The impact of stakeholder attributes on performance of disaster recovery projects: The case of transport infrastructure

JPMA-01992; No of Pages 12

Available online at www.sciencedirect.com

ScienceDirect International Journal of Project Management xx (2017) xxx – xxx www.elsevier.com/locate/ijproman

The impact of stakeholder attributes on performance of disaster recovery projects: The case of transport infrastructure Mohammad Mojtahedi ⁎, Bee Lan Oo Faculty of Built Environment, University of New South Wales, Sydney, Australia Received 4 April 2016; received in revised form 25 January 2017; accepted 6 February 2017 Available online xxxx

Abstract How stakeholder attributes might influence the performance of disaster recovery project remains ambiguous. Stakeholder attributes are socially constructed variables and have been classified as power, legitimacy and urgency based on stakeholder theory. They are not the only factors to predict the overall performance of a project, the environmental factors such as socio-economic and project conditions should also be considered. We, therefore, hypothesised that direct relationship between stakeholder attributes and performance of disaster recovery projects might be mediated by socio-economic and transport infrastructure conditions. Using structural equation modelling with partial least square estimation approach, we analysed data collected from structured questionnaire survey involving local councils in New South Wales, Australia. The results suggest that stakeholders with more power, legitimacy and urgency attributes have managed disaster recovery projects with better performance. The results also show that the socio-economic and transport infrastructure conditions have mediating effects on performance of disaster recovery projects. © 2017 Elsevier Ltd, APM and IPMA. All rights reserved. Keywords: Disaster recovery; Project performance; Stakeholder attributes; Stakeholder management

1. Introduction Damage from disasters has increased 14-fold since the 1950s (UNISDR, 2011) and worldwide estimates of annual expenditure on disaster recovery projects has increased to US $200 billion since the 1980s (IPCC, 2012). In a review of management science research in disaster risk management, Altay and Green (2006) noted that almost 90% of research addressed mitigation, preparedness and response phases of disaster risk management, while less than 10% of the research contributed to managing disaster recovery projects. There are poor understanding and little consideration of managing disaster recovery projects (Kim and Choi, 2013, Chang et al., 2012).

⁎ Corresponding author at: Faculty of Built Environment, University of New South Wales, Kensington, NSW 2052, Sydney, Australia. E-mail address: [email protected] (M. Mojtahedi).

Disasters cannot be eliminated, even with proper planning. When a disaster does occur, recovery activities involve rehabilitation (short-term) and reconstruction (long-term) to restore vital support systems and return life to normal such as rebuilding residential and non-residential buildings, roads, bridges and infrastructure, and coordinating government activities (Altay and Green, 2006, Moe and Pathranarakul, 2006, Peek and Mileti, 2002). Disaster recovery requires timely, quality, high-performance and low-cost disaster recovery activities. A wide range of stakeholders, such as local governments, state emergency services, road and maritime services have a key role in disaster recovery (Mojtahedi and Oo, 2014, Bosher et al., 2009). Understanding the impacts of the wide range of stakeholders involved in disaster recovery projects is essential to achieve recovery performance targets. Effective stakeholder management can improve the performance of disaster recovery projects, while poor management can lead to low project performance in terms of schedule, cost, quality, environment, return on investment

http://dx.doi.org/10.1016/j.ijproman.2017.02.006 0263-7863/00 © 2017 Elsevier Ltd, APM and IPMA. All rights reserved. Please cite this article as: M. Mojtahedi, B.L. Oo, 2017. The impact of stakeholder attributes on performance of disaster recovery projects: The case of transport infrastructure, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.02.006

