01446193.2010.537354

This article was downloaded by: [Indian Institute of Technology - Kharagpur] On: 20 October 2014, At: 07:29 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Construction Management and Economics Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/rcme20 Analysing the structure of public–private partnership projects using network theory Abu Naser Chowdhury a , Po‐Han Chen b & Robert L.K. Tiong a a School of Civil and Environmental Engineering , Nanyang Technological University , Singapore b Department of Civil Engineering , National Taiwan University , Taipei, Taiwan Published online: 09 Mar 2011. To cite this article: Abu Naser Chowdhury , Po‐Han Chen & Robert L.K. Tiong (2011) Analysing the structure of public–private partnership projects using network theory, Construction Management and Economics, 29:3, 247-260, DOI: 10.1080/01446193.2010.537354 To link to this article: http://dx.doi.org/10.1080/01446193.2010.537354 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. 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Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions Construction Management and Economics ( March 2011) 29 , 247–260 Construction Management and Economics ISSN 0144-6193 print/ISSN 1466-433X online © 2011 Taylor & Francis http://www.informaworld.com DOI: 10.1080/01446193.2010.537354 Analysing the structure of public–private partnership projects using network theory ABU NASER CHOWDHURY 1 *, PO-HAN CHEN 2 and ROBERT L.K. TIONG 1 1 School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 2 Department of Civil Engineering, National Taiwan University, Taipei, Taiwan Taylor and Francis Received 19 March 2010; accepted 29 October 2010 10.1080/01446193.2010.537354 In public–private partnership projects (PPP), the relationships between the participants are established through a variety of contractual agreements between financiers, government, contractors, operators and customers. Raising funds, linking various participants legally and financially, ensuring supply, and producing and marketing products depend on well-established financial and legal structures of PPP. Though numerous research studies have been conducted to establish and justify the structure of PPP projects based on contractual agreements between participating stakeholders and on existing legal frameworks of a host country, there are still questions left unanswered. Examples are: What are the factors that influence the structuring of PPP? Who are the key stakeholders? And what are the roles of participating partners in a PPP project? However, not much work has been done. Application of network theory can help fill these gaps and identify and distinguish potential stakeholders in PPP affiliation and can effectively contribute to an in-depth analysis of the relationships between participating partners. The analysis can identify important features like core–peripheral stakeholder(s), influential intermediary participants and their interdependence, and influences of a PPP structure on its substantive outcome. With the introduction of the network theory, a more thorough analysis of PPP structures can be achieved which may provide valuable information to project sponsors as well as legal and financial advisers. Keywords: Public–private partnership, structure, network, stakeholder, Pakistan. Introduction Public–private partnership (PPP) has become an icon of any public procurement. It has attracted much inter- est around the world and has been used in many infra- structure development projects with widespread purposes—ranging from construction of high revenue- generating projects, to economic projects, to provision of social services (Chowdhury, 2009). Various coun- tries have introduced PPP for different reasons, such as fiscal deficit, budgetary pressure, demand–supply gap, inefficient public services to infrastructure. Some coun- tries choose PPP with the expectation of gaining oper- ational efficiency, innovative technological and management skills, and expertise from the private sector and achieving more active involvement of private players in public service. However, PPP is a partnership for construction, operation and maintenance, and service delivery of public projects by the private sector. On the private sector side there are investors, lenders and companies providing construction and operational services and on the public sector side there are public authorities creating and implementing PPP policies as well as those actually procuring the PPP. Structuring PPP is quite complex because of the need to reconcile the interests of a large number of parties involved and relationships between them (Yescombe, 2007). In essence, stakeholders’ agreements have significant impact on the PPP structure and on the success of a project. The aim of the paper is to explore the structural properties of the network generated by PPP agreements. A number of key aspects of network * Author for correspondence. E-mail: [email protected] D o w n l o a d e d b y [ I n d i a n I n s t i t u t e o f T e c h n o l o g y - K h a r a g p u r ] a t 0 7 : 2 9 2 0 O c t o b e r 2 0 1 4 248 Chowdhury et al . analysis and its application to the structuring of PPP are therefore reviewed. It is believed that the legal and financial structure of PPP agreements is best positioned in close proximity to network analysis. The application of network theory to PPP structuring helps in under- standing how many agreements a