CONTEMPORARY CONCERNS STUDY

DETERMINING MODE OF ENTRY OF MULTINATIONALS: AN ANALYTICAL FRAMEWORK

Under the guidance ofProf. V. Ravi Anshuman

By

Hemant ChandrasekaranHemant Chandrasekaran(0511088)(0511088)Dharini Kannan Dharini Kannan (0511013)(0511013)

Submitted OnSubmitted On29TH AUGUST, 2006

INDIAN INSTITUTE OF MANAGEMENT, BANGALORE

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TABLE OF CONTENTSEXECUTIVE SUMMARY................................................3EMERGING MARKETS- EPICENTRE OF GROWTH.......................4INDIA- AN ATTRACTIVE DESTINATION FOR MNCS.....................5MODES OF ENTRY OF MULTINATIONALS.................................5

EXPORT........................................................................................................................5LICENSING.................................................................................................................6JOINT VENTURE......................................................................................................6WHOLLY OWNED SUBSIDIARY........................................................................6ACQUISITION............................................................................................................7GREENFIELD VENTURES...................................................................................7

LITERATURE SURVEY....................................................7RISK VS CONTROL ISSUES2...............................................................................8RESOURCE BASED VIEW FOR ENTRY MODE ISSUES........................10PRINCIPAL-AGENT THEORY IMPLICATIONS3.........................................11

LOGISTIC REGRESSION...............................................12STUDIES OF EARLIER DEVELOPED LOGISTIC REGRESSION MODELS....................................................................................................................13

1. Model 15.........................................................................................................132. Model 24.........................................................................................................143. Model 3...........................................................................................................154. Model 4...........................................................................................................15

BUILDING A LOGISTIC REGRESSION MODEL...........16DATA COLLECTION..............................................................................................17CASE STUDY ANALYSES....................................................................................17

QUANTITATIVE ANALYSIS..........................................19STEP 1: CORRELATION BETWEEN INDEPENDENT VARIABLES.. .19STEP 2: DATA REDUCTION USING PRINCIPAL COMPONENT ANALYSIS..................................................................................................................19STEP 3: MUTLINOMIAL LOGISTIC REGRESSION ON REDUCED DATA SET..................................................................................................................24

OUTPUT INTERPRETATION:........................................................................24TEST RESULTS...................................................................................................25

MODEL 1: WITH LOCATION OF ORIGIN VARIABLE U/E/J.........25MODEL 2: WITHOUT LOCATION OF ORIGIN VARIABLE U/E/J...............27

STEP 4: DISCRIMINANT ANALYSIS..............................................................28WHY DISCRIMINANT?....................................................................................28OUTPUT INTERPRETATION:........................................................................29TEST RESULTS...................................................................................................30

MODEL 1: WITH INCLUSION OF VARIABLE U/E/J........................30MODEL 2: DISCRIMINANT ANALYSIS WITHOUT THE U/E/J VARIABLE..........................................................................................................33

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PERCEPTUAL MAP.......................................................34DEVELOPMENT OF AN ANALYTICAL FRAMEWORK............................36

CONCLUSION..............................................................38ADDITIONAL REFERENCES.........................................39APPENDIX..................................................................40

EXECUTIVE SUMMARY

In recent years, several developing economies have seen a burst in growth, partly fuelled by Foreign Direct Investment or FDI flows. Post liberalization, several MNCs have been entering India. An MNC may enter through one of several modes of entry available to them, including Joint Ventures, Acquisitions, Wholly Owned Subsidiaries, Licensing route, etc. The decision of entry mode is governed by several factors.

This paper looks at several strategic theories governing entry modes and a few models developed in the past. Based on these, the paper attempts to develop a statistical model for predicting the entry mode of an MNC into India. A unique set of variables is identified on the basis of these, including a new variable, namely the country of origin (USA/Europe/Japan-Asia). These have been used in further statistical analysis.

Factor analysis using Principal Component Analysis has been done to reduce the variable set and combine certain relevant variables. This was then used to prediction models using Multinomial Logistic regression. Another statistical model, using Discriminant analysis, to classify a particular entry as one of the entry modes, has also been developed. This approach has not been attempted before in previous literature. The model was found to have a good predictive power

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compared to random selection. The model was also validated by applying it to a holdout sample. The prediction of the holdout sample was also found to be significant.

The results of the discriminant analysis, have been used to develop a 2-by-2 framework,

which captures the entry mode decision of a multinational based on 2 fundamental

factors. These have been identified to be the Risk-Return balance and the amount of

Local Resources needed (alternately, the amount of resources needed to be brought in

by the MNC). Though these two factors have never been combined before in previous

literature, it matches with earlier findings well.

EMERGING MARKETS- EPICENTRE OF GROWTH

As the markets in developed countries gradually mature, companies in these countries are looking for new avenues of growth. Emerging markets, particularly the BRIC block (Brazil, Russia, India, and China) offer huge potential for growth. The rapid globalization has triggered most of the companies in developed nations to look at these countries seriously.

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Source: JP Morgan Asia Pacific Equity Research Report, 07 March 2006, Adrian Mowat, Joanne Goh, Steve Malin

Other factors in these emerging markets like availability of cheap labour, huge size of potential market and the urge to catch up with the developed world are conducive to the entry of foreign multinationals into these countries.

INDIA- AN ATTRACTIVE DESTINATION FOR MNCSIndia in particular offers a huge target market owing to the rising middle class and has posted a GDP growth rate of 6.5% p.a.1 CAGR 1 http://meaindia.nic.in/indiapublication/The%20Indian%20economy.htm

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over the last decade. This makes it an attractive destination for multinational companies. Until 1991, the stiff regulatory framework, government imposed restrictions in favour of local companies, etc made it virtually impossible for MNCs to set shop in India. Following the liberalization of the economy, the markets have opened up for the MNCs. Most of the restrictions have been relaxed and the market as a whole has moved more towards free market economics. While this has spelled doom for some of the local companies, overall the competitiveness in the market has improved.

MNCs often tend to leverage on the technical expertise and brand value they are able to bring in. But several industries often require local support infrastructures. A combination of all these factors and many more ultimately determines the mode of entry of a multinational into India.

MODES OF ENTRY OF MULTINATIONALS

Mode of entry is the way in which a multinational decides to start its business in India. Selecting this institutional arrangement is one of the most crucial decisions for an MNC. An MNC has to ensure that its mode of entry is in line with its long term strategies and enables it to gain a competitive advantage in the target country by leveraging on its strengths. There are several modes of entry by which an MNC may enter India, as discussed below2:

2 Selecting international modes of entry and expansion, Gregory E Osland, Charles R Taylor, Shaoming Zou

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EXPORTIn export oriented entry, the manufacturing of the final product is done outside the destination country and finished goods are transported to the country. Indirect exports via intermediaries may also be done. E.g.: Boeing is a prime example of a major exporter. While the aeroplanes are manufactured in USA, most of is flights are sold to other countries.

LICENSING

This mode of entry is based on a contractual agreement and does not involve equity investment. The MNC transfers the right to use its patents, intellectual property, trademarks, technology, business methods, etc to a local company in the destination country for a fee. This fee could include an initial payment and subsequent payments based on annual earnings. (Actual structuring of the contract could vary from one company to another)

E.g. Most MNCs entering the retail segment have entered through the licensing route.

JOINT VENTURE

This involves collaboration with a local partner to share ownership, resources, business risks, management techniques, etc. Each partner brings in unique capabilities into the business, which together gives the joint venture the ability to build on these synergies and develop a competitive advantage. A company maybe forced to enter through this

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mode if the industry is not foully liberalized and the MNC has to have an Indian partner.

E.g. Allianz insurance formed a joint venture with Bajaj to form Bajaj-Allianz insurance, in which it has a 26% equity stake.

WHOLLY OWNED SUBSIDIARY

These are subsidiary of the parent company in the destination company, of which they have complete ownership. Thus the parent company has sole responsibility of the risks and returns associated with the business.

E.g.: When the electronics manufacturer Samsung entered India, it did so by setting up a wholly owned subsidiary, Samsung India Pvt. Ltd.

ACQUISITION

In this mode of entry, the parent company acquires an existing company in the destination country. This provides it access to all the local resources that a local company might enjoy, while reducing the hassles and time involved in setting up a WOS. The downside could be that government regulations might be very stringent in approving acquisitions, but this has been declining with increasing liberalization.

GREENFIELD VENTURES

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This involves an investment by a multinational in a project in the destination country. As in the case of a WOS, all the resources are invested by the parent company. This is particularly common in case of highly explorative R&D as in the case of oil drilling, etc. The disadvantage with this mode of entry is that it might take a long time to develop all the resources, both tangible and intangible.

LITERATURE SURVEY

The motivation for our study comes from the quest for an answer to the question to the factors determining entry modes of an MNC into an emerging market, particularly India, and developing an empirical model to predict the same. Several papers have attempted to identify these factors. The essence derived from these papers and their implications for developing a prediction model are discussed below.

RISK VS CONTROL ISSUESError: Reference source not found

This paper discusses the differences in modes of entry between countries based in US and Japan. As identified from previous research (Maignan and Lukas, 1997; Woodstock et al 1994) mode of entry is believed to be governed by three factors:

1. quantity of resource commitment required2. amount of control3. level of risk

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Resource commitments are investments in dedicated assets which cannot be put to alternative use. These may be tangibles, like machinery, plants, distribution infrastructure, etc. or intangibles in the form of managerial skill, distribution network and contacts, etc.

Figure 1

Source: Selecting international modes of entry and expansion, Gregory E Osland, Charles R Taylor, Shaoming Zou

As seen from the illustration above, export involves very little resource commitment while WOS require a high level of resource commitment.

The level of control is the extent to which the parent company wants to be involved in the day-to-day decisions and long term strategies of the operations in destination country. While a license agreement often relinquishes most of the control to the local licensee, control is shared in a JV based on the extent of equity investment. A WOS retains all the control with the parent company itself as seen in figure 1.

Technology risk refers to the possibility that the parent company’s knowledge may be transferred to a local firm. These are often

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referred to as “bleedthroughs”. In a license agreement, the risk that the licensee may end the contract and reproduce the technology on its own is fairly high. WOS has the least technology risk, as no external partners are involved.