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and communications (Bosher et al., 2009, Brilly and Polic, 2005). In addition, poor management of reconstruction projects can itself lead to disasters such as less resilient temporary buildings, waste of construction materials and environmental degradations and the highest ratio of recovery costs to losses (Yu et al., 2015). Although scholars have evaluated project performance in general, there has been little focus on the performance of disaster recovery projects, and there has not been much work in assessing the performance of disaster recovery projects. Although there are many factors involved in stakeholders' organisational capacity and performance in managing disaster recovery projects (Raschky, 2008), stakeholder attributes in managing disaster recovery projects have not been studied. This paper, therefore, addresses stakeholder management for disaster recovery projects by focusing on three stakeholder attributes: (i) power; (ii) legitimacy and (iii) urgency (Phillips et al., 2003, Mitchell et al., 1997, Freeman, 1984). These stakeholder attributes have not yet been evaluated in managing disaster recovery projects for good performance. Stakeholder attributes might influence the performance of disaster recovery projects. For instance, power enables stakeholders to use social and political forces and benefit from disaster recovery project management resources from their respective organisations. This might result in completing disaster recovery projects on time and on budget. On the other hand, legitimacy enables stakeholders to abide by beneficial or harmful risks pertinent to disaster recovery project management because legitimacy is a generalised assumption that a stakeholder will behave properly within socially constructed systems of norms, mandates and procedures. Legitimacy, hence, improve the quality of disaster recovery projects and finally, urgency enables stakeholders to coordinate immediate response and recovery activities in disaster recovery project management. Urgency accelerates the mobilisation tasks for sub-contractors during the reconstructing phase of disaster recovery projects. The paper is structured as follows. Section 2 reviews stakeholder engagement in disaster recovery projects, Section 3 reviews disaster recovery project performance, Section 4 provides a theoretical framework and hypothesis development, Section 5 outlines the research methodology, Section 6 presents the results, Section 7 discusses stakeholder attributes and Section 8 concludes.

2. Stakeholder engagement in disaster recovery projects A stakeholder is a person or an entity who gives an input into decision-making as well as one who benefits from the results of decision-making (Phillips et al., 2003). Stakeholders have an interest in the actions of an organisation, and have the ability to influence or be affected by the achievement of the organisation's objectives (Donaldson and Preston, 1995, Savage et al., 1991, Freeman, 1984). For disaster recovery projects, stakeholders are groups or individuals who can affect or be affected by the performance of a recovery project and include local councils, project managers, designers, subcontractors, suppliers and most

importantly users and community (Amaratunga and Haigh, 2011). Based on Freeman (1984) and the definition of project stakeholder management by the Project Management Institute (2013), disaster stakeholder management is the process of developing appropriate management strategies to effectively engage stakeholders throughout the mitigation, preparedness, response and recovery phases of the disaster risk management life cycle, based on analysis of their needs, interests, and potential impact on project success. One of the main aims of disaster stakeholder management is improving the performance of disaster recovery projects by engaging stakeholders effectively and strengthening stakeholders' attributes. Power, legitimacy and urgency are three distinct stakeholder attributes (Mitchell et al., 1997). Power allows a stakeholder to carry out its own will. The power of a stakeholder may arise from its ability to mobilise social and political forces as well as its capacity to withdraw resources from the organisation. Legitimacy gives the opportunity to a stakeholder to identify some sort of beneficial or harmful risk pertinent to its organisation. Urgency is the degree to which a stakeholder is able to call for immediate attention. Previous studies have focused on the role of stakeholder involvement in disaster risk management in the mitigation phase (Brilly and Polic, 2005). Only a few studies have scrutinised stakeholders' views and perspectives in the disaster recovery phase. For instance, Walker et al. (2016) studied the legitimacy attribute of stakeholders in Christchurch post-earthquake reconstruction in New Zealand. Almoradie et al. (2013) studied flood disaster in Cranbrook catchment (London, UK) and the Alster catchment (Hamburg, Germany) using web-based platforms. Bosher et al. (2009) studied flood disaster in the UK using questionnaire survey, and Vari et al. (2003) studied flood disasters in collaboration with Swedish and Hungarian researchers. This paper, therefore critically examines the role of key stakeholders and their characteristics in disaster recovery project performance. 3. Disaster recovery project performance Research has focused on different variables to measure project performance in general, including schedule, cost and quality variables (Yun et al., 2016, Popaitoon and Siengthai, 2014, Swarup et al., 2011), safety performance (Yun et al., 2016, Yeung et al., 2007, Cox et al., 2003), sustainability (Rankin et al., 2008, Yeung et al., 2007), and effective communication (Yeung et al., 2012, Yeung et al., 2007). Cox et al. (2003) developed a measure of performance in construction projects by combining six key performance indicators including quality control, on-time completion, cost, safety, cost per unit, and units per person-hour. Although time, cost, quality and safety are considered essential factors to measure project performance, this simple approach is not adequate in many complex cases such as disaster recovery projects because it involves a range of project types, urgent project planning with limited financial budget and different stakeholders engaged in project management. Although previous research has developed indicators, methods and procedures to evaluate project performance, there were some limitations in

