is easy to understand why control,
is an important focus of the entry mode literature before 1986. Anderson and
Gatignon (1986) argue that control is the single most important determinant
of both risk and return. Tang and Liu (2011) explain the entry mode choice for
a company in more practical business terms. The authors tell us that
multinational companies seeking to enter a new foreign market face the very
important strategic decision on two related but distinct issues. Tang and Liu
state that the first involves the choice between a non-equity entry mode such as exporting
through agents and licensing and an equity-based entry mode in which the local enterprise is
either partially owned or wholly owned. Second, if an equity mode of entry is selected, the
issue of whether to acquire an existing firm (acquisition), collaborate with a local firm (joint
venture) or establish a completely new plant has to be decided”(Tang and Liu 2011, p.50).
Tang and Liu (2011) explain their focus to be on the choice of equity-based
entry modes, described as investment that concerns ownership and confers
effective management control. They define four types of these, wholly owned
subsidiaries (WOS), equity joint ventures (JVs), acquisitions and capital
participation. They explain that each entry mode comes with its implications.
They state that at periods of high density, with the local network occupying the central
domain of resources distributions, a WOS would likely be positioned at the periphery of the
resource space. As such, a WOS will likely face higher selection pressures than entry
accomplished through acquisition. On the contrary, by acquiring an existing firm in a tightly
packed resource space, the entering firm could position itself into the central domain of the
resource distribution in the local environment (Tang and Liu 2011, p.56).
Datta et al. (2002) stresses a reoccurring point made in this research paper
which is that the choice of entry mode is a very important topic in academic
research in international business, and it is also a very important interest to
46
practicing managers and policy formulators. Datta et al. (2002) tell of an
unsurprising but impressive accumulation of academic research into choice of
market entry mode. However, the author has pointed out and Datta et al.
(2002) agree that most empirical research in the field of choice of entry mode
is ambiguous or not precise in their findings. This has made much of extant
literature of no practical use to real life practicing managers and policy
makers. However, some theoretical perspectives are useful in explaining the
choice of entry mode into new foreign markets. These include monopolistic
advantages, internalization, internationalization, transaction cost, strategic
behavior, bargaining, and eclectic theories. In the monopolistic advantages
theory, firm-specific advantages such as peculiar products or technology
could mean that such firms can enter markets with little fear of opportunism.
Furthermore scarce or unique firm specific advantages increase the
bargaining power of the same company in pre-market entry deliberations in
the context of the bargaining theory. In Hofstede (1980) we understand that
companies from cultures or countries that indicate greater power distance and
uncertainty avoidance could be anticipated to look for higher levels of
ownership and control in their choice of entry mode in to new foreign
markets. Datta et al (2002) tells us that theoretical arguments suggest a
relationship between the investor’s home country cultural managerial
characteristics and choice of entry mode it will make. Theoretical arguments
suggest that the desire for control is shaped by national culture. Agrarval and
Ramaswani (1991) tell us that the entry mode chosen would normally be the
one that offers the highest risk adjusted return on investment. However they
warn that evidence shows a need for control and resource availability
influences the choice of market entry mode. They explain that it is often
suggested that FDI businesses should not enter certain countries perceived
high investment risks; they warn that organizations vary in their capacity to
handle investment risks. For example certain firms with assets and skills
needed in markets such as Nigeria may be able to bargain with government
for concessions that make them immune to the perceived risks. Accenture,
(2011) explains their experience showing that successful organizations adopt
structured approaches to discerning and doing business with the African
consumer in Africa. They recommend a systematic and customized approach
to market entry in Africa in order for international businesses to benefit the
47
African opportunity. According to the Food and Agriculture Organization
(FAO) (2014), while businesses develop a market entry strategy, a crucial
determinant is time and cost. The time and cost involved in developing the
necessary intelligence system and the public’s perception for the company,
via promotion. FAO (2014) go on to explain that large investments in
promotion campaigns are needed, and contract expenses also are a critical
factor in developing up a market entry strategy. Transaction cost can be a
high barrier to FDI. Such costs include search and bargaining costs. Physical
distance, language barriers, logistics costs and risk limit the direct monitoring
of trade partners. According to FAO (2014), even the enforcement of contracts
may be costly legal integration can be fragile between countries makes things
cumbersome. Also, these factors are important when considering a market
entry strategy. The author points out that all the literature cited and reviewed
above point to the fact that the choice of market entry mode is a very
important topic in international business. However, in spite of the fact that
the Nigerian market remains a crucial and arguably the most attractive
market in Africa to the international investor, no other research before this
paper has produced a model for recommending the choice of entry mode in
the Nigeria market, let alone into its non-Oil & Gas sectors. In the author’s
view, this is a fact that needs further explanations; this is done in the
following paragraphs.
2.11 Identifying and explaining the gap in literature. A closer look at
examples of extant literature.
Extant research such as Obida Abu (2010) has explored the determinants or
importance of FDI in Nigeria. Mr. Obida Gobna Wafure and Mr. Abu
Nurudeen are academic lecturers at the University of Abuja in Nigeria. In
their paper published in 2010 and titled “Determinants of foreign Direct
investment in Nigeria: An empirical Analysis”, They carried out a
quantitative research with the purpose of investigating the determinants of
foreign direct investment in Nigeria. The error correction technique was
employed to analyze the relationship between foreign direct investment and
its determinants. Osinubi Amaghionyeodiwe, (2010) is authored by Nigerians,
Tokunbo Osinubi is of the department of Economics, division of business,
social & behavioral studies, prince George’s community college Largo,
48
Maryland, USA and Lloyd Amaghionyeodiwe is of the Department of
Economics, Faculty of the Social Sciences, University of the West Indies, Mona
Campus, Kingston, Jamaica. Their research paper published in 2010 and titled
“Foreign private investment and economic growth in Nigeria” is another
quantitative research with the purpose of analyzing the direction and
significance of the effect of foreign direct investment on the GDP of Nigeria.
Okon et al. (2012) titled “Foreign Direct Investment and Economic Growth in
Nigeria: An Analysis of the Endogenous Effects” is again authored by
university academics. The authors Okon Umoh, Augustine Jacob and Chuku
Chuku are lecturers at the Department of Economics, University of Uyo,
Nigeria the Heritage Polytechnic, Eket, Nigeria respectively. The purpose of
their paper was to empirically investigate the relationship between foreign
direct investment and economic growth in Nigeria. Ayanwale (2007) titled
“FDI and Economic Growth Evidence from Nigeria”, is authored by Adeolu
Ayanwale of the Department of Agricultural Economics Obafemi Awolowo
University Ole-Ife, Nigeria. It has the purpose of investigating the empirical
relationship between non-extractive FDI and economic growth in Nigeria and
examines the determinants of FDI into the Nigerian economy. All the above-
mentioned research works are quantitative in nature, all relying on a
quantitative, post positivist view in their research process. Obida Abu (2010)
and Okon et al. (2012) advice the government of Nigeria to ensure property
rights, bolster growth in market size, as well as moderate wages in order to
attract more FDI. Similarly Osinubi Amaghionyeodiwe (2010) advises the
Nigerian government that foreign direct and private investment should not
be ignored in policy decisions aimed at promoting the economic development
of Nigerian. Ayanwale (2007) tells us that FDI on the whole may not have
much impact on the Nigerian economy however the components positively
affect economic growth in Nigeria and therefore FDI always is to be
encouraged and sort after by the Nigerian government. The above-mentioned
research publications are representative in nature of the extant academic
research available pertaining to FDI, market entry mode into Nigeria, the
Nigerian Economy and Market. The author points out that they all focus on
producing findings, which enable them advice government on what polices to
adopt. The author asserts that the over focus on producing findings and
recommendations only for government has produced a gap and lack in extant
49
literature. There is an obvious gap and lack of research findings answering
questions coming from actual FDI business practice for Nigeria. Which is
what the attracted potential FDI business for Nigeria need and demand? In
the next paragraph the author will explain the gaps and lack in literature as
well as the reasons.
2.12 Explaining the Literature gap and its reason
The author points out that the representative research papers reviewed above
were all published after the year 2007. They all focus on researching into
information perceived to be important for government. They end up telling or
confirming to the Nigerian government of the importance of FDI or of what
policies it is to adopt to attract FDI. The flaw in this is that by the years 2003 -
2004 the Nigerian government had already convinced itself of the importance
of FDI to the Nigerian economy. By the year 2005, it was already deep into its
implementation of its economic reforms; these reforms were already seeing
success and yielding fast increasing FDI into the country. The Nigerian
government’s success in its reforms naturally lead to increased demand for
empirical practical business practice research findings on how to enter and do
business successfully in Nigeria, especially in the newly exposed non-oil gas
sectors of its economy. However, with exception of this research, such
research does not exist to this date. Instead, as exampled by Obida Abu,
(2010) and Okon et al. (2012) etc., the producers of extant research continued
to produce research focusing on what government should do and therefore
telling or confirming to government what had already been published. Many
of the recommendations from such research may have already been known to
government, and in some cases, was already being implemented. The
producers of extant academic research pertaining to FDI, market entry mode
into Nigeria, the Nigerian Economy and Market, seem to have missed the fact
that as a consequence of increased FDI and the interest for it in Nigeria,
demand for actual business practice intelligence and empirical findings on
Nigeria has increased enormously especially in its emerging non-oil and gas
sectors. Research has been done on the determinants of FDI location in
Nigeria, and others on the effects of FDI on the economy of Nigeria. Extant
literatures such as Hambrick and Mason (1984) and Thomas et al. (1991) have
investigated the relationship between CEO characteristics and the choice of
50
foreign market entry mode. Kogut and Singh (1988) investigate the
relationship between national characteristics and country culture on the
choice of foreign market entry mode. The author wishes to stress that before
this research no other research has been published on the critical success
factors for locating and operating FDI in Nigeria, let alone for its non-Oil and
Gas sectors. Furthermore no other research before this paper has investigated
from the CEO and manager perspective the relationship between the critical
success factors for FDI in Nigeria and the choice of entry mode into the
Nigerian market. This research produces a model for recommending the best
choice for an entry mode in to the Nigeria market; the focus on the non-Oil &
Gas sectors further establishes its uniqueness appeal and importance in the
context of Nigeria diversifying its economy from Oil & Gas. This research’s
research design, technique and method, results in the revealing among others,
the influence the CEO/manager characteristics and the firm’s internally
dominant country culture amongst managers, has on the choice of an entry
mode into Africa’s largest economy. The demand for business practice
intelligence and empirical findings on Nigeria has increased enormously
especially in its emerging non-oil and gas sectors. Prior to the author’s
research, International businesses have had to approach firms such as KPMG,
Deloitte, and Ernst Young etc…. for business practice knowledge on the
Nigeria Market. The scenario describe above explains the gap or lack of
research findings necessary for today’s Nigerian economy. This research not
only fills this gap, it removes the lack of such research literature by focusing
on Nigeria’s biggest and fastest growing economic sectors, the Non-oil and
gas sectors. From all the points and facts raised in this literature review, this
author can make the conclusions found in the next paragraph.
2.13 Conclusions (Literature Review)
For the informed persons, it is not a surprise that there is a huge demand for
knowledge/ research findings on how to enter and do business successfully
in Nigeria. However, the producers of extant research have continued to focus
on producing research findings tailored to influence Nigerian government
economic policies alone. This led to the non-existence of empirical research
findings tailored to guide the business practice on what best entry mode to
choose for the Nigerian market and what factors must be considered in order
51
to be successful in the Nigerian market. According to Thisday (2014), KPMG,
places Nigeria among the four major investment destinations and growth
areas in the world. IMF (2013) reports that Nigeria is the third fastest growing
economy in the world as attested by the UK government. The United Bank for
Africa UBA (2013) asserts that Nigeria is one of the key markets behind the
African growth story. They insist that any investor serious about Africa must
have a big presence in Nigeria. Thisday (2014) also tells us of the testimony of
the KPMG’s Michael Andrew, explaining that international businesses want
to know how to do business in Nigeria.
3.0 Research Methods – Procedures - Design
3.1 Introduction.
The research method used in this research is a dominantly quantitative mixed
method. Questionnaires were distributed to a purposive non-random sample
of CEOs and managers in 30 FDI companies that are located and have been
operating for a minimum of 20 years in the non-oil & gas sectors of Nigeria.
An extensive review of literature has been used to obtain the variables
measured via the questionnaire in a cross sectional sample. The variables
undergo quantitative statistical analysis. The factor analysis is to reduce or
extract the number of variables into the critical success factors, and the multi-
nominal logistic regression is to test the hypotheses and produce a set of
statistical probabilities of outcome for choice of entry mode as determined by
the Critical Success Factors. The need for the results of the hypotheses tested,
and the findings of this research are exposed by the extensive literature
review in this research. The research method here is a quantitatively
dominant mixed method, because as Johnson et al. (2007) explains, this
researcher relies on a quantitative post positivist view of the research process,
at the same time adding the use of qualitative approach such as a semi –
structured interview that is carried out to the benefit of the research project. A
formal, objective, systematic process leads up to the testing of the hypotheses
and all results.
52
3.2 Research Paradigm
The quantitative questionnaire approach, the testing of hypotheses and
statistical analysis used in this research are based on the research philosophy
of positivism, the foundation of scientific research in the belief that facts are
observable objectively and the truth can be captured if the right methods are
used. However, on the other hand a case study and an extensive review of
literature are qualitative research methods used in this research and are of
very significant input. They are based on interpretive research philosophy
belief that human interests drive science and the world is socially constructed
and subjective, the observer is part of what observed. Indeed as explained by
Johnson Onwuegbuzie (2004) pragmatism is this reasoning behind the choice
of a mixed method used for this research. Onwuegbuzie, (2004) explains that
more often mixing research methods puts forward a more practicable way out
and delivers a better research paper. The author finds this to be true in the
context of this proposed research.
3.3 Variables
The variables measured via the questionnaire are short listed after the
extensive literature review of established success factors for operating FDI
companies in Africa and internationally, determinant factors for successful
FDI location in Africa and internationally, determinant factors of choice of
foreign market entry mode and models for selection of choice of foreign
market entry mode. For example the United Nations Industrial Development
Organizations (UNIDO) (2008) and Musila and Sigue (2006) present in their
research a number of determinant factors for FDI location featured as
variables in this research, likewise Agarwal Ramaswami (1992) and
Universidad de Ibague (2011) present in their research a number of factors
that determine the choice of foreign market entry mode also featured as
variables in this research.
The independent variables derived from the extensive literature review and
used in this research are as follows: Size of Initial Investment (INIVEST), Knowledge of
the market (PREKNOW), Protection of Company Know How (KNOWHOW), Overall Size of
Company (SIZEOCOM), Special Concessions and Incentives (INCENTIVE), Understanding and
Integrating into local Perceptions and Practices (INTEGRATE), Political Stability (POLSTAB), Cross
53
Cultural Managerial Capabilities (CROSSCUL), Access to financing (FINANCE), Stable FDI
Friendly Economic Policies (FDIPOLICY), Security of Life and Property (SECURITY), Active
Government Support Services (GOVSUPP), Transparent Enforcement of Agreements & Contracts
(ENFORCE), Respect for the rule of law (RULEOFLAW), Quality of Infrastructure (INFRASTR),
Bilateral trade agreements (BITRADE), High return on investment (RETURN), W. Africa Trade
Agreements (ECOWAS), Africa Trade Agreements (AFRIUNI), Economic Growth (ECOGROWTH),
Low Cost of Labor (LABORCOS), Local Suppliers & Contractors (LOCALSUPP), Raw Materials
Availability (RAWMAT), Size of Nigeria Market (SIZEOMARK), Expandability to West Africa
(WAFRICA), A Local Partner (PARTNER). Data for Additional variables namely
Industry-Sector (SECTOR), Managerial level (LEVEL), Educational level (EDUCATION), Gender
(GENDER), Age (AGE), are also collected and measured by the questionnaire.
The dependent variable to be considered in this research is as follows:
Choice of Entry Mode (ENTRYMODE).
3.4 Hypotheses.
Many factors are found in extant literature as both determinants of foreign
market entry mode and success factors for FDI. For example UNIDO (2008)
and Musila and Sique (2006) present “Market potential” and “Market
knowledge” as success factors for FDI, while Agarwal and Ramaswami,
(1992) and Universidad de Ibague (2011) present the same as determinants of
foreign market entry mode. This research recognizes all of such as initial
variables provided they pass the correlation analysis, they are all the part of
the initial variables for factor analysis to determine the Critical Success
Factors for FDI, and therefore could emerge as critical success factors for FDI
in Nigeria. Factors such as the “quality of Infrastructure” cover many
dimensions ranging from roads, ports, railways and telecommunication
systems to institutional development. Studies such as Leigh (2013) and
Udeme (2011) explain that poor infrastructure such as bad roads, together
with the occasional “political instability”, and week or slow “enforcement of
legal obligations” or rule of law, affect negatively the locating of FDI in
Africa. However, we find that many FDI firms thrive in Nigeria. This research
therefore reveals whether factors such as infrastructure, active government
support services, market size, political state of affairs, emerge as Critical
Success Factors for FDI in Nigeria, and whether they have a statistical
significant predictable relationship with the choice of entry mode into the
54
non-oil& gas sectors of the Nigeria market. Together with the Critical Success
Factors these variables are used in the hypotheses.
H1 = There is a significant relationship between the quality of infrastructure
in Nigeria and choice of entry mode for FDI into its non-oil & gas sectors.
H2 = There is a significant relationship between the active government
support services for FDI in Nigeria and choice of entry mode for FDI into its
non-oil & gas sectors.
H3 = There is a significant relationship between the state of political stability
in Nigeria and the choice of entry mode for FDI into its non-oil & gas sectors.
H4 = There is a significant relationship between the size of the market in
Nigeria and the choice of entry mode for FDI into its non-oil & gas sectors.
H5 = There is a significant relationship between the extracted critical success
factors for FDI in Nigeria’s non-oil & gas sectors collectively, and the choice of
entry mode.
3.5 Research design, techniques and strategy
A Quasi – experimental research design is employed where non-random
sampling is executed and a Quantitative questionnaire method is used to
obtain data. An extensive review of literature is used to obtain the variables
measured via a scaled questionnaire in a cross sectional sample. All the
variables first undergo a correlation analysis and must pass the correlation
test. Correlation tests reveal whether an independent variable is related to the
dependent variable. The coefficient of correlation is a number between 0 and
1. If it is 0, then there is no correlation. 1 means perfectly positively correlated
and -1 means it’s Perfectly negatively correlated, and r can be between 0 and
1. The higher the value of r the better it is. If r is 0, it means the variable is not
significant, not related, does not have an impact on the dependent variable
and will therefore be dropped from further analysis. Then this research
proceeds with the factor analysis. Child (2006) and Thompson (2004) explain
that Factor analysis starts with a large number of variables and then tries to
55
reduce the interrelationships amongst the variables v’=[v1, v2,..., vq] to a few
numbers of clusters or factors f’=[f1, f2,..., fk]. Factor analysis is a correlation
technique to determine meaningful clusters of shared variance. It’s a
collection of statistical methods for reducing correlation data into a smaller
number of dimensions or factors. The Factor analysis in this research is
exploratory; in the spirit of what is explained in Kratzsch (2005) and Hair et
al. (1998), the researcher is simply seeking to reduce the number of variables,
without losing the underlying meaning or pattern in the variation of all
variables, in order to extract or obtain the “critical” variables needed to
represent the whole dimension. Which in this research are called the Critical
Success Factors, they are factors that are crucial to determining successful
non-oil & gas FDI in Nigeria. All the variables/ the questionnaire pass the
Cronbach's alpha test for measure of internal consistency or reliability. Then
the research goes on to test the hypotheses. By performing a multinomial
logistic regression. Greene (1993) explains that a multinomial logistic
regression is a regression model that allows for more than two discrete
outcomes. It is used to statistically predict the probabilities of the more than
two different possible outcomes of a dependent variable, given a set of
independent variables. Greene (1993) explains a formula for this is below.