Figure 2

Source: Selecting international modes of entry and expansion, Gregory E Osland, Charles R Taylor, Shaoming Zou

Some of the additional factors could include target market issues- competition, host government involvement, culture, partner availability, etc, company level issues- organization experience, resource needs, strategy. JVs offer some advantages over a WOS:

greater freedom for promotional activities lower taxes fewer government inspections more infrastructure support easier access to local government and institutions

While the other modes of entry involve lower levels of risk, the level of risk associated with WOS is very high, but the level of control that can

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be realised is also the highest. Hence a choice of entry mode is determined by the parent company’s risk appetite vs. need for control.

RESOURCE BASED VIEW FOR ENTRY MODE ISSUES3

The paper deals with the analysis of entry modes from a resource based view and developing an empirical model. The resource based view is based on the premise that entry strategies are dominated by the need of the foreign entrant to exploit the locally available resources in the destination country and to augment them with new resources available elsewhere. This is gradually being accepted as an alternative to the transaction theory based view to entry modes. In emerging economies, the markets are not as well developed and institutional frameworks not well established, due to which a large proportion of the local resources are often embedded in existing firms. Ability to work with the institutions like the government is valuable resources which could take a long time for a foreign entrant to develop on its own. Foreign investors could look at JVs (or acquisitions) for two reasons:

2. To gain experience in the new country before starting on their own

3. To circumvent institutional blocks which might otherwise be hard to work with

Emerging economies have been identified to have the following characteristics with regards to availability of resources:

1. Less sophisticated institutional framework

3 Greenfield versus Cooperative entries in Emerging Economies- A Resource-based and institutional perspective, Klaus E Meyer, Saul Estrin, Sumon Bhaumik, November 2004

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2. Weaker endowments of resources in the form of intellectual capital and infrastructure. Often, they are endowed with natural resources, low skilled cheap labour, etc.

3. Tangible and intangible assets could provide an important source of competitive advantage, such as control over natural resources, access to low skilled labour, etc.

Thus the choice of entry mode is based on an evaluation of the resources that can be acquired locally, resources that the MNC can bring in, and the ability to combine and exploit these in a way so as to obtain a competitive advantage.

Factors that favour entry to a JV or acquisition are identified to be:1. inefficient markets that make it more difficult to contract or

outsource2. weaker institutional framework3. lack of specialised intermediaries4. bureaucracy and corruption5. formal constraints like caps on FDI %

Factors that could work against in investment decision to enter through a JV include:

1. need to invest significantly to restructure the local organization

The paper goes on to build several hypotheses on entry strategies which are test based on empirically collected data. Need for local assets, prior experience in emerging markets, institutional support systems, etc are found to have an impact on entry mode decisions.

PRINCIPAL-AGENT THEORY IMPLICATIONSError: Reference source not found

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The subsidiary- headquarters relationship involves control issues and can be modelled along the lines of the classic principal-agent problem, as developed by Sumantra Ghosal in one of his papers. This can be seen particularly in WOS and acquisitions. The principal- namely the headquarters cannot make all the decisions as they don’t possess specialized local information, neither can they leave all the decision making to the subsidiary as the interests of the local management might differ from that of the parent. The parent company tries to develop a host of control mechanisms to work around this problem. The paper tests this hypothesis based on data collected from a range of companies in the manufacturing sector. This is done by developing a logistic regression model which predicts the mode of entry- Greenfield or acquisition.

Another view of the agency theory and its implications is in the case of a JV. The principal, namely the foreign entrant will need to monitor the activities of the agent, or the joint venture partner to ensure he acts in the best interests of the parent4. This entails a transactional cost of monitoring. Inability to resolve this conflict results in eventual dissolution of the JV. In the event of an acquisition, the transaction cost is in the form of restructuring of the organization to match the culture of the parent company.

LOGISTIC REGRESSION Logistic regression is a linear regression model which is used when the dependent variable is a categorical variable. In this model, the

4 Determinants of entry mode choice of MNCs in emerging markets: Evidence from South Africa and Egypt, Sumon Kumar Bhoumik, Stephen Gelb

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coefficients can be used to estimate the odds ratio for each of the variables in the model. E.g. the preference of consumers between 3 brands of health drinks may be identified to be a function of several variables like taste, health consciousness, energy, colour, etc.

Logistic data does not rely on any distributional assumptions, but usually the solution is likely to be more stable if the predictors have a multivariate normal distribution. Additionally, a low level of multi collinearity among predictors is desirable as it could lead to distortion and biases in the prediction. If all the predictors are categorical in nature, a log linear logit model may be used.

Predicting the mode of entry using a quantitative model is amenable to analysis by means of logistic regression. This is because the dependent variable, namely the mode of entry, is a categorical variable (JV/acquisition/WOS, etc). This has been explored in the past, and some important papers that attempted to develop a logit model have been identified. Developing a logistic regression model requires us to first identify the most important factors, collect necessary data and use this to build a model.

STUDIES OF EARLIER DEVELOPED LOGISTIC REGRESSION MODELS

1. Model 1Error: Reference source not foundThis paper identifies several variables that might impact the entry mode decision and builds a logit model to test the significance of these variables in predicting the entry mode. The

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paper identifies variables on a resource based view of the strategy. The variables identified include:

a. Need for local resources, both in the form of tangible and intangible assets. This test the fundamental premise of the resources based view on entry mode strategy.

b. Investment motive, as resource seeking or otherwisec. Experience in the destination countryd. Quality of local firms, taken as a rating on a five point

scalee. Quality of local institutionsf. Relative size of MNC, conglomerate or notg. Geographic distance, CDP per capita of MNCs home

countryThe model predicts whether the entry mode will be a Greenfield venture or not. Thus the model suggests that the most important local resource sought by MNCs is in the form of human capital which is usually embedded in existing organizations. In addition to these the paper identifies the strategy of the parent firm- global/ multinational/ multi domestic as another factor.

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Source: Acquisition vs. Greenfield investment: International strategy and management of entry modes, Anne-Wil Harzing, Strategic Management Journal

2. Model 2Error: Reference source not foundThis paper again explores the various factors determining modes of entry by developing and empirical model. The paper has a common author as the previous paper, Mr. Sumon Bhaumik. The variables used in the study include:

a. Growth of local industryb. Technology intensiveness of productc. Competition in local marketd. Resource needs of MNCe. Local institutionsf. Governance and business regulations

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g. Prior operating experience in developing country marketsh. Cultural distance between home country and host countryi. Extent of liberalizationj. Industry specific regulationsk. Perceptions of quality of host country’s managerial labourl. Sector of operation

The paper tries to predict the mode of entry to be JV, acquisition or a Greenfield venture. The data set used in this mode is based on companies in South Africa and Egypt. An additional variable predicted in the model was by making a distinction between two types of acquisitions: acquisitions with total control and acquisition with less than total control. The paper tries to contrast results between two countries, one being far more developed that the other. The model aims to validate the theoretical factors guiding entry mode and attempts to find deviations to the same if possible.

3. Model 35

This paper is based on the transaction model based approach to entry modes, which assumes that the MNC will chose an entry strategy which is the most efficient form of governance which is the least cost option. The model adds TC variables to previously identified models of entry mode strategies and analyses of the impact of the same on the output variable, namely the entry mode decision. The paper also makes reference to Shaver (1998) who suggested that post entry performance of firms also needs to be taken into consideration for the different entry modes to compare and contrast the performance of decisions

5 Transaction cost enhanced entry mode choices and firm performance, Keith D Brouthers, Lance Eliot Brouthers, Steve Werner

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that align with the decision criteria Thus a performance variable is introduced into the model.

4. Model 46

This paper analyses three possible modes of entry- JV, acquisition and Greenfield venture, while most papers often have analysed only the tradeoffs between JV and acquisition. The entry mode decisions are believed to depend on firm specific, industry specific and country specific factors. The model developed used the following variables:

a. R&D expenditureb. MNCs need for local resources- both tangibles and

intangiblesc. Local competitiond. Relative size of affiliatee. MNC experience in that host countryf. Institutional environmentg. Other control variables like geographic distance, extent of

liberalization, perception of the local quality of management, growth rate of relevant industry, manufacturing or not, year of entry (trend variable)

The results of the study suggest that most MNCs would not be willing to transfer cutting edge technologies to India due to weak IPR related laws, etc. Since most companies did not have a high exposure in India, it indicated that while they wanted to enter India to get a first mover advantage as the markets opened up, they were not yet contemplating setting up India as a serious hub of manufacturing or research, etc.

6 Determinants of MNCs mode of entry into emerging markets- some evidence from India, Sumon Kumar Bhaumik

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BUILDING A LOGISTIC REGRESSION MODEL

Based on the information obtained from all the above papers studied, we develop a set of variables to be used in the logit model. While an ideal model would try to add as many diverse variables as possible derived from the above papers, developing a good model lies in the ability to develop a good predictive model using as few variables as possible. This would be particularly useful in light of the fact as most of the data to be collected form India companies would be limited and obtaining all the data points (for a large set of variables) would be difficult. Thus we choose the following super set of variables for building the logit model:

1. Technological intensiveness of the product 2. Competition in the local market3. Local resource needs

a. need for a distribution networkb. need for technologyc. need for brandd. need for marketing capabilitiese. need for tangible assetsf. Need for business networks

4. Local institutions5. government support6. business procedures7. extent of R&D spend8. prior experience in emerging markets9. extent of liberalization10. manufacturing or not11. Country of origin

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12. time of entryA brief description of these variables is given in Appendix 1.

DATA COLLECTION

Data was primarily collected from company websites, annual reports and other case studies.

CASE STUDY ANALYSES

Case studies approach was used to study specific industries by taking one company from that industry as an example and building the variables around that. Existing reports and analyses on these companies in the context of their industries were studied and the logit variables defined in appropriate ranges as defined above. This was then used as a foundation to build for all the other companies in that industry. The data set was aimed to be as comprehensive as possible, with companies from several different industries included. The case studies analysed include:

1. Consumer electronics industry- SAMSUNG2. cement industry- HOLCIM3. automobile industry- FORD4. oil and gas industry- SHELL

A detailed analysis of these case studies and the result obtained from these are shown in Appendix 2. Based on this, we find that all the needs from local institutions can be combined into a single variable- local resource need. The time of entry and extent of liberalization are captured the competitiveness of local industry as later the time of entry, more the players that were already there in the market. Local institutions are part of local resource need while business procedures

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would be tied to government support. Thus the variables can be reduced to a smaller set.