Please cite this article as: M. Mojtahedi, B.L. Oo, 2017. The impact of stakeholder attributes on performance of disaster recovery projects: The case of transport infrastructure, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.02.006

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assessing the performance of disaster recovery projects. One of these limitations is a lack of attention to the role of stakeholders in managing disaster recovery projects. Successful disaster recovery projects can reduce the risks from disasters and minimise adverse impacts on human, social and economic environments. Evaluation of disaster recovery project performance is a critical managerial approach to achieve desired outcomes in disaster recovery projects (Moe et al., 2007). For example, performance auditing of disaster recovery projects could help create a more environmentally sustainable and physically survivable community (Labadie, 2008). Adapting and applying the process of performance auditing and performance measurement to disaster recovery projects and systematic evaluation of outcomes could help ensure disaster recovery efforts gain greater credibility with stakeholders. Lizarralde (2002) proposes a set of ten factors that need to be assessed to measure the performance of disaster recovery projects: “efficiency, results, timing, the quality of the product, pertinence, acceptability, strategy, scope, impacts/ objectives and external aspects”. Evaluation of project performance is heavily dependent on the nature of the individual project. Some researchers have suggested particular performance measurement should be applied to the specific project life cycle in construction projects (Yun et al., 2016). Performance evaluation for disaster recovery projects is different from measuring the performance of routine construction projects and more importantly is also different from the assessment of the performance of mitigation, preparedness and response projects. Therefore, we evaluate the role of stakeholder attributes in measuring the performance of disaster recovery projects in this study.

4. Theoretical framework and hypothesis development Scholars have studied the impact of disasters on the socioeconomic and built environment conditions quite extensively, but there are still three issues that suggest further theoretical development is needed. First, there is a lack of stakeholder management before and during disasters (Bosher et al., 2007, Pearce, 2003, Perry and Lindel, 1978). Bosher et al. (2009) noted there is still not enough evidence to indicate that key stakeholders are playing an effective role in the management of disaster recovery projects in the built environment and little attention has been given to systematically theorising the approaches taken by stakeholders to manage disaster recovery projects. Second, many researchers have focused on similar underlying theories and heuristics in the context of disaster recovery projects (Sementelli, 2007, McEntire, 2004). For example, crisis and chaos theory have become an increasingly fundamental theory used by scholars to support their research into disaster recovery projects (Ritchie, 2004, Pearson and Clair, 1998, Pauchant and Douville, 1993, Pearson and Mitroff, 1993, Shrivastava, 1993). Furthermore, previous research has tended to fall into the realm of rules, procedures, and policies that apply similar theories rather than integrating other organisational theories to address approaches and decisions made by stakeholders in managing disaster recovery projects.

