Academic literature and the media offer a variety of monikers for emerging states like Brazil, India and China, most prominently, ‘emerging powers’ and ‘emerging markets’. This article argues the terms used to describe these states create assumptions about their behaviour in global governance (GG). In order to accurately assess the impact of emerging states on international institutions, it is necessary to more systematically examine their current participation in GG. Does the use of power and economic interests in GG negotiations distinguish emerging states from traditional powers, as the ‘emerging’ part of these terms suggests? And can the content of GG negotiations predict the dominance of each factor, as implied by the ‘power/market’ part? This article tackles these questions by comparing the behaviour of one emerging state (India) and one traditional power (the United States) in negotiations at the World Trade Organisation and the United Nations Security Council. The results demonstrate that, while there is clearly something distinctive about at least India’s participation in GG, focussing on power or economic interests alone is insufficient to explain that distinctiveness or its implications for relations between rising and traditional powers in GG.

The primary objective of this study was to examine the broader impact of institutional quality on enhancing foreign direct investment (FDI) and foreign portfolio investment (FPI) inflows in a sample of twelve emerging market economies for the period 2007 to 2017. We specifically sought to answer questions related to the key drivers of FDI and FPI inflows into emerging markets, with a particular emphasis on the role played by institutional quality factors. We further sought to interrogate the long-run and causal relationships between the key variables of FDI, FPI and institutional quality, in respect of the sample of emerging markets. The study employed the Principal Components Analysis (PCA) to construct a composite index for institutional quality composed of the six Worldwide Governance Indicators. Various other econometric models were applied, including the dynamic panel data generalised method of moments (GMM) model, the panel autoregressive distributed lag (ARDL) model for dynamic heterogeneous panels, and the panel vector error correction model (VECM). The results revealed that FDI in the selected emerging markets was, in the main, attracted by economic growth and institutional quality. Institutional quality, economic rowth and capital account openness were positive determinants of FPI inflows; however, stock market development stood out as the foremost determinant factor. In addition to finding long-run, cointegrating relationships between the key variables, it emerged that there was bi-directional causality between FDI and FPI, as well as between FDI and institutional quality in the long run. Despite the latter findings, the results further suggested that the long-run relationship between the two foreign capital inflows, i.e. FDI and FPI, was more of a substitutability or trade-off nature in our sample of emerging markets. In light of these findings, we recommended that it would be in the best interests of these emerging markets if the responsible policymakers continued to liberalise these economies. Further, it was shown that in order to attract inward international capital flows, financial liberalisation should be coupled with the strengthening of the domestic institutional environment.Strengthening institutions could curtail the persistence of institutional weaknesses and insulate emerging market economies from the adverse effects of volatile capital flows, and in the long-run enhance international capital inflows. / Inhloso enkulu yalolu cwaningo kwaye kuwukuhlola umthelela obanzi kwizinga