Data was collected for this reduced variable set for around 60 companies (MNCs entering India). Of this 50 companies were used for building the models and 10 were used as holdout samples for checking the model. The primary data set used in building models consisted of 25 companies entering through the joint venture mode, 17 companies via wholly owned subsidiaries and 7 via acquisitions.

Entry mode variable

Frequenc

y PercentValid

PercentCumulative

PercentValid 1 25 51.0 51.0 51.0

2 17 34.7 34.7 85.73 7 14.3 14.3 100.0Total 49 100.0 100.0

321

entry mode variable

Data distribution

QUANTITATIVE ANALYSIS

STEP 1: CORRELATION BETWEEN INDEPENDENT VARIABLES

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A simple correlation table was generated to study the extent of correlation between the independent variables chosen (refer Appendix 3). It was found that government support had a high correlation with local resource needs, indicating that the extent of government support determined to what extent the MNC would be dependent on local partners resources. Hence this variable was eliminated. Further, it was also found that the manufacturing variable was correlated with several independent variables, namely technological intensiveness and R&D. Hence this was also reduced from the

independent variable set. Thus the variable set was reduced to be as follows:

Depentry mode jv=1, wos=2, acq=3

Indep1 tech intensiveness range 1-52 R&D3 attractiveness of local industry range 1-54 local resource need and govt support range 1-55 exp in emerging market range 1-56 % liberalization

7 country of originUSA=1, Europe=2, Japan/Asia=3

STEP 2: DATA REDUCTION USING PRINCIPAL COMPONENT ANALYSIS

Principal component analysis helps us to reduce a set if highly correlated variables to smaller set of independent variables. Thus we can run a principal component analysis for the independent variables. This would help us reduce these to set a set of independent variables with a low correlation amongst themselves. Reducing multi collinearity would help improve the subsequent prediction models that would be developed.

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OUTPUTCorrelation Matrix

Tech intensiveness

attractiveness

of local indus

try

local resource

needs

% R&D

exp in emerging mkts lib.

Correlation

Tech intensiveness 1.000 .156 .017 .739 .003 .301

attractiveness of local industry .156 1.000 .127 -.030 .003 .013

local resource needs .017 .127 1.000 -.069 .254 .184

% R&D .739 -.030 -.069 1.000 -.050 .344 exp in emerging

mkts .003 .003 .254 -.050 1.000 .164 lib. .301 .013 .184 .344 .164 1.000Sig. (1-tailed)

Tech intensiveness .143 .454 .000 .493 .018

attractiveness of local industry .143 .192 .418 .493 .466

local resource needs .454 .192 .319 .039 .102

% R&D .000 .418 .319 .365 .008 exp in emerging

mkts .493 .493 .039 .365 .129 lib. .018 .466 .102 .008 .129

The Kaiser-Meyer-Olkin (KMO) measure is indicator of how well suited the sample data are for factor analysis. It is the ratio of the sum of the squared correlations for all variables in the analysis to the squared correlations of all variables plus the sum of the squared partial correlations for all variables. The denominator of this ratio increases withvariation that is unique to pairs of variables (partial correlations), making the value of KMO less than one. Small values of KMO indicate that factor analysis may not be appropriate for the data. Kaiser (1974) values below .5 are not good. The value of KMO for the model is greater than 0.531 and indicates that PCA can be applied to a fair extent.

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Bartlett’s test of sphericity evaluates the null hypothesis that the correlation matrix is an identity matrix (all the values in the diagonal are 1 and all the off-diagonal values (correlations) are zero), which would indicate no relationships among the variables, and thus no basis on which to proceed with factor analysis. A significant test result allows us to reject this hypothesis. The Bartlett’s test of sphericity shows that PCA is found to be highly significant in this case.

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .531

Bartlett's Test of Sphericity

Approx. Chi-Square 52.416Df 15Sig. .000

The proportion of variance in a variable explained by the factors is called its communality. Communalities in the extracted solution should be examined for low (near zero) values. A low communality indicates a variable that shows little variation in common with the

others, and may lead to removing the variable from analysis. Communality may be interpreted as the reliability of the indicator. When an indicator variable has a low communality, the factor model is not working well for that indicator and possibly it should be removed from the model.

The communalities in this model are all high, except for liberalization. Hence, this variable could be dropped.

Communalities

InitialExtractio

nTech intensiveness 1.000 .842

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attractiveness of local industry 1.000 .944

Local resource needs 1.000 .814% R&D 1.000 .855exp in emerging mkts 1.000 .999lib. 1.000 .656

Extraction Method: Principal Component Analysis.

Rotation: in this context, refers to moving the axes to get an orientation in which factors would be strongly related to some variables, but weakly related to others. The ideal result of rotation is that each variable will have a high loading on a single factor (have a lambda coefficient near one) and small loadings (near zero) on the other factors. Therefore, the net effect of rotation, as well as its main motivation, is to facilitate interpretation.

Source: Advanced Statistical Analysis Using SPSS - SPSS Inc., Training Department, 2000

The table below shows the total variance explained by the various Principal Components

Total Variance ExplainedComponent Initial Eigenvalues

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Total % of Cumulative Total % of Cumulativ Total % of Cumulativ

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Variance % Variance e % Variance e %1 1.971 32.853 32.853 1.971 32.853 32.853 1.91

8 31.962 31.9622 1.370 22.838 55.690 1.370 22.838 55.690 1.14

5 19.084 51.0463 1.035 17.246 72.936 1.035 17.246 72.936 1.04

3 17.386 68.4324 .734 12.240 85.176 .734 12.240 85.176 1.00

5 16.744 85.1765 .657 10.953 96.1286 .232 3.872 100.000

Extraction Method: Principal Component Analysis.

Component Matrix (a)

Component

1 2 3 4Tech intensiveness .882 -.159 .138 .137attractiveness of local industry .138 .238 .921 .145

local resource needs .114 .774 .093 -.439% R&D .875 -.281 -.087 .060exp in emerging mkts .092 .712 -.299 .628lib. .622 .320 -.250 -.323

Extraction Method: Principal Component Analysis.a 4 components extracted.

Rotated Component Matrix(a)

Component

1 2 3 4Tech intensiveness .899 .019 .184 .019attractiveness of local industry .058 .068 .968 -.005

local resource needs -.135 .864 .177 .135% R&D .921 -.010 -.073 -.047exp in emerging mkts -.009 .145 -.006 .989lib. .491 .611 -.191 .073

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

a Rotation converged in 6 iterations.

The component matrix lists the factor loadings for each variable in the unrotated solution. In an orthogonal solution these factor loadings are both the correlations and the regression weights between factors

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(here components) and variables. The factor loadings, also called

component loadings in PCA, are the correlation coefficients between the variables (rows) and PCAs (columns). Analogous to Pearson's r, the squared factor loading is the percent of variance in that variable explained by the factor (PCA).

INTERPRETATION OF PCA: Tech. Intensiveness and % R&D are very heavily loaded onto component 1. Thus, they can be combined into one factor. This can be interpreted as Knowledge/Technology. The interpretation could be that when this component is high, there is a lot of proprietary and specific knowledge or technology and firms would prefer to have greater control over this. A simple average is taken to combine them into one factor.

Liberalization is not heavily loaded onto any one component and since the communality is also low, it can be dropped from the model.

Local resources needed, Attractiveness and Experience in emerging markets are individually heavily loaded onto components 2, 3, and 4 respectively. Hence, they can be retained separately for the model

Hence, the final set of variables after PCA is

1. Tech intensiveness and R&D combined2. Local resources needed3. Attractiveness of the local industry4. Experience in emerging markets

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STEP 3: MUTLINOMIAL LOGISTIC REGRESSION ON REDUCED DATA SET

Logistic regression is designed to use a mix of continuous and categorical predictor variables to predict a categorical outcome, or dependent, variable. If you have a categorical dependent variable with more than two possible values, you can use an extension of the binary logistic regression model, called multinomial or polytomous logistic regression, to examine the relationship between the dependent variable and a set of predictor variables. The models are called multinomial since for each combination of values (or covariate pattern) of the independent variables, the counts of the dependent variable are assumed to have a multinomial distribution. The counts at the different combinations are also assumed to be independent with a fixed total. The present data satisfies the conditions for running a logistic regression model (refer Appendix 4).

OUTPUT INTERPRETATION:

The Model Fitting Table gives us a brief summary of the how good the model as a whole is. Since logit uses loglinear transformations for developing the model, log likelihood. The Model chi-square is a statistical test of the null hypothesis that the coefficients for all of the terms in the model are zero. It is equivalent to the overall “F” test in regression. The chi-squared transformation of this should be as high as possible, or the significance as low as possible for the model to be a good fit.

Goodness of fit table: This is again a measure of the goodness of fit of this model for the data.

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Since R-squared cannot be directly calculated and tested for a logistic regression, pseudo R-squared measures are calculated. While these are not the same as the conventional R-squared, they give a fairly good idea of the extent of variation explained by the model. Usually the Nagelkerke pseudo R2 is to be preferred because it can, unlike the Cox and Snell R2, achieve a maximum value of one.

TEST RESULTS

MODEL 1: WITH LOCATION OF ORIGIN VARIABLE U/E/J

Model Fitting Information

Model

Model Fitting Criteria Likelihood Ratio Tests

-2 Log Likelihood Chi-Square df Sig.

Intercept Only 96.883Final 75.314 21.569 12 .043

We find that at a 5% level of significance, the model is significant (4.3% significance).

Goodness-of-Fit

Chi-Square Df Sig.Pearson 88.723 82 .287Deviance 75.314 82 .686

The chi-squared value is very high, but taking into account the degrees of freedom, the significance is also quite high and is cannot be rejected at a 10% level of significance.

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Pseudo R squared: We find that these measures are high (Cox and Snell R-squared value of 0.356), indicating that the model is significant in explaining the variance in the data.

