A review of public-private partnership: critical factors of concession period

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A review of public-private partnership: critical factors of concession period Fahim Ullah Bilal Ayub Siddra Qayyum Siddiqui Muhammad Jamaluddin Thaheem

Article information: To cite this document: Fahim Ullah Bilal Ayub Siddra Qayyum Siddiqui Muhammad Jamaluddin Thaheem , (2016),"A review of public-private partnership: critical factors of concession period", Journal of Financial Management of Property and Construction, Vol. 21 Iss 3 pp. 269 - 300 Permanent link to this document: http://dx.doi.org/10.1108/JFMPC-02-2016-0011 Downloaded on: 04 December 2016, At: 11:56 (PT) References: this document contains references to 90 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 243 times since 2016*

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A review of public-private partnership: critical factors of concession period Fahim Ullah, Bilal Ayub, Siddra Qayyum Siddiqui and Muhammad Jamaluddin Thaheem Department of Construction Engineering and Management (CE & M), National University of Sciences and Technology, Islamabad, Pakistan

Public-private partnership

269 Received 28 February 2016 Revised 22 April 2016 25 April 2016 8 May 2016 13 May 2016 Accepted 14 May 2016

Abstract Purpose – The purpose of this paper is to investigate the critical decision factors of public–private partnership (PPP) concession which is complex due to a number of uncertain and random variables. To identify critical factors contributing to determination of concession period, this study reviews the published literature. It also identifies countries contributing most in PPP research. As a whole, it provides a mutually beneficial scenario by formulating a decision-making matrix. Design/methodology/approach – This paper reviews the literature published during the period 2005-2015. A two-staged methodology is followed on retrieved scholarly papers: first, countries contributing to PPP are identified along with authors and affiliated institutions. Second, using frequency analysis of shortlisted critical factors, yearly appearance and stakeholders affected, a decision matrix is formulated. Findings – The most contributing country toward PPP research is China, followed by the USA both in terms of country- and author-based contribution. In total, 63 factors are identified that affect PPP concession out of which, 8 per cent are highly critical and 21 per cent are marginally critical for decision-making. Practical implications – Critical factors of PPP concession period will be identified with the help of decision-making matrix. This will help in adequate resource allocation for handling critical factors ensuring project success. Researchers may also understand the research trends in the past decade to usher ways for future improvements. Originality/value – This paper reports findings of an original and innovative study, which identifies critical success factors of PPP concession period and synthesizes them into a decision-making matrix. Many of the previous studies have identified and ranked the critical factors but such a synthesis has not been reported. Keywords Public-private partnership, Literature review, Concession period, Decision making matrix Paper type Literature review

Introduction Private sector’s participation in infrastructure funding and management is growing around the world and this increase has special relevance for road projects (Wang, 2015). Privatization of construction projects is an emerging trend in Europe as well as the USA. There have been significant greenfield projects since early 90s, such as the Dulles Greenway, opened in 1995. More recently, several brownfield privatizations have taken place: Chicago Skyway in 2005, Indiana Toll Road in 2006 and Pennsylvania Turnpike in 2008. Therefore, the construed wave of privatization is accompanied by a revived

Journal of Financial Management of Property and Construction Vol. 21 No. 3, 2016 pp. 269-300 © Emerald Group Publishing Limited 1366-4387 DOI 10.1108/JFMPC-02-2016-0011

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interest in public sector regulation. This can be explained by the redistributive effects arising from exploiting a network asset as a motorway (Albalate and Bel, 2009). There has been a proliferation of schemes promoting cooperation between public and private sectors to provide infrastructure development over a wide range of economic activities. Governments, in different parts of the world, have embraced public–private partnership (PPP) arrangements. One reason for such development is concern over public expenditure. These arrangements are presented as a core part of modernizing public services claiming to improve quality and efficiency of these services (Carbonara et al., 2014). While this is a positive outlook of PPP, a more skeptical aspect points toward its complexity in the form of multiparty contractual arrangements, short-term and long-term costs and consequences for labor and those reliant on public services (Demirag et al., 2011). This paper collects and synthesizes the decision factors, which propose policy guidelines regarding concession of PPP projects. Osei-Kyei and Chan (2015) recently studied detailed literature from 1990 to 2013 on PPP projects. As per their findings, researchers as well as practitioners are increasingly becoming aware of critical success factors (CSFs) of PPP projects. Risk allocation and sharing, strong consortium, political and public support and transparent procurement are at core of successful delivery of PPP. However, decision-making for optimum length of PPP concession remains risky for stakeholders; various factors have been identified over the years to propose duration allowing a symbiotic relationship. For the period 2005-2012, significant work has been performed on studying factors affecting concession length. From year 2012 onward, research has been more applied and repetitive in nature. During this time, severity of risk associated with PPP projects has received significant research attention, globally. Yet, no published literature organized the critical decision factors. The objectives of this paper are to highlight contribution to PPP research by various countries, respective authors and institutions, and to formulate a decision-making matrix for helping practitioners in estimating appropriate concession length. Findings of this study help both researchers and practitioners: researchers via a ranked list of CSFs affecting PPP concession established from extensive review of literature published during the period 2005-2015 and the practitioners via a decision-making matrix to determine major components of concession period by using analysis of factors identified from literature and their effects on three parties to PPP arrangement (user, concessionaire and public body). Further, the zoning in matrix ensures adequate resource allocation and due consideration to CSFs. Overview of PPP PPP is a contractual arrangement between public (local, state or federal) and private agencies to share resources, tools, assets and skills of each sector for successful delivery of a service or facility for public use (Bloomfield, 2006). It is a variant of private finance initiative and is globally recognized as an effective way of delivering value for money through public infrastructure or services (Takim et al., 2009). It seeks to combine advantages of competitive tendering and flexible negotiation, and allocates risk on an agreed-upon basis between public and private sectors (Carbonara et al., 2014). It is important that risk allocation is clearly communicated and understood between project stakeholders. Therefore, public clients and private bidders need to evaluate all

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potential risks throughout project life to ensure fair result (Carbonara et al., 2014). With fast pace of market-oriented transformation and increasing demand, a delicate balance has to be sought among private sector capacity, government regulatory function and public satisfaction (Demirag et al., 2011; Ameyaw et al., 2015). PPP projects are usually long-term contracts for services, providing associated facilities or properties. Under these contracts, private sector entity is responsible for designing and constructing the building or facility, and maintaining and servicing it throughout the contract term (Albalate and Bel, 2009). In practice, concessions do not fit neatly into a single category. However, technical distinction between concessions and operating concessions affect rules and regulations regarding public finances and tendering. Decision-making in PPP concession PPP projects raise a variety of concerns as they move from concept through project delivery. These concerns range from initial decision to use a PPP procurement mechanism: specifically, control over toll setting where applicable, sharing of risks and revenues and strategy for communicating complexities of agreements to public and decision makers (Kang et al., 2011). The general trend indicates that risk allocation between public and private sectors, involved in public-purpose transport investments, is an onerous and precautious matter owing to opportunistic behavior of stakeholders (Medda, 2007). Although allocation of risk between stakeholders in PPP construction projects varies from country to country, a major consensus is found over the assumption of entire financial risk by private sector undertaking long-term maintenance and operation responsibilities (Ke et al., 2010; Li et al., 2005). Thus, common contractual forms are build-operate-transfer and design-build-finance-operate-maintain. An exception to this risk sharing are the projects involving promotion and/or development of some underdeveloped and deprived parts of country. For this purpose, public planning authorities make use of various socio-economic indices such as Human Development Index and purchase power parity to statistically calculate the required funding to be paid by public agencies, usually called Viability Gap Fund. This ensures better results of projects subject to development of locality and public well-being. As a concluding thought, owing to their magnitude, highway infrastructure projects are threatened by emerging risks and their cost needs to be worked out in concession awards (Wirahadikusumah et al., 2014). Risk is further exasperated due to time factor; PPP concessions are usually very lengthy (25-30 years), giving rise to stochasticity of various estimates. This warrants for improved estimation of concession period, which is usually identified by three major elements: structure, length and incentive scheme (Ye and Tiong, 2003). The length of concession is critical due to sharing and allocation of rights and responsibilities between public and private sectors during project lifecycle (Zhang and AbouRizk, 2006). Estimating optimum concession period for any PPP-based infrastructure project is very important and sometimes conflicting; private parties attempt at not only ensuring payback of capital amount but also getting attractive return on investment (ROI) (profit), seeking lengthy concession period (Estache et al., 2007). However, public entity wants early transfer of the constructed facility/infrastructure against high customer satisfaction and quality of service (Akbıyıklı, 2007).