Figure 1.
Exp (xi Bj)
Pr (yi = j) ……………………………………………….
J
∑ Exp (xi Bj)
j
Where pr (yi=j) is the probability of belonging to group j, xi is a vector of
explanatory variables and Bj are the coefficients, which are estimated using
maximum likelihood estimation. The dependant variable is choice of entry
56
mode (Entry Mode) with the possible outcomes of Joint venture =1, sole
venture = 2 licensing = 3 Exporting = 4. A multi-nominal logistic regression is
used to test the hypotheses and evaluate the relationship between the CSFs,
other independent variables and the dependant variable in other words, the
goal of the multi-nominal logistic regression is to test the hypotheses and
determine the relationship between the CSFs, and the dependant variable,
which is in this case, “choice of entry mode” in order to reveal statistical
predictions for choice of entry mode that serves as a guide set for
recommending or deciding on successful entry modes into the Nigeria
market. In application of this guide set for recommending or deciding on
successful entry modes to business practice, the CEO/Managing Director and
managers in an FDI company not already part of the sample used for this
research, are selected as a case study. A semi – structured interview is carried
out with each of them and the interview determines which of the critical
success factors they consider most critical. The interview also determines the
hierarchical importance the interviewed gives for each Critical Success Factor.
Applying the statistical predictions for choice of entry mode as a guide, an
entry mode into Nigeria for the company is recommended. This
recommended entry mode is then compared to the actual entry mode used by
the company into Nigeria.
3.6 Validity and Reliability
The researcher has adopted measures to ensure proper and valid conclusions
can be made from this research data and findings. All variables obtained in
this research are obtained from an extensive peer reviewed literature review
and therefore would have been used or named in previous peer reviewed
research as FDI success factors. Only the variables that are correlated (pass
the researches correlation test) are used further in the research. A number of
research such as Agarwal and Ramaswami (1992) Korey (1995), Dunning
(1980), Dunning (1988), Hambrick and Mason (1984), Theory. Thomas et al.
(1991) Ross (1973), Kogut and Singh (1988), L, Brouthers, Brouthers and
Werner (1999) all give validity to research such as this that reveal relationship
between factors and choice of market entry mode. They also help expose joint
ventures, sole ventures, licensing and exporting as the most common and
basic entry modes considered by FDI companies. The sample used in this
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research is a more than adequate sample of carefully selected participants for
what the participants represents which are CEOs and managers in 30 FDI
companies (multi case study scheme) that are located and have been
operating for a minimum of 20 years in the non-oil & gas sectors of Nigeria.
The objective of such a sample is to sample a body of people that have been
and still are successful non-oil & gas FDI managers in Nigeria. With a
minimum experience of 20 years of doing business in the Nigerian
environment such companies have succeeded through different governments,
political and economic changes. For additional reliability the whole data in
this research passes a Cronbach’s reliability test in IBMs SPSS.
3.7 Populations and sample
Participants in this research are the CEOs and managers in 30 FDI companies
that are located and have been operating for a minimum of 20 years in the
non-oil & gas sectors of Nigeria. In other words, the objective of such a
sample is to sample a body of people that have been and still are successful
non-oil & gas FDI managers in Nigeria. With a minimum experience of 20
years of doing business in the Nigerian environment such companies have
succeeded through different governments, political and economic changes. 30
questionnaires were handed out in each of the 30 companies, The 30
companies case studied are made up of companies from each of the different
sectors of the Nigerian non-oil & gas economy, namely: 1.Agriculture,
Banking & Finance, Manufacturing & Production, Telecom, Building &
Construction, Mining, Trade & Goods, Health Care, Transport, Other.
According to the author’s unofficial probe of the National bureau of statistics
(NBC), and the Nigerian investment promotion council, there are an
estimated number of 1760 managerial level employees across 220 FDI
companies that meet the criteria of our purposive sampling. (Non-oil & gas
with 20 years of doing business in Nigeria). With a population size of 1760
and a 95 percent level of confidence, five percent margin of error the sample
size should be between 300 and 322. The approach to our dispensing the
questionnaire assured the high completion and return number of 414.
Permission and appointment was received from each company for a central
meeting facility where the questionnaires were filled supervised and returned
within the premises of the company. Trained survey administrators were
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used. In this way the researcher chose to use only the returned questionnaires
that were absolutely filled out correctly. This amounted to the 414 correctly
filled out and returned questionnaires used for this research.
3.8 Data Collection.
An extensive literature review used to obtain the variables to be measured in
a questionnaire. The pre-dominantly Likert scaled questionnaire is found in
(Appendices 3). The researcher has chosen a Likert scale of five because it
presents the respondents a balance between having enough choices to answer
from and the questionnaire being quick and easy to answer. Note that the
questionnaire is only administered to CEOs and managers in 30 case study
FDI companies that are located and have been operating for a minimum of 20
years in the non-oil & gas sectors of Nigeria. The function of the questionnaire
in this research is to obtain the following responses /data from all the
participants: Which of the various variables put forth in the questionnaire did
the participant determine to be critical to the success of his company in
Nigeria? What entry mode between joint venture, sole venture, licensing and
exporting did his or her company use into the Nigeria market? What industry
sector given did the participants company belong? Age? Sex? Educational
level? The questionnaire enabled this research to get responses from a large
number of participants in the most cost effective way. Reliability is
demonstrated via the Cronbach’s reliability test.
4.0 Analysis – Results - Findings 4.1 Reliability:
The Research continues with establishing reliability. That all variables in the
questionnaire (measured via a Likert scale) actually measure the success
factors for FDI in Nigeria. In cases such as in this research, where we have
multiple Likert questions in a survey/questionnaire that form a scale,
Cronbach's alpha is the most common measure of internal consistency or
reliability. Cronbach's alpha from Cronbach (1951), determines the internal
consistency or average correlation of items in a survey instrument to gauge its
reliability. Alpha coefficient ranges in value from 0 to 1. It is used to classify
the reliability of factors extricated from multi-point formatted questionnaires
or scales. The higher the score, the more the reliability of the generated scale.
59
In Nunnery (1978), 0.7 is indicated as recognized reliability coefficient,
however it should be noted that lower thresholds such as 0.5 are also used in
research. The formula for the standardized Cronbach's alpha is shown below:
N is equal to the number of items, c-bar is the average inter-item covariance
among the items and v-bar equals the average variance.
The Cronbach’s alpha test results, for all variables measuring critical success
factors for FDI in Nigeria, from the questionnaire (SPSS), is shown below: RELIABILITY /VARIABLES=POLSTAB FDIPOLICY SECURITY GOVSUPP ENFORCE RULEOFLAW INFRASTR RETURN ECOWAS AFRIUNI ECOGROWTH LOCALSUPP RAWMAT SIZEOMARK PREKNOW INTEGRATE INIVEST CROSSCUL SIZEOCOM LABORCOS WAFRICA LOCALEXPERT FINANCE BITRADE INCENTIVE PARTNER KNOWHOW /SCALE ('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE SCALE CORR /SUMMARY=TOTAL.
Reliability [DataSet1] /Users/antonychibo-christopher/Desktop/CSFRESEARCH.sav Scale: ALL VARIABLES
So it is seen here that the Cronbach’s alpha for our research variables is .861,
this represents good internal consistency and good reliability.
4.2 Correlation
Next the inter correlation between variables is examined, the aspects of the
inter correlation most important to this research are the strength and
significance. The strength of a correlation is measured by the correlation
coefficient r. Another name for r is the Pearson product moment correlation
coefficient in honor of Karl Pearson who developed it about 1900. The
formula for calculating “r” is shown below.
Table 1: Reliability Statistics
Cronbach's Alpha Cronbach's Alpha
Based on
Standardized Items
N of Items
.846 .861 27
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The inter correlation matrix is computed/generated. To show the inter
correlations value “r” between all the 27 variables measuring the critical
success factor in the questionnaire. This represents the next step after
establishing reliability above, see appendices 02- CSFs Variables Correlations
Table: 29. The correlation matrix result shows there are no r = 0 value in the
table/ matrix. The correlation matrix result shows all such variables are
correlated in varying degrees, and therefore none of the 27 variables is
dropped. The highest correlations are between variables that concern regional
agreements & opportunities, legal & law enforcement, company attributes
and government inputs.
(Table 2 below)
Table 2: Variables With High Inter Correlations
VARIABLE A VARIABLE B Correlation Coefficient “r” (All significant at the 0.01-level)
ECOWAS Trade Agreements African union trade agreements +0.704 Respect for the rule of Law Transparent enforcement of
agreements and contracts +0.503
Local suppliers & contractors Raw material availability +0.483 Overall size of company Size of companies initial
investment +0.463
Expandability to West African market
African union trade agreements +0.461
Security of life & property Quality of basic infrastructure +0.424 Active Government support
services Respect for rule of law +0.418
Quality of basic infrastructure Return on investment +0.417 4.3 The Factor Analysis: (The Critical Success Factors for FDI in Nigeria).
As explained in Hair et al. (1998), in this research, the Factor analysis is used
to perform a frugal reduction of the number of variables without losing the
fundamental structure in the variation of variable. The Kaiser’s criterion
explained in Kaiser (1960) is used to decide the number of factors to be
extracted. If any factor cannot explain the variance of at least a single variable
(“Eigen value” >1) it is disregarded. The explanatory ability is in the fact that
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factors extracted must explain at least 50 percent of the total cumulative
variance in the data. The inter correlations matrix has shown us that all the
variables are correlated. The Statistical Package for the Social Sciences (SPSS),
IBM’s software package remains the statistical analytic tool used. As
explained in Bryman & Cramer (2001) and Hair et al. (1998). The Principal
Component Analysis is selected as the SPSS technique for analyzing and
displaying how much of the variance in the variables is accounted for by
extracted factors, because the objective is to identify factors accounting for the
maximum variance in the variables. “Oblique rotation” is used within our
factor analysis, because in addition to developing the pattern matrix it also
develops a structure matrix and therefore in this case presents and helps to
analyze the rotated values and structure of variables more accurately than an
orthogonal rotation would. In established research such as Schwartz (1971) it
is expressed that variables with coefficient loadings between 0.30 and 0.60 are
common in factor analysis, in this research, variables with less than 0.60 co-
efficient loading are suppressed.
4.3.1 Bartlett’s test of sphericity (BTS); and the Kaiser-Meyer-Olkin
Measure of sampling adequacy (KMO)
The Factor analysis starts with executing and displaying the results of the
Bartlett’s test of sphericity (BTS); and the Kaiser-Meyer-Olkin measure of
sampling adequacy (KMO) to measure the suitability of the Factor analysis.
As explained in Dziuban & Shirkey (1974), BTS tests whether correlations
between variables are significantly greater than would be expected by chance,
while the KMO test from Kaiser & Rice (1974), compares the immensity of
observed correlation coefficients to the partial correlation coefficients. A large
KMO towards the value 1, means that patterns of correlations are compact,
and yield distinct and reliable factors. In this case, BTS is significant and KMO
at 0.834 is “meritorious” as described in Kim & Mueller (1978). See Table 3
below.
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Factor Analysis [DataSet1] /Users/antonychibo-christopher/Desktop/CSFRESEARCH.sav
Next is Table 4 below, showing the communalities. Communalities indicate
the amount of variance in each variable that is accounted for. Initial
communalities are estimates of the variance in each variable accounted for by
all components or factors. For principal components extraction, this is always
equal to 1.0 for correlation analyses. Extraction communalities are estimates of
the variance in each variable accounted for by the components. The
communalities in this table are all high, which indicates that the extracted
components represent the variables well. The communalities table indicates
that six of the variables namely “Size of the company’s initial investment”,
“Special incentives and concessions from Nigerian government”, ECOWAS trade
agreements”, “A local partner”, and “Size of company” have very high
communalities above .685 and are therefore more likely to be greatly affected
by extracted factors. On the other hand the variable, “Cross cultural
managerial capabilities” with a .363 value could be weakly and not affected
at all by the extracted factors
Table 3: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .834
Approx. Chi-Square 3221.578
df 351 Bartlett's Test of Sphericity
Sig. .000
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4.3.2 Factor Extraction
Factor extraction is now the next step in our factor analysis. Table 5 below
displays the initial solution. Eight factors with an Eigenvalue >1 explains
61.611 percent of the variation in the data. The first factor has an Eigenvalue
of 6.293 and explains 23.309 percent the variation. The next seven factors
together explain 37.302 percent of the variation. The first factor is therefore a
quiescent assemble of some variables, which is critical to the success of non-
oil & gas FDI in Nigeria. For shorter interpretation a smaller number of
extracted factors would have been easier, however this could not be so
because all eight extracted factors have Eigen values of above 1 and therefore
Table 4: Communalities
Initial Extraction
POLSTAB 1.000 .476
FDIPOLICY 1.000 .585
SECURITY 1.000 .563
GOVSUPP 1.000 .631
ENFORCE 1.000 .665
RULEOFLAW 1.000 .604
INFRASTR 1.000 .616
RETURN 1.000 .608
ECOWAS 1.000 .742
AFRIUNI 1.000 .735
ECOGROWTH 1.000 .472
LOCALSUPP 1.000 .560
RAWMAT 1.000 .614
SIZEOMARK 1.000 .599
PREKNOW 1.000 .611
INTEGRATE 1.000 .661
INIVEST 1.000 .780
CROSSCUL 1.000 .363
SIZEOCOM 1.000 .685
LABORCOS 1.000 .518
WAFRICA 1.000 .610
LOCALEXPERT 1.000 .500
FINANCE 1.000 .472
BITRADE 1.000 .629
INCENTIVE 1.000 .729
PARTNER 1.000 .685
KNOWHOW 1.000 .651
Extraction Method: Principal Component Analysis
64
represent significant and important variance. In effect 27 variables have been
reduced to 8 critical factors.
4.3.3 Identification and Labeling of the Extracted Factors.
Next is the identification and labeling of the extracted factors. To do this most
accurately an oblique rotation is performed within the Factor analysis in SPSS.
The pattern matrix is generated by the oblique rotation. The pattern matrix for
an oblique rotation in SPSS contains negative numbers. As Walker & Maddan
(2013) explains, these are not and should not be confused with negative
correlations. The delta values in SPSS measure the orientation of the angle of
axes. A 0 value for delta is when the factors are most oblique, and a negative
value means the factors are less oblique. Here the pattern Matrix below (table
6) is used to label the factors. A cut-off point of 0.60 co-efficient factor loading
has been set. In the Pattern Matrix each row represents one of the 27 research
variables and the eight columns represent the extracted factors. Rummel
(1970) expresses that the distinctive relationship between the factor and the
variable is displayed to us in the Pattern Matrix, which contrasts between
high and low loadings more clearly. See table 6 below
Table 5: Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Rotation
Sums of Squared Loadings
Component
Total % of Variance
Cumulative %
Total % of Variance
Cumulative %
Total
1 6.293 23.309 23.309 6.293 23.309 23.309 3.599 2 2.066 7.651 30.959 2.066 7.651 30.959 2.396 3 1.792 6.636 37.595 1.792 6.636 37.595 3.744 4 1.707 6.322 43.917 1.707 6.322 43.917 2.000 5 1.219 4.516 48.433 1.219 4.516 48.433 2.032 6 1.155 4.279 52.711 1.155 4.279 52.711 2.009 7 1.098 4.067 56.779 1.098 4.067 56.779 3.980 8 1.035 3.833 60.611 1.035 3.833 60.611 1.607 9 .986 3.653 64.265 10 .837 3.101 67.366 11 .816 3.023 70.390 12 .715 2.646 73.036 13 .692 2.564 75.600 14 .680 2.519 78.119 15 .635 2.350 80.469 16 .581 2.152 82.621 17 .560 2.075 84.695 18 .526 1.949 86.644 19 .517 1.917 88.561 20 .492 1.823 90.384 21 .482 1.785 92.169 22 .457 1.691 93.860 23 .397 1.471 95.331 24 .355 1.316 96.647 25 .347 1.284 97.931 26 .328 1.215 99.146 27 .231 .854 100.000 Extraction Method: Principal Component Analysis. a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance.
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Loadings on factors are graded and sorted above the cut-off point of 0.60.
Factor labeling/naming is influenced by the high grading value of the
variables loaded within the factor. In this case (Table 6: pattern matrix)
variables are spread quite clearly and explicably across the eight factors, and
therefore labeling and naming of factors is not complicated. The first factor
loads on three variables, it has loadings of 0.755 for Raw Materials Availability,
0.674 for local Suppliers & Contractors and 0.652 for Access to Financing. This
factor alone accounts for 23.309 percent of the total variance. It is therefore a
Table 6: Pattern Matrix
Component
1 2 3 4 5 6 7 8
POLSTAB
FDIPOLICY
SECURITY
GOVSUPP
ENFORCE
-.750
RULEOFLAW
-.687
INFRASTR
-.722
RETURN
ECOWAS
-.899
AFRIUNI
-.817
ECOGROWTH
LOCALSUPP .674
RAWMAT .755
SIZEOMARK
PREKNOW
.641
INTEGRATE
.757
INIVEST
.911
CROSSCUL
SIZEOCOM
.765
LABORCOS
WAFRICA
LOCALEXPERT
FINANCE .652
BITRADE
INCENTIVE
.840
PARTNER
.790
KNOWHOW
-.715
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization.
a. Rotation converged in 23 iterations.
66
very important factor in the analysis. This factor is appropriately labeled
“The Availability of Local Raw Materials, Suppliers and Financing”. The
second factor loads on two variables, 0.911 for Size of Company’s Initial
Investment and 0.765 for Size of Company. This factor accounts for 7.651 percent
of total variance. This factor is befittingly named “The Size of Initial
Investment and Company”. The third factor accounting for 6.636 percent of
the total variance, loads on two variables, ECOWAS Trade Agreements -0.899
and African Union Trade Agreements -0.817. This factor is therefore labeled
“Taking advantage of Ecowas and African Union Trade Agreements”. The
fourth factor is labeled, “Integrating Local practices in Market pre-
knowledge”. This is because this factor loads on two variables, 0.757 for
Understanding and Integrating into Local Perceptions and Practices, and 0.641 for
Pre-acquired knowledge of the market. This forth factor accounts for 6.322 percent
of the total variance. The fifth factor loads on a single variable, -0.715 for
Protection of Company’s Knowhow, it accounts for 4.516 percent of total
variance. Labeling for this factor is quiet straightforward, this factor is labeled
“Protecting of Company’s Knowhow”. The sixth factor accounting for 4,279
percent of total variance is labeled “A Local Partner” for it loads on a single
variable, 0.790 for a Local Partner. The seventh factor accounts for 4.067
percent of total variance, it loads on three variables, -0.750 for Transparent
Enforcement of Agreements and Contracts, -0.722 for Quality of Basic Infrastructure
and -0.667 for Respect for the Rule of Law. Therefore this factor is labeled
“Consideration for Quality of law Enforcement and state of infrastructure”.