Pseudo R-Square

Cox and Snell .356Nagelkerke .413McFadden .223

Parameter Estimates

entry mode variable(a) B

Std. Error Wald

DF Sig. Exp(B)

95% Confidence Interval for Exp(B)

1 Intercept 26.899 5.847 21.160 1 .000 TECHandRD .185 .420 .193 1 .660 1.203 .528 2.740 attrctivenssoflocalindu

stry -1.818 .900 4.078 1 .043 .162 .028 .948 localresourceneeds .775 1.316 .347 1 .556 2.171 .165 28.608 expinemrgngmkts -1.280 .596 4.612 1 .032 .278 .086 .894 [UEJ=1] -20.605 1.542 178.56

4 1 .000 1.13E-009 5.48E-011

2.31E-008

[UEJ=2] -19.786 1.021 375.745 1 .000 2.55E-009 3.45E-

0101.89E-

008 [UEJ=3] 0(b) . . 0 . . . .2 Intercept 29.572 5.840 25.643 1 .000 TECHandRD .574 .479 1.437 1 .231 1.775 .695 4.535 attrctivenssoflocalindu

stry -1.602 .927 2.986 1 .084 .201 .033 1.240 localresourceneeds -.458 1.355 .114 1 .735 .632 .044 9.002 expinemrgngmkts -1.101 .592 3.464 1 .063 .333 .104 1.060 [UEJ=1] -21.693 1.463 219.74

7 1 .000 3.79E-010 2.16E-011

6.68E-009

[UEJ=2] -20.244 .000 . 1 . 1.62E-009 1.62E-009

1.62E-009

[UEJ=3] 0(b) . . 0 . . . .a The reference category is: 3.b This parameter is set to zero because it is redundant.

OUTPUT VERIFICATION

This table gives the coefficients of the different variables to substitute in the equation for predicting the mode of entry. Most of these variables are found to be significant in predicting the output. The

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coefficients for the prediction of output categorical mode 3 are not given as it is used as a reference category. That is, if the probability that the mode of entry maybe 1 or 2 is found to be low from the model, the probability that it is 3 is high. The model can be used to predict the mode of entry and tested against the same sample as well as a hold out sample.

Thus, the regression equation is used to estimate the values of each of the three logits. Let this value be g1, g2. g3 = 0 since Acquisition is taken as the reference group.Now the probability of belonging to a particular group is given by

Source : Statistical Analysis – Advanced SPSS data

From this we find that the model has 59% accuracy in predicting the output within the original group. This is significantly higher than the 33.3% prediction that can be made through a random choice.

MODEL 2: WITHOUT LOCATION OF ORIGIN VARIABLE U/E/JModel Fitting Information

Model

Model Fitting Criteria Likelihood Ratio Tests

-2 Log Likelihood Chi-Square df Sig.

Intercept Only 96.883Final 84.170 12.713 8 .122

Goodness-of-Fit

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Chi-Square df Sig.Pearson 102.606 84 .082Deviance 84.170 84 .474

Pseudo R-Square

Cox and Snell .229Nagelkerke .265McFadden .131

Parameter Estimatesentry mode variable(a) B

Std. Error Wald df Sig.

Exp(B)

95% Confidence Interval for Exp(B)

Lower Bound Upper bound

1 Intercept 2.916 4.802 .369 1 .544 TECHandRD .107 .381 .079 1 .779 1.113 .528 2.347 attrctivenssofloc

alindustry -1.591 .816 3.801 1 .051 .204 .041 1.008 localresourcenee

ds 1.453 1.102 1.739 1 .187 4.276 .493 37.051 expinemrgngmkt

s -.851 .463 3.385 1 .066 .427 .172 1.0572 Intercept 3.388 4.854 .487 1 .485 TECHandRD .403 .395 1.044 1 .307 1.497 .690 3.246 Attrctivenssofloc

alindustry -1.318 .822 2.574 1 .109 .268 .054 1.339 localresourcenee

ds .656 1.098 .357 1 .550 1.928 .224 16.581 expinemrgngmkt

s -.622 .456 1.862 1 .172 .537 .220 1.312

a The reference category is: 3.

We can see that removal of the variable U/E/J has not altered the significance of the model and the other parameters too much. The

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prediction accuracy drops down but only slightly, to 55%. Hence, we can conclude that the U/E/J variable is not contributing significantly to the model and can hence be dropped.

STEP 4: DISCRIMINANT ANALYSIS

WHY DISCRIMINANT?

Discriminant Analysis is a technique for analysing data when the criterion or the dependent variable is categorical and the prediction or the independent variables are interval or categorical in nature.Thus, given: a) The existence of several distinct groups of individuals/objects, andb) A sample of observations from each group, we can find

a) Functions of these observations (variables) that will distinguish the groups (discrimination), and

b) Enable future unidentified individuals/objects to be classified to their correct group classification)?

It is thus appropriate tool for predicting the membership of observations in two or more groups. It is more of a predictive technique than logistic regression. The discriminant functions also help to build a perceptual map and the loadings on the functions, help interpreting them as real factors. The plot can then be attempted to be interpreted based on the factors, and insight can be drawn. There is also a dearth of literature on modes of entry which lacks an exploration of the discriminant model. Hence, discriminant analysis is explored as a technique to achieve the following ends.

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The goal of a PDA is to correctly classify cases into the appropriate group. Given this, parsimony is an important sub goal. This means using the fewest predictors needed for accurate classification, although not necessarily the smallest set of classification functions. This was the motivation for undertaking the Principal Component Analysis.

OUTPUT INTERPRETATION:

Box’s M test: An important assumption is that the covariance matrices of the various groups are equal. This is equivalent to the standard assumption in analysis of variance about equal variances across factor levels. When this is violated, distortions can occur in the discriminant functions and the classification equations. This assumption can be tested with the Explore procedure or with the Box’s M statistic.

The Box’s M test is quite powerful and leads to rejection of equal covariances when the ratio N/p is large, where N is the number of cases and p is the number of variables. The test is also sensitive to lack of multivariate normality, which applies to these data. Low probability value indicates that the covariance matrices are probably not equal. The effect on the analysis is to create errors in the assignment of cases to groups.

Eigen values: The Eigen Value is the ratio of the between group variance to the within group variance. (or the sum of squares). Ideally, the numerator or the between group variance should be maximized and the within group variance captured in the denominator should be minimized. Hence the eigen value should be as close to one as

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possible. Square of the canonical correlation tells us the % of group variance to the total variance.

Wilk’s Lambda: is the ratio of the within group sum of squares to the total sum of squares. Since this should be minimized, the Wilk’s Lambda should be as small as possible. This gives the portion of the total variance that is not explained by the discriminant model.

Structure Matrix is the most important table from the point of view if interpretation. The structure matrix tells us how the predictor variables are loaded on the two discriminant functions. A heavy loading indicates that the function is largely affected by this underlying variable.

The Classification Table gives us the accuracy of the model in predicting the output variable. Holdout samples that are excluded from the model are used to testing the model and verifying its ability to predict with accuracy.

TEST RESULTS

MODEL 1: WITH INCLUSION OF VARIABLE U/E/J

Box's M 37.827F Approx

. .949df1 30df2 1148.926Sig. .546

Tests null hypothesis of equal population covariance matrices.

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Here the p-value is 0.546, which is well greater than zero. Hence, the null hypothesis that the covariances matrices are equal cannot be rejected. Hence, the model validity is good.

Eigenvalues

Function

Eigenvalue

% of Variance

Cumulative %

Canonical Correlation

1 .374(a) 81.2 81.2 .5222 .087(a) 18.8 100.0 .283

a First 2 canonical discriminant functions were used in the analysis.

Wilks' Lambda

Test of Function(s)

Wilks' Lambda Chi-square df Sig.

1 through 2 .670 17.637 10 .0612 .920 3.663 4 .454

Here we find that percentage of variance explained is 27.2% from function 1 and 8 % from function 2. Thus we find that the eigen values are not very high. We find that about 65% of total variance is not explained by the model, which is a fairly high number. However, statistical applications of real data are bound to be imperfect, but this is still high and needs to be kept in mind.

Structure Matrix

Function

1 2attractiveness of local industry .513(*) .177

exp in emerging mkts .424(*) .184U/E/J -.374(*) .256TECH and R&D -.108 .646(*)local resource needs -.122 -.417(*)

Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions

Variables ordered by absolute size of correlation within function.* Largest absolute correlation between each variable and any discriminant function

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Thus we find that function 1 is predominantly a combination of the variables- attractiveness of the industry and experience in emerging markets. Function 2 is a combination of the variables, Technical intensiveness-R&D and Local Resource Needs with a negative correlation (which maybe interpreted as amount of resources brought in). Since the country of origin variable, U/E/J is loaded to almost the same extent on both the functions with opposite sign, we find that it is not highly useful in predicting the output variable and can be excluded.

Classification Results(a,b)

Predicted Group Membership 1 2 3 Total

Cases Selected Origina

lCount 1 13 6 6 25

2 4 10 3 17 3 1 1 5 7 % 1 52.0 24.0 24.0 100.0 2 23.5 58.8 17.6 100.0 3 14.3 14.3 71.4 100.0Cases Not Selected

Original

Count 1 3 0 2 5 2 2 0 2 4 3 0 1 1 2 % 1 60.0 .0 40.0 100.0 2 50.0 .0 50.0 100.0 3 .0 50.0 50.0 100.0

a 57.1% of selected original grouped cases correctly classified.b 36.4% of unselected original grouped cases correctly classified.

We find that 57.1% of the original groups have been correctly classified. When compared to chance, the probability of randomly selecting each entry mode is 1/3 or 33.3%. This is significantly greater than predicting the classification through a random choice. However, the prediction in the holdout sample is far lesser at 36.4%. This is not significantly greater than a random choice. Hence, this model does not have an acceptable predictive power.

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We find that the U/E/J variable may not be significant in prediction the output variable in the discriminant model. Upon generating a model without this variable, we found that:

The Box’s M was higher indicating that we can be more confident about making the assumption that the variances were equal.

The loadings of U/E/J variable in the structure matrix for the model with U/E/J were found to be more or less equal on each function. Thus this variable was not clearly loaded on either function.

Upon eliminating this variable from the model, we found that the other variables were more clearly loaded on the two functions.

The prediction on holdout cases was poor and not significantly better than a random choice.

This seems to indicate that the U/E/J variable might have been interacting with the other variables and distorting the model. Thus removing this variable would give us a better picture and greater interpretability of the two discriminant functions.

MODEL 2: DISCRIMINANT ANALYSIS WITHOUT THE U/E/J VARIABLE

Test Results

Box's M 18.764F Approx

. .756df1 20df2 1253.360Sig. .769

Tests null hypothesis of equal population covariance matrices.

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We find that the Box’s M statistic has a high significance of 0.769, indicating that we can be more confident of our assumption of equal variances.

Eigenvalues

Function

Eigenvalue

% of Variance

Cumulative %

Canonical Correlation

1 .255(a) 83.4 83.4 .4512 .051(a) 16.6 100.0 .220

a First 2 canonical discriminant functions were used in the analysis.