party participates in, how many parties are involved in an agreement and their influences on the structure. The analysis is limited to the formal contractual relationships between parties involved in a PPP agreement such as the concession contract, engineering procurement and construction (EPC) contract, operation and maintenance (O&M) contract, off-take contract, supply contract and the like. This theory has been used in many branches of scientific research, for example, in the analysis of marketing strategy, relational thinking, anthropol- ogy, disease outbreak, communication studies, economics, information science and organizational studies. The theory provides a powerful tool for the representation and analysis of complex PPP structures and their interacting stakeholders. The significance of using the network theory on a PPP structure helps: (1) to identify the most important stakeholder in a PPP structure; (2) to recognize systematically which stakeholders are participating in a specific agreement and how many agreements a stakeholder is involved in; (3) to classify how many stakeholders are involved in an agreement and the prominent stakehold- ers in those agreements; and (4) to analyse the structural constraints and oppor- tunities that a stakeholder faces as well as to understand the role that a stakeholder plays in the PPP structure. Legal and financial frameworks of PPP agreements PPP is a partnership between the public sector and the private sector for the purpose of delivering a project or service that was traditionally provided by the public sector. This partnership is an agreement between public and private parties to work together towards a common goal, share joint rights and responsibilities. They also rely on carefully crafted agreements defining the rights and obligations of the parties involved and establishing a framework for responding to new situa- tions as they arise. It is important to recognize that the different parties in PPP projects have distinct goals and requirements that need to be met in order to get effec- tive partnerships. A PPP structure is formed through various agree- ments with different bodies. Participants found in all project financing deals include sponsors, construction contractors, lenders, insurance providers and others (such as mezzanine investors or lenders, third party equity source, etc.). On the other hand, participants found in some, but not all project finance deals include government, off-taker, resource supplier and third party operators. In general, the stakeholders of PPP projects are: contractor, supplier, operator, equity holder, government and its agencies, financial institu- tions such as offshore and domestic banks, multilateral and bilateral agencies, export credit agencies and insur- ance companies. The promoters (i.e. contractor, supplier, operator, and government and its agencies) are normally the key stakeholders of a PPP project. Key stakeholders are a subset of stakeholders who, if their support were to be withdrawn, would cause the project to fail (Bourne, 2009). This group is important and influential. However, the transaction of PPP is constructed by using a special purpose vehicle (SPV) which acts as the management and operating company for the projects and is the legal owner of the concession that is granted by the public sector authority. Generally the private sector, especially the investors, contractors, subcontractors and suppliers, would form the SPV. Thus, the SPV is a type of consortium of private bodies who are interested in working on a project and are directly or indirectly related to it. The SPV (i.e. the ‘project company’ also known as the private party) is created by private sector investors especially to under- take the PPP contract. Figure 1 shows the outline of a typical PPP project and the interaction between the various parties in a project. Figure 1 Basic PPP structure (Delmon, 2009) The most common agreements in a PPP project are the loan agreement, off-take or purchase agreement, supply agreement, concession agreement, O&M agree- ment, EPC agreement or turnkey agreement and spon- sor’s support agreement. The participation of multilateral development banks (MDBs) and export credit agencies (ECAs) in PPP projects carries some special features. These agencies place strict require- ments on the project structure and lending agreements. Owing to their ability to potentially mitigate political risk, many offshore and domestic banks are willing to contribute to the project. It is also believed that govern- ments make greater effort to ensure that loans to MDBs are repaid even in difficult times. Similarly, ECAs are also popular stakeholders in the financing of PPP projects. They offer finance, insurance and guar- antee repayment of commercial lender-financing in case of political risk and/or commercial risk. All these stakeholders have specific roles and responsibilities in PPP structures. In the literature, PPP structures are presented by contractual and financial agreements D o w n l o a d e d b y [ I n d i a n I n s t i t u t e o f T e c h n o l o g y - K h a r a g p u r ] a t 0 7 : 2 9 2 0 O c t o b e r 2 0 1 4 Analysing PPP structure 249 between parties. For example, Wang and Tiong (2000) describe the PPP structure of the Laibin B project by its contractual agreement. Similarly, Tiong and Anderson (2003) sketch the HubCo PPP structure through contractual agreements between project participants. On