Mcdonald’s Five Forces

M ATTRACTIVENESS IN THE DEVELOPING WORLD Mergers and acquisitions form the majority of FDI call fors in the demonstr adequate to(p) terra unwaveringlya, and remain coiti scarce scarce as a mode of creation in the ontogenesis world. The infrequent social function of M as a overseas reign enthronization (FDI) compliance modality into create sections has motivated this study. As a first flavor in exploring the M paradigm in ontogenesis merchandises this paper depart classify and rank the M attracter of 117 develop economies.Further, the evidention in the midst of FDI drawing card and M attraction at a landed estate and regional head aim will be illustrated. Mergers and acquisitions, as a mode of FDI be r be in create countries. Only 26, 9 percent of the 11059 FDI develop economy deals record in this study and reason out mingled with 2004 and 2006 were track b regulate merger and acquisition deals, the remaining 73% of deals were solely greenfield. ins ide the period 2002 to 2004, mergers and acquisitions made up a mere 19% of the marrow bring out orthogonal operate investment (FDI) deals concluded in developing economies.In contrast, cross- hoidenish mergers and acquisitions held far great appeal in the positive world where M out subroutineed greenfield FDI deals by making up 51% of the come FDI deals concluded over the uniform period 2002 to 2004 (UNCTAD, 2007). The clear preference for greenfield deals in the developing world indicates that there come by elements within fixs sweet to M which be explicitive from those berths attracting greater greenfield natural action.In ball club to understand these elements, M showy and ugly locations must first be set and classified. M and greenfield be devil distinct modes of first appearance with differing motivations and dissimilar host field understand. M rents the purchase of a conquerling share of stock in an knowing host republic true with drudg ery capacity (Raff et al, H. , Ryan, M. and Stahler, 2008) whereas 1 greenfield investments see the impertinent firm building its own in cap fit business, and sourcing all resources right off from the foodstuff place ( noneke and Yeaple, 2007).The FDI drawing card of economies has been easy explored in the literature. However, seek on the role of FDI in economic breeding is dominated by a generalised view of FDI where the legal sepa ration of entry mode strategies was non central. several(prenominal)(prenominal) authors remove commented on the underreporting of M as a process distinct from the FDI umbrella in the literature these same authors gestate begun to explore in greater depth the M concept (Kogut & Singh, 1988 Raff et al, Ryan & Stahler, 2005 Nocke & Yeaple, 2007 & Haller, 2008).The M literature is pure on the veritable economies of the world as the greatest rule book of M activity has historically occurred in developed regions. Much of the literature on M de scribes the increase occur of these deals and its importance in global FDI, oft by referring to the global primitive (Haller, 2008 Bjorvatn, 2004 Horn & Persson, 2001, Shimizu, Hitt, Vaidyanath, Pisano, 2004). N unity of these studies give way referred to the sexual congress scarcity in utilisation of M in the developing world congenator to the developed regions of the globe.This paper aims to make a contribution non just to the acclivitous literature on M but to a fault to its particular developing economy paradigm. The methodology of this study al sufferings for the recognition and rank of FDI cute economies, M magnetic economies and for the distinction to be drawn among M amiable economies at the res publica aim and M attractive force at a regional train. At the democracy aim M attractive economies are economies which attracted more M than greenfield deals internally i. e. economies attracting a greater ratio of M activity to greenfield investments.regional M a ttractive economies were define as economies which whilst attracting heroic batchs of M activity within a region were not attracting a greater number of 2 M deals internally. Greenfield deals reside to dominate these trades. In another(prenominal) words these countries were M attractive by virtue of organism FDI attractive. FOREIGN DIRECT INVESTMENT IN highly-developed AND DEVELOPING ECONOMIES Understanding the distinction between developed and developing economies and foreign direct investment in these marts is fundamental to this study.Per capita income, an indicator of the wealth and authority of a market, is an important manifestation of the divagations between developing and developed economies. regrettably however, developing economies are subject to frequent policy regime switches and yield rate volatility when compared against the assembly of developed economies (Aguiar and Gopinath, 2007). Productivity in emergent markets is unstable, here the cycle of policy-ma king and economic shocks have become trends (Aguiar and Gopinath, 2007).The income in beity, higher(prenominal) poverty directs, governance, institutional mise en scenes (North, 1994 Peng and Heath, 1996) and the take of economic and military man development of developing economies is offset by the fact that since the early 1990s these countries have in any(prenominal) case been the fastest growing market in the world for products and divine services (Khanna and Palepu, 2005). The strategical plectrons made by multi subjects pursue in developing markets must ineluctably be considered with reward to the higher up mentioned host dry land parts.Many developing economies which are characterised by an accelerated pace of economic development and a liberalisation or opening of their economies by the application of free market principles are shapeed emerging economies (Hoskisson, Eden, Lau, Wright, 2000). Other rapid ripening countries include in this group are the transiti on economies of Eastern Europe which were historically think economies but have now adopted free market principles (Hoskisson et al, 2000). 