leziko ekuqiniseni uhlelo lokutshalwa ngqo kwezimali ezweni langaphandle (foreign direct investment; FDI) kanye nemali engena mayelana nokuthengwa kwamagugu (shares, stocks and bonds) angenisa imali ezweni elingaphandle (foreign portfolio investment; FPI) kwizimakethe zamazwe eziyishumi nambili esikhathini esiphakathi kuka 2007 ukufika ku 2017. Empeleni besifuna ukuphendula imibuzo emayelana nezikhwezeleli ezisemqoka eziheha uhlelo lwe-FDI kanye ne-FPI ezimakethe ezifufusayo, ikakhulu kugxilwe kwindima edlalwa yizinto ezihlobene nezinga leziko. Siqhubekela phambili nokuphenya izinhlobo zobudlelwano besikhathi esinde kanye nobudlelwano obuyimbangela phakathi kwamavarebuli asemqoka e-FDI, i-FPI kanye nezinga leziko, mayelana nesampuli yezimakethe ezisafufusayo. Ucwaningo lusebenzise uhlelo lwe-Principal Components Analysis (PCA) ukwakha imvange yezinkomba ukwenzela izinga leziko eliqukethe izinkomba eziyisithupha ezibizwa phecelezi nge-Worldwide Governance Indicators. Amanye amamodeli alinganisa ezomnotho asetshenzisiwe, kuxutshwa phakathi idatha yamaphaneli eguquguqukayo ebizwa nge-dynamic panel data generalised method of moments (GMM) model, uhlelo lwe-panel autoregressive distributed lag (ARDL) model ukwenzela amaphaneli ahlukahlukene futhi aguquguqukayo, kanye nohlelo lwe-panel vector error correction model (VECM). Imiphumela iveze ukuthi i-FDI ezimakethe ezikhethiwe ezisafufusa, esikhathini esiningi, iye yahehwa ukuhluma komnotho kanye nezinga leziko. Izinga leziko, ukuhluma komnotho kanye nokuvuleka kwe-akhawunti yemali bekuyizinto eziyizinkomba ezinhle zokungena kwe-FPI; yize-kunjalo, ukuthuthukiswa kwemakethe yesitoko kuvele kwagqama ngaphezulu njengenkomba ekhombisayo. Ukwengeza phezu kolwazi olutholakele esikhathini esinde, ukuhlangana kobudlelwano obuphakathi kwamavarebuli asemqoka, kuye kwavela ukuthi kwakunezimbangela ezikhomba izindlela ezimbili zokungena kwezimali ezitshalwa ngaphandle, zona yilezi i-FDI kanye nezinga leziko esikhathini esinde. Naphezu kolwazi olutholakele kamuva, imiphumela iqhubeka nokuphakamisa ukuthi ubudlelwano besikhathi eside obuphakathi kwezinhlelo zokutshalwa kwezimali ezivela emazweni angaphandle, lezo zinhlelo yilezi, i-FDI kanye ne-FPI, bezingendlela ikakhulukazi yokushintshana/yokumisela noma yokushintshelana ngokuhweba kwisampuli yethu 5 yezimakethe ezisafufusayo. Mayelana nalolu lwazi olutholakele, sincome ukuthi kuzohambisana nokuthandwa yilezi zimakethe ezisafufusa uma ngabe abenzi bemigomo ababandakanyekayo baqhubeke nokususa izihibe zomnotho kula mazwe asafufusa. Ngaphezu kwalokho, kuye kwavezwa ukuthi ukuze kuhehwe izimali zamazwe angaphandle, uhlelo lokususwa kwezihibe zomnotho lufanele luhambisane nokuqiniswa kwesizinda esiyiziko lasekhaya. Ukuqiniswa kwamaziko kungaqeda isimo esintengayo seziko futhi kungasusa izimakethe zamazwe asafufusayo kwisimo esingagculisi sezimali ezingenayo, kanti esikhathini eside lokhu kungaqinisa ukutshalwa ukungena kwezimali ezivela emzaweni angaphandle / Maikemisetso magolo a thutopatlisiso eno e ne e le go tlhatlhoba ditlamorago ka bophara tsa boleng jwa ditheo mo go tokafatseng keleloteng ya dipeeletso tsa tlhamalalo tsa kwa dinageng tse dingwe (FDI) le dipeeletso tsa dipotefolio tsa kwa dinageng tse dingwe (FPI) mo sampoleng ya diikonomi tse somepedi tsa mebaraka e e tlhagelelang mo pakeng ya 2007 go fitlha 2017. Re ne re totile go araba dipotso tse di malebana le ditsamaisi tsa botlhokwa tsa keleloteng ya FDI le FPI mo mebarakeng e e tlhagelelang, go lebeletswe thata seabe sa dintlha tsa boleng jwa ditheo. Gape re ne re lebeletse go tlhotlhomisa go nna sebaka se se telele le sebako sa dikamano magareng ga dipharologantsho tsa botlhokwa tsa FDI, FPI le boleng jwa ditheo, malebana le sampole ya mebaraka e e tlhagelelang. Thutopatlisiso e dirisitse Tokololo ya Dintlha