The eighth factor, explains 3.833 percent of total variance, it is labeled, “The
Advantage of Special Incentives and Concessions”, because it loads on one
variable, 0.840 for Special Incentives and Concessions.
4.3.4 Inter-Factor Correlation
As noted previously, no effort was made to reduce the number of extracted
factors bearing in mind that a key purpose here is to provide business practice
with all the new knowledge needed about what is critical for FDI success in
Nigeria. The matrix of inter-factor correlations, Table 7 below, shows the
results of how the variance is distributed between the factors labeled above. It
clearly shows low inter-correlations between the labeled factors, and therefore
corroborates that eight very precise factors have been arrived at.
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Knowing that the reliability test for our entire research variables measuring
Critical Success Factors showed a Cronbach’s alpha value of 0.861. We know
from extant established research such as Peterson (1994) and Nunnally (1978),
that the variables and the scale have good internal consistency and good
reliability. Therefore we conclude that from this research’s reliable scale, via
the factor analysis, we now can present the eight Critical Success Factors for
non-oil and gas FDI in Nigeria. This means the factors critical for
consideration and implementation when endeavoring to successfully locate
and operate a non-oil and gas FDI business in Nigeria.
4.4 The Critical Success Factors (The Critical Success Factors for non - oil and gas
FDI in Nigeria).
The 8 “Critical Success Factors” in order of importance are therefore:
1. The Availability of Local Raw Materials, Suppliers and Financing.
2. The Size of Initial Investment and Company
3. Taking advantage of ECOWAS and African Union Trade Agreements
4. Integrating into Local practices and Market pre-knowledge
5. Protecting of Company’s Knowhow
6. A Local Partner
7. Consideration for Quality of law Enforcement and state of infrastructure
8. The Advantage of Special Incentives and Concessions
Table 7: Inter-factor Correlation Matrix
Component Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8
Factor 1 1.000 .115 -.218 .105 -.128 .145 -.379 -.126
Factor 2 .115 1.000 -.176 .224 -.104 .205 -.115 -.068
Factor 3 -.218 -.176 1.000 -.099 .199 -.154 .300 .080
Factor 4 .105 .224 -.099 1.000 -.023 .118 -.121 -.019
Factor 5 -.128 -.104 .199 -.023 1.000 -.086 .094 .138
Factor 6 .145 .205 -.154 .118 -.086 1.000 -.126 -.023
Factor 7 -.379 -.115 .300 -.121 .094 -.126 1.000 .040
Factor 8 -.126 -.068 .080 -.019 .138 -.023 .040 1.000
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization.
68
4.5 Testing the hypotheses
H1 = There is a significant relationship between the quality of infrastructure
in Nigeria and choice of entry mode for FDI into its non-oil & gas sectors.
H2 = There is a significant relationship between the active government
support services for FDI in Nigeria and choice of entry mode for FDI into its
non-oil & gas sectors.
H3 = There is a significant relationship between the state of political stability
in Nigeria and the choice of entry mode for FDI into its non-oil & gas sectors.
H4 = There is a significant relationship between the size of the market in
Nigeria and the choice of entry mode for FDI into its non-oil & gas sectors.
H5 = There is a significant relationship between the extracted critical success
factors for FDI in Nigeria’s non-oil & gas sectors collectively, and the choice of
entry mode.
4.5.1 The Multinomial logistic regression (1)
This research will go on to test the hypotheses. By performing a multinomial
logistic regression. Greene (1993) explains that a multinomial logistic
regression is a regression model that allows for more than two discrete
outcomes. We use it to predict the probabilities of the more than two different
possible outcomes of a dependent variable, given a set of independent
variables. Greene (1993) explains a formula for this is (Figure 1). Where pr
(yi=j) is the probability of belonging to group j, xi is a vector of explanatory
variables and Bj are the coefficients, which are estimated using maximum
likelihood estimation. The dependant variable is “entry mode” with the
possible outcomes of Joint venture =1, sole venture = 2 licensing = 3 Exporting
= 4. A multi-nominal logistic regression is used to test the hypotheses and
evaluate the relationship between the variables in the four hypotheses as the
independent variables and the “entry mode” variable as the dependent
variable. Multinomial Logistic regression is appropriate when the outcome is
a polytomous variable (i.e. categorical with more than two categories) and the
69
predictors are of any type: nominal, ordinal, and / or interval/ratio
(numeric). Multinomial logistic regression compares multiple groups through
a combination of binary logistic regressions.
Figure 2.
Exp (xi Bj)
Pr (yi = j) ………………………………………………. J
∑ Exp (xi Bj)
j The group comparisons are equivalent to the comparisons for a dummy-
coded dependent variable, with the group with the highest numeric score
used as the reference group. Extant literature, for example, Starkweather and
Moske (2014) explains that the way multinomial logistic regression deals with
the variables in this case is somewhat similar to the concept of dummy
variables, in that it compares the probability of being in each of n-1 categories
compared to a baseline or reference category. In a way we can say that we are
fitting n-1 separate binary logistic models, where we compare category 1 to
the baseline category, then category 2 to the baseline and so on. In practice,
software algorithms allow us to model the comparisons to the baseline
simultaneously using maximum likelihood estimation, which is better as
doing it sequentially could lead to misestimating of the standard errors.
4.5.2 Statistical Software and The Model
The statistical software used here is again SPSS. The model used to test each
the hypotheses has “Entry Mode”(ENTRYMODE) as the dependent variable.
In each hypothesis test model, The Sector variable and the hypothesis variable
e.g. Political Stability are the independent variables. This is because this
research seeks to test the hypotheses not in isolation, but in consideration of
the various sectors of the Nigerian non-oil & gas market. Including the Sector
also improved the model’s fit and the model accuracy classification. The
statistical analysis and results involved in our multinomial logistic regression
will enable the confirmation or rejection of each of the hypotheses. Each
70
model can also predict the probability of the outcome category in Entry Mode,
as influenced by each independent variable in the model. However, this will
only be performed to describe relationships between the Critical Success
Factors determined earlier in this research and “Entry Mode”. The category
for reference in the dependent variable is set to “Licensing” (category “2” in
the multinomial logistic regression), which is the category with the highest
frequency of result, see Table: 31 entry mode frequencies Appendix 04.
Statistical probabilities of outcome are therefore compared between Joint
Venture and Licensing, Sole Venture and Licensing and Exporting and
Licensing.
4.5.3 Testing Hypothesis 1.
H1 = There is a significant relationship between the quality of infrastructure
in Nigeria and choice of entry mode for FDI into its non-oil & gas sectors.
Table 8: Case Processing Summary
N Marginal
Percentage
1.00 156 37.7%
2.00 188 45.4%
3.00 67 16.2% ENTRYMODE
4.00 3 0.7%
1.00 72 17.4%
2.00 89 21.5%
3.00 29 7.0%
4.00 42 10.1%
5.00 63 15.2%
6.00 14 3.4%
7.00 51 12.3%
SECTOR
10.00 54 13.0%
1.00 6 1.4%
2.00 13 3.1%
3.00 13 3.1%
4.00 201 48.6%
INFRASTR
5.00 181 43.7%
Valid 414 100.0%
Missing 0
Total 414
Subpopulation 31a
a. The dependent variable has only one value observed in 14
(45.2%) subpopulations.
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The above Table 8 is the Case/Model Processing Summary for Hypothesis 1.
Sloan (2014) remarks that very good and important is the fact that there are
100 percent valid cases (cases without missing data). The marginal percentage
values here for the dependent variable will be used in calculating the
“proportional by chance accuracy rate” and the proportional by chance
accuracy criteria for the model soon, but first the overall test of relationship
between the dependent and independent variables will be described. Many
extant literature such as Bayaga (2010) point out that the existence of a
relationship between the dependent and independent variables in the model
is founded on the statistical significance of the final model chi-square in the
Model Fitting Information. Table 9 shows that the probability of the model
chi-square (358.891) is 0.000, p < 0.001. Therefore the null hypothesis that
there was no difference between the model without independent variables
and the model with independent variables was rejected. Results from table 9
indicate the existence of a relationship between the independent variables and
the dependent variable.
Next it is important to establish the usefulness for the logistic model used
here. This is achieved by first obtaining our proportional by chance accuracy
rate. Extant research such as Starkweather and Moske (2014) explain that, this
is calculated by using all the marginal percentage values for the dependent
variable or in other words the proportion of cases for each group based on the
number of cases in each group of the outcome variable (dependent). See Table
8. Then squaring these values and adding them all up. 0.142 + 0.206 + 0.026 +
0.000049 = 0.371 = 37.1 percent the proportional by chance accuracy criteria is
therefore 1.25 x 37.1 percent = 46.3 percent. The classification accuracy rate
produced by this model as depicted in Table 10 is 70.5 percent which is much
more than the proportional by chance accuracy criteria at 46.3 percent. This
indicates that the model being used here is useful.
Table 9: Model Fitting Information (INFRASTR) H1
Model Fitting Criteria Likelihood Ratio Tests Model
AIC BIC -2 Log Likelihood Chi-Square df Sig.
Intercept Only 473.061 485.139 467.061
Final 180.170 325.101 108.170 358.891 33 .000
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Table 10: Classification (INFRASTR) H1
Predicted Observed
1.00 2.00 3.00 4.00 Percent Correct
1.00 113 27 16 0 72.4%
2.00 22 156 10 0 83.0%
3.00 41 3 23 0 34.3%
4.00 0 0 3 0 0.0%
Overall Percentage 42.5% 44.9% 12.6% 0.0% 70.5%
The Goodness-of-Fit table (table 11) provides further evidence of good fit for
our model. Again, both the Pearson and Deviance statistics are chi-square
based methods. In this case we interpret lack of significance as indicating
good fit. Sig p> 0.05 is a good fit.
Table 11: Goodness-of-Fit H1
Chi-Square df Sig.
Pearson 46.804 57 .830
Deviance 41.252 57 .942
While Results from table 9 indicate the existence of a relationship between the
independent variables and the dependent variable, it does not tell us which
independent variables in the model have a significant statistical/predictable
relationship with our dependent variable. For that we need to generate the
results from our Likelihood Ratio Tests. Extant research and literature such as
Greene (1993) and Starkweather and Moske (2014) tell us that the likelihood
ratio test evaluates the overall relationship between an independent variable
and the dependent variable or variables. The interpretation for an
independent variable focuses on its ability to distinguish between pairs of
groups and the contribution that it makes to change the odds of being in one
dependent variable group rather than the other. The results of the Likelihood
Ratio Tests are displayed below in Table 11, Likelihood Ratio Tests table.
Table 12: Likelihood Ratio Tests (INFRASTR) H1
Model Fitting Criteria Likelihood Ratio Tests Effect
AIC of Reduced
Model
BIC of Reduced
Model
-2 Log Likelihood
of Reduced Model
Chi-Square df Sig.
Intercept 180.170 325.101 108.170a .000 0 .
SECTOR 475.508 535.896 445.508 337.337 21 .000
INFRASTR 168.135 264.756 120.135 11.965 12 .449
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The results from the ratio likelihood tests above clearly show a 0.000 Sig p
<0.001 values for our SECTOR variable. This means that there is a statistically
significant/predictable relationship between the Sector of the Nigerian non-
oil & gas economy and the choice of entry mode recommended. However our
hypothesis variable (INFRASTR) Quality of Basic Infrastructure, has a value
of 0.449, Sig p> 0.05 value for chi square model probability. Therefore all
things being equal, this means there is no statistically significant/predictable
relationship between the Quality of Basic Infrastructure in Nigeria and the
choice of entry mode.
The result above does not support H1. The results show that there is no
significant relationship between the quality of infrastructure in Nigeria
and choice of entry mode for FDI into its non-oil & gas sectors.
4.5.4 Testing Hypothesis 2.
H2 = There is a significant relationship between the active government
support services for FDI in Nigeria and choice of entry mode for FDI into its
non-oil & gas sectors.
(Following the same process as above): SPSS COMMAND CODE NOMREG ENTRYMODE (BASE=2 ORDER=ASCENDING) BY SECTOR GOVSUPP /CRITERIA CIN (95) DELTA (0) MXITER (100) MXSTEP (5) CHKSEP (20) LCONVERGE (0) PCONVERGE (0.000001) SINGULAR (0.00000001) /MODEL /STEPWISE=PIN (.05) POUT (0.1) MINEFFECT (0) RULE (SINGLE) ENTRYMETHOD (LR) REMOVALMETHOD (LR) /INTERCEPT=INCLUDE /PRINT=CELLPROB CLASSTABLE FIT PARAMETER SUMMARY LRT CPS STEP MFI IC /SAVE ESTPROB PREDCAT PCPROB ACPROB.
Table 13: Model Fitting Information (GOVSUPP) H2
Model Fitting Criteria Likelihood Ratio Tests Model
AIC BIC -2 Log Likelihood Chi-Square df Sig.
Intercept Only 465.943 478.021 459.943
Final 164.662 309.593 92.662 367.282 33 .000
Table 14: Goodness-of-Fit (GOVSUPP) H2
Chi-Square df Sig.
Pearson 20.316 63 1.000
Deviance 22.714 63 1.000
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Table 15: Classification (GOVSUPP) H2
Predicted Observed
1.00 2.00 3.00 4.00 Percent Correct
1.00 108 29 19 0 69.2%
2.00 22 158 8 0 84.0%
3.00 31 4 32 0 47.8%
4.00 0 0 3 0 0.0%
Overall Percentage 38.9% 46.1% 15.0% 0.0% 72.0%
The Hypothesis 2 variable (GOVSUPP) Active Government Support Services,
has a value of 0.061, Sig p> 0.05 value for chi square model probability.
Therefore all things being equal, this means there is no statistically
significant/predictable relationship between the Active government support
services in Nigeria and the choice of entry mode.
The result above does not support H2. The results show that there is no
significant relationship between the Active government support services in
Nigeria and choice of entry mode for FDI into its non-oil & gas sectors.
4.5.5 Testing Hypothesis 3
H3 = There is a significant relationship between the state of political stability
in Nigeria and the choice of entry mode for FDI into its non-oil & gas sectors.
Table 17 Model Fitting Information (POLSTAB) H3
Model Fitting Criteria Likelihood Ratio Tests Model
AIC BIC -2 Log Likelihood Chi-Square df Sig.
Intercept Only 529.079 541.156 523.079
Final 214.087 359.018 142.087 380.992 33 .000
Table 16: Likelihood Ratio Tests (GOVSUPP) H2
Model Fitting Criteria Likelihood Ratio Tests Effect
AIC of Reduced
Model
BIC of Reduced
Model
-2 Log Likelihood
of Reduced Model
Chi-Square df Sig.
Intercept 164.662 309.593 92.662a .000 0 .
GOVSUPP 161.017 257.638 113.017 20.356 12 .061
SECTOR 467.551 527.939 437.551 344.889 21 .000
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Table 19 Classification (POLSTAB) H3
Predicted Observed
1.00 2.00 3.00 4.00 Percent Correct
1.00 102 30 24 0 65.4%
2.00 18 158 12 0 84.0%
3.00 31 3 33 0 49.3%
4.00 1 0 2 0 0.0%
Overall Percentage 36.7% 46.1% 17.1% 0.0% 70.8%
The Hypothesis 3 variable (POLSTAB) Political Stability, has a value of 0.001,
Sig p < 0.05 value for chi square model probability. Therefore all things being
equal, this means there is a statistically significant relationship between the
political stability in Nigeria and the choice of entry mode.
The result above supports H3. The results show that there is a significant
relationship between political stability in Nigeria and choice of entry mode
for FDI into its non-oil & gas sectors.
4.5.6 Testing Hypothesis 4
H4 = There is a significant relationship between the size of the market in
Nigeria and the choice of entry mode for FDI into its non-oil & gas sectors.
Table 18 Goodness-of-Fit (POLSTAB) H3
Chi-Square df Sig.
Pearson 65.835 69 .586
Deviance 67.281 69 .536
Table 20 Likelihood Ratio Tests (POLSTAB) H3
Model Fitting Criteria Likelihood Ratio Tests Effect
AIC of Reduced
Model
BIC of Reduced
Model
-2 Log Likelihood
of Reduced Model
Chi-Square df Sig.
Intercept 214.087 359.018 142.087a .000 0 .
SECTOR 511.461 571.849 481.461 339.374 21 .000
POLSTAB 224.152 320.773 176.152 34.066 12 .001
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The Hypothesis 4 variable (SIZEOMARK) Size of Nigeria’s Market, has a
value of 0.040, Sig p < 0.05 value for chi square model probability. Therefore
all things being equal, there is a statistically significant/predictable
relationship between the Size of Nigeria’s Market and the choice of entry
mode.
The result above supports H4. The results show that there is a significant
relationship between size of the market in Nigeria and choice of entry
mode for FDI into its non-oil & gas sectors.
Table 21: Model Fitting Information (SIZEOMARK) H4
Model Fitting Criteria Likelihood Ratio Tests Model
AIC BIC -2 Log Likelihood Chi-Square df Sig.
Intercept Only 475.064 487.141 469.064
Final 172.332 317.263 100.332 368.732 33 .000
Table 22: Classification (SIZEOMARK) H4
Predicted Observed
1.00 2.00 3.00 4.00 Percent Correct
1.00 111 28 17 0 71.2%
2.00 21 157 9 1 83.5%
3.00 37 2 28 0 41.8%
4.00 0 0 2 1 33.3%
Overall Percentage 40.8% 45.2% 13.5% 0.5% 71.7%
Table 23: Goodness-of-Fit (SIZEOMARK) H4
Chi-Square df Sig.
Pearson 37.470 60 .990
Deviance 34.489 60 .997
Table 24: Likelihood Ratio Tests (SIZEOMARK) H4
Model Fitting Criteria Likelihood Ratio Tests Effect
AIC of Reduced
Model
BIC of Reduced
Model
-2 Log Likelihood
of Reduced Model
Chi-Square df Sig.
Intercept 172.332 317.263 100.332a .000 0 .
SECTOR 473.287 533.675 443.287 342.955 21 .000
SIZEOMARK 170.138 266.758 122.138 21.805 12 .040
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4.6 The Critical Success Factors for FDI in non-oil& gas sectors of Nigeria,
and their statistical /predictable relationship to choice of entry mode.
H5 = There is a significant relationship between the extracted critical success
factors for FDI in Nigeria’s non-oil & gas sectors collectively, and the choice of
entry mode.
4.6.1 Testing Hypothesis 5
Analysis and Results
The first step in indentifying and describing these relationships is to create
new variables in our data set according to the variable loadings of each critical
success factor. Each Critical Success Factor is represented in the dataset by
the mean value of all the variables it loads on. The new variables (factors) are
labeled as “Factor 1 through Factor 8”. See Table 25.