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Decision-making implications of this study deal with donor’s confidence at planning phase as the increased certainty of concession period will encourage other funding agencies to invest in infrastructure development (Rouhani et al., 2013). It impacts economic and social sector development indices of any country. This study explains how various factors affect three main parties to a PPP project, and provides a systematic approach for factor selection to evaluate PPP arrangements. Methodology and results Methodology of this study consists of reviewing research papers published between 2005 and 2015 on PPP and its concession. Though PPP has gained considerable attention since 1991, recent trends are changing, as it is becoming more popular in underdeveloped countries (Lakshmanan, 2008). Hence, to incorporate the due proportion of underdeveloped countries and to deliberate recent trends in this area of research, study limit is set between 2005 and 2015. Literature searching In recent practices of literature review, use of databases and keywords to sample both journals and articles has emerged as a standard practice (Keegan and Boselie, 2006; Ke et al., 2009). Alternatively, selection of journals by reputation or impact rating is also emerging as a useful technique (Lockett et al., 2006; Glynn and Raffaelli, 2010). For searching-related literature, Google Scholar, Science Direct, Taylor & Francis Online, Emerald Insight, ASCE and Scopus libraries were used. The search process consisted of both keywords-based and semantic techniques. Keywords such as concession, public private partnerships, private finance initiative and public infrastructure were used. Moreover, search was further restricted to the areas of “engineering”, “social sciences”, “accounting”, “management”, “business”, “energy”, “econometrics, finance and economics”, “environmental science” and “decision sciences”. As a result, 141 unique research publications were retrieved. To avoid repetition in papers obtained from different journals, their titles and authors’ names were carefully read and highlighted. A spreadsheet was maintained for this purpose and papers were arranged alphabetically to avoid any chance of repetition. After retrieval of papers, a two-staged process was followed. In the first stage, analysis was performed to identify the countries contributing to PPP during specified study period. Number of authors and institutions of each country were also enlisted. In the second stage, detailed literature review was performed related to PPP concession to formulate the decision-making matrix. Academic journals breakdown Publications, retrieved in previous step, were summarized according to their journal of publication. Detailed analysis was carried out, and papers focused on PPP in general were separated from those relevant to study of its concession as shown in Table I. Though a large number of journals were identified, for sake of brevity, ones with five or more concession relevant papers are cited. International Journal of Project Management leads PPP publications closely followed by Journal of Financial Management of Property and Construction and Project Management Journal. This is evident from recent publications of these journals where issues like PPP and its complexity are thoroughly discussed.

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Journal International Journal of Project Management Journal of Financial Management of Property and Construction Project Management Journal Transportation Research Part A: Policy and Practice Research in Transportation Business and Management Journal of Construction Engineering and Management Research in Transportation Economics Others

Retrieved papers

Concession relevant papers

15 11 11 10 9 8 7 70

9 6 6 5 5 5 5 19

Stage A In this stage, countries were ranked based upon their contribution to PPP by analyzing and listing papers published each year. Afterward, author-based analysis was carried out to discern the quantum of contribution made by authors of each country and finally, authors’ affiliated institutions were listed. General contribution by the countries To assess research contribution by various countries, retrieved papers were studied and analyzed in detail. Origin of papers was identified based upon authors’ country of origin. To observe research evolution, a temporal analysis was carried out as shown in Table II. Papers exclusive to a country were given one point, whereas papers shared by authors from two different countries were given 0.5 point each. Similarly, for three or more authors, score was equally distributed, giving a total sum of 1 (i.e. 0.33 ⫹ 0.33 ⫹ 0.33). In Table II, the number with an asterisk (*) sign refers to joint publication. Total marks were adjusted accordingly as per previously mentioned criteria. The “% contribution” column shows contribution of each country to PPP literature and research during period 2005-2015. China, being a rapidly developing nation, is contributing maximum to this field (15.1 per cent) followed by the USA (13.3 per cent), whereas Sweden is contributing the least in the studied countries. But, this does not discredit the country, as it is among those countries that are producing substantial work on PPP and hence qualifies the current study’s criteria. Also, around 12 per cent work on PPP is conducted by various countries whose individual contribution is marginal for detailed study. Author-based contribution by the countries For calculating per cent contribution of authors of various countries, the methodology of Ke et al. (2009) was followed. Various authors have contributed to this methodology in recent times: Schweber and Leiringer (2012) used meta-analysis to specify relevant journals and research areas, Yi and Chan (2013) used three-staged content analysis and recently, Chou and Pramudawardhani (2015) used confirmatory factor, mean value and two-dimensional analysis in conjunction with it. Although these authors have contributed significantly to review studies, the work of Ke et al. (2009) stands out due to its simplistic approach. Based on it, authors were given scores as per equation (1):

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273 Table I. List of journals and relevant papers

Table II. Country-wise yearly contribution

2006

2007

2008

2009

2010 2011 2012

2013

2014

Note: * represents joint publications as explained in the text

17 15 11 9 5 5 5 4 3 3 3 3 3 3 2 2 2 1 0 13 109

4 2 2 1 2 0 0 2 2 0 0 0 0 0 1 1 1 2 3 9 32

0.8 0.66 0.66 0.5 0.66 0 0 0.66 0.66 0 0 0 0 0 0.5 0.5 0.5 0.66 0.75 0.9 8.45

17.8 15.6 11.6 9.5 5.6 5 5 4.6 3.6 3 3 3 3 3 2.5 2.5 2.5 1.6 0.7 13.9 117.45

15.1 13.3 9.9 8.1 4.8 4.2 4.2 3.9 3.1 2.5 2.5 2.5 2.5 2.5 2.1 2.1 2.1 1.4 0.6 11.8

2015 Independent Shared Shared score Score (%) contribution

China 1 1 2 2 2 ⫹ 1* 3 ⫹ 3* 3 3 USA 3 ⫹ 1* 1 1 1 2 3 ⫹ 1* 3 1 UK 2 1 2 1 ⫹ 1* 2 2 1 ⫹ 1* Spain 1* 1 1 1 1 1 1 3 Australia 1 1 ⫹ 1* 1 1 ⫹ 1* 1 Portugal 1 1 2 1 Italy 2 1 2 France 1 1 ⫹ 1* 1 1 ⫹ 1* Brazil 1 1* 1 1* 1 Pakistan 1 1 1 The Netherlands 1 1 1 Malaysia 1 1 1 Iran 1 1 1 India 1 1 1 New Zealand 1 1* 1 Japan 1 1 ⫹ 1* Belgium 1 1 ⫹ 1* Argentina 1 1* 1* Sweden 1* 1* 1* Others 1 1 ⫹ 1* 2 ⫹ 1* 1 2 ⫹ 1* 1 ⫹ 1* 1 2* 2* 2 ⫹ 1* 2 Total

2005

274

Country

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1.5n⫺i

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兺

n i⫽1

1.5

(1) n⫺i

Where “n” represents number of authors and “i” indicates order of each author. For single author, a score of 1 was assigned whereas for four authors in a paper, scores given to them in order of their position in publication were 0.42, 0.28, 0.18 and 0.12, respectively. In Table III, the score is sum of single author contribution and corresponding score of being the 1st, 2nd, 3rd or 4th author. The “% contribution” column shows contribution of authors of a specific country in terms of all reviewed papers. Results show that authors from China are making maximum contribution to PPP with 17.9 per cent relative score, followed by the USA (14.22 per cent). Around the world, 8.21 per cent of the authors contributing to PPP research are not a part of any country listed in this study.

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Institute-based contribution by the countries The country-wise analysis was taken further to highlight affiliated institutions of authors. The exercise synthesized the contribution of various institutions toward development of PPP during study period. Countries with a minimum of three publications are tabulated in Appendix. China tops the list in terms of both number of publications and specific institution producing PPP publications. The Hong Kong Polytechnic University (China), Universitat de Barcelona (Spain), Technical University of Lisbon (Portugal), National Highway Authority (Pakistan), University of Auckland

Country China USA UK Spain Australia France Portugal Italy Brazil Malaysia Iran The Netherlands India Japan Pakistan Argentina Belgium New Zealand Sweden Others

Single author

1st author

2nd author

3th author

4th author

Total papers

Score

(%) contribution

20 15 13 12 6 5 6 4 4 4 4 3 2 2 1 1 1 1 0 10

14 12 4 5 4 3 1 3 2 2 1 1 1 0 1 0 1 0 1 3

6 6 4 5 2 3 1 2 2 1 1 0 1 1 1 1 0 1 1 3

3 3 5 2 1 1 0 2 1 1 2 0 0 1 0 1 0 0 1 3

2 2 1 3 1 1 2 1 2 3 0 1 1 1 0 0 0 0 0 3 Total

45 38 27 27 14 13 10 12 11 11 8 5 5 5 3 3 2 2 3 22 244

28.34 22.5 16.82 16.22 8.54 7.4 6.94 6.3 5.82 5.66 5.06 3.54 2.82 2.58 1.7 1.46 1.42 1.28 0.88 13 158.28

17.90 14.22 10.62 10.25 5.4 4.67 4.38 3.98 3.67 3.57 3.2 2.23 1.78 1.63 1.07 0.92 0.9 0.81 0.55 8.21