the other hand, Tinsley (2000) repre- sents the HubCo PPP structure by contractual and financial agreements. In all cases the following ques- tions are unanswered. They are: What are the factors that influence the structure? Who are the key stake- holders? Why are they powerful? Who are the weak stakeholders? And what are the obstacles that cause them to be less powerful? Therefore, this research is focused on trying to find the answers to these ques- tions. Research methodology Since the questions are descriptive (e.g. what are the factors that influence the structure of PPP projects?) and explanatory (e.g. how well connected are the stake- holders? What are their roles?), qualitative research is justified. Two sources of data have been used. First, a set of secondary data sources such as articles, newspa- per reports, online databases and world wide web pages are reviewed to shape the basic understanding of network theory. Baines and Hale (2004) show that this theory can be applied to develop services because it can explain how things are linked to each other in order to grow. Similarly, network diagrams are used by medical researchers to trace the spread of contagious diseases. Marketing researchers use network-related data and conduct analysis to increase the effectiveness of their marketing strategies. Thus, the theory offers a useful conceptual framework to deal with the structure of many complex systems. Hence, the rationale for choos- ing network theory is its suitability to substantiate the arguments made in this paper. Second, five cases of PPP projects are documented in order to analyse PPP structures. Each case involves project documents such as concession agreements. These agreements are used to identify key stakeholders, roles of the stakeholders in the embedded structure, and the relationships between stakeholders. Therefore, the key tests are network diagram and stakeholder analysis. Method The method comprises two steps: (1) a comparative study of PPP infrastructure projects and selection of the best case; and (2) analysis of the best case using network theory. Figure 2 shows the research proce- dure. Figure 2 Research design A comparative study on PPP infrastructure projects is carried out to get additional insight concerning legal and financial structure. Data used for this purpose are concession agreements of power genera- tion and desalination projects in Pakistan, China, Bangladesh, Singapore and Israel. These are the HubCo Independent Power Project (IPP) in Figure 1 Basic PPP structure (Delmon, 2009) D o w n l o a d e d b y [ I n d i a n I n s t i t u t e o f T e c h n o l o g y - K h a r a g p u r ] a t 0 7 : 2 9 2 0 O c t o b e r 2 0 1 4 250 Chowdhury et al . Pakistan, the Laibin B IPP in China, the Meghnaghat power project in Bangladesh, the Tuas desalination project in Singapore, and the Ashkelon desalination project in Israel. Concession agreements of these five cases are then compared in terms of financial and legal structures and the involvement of stakeholders in the project. Since the aim is to find a case that represents all the interests in terms of PPP, the authors therefore set the criteria as follows: (1) Involvement of various contractors/suppliers/ operators from the locality and abroad. (2) Involvement of multilateral and bilateral agen- cies. (3) Involvement of export credit agencies in terms of debt and/or political risk guarantees. (4) The project gets as many guarantees as possible from the government, government agencies and its central bank (i.e. off-take, supply, performance and foreign exchange guarantees). All these criteria emerged when comparing the five cases. Table 1 shows the projects and the presence of criteria. Considering all these criteria and project features, it is found that the HubCo project of Pakistan is the most complete one among the five cases (as shown in Table 1). The authors believe that all the PPP project structures can be analysed using the network theory, but for the sake of better understanding, the HubCo project is most representative as it contains almost all possible factors in terms of stakeholders and their agreements. HubCo project: a case for network analysis The HubCo project of Pakistan is a good case to examine the structure of PPP because of the compre- hensive set of contractual agreements that contain the Figure 2 Research design Table 1 Selected cases and their features Sl. No Name of the project Presence of criteria (features of the project) 1 HubCo IPP, Pakistan (a), (b), (c), (d) 2 Laibin B IPP, China (a), (c), (d) 3 Meghnaghat power project, Bangladesh (a), (b), (d) 4 Tuas desalination project, Singapore (a), (d) 5 Ashkelon desalination project, Israel (a), (d) D o w n l o a d e d b y [ I n d i a n I n s t i t u t e o f T e c h n o l o g y - K h a r a g p u r ] a t 0 7 : 2 9 2 0 O c t o b e r 2 0 1 4 Analysing PPP structure 251 necessary security to ensure the viability of the project. This case has been described by many authors such as Delmon (2009), Chowdhury and Charoenngam (2009), Tiong and Anderson (2003), Tinsley (2000), Luxton (1998) in several journals, books, the World Bank notes as well as by the Public- Private Infrastructure Advisory Facility (PPIAF). This case will help to better understand PPP agreements and their structure. At the outset, international finan- cial institutions and banks had modest credit limits for Pakistan and domestic banks had limited lending capacity. HubCo is a 4 × 323 MW power plant project and also the first build-own-operate (BOO) plant project in Pakistan with 25:75 equity–debt ratio (Tiong and Anderson, 2003). The World Bank and a consortium of foreign banks and agencies were committed to finance the project. Debt was provided in two forms: a subordinated debt from the Private Sector Energy Development Fund (PSEDF) organized by the World Bank and other debts from Japanese, Italian and French export credit agencies (Luxton, 1998). A 30- year power purchase agreement was signed by the Water and Power Development Authority (WAPDA, a state-owned entity) with a provision of certain performance-based bonuses and penalties for the project. National Power UK, an equity holder was engaged to witness the construction and commission- ing and was also responsible for ensuring high plant performance during operation. The government of Pakistan had provided HubCo a sovereign guarantee of the financial obligations of some of the state entities involved such as WAPDA and Pakistan State Oil Co. and a grant of a 30-year concession. Moreover, HubCo (here the special purpose vehicle, SPV) entered into an exchange risk insurance scheme with the State Bank of Pakistan (SBP) to cover the currency-related risk. The structure of the project is shown in Figure 3. Figure 3 PPP structure of the HubCo project, Pakistan (Tinsley, 2000) The World Bank provided partial risk guarantee in (1) conversion of local currency to foreign currency if the SPV is unable to do so; (2) any change in law that affects the economy of the project; (3) the govern- ment’s failure to fulfil its obligation under the project agreement; (4) expropriation of project assets by government; and (5) the project’s failure to generate sufficient revenue due to war and civil unrest on this project (Delmon, 2009). To better understand the HubCo project, the network theory is employed to analyse the project in the following section. Figure 3 PPP structure of the HubCo project, Pakistan (Tinsley, 2000) D o w n l o a d e d b y [ I n d i a n I n s t i t u t e o f T e c h n o l o g y - K h a r a g p u r ] a t 0 7 : 2 9 2 0 O c t o b e r 2 0 1 4 252 Chowdhury et al . Network theory and bipartite graph Recent advancement of the network theory and its analysis in physics, biology and social science has attracted considerable attention in the fields of engi- neering, computer science, genetic science, mathemat- ics and management (Suh et al. , 2008). Network analysis (social network theory) is the study of how the social structure of relationships around a person, group or organization affects beliefs or behaviours. The theory is enormously helpful to the understanding of relationships and the design of many-to-many market- ing (Gummesson, 2007). A network is set based on the relationships, contains a set of objects (nodes) and a mapping or description of relations between the objects or nodes (Kadushin, 2004). The main focus is on the relationship between people instead of on the charac- teristics of people. These relationships may comprise the feelings people have for each other, the exchange of information, or more tangible exchanges such as goods and money. The simplest network contains two objects (e.g. 1 and 2) and one relationship (e.g. 1 and 2 might be standing in a same room) that links them. The rela- tionship could be directional such as 1 likes 2. There can be multiplex relationships (i.e. more than one). For example 1 and 2 are in the same room and both like each other. Figure 4 shows the graphical repre- sentation of networks. Figure 4 Simple network diagrams The network theory helps map out relationships between people, thus identifying the opinion leader. An opinion leader is the potential person who holds the core position and many people are linked with him/her in the network. The growth of a business depends on identifying the opinion leader and how he/she can help in communication strategy. In a network, nodes are represented by the circles and the lines that connect them represent the edges. In Figure 5, 1, 2, 3, 4 …, etc. all are actors/nodes and the lines between them are termed edges. It is observed that 5 is the most important node because he has many links to other node members, thus residing in the core position in the social network. Therefore 5 can help to get the business or personal contacts one needs. Figure 5 A basic social network (Baines and Hale, 2004) When each of the edges between two nodes of a network has a specific value assigned to it, the network is termed a weighted network. An example of this type of network can be cities and the distances between them (Diaz, 2008). Figure 6 shows an example of unweighted and weighted networks. In this paper, for Figure 4 Simple network diagrams Figure 5 A basic social network (Baines and Hale, 2004) Figure 6 Type of network D o w n l o a d e d b y [ I n d i a n I n s t i t u t e o f T e c h n o l o g y - K h a r a g p u r ] a t 0 7 : 2 9 2 0 O c t o b e r 2 0 1 4 Analysing PPP structure 253 the sake of simplicity, a simple, undirected and unweighted network is used to analyse PPP structures. A weighted network requires weighing of various PPP agreements in terms of criticality/importance from the response data, which is quite difficult to achieve and the process is also complex. Figure 6 Type of network When the nodes of a network are divided into two sets so that no edge connects two nodes in the same set, the network is defined as a