SPSS COMMAND CODE COMPUTE FACTOR1=MEAN(LOCALSUPP, RAWMAT, FINANCE). EXECUTE. COMPUTE FACTOR2=MEAN(INIVEST, SIZEOCOM). EXECUTE. COMPUTE FACTOR3=MEAN(ECOWAS, AFRIUNI). EXECUTE. COMPUTE FACTOR4=MEAN(PREKNOW, INTEGRATE). EXECUTE. COMPUTE FACTOR5=KNOWHOW. EXECUTE. COMPUTE FACTOR6=PARTNER. EXECUTE. COMPUTE FACTOR7=MEAN(ENFORCE, RULEOFLAW, INFRASTR). EXECUTE. COMPUTE FACTOR8=INCENTIVE. EXECUTE. DESCRIPTIVES VARIABLES=FACTOR1 FACTOR2 FACTOR3 FACTOR4 FACTOR5 FACTOR6 FACTOR7 FACTOR8 /STATISTICS=MEAN STDDEV MIN MAX.
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Descriptives [DataSet1]\CSFRESEARCH.sav
Table 25: Descriptive Statistics CSFs
N Minimum Maximum Mean Std. Deviation
FACTOR1 414 1.33 5.00 4.2480 .64176
FACTOR2 414 1.00 5.00 3.4529 .99737
FACTOR3 414 1.00 5.00 3.7597 .80078
FACTOR4 414 1.00 5.00 3.7065 .75447
FACTOR5 414 1.00 5.00 4.1618 .84681
FACTOR6 414 1.00 5.00 3.6908 .98711
FACTOR7 414 1.00 5.00 4.1715 .60919
FACTOR8 414 1.00 5.00 3.1787 1.33927
Valid N (listwise) 414
4.6.2 The multinomial logistic Regression (2)
The model used here to test the hypotheses 5 and identify significant
statistical probabilities of outcome (statistical predictions) of the choice of
Entry Mode as determined by each individual Critical Success Factor,
includes the variables for sector, management level and age as the other
independent predictor variables together with the hypothesis independent
variables. This researcher has done this, because it greatly improved the
model fit and the model accuracy classification. This also means that the
hypothesis 5 as well as all the statistical predictions, has been tested to be true
or false or determined with the statistical consideration of the influence
industry/sector, management level and age, have on the model. SPSS COMMAND CODE GET FILE='/Users/antonychibochristopher/Desktop/Attachments_20141119/CSFRESEARCHnew.sav'. DATASET NAME DataSet2 WINDOW=FRONT. NOMREG ENTRYMODE (BASE=2 ORDER=ASCENDING) BY FACTOR1 FACTOR2 FACTOR3 FACTOR4 FACTOR5 FACTOR6 FACTOR7 FACTOR8 SECTOR/CRITERIA CIN(95) DELTA(0) MXITER(100) MXSTEP(5) CHKSEP(20) LCONVERGE(0) PCONVERGE(0.000001) SINGULAR(0.00000001)/MODEL/STEPWISE=PIN(.05) POUT(0.1) MINEFFECT(0) RULE(SINGLE) ENTRYMETHOD(LR) REMOVALMETHOD(LR)/INTERCEPT=INCLUDE/PRINT=CELLPROB CLASSTABLE FIT PARAMETER SUMMARY LRT CPS STEP MFI.
Table :26 Model Fitting Information CSFs
Model Fitting Criteria Likelihood Ratio Tests Model
AIC BIC -2 Log
Likelihood
Chi-Square df Sig.
Intercept Only 880.939 893.017 874.939
Final 622.099 1491.686 190.099 684.841 213 .000
Results displayed in the Model fitting information, (table 26) show that the
model used fits. (Will provide valid answers to the investigation), shows that
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the probability of the model chi-square (684.841) is 0.000, (i.e. p<0.05).
Therefore the null hypothesis that there was no difference between the model
without independent variables and the model with independent variables
was rejected it also shows that there is a significant collective, predictable
relationship between the independent variables in the model (the Critical
Success Factors) and the dependent variable (Entry Mode). The classification
accuracy rate produced by this model as depicted in (Table 27) is 91.3 percent,
which is much more than the proportional by chance accuracy criteria at 46.3
percent. This indicates that the model being used here is quite useful.
Table: 27 Classification CSFs
Predicted Observed
1.00 2.00 3.00 4.00 Percent Correct
1.00 141 6 9 0 90.4%
2.00 8 180 0 0 95.7%
3.00 11 2 54 0 80.6%
4.00 0 0 0 3 100.0%
Overall Percentage 38.6% 45.4% 15.2% 0.7% 91.3%
Results displayed in the Model fitting information, (table 26) show that the
model used fits. (Will provide valid answers to the investigation), shows that
the probability of the model chi-square (684.841) is 0.000, (i.e. p<0.05). Shows
that there is a significant collective, predictable relationship between the
independent variables in the model (the Critical Success Factors) and the
dependent variable (Entry Mode). Therefore all things being equal, this means
there is a significant predictable relationship between the extracted critical
success factors for FDI in Nigeria’s non-oil & gas sectors collectively, and the
choice of entry mode.
The result above supports H5. The results show that there is a significant
relationship between the extracted critical success factors for FDI in
Nigeria’s non-oil & gas sectors collectively, and the choice of entry mode.
Furthermore the results from the ratio likelihood tests displayed in table 28
below, clearly show Sig p<0.05 values for all our factors (independent/
predictor variables, This means that there are statistically
significant/predictable relationship between each of the Critical Success
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Factors, and the choice of entry mode recommended for FDI into the non-oil
and gas sector of the Nigeria market.
Table: 28 Likelihood Ratio Tests CSFs
Model Fitting Criteria Likelihood Ratio Tests Effect
AIC of
Reduced
Model
BIC of
Reduced
Model
-2 Log
Likelihood of
Reduced
Model
Chi-Square df Sig.
Intercept 622.099 1491.686 190.099a .000 0 .
FACTOR1 625.203 1374.014 253.203b 63.104 30 .000
FACTOR2 656.848 1429.814 272.848b 82.749 24 .000
FACTOR3 634.935 1407.901 250.935b 60.836 24 .000
FACTOR4 625.941 1398.907 241.941b 51.842 24 .001
FACTOR5 619.189 1440.466 211.189b 21.091 12 .049
FACTOR6 973.609 1794.885 565.609b 375.510 12 .000
FACTOR7 629.209 1365.943 263.209b 73.111 33 .000
FACTOR8 629.926 1451.203 221.926b 31.828 12 .001
SECTOR 926.807 1711.851 536.807b 346.708 21 .000
LEVEL 629.356 1474.787 209.356b 19.257 6 .004
AGE 601.332 1410.531 199.332b 9.233 15 .865
We see from the results displayed in Likelihood Ratio Tests table individual
statistical predictable relationship significance p values. (CSF) Critical Success
Factor 1 has the value of 0.000 Sig p <0.001 for chi square model probability in
the likelihood ratio tests table (table 28). Critical success factor 2 has the value
of 0.000 Sig p <0.001. CSF 3 has the value of 0.000 Sig p <0.001, CSF 4 has the
value of 0.001 Sig p <0.05, CSF 5 has the value of 0.049 Sig p <0.05, CSF 6 has
the value of 0.000 Sig p <0.001, CSF 7 has the value of 0.000 Sig p <0.001, and
CSF 8 has the value of 0.001 Sig p <0.05 respectively.
4.7 The Parameters Estimates Table
Identification of significant statistical probabilities for outcome of
”ENTRYMODE” as determined by each individual Critical Success Factor
At this point, we move on with our multi nominal logistic regression and
generate the parameters estimates table. Here we have the probability of the
outcome category in the dependent variable “ENTRYMODE”, as influenced
by each independent variable in the model. In the regression, the category for
reference in the dependent variable is set to “Licensing” (category “2” of the
dependent variable in the multi nominal logistic regression and the category
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with the highest frequency result (see Table 31 appendixes 03). Statistical
probabilities of outcome are therefore compared between Joint Venture and
Licensing, Sole Venture and Licensing and Exporting and Licensing. It should
be noted that responses from the questionnaire measured by the Likert scale,
have been coded into SPSS (the statistical and analysis software) as: strongly
disagree = 1, disagree = 2, not sure = 3, agree = 4, strongly agree = 5.
Significant statistical probabilities in the parameters estimates table for such
values as 4.50 and 3.33 not coded into SPSS are ignored and not selected as
part of the statistical predictions/ probabilities of outcome for choice entry
mode that form the choice of entry mode guide set. Using the statistical
predictions/ probabilities of outcome for choice entry mode as a guide set for
the CEO or consultant to choose or recommend an appropriate entry mode, is
based on one or some of this research’s determined critical success factors for
non - oil and gas FDI in Nigeria being determined or agreed to as critical for
success in Nigeria by the consultant or and the CEO. Therefore statistical
predictions/ probabilities of outcome for strongly disagree = 1, disagree = 2,
are not included as part of the guide set. Statistical predictions/ probabilities
of outcome for not sure = 3, have been included in the guide set to enhance
the processes of choosing or recommendation of the appropriate entry mode
using the guide set.
The Parameters Estimates Table
The Parameter estimates table summarizes the effect of each predictor. The
parameter estimates table (table 30: see appendix 06) is generated here to
enable the identification of significant statistical probabilities of outcome of
”ENTRYMODE” as determined by each of the 8 individual Critical Success
Factor here. Each statistically significant probability of outcome is extracted
from the parameters table by this researcher, with a view of using them as a
guide set/model for recommending the best entry mode for future potential
foreign direct investors for the Nigerian market. Attention is paid to the Exp
(B), B, and Sig values displayed in the parameters estimates table.
The Institute for Digital research and education, IDRE (2014) explain that the
“Exp (B)” values are the odds ratios for the predictors. They are the
exponentiation of the coefficients. The odds ratio of a coefficient indicates
82
how the risk of the outcome falling in the comparison group compared to the
risk of the outcome falling in the referent group changes with the variable in
question. An odds ratio > 1 indicates that the risk of the outcome falling in
the comparison group relative to the risk of the outcome falling in the referent
group increases as the variable increases. In other words, the comparison
outcome is more likely. An odds ratio < 1 indicates that the risk of the
outcome falling in the comparison group relative to the risk of the outcome
falling in the referent group decreases as the variable increases. Exp (B)
values are chosen here, because they are the odds ratios for the predictors.
They explain in the clearest presentable way, the odds - quantified likelihood
or not for those (companies/managers) who or which fall into the
categorizations described in each prediction, to choose one entry mode over
the other.
The “B” values are the estimated multinomial logistic regression coefficients
for the models. An important feature of the multinomial logit model is that it
estimates k-1 models, where k is the number of levels of the outcome variable.
As mentioned previously, SPSS has been set to treat the “licensing” category
of the dependent variable “ENTRYMODE” as the referent group and
therefore estimated a model for joint venture relative to licensing, sole
venture relative to licensing and exporting relative to licensing. Therefore,
since the parameter estimates are relative to the referent group, the standard
interpretation of the multinomial logit is that for a unit change in the
predictor variable, the logit of outcome m relative to the referent group is
expected to change by its respective parameter estimate (which is in log-odds
units) given the variables in the model are held constant.
The Sig values are the p-values of the coefficients or the probability that,
within a given model, the null hypothesis that a particular predictor's
regression coefficient is zero given that the rest of the predictors are in the
model.
Only parameters from the parameters estimates table with relevant significant
sig p<0.05 negative or positive coefficients and relevant are of value to and
83
used in this research. In other words, each and only statistically significant
probability of outcome and relevant to the goals of this research are extracted
from the parameters table.
4.8 Significant statistical probabilities of outcome for ”ENTRYMODE” as
determined by each individual Critical Success Factor
Bearing in mind that Results displayed in the Model fitting information, (table
26) show that the model used fits, and the classification accuracy rate
produced by this model as depicted in (Table 27) is 91.3 percent which is a lot
much more than the proportional by chance accuracy criteria at 46.3 percent.
Results from the parameter estimates table show:
Critical Success Factor One
1.CEOs and Managers of current FDI companies with a minimum of 20 years
of operating successfully in the Nigerian market, who determine or agree that
The Availability of Local Raw Materials, Suppliers and Financing, is critical
or to their success in the Nigeria market, are 181 times more likely to choose
a joint venture as an entry mode over licensing. B = 5.198, Sig p= 0.005 (<0.05)
Exp (B) =180.827.
2. CEOs and Managers of current FDI companies with a minimum of 20 years
of operating successfully in the Nigerian market, who determine or agree that
The Availability of Local Raw Materials, Suppliers and Financing, is critical
to their success in the Nigeria market, are 211 times more likely to choose a
sole venture as an entry mode over licensing. B = 5.352, Sig p = 0.009 (<0.05)
Exp (B) = 211.058.
Critical Success Factor Two
3. CEOs and Managers of current FDI companies with a minimum of 20 years
of operating successfully in the Nigerian market, who are not sure that the
Size of Initial Investment and Company, is critical to their success in the
Nigeria market, are 0.001 times less likely to choose a joint venture as an
entry mode over licensing. B = -6.744, Sig p = 0.000 (<0.001) Exp (B) = 0.001.
84
4. CEOs and Managers of current FDI companies with a minimum of 20 years
of operating successfully in the Nigerian market, who determine or agree that
the Size of Initial Investment and Company, is critical to their success in the
Nigeria market, are 0.018 times less likely to choose a joint venture as an
entry mode over licensing. B = -4.027, Sig p = 0.012 (<0.05) Exp (B) = 0.018.
5. CEOs and Managers of current FDI companies with a minimum of 20 years
of operating successfully in the Nigerian market, who determine or agree that
the Size of Initial Investment and Company, is critical to their success in the
Nigeria market, are 0.002 times less likely to choose a sole venture as an
entry mode over licensing. B = -6.064, Sig p = 0.006 (<0.50) Exp (B) = 0.002.
6. CEOs and Managers of current FDI companies with a minimum of 20 years
of operating successfully in the Nigerian market, who are not sure that the
Size of Initial Investment and Company, is critical to their success in the
Nigeria market, are 4.392e-005 times less likely to choose a sole venture as an
entry mode over licensing. B = -10.033, Sig p = 0.000 (<0.001) Exp (B) = 4.392e-
005.
Critical Success Factor Three
7. CEOs and Managers of current FDI companies with a minimum of 20 years
of operating successfully in the Nigerian market, who are not sure that
Taking advantage of ECOWAS and African Union Trade Agreements, is
critical to their success in the Nigeria market, are 350 times more likely to
choose a joint venture as an entry mode over licensing. B = 5.858, Sig p = 0.017
(<0.05), Exp (B)= 349.877.
8. CEOs and Managers of current FDI companies with a minimum of 20 years
of operating successfully in the Nigerian market, determine or agree that
Taking advantage of ECOWAS and African Union Trade Agreements, is
critical to their success in the Nigeria market, are 949 times more likely to
choose a joint venture as an entry mode over licensing. B = 6.856, Sig p = 0.001
(<0.50), Exp (B)= 949.338.
85
Critical Success Factor Four
9. CEOs and Managers of current FDI companies with a minimum of 20 years
of operating successfully in the Nigerian market, who determine- or agree
that Integrating into Local practices and Market pre-knowledge, is critical to
their success in the Nigeria market, are 77 times more likely to choose a sole
venture as an entry mode over licensing. B = 4.340, Sig p = 0.042 (<0.05) Exp
(B) = 76.691.
Critical Success Factor Five
10. CEOs and Managers of current FDI companies with a minimum of 20
years of operating successfully in the Nigerian market, who are not sure that
the Protecting of Company’s Knowhow, is critical to their success in the
Nigeria market, are 1852 times more likely to choose a joint venture as an
entry mode over licensing. B = 7.524, Sig p = 0.003 (<0.05) Exp (B) = 1851.569.
11. CEOs and Managers of current FDI companies with a minimum of 20
years of operating successfully in the Nigerian market, who are not sure that
the Protecting of Company’s Knowhow, is critical to their success in the
Nigeria market, are 365 times more likely to choose a sole venture as an entry
mode over licensing. B = 5.900, Sig p = 0.030 (<0.05) Exp (B) = 364.917.
Critical Success Factor Six
12. CEOs and Managers of current FDI companies with a minimum of 20
years of operating successfully in the Nigerian market, who determine or
agree that the A Local Partner, is critical to their success in the Nigeria
market, are 0.059 times less likely to choose a joint venture as an entry mode
over licensing. B = -2.839, Sig p = 0.022 (<0.05) Exp (B) = 0.059.
13. CEOs and Managers of current FDI companies with a minimum of 20
years of operating successfully in the Nigerian market, who determine or
agree that the A Local Partner, is critical to their success in the Nigerian
market, are 0.010 times less likely to choose a sole venture as an entry mode
over licensing. B = - 4.633, Sig p = 0.022 (<0.05) Exp (B) = 0.010.
Critical Success Factor Seven
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14. CEOs and Managers of current FDI companies with a minimum of 20
years of operating successfully in the Nigerian market, who determine or
agree that the Consideration for Quality of law Enforcement and state of
infrastructure, is critical to their success in the Nigerian market, are 0.004
times less likely to choose a joint venture as an entry mode over licensing.
B = -5.460, Sig p = 0.000 (<0.05) Exp (B) = 0.004.
15. CEOs and Managers of current FDI companies with a minimum of 20
years of operating successfully in the Nigerian market, who determine or
agree that the Consideration for Quality of law Enforcement and state of
infrastructure, is critical to their success in the Nigerian market, are 0.015
times less likely to choose a sole venture as an entry mode over licensing. B
=-4.232, Sig p = 0.019 (<0.05) Exp (B) = 0.015.
Critical Success Factor Eight
16. CEOs and Managers of current FDI companies with a minimum of 20
years of operating successfully in the Nigerian market, who agreed that the
Advantage of Special Incentives and Concessions, is critical to their success
in the Nigeria market, are 19 times more likely to choose a joint venture as an
entry mode over licensing. B = 2.926, Sig p = 0.011 (<0.05) Exp (B) = 18.660.
17. CEOs and Managers of current FDI companies with a minimum of 20
years of operating successfully in the Nigerian market, who are not sure that
the Advantage of Special Incentives and Concessions, is critical to their
success in the Nigerian market, are 227 times more likely to choose a sole
venture as an entry mode over licensing. B = 5.426, Sig p = 0.007 (<0.05) Exp
(B) = 227.000.
18. CEOs and Managers of current FDI companies with a minimum of 20
years of operating successfully in the Nigerian market, who agreed that the
Advantage of Special Incentives and Concessions, is critical to their success
in the Nigerian market, are 96 times more likely to choose a sole venture as
an entry mode over licensing. B = 4.562, Sig p = 0.004 (<0.05) Exp (B) = 95.810.
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5.0 Discussions and Interpretations for Results
5.1 Discussions & interpretations for Factor Analysis Results
The analysis completed above has determined eight critical factors the
intending foreign direct investor must consider in order to achieve successful
non-oil & gas FDI in Nigeria. This research has done so from a sample of 414
CEOs and managerial staff of FDI companies that is located and has been
operating for a minimum of 20 years in the non-oil & gas sectors of Nigeria.
The reason for such a sample is to sample a body of people that have been
and still are successful non-oil & gas FDI managers in Nigeria. With a
minimum experience of 20 years of doing business in the Nigerian
environment such companies have succeeded through different governments,
political and economic changes.