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The institutional capacity of stakeholders is critical for effective implementation of structural and non-structural recovery in disaster risk management (Brody et al., 2010). This study empirically investigates the effects of the stakeholder attributes of power, legitimacy and urgency on disaster recovery project performance. Stakeholder attributes – power, legitimacy and urgency – have been playing an essential role in project's performance (e.g., Olander, 2007, Phillips et al., 2003, Freeman, 1984) and stakeholder theory has supported this claim (Mitchell et al., 1997). Hence, we hypothesised: H1. Stakeholder attributes have a direct effect on performance of disaster recovery projects. However, the stakeholder attributes is not the only construct that determines the performance of disaster recovery projects. Socio-economic has also a direct impact on the performance of disaster recovery projects. The magnitude of project damage in disaster is dynamic and depends on economic, social, geographic, demographic, cultural, institutional, governance and environmental factors (IPCC, 2012). The socio-economic condition is the main drivers of economic losses due to some climate extremes and disasters. Many studies, (e.g., IPCC, 2012, Nicholls and Tol, 2006, O’ Brien and Leichenko, 2000), showed that increases in exposure and vulnerability of society and economy will result in higher direct economic losses from disasters and makes disaster recovery projects more complex. For example, a local council with higher Gross Regional Product (GRP) has more power and legitimacy in comparison with other local councils with lower GRP. Therefore, the size of a local council has been measured by their density, population, income level and most importantly with their GRP. Hence, the socio-economic condition of local councils has been modelled in this study. H2. Socio-economic condition has a direct effect on the performance of disaster recovery projects. All types of built environments, such as buildings (residential, commercial and industrial), transport infrastructure of roads, railways, bridges, airports and ports, and water and power infrastructure, can be at risk of direct damage from disasters. For example, transport infrastructure is vulnerable to extremes in temperature, precipitation, river floods and storm surges, which can lead to damage in road, rail, airports and ports (IPCC, 2012). Transport infrastructure is considered to be vulnerable to disasters, but the exposure and impact will vary by region, location, elevation and condition of the infrastructure (UNCTAD, 2009, Humphrey, 2008). Roads, bridges and culverts are the most vulnerable elements in transport infrastructure in US research with projected increases in flooding, because the lifetime of these rigid structures is longer than most road surfaces and they are costly to repair or replace (Meyer, 2008). Although there are different types of categorisation of infrastructure, based on Australian Government, Department of Infrastructure and Regional Development, Australian infrastructure is classified by transport (roads, rail, ports, etc.), energy (electricity and gas transmission networks, etc.), telecommunications

Please cite this article as: M. Mojtahedi, B.L. Oo, 2017. The impact of stakeholder attributes on performance of disaster recovery projects: The case of transport infrastructure, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.02.006

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networks, and supply and distribution networks (BITRE, 2012). The increasing number of disasters in Australia has demonstrated the importance of managing disaster recovery in transport infrastructure projects (Blong, 2004). Transport infrastructure is generally less resilient to flooding than to other forms of disaster (Humphrey, 2008, Meyer, 2008, McKenzie et al., 2005). Most Australian roads and bridges are located in coastal and riverine areas, where they are more vulnerable to rises in sea level and localised flooding. New South Wales (NSW) is one of the most susceptible states in Australia for flood damage, particularly to its transport infrastructure (Bureau of Transport Economics, 2001). In addition, transport infrastructure disaster recovery time in the riverine areas with lower socio-economic status is particularly susceptible to lengthy recovery projects. Therefore, we hypothesised: H3. Transport infrastructure condition has a direct effect on the performance of disaster recovery projects. It is essential to investigate the role of stakeholder attributes in exacerbating or ameliorating the exposure and vulnerability of the socio-economic and transport infrastructure conditions of a specific region. The stakeholder attributes of power, legitimacy and urgency could be important in reducing the devastating consequences of disasters. Socio-economic and transport infrastructure conditions are important factors that can influence the local councils' stakeholder attributes and performance of disaster recovery projects. Therefore, apart from a direct relationship between socio-economic and transport infrastructure conditions with the performance of disaster recovery projects, the socioeconomic and transport infrastructure conditions would most likely play mediating roles in the relationship between stakeholder attributes and performance of disaster recovery projects. Thus, we hypothesised: H4. (a) The socio-economic condition mediates the relationship between stakeholder attributes and performance of disaster recovery projects and (b) transport infrastructure condition mediates the relationship between stakeholder attributes and performance of disaster recovery projects. Addressing the aforementioned issues in managing disaster recovery projects, the conceptual framework for analysing the factors that determine the performance of disaster recovery projects is developed in Fig. 1. It brings all the above hypotheses together and indicates that three primary factors influence stakeholder disaster recovery project performance (Y): (i) stakeholder attributes (X1), (ii) socio-economic condition (X2) and (iii) transport infrastructure condition (X3). Each of these three primary factors is then comprised by more specific sub-factors or indicators in Table 1. In addition, stakeholder attributes and environmental factors influence each other during business as usual time. For example, a local council with higher Gross Regional Product (GRP), higher income rate and lower density has more power, legitimacy and urgency in comparison with other local councils with lower GRP, lower income rate and higher population density.