3 The literature is dominated by developed economy FDI.However, FDI patterns observed in developed countries cannot be generalized to transitional or developing economies (Pan, 2003). Blonigen and Wang (2005) have established that the computes determining the location of FDI vary systematically between developing and developed countries (Blonigen and Wang, 2005). In their paper, Phylatakis and Xia (2006) investigate the dynamics of global, terra firma and effort cause in firm level re staves between developed and emerging, markets. Their findings delegate that specially for emerging markets, land personal personal set up are more important than ndustry effects in exempting return variation for firms (Phylatakis and Xia, 2006). Sethi, Guisinger, Phelan and Berg (2003) believe that FDI draw should not only be studied at a firm level but supererogatoryly at a pastoral level as orbit level factors affect the decisions of all firms over sequence (Sethi et al, 2003). In addition, not all of the hypothesized relationships in the literature on FDI (e. g. exchange order and source state size) were supported in a study on the transitional economy of China (Pan, 2003).This extracts that the developed and developing region FDI paradigms should be studied as distinct entities. LOCATION FACTORS Encouraged by captain technology, faster and cheaper communications and motivated by intensifying competition, businesses are able to scour the globe in search of locations whirl advantages which increase the competitiveness of the firm. Location advantages refer to the institutional and productive factors which are present in the particular geographical area chosen for FDI (Galan and Gonzalez-Benito, 2006).Dunnings OLI theory explains a firms quality for a particular FDI ending. First the al-Qaeda based firm must posse ss an ability which it is able to 4 exploit abroad and which is portable. This is termed the ownership advantage (the O advantage) of the firm. The L, which is the counseling of our research, refers to the location which must have desirable qualities and offer advantages to the firm. Examples of this would include monumental markets, production factors including cheap or skilled labour or indwelling resources. A locational advantage would enhance the profits of a firm.The I refers to internalisation, which implies the firm has more to gain from the essence authorization of the asset than by allowing control to easement with export agents or licensees (Dunning, 2001). Tong, A littleandri, Reur and Chintakananda (2008) find that untaught and industry effects and their interaction substantially influence firm mental process. The authors advocate that industries with growth opportunities escort how to exploit state specific factors by kettle of fish operations there. withal though low labour be are utilize by many developing economies to attract FDI (e. g.China and Vietnam) studies show that it is of far slight consequence to FDI inclineerness than host market size and distance. Total be of production taken together are however largely authoritative in the direction of FDI flows. High labour be whitethorn be mitigated by the infrastructural spend on health and education which would head in a healthy, skilled and more efficient workforce which in turn acts to demean approach (Bellak, Leibrecht and Riedl, 2008). In collar M attachment it is important to first mention the literature on FDI attraction, that is why firms go to foreign locations.According to Fontagne and Mayer (2005), firms will go to foreign locations if there exists adapted demand in the country or region, make sense production cost incurred at the location are low, intense competition is not a threat, public policies are advantageous and institutions create productive and e fficient economies in which to operate. Foreign locations whitethorn similarly be desirable in order to leverage economies of scale, take advantage of arbitrage opportunities involving factor cost, to diversify and prune attempt, exploit distinctive 5 dvantages to gain market and to escape from increasing groundwork market competition (Rugman & Li, 2007 and Rugman and Verbeke, 2001). because we may expect that economies offering locational factors conducive specifically to M will display greater attracter harbors. In light of the statements preceding(prenominal), host country demand amongst other factors is prudent for the decisions of firms to choose foreign locations it leads us to believe that market size or the GDP of a country has an important role to play in M attraction.Therefore it may be expected that the larger a countries GDP the greater the M activity it will attract. First documented by Knickerbocker (1973) is an idiosyncrasy in the movement of firms. Firms f ollow into locations where other firms from their industry have al recordy entered despite the increase in competitive transport this gene evaluate. Therefore M draw may also be cogitate to the number of firms already functioning within the host market. This agglomeration tendency may be linked to supply chain and input-output linkages.Further by locating affiliates close to other international affiliates they may be able to realise from absorbing technological spillovers. The effect of this would be the lowering of R be and raising the firms competitiveness by enabling it to stick out abr easternmost of competitor outline (Fontagne and Mayer, 2005). REGIONAL rustic LEADER emergence Part of the focus of this paper is to explore a regional symmetry of FDI and M. Much of the literature on regional attractionship effects concerns Japanese FDI into the Asia-Pacific region.The flying geese model by Ozawa describes the trend where mature products and industries are shifted fro m one country to another(prenominal) more peripheral lower cost destination within the region 6 (Ozawa, 2003 and Kojima, 2000). As the host country costs rise so it too moves toward higher value add products and the production of the good moves to the next low cost destination (Edgington and Hayter, 2000 Hart-Landsberg and Burkett, 1998). In this commission advantages such(prenominal) as technology, employment, real incomes and innovation may cascade by means of a region (Clark, 1993).Several studies have shown that when MNCs first conception to internationalise they choose geographically and heathenly proximate regions, this is known as the market familiarity principle. In this way home based skills, advantages, vigilance and resources may be leveraged to minimize transaction costs (Gomes and Ramaswamy, 1999). In regionalism and the regionalisation of internationa describe Trade, Gaulier, Sebastien and UnalKesenci (2004) explain the idea that regionalisation is a natural patt ern and that the pile of inter-neighbour championship between countries is high due to the economic sense of affair over shorter distances.Various studies find that countries have the bulk of their foreign workmanship concentrated within a particular triad region (Gaulier, Sebastien and Unal-Kesenci, 2004 Rugman and Verbeke, 2004). In their study on 64 Japanese multinationals Collinson and Rugman (2008) found that only 3 operated globally with the remainder concentrating 80 % of their operations (gross revenue & assets) intra-regionally. More importantly, with implications for