In the Inter Factor correlation matrix, the low inter correlation values between
the eight critical success factors is ideal, for it shows that the factors are
greatly distinct from one another. Meaning that each one of them is a critical
factor to be considered independently. The large number of negative values
here simply means in such negative correlation relationships, that the higher
presence of one Critical Success Factor, the lower the necessity for the other in
the negative correlation relationship. For example, the results show that the
more the integration into local practices and pre - knowledge of the Nigeria
non- oil & gas markets possessed by a company, the less necessary it is for it
to have special incentives and concessions from the Nigerian government.
The positive valued relationships also make much sense. For example from
the results it can be seen that the more important it is for a company to have
special incentives and concessions from the Nigerian government, it would
also be more important for such to protect its company knowhow or in other
word keep its know-how from public knowledge. Variables such as Political
stability and Security of life and property did not emerge as any of the eight
Critical Success Factors identified. Asiedu (2001) proposed rational for this
observation is that the FDI inflows to Nigeria are so profitable that the return
after considerations of any risk is quite substantial, therefore investors are not
discouraged by political instability. However the researcher puts forward
that, this simply means that in the minds of CEOs and mangers of successful
non- oil & gas FDI companies in Nigeria with a minimum of 20 years’
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experience, the political stability of the country and security of life and
property are not and have not been critical factors to consider in their
experience of being successful FDI companies in Nigeria. This implies that the
political climate and general security in Nigeria have not had a negative effect
towards the success of their companies in Nigeria.
The results show that first and foremost The Availability of Local Raw
Materials, Suppliers and Financing is the most important critical success
factor of the eight, from the point of view of CEOs and mangers of successful
non- oil & gas FDI companies in Nigeria with a minimum of 20 years’
experience. This factor alone accounts for 23.309 percent of the total variance.
23.309 percent of the total variance sends the message that what should be of
utmost importance to the FDI company in Nigeria, should be to get or
continue to have profitable access to its necessary raw materials, and or
successfully arranging the supply of all necessary for the firm to produce or
give its services, which includes suitable financing, it suggests that achieving
this should be the primary goal, because other factors can be worked out. This
result denotes the very goal and business practice oriented nature across all
the eight Critical Success Factors.
The second most Critical Success Factor turns out to be The Size of Initial
Investment and Company. This is a message of pre - caution for all intending
FDI firms for the Nigerian market. The size of the initial investment must
consider and be big enough to accommodate for the inadequacy of and
therefore the high cost of crucial infrastructure such as power and
transportation. Although this critical success factor accounts for 7.651 percent
of the total variance, a distant second from “Availability of Local Raw
Materials, Suppliers and Financing,” it has emerged with the other seven, as a
highly distinct critical success factor, given it low correlation value with the
others, more so its positive correlation value with Critical Success Factor one,
tells us that when in any scenario the Availability of Local Raw Materials,
Suppliers and Financing grows in importance so also does the Size of Initial
Investment and Company.
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With 6.636 percent of the total variance, and in third place is Taking
advantage of ECOWAS and African Union Trade Agreements. The
emergence of this as a Critical Success Factor is as knowledge refreshing and
revealing as the emergence of the other seven here. It stems from and
underlines the fact that Nigeria is not only Africa’s and West. Africa’s
dominant economy, but West Africa’s economic and industrial hub. Nigeria
Trade Hub NTH (2014), revealed that according to the European Union EU,
the world sees Nigeria as the economic and business gateway to Africa, it
quotes the EU as explaining that Nigeria, as the largest economy in Africa and
the industrial hub of West Africa, the West African market was in fact an
extension of Nigeria’s domestic economy, Nigeria must always take the
leadership role and drive the further integration of West Africa. This suggests
that among the initial goals of an FDI company entering the Nigeria market is
to take advantage of Nigeria’s position in the African and W. African markets
to expanding easily further into other W. African markets. It therefore makes
much sense that Taking advantage of ECOWAS and African Union Trade
Agreements would be critical to the success of the goals of such FDI
companies. However interestingly, the results show a negative correlation
relationship between the first and most important critical success factor
“Availability of Local Raw Materials, Suppliers and Financing” and the
third “Taking advantage of ECOWAS and African Union Trade
Agreements”. This tells us that the more successful a company is in ensuring
critical success factor one, it would find that it becomes less necessary to
obtain critical success factor three, for that would come consequentially.
Integrating into Local practices and Market pre-knowledge emerged as one
of the eight critical success factors with 6.322 percent of the total variance.
Interestingly it has negative correlations with Critical Success Factors, three,
five, seven and eight. Accenture (2010) conceptualizes that this Critical
Success Factor gives the FDI Company the ability to recognize and take
advantage of new opportunities successfully. The researcher agrees and adds
that this Critical Success Factor is essential today where there is a growing
consciousness for responsible FDI among Nigerians, for it turns aside
resentment for FDI firms that may resemble exploitative colonial
relationships.
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Protecting of Company’s Knowhow with 4.516 percent of the total variance
emerged as one of the critical success factors. This is quite obvious, especially
for FDI firms seeking to enjoy special incentives and concessions from the
Nigeria government owing to their special and or uncommon company skills
and knowhow. It also makes sense that there is a negative correlation between
critical success factor 4, Integrating into Local practices and Market pre-
knowledge and 5, Protecting of Company’s Knowhow. It is logical that the
more a company is integrated into the Nigerian society and its practices, the
more its success in getting contracts would be owing to the relationships and
connections it has fostered and less on solely its company knowhow.
A Local Partner is the sixth critical success factor with 4.279 percent of total
variance. This researcher agrees with Frontier Market Intelligence FMT (2010)
it expresses that a local business partner in Nigeria is highly recommended,
however the task is in finding an effective one. FMT explains that the risk, as
in any other country, is not in ending up with a fraudulent partner but an
ineffectual one. UBA (2014) also expresses that it is good for the investors to
partner with locals who understand the terrain. The researcher will also add
that when or if a decision is made to get a local partner, It is best to allocate
resources towards finding an effective one via specialized locally based
business consultancies, also important is to note that organizations such as the
Nigeria Trade Commission and the Nigerian Investment Promotion Council
both offer screening services to validate the authenticity of companies. It is
also important to note that the results show that this critical success factor “A
Local Partner” has a negative correlation with critical success factors five,
seven and eight. This again makes much sense, for with an effective local
partner, the overall burden of navigating the quality of law enforcement and
getting special incentives and concessions is lowered.
The seventh Critical Success Factor is Consideration for Quality of law
Enforcement and state of infrastructure. It accounts for only 4.067 percent of
the total variance. The emergence of this as a Critical Success Factor means
that in the minds of CEOs and mangers of successful non – oil & gas FDI
companies in Nigeria with a minimum of 20 years’ experience, knowing how
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to successfully navigate and understand local practices in law enforcement
and to compensate for the inadequacy of some infrastructure is critical to their
success. The researcher will point out that while the inadequacy of certain
infrastructure can be compensated for by increased spending on
transportation and electrical generators for power, extended dissatisfaction in
the enforcement of the law will lead to an eventual lack of FDI. The fact that
FDI inflows continues to increase to Nigeria and it maintains its place as the
top FDI recipient on the African continent, suggests that FDI companies in
Nigeria over the years receive satisfaction with the enforcement of law
concerning their matters, or in other words have adapted their satisfaction to
the type of law enforcement in Nigeria. However as mentioned before, the
emergence of “Consideration for Quality of law Enforcement and state of
infrastructure” as a Critical Success Factor means knowing how to
successfully navigate and understand local practices in law enforcement has
been critical to their success in the non- oil & gas market on Nigeria.
The Advantage of Special Incentives and Concessions has emerged has the
least important Critical Success Factor, with just 3.833 percent of the total
variance. It just made it as a Critical Success Factor. This is not surprising
when one considers that many extant research literatures such as Loree &
Guisinger (1995) and Wells et al. (2003) state that special incentives and
concessions are not even influential in the decision to locate FDI. However,
results from this research, which uses the sample of the business practice
CEOs and managers, drawing from their experience and point of view,
anything that allows for their quick setup and savings in their initial set up
cost was and is critical to their continued success. Such incentives freed
crucial capital to be used to set up in such a way that guaranteed continued
and profitable operation over time. For example, The Embassy of the Federal
Republic of Nigeria. Washington D.C (2014) gives us a list of the incentives
and concessions offered to FDI companies in and bound for Nigeria. They
include, on infrastructure 20 percent of the cost of providing basic
infrastructures such as roads, water, electricity, where they do not exist, is tax
deductible once and for all, on investment in economically disadvantaged
areas, 100 percent tax holiday for seven years and additional 5 percent
depreciation over and above the initial capital depreciation. On local raw
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materials utilization, 30 percent tax concession for five years to industries
that attain minimum local raw materials utilization as follows: - agro 80
percent - agro allied 70 percent - engineering 65 percent - chemical 60 percent
- petro-chemical 70 percent On labor intensive mode of production, 15
percent tax concession for five years. The rate is graduated in such a way that
an industry employing one thousand persons or more will enjoy 15 percent
tax concession while an industry employing one hundred will enjoy only 6
percent, while those employing two hundred will enjoy 7 percent, and so on.
All the above incentives are designed to make it easy for such companies to
set up and move on to success. It is clear how and why CEOs and managers
of such companies consider these incentives and concessions critical to the
setup and eventual success in Nigeria.
5.2 Discussions & interpretations for Hypotheses testing Results H1 to H4
The model used here to test the hypotheses, includes the industry of the
Nigerian non-oil & gas sector as the other independent predictor variable
with the hypothesis independent variable. This means that the hypotheses in
context here, have been tested to be true or false with the consideration of the
influence industry/sector may have on the choice of entry mode. Also
noteworthy is the fact that the least model accuracy classification across the
four tests is 70.5 percent, which is not only good; it’s much more than the
proportional by chance criteria which is 46 percent.
Hypothesis 1
The results do not support the first hypothesis, H1. The results show that all
things being equal, there is no significant relationship between the quality of
infrastructure in Nigeria and choice of entry mode for FDI into its non-oil &
gas sectors. A value of 0.449, Sig p> 0.05 value for chi square model
probability is recorded. While many extant literature such as Ayanwale
(2007), have stressed the importance of infrastructure in attracting FDI, hence
the formulation of the hypothesis in the first place, however, upon this result,
we can note, that Asiedu (2002), research work from a leading researcher on
FDI in sub Saharan Africa, tells us that determinants of FDI have a different
effect on the FDI inbound of Sub- Saharan countries than that of other
developing countries. Asiedu (2002) found that some determinants that
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normally correlate with FDI have a different effect on Sub-Saharan Africa. She
asserts that infrastructure development and a higher return on capital are
important determinants for the other developing countries but not for Sub-
Saharan countries. According to her, the perceived risky nature of Africa
cancels these normally reasonable relationships. Bearing in mind the critical
success factors identified earlier, we can see that while it is critical to know
how to operate in spite of the poor state of basic infrastructure, in the minds
of CEOs and managers of currently successful FDI firms in Nigeria, the poor
basic infrastructure does not significantly influence the decision for what
entry mode to use into Nigeria. This result is even clearer to understand,
when one considers that Nigeria receives the highest amount of FDI coming
into Africa, in spite of its poor quality of basic infrastructure. This suggests
and supports this result, which shows, that with regards to successful CEOs
and managers in Nigeria, the decision to enter the Nigerian market and how,
is not influenced significantly by the state of basic infrastructure in Nigeria.
Hypothesis 2
The results do not support the second hypothesis H2. The results show that
all things being equal, there is no significant relationship between the Active
government support services in Nigeria and choice of entry mode for FDI into
its non-oil & gas sectors. ) A value of 0.061, Sig p> 0.05 value for chi square
model probability. Active government support services here should not be
confused with Special Incentives and Concessions from the Nigeria
government. Active government support services here, refers to services
offered to FDI and or prospective FDI companies in Nigeria by agencies of the
federal government. Examples of such services are background checks on
possible business partners, one stop centers for obtaining business
incorporation papers and so on. This result simply means that in the minds of
CEOs and managers of successful FDI companies in Nigeria, these services do
not influence their decision on what entry mode to use into the Nigerian
market.
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Hypothesis 3
The results support the third hypothesis H3. The results show that all things
being equal, there is a significant relationship between political stability in
Nigeria and choice of entry mode for FDI into its non-oil & gas sectors.
Political Stability, has a value of 0.001, Sig p < 0.05 value for chi square model
probability. Many extant literatures such as Kabananiye (2011) have asserted
an important relationship between political stability, and FDI inflow into a
country. In the case of Nigeria, research works, such as Umoh (2011) has also
explained an important relationship between political stability and FDI
inflow. In contrast, Interestingly, Asiedu (2001) explains that political
instability or risk, has an insignificant effect on FDI when it comes to Nigeria,
she asserts that political stability has not discouraged FDI inflows into
Nigeria, because even after adjusting for risk the profits are very high. Now,
the results from this research, reveal new knowledge that could expound
Asiedu (2011)’s assertation, as well as agree with the important relationship
political stability has with FDI as asserted by many extant literature. Bearing
in mind our critical success factors identified earlier, the results in this
research show that while there is a significant predictable relationship
between political stability in Nigeria and choice of entry mode for FDI into its
non-oil & gas sectors. In the minds of CEOs and managers of successful FDI
companies in Nigeria, political stability in Nigeria is not a critical
consideration for an FDI Company’s success in the Nigeria non-oil & gas
market.
Hypothesis 4
The results support the fourth hypothesis H4. The results show that all things
being equal, there is a significant relationship between size of the market in
Nigeria and choice of entry mode for FDI into its non-oil & gas sectors. Size of
Market, has a value of 0.040, Sig p < 0.05 value for chi square model
probability. Anyanwu (1998) identified market size as a major determinant of
FDI inflows into Nigeria. Ayanwale (2007) identifies Nigeria’s Market size as
a determinant of FDI in Nigeria. Nigeria is Africa’s largest market, Ogunkeye
(2014) goes on to explain Nigeria is ranked number one in Africa in terms of
GDP Purchasing Power Parity (PPP) as of 2013, and as mentioned before has
just become the largest economy in Africa in 2014. The result in this research
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shows that the choice of entry mode into Nigeria’s market is predictably
related to a significant degree to the size and opportunities of the Nigeria
market in the minds of CEOs and managers of successful FDI ventures in
Nigeria.
5.3 Discussions & interpretations for Hypotheses testing Results H5
The results support the fifth hypothesis H5. The results show that all things
being equal, there is a significant relationship between all the eight Critical
Success Factors for non-oil and gas FDI in Nigeria identified by this research
and the choice of entry mode for FDI into its non-oil & gas sectors. From the
Model fitting information generated, we see that all the 8 critical success
factors collectively have the value of 0.000 Sig p <0.05 values for chi square
model probability. Therefore, confirming hypothesis 5. The model used here
to test the hypotheses 5 includes the variables for industry/sector of the
Nigerian non-oil & gas market, management level and age as the other
independent predictor variables together with the hypothesis independent
variables. The researcher has done this, because it greatly improved the
model fit and the model accuracy classification. This also means that the
hypothesis 5 has been tested to be true or false with the statistical
consideration of the influence industry/sector, management level and age
may have on the choice of entry mode. Also noteworthy is the fact that the
model accuracy classification is 91.3 percent, which is not only good; it’s
much more than the proportional by chance criteria, which is 46 percent.
Furthermore we see from the results of the likelihood ratio tests that each
different critical success factor has a significant, predictable relationship with
the choice of entry mode. This tells us that all things being equal, the 8 critical
success factors for FDI into the non-oil & gas sectors of the Nigerian market
statistically identified via the statistically appropriate sample of CEOs and
managers of successful FDI companies in Nigeria, truly have a predictable
relationship or influence with the outcome choice of FDI entry mode into the
Nigeria’s non-oil & gas sectors. This leads to identifying the significant
statistical probabilities of outcome of ”ENTRYMODE” as determined by each
individual Critical Success Factor.
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5.4 Discussions & interpretations for significant statistical probabilities of
outcome for ”ENTRYMODE” as determined by each individual Critical
Success Factor results.
The results show that all things being equal, the 8 critical success factors for
FDI into the non-oil & gas sectors of the Nigeria Market which have been
statistically identified via the appropriate sample of CEOs and managers of
successful FDI companies in Nigeria, truly have a predictable relationship or
influence with the outcome choice of FDI entry mode into the Nigeria’s non-
oil & gas sectors. (CSF) Critical Success Factor 1 has the value of 0.000 Sig p
<0.001 for chi square model probability in the likelihood ratio tests table (table
28). Critical success factor 2 has the value of 0.000 Sig p <0.001. CSF 3 has the
value of 0.000 Sig p <0.001, CSF 4 has the value of 0.001 Sig p <0.05, CSF 5 has
the value of 0.049 Sig p <0.05, CSF 6 has the value of 0.000 Sig p <0.001, CSF 7
has the value of 0.000 Sig p <0.001, and CSF 8 has the value of 0.001 Sig p
<0.05 respectively. Using our model we have by generating the Parameters
Estimates table calculated and identified 18 significant statistical probabilities
of outcome (or statistical predictions) for “ENTRYMODE” choice of entry
mode as determined by each and every one of the 8 critical success factors.
These 18 statistical predictions by the model for the outcome of the choice of
entry mode, will now serve as the guide set for recommending the best entry
mode on a case by case basis to future potential foreign direct investors for
the Nigerian market.
The researcher notes that the model includes the variables for sector,
management level and age as the other independent predictor variables
together with the 8 critical success factors. The researcher has done this,
because it greatly improved the model fit and the model accuracy
classification. This also means that all the statistical predictions have been
determined with the statistical consideration of the influence industry/sector,
management level and age may have on the model. There are no significant
statistical predictions by the model for the outcome of entry mode as
determined by the sector and age. There are significant predictions
determined by the management level, however any predictions determined
by sector, age, and management level are ignored by this research, and left for
other further research. This research does not set out to determine such. The
model used here, has a model accuracy classification of 91.3 percent (a lot
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greater than the proportional by chance criteria of 46 percent) in executing
what its designed for, which is making statistical predictions for the choice of
entry mode as determined by each and every one of the 8 critical success
factors and enabling the identification and use of the relevant statistically
significant ones. The researcher also notes that there are no significant
statistical predictions for “exporting” as the outcome category for choice of
entry mode. This is simply because the number of entries selected for
exporting is very low in the first place in the questionnaire at 0.7 percent See
ENTRYMODE frequencies, (Table 31 appendixes 03). As mentioned
previously, The 18 statistical predictions revealed by this research are a guide
set to CEOs, managers or Consultants in making or recommending the best
entry mode on a case-by-case basis with consideration of company specific
circumstances and other relevant factors. The entry mode finally chosen or
recommended is primarily determined by the critical success factor agreed by
the CEO/manager or consultant to be the most critical to the company.
Critical Success Factor One and Choice of Entry Mode
The model predicts that those who determine or agree that The Availability of
Local Raw Materials, Suppliers and Financing is critical to their success in the
Nigeria market, are 181 times more likely to choose a joint venture as an entry
mode over licensing. However they are also 211 times more likely to choose a
sole venture as an entry mode over licensing. This indicates that
recommending a sole venture entry mode to such persons who determine or
agree that the Availability of Local Raw Materials, Suppliers and Financing, is
critical to their success in the Nigeria would be a good recommendation. It is
worth noting that this critical success factor The “Availability of Local Raw
Materials, Suppliers and Financing” emerged from the factor analysis, as the
most important critical success factor of the eight. This factor alone accounted
for 23.309 percent of the total variance.