Table III. Country-wise authors contribution

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(New Zealand), Universidad Nacional de Córdoba (Argentina) and University of Antwerp (Belgium) are leading the race with three publications each. Stage B This stage shortlists the literature relevant to PPP concession and sets base for decision matrix. The pertinent steps are as below. Shortlisting the relevant literature Retrieved papers were studied in detail to only consider the ones containing the phrase “concession period”, “PPP concession”, “optimum concession period of PPP” and “deciding the concessions”. These phrases were searched for in the title, abstract or main body of retrieved literature. As a result, 60 papers were found relevant as previously shown in Table I. Factor identification From relevant papers, 63 factors were identified having more than 10 appearances based upon a frequency analysis as shown in Table IV. Yearly appearance was also noted to get annual assessment of various factors, i.e. when were they introduced. To understand the influence of identified factors, the affected stakeholders were also studied. Based on these analyses, zoning was done to segregate the significant factors. In the zoning process, factors are divided into four zones: Red, Pink, Yellow and Green owing to their relative importance and significance, with red zone being the most influential. This helps in formulating the decision-making matrix supporting critical PPP concession decisions. Though an extensive search was performed for finding the relevant literature, the retrieved papers from selected journals may not include all the publications in this area. Therefore, it is highlighted that the findings of this paper are solely based on the data collected from specific sampling procedure. Findings and discussion After careful observation and using extracted knowledge of related literature, 63 previously identified factors were divided into six groups. The classification was done on the basis of both functional and characteristic-based criteria: three groups on the basis of functional attributes, while the remaining three on the basis of characteristics. The functional groups include Financial Indicators, Project Executability and Revenue, whereas the characteristic-based groups are National Attributes, Consortium Attributes and Purchase Power. In the next step, the identified factors were assigned to functional and characteristic groups as reported in Table V. Though recent in its application, extensive research has been reported on PPP since a considerable amount of time. The evolution since 2005 reports that the factors identified initially could only be classified into five of the previously mentioned groups except “purchase power”, as no constituent member of this group was identified at that time as per reviewed literature. Considerable attention was given to the users affected by PPP projects, and the interaction of traffic and construction machinery in literature following 2005, leading to introduction of new factors i.e. factor 62 and 63: population in area and toll adjustment, respectively. This resulted in addition of “purchase power” group in 2006.

12

11

10

9

8

7

6

5

4

3

2

1

Sr. no.

Toll adjustment (Yu and Lam, 2013) Strength of the consortium (Caicedo and Diaz, 2013) Discount rate (Wang et al. 2014) Procurement (Zhang and Chen, 2013) Government effectiveness (Hanaoka and Palapus, 2012; Lundy and Morin, 2013) Profitability (Carbonara et al., 2014; Hwang et al., 2013) Size of investment (Niu and Zhang, 2013) Differentiation in guarantees (Cantos-Sánchez et al., 2011)

24 25

27

29 30 31

Construction cost (Jain, 2014)

O and M costs (Geenen, 2014)

Return on investment (Ubbels and Verhoef, 2008; Takim et al., 2009; Ameyaw et al., 2015) Investment attraction (Wang et al., 2014) Market situation (Roumboutsos and Chiara, 2010; Hu and Zhu, 2015; Ameyaw et al., 2015) 33

32

28

Construction period (Zhang and AbouRizk, 2006; Santos et al., 2010)

Strategic quality management (Wang, 2015) Poorly defined sector policies (Takim et al., 2009)

Service price (Takim et al., 2009)

23

26

Inflation rate (Hwang et al., 2013)

22

Toll price (Wamuziri and Clearie, 2005; Ameyaw and Chan, 2015) Severity of risks involved (Carbonara et al., 2014; Ibrahim et al., 2006) Net present value (Ubbels and Verhoef, 2008) Operation period (Hanaoka and Palapus, 2012;Jain, 2014) Operation revenue in year (Zhang and Chen, 2013) Revenue stream (Zhang and Chen, 2013)

Factors

Sr. no.

Factors

54

53

52

51

50

49

48

47

46

45

44

43

Sr. no.

(continued)

Constructability (Ferrari et al., 2013; Zhang and Chen, 2013) Right project identification (Yu and Lam, 2013)

Lack of competition (Rolim et al., 2014; Demirag et al., 2011) Size of project (Bourguignon, 2013)

Adequacy of funding (Willoughby, 2013)

Innovative design (De Marco et al., 2012; Takim et al., 2009) No of partners (Ng et al., 2007; Yang et al., 2010; Ameyaw and Chan, 2015)

Social welfare (Niu and Zhang, 2013; Hu and Zhu, 2015) Economic viability (Hanaoka and Palapus, 2012; Rolim et al., 2014) Government’s interests (Willoughby, 2013; Ismail and Azzahra Haris, 2014) Political stability (Yu and Lam, 2013; Ibrahim et al., 2006) Type of project (Kang et al., 2011)

Factors

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Table IV. Identified factors with their references

Operational life (Zhang and AbouRizk, 2006; Ferrari et al., 2013) No of vehicles (Athias and Nuñez, 2008; Albalate et al., 2015) Equity allocation (Scandizzo and Ventura, 2010; Yang et al., 2010)

Sale price (Bourguignon, 2013; Wang, 2015) Toll adjustment (Wamuziri and Clearie, 2005; Yu and Lam, 2013)

13

16

Travel time (Niu and Zhang, 2013; Rolim et al., 2014) Market demand (Albalate and Bel, 2009; Geenen, 2014) Complexity (Ameyaw and Chan, 2015)

19

21

20

Operation cost (Ng et al., 2007)

18

17

15

42

41

40

39

38

37

36

35

34

Sr. no.

Project development cost (Zhang and Chen, 2013) Capital structure of company (Willoughby, 2013; De Marco et al., 2012) Lack of clear government objectives and commitment (Willoughby, 2013; Kumaraswamy and Ling, 2010) Traffic congestion (Ubbels and Verhoef, 2008; Albalate et al., 2015) Securitization of asset (Willoughby, 2013; Takim et al., 2009) Poor transparency (Zhang and Chen, 2013; Santos et al., 2010)

Strength of SPV (Demirag et al., 2011; Burke and Demirag, 2015)

Credibility of government policies (Kumaraswamy and Ling, 2010) Corruption (Santos et al., 2010)

Factors

63

62

61

60

59

58

57

56

55

Sr. no.

Entrepreneurship and leadership (Yu and Lam, 2013) Site limitation (Yang et al., 2010; Ke et al., 2010)

Refinancing (Chou et al., 2012)

Construction logistics (Patel and Bhattacharya, 2010; Junge and Levinson, 2013) Securitization of loan (Rolim et al., 2014; Iseki and Houtman, 2012) Organizational structure of project (De Marco et al., 2012;Auriol and Picard, 2013) Income in year (Khanzadi et al., 2012)

Population in area (Caicedo and Diaz, 2013) Promotion (Jain, 2014)

Factors

278

14

Factors

Table IV.

Sr. no.

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Sr. no.

Group

Factors

1

Financial indicators

Discount rate O & M costs Inflation rate Profitability Net present value Income in year Economic viability Construction cost Operation cost Project development cost Securitization of loan Refinancing Construction period Operation period Type of project Size of project Complexity Constructability Size of investment Procurement Innovative design Construction logistics Site limitation Severity of risks involved Securitization of asset Service price Toll price Sale price Market demand Operational life Equity allocation Operation revenue in year Return on investment Market situation Revenue stream No of vehicles Project promotion Government effectiveness Investment attraction Poorly defined sector policies Lack of clear government objectives and commitment Credibility of government policies Poor transparency Government’s interests Political stability Corruption (continued)

2

Project executability

3

Revenue

4

National attributes

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Table V. Groups with their factors

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Sr. no.

Group

Factors

5

Consortium attributes

6

Purchase power

Organizational structure of project Adequacy of funding Entrepreneurship and leadership No of partners Strength of SPV Capital structure of company Right project identification Risk management maturity Differentiation in guarantees Lack of competition Strategic quality management Social welfare Travel time Traffic congestion Population in area Toll adjustment

280

Table V.