this study and the attraction of M, was the finding that region-specific regionalisation trends are linked to changes in infrastructure, information or ethnical ties.Large regional trade agreements, especially when a custom amount of money exists, were also shown to have positive effects on trade mint and created lucrative opportunities for foreign producers. The trade agreements allowed access to a lar ge market from a single country, even if it was a small market than its neighbours (Gaulier, Sebastien and Unal-Kesenci, 2004). This paper 7 reinforces the importance of institutions in developing regional trade and mentions specifically that a positive gravity factor of regionalisation could be the swift quickening of GDP growth of other countries within a region.Policy makers should take note that contractual relationships present significant risks to foreign MNEs in host countries which have linguistic, legal and economic institutions systems vastly different from the home country (Clark, 1993). Promoting and facilitating corporate governance would have a positive regard on inter-company linkages with the extremumant promotion of regional development. The ability to access risk finance and instruments make it critical for a firm to operate in an advantageous national location within a region (Clark, 1993).Pajunen (2008) reinforces the above idea of a MNE firm hard-hitting f or the or so advantageous location within a region. In order to access the rapidly expanding emerging economy market a firm may make a strategic decision to enter south intimately America or SouthEast Asia and will then search for the most attractive location within that region to trade from (Pajunen, 2008). As we have seen in an previous paragraph, the growing number of regional trade agreements allows the MNE to transact with minimal trade costs within a region. The regional leader attracts the most FDI in a region. This research asks the hesitancy who attracts the most M and why?This question may be answered by the findings of Qian, Li, Li and Qian (2008). Qian, Li, Li and Qian (2008) confirm that firms are regionally focused and also offer an chronicle for the regional internationalization of firms quite a than a fully global expansion. They find that firms costs are lower intra-regionally and hence consummateance is enhanced. They add however that a doorsill to performa nce is r distributivelyed intra-regionally and that a developed country MNE may maximise performance by go into into a moderate number of developed country regions and a strictly limited number of developing regions as costs here are substantially 8 ifferent. They advocate the careful plectron and allocation of resources in developing regions as over-diversification here will number in costs outweighing benefits (Qian et al, 2008). This reinforces the idea of a regional FDI leader in the developing country context that is a safer haven for MNE resource allocation. taking into account this evidence, it is possible to assume that as regional cooperation is enhanced so inter-regional trade is encouraged which results in greater amounts of FDI and M which will flow into a regional leader country with the safest reputation. MERGERS AND ACQUISITIONSAn imperative of a foreign investment entry strategy is to minimise the cost of entry in order to render the venture more profitable. Cult ural barriers and socio-political differences between the fresher and host raise the cost of transacting and thus the entry mode chosen will attempt to reduce this. M AND CAPABILITY SEEKING MULTINATIONALS Firms have capabilities in their own markets which are not necessarily internationally mobile, may not be useful in a foreign market or the firm may require a set of additional competencies to operate successfully in the foreign market (Anand and Delios, 2002).Anand and Delios (2002) offer a description of upstream capabilities which are described as fungible and portable an example of this may be intangible technological know-how. By engaging in a cross- boundary M the firm is able to access the local knowledge and downstream capabilities of a local firm and use this to attachment its portable advantages in serving the new host market (Nocke and Yeaple, 2007). Examples of capabilities or advantages which the local firm may possess include taint, marketing and gross revenue for ce knowledge, privileged access to 9 istribution channels, a capability to shimmer through local institutional voids and take exceptions (Khanna and Palepu, 2005), emission rights for environmental pollution, landing slots at airports, scarce land or oil/mineral extraction rights amongst others (Horn and Persson, 2001). exchangeable upstream capabilities are a stronger driver for acquisitions than downstream capabilities which are less fungible (Anand and Delios, 2002). Developing countries are less likely to have blue-ribbon(prenominal) technological capabilities than the potential developed country acquiring firm.The lower ordinariness of the developing market would thereof limit the number of acquisition targets operational for a developed country MNE. Acquisition targets for downstream capabilities (marketing, brand etcetera ) would hold greater appeal in countries with large target markets. The number of M deals can therefore be expected to relate to market size (GDP) a nd market sophistication (represented by aspects like the level of human development and infrastructure). The number of M deals will also be relate to the number of local acquisition targets available which in turn is dependent on the level of development of the country.ACQUISITION DRIVERS The initial choice to soak up in FDI over export is dependent on how profitable the firm expects the greenfield or M to be. The second strategic choice of greenfield over M is related to the firms ownership of productive assets and varies both across and within industries (Raff, Ryan and Stahler, 2005). A cross border-merger provides access to a foreign market whilst a national merger relieves domestic competitive contract. When trade costs are low however national mergers do not reduce competitive pressure and firms will seek access to foreign markets through a cross-border merger. sparing integration results in lowered trade costs and therefore change magnitude competition which is likely to increase the profitableness of acquisitions (Bjorvatn, 2004). The lowering of trade costs 10 which is dependent on host country regulations will therefore increase the level of cross-border M activity. The literature describes one of the main advantages of cross-border M to be the access which it provides to a foreign