The square of the canonical correlation shows that the model explains about 25% of the total variance in the data set. While this is not very high, for real world data sets this acceptable.

Structure Matrix

Function

1 2attractiveness of local industry .626(*) -.155

exp in emerging mkts .524(*) -.080TECH and R&D -.024 .894(*)local resource needs -.208 -.435(*)

Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions

Variables ordered by absolute size of correlation within function.* Largest absolute correlation between each variable and any discriminant function

The structure matrix shows that attractiveness of local industry and experience in emerging markets are heavily loaded on function 1, while technological intensiveness and local resource needs (with a negative correlation) are heavily loaded on function 2.

Classification Results(a,b)

Predicted Group Membership 1 2 3 Total

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Cases Selected Original

Count 1 15 6 4 25 2 5 7 5 17 3 1 1 5 7 % 1 60.0 24.0 16.0 100.0 2 29.4 41.2 29.4 100.0 3 14.3 14.3 71.4 100.0Cases Not Selected

Original

Count 1 3 0 2 5 2 0 2 2 4 3 0 1 1 2 % 1 60.0 .0 40.0 100.0 2 .0 50.0 50.0 100.0 3 .0 50.0 50.0 100.0

a 55.1% of selected original grouped cases correctly classified.b 54.5% of unselected original grouped cases correctly classified.

The classification table shows the accuracy of the model in predicting within the sample and for the holdout sample. We find that the model has 55% prediction accuracy for prediction within sample and 54.5% prediction accuracy for the holdout sample. This is well above the 33.33% prediction accuracy that might be expected from a normal random sample. Hence, this model has a fairly acceptable prediction capability.

PERCEPTUAL MAPThe territorial map or the perceptual map is useful for visualizing the plot of how the output variables vary with the 2 most important discriminant functions. This is very commonly used in several real time applications of discrimiant analysis to understand the functional drivers of the output variable and get a simple visual representation.The territorial map plots the two functions on the 2 axes and the shows data points of each categorical output variable plotted in the plot area as a function of these two. Thus plot area thus gets divided into three segments in this case, representing the combination of function values that JV, WOS and acquisitions take. The numbers in

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the lot area show the boundaries between the categorical variables. Thus the segmentation of the plot area is seen in the territorial map

Canonical DiscriminantFunction 2

-6.0 -4.0 -2.0 .0 2.0 4.0 6.0 6.0 23 23 23 23 23 23 4.0 22 23 11222 23 11122 WOS 23 11222 23 11122 23 11222 23 2.0 11122 23 11222 23 11122 23 1122 23 11222 * 23 11122 2*3 .0 1*222 23 11123 13 13 13 ACQ 13 -2.0 13 13 13 JV 13 13 13 -4.0 13 13 13 13 13 13 -6.0 13 -6.0 -4.0 -2.0 .0 2.0 4.0 6.0 Canonical Discriminant Function 1

Symbols used in territorial mapSymbol Group Label------ ----- --------------------

1 1 2 2 3 3 * Indicates a group centroid

DEVELOPMENT OF AN ANALYTICAL FRAMEWORK

From the structure loadings matrix, we know that function 1 is essentially a combination of attractiveness of the industry and

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experience in emerging markets. This information can now be superimposed on the plot to get a deeper understanding. These two variables are captured in function 1 as a RISK-TO-RETURN ratio, with experience in emerging markets as a proxy for lack of risk and attractiveness of the local industry as a proxy for returns. It shows whether the Returns outweigh the Risk or vice versa.

When a company has very low experience in emerging market conditions, the risk in entering emerging markets like India would be high. If at the point of time they were contemplating entry the industry was not very attractive, the returns they could expect would be low. Hence the level of risk would be substantially higher compared to the returns, captured in a high RISK-TO-RETURN ratio. The company would hence choose the safest mode of entry, namely JV. As the level of returns from the industry increases and the company has a higher experience in emerging markets, the level of risk relative to the returns from the industry gradually reduces. Thus we see a transition to acquisition mode of entry. Finally, when the level of returns relative to risk is very high, that is function 1 takes a low value, the company needs to enter at the earliest to obtain an early mover advantage and reap significant returns from the industry. This scenario suggests a quick mode of entry via an acquisition. (This theory supporting acquisition as a mode of entry for quick entry into attractive markets has been established in several papers before7). This is also seen in the framework map. Thus statistically obtained prediction is in line with intuitive logic as well.

Consider the function 2. When a company has a high proprietary technology and R&D expenses of its own, it is likely to be less 7 Global strategy and the acquisition of local knowledge: How MNCs enter regional knowledge clusters, Mark Lorenzen, Volker Mahnke, DRUID Summer Conference 6-8 June 2002

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dependent on the local resource needs. This is because the most important resources the firm would bring into the country on its own than use locally. This relationship can be seen by the fact that local resource needs and extent of technological intensiveness are loaded on function 2 with opposing sign. Thus when the company has high technology, it is likely to be less dependent on local resources, that is, function 2 overall would take a high value, the company would prefer to enter as a wholly owned subsidiary as it is bringing in its own resources and would like to safeguard them. As the extent of proprietary technology owned by the company reduces, its needs from the local industry will also increase, seen as a drop in value of function 2. Thus the as the local resource needs increase, we see a transition in mode of entry from WOS towards acquisitions and eventually joint ventures. In JV, since the local partner is still a firm in the local industry, their ability to work with the government and distributors would be higher when compared to a company the firm acquired.

Thus we find that the territorial map and its implications can be captured in the framework or 2 by 2 as shown, with the variables along the two axes and the plot areas showing the various modes of entry. This is a new finding, and while some of the factors seen in this framework have been intuitively suggested in prior papers, this is the first time the entire framework and the statistical backing for the same has been developed.

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CONCLUSION

Thus a logistic model was developed for the prediction of entry mode of an MNC into India. Further, a discriminant model was also developed. While the discriminant model has more rigid assumptions, it often more useful in building models from data for discrimination and prediction. It is also useful for developing a territorial map which helps us visualize the major factors governing the output.

The territorial map developed for discriminant analysis indicated the distribution of the output variable in a 2-dimensional space, varying with the two discriminant functions. This shows that that the local resource needs of the MNC and the risk-return tradeoffs are the two major factors governing the mode of entry of an MNC into India.

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While the model is fairly accurate in predicting the output, the data is subjective in nature and hence subject to some errors.

ADDITIONAL REFERENCES

1. Analyzing Modes of Foreign Entry: Greenfield Investment versus Acquisition,

Thomas M¨uller, University of Munich, December 2000

2. Entering India- Licensing or Joint Venture?, Alex Eapen, Jean Francois Hennart

3. Determinants of Entry Mode Choice of MNCs in Emerging Markets: Evidence from

South Africa and Egypt, Sumon Kumar Bhaumik, Stephen Gelb

4. Key trade ideas- Perspectives and portfolios, JP Morgan Asia Pacific Equity

Research, 07 March 2006, Adrian Mowat, Joanne Goh, Steve Malin

5. Entry Strategies in Emerging Economies: The Case of the Indian Automobile

Industry, Avinandan Mukherjee and Trilochan Sastry Indian Institute of

Management, Ahmedabad, March 1996

6. Entry Strategies in Emerging Economies: The Case of the Indian Automobile

Industry, Avinandan Mukherjee and Trilochan Sastry Indian Institute of

Management, Ahmedabad, March 1996

7. The Institutional environment for Multinational Investment, Witold J Henisz,

University of Pennsylvania, Oxford University Press 2006

8. Modes of Foreign Entry under Asymmetric Information about Potential

Technology Spillovers, Thomas MÄullery, University of Munich September 2002

9. Multinational Enterprises and M&As in India: Patterns and Implications, Nagesh

Kumar Published in Economic and Political Weekly,5 August 2000: 2851-8

10. Conclusions for economic strategy- Investments in Emerging Markets, Saul Estrin

and Klaus E Meyer

11. Indian Case Studies, PL Beena, Subir Gokarn, Anjali Tandon

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12. India Automobiles- Growth Momentum Continues, Morgan Stanley Equity

Research Asia/Pacific, March 14 2006, Satish Jain and Deepak Gupta

13. Advanced Statistical Analysis using SPSS, SPSS Inc., 2000

14. A Swedish MNCs government strategy in India- A case study of Volvo Truck

Corporation, Pär Björckebaum and Peter Säle, Graduate Business School- School

of Economics and Commercial Law Göteborg University

APPENDIX

Appendix 1Explanation of logit variables

1. Technological intensiveness of the product : 1-5 This refers to the degree of high technology involved in the

product. If the product is of cutting edge technology and constant innovation is extremely important to remain competitive in the industry, the parent company may not want to share this information with local partners as the risk of the local partner gaining this sensitive information and using it to start its own business is high. A low rating close to 1 indicates the product involves very low technology, a high rating close to 5 indicates the product has a high degree of propriety technology.

2. Competition in the local market : 1-5 This gives us a picture of the attractiveness of the industry.

Depending on the time of entry, the numbers of local and international players in the industry vary. As we know from Porter’s framework, the larger the number of players in an industry, the less attractive it is as price competition is likely to be high, products would be highly commoditized and margins would be squeezed. A low rating indicates the industry is

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attractive and a high rating shows the attractiveness of the industry is low.

3. Local resource needs : 1-5a. need for a distribution network

This encapsulates need for having a strong distribution set up. Consumer goods, etc require very strong distribution capabilities, especially due to the geographic expanse of India. A low rating shows the industry does not need high distribution networks, as it might be a highly customized niche product.

b. need for technology This tells us what the extent of technological contribution from

an Indian partner is. Usually this is low for most companies entering India, as they are looking to enter India for its cheap resources, bringing in their own technology.

c. need for brand This tells us what the requirement for the local company to have

brand recognition is. If the international brand is not recognized in India, it might expect a partner with a good brand value. This is again give as rating variable range 1-5

d. need for marketing capabilities This indicates the need for marketing capabilities from the local

partner. A high rating indicates the parent company is largely dependent on the local player for marketing the product.

e. need for tangible assets This tells us what is the need for tangible assets like plants,

land, etc from the local partner.f. Need for business networks

This captures the importance of having networks with government and local institutions. A high rating shows high dependence on local networks.

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4. Local institutions : 1-5 This variable captures the need for local institutions. A high

rating shows the company is highly dependent on local resources like infrastructure, etc.