bipartite (two-mode) network and can be represented by a bipartite graph. A bipartite graph is very useful in representing two- mode networks (Ohn et al. , 2006). Two-mode networks have been studied in a wide variety of contexts such as movie actors (Watts and Strogatz, 1998; Newman, 2001), financial networks (Young- Choon, 1998; Caldarelli et al. , 2004; Dahui et al. , 2005) and management science (Kogut et al. , 2007). In social science literature, the bipartite graph is called a collaboration network. In Figure 7, there are two sets of nodes, one representing a set of actors and the other a set of collaboration acts. An edge is a connection between an actor and a collaboration act in which the actor participates. According to Ohkubo et al . (2005) the actors participating in a common collaboration act relate to each other through that act. Figure 7 Bipartite graph (collaboration network) (Ohkubo et al ., 2005) Freeman (1979) states that one of the main focuses of the network theory is to measure the centrality of actors and events through three widely used measures: (1) degree; (2) closeness; and (3) between- ness. Measuring node degree centrality helps to iden- tify the actor/node that has the most ties to other actors/nodes in a network. Node closeness centrality measures how close an actor/node is to all the other actors/nodes. In this case, the actor is connected to many actors in the network, but those actors have few connections between them. High betweenness means that other actors/nodes depend on this actor/node to communicate with each other and the actor/node might have some control over the network (i.e. broker role into power). Each of the three approaches describes the locations of individuals in terms of how close they are to the centre of action in a network (Hanneman and Riddle, 2005). And the power arises from occupying advantageous positions (i.e. high degree, high closeness and high betweenness) in networks of relations. In this paper, UCINET 6.0 software is used to measure the centrality index. More information on the background of network theory and its basic understanding can be found in the cited references in e.g. Kadushin (2004), Gummesson (2007), Hanneman and Riddle (2005) and Diaz (2008). Application of network theory on the HubCo project and the findings The aim is to analyse the PPP structure of the HubCo project using the network theory. Now consider a bipartite (two-mode) graph which represents the PPP agreements of the HubCo project. Group 1 is the related parties of the PPP agreements and Group 2 is the contracts/agreements. An edge exists only between an agreement and an actor (i.e. a stakeholder of PPP), but there is no edge between two actors in the same set. Figure 8 shows the bipartite graph of the HubCo project. Figure 8 Bipartite graph of the HubCo project Now, this graph can be analysed algebraically by introducing adjacency and incidence matrices. In the matrix notation, B ij = 1, if node i from the first group links to the node j of second group; = 0, otherwise. In this matrix, each row represents the stakeholders of Group 1 and the columns represent the underlying contract/agreements with that stakeholder. For exam- ple, row 1 represents the Pakistan government and its agreements in the HubCo project. Similarly, row 2 represents WAPDA and its agreements in the HubCo project. Thus, adjacency matrix B is a binary matrix. It is neither square nor symmetric in general. i and k are linked if both of them are linked to j (as shown in Figure 8). A ik = Σ j B ij B ji ; thus collapsing a two-mode network into a one-mode network. A = BB T ; transposition of a matrix swaps B xy and B yx , if B is a m-by-n matrix B T is n-by-m matrix. Figure 7 Bipartite graph (collaboration network) (Ohkubo et al., 2005) D o w n l o a d e d b y [ I n d i a n I n s t i t u t e o f T e c h n o l o g y - K h a r a g p u r ] a t 0 7 : 2 9 2 0 O c t o b e r 2 0 1 4 254 Chowdhury et al . The general formula for matrix multiplication is Z ij = Σ k X ik Y kj . The diagonal entities of A give the number of agree- ments in which each party is involved. For example, HubCo (SPV) is involved in eight agreements; similarly Pakistan State Oil Co. is involved in two agreements. Off-diagonal elements of A give the number of agree- ments in which both parties are involved. For example there are four agreements between equity holders and HubCo. Similarly, there are two agreements between the government of Pakistan and Pakistan State Bank (i.e. foreign exchange guarantee and guarantee on performance). Software for social network analysis (computer pack- age UCINET 6.0) is used to draw the network diagram of the HubCo case. The package incorporates models which are suggested by Borgatti et al . (2002) for detect- ing core–periphery structures in network data. The components of matrix A are now being inserted into the data spreadsheets matrix of UCINET 6.0 and then the network diagram is visualized with NetDraw. Figure 9 shows the network diagram generated by NetDraw (UCINET 6.0). Figure 9 PPP structure of the HubCo project by NetDraw The drawing by NetDraw helps to better understand how a particular node is embedded in its neighbour- hood and in the larger graph. It gives a sense of the structural constraints and opportunities that an actor faces and also makes it possible to understand the role that an actor plays in a structure. In order to explain the location of each stakeholder in terms of how