market (Horn and Persson, 2001) whilst within border mergers are generally attributed to relieving domestic competitive pressure (Bjorvatn, 2004).Raff et al (2008) explains that firms entering a foreign market will approach local firms with a merger and acquisition or joint venture proposal in order to enjoy the synergies of such a relationship. Raff et al (2008) maintain that a merger & acquisition offer will be accepted by the local firm if the profitability and success of a greenfield investment by the multinational is likely and credible. Further, the greater the anticipated profitability of the greenfield investment the lower the merger & acquisition price offer ed to the local firm.Hence M& A would be favorite(a) over greenfield as the entry costs would be lowered. The choice of greenfield over M will depend on the number of competitors in the market and the market potential as this affects the anticipated profitability of the greenfield venture or the cost of the M (Raff et al, 2007). This leads us to hypothesize that countries with greater market potential (GDP, GDP per capita and HDI) and fewer local competitors will result in a lowering of the cost of an M which in turn results in increased volumes of M.CULTURAL CHALLENGES AND THE LIABILITY OF FOREIGNNESS Mergers and acquisitions and partially possess ventures offer the opportunity for a foreign MNE to access local assets such as brand, distribution ne twainrks and a client-base which is difficult to mobilise from home by working with local established companies (Petrou 2007). In instances where large cultural distances exist between home and host countries, Brouthers and Brouthers 1 1 (2000) advocate the use of acquisitions in order to confer legitimacy and cceptance on the foreign MNE. However, M involve greater costs when the cultural distance is high and therefore Chang and Rosenzweig, (2001) declare that firms would be more likely to choose greenfield entry to avoid the costs of integrating diverse company cultures. Greenfield investments offer total affiliate control and avoid post merger cultural difficulties but take a far longer time period to establish market presence and require substantial experience and know-how of local conditions (Chang and Rosenzweig, 2001).Most recently Slangen and Hennart (2008) have found that MNEs will prefer acquisitions in culturally out-of-town locations if they have little international experience or if they plan to select the subsidiary autonomy in marketing. If they are internationally experienced or have no market related concerns then a greenfield is preferred in culturally distant locations. The entry choice is a lso industry-specific depending on the resource requirements of the firm.Manufacturing operations tend to favour greenfield deals whereas in advertising where brand and product are tailored to local tastes acquisitions are preferred as FDI entry strategies (Kogut and Singh, 1988). The above information alludes to the idea that M will tend to occur in the services industry as it confers on the MNE an understanding of, acceptance within and access to a foreign market. The information examined above dealt with the cultural challenges of M. The next section will broach the subject of institutional challenges in M deals especially in developing economies.M chastening 12 Approximately 70%-80% of all mergers fail (Bretherton, 2003) and KPMG reports only 17 % of cross border M s create value magic spell 53% destroy value (Shimizu, Hitt, Vaidyanath, Pisano, 2004). These statistics may be part of the explanation for the lower volumes of M deals in developing economies where investor firms m ay be vigilant of entering into deals already known to have high failure rates and then compounding this in an environment fraught with challenges i. . developing regions. Therefore many organisations choose to enter into strategic alliances and joint ventures which allow them the benefits of searching for new market opportunities, sharing in innovation and technology, overcoming host regulatory requirements and developing new capabilities. Importantly however these alliances are easier and less dear(p) for companies to enter and exit should the need arise. IMPORTANCE OF LEGAL AND FINANCIAL FRAMEWORKS TO stand out MNESMarket inefficiencies related to the resource visibleness and institutional profile of a host economy may be overcome by the entry strategy of the MNE. Chang and Rosenzweig (2001) assert that an acquisition is the quickest way for a firm to build a sizable presence in a foreign market. The challenges of this mode however involve the post acquisition cultural merge , the risk of overpaying and an inability to fully assess the value of the acquired assets (Chang and Rosenzweig, 2001).In a developing market context additional challenges to M include the scarcity or absence of legal, pecuniary and institutional organisations and structures through which the deal could be investigated, formalised and protected and is further complicated by the existence of heavy host country regulations relating to ownership (Khanna and Palepu, 2005). HYPOTHESIS 13 It is expected that M attractive economies in the developing world may be identified as a group distinct from FDI attractive economies depending on the context of the location factors of the host economies.It can therefore be hypothesised that M magnet does not equal FDI magnet and that varying levels of M attractiveness occur. search DESIGN SAMPLE AND DATA SOURCES The World Bank and UNCTAD, through the annual World investiture bailiwick and World Investment directory, publish selective informat ion on over 210 economies which are divided into developed and developing economies. In this study info were assembled for 117 developing and transition economies.Blonigen and Wang (2004) in their examination of the FDI experiences of developed and developing economies conclude that the variation of selective information across these groups makes it inappropriate to puddle data on them in confirmable analyses. A further systematization for the isolation of developing economies from developed economies in this paper can be found in North (1994), he writes that the experiences of actors in highly developed modern economies may not be compared to that of individuals operating under conditions of uncertainty, political or economic.In order to identify regional FDI leaders, for the purpose of this study, the country data was divided into regional groupings (see table under) according to the unit of measuremented Nations statistical Office as published in the UNCTAD World Investment Report classification for 2007. card 1 