5. government support : 1-5 This variable captures the extent of support rendered by the

government. Companies that entered in the early years of liberalization received lesser government support compared to companies that entered later, as by then the economy was more liberalized and the government was willing to make relaxations to norms and restrictions to attract FDI.

6. business procedures : 1-5 This tells us the extent of business procedures required like

legal registration, government grants and allotment, approvals, etc.

7. extent of R&D spend - 1-5 This variable tells us how much the company spends on R&D

relative to other players in the industry. This is indicative of the extent of the proprietary technology. A high rating shows high R&D.

8. prior experience in emerging markets - 1-5 This variable is used to capture the experience the company

might have due already existing establishments in emerging markets. Higher level of experience might make an MNC more willing to take risks as it enters India.

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9. extent of liberalization : 1-5 This captures the % FDI that was allowed by the government

into that sector. Several sectors were 100% deregulated early in the process of liberalization, but some sectors like insurance and telecom have been liberalized only very gradually over time.

10. manufacturing or not - 1/0 this variable tells us if the industry is manufacturing intensive

(1) or not (0)

11. Country of origin – U/E/J This variable tells us if the country of origin is in USA, Europe

or Japan (Asia). This captures the mentality of companies in each of these regions. For example, Japanese are very particular about tight control while USA has a policy of decentralized management.

12. time of entry - year (trend variable)

Appendix 2CASE STUDY ANALYSIS

Appendix 2.1: Consumer Electronics Industry

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CONSUMER ELECTRONICS INDUSTRY ANALYSIS8, 9

Growth of the industry:The consumer electronics industry originally consisted of several sub segments, including TVs, audio equipments and components. With development of fuzzy technology, etc, washing machines, microwaves are also seen to be a part of this segment in present day context.

In the early 90s, the B/W TV market had a CAGR of around 12% which saw a spike in ’94 owing to relaxation of excise duties on imports. This gradually began to decline with the advent of colour televisions. As the penetration of B/W TVs in urban areas declined, they moved into semi-urban and rural areas. On the production side, this industry consists of a large unorganized sector, accounting for 41%. The major players in the market before liberalization were local players like Dynora, Crown, Uptron, Solidaire, etc. The entry of global giants like Philips gradually ousted them from the market as they were able to leverage on higher brand value and dealer networks. This shows that two of the important requirements for success in this industry were:

Distribution through dealer networks Brand equity

While an MNC could bring the latter, the former needs to build from scratch in the country or obtained through an acquisition.

8 Trends in Consumer Electronics, Kumar Shankar Vellal, Parveen S. Jamooji and Anjan Kumar J.V., Technological Review# 2002-02, Tata Consultancy Services, March 2002, 9 Consumer Durables, Consumer Electronics, ICRA Sector Research-ICRA Information Services, February 2003

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Colour televisions:From 1996 to 2000 this market has grown at a CAGR of 3.4%. While India housed nearly 17% of the world population at that time, it accounted for merely 3.8% of the colour TV market. Thus MNCs saw potential for entering a huge untapped market. Further, principal producers also started production outside US from countries like Mexico due to availability of cheap labour to compete with the cost effective products coming from Japan and China. Japan is home to some of the major MNCs in the field of consumer electronics including companies like Matsushita, Sony, Sanyo, Toshiba, Hitachi, etc. nearly 91% of Japan’s production comes from the neighbouring countries of china, Korea, Indonesia, etc to be cost competitive.

The advent of satellite channels and cable TV triggered growth of televisions in India post 1993. The CTV market grew at a CAGR of 32.1% from 1995-2000.

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The penetration has been conventionally higher in urban areas and in the west/ south compared to north/ east.

A porter framework analysis of the industry gives the following insights:1. Competition:

Liberalization brought several players into the untapped market. Hence competition increased manifold; market was becoming fragmented

The raw materials to manufacturing of TVs in terms of simple electronics, picture tubes, etc. were fairly commoditized and outsourced by most people. Even assembly was outsourced in some cases.

Buyer concentration was seen in certain areas Dealer networks were extremely important, especially to

penetrate rural markets Brand identity was also important to differentiate Substitutes were emerging in the form of new technology

replacing picture tubes, etc but these catered to the niche. Hence the basic product was still appealing to the masses and

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the real challenge was to bring down the prices to make it attractive to the rural markets.

The real value add brought by the MNCs was in terms of technology and branding

Economies of scale can be derived from large installed capacities

HOW DID PLAYERS ENTER? Sanyo – technological collaboration with BPL Philips India ltd.- one of the first to enter through WOS (?),

quality leader brand eventually diluted Sony India ltd- import PCT from facility in Singapore,

proprietary technology hence unique quality positioning, LG electronics India ltd.- entered through WOS, PCTs also

manufactured by group holding, aggressive marketing and technology differentiation

Samsung India ltd.- brought in aggressive marketing, productivity improvements and working capital control

Matsushita, National Panasonic India ltd. - entered through acquisition of Salora and NPIL and outsourced marketing function to NPIL, cluster management with most functions centralised,

TCL from china- cost driven, collaboration with Baron International

New technology has lead to identification of niches within which companies try to dominate. We look at this industry from the point of view of a specific company, namely Samsung Electronics.

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SAMSUNG ELECTRONICS INDIA LTD.

Samsung Electronics, established in Japan, entered India through the wholly owned subsidiary route post liberalization. Their chief attraction in coming to India was possibly the large untapped market and the availability of cheap labour, etc. The major factors form the point of view of the variables and their ratings maybe seen as follows:

LOGIT VARIABLES DEFINED Technology intensiveness : fairly high

o While most part of their work, especially in India was for bulk manufacturing, they were trying to use technology to differentiate

Competition in local market : fairly higho competition from incumbents and other new entrants

Need for distribution network : very higho This was one of the key success factors in this industry

especially in India owing to its geographic expanse Need for technology - not high

o Would bring in their own technology

Need for brand - not higho Would try to leverage on their own international brand,

hence brand of a potential local partner would not be as important

Need for marketing capabilities - very higho Marketing is extremely important especially in urban

segments which were the major markets for most MNCs Need for tangible assets - medium

o Since most of the production and assembly can be outsourced, it can be argued that the need for tangible assets

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is not high in general. But the higher the proprietary technology the MNC brings in, the greater the need for them to maintain the production in-house.

Local institutions - mediumo Local infrastructure support was not very high, but as FDI

was increasing, legal support systems, etc were still weak. Business procedures - high

o This was because setting up new plants, etc required lot of approvals. Further, IP protection, etc were still in the nascent stages.

Government support - lowo Government has supported, especially at time of entry, by

reducing excise duties and tariffs. But the economy was still in stages of liberalizing, hence the government tended to favour local companies and imposed stiff regulations and restrictions on foreign entrants.

% R&D- higho Involved technological innovations, hence required a fair

amount of R&D. o Samsung in particular, posted higher R&D investments than

competitors (around 8.3% in 2004) Experience in emerging markets- high

o Had entered developing countries like Thailand, Mexico and had also formed joint ventures in China and Korea10.

Extent of liberalization - The industry was 100% liberalized to allow foreign players to enter.

Manufacturing or not - yeso This was essentially large scale cost effective manufacturing

rather than providing service.

10 http://www.samsung.com/AboutSAMSUNG/ELECTRONICSGLOBAL/CompanyProfile/TimelineHistory/sec_timeline08.htm

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Maturity of capital markets - mediumo At the time of entry, the capital markets were not as mature

for both the MNCs (in terms of raising equity or debt, etc) and for consumers ( in terms of credit cards, loans, etc to aid purchase)

Country of origin: Japan

Appendix 2.2 : Cement Industry

CEMENT INDUSTRY ANALYSIS: HOLCIM’s ENTRY11, 12, 13

INTRODUCTIONThe Indian cement industry consists of over 54 cement players and more than 129 manufacturing plants and is highly fragmented and regional in nature. Its estimated size in value terms is around Rs 400 billion and around 130 metric tonnes per annum (mtpa) in volume terms. It owes its regional nature to the concentration of cement plants near the clusters of limestone reserves, located only in few states. This has led to a surplus situation in some regions and vice versa in others. As cement is a low value and high volume product, it is uneconomical to transport it over long distances. In order to reduce transportation costs, players either set up plants on a regional basis or have a pan-India presence.

Industry characteristicsThe major characteristics of the cement industry are:

Units concentrated near raw material sources or markets

11 INDUSTRY STATISTICS, January 2006, CRIS-INFAC Report, CRIS-INFAC Cement Annual Review12 STATE OF THE INDUSTRY, February 2006, CRIS-INFAC Report, CRIS-INFAC Cement Annual Review13 STATE OF THE INDUSTRY, October 2003, CRIS-INFAC Report, CRIS-INFAC Cement Annual Review

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Proximity to key markets reduces freight cost High power and fuel costs:

Energy costs account for 35 per cent of the total production costs. Focus towards backward integration

Owing to the large power and fuel costs affecting the cost structure of companies and irregularity of power supply in most of the states, the companies have opted to set up their own captive power units using diesel generating (DG) sets or thermal power sets in order to get a regular supply of power and optimize costs.

Small value chainThe industry’s supply chain comprises raw material suppliers, cement manufacturers, distributors and end users.

Regional variations and volatility in prices and marginsCement prices and margins vary across regions, due to the variation in demand-supply balance, level of concentration and demand growth.

High debt levelsThe debt levels in the industry are high because of the capital-intensive nature of cement projects. Moreover, low prices and low profitability have also added to the high debt levels.

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Regional distribution of demandCement consumption varies across regions because of the differences in the demand-supply balance, per capita income and the level of industrial development in each state.

DEMAND DRIVERSCement demand is closely related to the growth in the construction sector. Consequently, cement demand has posted a healthy growth rate of around 8 per cent since 1997-98, propelled by the increased focus on infrastructure development, and higher demand from the housing sector and industrial projects.

The strong demand growth can be driven by the following factors:1. Strong housing demand2. Higher level of commercial construction activity3. Increased government focus on infrastructure spending and4. Higher investment in industrial projects

PROCESSCement is stored in silos, and aggregates (sand and stone chips) are stored in stockpiles or hoppers. These are then transported to an elevated tower for batching. The batched materials are then fed into the mixer, where they are mixed at a regulated speed, in order to obtain a concrete mix of the desired quality.