Fig. 1. Conceptual diagram of structural model.

5. Research methodology 5.1. Sample framework New South Wales (NSW) is one of the most susceptible areas for flood damage, particularly its transport infrastructure (Bureau of Transport Economics, 2001) and almost 24% of Australian roads are located in NSW, which has the highest amount of transport infrastructure across Australia. Transport infrastructure in NSW including roads and bridges was selected as the built environment type for this study. Roads in NSW are divided into three main categories: (i) state roads; (ii) regional roads; and (iii) local roads. Funding for restoration against disasters on state roads is the responsibility of the Road and Maritime Services (RMS), while funding for restoration works on regional and local roads is the responsibility of local councils (RMS, 2012). Local councils are responsible for investing, constructing, maintaining and restoring a major portion of regional and local roads and bridges across NSW, they were selected as stakeholders or sampling frame in this research. Since not all local councils in NSW are susceptible to flood disaster, the sampling frame was filtered by focusing on 75 local councils who are members of the Flood Management Australia (FMA) which they have been affected by major flood disasters over the past decades.

5.2. Data collection method This study used a survey research design because it provides a relatively prompt and efficient method of collecting information from targeted samples and addressing research objectives (Creswell, 2013). Collecting flood disaster data is a timeconsuming process, and all data has not been recorded; thus, a questionnaire is a fast and economical way of collecting data

Please cite this article as: M. Mojtahedi, B.L. Oo, 2017. The impact of stakeholder attributes on performance of disaster recovery projects: The case of transport infrastructure, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.02.006

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Table 1 Constructs and initial measurement indicators. Construct

Indicator Measurement indicators

Reference

Stakeholder attributes (X1)

X11 X12 X13 X21 X22 X23 X24 X25 X26 X31 X32 X33 X34 X35 Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9

(Olander, 2007); (Phillips et al., 2003); (Freeman, 1984); (Mitchell et al., 1997)

Socio-economic condition (X2)

Transport infrastructure condition (X3)

Disaster recovery project performance (Y)

Power Legitimacy Urgency Density (person/km2) Gross Regional Product (GRP) per capita Population Age structure Population at risk from disaster Income level Local urban roads (km) Local non-urban sealed roads (km) Local non-urban unsealed roads (km) Total bridge and culverts length on local roads Roads and bridges at risk from flood Response time for reconstruction Sustainability in reconstruction Quick mobilisation Approved contractor list Temporary roads and bridges Execution plan for reconstruction Lessons learned and best practices Lean reconstruction Stakeholder engagement

pertinent to flood events and it is an appropriate tool for empirical research and can generalise findings by testing the hypotheses (Flynn et al., 1990). This study used a survey research design to investigate the time period from 1992 to 2014. The decision to focus on a 22-year time period is because data and information pertinent to flood disasters were not kept in local councils' databases before 1992. Disasters occur over time and the exposure and vulnerability of a specific region to disaster are dynamic and depend on unstable conditions such as economic, social, geographic, demographic, cultural, institutional, governance and environmental factors which change over time (IPCC, 2012). In addition, stakeholder attributes and approaches are volatile and would most likely change over time (Olander, 2007). Thus, observing stakeholder attributes over time is essential for deducting a valid conclusion. Furthermore, socio-economic measurement indicators and transport reconstruction projects due to flooding were recorded several times over the past 22 years in the relevant Australian databases. Primary data collected using a structured questionnaire. A questionnaire comprised of tailored measurement scales was designed and asked local councils to assess the role of their local councils' stakeholder attributes in managing flood recovery projects in transport infrastructure across NSW. Local councils were also required to provide general information about the socio-economic condition of their local areas. The responses to most questions were on a seven point Likert design, unless otherwise stated. Local council staff including floodplain engineers, planning and infrastructure engineers and emergency management officers was identified as prospective respondents for this questionnaire. Table 2 shows the respondents' profile in terms of field of work and years of experience in disaster recovery projects. The measurement indicators were generated through a review of the literature. To increase the validity of these