astir(predicate) here VARIABLES AND MEASURES The outline aims to separate FDI attractiveness from M attractiveness and to rank the attractiveness of developing countries to mergers and acquisitions.The data for value and volume 14 of M in the sample of developing economies was taken from the up-to-the-minute available M and greenfield data published by UNCTAD (based on data from Thomson Financial) over the period 2004 to 2006. Six uncertain quantitys were created. The table below describes, explains and shows the grouping of the variables. root A in table 2 below represents country M attractiveness. Two ginmills numbers 1 and 2 were used to measure attractiveness at the country level.One is volume based that is the number of deals in one country as a percentage of the countrys total deals, whilst two is value based that is the long horse value of deals which flowed into the individual(prenominal) country as a percentage of GDP. Thus the measure for country level M activity has two dimensions in this way the variable carries richer information and is less likely to be skewed by a single, large dollar value deal. As this measure is computed using per country total deals and per country GDP as the denominator, it is an intra-country measure. concourse B in table 2 represents regional M attractiveness and contains 3 measures. again both a volume and a dollar value were used to measure regional M activity for the same reasons identifyed above for country attractiveness. If for example a country attracted one real large dollar value deal, but no other deals, it may be read as an M attractive economy when in fact it only attracted a single deal. This regional group of variables is computed using the number of total regional M deals, the number of total regional FDI deals and the dollar value of the total regional FDI inflow as the denominators. Thus it measures the countrys M volume and value respective to the regi onal total.It is an intra- regional value. Group C in table 4 contains one measure for the FDI attractiveness of a country in a region. This measure includes all deals (greenfield and M) which a country attracts with respect to the total number of deals concluded in its geographic region. 15 evade 2 near here METHOD OF ANALYSIS The statistical challenge in this study was to find a method which would allow for the separation of FDI attractive economies from M attractive economies and of M attractive from M unattractive economies. Two statistical methods were utilised to test the variables.A plunk compendium allowed for countries with similarities based on the variables to be foregathered together. A spark advance destiny summary was performed in order to create an M attractiveness be of the sample countries. bunch ANALYSIS INTRODUCTION TO foregather THEORY A cluster outline is a statistical tool which allows for the discovery of pregnant structures within data without ex plaining why they exist. This allows data to be sorted into groups or categories where the members of apiece group have a high degree of connexion with all(prenominal) other and a minimal association if they belong to another group.Thus this technique places the economies under study into clusters based on well defined similarity rules and finds the most significant groups of objects. (http//www. statsoft. com/textbook/stcluan. html) Clustering is the term used to describe the presence of separate and distinct groups in the data however if clustering is not recognized by failing to visually inspect the data (scatterplots or another graphing technique), the correlation coefficient may suggest that no relationship exists even though within each cluster a clear relationship may indeed exist (Siegel, 2000).As an initial exploratory step and in order to determine which of the variables listed in Table1 were most successful in dividing the economies a cluster depth psychology was perfo rmed. 16 The data for some variables such as GDP had a very different scale to the some of the smaller scale set e. g. Polcon 3 index. The data was thus standardized to allow each variable an equal opportunity to display significance in the cluster analysis and continue any one variable dominating (Boudier-Bensebaa, 2008). A cluster analysis was run on the variables listed in table 2 above.A iv cluster solution was accepted as all the clustering variables proven to be significant. PRINCIPAL COMPONENTS ANALYSIS A principal components analysis allows for the identification of vestigial factors in the variables which account for the largest sectionalisation amongst the data set of 117 countries. Table 3 below shows the variables used in the principal component analysis grouped at the country and regional level. This analysis is undertaken in order to create an attractiveness value per country which allows the developing countries to be be based on their M attractiveness score.Und erstanding Principal portion abridgment The principal component analysis (PCA) is a data decrement technique that distils the essence of several variables into a smaller number of components which explain the variance in the data. The regional and country variables listed above showed correlations but rather than discard them they are rolled into a two factor tangled M attractiveness value one factor for regional attractiveness and one factor for country attractiveness.The principle of parsimony (simplicity and reduction) is followed by creating an attractiveness value out of the variables, in this way more meaningful and richer measure is created and the dimensions of the data set become more manageable (Siegel, 2000 p586 Berenson & Levine, 1986). 17 The Eigen analysis is the name of the mathematical technique used in PCA. Eigen values show the percentage of variance explained by each component, the largest Eigen value is the first principal component, the second largest Eigen value is the second principal component, and so on. (http//www. fon. hum. uva. nl/praat/manual/Principal_component_analysis. tml). The Eigen values for our study were determined these values were then plot on a scree plot to illustrate the importance of each of the components. A factor analysis was performed on the all the variables in table 3 above. The PC analysis will create factors by reducing the data into its underlying dimensions. These factors allow for an attractiveness score to be generated for each country. THE VARIABLE DENOMINATORS Table 3 about here The country level variables were verbalized as percentages of per country GDP, per country FDI inward stock and total number of per country FDI deals.Therefore outcome values expressed are all calculated