close they are to the centre of action in a PPP structure, it is necessary to analyse degree centrality, closeness centrality and betweenness centrality indices. Table 2 Figure 8 Bipartite graph of the HubCo project D o w n l o a d e d b y [ I n d i a n I n s t i t u t e o f T e c h n o l o g y - K h a r a g p u r ] a t 0 7 : 2 9 2 0 O c t o b e r 2 0 1 4 Analysing PPP structure 255 shows the degree centrality indices of HubCo project stakeholders. HubCo, the SPV (i.e. stakeholder #6) is regarded as the most influential stakeholder, as it has the highest degree. The government of Pakistan (i.e. stakeholder #1) is also a prominent and influential party (as shown in Table 2) with a degree centrality index 12 in this PPP structure. The results of the last column of the first panel show the normalized degree centrality of HubCo stakeholders (called NrmDegree in UCINET). The second panel of results shows what the distribution of the actor’s degree centrality scores look like. On average, stakeholders have a degree of 7.8 (i.e. the mean), which is quite high, given that there are only nine other stakeholders. The variability that is the coefficient of variation (standard deviation divided by mean, times 100) is 64.10 which indicates that the population in the dataset is quite homoge- neous. Closeness centrality Closeness centrality approaches emphasize the distance of an actor to all others in the network by focusing on the distance from each actor to all others. Closeness provides a number of alternative ways of calculating the ‘farness’ of each actor from all others. Farness is the sum of the distance from each actor to all others in Figure 9 PPP structure of the HubCo project by NetDraw Table 2 Degree centrality index of HubCo project stakeholders 1 2 Stakeholders ID Degree NrmDegree HubCo SPV 6 20.0 44.4 Pakistan government 1 12.0 26.7 Equity holders 8 12.0 26.7 MDBs 9 7.0 15.6 ECAs 10 5.0 11.1 WAPDA 2 5.0 11.1 Pakistan State Bank 7 5.0 11.1 Pakistan State Oil Co. 3 5.0 11.1 Contractors 4 4.0 8.9 National Power UK 5 3.0 6.7 1 2 Descriptive statistics Degree NrmDegree Mean 7.8 17.3 Std dev. 5.0 11.2 Sum 78.0 173.3 Variance 25.4 125.2 SSQ 862.0 4256.8 MCSSQ 253.6 1252.3 Euc norm 29.4 65.2 Minimum 3.0 6.7 Maximum 20.0 44.4 D o w n l o a d e d b y [ I n d i a n I n s t i t u t e o f T e c h n o l o g y - K h a r a g p u r ] a t 0 7 : 2 9 2 0 O c t o b e r 2 0 1 4 256 Chowdhury et al . the network. Table 3 shows the closeness centrality measures of the HubCo project. National Power UK, the operator (i.e. stakeholder #5) and Pakistan State Bank (i.e. stakeholder #7) have the largest sum of geodesic distances from other stakeholders (i.e. farness is 15). The farness is re- expressed as closeness (i.e. reciprocal of farness) and the greatest closeness is observed in the table (here, the closeness of HubCo, the SPV). Summary statistics are shown in the last panel of the table. The distribu- tion of in-closeness and out-closeness variability is same (i.e. the minimum and maximum). This is also reflected in the table in-centralization (72%) and out- centralization (72%). Another way of measuring closeness is by eigenvec- tor of geodesic distances. The eigenvector approach is an effort to find out the most central actors in terms of ‘global’ or ‘overall’ structure of the network. It is a measure of the importance of a node in a network. It assigns relative scores to all the nodes in a network based on the principle that connections to high-scoring nodes contribute more to the score of the node in question than equal connections to low- scoring nodes. It pays attention to patterns that are more ‘local’. The location of each actor with respect to each dimension is called an ‘eigenvalue’ and the collection of such values is called an ‘eigenvector’. Table 4 shows the eigenvector centrality of HubCo project stakeholders. The first set of statistics—the eigenvalues—tell how much of the overall pattern of distances among actors can be seen as reflecting the global pattern (the first eigenvalue) and more local, or additional patterns. Here, the percentage 54% means that half of all of the distances between actors are reflective of the main dimension or pattern. The first eigenvalue is larger than the second. Here the ratio 2.9 means that the dominant pattern is 2.9 times more important than the secondary pattern. In the second set of statistics, the highest score indicates that an actor is more central to the main pattern of distances among all of the actors, whereas lower values indicate that actors are more peripheral. The second column indicates the normalized eigenvec- tor (called nEigenvec in UCINET software) of the Table 3 Closeness centrality index of HubCo project stakeholders Closeness centrality measures 1 2 3 4 Stakeholders ID inFarness outFarness inCloseness outCloseness HubCo SPV 6 9.0 9.0 100.0 100.0 Pakistan government 1 12.0 12.0 75.0 75.0 Equity holders 8 12.0 12.0 75.0 75.0 MDBs 9 13.0 13.0 69.2 69.2 ECAs 10 14.0 14.0 64.3 64.3 WAPDA 2 14.0 14.0 64.3 64.3 Contractors 4 14.0 14.0 64.3 64.3 Pakistan State Oil Co. 3 14.0 14.0 64.3 64.3 Pakistan State Bank 7 15.0 15.0 60.0 60.0 National Power UK 5 15.0 15.0 60.0 60.0 Descriptive statistics 1 2 3 4 inFarness outFarness inCloseness outCloseness Mean 13.2 13.2 69.6 69.6 Std dev. 1.7 1.7 11.3 11.3 Sum 132.0 132.0 696.4 696.4 Variance 3.0 3.0 128.0 128.0 SSQ 1772.0 1772.0 49773.5 49773.5 MCSSQ 29.6 29.6 1279.9 1279.9 Euc norm 42.1 42.1 223.1 223.1 Minimum 