Similarly, other factors, i.e. 1, 2, 10 and 12 were identified and added to “financial indicators” group; factors 14, 15, 16, 17, 20, 21 and 24 to “project executability”; factors 28, 29, 30, 31 and 34 to “revenue”; and factor 53 to “consortium attributes” group. There were no new factors identified in “national attributes”. During the next year, owing to the importance of financial decision-making, three new factors (6, 8 and 9) were added to “financial indicators” group. Similarly, factors 12 and 25 were added to “project executability”, whereas factor 37 was added to “national attributes” by the end of 2007. The remaining groups proceeded with no additions. The trend of “financial indicators” continued for the next year as well. Substantial considerations were given by researchers to social welfare aspects and hence, critical additions were made to “purchase power”. Additions were also made to groups “project executability” (factor 19), “revenue” (factors 26 and 27) and “national attributes” (factor 38). Later on, research trend started focusing on “national attributes”, i.e. corruption and political stability in 2009, leading to addition of factors 45 and 46. The previous trend of “purchase power” also continued with addition of factor 61. The remaining groups stayed dormant, with no additions of further factors, for another year. The research focus started shifting toward the “financial indicators”, “project executability” and “consortium attributes” in the next year with an addition of one new factor to each group: factor 4, 22 and 51, respectively. The group of “consortium attributes” continued its trend with addition of factor 47, without any changes to the remaining groups in 2011. The research focus shifted toward equity allocation and its effects on revenue. Hence, the list was completed with addition of factor 31 to “revenue” by the end of 2012. In light of reviewed literature, research matured after 2012 and no new factors have been identified as of 2015. Year 2012 onward, the already identified factors are utilized in decision-making; their effects being the focus of researchers and practitioners. Table VI shows the detailed yearly appearance of these factors.

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.

Sr. no. Discount rate O & M costs Inflation rate Profitability Net present value Income in year Economic viability Construction cost Operation cost Project development cost Securitization of Loan Refinancing Construction period Operation period Type of project Size of project Complexity Constructability Size of investment Procurement Innovative design Construction logistics Site limitation Severity of risks involved Securitization of asset

Financial indicators

Project executability

Factors

Gp

✓

✓

✓

✓

✓

✓

2005

✓ ✓

✓ ✓

✓ ✓ ✓ ✓

✓

✓

✓ ✓

2006

✓

✓ ✓

✓

✓ ✓

✓ ✓

✓

2007

✓

✓

✓

✓

✓

2008

✓

✓

✓

✓

✓ ✓ ✓

✓

✓ ✓ ✓

✓ ✓

✓ ✓

✓

✓

✓ ✓

✓

✓ ✓

Year of appearance 2009 2010 2011

✓

✓ ✓ ✓ ✓ ✓ ✓ ✓

✓ ✓ ✓ ✓

✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

2012

✓ ✓

✓

✓ ✓ ✓

✓ ✓ ✓ ✓ ✓

✓ ✓ ✓

✓ ✓ ✓ ✓

✓ ✓ ✓

✓

✓

✓

✓

2015

(continued)

✓

✓ ✓

✓ ✓

✓ ✓

✓ ✓

✓ ✓ ✓ ✓ ✓ ✓

✓ ✓ ✓

2014

2013

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Table VI. Yearly appearance of the factors

National attributes

Revenue

26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46.

Table VI.

Gp

Service price Toll price Sale price Market demand Operational life Equity allocation Operation revenue in year Return on investment Market situation Revenue stream No of vehicles Project promotion Government effectiveness Investment attraction Poorly defined sector policies Government objectives and commitment Credibility of government policies Poor transparency Government’s interests Political stability Corruption

Factors

✓ ✓ ✓ ✓ ✓ ✓

✓

✓

2005

✓ ✓

✓

✓ ✓ ✓

2006

✓

✓ ✓

✓

2007

✓ ✓ ✓ ✓

✓ ✓

✓ ✓

✓ ✓ ✓ ✓ ✓

2008

✓ ✓

✓ ✓

✓

✓

✓

✓ ✓

✓ ✓

✓

Year of appearance 2009 2010 2011

✓

✓ ✓ ✓ ✓

✓ ✓ ✓

✓

✓ ✓

2012

✓ ✓ ✓ ✓

✓ ✓

✓ ✓

✓ ✓ ✓ ✓

✓ ✓ ✓

2013

282

Sr. no.

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✓

✓

2015

✓ ✓ ✓ ✓ ✓ ✓ ✓ (continued)

✓ ✓

✓ ✓ ✓ ✓ ✓ ✓ ✓

✓ ✓

✓ ✓

2014

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47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63.

Sr. no. Organizational structure of project Adequacy of funding Entrepreneurship and leadership No of partners Strength of SPV Strength of consortium Capital structure of company Right project identification Risk management maturity Differentiation in guarantees Lack of competition Strategic quality management Social welfare Travel time Traffic congestion Population in area Toll adjustment

Consortium attributes

Purchase power

Factors

Gp

✓ ✓ ✓ ✓ ✓

✓

✓ ✓ ✓

2005

✓ ✓

✓ ✓ ✓

2006

✓

✓

✓

2007

✓ ✓

✓ ✓

2008

✓

✓

✓ ✓

✓ ✓ ✓ ✓

✓ ✓ ✓

✓

✓

✓

✓

✓

Year of appearance 2009 2010 2011

✓

✓

✓ ✓

✓ ✓

✓

2012

✓ ✓ ✓ ✓

✓ ✓ ✓ ✓ ✓

✓

✓ ✓

✓ ✓ ✓ ✓ ✓

✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

2014

2013

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✓

✓ ✓ ✓ ✓ ✓ ✓ ✓

2015

Public-private partnership

283

Table VI.

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In the next stage of analysis, effects of these factors were observed on the three parties of PPP arrangement. It was done to support decision-making of concession period to achieve a mutually beneficial scenario for all parties. In analyzing the factors, their effects, both direct and indirect, were studied and noted. Some factors directly affect all three parties, while some factors affect only two or one of them (Hu and Zhu, 2015). The systems diagram, as shown in Figure 1, depicts this phenomenon. A factor affecting a particular party has an arrow leading from the factor to that party. A factor affecting more than one party has multiple arrows corresponding to number of affected parties. Factors like service price, toll price, sale price and government’s effectiveness directly affect all three parties; hence, three arrows can be seen originating from these factors. Furthermore, factors like credibility of government’s policies and social welfare affect two, whereas some factors are exclusive to a specific party such as traffic congestion, travel time and toll adjustment. As an example, it can be understood that increased toll price affects all three concerned parties: user will have to pay more than his purchase power for persistent use, concessionaire will have a quick recovery of his investment whereas the public body can have a shorter concession duration and can acquire the project back swiftly (Rouhani et al., 2013). However, this is not necessarily a synergetic situation. Furthermore, mutual interaction of these factors creates an overarching effect that plays a significant role in enhancing or limiting overall effect of a factor or its group. For example, discount rate affects inflation rate: it has a direct proportionality (Del Negro et al., 2015). Corruption is related to political stability and poor transparency: it increases with unstable governments and politics, and also with transparency levels (Nurudeen et al., 2015; Quazi et al., 2015). Toll price is affected by the number of vehicles and investment size: it decreases with number of vehicles utilizing the project (Albert and Mahalel, 2006) and increases with investment size to ensure timely return on investment (Albalate and Bel, 2009). Similarly, market demand, economic viability and investment attraction are mutually dependent following an increasing trend (Coelho and de Brito, 2013; Sahasranaman and Kapur, 2014). The list of these interactions continues but for sake of brevity, the diagram only shows direct relations between the factors. Further, only relation to parties is considered and mutual interaction is not shown to incorporate all the identified factors and make the diagram simple. After establishing the relationship between factors and the parties affected, next step was carrying out a frequency analysis of identified factors and their total appearances as observed in 60 relevant research papers. This helped in understanding research trends and focus for last decade based on number of appearances of CSFs and their relative importance. Similar work has been carried out by Shaya et al. (2008) for their study on school-based obesity conducted for literature published during period 1986-2006, and Schweber and Leiringer (2012) in their article for buildings and energy research conducted during 2000-2011. In current study, the factors are studied on yearly basis, highlighting the importance given to a particular factor in a specific year, as previously discussed in Table VI. Further, reviewed literature was retrieved using certain limiting factors, as mentioned in literature searching section. Thus, possibility of a less important factor making into the shortlisting is minimum. Also, to counter any ambiguity, a panel of three local experts on PPP was taken on board to systemize the review process in accordance with Tranfield et al. (2003). These experts had an