with respect to intra-country measures. The regional level variable denominators included the total FDI flows into a geographic region, the total number of M deals in a region and the total number of FDI deals in a region ( e. g. substitution America, North Africa etc) and are expressed as percentages. Therefore all values are calculated with respect to regional totals. By separating the variables a richer result is obtained, the analysis is able to pluck out regional leaders and interesting countries which may not be FDI attractive but nevertheless are M attractive.If the analysis had not made the distinction between attractiveness at the country level 18 and regional level the interesting case of Libya where M deals dominant would have been lost as its total FDI is so small. RESULTS THE FOUR CLUSTER SOLUTION, DESCRIPTIONS AND MEMBER COUNTRIES The results of the four cluster solution is summarised as a profile plot with the means percentages included in table 4 below. The assumption that a country level and regional level group exist in the data was confirmed with the cluster analysis.All the countries in cluster 1 showed a high value for the intra-country number (or volume) of M deals respective to the other clusters. Cluster 1 countries are intra-country performers. They do not perform well at a regional level. Cluster 4 countries are country level performers like cluster 1 but perform better on M dollar sales value than on M volume. For the purpose of this study clusters 1 and 4 are both considered as country level performers, their distinction lies in a difference of measure that is volume of M deals versus value of M deals respectively.Cluster 2 displays a strong performance on the regional level M variables. Cluster 2 also displays the strongest regional FDI attraction. Cluster 2 countries are regional performers. Table 4 about here Table 5 about here put down 1 about here Cluster 3 countries do not perform on any of the variables they may be labelled poor M performers. Table 5 above lists the member countries of each cluster. In light of the descriptions defined above, each of the four clusters has displayed distinctive mean characteristics based on a regional and c ountry distinction and on the strength of the M 19 ttraction. In order to illustrate each clusters level of attractiveness graphically, the clusters have been plotted onto the axes above (Figure 1), the y axis of rotation representing country attractiveness and the x axis representing regional attractiveness. PC ANALYSIS AND EIGEN VALUES The PC analysis in table 6 below shows the reduction of the five variables into a two factor solution which explains 80, 3% of the variance of the underlying variables. The Eigen value is the variance explained by each factor of the underlying variables. Table 6 about here The PC analysis onfirmed the premise held of there being both a regional and a country effect in the data by make full all the regional variables on factor 1 and the country variables on factor 2. Factor 1 is a regional M attractiveness factor and factor 2 is an intra- country M attractiveness factor. The 117 countries on the data table are run against these attractiveness values in order to obtain a regional and a country level attractiveness value for each. This is accomplished by multiplying each countrys variable score by the factors in the table.The regional PC factor value allows for the generation of a regional attractiveness value for each country whilst the intracountry PC value allows for the generation of an intra-country attractiveness value for each country. Two lists are thus created, a list of the 117 developing countries with regional attractiveness values and another containing the same 117 developing countries with intra-country attractiveness values. PER coarse ATTRACTIVENESS VALUES AND RANKING 20 In order to make sense of the country and regional attractiveness values each list was ranked and ordered so that the countries appear in order of attractiveness.The top quartile or quartile 1 (Q1) is the to the lowest degree attractive to M activity, the bottom quartile or quartile 4 (Q4) is the most attractive. Therefore the higher the be t he more M attractive the country is. The pursual countries were not ranked as they had no M activity Azerbaijan, Brunei Darussalam, Cameroon, Equatorial Guinea, Eritrea, Ethiopia, Guyana, Honduras, Myanmar, Nepal, Paraguay, Qatar, Senegal and Suriname. At the regional level the most M attractive economies were India, RSA and brazil nut, Russia, Turkey and Mexico, Table 7 below lists and ranks the most regionally M attractive economies.Table 8 ranks the least attractive regional economies with Burkina Faso, Yemen and Albania being the most unattractive M economies regionally. The countries most attractive to M at the country level that is those countries attracting a greater number of intra-country M than greenfield deals are listed in Table 9, the top ranked countries are Mauritius, Burkina Faso, Bulgaria, Panama, and gold coast. The most unattractive country level economies for M activity are listed in Table 10, with the UAE as the most unattractive followed by Tanzania and Saudi Arabia. Table 7 about here Table 8 about here Table 9 about here Table 10 about here Figure 2 about here 21 Figure 2 above is a scatter plot of the country level economies list on the y axis and the regional level economies list on the x axis. The most attractive country level economies (attract more M than greenfield internally) can be seen on the upper left section. The most attractive M economies on the regional list can be seen on the lower right section of the plotted area. These economies attract the most M deals in their geographic regions.The line drawn through the origin recreates the M attractiveness axes shown in Figure 1 which can be layered over this plot. DISCUSSION For both sets of analyses the regional FDI leaders correlated. This list included the Cluster 2 countries and top ranked regional M attractive countries (India, RSA and Brazil, Russia, Turkey and Mexico). The large market sizes of these regional leader countries have several implications in terms of M attr action. First, large markets attract market quest MNEs, the literature shows that these firms are likely to utilise M as a mode of entry (Buch and De Long, 2001).The fact that they are economic hubs and attract greater volumes of FDI than other developing countries also results in an increased presence of foreign affiliates operating in their markets (Qian and Delios 2008 and Kolstad and Villanger, 2008). These affiliates are likely to be followed by service industry firms (following