(IPCC, 2012); (Noy, 2009); (Raschky, 2008); (Ibarrarán, Ruth, Ahmad, & London, 2007); (Masozera et al., 2007); (Kahn et al., 2005); (Haque, 2003)

(Meyer, 2008); (Sohn, 2006); (Suarez, Anderson, Mahal, & Lakshmanan, 2005); (Chang, 2000, 2003) Discussion with experts from local councils

(Mojtahedi and Oo, 2014); (Altay and Green, 2006); (Haigh, Amaratunga, & Keraminiyage, 2006); (Kates, Colten, Laska, & Leatherman, 2006); (Freeman, 2004); (Barakat, 2003); (Chang et al., 2012); (Ofori, 2002); (Bolin & Stanford, 1991)

measurement indicators, one experienced academic, two experts from FMA and one expert from a local council assessed the structured questionnaire before the pilot study, particularly on issues involving the contents and wording of individual measurement indicators. Before the questionnaire was officially distributed to the 75 local councils across NSW, the questionnaire was pilot tested by four local councils to check the questionnaire to ensure the face and content validity in terms of assessing the degree to which a construct has been precisely translated into an operationalisation. They suggested that some overlapping indicators could be omitted, but some unclear statements and questions should be revised in plain English. These amendments and revisions were carried out before the questionnaire was officially distributed to the 75 Local Councils across NSW. The response rate was 48% with 37 local councils completed the questionnaire. Although only 37 responded, this was still a response rate of 48%, which was reasonable considering the normal rate of response in the construction industry (Kumaraswamy et al., 2005) and disaster risk management studies (Bharosa et al., 2010).

Table 2 Summary of respondents' profile. Field of work

Field of work (%)

Experience (years)

Experience (%)

Civil and infrastructure engineer Designer Project manager Planner Flood risk analyst Emergency management officer

33% 5% 19% 10% 15% 18%

b5 6–10 11–15 16–20 N 20 –

35% 15% 20% 8% 22% –

Please cite this article as: M. Mojtahedi, B.L. Oo, 2017. The impact of stakeholder attributes on performance of disaster recovery projects: The case of transport infrastructure, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.02.006

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5.3. Data analysis technique Partial least squares structural equation modelling (PLS-SEM) was selected as an analytical tool in this research because PLS-SEM works efficiently with small sample sizes and complex models and it needs no assumptions about data distributions (Henseler, 2010). Albeit the number of responses was relatively small, a statistical analysis could still be performed based on the central limit theorem that holds true if the sample size is more than 30 (Field, 2013). In addition, PLS-SEM is very efficient at estimating parameters, which results in high levels of statistical power. Greater statistical power means that PLS-SEM will probably generate a specific and significant relationship when in fact it's significant in the population (Hair et al., 2014a). PLS-SEM has been used in many other social science research to develop and test theories using survey data for studies in business marketing (e.g., Hair et al., 2011, Henseler et al., 2009, Fornell et al., 1996), organisational behavioural (Hair et al., 2014b), construction management (e.g., Oke et al., 2012, Aibinu and Al-Lawati, 2010, Lim et al., 2010) and project performance (Suprapto et al., 2015, Doloi, 2014). However, there have been no empirical studies which use PLS-SEM on evaluating the performance of disaster recovery projects. Previous studies showed that the PLS-SEM is a robust tool to analyse the mediation and moderation effects of factors and constructs. For example, Suprapto et al. (2015) used SEM to analyse how the contract types and incentives have impacts on project performance. They discussed PLS-SEM was a useful tool to analyse complex models with smaller sample size. Therefore, the PLS-SEM is used in this study to analyse the mediating effects of social-economic and transport infrastructure conditions on the relationship between stakeholder attributes and performance of disaster recovery projects. Although multiple regression, analysis of variance and logistic regression are other possible approaches for data analysis in this paper, PLS-SEM is one of the most suitable multivariate data analysis techniques when the research problem involves several relationships of dependent and independent variables (Hair, 2009). The SmartPLS software 2.0 (Ringle et al., 2005) was used to execute all the PLS-SEM analyses in this study because it has a graphical user interface that enables the user to estimate the PLS path model effectively. SmartPLS uses bootstrapping procedure which is suitable when the data size is small (Hair et al., 2014a). 6. Results The initial structural model hypothesised in Fig. 1 was analysed using SmartPLS 2.0 software and the results of the measurement model are discussed in the following sections. The adequacy of the structural model was tested using individual variable reliability analysis, convergent validity measures of the indicators, and discriminant validity of the measurement model. 6.1. Measurement models Individual indicator reliability is an interpretation of the extent to which measurements of the constructs taken with multiple-