Critical Success Factor Two and Choice of Entry Mode
The model predicts that those who are not sure that the Size of Initial
Investment and Company, is critical to their success in the Nigeria market, are
0.001 times less likely to choose a joint venture as an entry mode over
licensing. Those who determine or agree that the Size of Initial Investment
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and Company is critical or important to their success are 0.018 less likely to
choose a joint venture as an entry mode over licensing. The model also
predicts that those who agree are 0.002 times less likely to choose a sole
venture as an entry mode. Those who are not sure are also 4.392e-005 times
less likely to choose a sole venture as an entry mode. This indicates that in
cases where a company is not willing or not able to come up with the
necessary amount of initial FDI funds to overcome shortcomings such as poor
infrastructure or “bribes”, it is good to recommend an entry mode of licensing
into the Nigeria market for such. However it is noted that the degree of less
likelihood to choose a joint venture or sole venture over licensing here is
considerably small at 0.001, 0.018, 0.002 and 4.392e-005 times respectively.
This suggests that in some circumstances, the recommendation of a joint
venture or even a sole venture can be a good recommendation or choice.
Critical Success Factor Three and Choice of Entry Mode
The model predicts that those who determine or agreed that Taking
advantage of ECOWAS and African Union Trade Agreements, is critical to
their success in the Nigeria market, are 949 times more likely to choose a joint
venture as an entry mode over licensing. Those who are not sure are 350 times
more likely to choose a joint venture as an entry mode. This statistical
prediction by the model is even clearer to understand, when one understands
that to take advantage of ECOWAS and African Union Trade Agreements, a
company would logically not only be sited in Africa or West Africa, but also
be African. Its board of directors should be seen to be predominantly African
or W. African. This suggests that in cases where a potential foreign direct
investment business determines or agrees that taking advantage of the
benefits of ECOWAS and African Union Trade Agreements is critical to its
success, it will seek to create joint ventures that will not only be sited in West
Africa or Africa, but for which their board of directors are pre-dominantly
African. A new joint venture as an entry mode is an obvious good
recommendation to such that determine or agree that the benefits of such
agreements are critical to their success. In this prediction, it is obvious to see
that all the predictions have been statistically derived from the experience and
skills of CEOs and managers who are very experienced insiders in what it
takes to be a successful FDI in Nigeria.
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Critical Success Factor Four and Choice of Entry Mode
The model predicts that those who determine or agree that Integrating into
Local practices and Market pre-knowledge, is critical or important to their
success in the Nigeria market, are 77 times more likely to choose a sole
venture as an entry mode over licensing. This result shows and suggests that
for those who have done a thorough job in acquiring market pre-knowledge
for Nigeria, and therefore understand on their own to a good degree, the local
practices and attributions, such persons or firms prefer to be in full and direct
control of their investment in Nigeria. This result indicates that current
successful FDI CEOs and managers in the Nigerian market judge that the
local practices necessary to be successful in Nigeria are better controlled and
supervised directly via a locally sited sole venture. For such persons the
decision or recommendation of a sole venture as an entry mode is clear.
Critical Success Factor Five and Choice of Entry Mode
The model predicts that those who are not sure that the Protecting of
Company’s Knowhow is critical to their success in the Nigeria market are
1852 times more likely to choose a joint venture as an entry mode over
licensing. They are also 365 times more likely to choose a sole venture as an
entry mode over licensing. For some not yet privileged with the knowledge
that the findings of this research provides, a joint venture may not come to
mind when trying to protect company knowhow is the issue or when the
protection of company knowhow is determined critical for success. However,
this prediction which as we know is derived statistically from the experience
and skills of CEOs and mangers of currently successful FDI companies in the
Nigeria market reveals a joint venture to be the recommended entry mode in
this context. An interpretation for this is that these CEOs and mangers know
that as Mckinsey (2014) explains many of such FDI companies with such
concerns, set up joint ventures that are restricted to those steps in the value
chain that involve limited or no intellectual property, like assembling and
packaging, where the FDI company appears to have a manufacturing
presence in the local economy with its joint venture partners, but company
knowhow is protected because the actual intellectual and company knowhow
takes place only in the companies country of origin. In the context where
Critical Success Factor five is determined most critical, this sort of joint
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venture option can be seen as better in protecting company knowhow than in
a sole venture, for in a sole venture, local employees will be exposed to the
manufacturing process to a large degree. This sort of joint venture as an entry
mode option can also be seen as better than that of a licensing agreement, for
in a manufacturing licensing agreement, significant company knowhow will
be transferred in order to ensure and maintain product quality. It is good to
mention that the option or scenario of exporting a finished manufactured
product into the Nigerian market, could likely mean not being able to
compete with the prices of similar products in the Nigerian market produced
by the means of such above explained joint ventures. Therefore, as the model
predicts, where Protecting of Company’s Knowhow is agreed to be critical is
to their success in the Nigeria market, a joint venture that is restricted to those
steps in the value chain that involve limited or no intellectual property is an
appropriate recommendation for an entry mode.
Critical Success Factor Six and Choice of Entry Mode.
The model predicts that those who determine or agree that a Local Partner is
critical to their success in the Nigeria market are 0.010 times less likely to
choose a sole venture as an entry mode over licensing. They are also 0.059
times less likely to choose a joint venture as an entry mode over licensing.
These results/predictions are quite intriguing for to an extent it contradicts
some results in extant such as Luo (2007) who expresses the availability of
possible partners in a location as a positive determinant for FDI, leading to
increasing incidence of joint ventures. However, extant literature such as
UNIDO (2008) support such results/predictions, as they explain that many
FDI companies see a potential joint venture partner as a competitor, a possible
threat to large market share. They express that foreign investors seem to
forego the opportunity to use a joint venture, opting for a highly controlled
licensing deal or even a sole venture instead of a joint venture. The
predictions we see here for critical success factor six, suggest just that. In this
context, in view of the predictions, the discussion of an entry mode such a
controlling licensing deal would be a good first recommendation.
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Critical Success Factor Seven and Choice of Entry Mode.
The model predicts that those who determine or agree that the Consideration
for Quality of law Enforcement and state of infrastructure, is critical to their
success in the Nigeria market, are 0.004 times less likely to choose a joint
venture as an entry mode over licensing. They are also 0.015 times less likely
to choose a sole venture as an entry mode. This prediction suggests that For
those who determine or agree Critical Success Factor seven is critical to their
success but are not confident of being able to set up in Nigeria in a way that
cancels the negative effects of poor infrastructure and or a different sort of
law enforcement, a decision or recommendation of licensing, as an entry
mode is appropriate. However, noting that the predicted degree of likelihood
of choosing licensing over joint venture is quiet low at 0.004 times, one must
also note that a joint venture could be an appropriate decision or
recommendation for entry mode in such circumstances.
Critical Success Factor Eight and Choice of Entry Mode.
The model predicts that those who are not sure that the Advantage of Special
Incentives and Concessions, is critical to their success in the Nigerian market,
are 227 times more likely to choose a sole venture as an entry mode over
licensing. Those who agreed are 96 times more likely to choose a sole venture
as an entry mode over licensing. The model also predicts that those who
determine or agree that the Advantage of Special Incentives and Concessions,
is critical to their success are 19 times more likely to choose a joint venture
over licensing as their entry mode into the Nigeria market. The
results/predictions suggest that for those who agree or are not sure about
critical success factor eight being critical to their success, a sole venture is an
appropriate decision or recommendation.
As mentioned previously, the 18 statistical predictions revealed by this
research are a guide set to CEOs, managers or Consultants in making or
recommending the best choice of entry mode on a case-by-case basis. The
entry mode finally chosen or recommended is greatly influenced by the
critical success factor agreed by the CEO/manager or consultant to be the
most critical to the company. The business practice application of this guide
set in recommending or deciding on successful entry modes in business
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practice, is executed via carrying out a semi – structured interview with the
CEO and or decision making managers in a given (potential) FDI company.
An important goal in carrying out the semi – structured interview is to
determine which of the 8 Critical Success Factors they consider most critical in
the context of their company’s anticipated success in Nigeria. The interview
would also bring to bare their understanding of the hierarchical importance
for the other seven critical success factors as determined according to the
answers given by the interviewee. After the answers to the questions in the
semi-structured interview are obtained, the (guide set) 18 statistical
predictions for choice of entry mode, is then consulted and applied, the most
suitable, fitting and corresponding statistical prediction is chosen, and an
entry mode into Nigeria for the company is recommended or chosen.
6.0 Further Research and Recommendations
This research contributes and betters knowledge and practice in international
business, it provides the Critical Success Factors and entry mode
recommendations to be considered in order to achieve a successful non-oil &
gas FDI project and entry mode into Africa’s largest economy at a time when
Africa provides a significant part of opportunities for business growth
worldwide. This research also contributes and betters knowledge and practice
in international business by exposing or suggesting the need for further
research into topics related to it. For example, according to extant literature
such as UBA (2014) Nigeria is the dominant economy in West Africa, it
accounts for almost 50 percent of W. Africa’s GDP and 60 percent of its
population. The Nigerian market population is an attractive market to enter
for FDI companies, not only for its population and therefore high demand for
goods and services, but also for the opportunity it offers into the even larger
W. Africa market. With Nigeria accounting for about 50 percent of W. Africa’s
GDP and about 60 percent of its population, are the critical success factors for
non-oil & and gas FDI in Nigeria here determined in this research, the same
as the critical success factors for non-oil & and gas FDI in W. Africa? The
results and findings of this research open the door for further research into
answering this question. Similarly, as Nigeria is the biggest market and
economy in Africa, and having determined in this research, the critical
success factors for non-oil and gas FDI in Nigeria and their statistical
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relationship with the choice of entry mode into the Nigeria market, other
significant findings can be determined by further research for example, the
critical success factors for doing business in Africa, and what their statistical
predictable relationships with the choice of entry mode into Africa’s different
economic or geographical market regions. Furthermore, the data collected by
this research can be used in other further research to generate models that
predict the outcome of entry mode as determined by the sector of the
Nigerian economy and or the ages of the CEOs or the level of management.
The data collected by this research can also be used in other further research
to generate models that predict the outcome of entry mode as determined by
the educational level of the CEO or managerial staff (in the context of
Nigeria). However as mentioned previously, this research does not set out to
determine such. The model used here, has a model accuracy classification of
91.3 percent (proportional by chance criteria of 46 percent) in executing what
its designed for, which is making statistical predictions for the choice of entry
mode as determined by each and every one of the 8 Critical Success Factors
determined in this research. The relevant statistical significant predictions are
identified and used. Designing and implementing a model using this
research’s data, which will provide accurate statistical choice of entry mode
predictions as determined by sector, age, management level and level of
education, is ignored by this research, and left for other further research.
In addition, results from this research tell us that all things being equal there
is no significant predictable relationship between the quality of infrastructure
in Nigeria and choice of entry mode for FDI into its non-oil & gas sectors.
From this finding raises the question, should the authorities in Nigeria seek to
use developing its infrastructure to attract and influence FDI and its entry
mode? If so, what infrastructural changes must be embarked upon to attract
FDI and influence the choice entry mode to the benefit of the Nigeria
economy? These are significant research topics brought to bare by this
research to be answered by further research. Again, the results show that all
things being equal, there is no significant predictable relationship between the
Active government support services in Nigeria and choice of entry mode for
FDI into its non-oil & gas sector. Active government support services here
should not be confused with Special Incentives and Concessions from the
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Nigeria government. Active government support services here, refers to
services offered to FDI and or prospective FDI companies in Nigeria by
agencies of the federal government, such as background checks on possible
business partners, one stop centers for obtaining business incorporation
papers and so on. Again from the findings of this research raises the question,
should the authorities in Nigeria seek to attract and influence FDI and its
entry mode via the support services it offers to potential FDI companies? If
so, then why? What changes to the government support services offered
currently is required to achieve beneficial influence over the choice of FDI
entry mode?
This research and it findings come appropriately at a time when and where
there is much realization among oil and gas revenue dominant economies of
the need for the diversification of their economies, from oil and gas revenue
dependent to vibrant multi sector/ industry revenue economies. Nigeria is
one of such countries. This research in revealing important new economic
findings specifically for the non-oil and gas sector of Nigeria, not only helps
and inspires further towards the diversification of the economy, but exposes
the need for other economic and business practice research to be conducted to
revealing findings specifically for and about the non-oil and gas sectors of
Nigeria. Indeed there is now the need for further research investigating how
best to use FDI responsibly and beneficially to develop the most under
developed sectors of the Nigerian economy.
7.0 Summary and Conclusions
This research contributes and betters knowledge and practice in international
business, it provides the 8 critical success factors and 18 statistical predictions
for choice of entry mode to be considered in order to achieve a successful non-
oil & gas FDI project and entry mode into Africa’s largest economy and most
populous nation Nigeria. This research does this at a time when Africa
provides a significant part of opportunities for business growth worldwide,
and at a time when there is much realization among oil and gas revenue
dominant economies such as Nigeria of the need for the diversification and
transformation into vibrant multi sector/ industry revenue economies. The
findings from this research are important to Business practice across the
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world, as businesses increasingly seek out international-foreign markets and
decide on their most beneficial market entry modes, as they seek
opportunities for growth. This research reveals the statistical predictable
relationships between factors in Nigeria, such as infrastructure, Political
Stability, Size of Market, Government Support Services and the choice of
entry mode into the Nigerian market. It then goes on to determine and
present a set of significant statistical probabilities of outcome (or statistical
predictions) for choice of entry mode as determined by each and every one of
the critical success factors. These statistical predictions for the outcome of the
choice of entry mode serve as a new guide set for recommending the best
entry mode on a case by case basis to future potential foreign direct investors
for the Nigerian market. This research is unique and contributes new
knowledge to business practice, for aside from this research, there is no other
empirical study into the critical success factors for locating and operating a
non-oil & gas FDI company in Nigeria and there also is no other research that
presents a set of significant statistical probabilities of outcome for choice of
entry mode as determined by each and every one of the critical success
factors. This research is timely because its findings come at a time when
interest in Nigeria has meant that it receives the most FDI in Africa and
people such as Michael Andrew the Global Chairman, KPMG International,
assert that offers to invest in Nigeria are enormous and intense and that
people want to know how to do business in Nigeria and know how to access
the Nigerian market in the most beneficial ways for them. This research is also
functional and timely because leadership in Nigeria recognizes and reaffirms
their determination to do all within its powers to facilitate and encourage the
rapid diversification of Nigeria’s economy away from oil and gas
dependency. Therefore this research is also functional for the leadership in
Nigeria.
Participants in this research are the CEOs and managers in 30 FDI companies
that are located and have been operating for a minimum of 20 years in the
non-oil & gas sectors of Nigeria. The objective of such a sample is to sample a
body of people that have been and still are successful non-oil & gas FDI
managers in Nigeria. With a minimum experience of 20 years of doing
business in the Nigerian environment, such companies have succeeded
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through different governments, political and economic changes. A Quasi –
experimental research design is employed where non-random sampling is
executed and a Quantitative questionnaire method is used to obtain data. An
extensive review of established literature is employed here to obtain the initial
variables. Only variables that pass the correlation test undergo the statistical
factor analysis to determine the Critical Success Factors. All the variables/ in
the questionnaire pass Cronbach's alpha test for measure of internal
consistency or reliability. . A multi-nominal logistic regression is used to test
the hypotheses and determine the relationship between the CSFs, and the
dependant variable, which is “choice of entry mode” in order to reveal
statistical predictions for choice of entry mode that serves as a guide set for
recommending or deciding on successful entry modes into the Nigeria
market. This research tells the business practitioner how best to enter the
Nigerian market and the above-mentioned facts are just a few reasons why
this author’s research is critically important and timely.
8.0 Applying the research findings in a Case Study
Overview
This chapter applies the findings of the research to business practice. The 18
statistical predictions - probabilities of outcome for choice of successful entry
mode, revealed by this research, is applied to a company set up through FDI,
currently operating in Nigeria. The utility of the 18 statistical predictions in
recommending a successful Nigerian market entry mode is demonstrated and
revealed by applying the statistical predictions as a guide to the selected
company and arriving at a recommended successful Nigeria market entry
mode for the company. The successful Nigerian market entry mode
recommended as a result of applying the 18 statistical predictions as a guide,
is then compared to the actual successful market entry mode used by the
company into the Nigerian market. In the case where the entry mode
recommended and the entry mode actually used are the same for the
company, the utility of the findings from the research to business practice, is
therefore verified. In this chapter, the company to which the 18 statistical
predictions are applied to, is referred to as company X. Company X has not
been included as part of the sample used in the research. However, it
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complies with the research sampling criteria of having operated successfully
with its managers for over twenty years in the non-oil and gas sector of the
Nigerian market. The researcher conducts a semi – structed interview with
the decision maker or makers of company X. The 18 statistical predictions are
applied to the answers obtained from the interview as a guide in arriving at
the recommended successful Nigerian market entry mode for company X.
8.1 Introduction
The findings and conclusions from this research include 8 critical success
factors and the 18 statistical predictions - probabilities of outcome for choice
of entry mode, revealed by this research to serve as a guide set to CEOs,
managers or Consultants in making or recommending the best choice of entry
mode on a case-by-case basis. The entry mode finally chosen or recommended
is greatly influenced by the critical success factor agreed or determined by the
CEO/manager or consultant to be the most critical to the company. The
business practice application of this guide set in recommending or deciding
on successful entry modes to business practice is executed and demonstrated
in this chapter. An existing successful FDI Company X in Nigeria is here
selected. This company X chosen has not been included as part of the sample
used in the research above. However, it too complies with the research
sampling criteria of having operated with its managers for over twenty years
in the non-oil and gas sector of Nigeria.
Executing and demonstrating the 18 probabilities of outcome for entry mode
as a guide set, started with the researcher conducting a semi- structured
interview with the decision maker or makers of the given company. In this
case company X. In other words, the CEO and or decision-making managers
in company X are interviewed in a semi-structured form. A primary goal in
carrying out the semi – structured interview is to determine which of the 8
Critical Success Factors they consider most critical in the context of their
company’s success in Nigeria. The interview would also bring to bare their
understanding of the hierarchical importance for the other seven critical
success factors as determined according to the answers given by the
interviewee. The semi – structured interview format enables the interviewee
to make additional comments and contributions not necessarily as direct
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responses to a question asked. After the answers have been obtained in the
semi - structured interview, the 18 statistical predictions for choice of entry
mode as a guide, is then consulted and applied, the most suitable, fitting and
corresponding statistical prediction is chosen, and an entry mode into Nigeria
for the company is recommended or chosen. This chosen entry mode is then
compared to the actual entry mode used by company X into Nigeria. In so
doing, the business practice application of this research’s findings is thus
applied, demonstrated and accuracy verified.
8.2 Introducing Company X
Company X is an established and one of the leading Building Construction
companies in Nigeria. It entered as a sole venture in 1971 to Nigeria, from its
foreign origin base in Western Europe. Since 1970, company X has executed
and continues to execute several large/ costly construction projects especially
for the federal and state government authorities in Nigeria. These include
bridges, roads, buildings, and the like. Company X has also since expanded
into other related business activities such as the quarrying and polishing of
granite and marble for local building construction needs, as well as export
from Nigeria. Since 1970 company X has grown from a company with 100
permanent employees, to the company of over 13000 permanent employees
today with about three times as much non- permanent an auxiliary project
staff. According to the CEO, company X has always exceeded its profit
expectations by a minimum of 20 percent every year since 1976. Today and
for many years, company X has the reputation in Nigeria of delivering very
high quality built and constructed structures. As part of it corporate social
responsibility scheme, company X runs ambulance and firefighting services in
assistance to the local authority services.