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PLANT AND EQUIPMENTA typical RMC facility includes a central batching and mixing plant, revolving transit mixers, and concrete pumps and conveyors.The following operations are carried out at the central batching and mixing plant:• Storage of materials• Weighing as per the required proportion mix• Discharging the weighed materials to the mixer• Mixing

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Batching and control operations are completely automated. A batching and mixing plant can store around 100 mixes. (A mix is a particular proportion of cement and aggregates.) The revolving transit mixer could be a truck mixer or truck agitator, which is used to transport RMC to the construction site. The mixer continuously agitates the mix to prevent early hardening. Concrete pumps and conveyors are used to pump concrete at the construction site.

TECHNOLOGICAL DEVELOPMENTS IN CEMENT INDUSTRY AND FUTURE OUTLOOKUse of alternative fuels in cement kilnsThe cement industry is thermal energy intensive. The average heat generated during the process is 3,200-3,300 kilo joules/kg. Burning fossil fuels, such as pulverised coal/oil in the rotary kiln, generates high-grade heat. However, as these fuels are becoming expensive and difficult to procure, there is an increasing need for alternative fuels for kilns.

This uncertainty in availability has led to the usage of different fuels in various cement plants in the world, either as alternative fuels or as supplementary fuels. The use of supplementary fuels to fire the kiln, along with regular fuels, is called co-processing. The choice of alternative fuel depends on various factors, viz, physical

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characteristics, chemical composition, toxicity, ash content, particle size, homogeneity, reactivity and calorific value of the fuel. In addition, the fuel has to be available regularly, so as to maintain continuous kiln operations and consistency in cement quality.In future, alternative fuels would assume significant importance on account of high cost and inadequate availability of conventional fuels. The use of alternative fuels in cement plants is expected to increase.

Waste heat recovery potential and co-generation of powerThe manufacture of cement requires considerable amount of thermal and electrical energy. Substantial amounts of energy are consumed in the pyro-processing and grinding sections respectively. A lot of energy generated during pyro-processing is released into the atmosphere and wasted. In many cement plants, this waste heat is recovered and converted into electrical energy.

FIVE FORCES FRAMEWORKEntry barriersCement industry witnesses HIGH entry barriers owing to the gestation period of 36 months involved in setting up a million tonne cement plant along with a DG set.Along with the gestation period, other factors that play an important role in determining the feasibility of setting up a cement plant are:• Availability of raw material – limestone• High initial investments (around Rs4000 million for a 1 million tonne capacity and a DG set)• Fuels — coal• Logistics• Demand-supply dynamics• Policies or incentives by various state governments• Brand recognition and market shares of existing players

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Bargaining power of suppliersThe suppliers to the cement industry have HIGH bargaining power owing to their monopoly over the supply.• Licensing of coal and limestone reserves• Availability of railway wagons for transportation• Supply of power by the state gridIn all the above cases, the supplier is the government. In this way, the supplying industry hinders the productivity and profitability of the cement industry.

Bargaining power of customersCement being high volume and low value commodity, the bargaining power of the customers is HIGH due to the following factors:• Low cost of switching brands• Large number of players• Distribution reach of a player• Less differentiation in product

Substitutes:The diluting effect of substitutes on the industry in very less as cement is one of the essential items in any construction and cannot be replaced by any other material easily. Hence, the effect of substitutes is LOW

Extent of competitionCurrently, the industry is oligopolistic in nature as the top five players account for 50 per cent of market share owing to improved level of consolidation.

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However earlier, the presence of many players and the war to gain market share had led to intense competition among players and there also existed price cuts to gain market shares. The industry is divided into large, medium, small and mini players. The market shares of the large players increased owing to the consolidation that followed the acquisitions witnessed in the past few years.

As on March 2005, the large players accounted for 48 per cent of the total capacity compared to 11 per cent in 1999-2000. Thus, the industry was fairly fragmented in 2000. Since, this is an extremely capital-intensive industry with huge fixed costs, economies of scale are extremely important. This made the industry vulnerable to the acquisitions and subsequent consolidation within a quick span of time.

Source: Professor J. Ramachandran’s Class slides

Overall attractiveness of the industry: HIGH

SUBSTITUTES

INDUSTRY COMPETITORS

Rivalry among Existing Firms

BUYERS

POTENTIAL ENTRANTS

Threat of entry Bargaining power of suppliers

Bargaining power of buyers

Threat of Substitute Products or Services

SUPPLIERS

LOW

HIGH

HIGH

HIGH

LOW

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GOVERNMENT REGULATIONSThe growth of the cement industry, however, has been uneven (with most of the capacity additions happening only during the last decade). Government regulations stunted the growth of this industry. In the past, the government regulated the industry with licensing, price and distribution controls. The removal of these controls resulted in rapid progress in terms of capacity additions and higher production, and India moved from a cement scarcity situation to a surplus position. It now ranks as the second largest producer of cement in the world.The evolution of the cement industry in India can be broadly classified into three periods — the period up to partial decontrol (up to 1982), the period up to total decontrol (1982-89) and the period after total decontrol (after 1989 to date).

The following table summarises the events in the cement industry:

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HOLCIMHolcim was incorporated in 1912 in the village of Holderbank, Canton, Switzerland and is currently the second largest player in the global cement industry and has a presence in nearly 70 countries. It is one of the key playersIn the aggregates, concrete and other construction related services. The company has strong presence in countries spread across Africa, Asia-Pacific, Europe, Latin America and North America.

Holcim derives nearly 40 per cent of its revenues from Europe, followed by Latin America — 21 per cent and North America — 20 per cent. Africa, Middle East and Asia Pacific account for the remaining 19 per cent of therevenues.

VARIABLES FOR LOGIT MODEL

Technology intensiveness – highThe data on technology in the industry suggests that technology-intensiveness in the industry is extremely HIGH

Attractiveness of the industry - high Need for distribution network – high

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Extremely important, especially as transportation costs are relatively a very high component

Need for local technology - medium-high Need for brand - low Need for marketing capabilities - low

Fairly commoditized product, hence, not extremely important Need for business networks - high

Lot of it is Industrial and Govt construction for infrastructure development; hence government contacts and networks to win bids, etc are important.

Need for tangible assets - high Highly capital intensive industry; need for large manufacturing plants and transportation capabilities very importantLocal institutions- low Institutions which support the industry are predominantly Power, Transport and Mining. Power is still mostly under the government and hence not as efficient and reliable, which is why most companies prefer to have their own captive power plant. Transport is still suffering from inadequate road infrastructure. This is improving, but as yet inadequate. Mining industry is also not as efficient and uses less superior technology compared to the western world. Hence, the dependence and risk of the local institutions is higher. This therefore gets a LOW rating

Government support - low-mediumIndustry was under the grip of licensing and regulation. Liberalization did attempt to reduce this, but government tended to favour local companies for major projects.

Business procedures - medium Getting approvals, land allotment, etc was tedious processes

compared to western countries. Legal framework was also weak.

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% R&D spend - low Prior experience in emerging markets - high

Significant portion of their revenues come from emerging market operations, with presence in over 70 countries

Manufacturing or not - yes Maturity of capital markets - low-medium Country of origin - Europe based

Additional variables: Relative size of the affiliate - HIGH Quality of executive management - HIGH (esp. in Gujarat

Ambuja) Year of entry - 2005 Recognition of brand in local country - LOW Availability of funds - HIGH State of country economy at time of entry -

o COUNTRY SOVEREIGN RATING - MEDIUM

Sector : CEMENT Growth of local industry : 8.5 - 9.5 % ( v high)

(http://www.indiainfoline.com/nevi/holc.html)

Appendix 2.3: Automobile industryEVOLUTION OF THE INDUSTRY

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Automotive sector- Passenger Cars14

The post-liberalization period saw a boom in passenger car sales primarily due changes in government policies, growing middle class and availability of car finance. The industry was deregulated in 1993 (100% FDI) and several MNCs entered at this time including Ford, GM and Daimler Chrysler. These companies could capitalise on their global economies of manufacturing and R&D in targeting the untapped Indian market.

GrowthThe auto industry has a reported CAGR of 13.4% between 1994-2002. Between 1994-1997 the CAGR was around 24%, a boost owing to the entry of several players post liberalization. Key demand drivers for growth of car industry include:

1. growing middle class with higher levels of disposable income2. availability of low cost finance for purchase of cars3. availability of more options

14 ICRA Automotive Industry- The Indian Passenger Car Industry, Nov 2003

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4. competitive pricing, aided by government reduction in taxes, excise duties, etc

5. a significant used car market

The cars could be divided into segments based on price and size. Post liberalization, the market was still highly under penetrated, thus offering huge potential for MNCs. Prior to liberalization, there were very few players in the market- Maruti Udyog Ltd, Premier Automobiles Ltd and Hindustan Motors. The entry of MNCs over the 90’s maybe summarized as follows:

Most MNCs entered India through JVs with local companies. This was essential as they needed the local companies to provide them insights into the local market conditions, building dealer and distribution networks, etc while they brought in technological expertise. Later on, when the Indian partners in the JVs were unable to pump in required amount of money for expansion, the MNCs converted the JVs into WOS or increased their stake significantly.

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A 60% tariff on completely built up imports turns the production in favour of domestic manufacturers. Hence MNCs would be looking to set up larger plant capacities than import from elsewhere. The excise (40%, revised to 32% in 2002) and customs duties (around 30%) also continue to be high.

Auto Industry Analysis- post liberalization (around 1992-96)

Rivalry among Existing Firms

LOW, Increasing

Mainly only Maruti, HM and TATA in the fray in PV market

Liberalization of the economy was set to boost demand and several MNCs were set to enter

Threat from Potential Entrants

HIGH, Increasing

Capital intensive industry hence financial strength important to develop adequate capacity and distribution network

Cheap labour available for manufacturing for MNCs

Govt was favourable to potential entrants with sops, land, etc

Lower tariffs post-WTO may expose Indian companies to threat of imports

Dealer networks important which MNCs would not have

Huge untapped market, esp., in urban segments

Bargaining power of Buyers

Low Customers had limited choicesQuality perceptions of

international brands were

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higher

Bargaining power of Suppliers

Low

Large number of suppliers in a fragmented industry

Automotive players are aiming at rationalizing their vendor base in order to achieve consistency in quality

Threat of substitutes

Low

Hardly any substitutes in local market

Nearest substitutes- two wheelers- cheaper

Public transportation- inconvenient

Low vehicle density, traffic congestion low even in urban areas

Overall industry attractiveness- HIGH

SALIENT POINTS FROM PORTER ANALYSIS Need for distribution network Need for large manufacturing capacities Economies of scale Branding and marketing are gaining importance as several

players enter the industry.