their domestic clients) into these foreign markets (Qian and Delios 2008) thereby creating a virtuous plenty for increased FDI and M activity. These countries are FDI poster boys in their respective regions and are M attractive by virtue of being FDI attractive.A distinct group of countries emerged as country level M leaders in the PC analysis and as the members of clusters 1 and 4. These comprise an interesting and eclecticist mix of countries which include amongst others Mauritius, Burkina Faso, Bul garia, Panama, gold coast, Kyrgyzstan, Armenia, Croatia, Ukraine, Colombia, Yemen and Azerbaijan. They are not regional FDI leaders but 22 attracted a greater amount of M activity than greenfield activity. In these countries, M attractiveness is not distorted by the regional leader effect and associated FDI attractiveness hence M host location attractiveness can be studied in a purer form.Differences exist between the regional leader group and the country level leader groups which make these groups unique. The Cluster 4 and top ranked country level M attractive economies must possess some interesting locational features considering that these are smaller economies which do not comprise the largest markets in the sample. Given that M are more frequently used as a mode of entry in developed countries, location features may exist in the country level attractive group which mimic certain developed market conditions. M attractiveness at the country level may be a marker for development.T he cluster 2 and regional leader groups whilst attracting large volumes of M activity within a region were not attracting a greater number of M deals internally. Greenfield deals continue to dominate these markets. In other words, it is partly true that these countries were M attractive by virtue of being FDI attractive. Examining however the PC analysis at the country level of M attraction and the cluster 4 countries in the cluster analysis, we are able to identify true M attractive economies i. e. economies attracting a greater ratio of M activity to greenfield investments.It can now be stated that FDI attractiveness does not automatically mean M attractiveness as the analysis has isolated clear groups of countries which are FDI attractive and which attract more greenfield activity and those which are M attractive. Lipsey comments on the absence in the literature of the effects which FDI may have on a countrys consumers. Mergers and acquisitions may result in the consolidation of industries increasing the monopoly king of firms with resulting higher prices (Haller, 2008 Nocke and 23 Yeaple, 2007).Greenfield operations would have the opposite effect by reducing the power of local producer monopoly positions and increasing local competition. At the same time superior technology and innovation brought in by the acquiring firms may improve local production efficiencies thereby lowering the local cost of goods (Lipsey, 2002). The dissimilar spillover effects of greenfield versus M is a clear motivation for the two modes of entry to be analysed and understood as distinct entities, even though much of the literature on the developmental role of FDI treats FDI as a single entity (Dunning & Narula, 1996 Dunning 2001 Rugman & Li, 2007).The effects of M investment into developing regions, local linkages and their equal on growth and development in the host may also be areas of great interest especially to policy makers. Future research directions would be to identify exactly what the macro-economic markers of development are which attract M to certain developing economies. 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Regional Divisions Central America Middle East (West Asia) South Asia South-East Asia selenium Europe CIS (Transition economies) 8 TABLE 2 EXPLANATION OF VARIABLES Variables for the Cluster Analysis Value Based or Volume Explanation of Variable line A Country level attractiveness variables 1 M deals per country as a % of total number of country deals 2 MA sales as % of GDP avg 2004-2006 volume based Examines the volume of per country M deals relative to the total number of FDI deals entering that country. The intra- country equipoise of M to FDI in terms of volume. Examines the value of per country M deals relative to the GDP of the same country.An intra-country measure of the proportion of M to GDP in terms of value. Examines the volume of per country M deals relative to the M deal volume of countries in the region. An inter-country but intra-regional measure. Examines the vo lume of per country M deals relative to the volume of total FDI deals (greenfield & M) of countries in the region. An inter-country but intraregional measure. Examines the value in $s of per country M sales relative to the value of all FDI inflows into the region present the countrys share or proportion of M sales value in the region.Examines which country in a region attracts the most FDI deals in total (greenfield & M) to show regional FDI leader. value based in US $s B Regional level attractiveness variables 1 M deals per country as a % of total regional Ms 2004-2006 2 no of per country MA deals as a % of all regional deals 2004-2006 3 M sales per country as a % of total regional FDI inflow ( US$ millions) 20042006 no of deals per country as % of total regional deals 20042006 volume volume value in US $s C Overall FDI attractiveness variable volume 29 TABLE 3 PRINCIPAL COMPONENT VARIABLES train attraction Country level of Combined Country direct And Regional Level Variable s In Order To Create Component magnet set At The Country Level And At The Regional Level M sales per country as a % of FDI inward stock per country (US $millions) 2004 -2006 MA sales as % of GDP average 2004-2006 M deals per country as a % of total regional Ms 2004-2006 no of per country MA deals as a % of all regional deals 2004-2006 M sales per country as a % of total regional FDI inflow ( US$ millions) 20042006 Regional level 30 Table 1 profiles of cluster means for a 4 cluster solution 31 Table 5 CLUSTER COUNTRY MEMBERS Cluster 1 Belize Brunei Daruss Burkina Faso congo Guatemala Kyrgyzstan Libya Macedonia, Mozambique Nicaragua Paraguay Qatar Rwanda Swaziland Zimbabwe Cluster 2 Brazil India Indonesia Malaysia Mexico Romania Russian Fed South Africa Thailand Turkey UAE Cluster 4 Armenia Bulgaria Colombia Croatia Ghana Mauritius Panama Ukraine Cluster 3 Albania Algeria Angola Argentina Azerbaijan Bahrain Bangladesh Belarus Bolivia Bosnia & Herz Botswana Cambodia Cameroon chile Congo, DRC costa Rica Cote d Ivoire Ecuador Egypt El Salvador Equatorial Guinea Eritrea Cluster 3Ethiopia Gabon Georgia Guinea Guyana Honduras