Strengths
Company X understands and has over the years integrated into the local
culture, attributions, and practices in Nigeria. Its ability to do this has meant
that it has always enjoyed Political support through various governments’
authorities in Nigeria. This has translated to continuous patronage in terms of
lucrative contracts and special concessions. This has also meant that it has
fostered strong relationships and partnership with local banks and therefore
109
enjoys adequate project financing. The good internal leadership it enjoys and
its long stay in this market has translated to very valuable market experience
in Nigeria. Company X has been able to create the reputation of a company
that delivers quick responsiveness to its customer’s demands and high quality
constructed structures.
Weaknesses
Company X only delivers good quality jobs at high costs. This strategy,
structure and tradition means it operates almost exclusively with government
as its client, and is out of reach and not operating in the other strata’s of the
building construction market in Nigeria. If ever it losses government as it
client, it should affect it profits significantly.
Opportunities
The nature of the Nigeria building construction market means that company
X continues to operate in an environment where there are many opportunities
for further growth. This includes further expanding its lucrative granite and
marble quarry concessions. Company X could also expand along with many
Nigerian entrepreneurs and businesses into other countries in W. Africa.
Company X could also restructure its operations to include taking on more
new construction jobs from clients other than government. This can only
bring in more profits and growth.
Threats
In recent years, Chinese constructions companies have been making progress
into Africa. They have executed several significant construction projects for
governments across Africa including Nigeria. Nigeria remains an African
nation with the least Chinese construction presence. However, the
comparatively low cost bids for contracts from such Chinese construction
firms suggest a threat to company X. However, according to company X, it
continues to overcome such threat with its long time market experience in
Nigeria and its integration into the local culture, attributions, and practices in
Nigeria.
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8.3 The Semi Structured Interview
A 30 minutes face–to-face semi structured interview was agreed on and
executed by this researcher with the CEO of company X. The senior Vice
President “New Business”, manager legal affairs, and the manager research
and development, were invited to the interview by the CEO to be interviewed
collectively with him. He explained that these top managers formed the team
within company X that made decisions on expansion and probing into new
markets for jobs, and their collective response to the interview topics would
serve the purpose better. The researcher agreed, for the aim was to interview
the real decisions makers in company X for new markets and choice of entry
mode. In this way, responses to the interview topics would be collective and
in consensus from company x. For the interview questionnaire/ topics, see
appendix 04 (Semi structured interview questionnaire topics)
The primary goal for the semi – structured interview was to determine which
of the 8 Critical Success Factors determined by this research, the decisions
makers in company X collectively considered most critical in the context of
their company’s success in Nigeria. Another purpose for the interview was to
understand the hierarchical importance they determined for the other seven
critical success factors. The interview simulates the true scenario international
business strategy consultant – client interview in order to determine the most
beneficial new market entry mode to be recommended to the client. In this
way the business practice application of the new findings of this research is
demonstrated, and its accuracy verified. Therefore, a main advantage of using
the face-to-face survey is its appropriateness for covering complex issues that
may need detailed explanations. Flexibility to the interview is achieved by
using a semi-structured questionnaire with topics and not questions.
Clarifying questions and exploratory questions are easily applied within such
interviews.
8.4 The Interview Answers
The interview revealed the consensus answer that critical success factor 4
“Integrating into Local practices and Market pre-knowledge” was the most
critical success factor for company X. It was explained that integrating in to
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the Nigerian society, cultivating and fostering friendships with prominent
and influential members of the Nigerian society including government, was
critical to company X getting construction project to execute. It was also
critical in their succeeding in getting granite and marble concessions to
quarry. The Vice President – New Business expressed “one can get very many
Jobs here, but you must be seen as a local friend first…a friend with the
capability to execute the Job”. The rest of the interviewees agreed. The
interview also revealed Critical Success Factor 1, “Availability of Local Raw
Materials, Suppliers and Financing”, as the second most Critical Success
Factor to company’s X. It was explained in the interview that the readily
available basic natural raw materials for construction such as granite, marble,
lime stone, fine and coarse sands etc… in Nigeria was critical to the profits
company X makes in their construction projects and the quarrying, polishing
and exporting of such. The third most Critical Success Factor to company X,
was determined in the interview to be critical success factor 8 “Advantage of
Special Incentives and Concessions”. It was explained that granite and marble
quarrying, were concessions and licenses granted by the Nigeria government
to company X and represented a significant part of their success in Nigeria.
8.5 Applying the Guide Set/Probabilities of Outcome for Choice of Entry
Mode.
Critical Success Factor 4 “Integrating into Local practices and Market pre-
knowledge” determined as most critical of all the 8 critical success factors to
company X.
This research’s model has predicted in the Guide Set/18 statistical
Probabilities of Outcome for Choice of Entry Mode, that CEOs and Managers
of current FDI companies with a minimum of 20 years of operating
successfully in the Nigerian market, who determine- or agree that Integrating
into Local practices and Market pre-knowledge, is critical to their success in
the Nigeria market, are 77 times more likely to choose a sole venture as an
entry mode over licensing. The model does not make any other statistically
significant prediction for Critical Success Factor 4. Therefore at this stage, a
sole venture as an entry mode is an appropriate but tentative
recommendation. It is not always necessary, but is good for added support
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towards the final choice of entry mode entry recommended, to consider the
second and third most Critical Success Factors determined by the given
company, in this case company X.
Critical Success Factor 1 “Availability of Local Raw Materials, Suppliers and
Financing” and Critical Success Factor 8 “Advantage of Special Incentives and
Concessions” are determined as second and third most critical of all the 8
critical success factors to company X.
The research’s model has predicted in the Guide Set/18 statistical
Probabilities of Outcome for Choice of Entry Mode, that CEOs and Managers
of current FDI companies with a minimum of 20 years of operating
successfully in the Nigerian market, who determine or agree that Critical
Success Factor 1 “The Availability of Local Raw Materials, Suppliers and
Financing”, is critical or to their success in the Nigeria market, are 211 times
more likely to choose a sole venture as an entry mode over licensing. (They
are only 181 times more likely to choose a joint venture over licensing). The
research’s model has also predicted in the Guide Set that CEOs and Managers
of current FDI companies with a minimum of 20 years of operating
successfully in the Nigerian market, who agreed that Critical Success Factor 8
“Advantage of Special Incentives and Concessions”, is critical to their success
in the Nigerian market, are 96 times more likely to choose a sole venture as
an entry mode over licensing. (They are only 19 times more likely to choose a
joint venture over licensing).
8.6 The recommendation and comparison
Matching the three most Critical Success Factors determined by and for
company X in the interview to, this research’s statistical predictions for those
three Critical Success Factors shows us clearly that an entry mode of Sole
Venture is recommended for company X. When that company recalled X
actually entered the Nigerian Market in 1971 via a Sole Venture as an entry
mode. The usefulness, applicability and importance of the findings from this
research are here by applied in international business practice and verified.
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9.0 Ethics Ethics in research refers to the standard of proper conduct that dictates
research. According to Sekaran and Bougie (2009), Ethics in business research
are the principles of etiquette during carrying out research. There are a
number of principles dictating good conduct in carrying out research. Sekaran
and Bougie (2009) explain the main principles to be: 1. The confidentiality of
all participants. 2. Consent from all participants. 3. Clarity and honesty in
communicating the nature and purpose of the research to all participants. 4.
Respect for all participants and their opinions. 5. Consideration for ensuring
that no harm of any sought comes to anyone as a result of participating. 6.
Integrity and honesty in the reporting of results and findings.
Much care has been taken by the researcher to ensure that the entire research
has been conducted in the highest ethical standards. All participants in the
research, at any of its stages were made to understand that they were to
participate only if they wanted to. The confidentiality of all participants has
been kept and this was communicated in writing and verbally to all
participants during the process of administering the questionnaires or semi-
structured interview. All questionnaires and the semi-structured interview
were executed only after consent and the participant gave appointment. The
Nature and purpose of the research was clearly communicated to all
participants. A situation where the self-respect or self-esteem of any
participant could be hurt never arose. No harm came to anyone as a result of
participating in this research. There is absolutely no misrepresentation or
distortion in the reporting of results and findings in the research.
The researcher recognizes the fact that keeping the confidentiality and
anonymity of all the participants is also important to ensure that company
trade secrets and advantages are not unintentionally communicated to the
public as a consequence of their participating in the research. All opinions
and points of view encountered in the process of the research are respected.
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10.0 Appendices
Appendix 01: The questionnaire: QUESTIONNAIRE. To Be Filled Out By Managerial Staff Only. Please Answer All Questions. Note: “Critical” Implies That Your Company Would Not Be Successful In The Absence Of The Subject Matter Of The Question. Note: Guaranteed Anonymity: In Filling Out This Questionnaire Your Individuality Is Unknown To The Researcher, Your Company And All Concerned.
1. Age
2. Gender
3. Educational level (Tick The Highest Attained)
Below 35 46 -‐50 35 -‐ 45
51-‐55 Above 60 56-‐60
Male Female
Secondary School/ professional equivalent
Masters degree/professional equivalent
Bachelors Degree/professional equivalent
Doctorate Degree/professional equivalent
Other
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4. What is your current managerial level in your company?
5. In what sector of the Nigerian non-Oil & Gas economic sector would you place your place your company?
6. Which of the following best describes the entry mode chosen by your company into Nigeria? Joint Venture Licensing Sole Venture Exporting 7. Which of the following best describes the entry mode you would recommend if you were to enter Nigeria today? Joint Venture Licensing Sole Venture Exporting Please tick the option that best suits your experience on all the statements below
Agriculture Manufacturing & Production
Banking & finance
Telecom Mining Building & Construction
Trade/Goods export/import
Other
Lower Management
Top Management
Mid-‐level Management
Health care
Transport
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8. Political stability in Nigeria is critical to the success of your company in Nigeria.
AGREE STRONGLY AGREE
9. Stable FDI friendly Economic Policies in Nigeria are critical to the success of your company in Nigeria.
10. Security of Life & Property in Nigeria is critical to the success of your company. 11. Active government support services are critical to the success of your company in Nigeria.
12. Transparent enforcement of agreements & contracts in Nigeria are critical to the success of your company.
13. Respect for the rule of law in Nigeria is critical to the success of your company in Nigeria.
14. The Quality of basic infrastructure in Nigeria is critical to the success of your company.
DISAGREE NOT SURE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
STRONGLY DISGREE
117
15. A High return on investment is critical to the success of your company in Nigeria.
16. ECOWAS Trade Agreements are critical to the success of your company in Nigeria.
17. Africa Union trade Agreements are critical to the success of your company in Nigeria.
18. Nigeria’s Economic Growth is critical to the success of your company in Nigeria.
19. Local suppliers & contractors are critical to the success of your company in Nigeria.
20. Raw Materials Availability in Nigeria is critical to the success of your company in Nigeria
21. The Size of Nigeria’s Market is critical to the success of your company in Nigeria.
22. Pre -‐ Acquired knowledge of the Market in Nigeria was critical to the success of your company in Nigeria.
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
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23. Understanding and Integrating into Local Perceptions and Practices is critical to the success of your company in Nigeria.
24. The Size of Your companies Initial Investment was critical to the success of your company in Nigeria.
21. Cross cultural managerial capabilities of your company is critical to the success of your company in Nigeria.
25. The overall size of your company is critical to the success of your company in Nigeria.
26. The Cost of labor in Nigeria is critical to the success of your company in Nigeria
27. The Expandability to the West African market is critical to the success of your company in Nigeria
28. The Existence of Local Expertise in Nigeria is critical to the success of your company in Nigeria.
29. The Access to financing in Nigeria is critical to the success of your company in Nigeria.
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
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30. The A Bi-lateral trade agreement between Nigeria and another country is critical to the success of your company in Nigeria
31. Special Incentives and Concessions from the Nigeria government are critical to the success of your company in Nigeria
32. A Local partner in Nigeria is critical to the success of your company in Nigeria
33. The protection of your company’s know-how is critical to the success of your company in Nigeria
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
DISAGREE STRONGLY DISGREE
NOT SURE STRONGLY AGREE
AGREE
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Appendix 02 The Correlation Table. (Table 29) Too bulky for word document formatting.
Available in SPSS output format on request
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Appendix 03: Entry Mode Frequencies
Table: 31 ENTRYMODE FREQUENCIES
Frequency Percent Valid Percent Cumulative
Percent
1.00 156 37.7 37.7 37.7
2.00 188 45.4 45.4 83.1
3.00 67 16.2 16.2 99.3
4.00 3 .7 .7 100.0
Valid
Total 414 100.0 100.0
122
Appendix 04: Semi Structured Interview Questionnaire - Topics SEMI STRUCTURED INTERVIEW QUESTIONNAIRE - TOPICS For Face-to-Face Interviews with CEO and or Decision Making Managerial Team of Potential FDI Company for Nigeria Market. For the purpose of using the Guide Set of 21 statistical predictions for choice of market entry mode in recommending the most beneficial entry mode to potential FDI company. Participant/s must be assured of anonymity and that the interview is taking place only because their consent has been given. Answers Must be Received for All Questions / Topics. Note: “Critical” Implies That Your Company Would Not Be Successful In The Absence Of The Subject Matter Of The Question. Note: Guaranteed Anonymity: In Taking part in this interview, your company and Individuality Is known only to the Interviewer (Consultant). 1. Name of Company ………………………………… 2. Manager/s Interviewed ……………………………………………………………………………………. The 8 Critical Success factors for non – Oil and Gas FDI in Nigeria 1. The Availability of Local Raw Materials, Suppliers and Financing. 2. The Size of Initial Investment and Company 3. Taking advantage of ECOWAS and African Union Trade Agreements 4. Integrating into Local practices and Market pre-knowledge 5. Protecting of Company’s Knowhow 6. A Local Partner 7. Consideration for Quality of law Enforcement and state of infrastructure 8. The Advantage of Special Incentives and Concessions 3. From the 8 critical success factors listed above, which do you determine to be the most critical for your success in the Nigeria market and why? 4. Please arrange the other seven critical success factors in the order of their importance for the success of your company. 2nd…………………………… 3rd…………………………… 4th…………………………… 5th…………………………… 6th…………………………… 7th…………………………... 8th…………………………… 5. Give some explanation for why you have placed your 2nd and 3rd placed Critical Success Factors as such. Thank You.
123
Appendix 05: Figure 5. Africa: Top 5 recipients of FDI inflows, 2011 and 2012 (billions of US dollars) Source: UNCTAD, World Investment Report 2013.
124
Appendix 06: The parameters Estimates Table
125
Table 30: Parameter Estimates
95% Confidence Interval for
Exp(B)
ENTRYMODEa B Std. Error Wald df Sig. Exp(B)
Lower Bound Upper Bound
Intercept -35.078 1999.000 .000 1 .986
[FACTOR1=1.33] 24.763 380.112 .004 1 .948 56794485305
.747
.000 .b
[FACTOR1=808944768
1004E2.00]
5.984 369.493 .000 1 .987 397.210 .000 .b
[FACTOR1=2.33] 10.844 291.871 .001 1 .970 51213.955 1.000E-013 1.413E+253
[FACTOR1=2.67] 5.810 2.296 6.401 1 .011 333.640 3.703 30061.432
[FACTOR1=3.00] .695 2.571 .073 1 .787 2.003 .013 309.216
[FACTOR1=3.33] 9.245 3.276 7.963 1 .005 10351.554 16.842 6362337.711
[FACTOR1=3.67] -2.132 2.249 .899 1 .343 .119 .001 9.740
[FACTOR1=4.00] 5.198 1.831 8.058 1 .005 180.827 4.997 6543.347
[FACTOR1=4.33] -.501 1.380 .132 1 .717 .606 .041 9.066
[FACTOR1=4.67] -1.864 1.393 1.790 1 .181 .155 .010 2.379
[FACTOR1=5.00] 0c . . 0 . . . .
[FACTOR2=1.00] .448 3.895 .013 1 .908 1.565 .001 3239.302
[FACTOR2=1.50] -21.588 20.116 1.152 1 .283 4.212E-010 1.000E-013 55834698.91
4
[FACTOR2=2.00] -16.903 4.049 17.427 1 .000 4.563E-008 1.642E-011 .000
[FACTOR2=2.50] -8.351 2.325 12.903 1 .000 .000 2.478E-006 .022
[FACTOR2=3.00] -6.744 1.860 13.143 1 .000 .001 3.073E-005 .045
[FACTOR2=3.50] -5.029 1.745 8.304 1 .004 .007 .000 .200
[FACTOR2=4.00] -4.027 1.595 6.370 1 .012 .018 .001 .407
[FACTOR2=4.50] -2.433 1.582 2.366 1 .124 .088 .004 1.949
[FACTOR2=5.00] 0c . . 0 . . . .
[FACTOR3=1.00] 4.078 125.238 .001 1 .974 59.014 1.000E-013 2.365E+108
[FACTOR3=1.50] -20.423 370.574 .003 1 .956 1.350E-009 .000 3.658E+306
[FACTOR3=2.00] 2.767 5.011 .305 1 .581 15.918 .001 292984.156
[FACTOR3=2.50] -3.204 2.450 1.710 1 .191 .041 .000 4.946
[FACTOR3=3.00] 5.858 2.456 5.688 1 .017 349.877 2.840 43097.328
[FACTOR3=3.50] 2.308 2.100 1.208 1 .272 10.050 .164 615.722
[FACTOR3=4.00] 6.856 2.116 10.493 1 .001 949.338 14.994 60106.418
[FACTOR3=4.50] 1.970 2.298 .735 1 .391 7.170 .079 648.516
[FACTOR3=5.00] 0c . . 0 . . . .
[FACTOR4=1.00] -12.953 369.484 .001 1 .972 2.368E-006 .000 .b
1.00
[FACTOR4=1.50] 9.222 163.708 .003 1 .955 10121.054 1.000E-013 2.259E+143
126
[FACTOR4=2.00] 12.626 70.695 .032 1 .858 304508.196 1.000E-013 4.566E+065
[FACTOR4=2.50] -7.997 3.597 4.944 1 .026 .000 2.922E-007 .388
[FACTOR4=3.00] -3.442 1.784 3.720 1 .054 .032 .001 1.057
[FACTOR4=3.50] -1.247 1.543 .653 1 .419 .287 .014 5.913
[FACTOR4=4.00] .950 1.565 .369 1 .544 2.587 .120 55.605
[FACTOR4=4.50] -.164 1.464 .013 1 .911 .849 .048 14.960
[FACTOR4=5.00] 0c . . 0 . . . .
[FACTOR5=1.00] -28.520 119.581 .057 1 .811 5.110E-013 1.000E-013 2.521E+089
[FACTOR5=2.00] 3.844 2.534 2.302 1 .129 46.726 .326 6703.729
[FACTOR5=3.00] 7.524 2.558 8.648 1 .003 1851.569 12.296 278818.162
[FACTOR5=4.00] -.491 1.203 .167 1 .683 .612 .058 6.471
[FACTOR5=5.00] 0c . . 0 . . . .