VALUE CHAINSourcing raw manufacturing distribution via

branding, marketing,Materials and dealer networks and advertising

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Auto components

Due to availability of cheap labour, most MNCs set up manufacturing plants in India not only to cater to local demand but also for exporting to other countries, making India the regional hub for their activities in Asian countries.

The logit variables for the auto industry maybe defined from the point of view Ford Motor Company and its entry into India.

FORD MOTOR COMPANYFord Motor Co., an American automobile manufacturer entered India through the joint venture route. They formed a joint venture with Mahindra & Mahindra as a technical collaboration, where Ford would bring in technical expertise and M&M would provide manufacturing capabilities. Later on, they went on to set up their own manufacturing plant in India.

LOGIT VARIABLES DEFINED Technology intensiveness - Medium-high

o MNCs like Ford bring in technology and quality standards to differentiate

Competition in local market - Lowo At the point of entry, very few local players

o Several MNCs looking to enter around the same time

Need for distribution network - Higho MNCs lacked dealer and distribution networks, which were

extremely important in the industry Need for local technology - Low

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o Ford would bring in the required production and manufacturing technology, designs, etc.

Need for brand - Lowo The local company ford might want to tie up with was mainly

to provide dealer networks Need for marketing capabilities- Medium-high

o Local company would have to have a basic understanding of local consumer needs, etc.

Need for business networks- Higho Local dealer networks were extremely important for success

in this industry Need for tangible assets - Medium-High

o Local manufacturing capacities could be used

o MNCs were also ready to invest in new plants adapted to their technologies

Local institutions - Mediumo MNCs would require infrastructure support for their

manufacturing plants, transportation of vehicles, etc. Government support - low-Medium

o growing with deregulation, reductions in taxation, etc

Business procedures - Lowo 100% deregulated, easy for MNCs to set up manufacturing

centres here Extent of R&D spend - medium

o % of rev= 4.5%

Prior experience in emerging markets - none o India was among the first emerging markets they entered

o China, Turkey: China entry in 2001, Turkey- 2002

quality of executive management- medium-higho ford formed a JV with M&M

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o they had long term experience in Indian market

o but their focus had been tractors, jeeps, etc. Hence they might have been unfamiliar with the passenger car market.

Year of entry o 1995

Extent of liberalization o FDI cap= 100%

Manufacturing or not - yes Maturity of capital markets- Low-medium (India as a whole)

o Markets were still in stage of maturing, no easy access to capital, etc.

Country of origin - AMERICA o where the philosophy was to decentralise holdings and let

the subsidiaries manage their operations.

Other variables: recognition of brand in India

o high

o Ford was well recognized international company

Appendix 2.4: Oil and Gas industry

INDUSTRY CHARACTERISTICS15, 16

A porter framework analysis of the industry yields following results:

1. Annual turnover of US$ 80 billion, accounts for 30% of imports bill

15 Upstream Oil & Gas Sector, ICRA Sector Analysis- ICRA Information, Grading and Research Service, February 200516 The Indian Oil & Gas Sector, ICRA Sector Analysis- ICRA Information, Grading and Research Service, October 2004

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2. Historically, a government controlled sector with limited deregulation in march 2002.

3. While global players have integrated operations, the Indian industry is broken down into Upstream exploration and production, Downstream refining and marketing, natural gas distribution. Deregulation is aiding integration across segments.

4. Segments like lubricants which have been deregulated see intense competition among existing players. Other sectors too are seeing a gradual increase in competition

5. Refiners do not have too much bargaining power are their fortunes depend on their suppliers. But the end consumers have higher bargaining power as there is not much differentiation between products and hence no loyalties.

6. Entry barriers include:a. Huge capital outlayb. Restrictions on entry into automotive fuel marketingc. Technology intensive (upstream)d. Distribution and logistics intensive (downstream)

7. In markets like transportation, there is no threat of substitution. Whereas in power segments, other sources of power become substitutes and hence price becomes a discriminating factor.

SUCCESS FACTORSSuccess depends on:

1. product mix2. cost competitiveness3. infrastructure4. integration into attractive segments along value chain

UPSTREAM SECTOR

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The major player here is ONGC. Other players include REL, British Gas, Oakland international, Tata Petrodyne, etc.Most of the major players have been making losses in recent years.

6.5% decline in net sales 3.9% decline in operating profit 17.7% decline in net profit

The level of risk and volatility involved in this sector is high. While the demand has been steadily rising, production has almost stagnated.

Limited success by private players:1. only 15% of potential oil bearing fields have been given out to

private sector2. considerable delay in awarding contracts by government. Since

1999, the government is trying to expedite the process to attract FDIs

3. absence of major oil discoveries in past 15 years has discouraged oil majors.

GAS SECTORKey features are:

no major discoveries in recent years

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GAIL (government) has virtual monopoly over distribution Private players like Shell are working on Liquefied gas, etc Downstream refining and marketing sectors are seeing growing

levels of competition.o E.g. Speed petrol, by BPCL

Price competition has not yet started, but is anticipated due to the growing presence of large players like Reliance and Essar

The two most common entry routes in this sector are acquisitions/ green field ventures

Refining complexities is below global averages; marketing throughput is higher the international standards

Commissioning Reliance refinery has resulted in surplus in the industry

Market players in oil sector:

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80

81

Taxation:This is a heavily taxed sector.

35% corporate tax 20% royalties on Crude oil, customs duty of 10% on imported

crude, 4% sales tax Natural Gas: 10% royalty, 5% customs duty, 16% excise duty,

sales tax varying from state to state.

LOGIT VARIABLES DEFINED- ROYAL DUTCH SHELL

Royal Dutch-Shell entered India through the joint venture route, forming a JV with Bharath Petroleum Corporation Ltd. forming Bharath Shell Ltd, with a 51% stake in the venture. Their undertakings in India include:

Bharat Shell Limited    Bharat Shell Limited, a joint venture between Shell Overseas Investments B.V. (an affiliate of The Shell Group) and Bharat Petroleum Corporation Ltd. (BPCL), markets a range of Shell branded lubricants in India.

Shell Hazira Project    The Hazira LNG Terminal & Port Project near Surat is currently the largest of The Shell Group ventures in India and includes an LNG receiving and storage terminal within a functional port. It is one of the largest Foreign Direct Investment projects in energy sector in India. Shell’s Hazira project brings world-class experience in delivering liquefied natural gas.  

Shell Gas (LPG) India Private Limited    Shell Gas (LPG) India Private Limited, a fully-owned Shell subsidiary, markets LPG under the brand name "Shell Gas" in parts of Western and Central India. Shell Gas services both the packed and bulk segments of the LPG market.  

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Shell Solar India Private Limited    Shell Solar India Limited brings to India Shell's commitment to renewable energy. Shell Solar markets solar-powered home lighting units in rural districts of South India.

LOGIT MODEL VARIABLES Technology intensiveness - medium

o In general the technology intensiveness depended on the nature of the processing:

Upstream process-High Refining- medium Marketing, distribution- low

Attractiveness of industry - low-mediumo Few players, low competition

o Very high capital expenditure

Need to build distribution network - higho This depended on the nature of the processing:

Upstream, refining- low Distribution- High Need to lay pipelines, etc

Need for local technology - Medium-higho Knowledge of local geographies, etc required

Need for brand - low-mediumo Exploration, refining- low

o Distribution- medium

o Not an end user product, more for industrial uses

o Few suppliers, lower need to differentiate

Need for marketing capabilities - medium

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o Exploration, refining- low

o Distribution- high

o local knowledge for marketing to Indian consumers needed (local consumer behaviour)

Need for business networks - Higho Approvals, licence, for securing government projects, etc

Need for tangible assets - Higho Capex requirements high, investments in equipment, etc

Local institutions - Medium-higho Need for local power supply, equipment sourcing, political

contacts, transportation, etc. Government support- low-medium

o been under government monopoly all this while

o but government wants to encourage FDI

Business procedures - Higho Licensing, project approvals, permissions, etc required

Extent of R&D spend - lowo The drilling and refining process technologies were fairly

well established and Shell was not innovating on that front prior experience in emerging markets - low

o India was among the first few emerging markets Shell was entering

quality of executive management- N.A year of entry

o distribution company set up in 1928

o 1994 BSL entered lubricant market

Extent of liberalization o FDI- 100%

Manufacturing or not o not

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Maturity of capital markets - Low-Mediumo Foreign companies can’t raise equity at that time?

o Markets not mature, scams, inefficiencies, not yet computerized? , etc.

Country of origin - UK (Europe)o Where the philosophy was to decentralise holdings but

maintain control over the company

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Appendix 3Correlation Matrix

 

entry mode variable

Tech intensive

ness

attractiveness of local

industry

local resource

needs

govt support

% R&D

exp in mergi

ng mkts lib.

manu. U/E/J

Tech & R&D

combined

entry mode variable 1                    

Tech intensiveness0.080

1 1                  attractiveness of local industry

0.2603 0.1000 1                

local resource needs

-0.153

1 -0.0386 0.0759 1              

govt support0.131

4 0.1181 -0.2100-

0.7034 1            

% R&D0.103

3 0.7387 -0.0453-

0.05190.062

2 1          

exp in merging mkts0.212

8 0.0508 0.0388 0.0979

-0.145

1

-0.00

54 1        

lib.0.189

3 0.5034 0.0590 0.01980.151

70.53

54 0.0222 1      

manu.0.104

4 0.8993 0.0890-

0.01140.073

40.66

28 0.07780.51

06 1    

U/E/J

-0.168

7 -0.0637 0.0228 0.3483

-0.268

6

-0.08

68 0.2527

-0.05

000.01

55 1  

Tech & R&D combined

0.0999 0.9126 0.0190

-0.0494

0.0926

0.9497 0.0203

0.5586

0.8198

-0.082

3 1

86

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Appendix 4Assumptions of Logistic Regression

Logistic requires that:1) The independent variables be interval, ratio, or dichotomous;2) All relevant predictors be included, no irrelevant predictors be included, and the form of the relationship is linear;3) The expected value of the error term is zero;4) There is no autocorrelation;5) There is no correlation between the error and the independent variables;6) There is an absence of prefect multicollinearity between the independent variables.

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