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285

Figure 1. Systems diagram for parties affected

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experience of at least three PPP projects and belonged to upper managerial positions in construction industry of Pakistan. In-house discussions were carried out regularly on shortlisting of factors. As a result, the list was finalized only after the experts agreed to their importance and criticality. For inclusion in the analysis, factors with at least three appearances were considered. The analysis shows a maximum appearance of 44 for severity of risks involved. Different zones are formulated on the basis of frequency analysis: red or most critical zone contains the factors having an appearance of 76 per cent and above, pink zone houses factors having 75 to 51 per cent appearance, yellow zone caters for an appearance between 50 and 26 per cent and the green zone has the factors having appearance less than or equal to 25 per cent. These zones correspond to the quartiles accommodating the factors. This exercise of quartiles in research studies has previously been used in the medical field by Flum and Dellinger (2004), in banking sector by Bolt and Humphrey (2015) and, more relevantly, in construction by Lee et al. (2005). Further, the concept of zoning, in literature review, is recently used in a study conducted by Siddiqui et al. (2016), which categorized the factors into three zones: red, yellow and green. The zoning corresponds to criticality of factors being studied. Starting from the most critical factors (red zone) to the least critical (green zone), these ranges are used to formulate the decision-making matrix. The analysis shown in Table VII concludes that 5 factors of 63 (8 per cent) are placed in the red zone, 13 (21 per cent) in the pink zone, 32 (51 per cent) in the yellow zone and remaining 13 factors (21 per cent) are placed in the green zone. This points to a very pertinent conclusion that more than two-third (71 per cent) of PPP decision factors fall into the less critical or safe zones, an argument that may easily be used in favor of these projects. Nevertheless, a considerable portion of 29 per cent factors falls into red and pink zones, which suggests the level of criticality PPP projects face. Combining this with the magnitude of these projects, it results into an alarming situation, which needs highly efficient management to ensure project success. The zoning not only takes into consideration the frequency of appearance but also the extent of effect each factor has on the three parties. So, it must be noted that though factors 26, 27, 28 and 38 have lesser frequency, but due to their overarching effect, they are placed in the red zone. On the contrary, some factors, such as 19 and 34, report a considerably high frequency, but due to their effect on only two parties, they are placed in less critical yellow zone. The decision-making matrix After frequency analysis, the decision-making matrix is formulated as shown in Figure 2. It places the identified factors in various zones based upon their appearances in literature and analyzes them against the number of parties affected. The matrix has four zones corresponding to four quartiles: 1st quartile (⬎75 per cent effect) is the red zone, 2nd quartile (⬎50-75 per cent effect) is the pink zone, 3rd quartile (⬎25-50 per cent effect) is the yellow zone, whereas 4th quartile (0-25 per cent effect) is the green zone. The red zone has the most significant factors followed by pink and yellow zones, while factors falling in green zone have least significance. The matrix has four portions: Portion 1 consists of factors affecting only one party. Though Portions 2 and 3 contain factors affecting two parties, the segregation is performed on the basis of per cent appearance. Finally, Portion 4 consists of factors affecting all three parties. The flexibility of this decision matrix is that a factor can move within a portion from one zone

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Sr. no. Red zone 24 27 38 26 28 Pink zone 58 32 36 5 39 45 6 52 35 48 25 31 33 Green zone 12 37 60 44 4 57 18 54 15 21 47 61 49 Yellow zone 19 34 8 2 56 9 13 30 14 43 11 46

Factor name

Count

Parties Affected

Percentage

Severity of risks involved Toll price Government effectiveness Service price Sale price

44 14 11 7 6

3 3 3 3 3

73 23 18 12 10

Strategic quality management Operation revenue in year No of vehicles Net present value Investment attraction Political stability Income in year Strength of consortium Revenue stream Adequacy of funding Securitization of asset Equity allocation Return on investment

35 34 33 32 32 32 31 31 40 33 32 32 32

2 2 2 2 2 2 2 2 1 1 1 1 1

58 57 55 53 53 53 52 52 67 55 53 53 53

Refinancing Project promotion Travel time Government’s interests Profitability Lack of competition Constructability Right project identification Type of project Innovative design Organizational structure of project Traffic congestion Entrepreneurship and leadership

15 13 13 12 11 10 7 7 5 5 5 4 3

1 1 1 1 1 1 1 1 1 1 1 1 1

25 22 22 20 18 17 12 12 8 8 8 7 5

Size of investment Market situation Construction cost O & M costs Differentiation in guarantees Operation cost Construction period Operational life Operation period Poor transparency Securitization of loan Corruption

30 30 26 25 25 22 20 20 19 18 17 16

2 2 2 2 2 2 2 2 2 2 2 2

50 50 43 42 42 37 33 33 32 30 28 27 (continued)

Public-private partnership

287

Table VII. Zonal allocation

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Table VII.

Sr. no.

Factor name

1 3 29 59 16 40 41 42 7 10 20 55 63 17 53 50 62 51 22 23

Discount rate Inflation rate Market demand Social welfare Size of project Poorly defined sector policies Government objectives and commitment Credibility of government policies Economic viability Project development costs Procurement Risk management maturity Toll adjustment Complexity Capital Structure of company No of partners Population in area Strength of SPV Construction logistics Site limitation

Count

Parties Affected

Percentage

13 10 8 8 7 6 5 5 4 3 30 27 24 23 22 18 18 17 16 16

2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1

22 17 13 13 12 10 8 8 7 5 50 45 40 38 37 30 30 28 27 27

to another based on its frequency of appearance, but cannot cross the portion since its effect is limited to a certain number of parties, unless functionally altered. On Y-axis, the matrix represents percentage of appearance and count of each factor. The axis is linearly distributed into 20 ticks. The X-axis represents two details: on top, it represents parties affected and on bottom, frequency of appearance is mapped. Since the ticks on Y-axis have five-point range, the bottom of X-axis contains five blocks where the factor is placed using its frequency beyond minima of range. For example, a factor having a count of 22 of 100 and affecting only one party will be placed in 21-25 tick. But, since the frequency is one number larger than minima of range, the factor will eventually be placed in column 2 in green zone. In another example, a factor with 48 per cent score and affecting two parties will be placed in 46-50 tick and its position will be column 3 in yellow zone. Placing a factor in the matrix To place a factor appropriately into the matrix, the following steps were applied: Step 1: Parties affected by the factor. • If it affected one party, it was placed in Portion 1 (zone depends upon its percentage). • If it affected two parties, it could be placed in Portions 2 or 3 (either yellow or pink zone). If factor’s per cent appearance was less than or equal to 50, it was placed in Portion 2 (yellow zone). Whereas, in case of per cent appearance greater than 50, the factor was placed in Portion 3 (pink zone). • If it affected three parties, it was placed in Portion 4 (red zone).

96 to 100 91 to 95 86 to 90 81 to 85 76 to 80 71 to 75 66 to 70 61 to 65 56 to 60 51 to 55 46 to 50 41 to 45 36 to 40

58 to 60 55 to 57 52 to 54 49 to 51 46 to 48 43 to 45 40 to 42 37 to 39 34 to 36 31 to 33 28 to 30 25 to 27 22 to 24

Zones

Red Zone

Pink Zone

Yellow Zone

Green Zone

1

Pares affected

35

53 22, 23 37, 60 57 18, 54 61

2

Poron 1

25, 31, 33

17 51 4 15, 21, 47

3

1 Party 4

48 20 55 63 50,62 12 44

49

5

1

2, 56 9

13, 30

8

Poron 2

14

29, 59 41, 42

3

11

46 1 3 16 7

2

4

19, 34

43

40 10

5

2 Pares 1

32 6, 52

2

Poron 3

58 5, 39, 45

3

4

36

5

1

26

2

Poron 4

24

27 38

3

3 Pares

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(Count for This Study)

31 to 35

Percent Absolute Frequecy Frequecy

289 19 to 21 16 to 18 13 to 15 10 to 12 7 to 9 4 to 6 1 to 3

26 to 30 21 to 25 16 to 20 11 to 15 6 to 10 1 to 5

Appearance (%)

Public-private partnership

4

5

28

Figure 2. Decision-making matrix

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Step 2: Appearance percentage of the factor: • A factor with up to 25 per cent appearance was placed in green zone. • From 26 up to 50 per cent, it was placed in yellow zone. • From 51 up to 75 per cent, it was placed in pink zone. • Finally, a factor with 76 per cent and above appearance was placed in red zone. Step 3: Movement within the portions. Portions were provided on top of the matrix. If a factor affecting one party had a score of more than 25 per cent, it could move from green to yellow zone and above, accordingly. It must be noted that a factor cannot move from one portion to another, as it will interrupt the parties affected, but within its portion, it can move from green to red zone, depending upon its appearance percentage. For current study, the header row is also supported by the factor count. Hence, a factor with 30 appearances of 60 papers (50 per cent) and affecting one party was placed in Portion 1 in the 46-50 tick. Thus, all factors were placed as per the mentioned technique. The maximum appearance recorded was 44 of 60, while the minimum appearance and consideration limit was 3 of 60. The most appearing factor was placed at upper right corner of the matrix, confirming that it is the most critical factor on the basis of research trends of past decade. Similarly, all of the previously identified factors were placed in the matrix on the basis of their appearance versus parties affected by them. Application of decision-making matrix The decision-making matrix has applications for both researchers and practitioners. This mechanism of formulation of the decision matrix can be used for any literature review work, as it is simplistic and ever evolving. First, a CSF is identified by searching relevant literature; second, its frequency needs to be calculated; and lastly, the stakeholders affected by the particular CSF need to be identified. Once these steps are successfully carried out, the CSF can be mapped in the decision matrix. For this study, the factors placed in red zone demand higher level of attention during decision-making of concession length. For example, factor 24, severity of risks involved, has the highest position as per results of current study. Risks in construction have sought attention of researchers in recent times, especially since 2000 (Tah and Carr, 2000; Larsson and Field, 2002; Hollowell and Gambatese, 2009; Barlish et al., 2013; De Marco and Thaheem, 2014; Sarkar and Panchal, 2015). This clearly points to the need of collaboration between researchers and practitioners. The factors in pink zone need proper considerations since they are also crucial due to higher frequency of appearance. For example, factor 58, strategic quality management, has an effect of 58 per cent, pointing to its criticality and if not properly managed, may lead to disastrous situations. The effects of these failures have been highlighted by Barber et al. (2000), Kagioglou et al. (2001) and recently by AlMaian et al. (2015). The factors in yellow and green zones should be observed with less intensive management effort. Once the analysis is performed and the factors are placed in the matrix, different management techniques should be used as per their criticality. The practitioners benefit from the matrix by having a list of CSFs and their respective influence. The zonal allocation of these CSFs reports about their importance to the practitioners leading to better decision-making. Also, the practitioners can allocate adequate resources to CSFs needing more attention. Furthermore, boundaries of