[FACTOR6=1.00] -4.649 2.795 2.766 1 .096 .010 3.994E-005 2.293
[FACTOR6=2.00] -6.297 2.042 9.505 1 .002 .002 3.364E-005 .101
[FACTOR6=3.00] .174 1.271 .019 1 .891 1.190 .098 14.378
[FACTOR6=4.00] -2.839 1.240 5.241 1 .022 .059 .005 .665
[FACTOR6=5.00] 0c . . 0 . . . .
[FACTOR7=1.00] -17.991 369.464 .002 1 .961 1.536E-008 .000 4.731E+306
[FACTOR7=1.33] -6.191 1998.827 .000 1 .998 .002 .000 .b
[FACTOR7=2.00] -10.537 3.208 10.786 1 .001 2.654E-005 4.931E-008 .014
[FACTOR7=2.33] 27.606 396.409 .005 1 .944 97549246690
8.572
.000 .b
[FACTOR7=2.67] -9.326 3.868 5.815 1 .016 8.906E-005 4.545E-008 .175
[FACTOR7=3.00]
9.331 28.675 .106 1 .745 11286.597 1.000E-013 28912910124
10756000000
0000000.000
[FACTOR7=3.33] -7.122 2.523 7.968 1 .005 .001 5.748E-006 .113
[FACTOR7=3.67] -2.642 1.823 2.101 1 .147 .071 .002 2.536
[FACTOR7=4.00] -5.460 1.554 12.350 1 .000 .004 .000 .089
[FACTOR7=4.33] -2.070 1.363 2.307 1 .129 .126 .009 1.825
[FACTOR7=4.67] -5.670 1.619 12.262 1 .000 .003 .000 .082
[FACTOR7=5.00] 0c . . 0 . . . .
[FACTOR8=1.00] 5.962 1.582 14.207 1 .000 388.305 17.492 8619.853
[FACTOR8=2.00] 5.501 1.681 10.704 1 .001 244.861 9.074 6607.432
[FACTOR8=3.00] 2.711 1.685 2.588 1 .108 15.037 .553 408.614
[FACTOR8=4.00] 2.926 1.154 6.425 1 .011 18.660 1.942 179.307
[FACTOR8=5.00] 0c . . 0 . . . .
[SECTOR=1.00] 31.091 40.194 .598 1 .439 31818864921
288.950
1.000E-013 5.197E+047
127
[SECTOR=2.00] 22.654 40.140 .319 1 .573 6891435689.
459
1.000E-013 1.013E+044
[SECTOR=3.00]
43.544 40.523 1.155 1 .283 81431280906
95284700.00
0
1.003E-013 2.534E+053
[SECTOR=4.00] 22.599 40.137 .317 1 .573 6524896601.
849
1.000E-013 9.532E+043
[SECTOR=5.00]
70.786 52.397 1.825 1 .177 55208557652
20307000000
000000000.0
00
1.139E-013 2.198E+075
[SECTOR=6.00]
72.821 84.468 .743 1 .389 42257474188
61126000000
0000000000.
000
1.000E-013 3.351E+103
[SECTOR=7.00] 28.820 40.190 .514 1 .473 32851326918
42.004
1.000E-013 5.328E+046
[SECTOR=10.00] 0c . . 0 . . . .
[LEVEL=1.00] -3.597 1.827 3.878 1 .049 .027 .001 .983
[LEVEL=2.00] -3.116 1.616 3.717 1 .054 .044 .002 1.053
[LEVEL=3.00] 0c . . 0 . . . .
[AGE=1.00] 13.616 1998.595 .000 1 .995 818944.302 .000 .b
[AGE=2.00] 13.528 1998.595 .000 1 .995 750129.952 .000 .b
[AGE=3.00] 10.189 1998.595 .000 1 .996 26598.430 .000 .b
[AGE=4.00] 4.245 1998.619 .000 1 .998 69.733 .000 .b
[AGE=5.00] 1.652 2821.382 .000 1 1.000 5.216 .000 .b
[AGE=6.00] 0c . . 0 . . . .
Intercept -12.059 1344.176 .000 1 .993
[FACTOR1=1.33] 12.799 741.556 .000 1 .986 361917.365 .000 .b
[FACTOR1=808944768
1004E2.00]
34.505 567.603 .004 1 .952 96685212546
4154.600
.000 .b
[FACTOR1=2.33] 34.910 227.553 .024 1 .878 14495231556
44615.000
1.000E-013 7.156E+208
[FACTOR1=2.67] 5.620 5.788 .943 1 .332 275.857 .003 23317966.35
1
[FACTOR1=3.00] 3.570 2.852 1.567 1 .211 35.534 .133 9520.668
[FACTOR1=3.33] -.535 92.189 .000 1 .995 .586 1.000E-013 1.736E+078
[FACTOR1=3.67] -.322 2.691 .014 1 .905 .725 .004 141.558
[FACTOR1=4.00] 5.352 2.046 6.842 1 .009 211.058 3.825 11644.668
[FACTOR1=4.33] -4.220 1.953 4.672 1 .031 .015 .000 .675
3.00
[FACTOR1=4.67] -.493 1.523 .105 1 .746 .611 .031 12.084
128
[FACTOR1=5.00] 0c . . 0 . . . .
[FACTOR2=1.00] -5.801 4.552 1.624 1 .203 .003 4.039E-007 22.666
[FACTOR2=1.50] -12.593 3.478 13.107 1 .000 3.397E-006 3.719E-009 .003
[FACTOR2=2.00] -19.220 4.825 15.865 1 .000 4.497E-009 4.511E-013 5.758E-005
[FACTOR2=2.50] -19.222 4.632 17.221 1 .000 4.488E-009 6.121E-013 3.933E-005
[FACTOR2=3.00] -10.033 2.543 15.572 1 .000 4.392E-005 3.010E-007 .006
[FACTOR2=3.50] -9.887 2.664 13.772 1 .000 5.083E-005 2.744E-007 .009
[FACTOR2=4.00] -6.064 2.217 7.482 1 .006 .002 3.015E-005 .179
[FACTOR2=4.50] -4.563 2.207 4.274 1 .039 .010 .000 .789
[FACTOR2=5.00] 0c . . 0 . . . .
[FACTOR3=1.00] -11.132 302.988 .001 1 .971 1.463E-005 1.000E-013 1.172E+253
[FACTOR3=1.50] -34.298 564.493 .004 1 .952 1.013E-013 .000 .b
[FACTOR3=2.00] 2.824 5.403 .273 1 .601 16.850 .000 669681.928
[FACTOR3=2.50] -2.987 3.018 .980 1 .322 .050 .000 18.670
[FACTOR3=3.00] 3.494 2.927 1.425 1 .233 32.925 .106 10217.259
[FACTOR3=3.50] 2.316 2.616 .783 1 .376 10.132 .060 1708.897
[FACTOR3=4.00] 1.670 2.828 .349 1 .555 5.310 .021 1356.183
[FACTOR3=4.50] .297 2.860 .011 1 .917 1.346 .005 366.075
[FACTOR3=5.00] 0c . . 0 . . . .
[FACTOR4=1.00] -8.583 571.250 .000 1 .988 .000 .000 .b
[FACTOR4=1.50] 5.637 280.885 .000 1 .984 280.583 1.000E-013 3.452E+241
[FACTOR4=2.00] 21.442 70.736 .092 1 .762 2051692756.
853
1.000E-013 3.331E+069
[FACTOR4=2.50] -2.219 4.159 .285 1 .594 .109 3.136E-005 377.236
[FACTOR4=3.00] -3.556 2.216 2.575 1 .109 .029 .000 2.198
[FACTOR4=3.50] .927 2.134 .189 1 .664 2.527 .039 165.664
[FACTOR4=4.00] 4.340 2.131 4.149 1 .042 76.691 1.178 4992.028
[FACTOR4=4.50] -2.595 2.122 1.496 1 .221 .075 .001 4.774
[FACTOR4=5.00] 0c . . 0 . . . .
[FACTOR5=1.00] -28.469 229.813 .015 1 .901 5.326E-013 1.000E-013 1.792E+183
[FACTOR5=2.00] 6.392 2.827 5.114 1 .024 597.018 2.344 152060.636
[FACTOR5=3.00] 5.900 2.712 4.734 1 .030 364.917 1.795 74180.186
[FACTOR5=4.00] .054 1.396 .001 1 .969 1.055 .068 16.263
[FACTOR5=5.00] 0c . . 0 . . . .
[FACTOR6=1.00] -29.548 225.672 .017 1 .896 2.470E-013 1.000E-013 1.817E+179
[FACTOR6=2.00] -5.092 2.301 4.897 1 .027 .006 6.755E-005 .559
[FACTOR6=3.00] -1.794 1.692 1.125 1 .289 .166 .006 4.581
[FACTOR6=4.00] -4.633 1.471 9.918 1 .002 .010 .001 .174
[FACTOR6=5.00] 0c . . 0 . . . .
[FACTOR7=1.00] -12.784 563.759 .001 1 .982 2.804E-006 .000 .b
[FACTOR7=1.33] 25.221 1371.373 .000 1 .985 89830870241
.173
.000 .b
[FACTOR7=2.00] -11.203 8.932 1.573 1 .210 1.364E-005 4.405E-013 546.255
129
[FACTOR7=2.33] -4.690 612.957 .000 1 .994 .009 .000 .b
[FACTOR7=2.67] -22.061 125.502 .031 1 .860 2.626E-010 1.000E-013 1.763E+097
[FACTOR7=3.00]
15.830 28.784 .302 1 .582 7500328.376 1.000E-013 23753579050
96051000000
0000000000.
000
[FACTOR7=3.33] -39.515 65.784 .361 1 .548 1.000E-013 1.000E-013 6.833E+038
[FACTOR7=3.67] 2.280 2.399 .903 1 .342 9.773 .089 1077.014
[FACTOR7=4.00] -4.232 1.805 5.498 1 .019 .015 .000 .499
[FACTOR7=4.33] -3.385 1.850 3.349 1 .067 .034 .001 1.272
[FACTOR7=4.67] -3.544 1.980 3.203 1 .074 .029 .001 1.401
[FACTOR7=5.00] 0c . . 0 . . . .
[FACTOR8=1.00] 6.373 1.871 11.599 1 .001 586.000 14.962 22951.889
[FACTOR8=2.00] 4.930 1.986 6.159 1 .013 138.328 2.819 6788.494
[FACTOR8=3.00] 5.425 2.015 7.247 1 .007 227.000 4.372 11786.296
[FACTOR8=4.00] 4.562 1.582 8.319 1 .004 95.810 4.315 2127.606
[FACTOR8=5.00] 0c . . 0 . . . .
[SECTOR=1.00] 23.828 56.567 .177 1 .674 22297180594
.072
1.000E-013 3.150E+058
[SECTOR=2.00] 13.134 56.508 .054 1 .816 506000.281 1.000E-013 6.360E+053
[SECTOR=3.00] 22.976 97.600 .055 1 .814 9514338910.
781
1.000E-013 1.137E+093
[SECTOR=4.00] -2.539 73.049 .001 1 .972 .079 1.000E-013 1.192E+061
[SECTOR=5.00]
65.684 65.806 .996 1 .318 33590487673
93940000000
0000000.000
1.000E-013 3.469E+084
[SECTOR=6.00]
64.947 93.381 .484 1 .487 16067682740
52592200000
0000000.000
1.000E-013 4.918E+107
[SECTOR=7.00] 25.512 56.556 .203 1 .652 12019207701
9.126
1.000E-013 1.661E+059
[SECTOR=10.00] 0c . . 0 . . . .
[LEVEL=1.00] -7.155 2.090 11.722 1 .001 .001 1.299E-005 .047
[LEVEL=2.00] -4.433 1.714 6.689 1 .010 .012 .000 .342
[LEVEL=3.00] 0c . . 0 . . . .
[AGE=1.00] .915 1342.987 .000 1 .999 2.497 .000 .b
[AGE=2.00] -.025 1342.987 .000 1 1.000 .976 .000 .b
[AGE=3.00] -1.867 1342.986 .000 1 .999 .155 .000 .b
[AGE=4.00] -10.319 1343.022 .000 1 .994 3.302E-005 .000 .b
[AGE=5.00] 3.666 1891.733 .000 1 .998 39.109 .000 .b
[AGE=6.00] 0c . . 0 . . . .
4.00 Intercept -24.125 7608.091 .000 1 .997
130
[FACTOR1=1.33] 30.903 3040.868 .000 1 .992 26357366756
552.140
.000 .b
[FACTOR1=808944768
1004E2.00]
26.408 2671.435 .000 1 .992 29447776359
7.308
.000 .b
[FACTOR1=2.33] 26.170 228.760 .013 1 .909 23200307886
8.965
1.000E-013 1.221E+206
[FACTOR1=2.67] -4.427 142.347 .001 1 .975 .012 1.000E-013 1.751E+119
[FACTOR1=3.00] 1.209 119.132 .000 1 .992 3.348 1.000E-013 8.511E+101
[FACTOR1=3.33] 11.953 98.190 .015 1 .903 155267.089 1.000E-013 5.892E+088
[FACTOR1=3.67] 10.989 66.268 .027 1 .868 59228.398 1.000E-013 1.514E+061
[FACTOR1=4.00] 6.074 60.578 .010 1 .920 434.458 1.000E-013 1.592E+054
[FACTOR1=4.33] 1.356 64.358 .000 1 .983 3.882 1.000E-013 2.350E+055
[FACTOR1=4.67] -2.950 57.296 .003 1 .959 .052 1.000E-013 3.084E+047
[FACTOR1=5.00] 0c . . 0 . . . .
[FACTOR2=1.00] -12.696 120.309 .011 1 .916 3.062E-006 1.000E-013 7.817E+096
[FACTOR2=1.50] -17.027 88.660 .037 1 .848 4.031E-008 1.000E-013 1.182E+068
[FACTOR2=2.00] -17.360 93.505 .034 1 .853 2.888E-008 1.000E-013 1.127E+072
[FACTOR2=2.50] -16.864 85.995 .038 1 .845 4.743E-008 1.000E-013 7.503E+065
[FACTOR2=3.00] -12.180 82.607 .022 1 .883 5.131E-006 1.000E-013 1.060E+065
[FACTOR2=3.50] -12.308 68.461 .032 1 .857 4.516E-006 1.000E-013 8.488E+052
[FACTOR2=4.00] -8.666 55.128 .025 1 .875 .000 1.000E-013 1.451E+043
[FACTOR2=4.50] -9.126 63.603 .021 1 .886 .000 1.000E-013 1.497E+050
[FACTOR2=5.00] 0c . . 0 . . . .
[FACTOR3=1.00] 24.900 267.160 .009 1 .926 65155929483
.869
1.000E-013 1.664E+238
[FACTOR3=1.50] 16.059 2691.875 .000 1 .995 9429713.470 .000 .b
[FACTOR3=2.00] 2.286 162.017 .000 1 .989 9.837 1.000E-013 7.974E+138
[FACTOR3=2.50] 2.211 118.763 .000 1 .985 9.121 1.000E-013 1.125E+102
[FACTOR3=3.00] 15.263 88.364 .030 1 .863 4253954.769 1.000E-013 6.994E+081
[FACTOR3=3.50] 9.612 106.347 .008 1 .928 14942.198 1.000E-013 4.976E+094
[FACTOR3=4.00] 13.570 82.700 .027 1 .870 782382.401 1.000E-013 1.938E+076
[FACTOR3=4.50] 12.923 91.334 .020 1 .887 409664.368 1.000E-013 2.269E+083
[FACTOR3=5.00] 0c . . 0 . . . .
[FACTOR4=1.00] -5.648 2667.855 .000 1 .998 .004 .000 .b
[FACTOR4=1.50] 25.848 307.578 .007 1 .933 16820627682
8.001
1.000E-013 1.089E+273
[FACTOR4=2.00] 27.278 111.440 .060 1 .807 70280833061
1.906
1.000E-013 5.065E+106
[FACTOR4=2.50] 1.193 107.213 .000 1 .991 3.297 1.000E-013 5.997E+091
131
[FACTOR4=3.00] 6.477 68.471 .009 1 .925 649.967 1.000E-013 1.247E+061
[FACTOR4=3.50] 4.657 69.913 .004 1 .947 105.328 1.000E-013 3.412E+061
[FACTOR4=4.00] 4.472 70.063 .004 1 .949 87.547 1.000E-013 3.805E+061
[FACTOR4=4.50] 2.840 81.747 .001 1 .972 17.108 1.000E-013 6.550E+070
[FACTOR4=5.00] 0c . . 0 . . . .
[FACTOR5=1.00] -34.572 16391.493 .000 1 .998 1.010E-013 .000 .b
[FACTOR5=2.00] -4.656 112.722 .002 1 .967 .010 1.000E-013 8.448E+093
[FACTOR5=3.00] -3.512 80.428 .002 1 .965 .030 1.000E-013 8.613E+066
[FACTOR5=4.00] -1.290 49.129 .001 1 .979 .275 1.000E-013 1.813E+041
[FACTOR5=5.00] 0c . . 0 . . . .
[FACTOR6=1.00] -14.226 171.739 .007 1 .934 6.631E-007 1.000E-013 1.015E+140
[FACTOR6=2.00] -6.529 60.469 .012 1 .914 .001 1.000E-013 4.324E+048
[FACTOR6=3.00]
-4.304 42.575 .010 1 .919 .014 1.000E-013 23488069084
01325000000
00000000000
00.000
[FACTOR6=4.00]
-6.557 38.200 .029 1 .864 .001 1.000E-013 46593561738
27935000000
00000000.00
0
[FACTOR6=5.00] 0c . . 0 . . . .
[FACTOR7=1.00] -4.996 2666.100 .000 1 .999 .007 .000 .b
[FACTOR7=1.33] -25.634 .000 . 1 . 7.464E-012 7.464E-012 7.464E-012
[FACTOR7=2.00] -5.439 173.711 .001 1 .975 .004 1.000E-013 3.169E+145
[FACTOR7=2.33] 18.726 16607.001 .000 1 .999 135714192.9
93
.000 .b
[FACTOR7=2.67] -9.918 191.130 .003 1 .959 4.926E-005 1.000E-013 2.414E+158
[FACTOR7=3.00] -8.954 305.663 .001 1 .977 .000 1.000E-013 1.959E+256
[FACTOR7=3.33] -13.216 144.314 .008 1 .927 1.822E-006 1.000E-013 1.261E+117
[FACTOR7=3.67] -.303 85.531 .000 1 .997 .739 1.000E-013 4.711E+072
[FACTOR7=4.00]
-11.494 43.543 .070 1 .792 1.019E-005 1.000E-013 11814347239
49141000000
00000000000
.000
[FACTOR7=4.33] -5.085 60.463 .007 1 .933 .006 1.000E-013 1.809E+049
[FACTOR7=4.67] -4.008 75.071 .003 1 .957 .018 1.000E-013 1.445E+062
[FACTOR7=5.00] 0c . . 0 . . . .
[FACTOR8=1.00] -.795 60.947 .000 1 .990 .452 1.000E-013 3.411E+051
[FACTOR8=2.00] -1.646 61.182 .001 1 .979 .193 1.000E-013 2.308E+051
132
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