9.0 9.0 60.0 60.0 Maximum 15.0 15.0 100.0 100.0 Network in-centralization = 72% Network out-centralization = 72% Note : The last two rows (i.e. network in-centralization and out-centralization) of the table are in percentage figures. D o w n l o a d e d b y [ I n d i a n I n s t i t u t e o f T e c h n o l o g y - K h a r a g p u r ] a t 0 7 : 2 9 2 0 O c t o b e r 2 0 1 4 Analysing PPP structure 257 HubCo stakeholders. The results are very similar to those for earlier analysis of closeness centrality. This indicates that the Pakistan government, HubCo SPV and equity holders (i.e. stakeholders #1, #6 and #8) are the most central, and the contractors (i.e. stake- holder #4) and National Power UK, the operator (i.e. stakeholder #5) are most peripheral. But the most peripheral stakeholder in the HubCo PPP structure is National Power UK, which is the operator, as its eigenvector value (i.e. 0.06) is the lowest among all stakeholders. Betweenness centrality Freeman (1979) created some measures of centrality of an individual actor based on its betweenness as well as overall graph centralization. The more actors depend on a particular actor to make connections with other actors, the more powerful that particular actor is. The results of the HubCo project are shown in Table 5. There is a lot of variation in stakeholder between- ness, ranging from 0 to 28.6. There is also quite a bit of variation (std. dev. = 8.4 and mean betweenness = 4.2). Despite this, overall network centralization index is low (38%), which means that one-third of all connec- tions can be made in this network without the aid of any intermediary. HubCo SPV, the Pakistan government and MDBs (i.e. stakeholders #6, #1 and #8) appear to be relatively more powerful than others by this measure due to high betweenness value (i.e. 28.6, 6.7 and 4 respectively). There is a structural basis for these stake- holders to perceive that they are different from others in the population. In this figure, nBetweeness means the normalized betweenness centrality indices of the HubCo stakeholders. Another way of measuring betweenness is to find out what relations are most central rather than finding which actors. The results for these measures are shown in Table 6. A number of potential relations between parts of actors are not parts of any geodesic paths such as the relation from stakeholder #1 to stakeholders #4, #5 Table 4 Closeness centrality index of HubCo project stakeholders (by eigenvalue) Eigenvalues Factor Value Percent Cum % Ratio 1 16.2 54 54 2.9 2 5.6 19 73 1.6 3 3.5 12 85 2.5 4 1.4 5 90 1.4 5 1.0 3 93 1.0 6 1.0 3 96 1.2 7 0.8 3 99 1.9 8: 0.4 1 100 6.4 9 0.1 0 100 30.0 100.0 Bonacich eigenvector centralities 1 2 Stakeholders ID Eigenvec nEigenvec Pakistan government 1 0.46 64.95 WAPDA 2 0.14 20.36 Pakistan State Oil Co. 3 0.14 20.36 Contractors 4 0.09 13.33 National Power UK 5 0.06 9.08 HubCo SPV 6 0.69 96.97 Pakistan State Bank 7 0.19 26.99 Equity holders 8 0.41 58.38 MDBs 9 0.20 28.74 ECAs 10 0.13 18.06 Table 5 Betweenness centrality index of HubCo project stakeholders 1 2 Stakeholders ID Betweenness nBetweenness HubCo SPV 6 28.6 39.8 Pakistan government 1 6.7 9.3 Equity holders 8 4.0 5.6 MDBs 9 1.3 1.9 WAPDA 2 0.7 0.9 Pakistan State Oil Co. 3 0.7 0.9 National Power UK 5 0 0 Pakistan State Bank 7 0 0 Contractors 4 0 0 ECAs 10 0 0 Descriptive statistics 1 2 Betweenness nBetweenness Mean 4.2 5.8 Std dev. 8.4 11.7 Sum 42 58.3 Variance 70.8 136.7 SSQ 884.9 1706.9 MCSSQ 708.5 1366.7 Euc norm 29.7 41.3 Minimum 0 0 Maximum 28.6 39.8 Network centralization index = 38% Note : Only the last row of the table (i.e. network centralization index) is in percent figure. D o w n l o a d e d b y [ I n d i a n I n s t i t u t e o f T e c h n o l o g y - K h a r a g p u r ] a t 0 7 : 2 9 2 0 O c t o b e r 2 0 1 4 258 Chowdhury et al . and #10. Betweenness is zero if there is no tie or if a tie that is present is not part of any geodesic paths. There are some central relations in the table. For example, the ties from HubCo SPV (i.e. stakeholder #6) to contrac- tors (i.e. stakeholder #4), to National Power UK (i.e. stakeholder #5) and to ECAs (i.e. stakeholder #10) have values of 4.8, 6.3 and 4.8 respectively. These high values arise because without the tie to HubCo SPV, contractors, National Power UK and ECAs would be largely isolated. If this stakeholder (i.e. HubCo, the SPV) is removed from the table, stakeholders #4, #5 and #10 would not be there anymore. Therefore they are one step up in the hierarchy. Hierarchy reduction by betweenness for the HubCo project is shown in Table 7. In this dataset, it is seen that a three-level hierarchy exists. The first portion of the output shows a partition of the node’s level in the hierarchy. Stakeholders #5, #7, #4 and #10 are at the lowest level (1) of the hier- archy. The second portion of the output has rearranged the nodes to show which actors are included at the lowest level of betweenness, which drop out at level 2 (i.e. are most subordinate e.g. actors 5, 7, 4, 10) and successive levels. The dataset has a hierarchical depth of three. According to this measurement, the Pakistan government (i.e. stakeholder #1) and HubCo, the SPV (i.e. stakeholder #6) are at the highest level of hierarchy (i.e. at level 3). Discussion of findings and conclusion From the analysis of indices, it is quite clear that four parties—HubCo (the SPV), the Pakistan government, equity holders and MDBs are the influential stakehold- ers