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the proposed decision matrix are not limited to construction industry only and can be enhanced to incorporate other fields as well, such as social sciences, medical, management, etc. Conclusions This paper reviews literature published on PPP and its concession during the period 2005-2015, highlighting the research contributions by various countries with respect to their authors and institutions. The focus is on overall PPP arrangement and the critical factors affecting concession period in published literature. Initially, literature is scrutinized based upon country of origin of publication. As a result, a generic country-wise research contribution is obtained where China leads, closely followed by the USA. Similar pattern is observed for author-based and institutional contribution, delineating that authors and institutions from China have contributed maximum toward PPP research. To help in decision-making of PPP concession, based on the research trends of the past decade, factors affecting length of concession period are investigated. A total of 63 factors are identified by scrutinizing the literature published in various journals, their effects on the three parties to concession (user, concessionaire or public body) are studied and results are compiled. Furthermore, based upon the frequency, the factors are divided into four zones corresponding to their relevance in decision-making. The red zone is the most critical and green is the least. The findings are summarized into a decision-making matrix, which supports the argument that most of the factors for PPP fall into safe (green and yellow) zones; hence, more of these projects are encouraged by stakeholders. In addition, some factors, like severity of involved risks and revenue stream, fall into very critical zone pointing to importance of proper consideration demanded by them. Overall, 8 per cent of the factors from the published literature fall into the red zone demanding due attention of decision makers in determining concession length. The current study is the first step in wider analysis of arrangement of available literature on concession determination of PPP projects and its CSFs. The recent contribution by Osei-Kyei and Chan (2015) in establishing an increased research interest in the exploration of PPP CSFs since 1990 is acknowledged. To continue this knowledge gathering exercise, for facilitating the decision makers, further assessment is required on the identified factors. More factors can be incorporated by enhancing the searching criteria followed in this study. It is recommended to apply proper management techniques on these factors and observe their effects to help future decision makers to learn from past experiences. The major deliverable of this study, the decision-making matrix, is an evolving synthesis of existing knowledge. Moreover, with reference to internal organization of the three involved parties, a critical point in the decision-making efficiency is connected with difficulty in collecting data. This information gap could easily be exploited by incentivizing the concessionaire of the terminal area to release necessary information due to the new proposed link between identified factors on concession period and performance achievements. This study contributes to a more transparent concession system through introduction of decision-making matrix that can help the three parties to perceive and achieve their goals.

Public-private partnership

291

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An interesting evolution of presented work considers assessment of effects of social welfare and users’ expectations on length of concession period to better understand the impact of proposed methodology in different PPP projects. The proposed methodology is an original approach to set a conceptual framework; in this context, future studies and research may work on the above-mentioned considerations to enrich its potential applications. For futuristic studies, other dimensions can be introduced in the decision matrix, such as experts’ opinion. The list of papers, journals and countries provided aids the researchers in their quest for relevant literature on PPP. It is envisioned that, owing to constant research and publications in this area, and a need to update the published CSFs, more countries and journals will be added to the presented list. Also, the identified CSFs can be used for further exploration of PPP-related dimensions. Better understanding of the latest PPP research trends will enable the practitioners to understand key problems in PPP development leading to better project delivery. Further, current analysis provides an opportunity of cooperation between scholars and practitioners on the basis of identified CSFs. In future, current study can be repeated for PPP as well as other areas of construction and industries to enrich the overall body of knowledge. References Akbıyıklı, R. (2013), “Performance assessment of a private finance initiative road project”, Transport, Vol. 28 No. 1, pp. 11-24. Albalate, D. and Bel, G. (2009), “Regulating concessions of toll motorways: an empirical study on fixed vs. variable term contracts”, Transportation Research Part A: Policy and Practice, Vol. 43 No. 2, pp. 219-229. Albert, G. and Mahalel, D. (2006), “Congestion tolls and parking fees: a comparison of the potential effect on travel behavior”, Transport Policy, Vol. 13 No. 6, pp. 496-502. AlMaian, R.Y., Needy, K.L., Walsh, K.D. and Alves, T.D.C. (2015), “Supplier quality management inside and outside the construction industry”, Engineering Management Journal, Vol. 27 No. 1, pp. 11-22. Ameyaw, C., Adjei-Kumi, T. and Owusu-Manu, D.G. (2015), “Exploring value for money (VfM) assessment methods of public-private partnership projects in Ghana: a theoretical framework”, Journal of Financial Management of Property and Construction, Vol. 20 No. 3, pp. 268-285. Ameyaw, E.E. and Chan, A.P.C. (2015), “Evaluation and ranking of risk factors in public–private partnership water supply projects in developing countries using fuzzy synthetic evaluation approach”, Expert Systems with Applications, Vol. 42 No. 12, pp. 5102-5116. Athias, L. and Nuñez, A. (2008), “Winner’s curse in toll road concessions”, Economics Letters, Vol. 101 No. 3, pp. 172-174. Auriol, E. and Picard, P.M. (2013), “A theory of BOT concession contracts”, Journal of Economic Behavior & Organization, Vol. 89, pp. 187-209. Barber, P., Graves, A., Hall, M., Sheath, D. and Tomkins, C. (2000), “Quality failure costs in civil engineering projects”, International Journal of Quality & Reliability Management, Vol. 17 Nos 4/5, pp. 479-492. Barlish, K., De Marco, A. and Thaheem, M.J. (2013), “Construction risk taxonomy: an international convergence of academic and industry perspectives”, American Journal of Applied Sciences, Vol. 10 No. 7, pp. 706-713.

Bloomfield, P. (2006), “The challenging business of long-term public–private partnerships: reflections on local experience”, Public Administration Review, Vol. 66 No. 3, pp. 400-411.

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Bolt, W. and Humphrey, D. (2015), “A frontier measure of US banking competition”, European Journal of Operational Research, Vol. 246 No. 2, pp. 450-461. Bourguignon, C. (2013), “Leases, concessions, and authorizations: searching for an alternative to the privatization of federal domain in Brazil”, Land Use Policy, Vol. 33, pp. 100-110. Burke, R. and Demirag, I. (2015), “Changing perceptions on PPP games: demand risk in Irish roads”, Critical Perspectives on Accounting, Vol. 27, pp. 189-208. Caicedo, F. and Diaz, A. (2013), “Case analysis of simultaneous concessions of parking meters and underground parking facilities”, Transportation Research Part A: Policy and Practice, Vol. 49, pp. 358-378. Cantos-Sánchez, P., Moner-Colonques, R., Sempere-Monerris, J.J., and Álvarez-SanJaime, Ó. (2011), “Viability of new road infrastructure with heterogeneous users”, Transportation Research Part A: Policy and Practice, Vol. 45 No. 5, pp. 435-450. Carbonara, N., Costantino, N. and Pellegrino, R. (2014), “Concession period for PPPs: a win–win model for a fair risk sharing”, International Journal of Project Management, Vol. 32 No. 7, pp. 1223-1232. Chou, J.S. and Pramudawardhani, D. (2015), “Cross-country comparisons of key drivers, critical success factors and risk allocation for public-private partnership projects”, International Journal of Project Management, Vol. 33 No. 5, pp. 1136-1150. Chou, J.S., Tserng, H.P., Lin, C. and Yeh, C.P. (2012), “Critical factors and risk allocation for PPP policy: comparison between HSR and general infrastructure projects”, Transport Policy, Vol. 22, pp. 36-48. Coelho, A. and de Brito, J. (2013), “Economic viability analysis of a construction and demolition waste recycling plant in Portugal – part I: location, materials, technology and economic analysis”, Journal of Cleaner Production, Vol. 39, pp. 338-352. De Marco, A. and Thaheem, M.J. (2014), “Risk analysis in construction projects: a practical selection methodology”, American Journal of Applied Sciences, Vol. 11 No. 1, pp. 74-84. De Marco, A., Mangano, G. and Zou, X.Y. (2012), “Factors influencing the equity share of build-operate-transfer projects”, Built Environment Project and Asset Management, Vol. 2 No. 1, pp. 70-85. Del Negro, M., Giannoni, M.P. and Schorfheide, F. (2015), “Inflation in the great recession and new Keynesian models”, American Economic Journal: Macroeconomics, Vol. 7 No. 1, pp. 168-196. Demirag, I., Khadaroo, I., Stapleton, P. and Stevenson, C. (2011), “Risks and the financing of PPP: perspectives from the financiers”, The British Accounting Review, Vol. 43 No. 4, pp. 294-310. Estache, A., Juan, E. and Trujillo, L. (2007), “Public-private partnerships in transport”, World Bank Policy Research Working Paper No. 4436. Ferrari, C., Puliafito, P.P. and Tei, A. (2013), “Performance and quality indexes in the evaluation of the terminal activity: a dynamic approach”, Research in Transportation Business & Management, Vol. 8, pp. 77-86. Flum, D.R. and Dellinger, E.P. (2004), “Impact of gastric bypass operation on survival: a population-based analysis”, Journal of the American College of Surgeons, Vol. 199 No. 4, pp. 543-551.