tsa Botlhokwa (PCA) go aga tshupane ya dikarolo ya boleng jwa ditheo e e nang le Disupi di le thataro tsa Lefatshe lotlhe tsa Bolaodi. Go dirisitswe gape dikao tse dingwe tse di farologaneng tsa ikonometiriki, go akarediwa sekao sa dynamic panel data generalised method of moments (GMM) sa data ya phanele e e farologaneng, sekao sa panel autoregressive distributed lag (ARDL) sa diphanele tse di farologaneng le sekao sa panel vector error correction (VECM). Dipholo di senotse gore FDI mo mebarakeng e e tlhophilweng e e tlhagelelang e ne tota e ngokiwa ke kgolo ya ikonomi le boleng jwa ditheo. Boleng jwa ditheo, kgolo ya ikonomi le go bulega ga akhaonto ya kapitale e nnile diswetsi tse di siameng tsa keleloteng ya FPI; fela tlhabololo ya mebaraka ya setoko e tlhageletse jaaka ntlha e e kwa pele e e swetsang. Go tlaleletsa mo go fitlheleleng botsalano jwa pakatelele le jo bo kopanang jwa dipharologantsho tsa botlhokwa, go tlhageletse gore go na le go sebako sa dintlhapedi magareng ga FDI le FPI gammogo le magareng ga FDI le boleng jwa ditheo mo pakeng e e telele. Le fa go ntse go na le diphitlhelelo tse di kailweng la bofelo, dipholo gape di tshitshinya gore botsalano jwa paka e e telele magareng ga keleloteng ya kapitale ya kwa ntle k.g.r. FDI le FPI ke jwa mofuta wa go emisetsa se sengwe ka se sengwe mo sampoleng ya rona ya mebaraka e e tlhagelelang. Ka ntlha ya diphitlhelelo tseno, re atlenegisa gore go tlaa bo go le mo dikgatlhegelong tsa mebaraka eno e e tlhagelelang gore ba ba rweleng maikarabelo a go dira dipholisi ba ka tswelela go repisa diikonomi tseno. Mo godimo ga moo, go bonagetse gore go ngokela kelelo e e tsenang ya kapitale ya boditšhabatšhaba, go repisiwa ga merero ya ditšhelete go tshwanetse ga tsamaisiwa le maatlafatso ya tikologo ya ditheo tsa 7 selegae. Go maatlafatsa ditheo go ka fedisa go tswelela pele ga makoa a ditheo le go sireletsa diikonomi tsa mebaraka e e tlhagelelang mo ditlamoragong tse di maswe tsa dikelelo tse di maswe tsa kapitale, mme kwa bokhutlong, go tokafadiwe kelelo ya kapitale ya boditšhabatšhaba / Finance, Risk Management and Banking / M. Com. (Financial Management)

Personliga eldrivna fordon (EV) är ett nytt teknikområde som är på väg att uppnå stort momentum på flera av världens marknader. Eftersom branschen fortfarande ligger i sin linda finns det i nuläget inga tydliga strukturer, som gäller för alla marknader världen över, gällande relationerna mellan aktörer, vilket leder till osäkerheter när det kommer till att ta strategiska beslut. Uppdragsgivaren för denna studie är Hong Kong EV Power Ltd. (EV Power), en Hongkong-baserad leverantör av laddningsstationer för elbilar och relaterade tjänster, som har ambitionen att inträda marknaden på det kinesiska fastlandet inom den närmaste framtiden. Emellertid har EV Power ännu inte bestämt sig vilken stad de vill rikta in sig på i det första skedet. Denna avhandling ämnar formulera en modell som kan användas för att utvärdera och jämföra geografiska marknader med avseende på lämpligheten för ett marknadsinträde av en leverantör av laddningsstationer för elbilar. Dessutom kommer modellen testas på tre städer på kinas fastland (Peking, Shanghai och Shenzhen), med syfte att komma fram till vilken stad som är mest attraktiv för EV Power, samt att utvärdera modellens funktionsduglighet. Sist kommer resultaten från utvärderingen av de tre städerna att tjäna som utgångspunkten för en analys som ämnar ta fram framgångsfaktorer för ett marknadsinträde på kinas fastland. För att uppnå detta har fyra olika datainsamlingsmetoder använts: Först studerades teori, med syfte att få bakgrundskunskap likväl som att få förståelse för specifika faktorer som påverkar ett marknadsinträde som detta. För det andra observerades EV Powers nuvarande verksamhet i Hong Kong, i avsikt att