in the PPP structure of this project. All the other stakeholders are surrounded by them. HubCo (the SPV) has the highest degree and betweenness central- ization indices (as shown in Tables 2 and 5) and main- tains numerous contracts with other stakeholders in PPP structure. This implies that HubCo (the SPV) is a cohesive core actor in the PPP structure of this project. This actor is more influential, has greater access to information and can communicate with others more efficiently. Moreover, it has the highest closeness index (as shown in Tables 3 and 4). Therefore, the opinion leader of this network is HubCo, the SPV. On the other hand, node degree centrality of the Pakistan government and of the equity holders are the same but the second most influential and cohesive actor is the Pakistan government due to its higher betweenness index (as shown in Table 5). This indicates that the Pakistan government has more control over the flow of communication (i.e. the contracts) and can connect more actors indirectly through its direct links. Also, MDBs is considered an important actor as it is rela- tively close to all the other actors. According to the hierarchy of power, it is in the fourth position just after equity holders (as shown in Table 2). In contrast, contractors and National Power UK (i.e. the operator) have the least degree and betweenness indices. Both of them are peripheral actors, maintaining few contracts in the network and, hence are located spatially at the margins of the network. However, National Power UK is the most peripheral actor in the PPP structure as it has the least degree centrality and eigenvector (as shown in Tables 2 and 4). Degree and closeness indi- ces of WAPDA and ECAs are same (as shown in Tables 2 and 3) and the betweenness index of ECAs is lower than WAPDA (as shown in Table 5). Among these two stakeholders, WAPDA is powerful due to its higher value in betweenness, eigenvector and hierarchy reduction by betweenness index. WAPDA is in an Table 6 Betweenness centrality index of HubCo project stakeholders (edge betweenness) Edge betweenness 1 2 3 4 5 6 7 8 9 10 1 0 2.83 2.83 0 0 2 2.3 2.67 3 0 2 2.83 0 1 0 1.3 4.5 0 0 0 0 3 2.83 1 0 0 1.3 4.5 0 0 0 0 4 0 0 0 0 0 4.8 0 1.8 1.3 1 5 0 1.3 1.3 0 0 6.3 0 0 0 0 6 2 4.5 4.5 4.8 6.3 0 4.3 3 3.3 4.8 7 2.3 0 0 0 0 4.3 0 2.3 0 0 8 2.67 0 0 1.8 0 3 2.3 0 1.3 1.8 9 3 0 0 1.3 0 3.3 0 1.3 0 1.3 10 0 0 0 1 0 4.8 0 1.8 1.3 0 Note : 1 = Pakistan government, 2 = WAPDA, 3 = Pakistan State Oil Co., 4 = Contractors, 5 = National Power UK, 6 = HubCo SPV, 7 = Pakistan State Bank, 8 = Equity holders, 9 = MDBs, 10 = ECAs. Table 7 Betweenness centrality of HubCo project stakeholders (hierarchical reduction) Partition based on successive reduction of HubCo project via betweenness 1 2 3 4 5 6 7 8 9 10 1 3 2 2 1 1 3 1 2 2 1 Successive reduction of HubCo project via betweenness 5 7 4 10 3 2 9 8 1 6 3 3 . . . . . . . . 1 1 2 2 . . . . 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Note : 1 = Pakistan government, 2 = WAPDA, 3 = Pakistan State Oil Co., 4 = Contractors, 5 = National Power UK, 6 = HubCo SPV, 7 = Pakistan State Bank, 8 = Equity holders, 9 = MDBs, 10 = ECAs. D o w n l o a d e d b y [ I n d i a n I n s t i t u t e o f T e c h n o l o g y - K h a r a g p u r ] a t 0 7 : 2 9 2 0 O c t o b e r 2 0 1 4 Analysing PPP structure 259 advantageous position in the PPP structure as it is not only connected with two influential and dominant parties (i.e. the Pakistan government and the SPV) but also able to connect more parties indirectly than ECAs. This is how the network theory speaks the language of nodes and links, and provides a foundation for graphical and mathematical representation without rejecting verbal languages, as done by case studies. Another stakeholder analysis method is the stakeholder analysis matrix. It requires systematic gathering and analysing qualitative information (Schmeer, 2000). Workshops, focus group discussion and interviews are the three common approaches of this method. The method is quite subjective and requires prudent judg- ment on identifying influential stakeholders and their impact assessment on a project. Customer relationship management (CRM) is another stakeholder analysis method which requires substantial datasets and it uses data mining technique to assess opportunity for busi- ness growth. On the other hand, network theory focuses on the importance of relationships between stakeholders. Through mapping the relationships, this method is able to identify position, power and influ- ences of each stakeholder. Here, the application of network theory on the structure of PPP projects addresses some important aspects, such as the distribu- tion of power of related parties in PPP agreements and the sources. Core–periphery structure and a dense, cohesive core and a sparse, unconnected periphery are also sought in the analysis. It is found that the power of any individual actor (i.e. party) is not an individual attribute but arises from the relationships with other actors in the network. Finally, the use of the network theory on the PPP structure reveals some hallmark perspectives: relationships between actors rather than attributes of actors, interdependence of actors, and structure affect substantive outcome. Recommendation Thus, the application of the network theory for analys- ing the structure of PPP projects shows a new dimen- sion in research study. 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