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Geenen, S. (2014), “Dispossession, displacement and resistance: artisanal miners in a gold concession in South-Kivu, Democratic Republic of Congo”, Resources Policy, Vol. 40, pp. 90-99. Glynn, M.A. and Raffaelli, R. (2010), “Uncovering mechanisms of theory development in an academic field: lessons from leadership research”, Academy of Management Annals, Vol. 4 No. 1, pp. 359-401.

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Hanaoka, S. and Palapus, H.P. (2012), “Reasonable concession period for build-operate-transfer road projects in the Philippines”, International Journal of Project Management, Vol. 30 No. 8, pp. 938-949. Hu, H. and Zhu, Y. (2015), “Social welfare – based concession model for build/operate/transfer contracts”, Journal of Construction Engineering and Management, Vol. 141 No. 1, p. 04014064. Hwang, B.G., Zhao, X. and Gay, M.J.S. (2013), “Public private partnership projects in Singapore: factors, critical risks and preferred risk allocation from the perspective of contractors”, International Journal of Project Management, Vol. 31 No. 3, pp. 424-433. Ibrahim, A.D., Price, A.D.F. and Dainty, A.R.J. (2006), “The analysis and allocation of risks in public private partnerships in infrastructure projects in Nigeria”, Journal of Financial Management of Property and Construction, Vol. 11 No. 3, pp. 149-164. Iseki, H. and Houtman, R. (2012), “Evaluation of progress in contractual terms: two case studies of recent DBFO PPP projects in North America”, Research in Transportation Economics, Vol. 36 No. 1, pp. 73-84. Ismail, S. and Azzahra Haris, F. (2014), “Rationales for public private partnership (PPP) implementation in Malaysia”, Journal of Financial Management of Property and Construction, Vol. 19 No. 3, pp. 188-201. Jain, G. (2014), “The role of private sector for reducing disaster risk in large scale infrastructure and real estate development: case of Delhi”, International Journal of Disaster Risk Reduction, pp. 2212-4209. Junge, J. and Levinson, D. (2013), “Property tax on privatized roads”, Research in Transportation Business & Management, Vol. 7, pp. 35-42. Kagioglou, M., Cooper, R. and Aouad, G. (2001), “Performance management in construction: a conceptual framework”, Construction Management and Economics, Vol. 19 No. 1, pp. 85-95. Kang, C.C., Feng, C.M. and Kuo, C.Y. (2011), “A royalty negotiation model for BOT (build– operate–transfer) projects: the operational revenue-based model”, Mathematical and Computer Modelling, Vol. 54 No. 9, pp. 2338-2347. Ke, Y., Wang, S., Chan, A.P. and Cheung, E. (2009), “Research trend of public-private partnership in construction journals”, Journal of Construction Engineering and Management, Vol. 135 No. 10, pp. 1076-1086. Ke, Y., Wang, S., Chan, A.P. and Lam, P.T. (2010), “Preferred risk allocation in China’s public– private partnership (PPP) projects”, International Journal of Project Management, Vol. 28 No. 5, pp. 482-492. Keegan, A. and Boselie, P. (2006), “The lack of impact of dissensus inspired analysis on developments in the field of human resource management”, Journal of Management Studies, Vol. 43 No. 7, pp. 1491-1511. Khanzadi, M., Nasirzadeh, F. and Alipour, M. (2012), “Integrating system dynamics and fuzzy logic modeling to determine concession period in BOT projects”, Automation in Construction, Vol. 22, pp. 368-376.

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Kumaraswamy, M. and Ling, F.Y. (2010), “Public private partnerships: improving governance and performance amidst emerging economic realities”, Journal of Financial Management of Property and Construction, Vol. 15 No. 3.

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Lakshmanan, L. (2008), “Public private partnership in Indian infrastructure development: issues and options”, Reserve Bank of India Occasional Papers, Vol. 29 No. 1, pp. 37-41. Larsson, T.J. and Field, B. (2002), “The distribution of occupational injury risks in the Victorian construction industry”, Safety Science, Vol. 40 No. 5, pp. 439-456. Lee, S.H., Thomas, S.R. and Tucker, R.L. (2005), “Web-based benchmarking system for the construction industry”, Journal of Construction Engineering and Management, Vol. 131 No. 7, pp. 790-798. Li, B., Akintoye, A., Edwards, P.J. and Hardcastle, C. (2005), “Critical success factors for PPP/PFI projects in the UK construction industry”, Construction Management and Economics, Vol. 23 No. 5, pp. 459-471. Lockett, A., Moon, J. and Visser, W. (2006), “Corporate social responsibility in management research: focus, nature, salience and sources of influence”, Journal of Management Studies, Vol. 43, pp. 115-136. Lundy, V. and Morin, P.P. (2013), “Project leadership influences resistance to change: the case of the canadian public service”, Project Management Journal, Vol. 44, pp. 45-64. Medda, F. (2007), “A game theory approach for the allocation of risks in transport public private partnerships”, International Journal of Project Management, Vol. 25 No. 3, pp. 213-218. Ng, S.T., Xie, J., Cheung, Y.K. and Jefferies, M. (2007), “A simulation model for optimizing the concession period of public–private partnerships schemes”, International Journal of Project Management, Vol. 25 No. 8, pp. 791-798. Niu, B. and Zhang, J. (2013), “Price, capacity and concession period decisions of Pareto-efficient BOT contracts with demand uncertainty”, Transportation Research Part E: Logistics and Transportation Review, Vol. 53, pp. 1-14. Nurudeen, A., Abd Karim, M.Z. and Aziz, M.I.A. (2015), “Corruption, political instability and economic development in the Economic Community of West African States (ECOWAS): is there a causal relationship?”, Contemporary Economics, Vol. 9 No. 1, pp. 45-60. Osei-Kyei, R. and Chan, A.P. (2015), “Review of studies on the Critical Success Factors for Public– Private Partnership (PPP) projects from 1990 to 2013”, International Journal of Project Management, Vol. 33 No. 6, pp. 1335-1346. Patel, U.R. and Bhattacharya, S. (2010), “Infrastructure in India: the economics of transition from public to private provision”, Journal of Comparative Economics, Vol. 38 No. 1, pp. 52-70. Quazi, R., Alam, A. and Tandon, S. (2015), “Impact of foreign aid on corruption: an econometric case study of South Asia and East Asia”, Global Journal of Business Research, Vol. 9 No. 4, p. 17. Rolim, F., Santos, E. and Meira, L. (2014), “Competitiveness in the invitation to bid for the concession of the Urban bus system of the metropolitan region of recife”, Procedia-Social and Behavioral Sciences, Vol. 160, pp. 160-169. Rouhani, O.M., Niemeier, D., Knittel, C.R. and Madani, K. (2013), “Integrated modeling framework for leasing urban roads: a case study of Fresno, CA”, Transportation Research Part B: Methodological, Vol. 48, pp. 17-30.

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Roumboutsos, A. and Chiara, N. (2010), “A strategic partnering framework analysis methodology for public-private partnerships”, Journal of Financial Management of Property and Construction, Vol. 15 No. 3, pp. 235-246. Sahasranaman, A. and Kapur, V. (2014), “The practice of PPP in Urban infrastructure”, Urbanisation in India: Challenges, Opportunities and the Way Forward, p. 176.