Iran Iraq Jordan Kazakhstan Kenya capital of Kuwait Lao PDR Lebanon Madagascar Mali Mauritania Moldova Morocco Myanmar Namibia Nepal Cluster 3 Nigeria Oman Pakistan Peru Philippines Saudi Arabia Senegal Sierra Leone Sri Lanka Sudan Suriname Syria Tajikistan Tunisia Turkmenistan Uganda Tanzania Uruguay Uzbekistan Venezuela Viet Nam Yemen, Zambia 32 Table 6 Results of PC Analysis Level Of Attraction Country level Regional level Combined Country Level And Regional Level Variables In Order To Create Component showyness Values At The Country Level And At The Regional Level.M sales per country as a % of FDI inward stock per country (US $millions) 2004 -2006 MA sales as % of GDP average 2004-2006 M deals per country as a % of total regional Ms 20042006 no of per country MA deals as a % of all regional deals 20042006 M sales per country as a % of t otal regional FDI inflow ( US$ millions) 2004-2006 Expl. Var Regional Attractiveness Factor 1 Intra-Country Attractiveness Factor 2 %Variance Explained Components by -0. 015066 0. 857492 0. 085347 0. 847898 0. 936657 0. 036875 0. 962411 0. 013174 0. 864350 2. 558174 0. 051764 1. 458437 80. 3 % 33 Table7 REGIONAL LEVEL ATTRACTIVENESS- most attractive rankingRegional Level M Attractiveness Quartile 4 -Most Attractive clique Regional Attractiveness M Attractiveness Value Above bonnie India South Africa Brazil Russian Federation Turkey Mexico Indonesia Malaysia Thailand Romania Argentina UAE Egypt Bulgaria Ukraine Chile Colombia Peru Pakistan Philippines 87 86 85 84 83 82 81 80 79 78 77 76 75 74 73 72 71 70 69 68 4. 47456 3. 59947 3. 11423 2. 70295 2. 18032 2. 10503 1. 96844 1. 83932 1. 50218 1. 00295 0. 95504 0. 71507 0. 58127 0. 49219 0. 48130 0. 41931 0. 40345 0. 13893 0. 12567 0. 10631 34 Table 8 Regional level attractiveness- least attractive east attractive Regional Level M Attr activeness Quartile 1Least Attractive social station Regional M Attractiveness Attractiveness Value Below bonny Regional Level M Attractiveness Quartile 1Least Attractive2 Rank Regional M Attractiveness 2 Attractiveness Value Below Average 2 Burkina Faso Yemen Albania Tajikistan Belize Turkmenistan Lao PDR Gabon Sri Lanka Botswana Guinea Kuwait Cote d Ivoire Kyrgyzstan Iran Swaziland Sierra Leone Mali Libyan Arab Jamahiriya Mauritania Armenia Algeria Bolivia Cambodia Moldova, state of Belarus Macedonia, TFYR Lebanon Nicaragua Congo, Republic of Angola Congo Democratic 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 -0. 81391 -0. 62301 -0. 59695 -0. 58134 -0. 56980 -0. 56586 -0. 55855 -0. 54206 -0. 53908 -0. 53824 -0. 53655 -0. 53403 -0. 53331 -0. 52797 -0. 52388 -0. 51088 -0. 51028 -0. 50993 -0. 50966 -0. 50856 -0. 50707 -0. 50669 -0. 50637 -0. 50389 -0. 50075 -0. 49762 -0. 49691 -0. 49085 -0. 48372 -0. 48345 -0. 48291 -0. 48068 Costa Rica El Salvador Rwanda Madagascar Syrian Republic Bangladesh Uzbekistan Georgia Iraq Viet Nam Bosnia Herzegovina Tanzania KenyaMozambique Namibia Oman Bahrain Saudi Arabia Zimbabwe Zambia Ecuador Uganda Panama Sudan Venezuela Kazakhstan Mauritius Ghana Tunisia Nigeria Jordan Croatia and Arab 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 -0. 46264 -0. 46137 -0. 46100 -0. 45911 -0. 45391 -0. 45035 -0. 44220 -0. 42553 -0. 42284 -0. 41269 -0. 41006 -0. 40278 -0. 37712 -0. 37626 -0. 36841 -0. 35828 -0. 35541 -0. 35395 -0. 35140 -0. 34751 -0. 31359 -0. 31281 -0. 31113 -0. 30115 -0. 25848 -0. 22807 -0. 21374 -0. 21133 -0. 17359 -0. 13017 -0. 12656 -0. 09001 35 Uruguay Guatemala 33 34 -0. 46757 -0. 6471 Morocco 67 -0. 07754 Table 9 Country level M attractiveness- most attractive countries Country Level M Attractiveness Quartile 4 Most Attractive Rank Attractiveness Value Above Average Mauritius Burkina Faso Bulgaria Panama Ghana Kyrgyzstan Armenia Croatia Ukraine Colombia Yemen Romania Turkey Sudan Tunisia Uzbekistan Mauritania Peru Ecuador Indonesia Lao PDR South Africa Macedonia Pakistan Belize Kuwait 87 86 85 84 83 82 81 80 79 78 77 76 75 74 73 72 71 70 69 68 67 66 65 64 63 62 5. 44211 4. 67217 2. 45823 2. 04796 1. 89195 1. 06603 0. 90303 0. 87151 0. 82457 0. 81623 0. 78430 0. 77845 0. 71227 0. 65421 0. 2570 0. 36499 0. 32190 0. 26612 0. 24742 0. 23859 0. 20139 0. 10116 0. 04362 0. 04359 0. 03089 0. 01879 36 Table 10 Country level attractiveness- least attractive Country level M attractive Q1- least attractive UA E Tanzania Saudi Arabia Angola Libya Belarus Sri Lanka Algeria Guinea Iraq Iran Sierra Leone Mali Zimbabwe Cote d Ivoire Viet Nam Mozambique Bahrain Madagascar Oman Tajikistan Cambodia Congo Turkmenistan Mexico Zambia Lebanon Venezuela Congo Swaziland Rank Attractiveness value below average -0. 69652 -0. 68043 -0. 68009 -0. 67564 -0. 67419 -0. 66567 -0. 66410 -0. 66351 -0. 66076 -0. 66060 -0. 64409 -0. 3906 -0. 62 707 -0. 62270 -0. 62038 -0. 61471 -0. 61461 -0. 59631 -0. 58028 -0. 57740 -0. 57596 -0. 56811 -0. 56112 -0. 55555 -0. 55058 -0. 54445 -0. 53035 -0. 51967 -0. 50304 -0. 48027 Country level M attractive Q1- least attractive2 Rwanda Russian Fed Guatemala Philippines Gabon Brazil Bangladesh Uruguay Costa Rica Botswana India Moldova Bolivia Egypt Nigeria Argentina Thailand Namibia Albania Bosnia & Herzeg Malaysia Kazakhstan Kenya Georgia Morocco Chile Uganda Nicaragua Jordan Syria El Salvador Rank2 Attractiveness value below average2 -0. 46953 -0. 46579 -0. 46387 -0. 45862 -0. 43042 -0. 40607 -0. 39852 -0. 8454 -0. 38399 -0. 33595 -0. 31087 -0. 30362 -0. 28460 -0. 28442 -0. 28428 -0. 25341 -0. 23769 -0. 22207 -0. 22091 -0. 22082 -0. 21129 -0. 18592 -0. 18396 -0. 16633 -0. 14784 -0. 09800 -0. 06308 -0. 03914 -0. 03806 -0. 01932 -0. 00700 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 37 Figure 1 M attractiveness axes -regional/country 38 Figure 2 REGIONAL LEVEL ATTRACTIVENESS COUNTRIES PLOTTED ON Y AXIS COUNTRY LEVEL M ATTRACTIVE COUNTRIES PLOTTED ON X AXIS. 39 APPENDIX 1-EXCLUDED DATAIn addition to the developed economy data, the following economies were also excluded from the study Caribbean and Oceania economies (many of these island economies were very small, atypical and had missing data) China (over 48 % of the total number of deals for South and SouthEast Asian region were concluded in China in order to avoid skewing the findings for the rest of the region, Chinese data was excluded) Hong Kong, Singapore, Taiwan and Korea (these economies exhibit higher levels of development and sophistication than the rest of the sample and exhibit FDI levels higher than the typical developing countries of the sample group of this study) St Helena, Guinea Bissau, Mayotte, Reunion, Falkland Islands, French Guiana, Palestinian Territory, Afghanistan, B hutan, Maldives and Timor Leste (these