förstå vad som har lett till den framgång som företaget upplevt på sin hemmamarknad. För det tredje intervjuades branschexperter för att få ett perspektiv på branschen som helhet. Sist samlades sekundär data kring de tre städerna in, för att kunna utvärdera de olika faktorerna som ingår i den framtagna modellen. Den slutgiltiga modellen består av fem faktorer som påverkar en stads attraktivitet för ett marknadsinträde av en leverantör av laddningsstationer för elbilar. De identifierade faktorerna är: ’Marknadens tillgänglighet’, ’Kortsiktig efterfrågan’, ’Förväntad marknadsandel’, ’Vinstmarginal’ och ’Långsiktig produktpotential’. Dessa faktorer är i sin tur indelade i subfaktorer som har sina egna uppsättningar av drivare. Efter att ha använt modellen för att utvärdera de tre städerna konstaterades det att Shanghai är den lämpligaste staden för ett marknadsinträde av EV Power, främst på grund av stadens dominans på marknaden för privatanvända elbilar och ett gynnsamt regelverk. Slutligen hittades tre framgångsfaktorer för ett sådant inträde: ’Fokusera på tjänster’, ’Bibehåll partner-relationer’ och ’Inträd tidigt’. / Personal electric vehicles (EV) is an emerging technology that has gained much momentum in several markets during the past decade, and China is currently one of the markets where the growth in EV sales is the highest. Since the industry is still in its infancy, there are currently no clear structures regarding the relationships between different actors that apply to all markets globally, leading to great uncertainty in strategic decisions. The commissioner of this study is Hong Kong EV Power Ltd. (EV Power), a producer of EV supply equipment (EVSE) and related services in Hong Kong, which aspires to enter the Chinese mainland in the near future. However, EV Power has yet to decide which city they want to target first. This thesis aims to formulate a model that can be used to evaluate and compare geographic markets for a market entry by an EVSE company. Furthermore, the model is tested on three cities in Mainland China (Beijing, Shanghai and Shenzhen), in order to derive the most attractive city for EV Power and to evaluate the adequacy of the model. Lastly, with the results from the city evaluation, as a point of departure, success factors for an entry into Mainland China by the commissioning company will be summarized. In order to achieve this objective, four distinct data collection methods have been used: First, theory was studied, in order to gain background knowledge as well as to understand specific factors that impact a market entry decision such as this. Second, EV Power’s current business in Hong Kong was observed, with a view to achieve an understanding of what has led the company to experience success in its home market. Third, Interviews with industry experts were conducted, so as to get a perspective on the industry as a whole. Fourth and last, secondary data for the different cities was collected, for the sake of evaluating them according to the developed model. The final model consists of five main factors that encompass the elements that influence a cities level of attractiveness for entry by an EV charging station supplier. The identified factors are: ‘Market accessibility’, ‘Short-term demand’, ‘Expected market share’, ‘Profit margin’, and ‘Long-term product potential’. These factors are in turn divided into sub factors that have their own set of drivers. Using the model to evaluate the cities, it was found that Shanghai is the most suitable city for a market entry by EV Power, mainly due to its dominance in the market for private EVs and a favourable regulatory environment. Finally, three main success factors, for such a market entry, were found: ‘Focus on services’, ‘Maintain partner relationships’, and ‘Enter early’.