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Santos, G., Behrendt, H., Maconi, L., Shirvani, T. and Teytelboym, A. (2010), “Part I: externalities and economic policies in road transport”, Research in Transportation Economics, Vol. 28 No. 1, pp. 2-45. Sarkar, D. and Panchal, S. (2015), “Integrated interpretive structural modeling and fuzzy approach for project risk management of ports”, International Journal of Construction Project Management, Vol. 7 No. 1, p. 17. Scandizzo, P.L. and Ventura, M. (2010), “Sharing risk through concession contracts”, European Journal of Operational Research, Vol. 207 No. 1, pp. 363-370. Schweber, L. and Leiringer, R. (2012), “Beyond the technical: a snapshot of energy and buildings research”, Building Research & Information, Vol. 40 No. 4, pp. 481-492. Shaya, F.T., Flores, D., Gbarayor, C.M. and Wang, J. (2008), “School-based obesity interventions: a literature review”, Journal of School Health, Vol. 78, pp. 189-196. Siddiqui, S.Q., Ullah, F., Thaheem, M.J. and Gabriel, H.F. (2016), “Six Sigma in construction: a review of critical success factors”, International Journal of Lean Six Sigma, Vol. 7 No. 2, pp. 171-186. Tah, J.H.M. and Carr, V. (2000), “A proposal for construction project risk assessment using fuzzy logic”, Construction Management & Economics, Vol. 18 No. 4, pp. 491-500. Takim, R., Ismail, K., Nawawi, A.H. and Jaafar, A. (2009), “The Malaysian private finance initiative and value for money”, Asian Social Science, Vol. 5 No. 3, p. 103. Tranfield, D., Denyer, D. and Smart, P. (2003), “Towards a methodology for developing evidence-informed management knowledge by means of systematic review”, British Journal of Management, Vol. 14, pp. 207-222. Ubbels, B. and Verhoef, E.T. (2008), “Auctioning concessions for private roads”, Transportation Research Part A: Policy and Practice, Vol. 42 No. 1, pp. 155-172. Wamuziri, S.C. and Clearie, A.G.F. (2005), “Economic feasibility of the proposed second forth road bridge using public private partnership procurement”, Journal of Financial Management of Property and Construction, Vol. 10 No. 2, pp. 95-106. Wang, G.W., Pallis, A.A., and Notteboom, T.E. (2014), “Incentives in cruise terminal concession contracts”, Research in Transportation Business & Management, Vol. 13, pp. 36-42. Wang, Y. (2015), “Evolution of public–private partnership models in American toll road development: learning based on public institutions’ risk management”, International Journal of Project Management, Vol. 33 No. 3, pp. 684-696. Willoughby, C. (2013), “How much can public private partnership really do for urban transport in developing countries? ”, Research in Transportation Economics, Vol. 40 No. 1, pp. 34-55. Wirahadikusumah, R.D., Sapitri, B.S. and Soemardi, B. (2014), “Risk inclusion in the reserve price estimation for toll road concession award”, Journal of Traffic and Logistics Engineering, Vol. 2 No. 1, pp. 34-39. Yang, J.B., Yang, C.C. and Kao, C.K. (2010), “Evaluating schedule delay causes for private participating public construction works under the Build-Operate-Transfer model”, International Journal of Project Management, Vol. 28 No. 6, pp. 569-579.

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Ye, S. and Tiong, R.L. (2003), “The effect of concession period design on completion risk management of BOT projects”, Construction Management and Economics, Vol. 21 No. 5, pp. 471-482.

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Yi, W. and Chan, A.P. (2013), “Critical review of labor productivity research in construction journals”, Journal of Management in Engineering, Vol. 30 No. 2, pp. 214-225. Yu, C.Y. and Lam, K.C. (2013), “A decision support system for the determination of concession period length in transportation project under BOT contract”, Automation in Construction, Vol. 31, pp. 114-127. Zhang, X. and AbouRizk, S.M. (2006), “Determining a reasonable concession period for private sector provision of public works and service”, Canadian Journal of Civil Engineering, Vol. 33 No. 5, pp. 622-631. Zhang, X. and Chen, S. (2013), “A systematic framework for infrastructure development through public private partnerships”, IATSS Research, Vol. 36 No. 2, pp. 88-97. Further reading Albalate, D., Bel, G. and Fageda, X. (2015), “When supply travels far beyond demand: causes of oversupply in Spain’s transport infrastructure”, Transport Policy, pp. 80-89. Cheung, E. and Chan, A.P. (2011), “Risk factors of public-private partnership projects in China: comparison between the water, power, and transportation sectors”, Journal of Urban Planning and Development, Vol. 137 No. 4, pp. 409-415. Hallowell, M.R. and Gambatese, J.A. (2009), “Construction safety risk mitigation”, Journal of Construction Engineering and Management, Vol. 135 No. 12, pp. 1316-1323. Hertzog, M.A. (2008), “Considerations in determining sample size for pilot studies”, Research in Nursing & Health, Vol. 31 No. 2, pp. 180-191. Huff, A.S. (2000), “Changes in organizational knowledge production”, Academy of Management Review, Vol. 25 No. 2, pp. 288-293. Klakegg, O.J., Williams, T., Magnussen, O.M. and Glasspool, H. (2008), “Governance frameworks for public project development and estimation”, Project Management Journal, Vol. 39, pp. 27-42. Vassallo, J.M. (2010), “The role of the discount rate in tendering highway concessions under the LPVR approach”, Transportation Research Part A: Policy and Practice, Vol. 44 No. 10, pp. 806-814. Zhao, Z.Y., Zuo, J. and Zillante, G. (2013), “Factors influencing the success of BOT power plant projects in China: a review”, Renewable and Sustainable Energy Reviews, Vol. 22, pp. 446-453. Corresponding author Fahim Ullah can be contacted at: [email protected]

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Appendix.

Country

Affiliated institute

China

The Hong Kong Polytechnic University Huazhong University of Science and Technology The Hong Kong University of Science & Technology, Hong Kong The University of Hong Kong, Hong Kong Shanghai Jiao Tong University Shanghai University of Finance and Economics Southeast University, Nanjing Zhejiang University of Finance & Economics, Hangzhou Nottingham University Business School China, Ningbo City University of Hong Kong, Hong Kong North China Electric Power University, Beijing Sun Yat-Sen University National Taiwan University of Science and Technology Chunghua University, National Chiao Tung University Providence University Total University of California, San Diego, United States Minnesota Department of Transportation Victoria Transport Policy Institute Indiana University National System of Researchers, Mexico Stanford University University of Kentucky Colorado State University University of California, Davis Jackson State University University of Maryland University of Nevada, Las Vegas Utah State University, Logan Texas A&M University, Galveston Autonomous University of Mexico City, Mexico Total Cardiff University Edinburgh Napier University, Scotland University of Bath University of Salford The University of Manchester University of Oxford Queen’s University Belfast University of Bedfordshire Business School Warwick Business School, Coventry University of Keele Edinburgh Napier University Newcastle University Total

298

USA

UK

Table A1. Country wise institution contribution

Publications 3 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 21 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 17 2 1 1 1 1 1 1 1 1 1 1 1 13 (continued)

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Country

Affiliated institute

Spain

Universitat de Barcelona University of Las Palmas de Gran Canaria Universidad Politécnica de Madrid Universidad Autonoma de Madrid Universidad Politécnica de Cartagena University of Valenci Total The University of Queensland University of Newcastle Southern Cross University, New South Wales Deakin University The Australian National University, Canberra The University of Sydney Total University of Paris Sorbonne Université de Toulouse I Panthéon-Sorbonne University Cirad/Institute of Political Studies Universite des Sciences Sociales de Toulouse Total Polytechnic of Bari University of Rome “Tor Vergata” TRT Trasporti e Territorio Srl, Milan University of Sannio University of Genoa Total Paraná Federal University Cidade Universitaria, Sao Paulo Harvard University’s Kennedy School of Government Institute for Applied Economic Research of the Brazilian Government Universidade Federal do Rio Grande do Norte Total University Technology MARA (UiTM) Shah Alam UTHM, Johor Universiti Teknologi PETRONAS Total Technical University of Lisbon Instituto Universitário de Lisboa Quinta de São Miguel Total Iran University of Science & Technology Payame Noor University (PNU) Total Reliance Industries Limited, Mumbai Indian Institute of Technology Madras Indian Institute for Human Settlements, New Delhi Total

Australia

France

Italy

Brazil

Malaysia

Portugal

Iran

India

Publications 3 2 2 1 1 1 10 2 1 1 1 1 1 7 2 1 1 1 1 6 1 1 1 1 1 5 1 1 1 1 1 5 1 1 1 3 3 1 1 5 2 1 3 1 1 1 3 (continued)

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299

Table A1.

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Country

Affiliated institute

Pakistan

National Highways Authority Total VU University Amsterdam Eindhoven University of Technology Total University of Antwerp Total Swedish University of Agricultural Sciences Gothenburg University Total Tokyo Institute of Technology Shimizu Corporation Total The University of Auckland Total Universidad Nacional de Córdoba Total Others

Netherland

300

Belgium Sweden

Japan

New Zealand Argentina

Table A1.

Publications 3 3 2 1 3 3 3 2 1 3 2 1 3 3 3 3 3 32