indicator scale manifests the true score of the constructs relative to any errors (Hulland, 1999). It is the correlations of the indicators with their respective constructs (Hair, 2009). Higher loadings on a construct indicate that the associated indicators have much in common and they are captured by the construct (Carmines and Zeller, 1979). In this study, to minimise the errors in measurement models and enhance the precision and validity of the scales and exploratory power of the developed model, a conservative value of 0.70 was used as the threshold value. Nonetheless, before removal, the potential practical significance of indicators with loadings lower than 0.70 was meticulously investigated. Thus, six measured variables from Table 1 (X23 = 0.45, X24 = 0.32, X31 = 0.64, Y4 = 0.52, Y7 = 0.37 and Y9 = 0.55) were dropped before accepting the final structural model. Convergent validity is the extent to which a measure correlates positively with alternative measures of the same construct (Hair et al., 2014a). Indicators of a specific measure should converge or share a high portion of the variance. Convergent validity is estimated to ensure that the indicators are assumed to measure each respective construct and not another construct (Hulland, 1999). In PLS-SEM, two tests can be used to determine the convergent validity of the measured constructs (Fornell and Larcker, 1981): (i) a composite reliability score and Cronbach's Alpha for the constructs; and (ii) the average variance extracted (AVE). Cronbach's alpha and composite reliability both vary between 0 and 1, with higher values indicating higher levels of reliability. Churchill (1979) suggested that a Cronbach's alpha value of 0.6 would be acceptable, whereas Nunnally et al. (1967) proposed 0.7 as a benchmark for modest composite reliability. A common measure to establish convergent validity at the construct level is the average variance extracted (AVE) (Hair et al., 2014a). AVE measures the amount of variance that a construct obtains from its indicators relative to the amount due to measurement errors (Fornell and Larcker, 1981). They stated that the AVE should be higher than 0.5, which indicates that on average, the construct explains more than half of the variance of its indicators. Conversely, an AVE of less than 0.5 means that on average, more errors remain in the indicators than the variance explained by the construct (Hair et al., 2014a). The AVE can be calculated as follows: AVE ¼

∑λ2i ∑λ2i þ ∑i varðϵ i Þ

ð1Þ

where λi is the component loading of each indicator to a latent construct and var(ϵi) = (1 − λi2). The outer loadings and statistical significance of all the indicators used in the final model are shown in Table 3. They all have loadings above 0.70, which implies that less than 50% of an indicator's variance was owing to errors. All the indicators presented a satisfactory level of individual reliability, and Table 3 shows that the outer loadings were all statistically significant. In this study, Cronbach' alpha and composite reliability generated by SmartPLS 2.0 software and results are

Please cite this article as: M. Mojtahedi, B.L. Oo, 2017. The impact of stakeholder attributes on performance of disaster recovery projects: The case of transport infrastructure, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.02.006

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Table 3 Measurement model evaluation result. Construct

Indicator

Loading

Cronbach's alpha

Composite reliability (CR)

Average variance extracted (AVE)

Stakeholder attributes (X1)

X11 X12 X13 X21 X22 X25 X26 X32 X33 X34 X35 Y1 Y2 Y3 Y5 Y6 Y8

0.950 0.939 0.914 0.969 0.804 0.954 0.848 0.910 0.837 0.862 0.750 0.702 0.840 0.741 0.877 0.820 0.719

0.827

0.954

0.873

0.788

0.897

0.815

0.642

0.781

0.650

0.865

0.802

0.752

Socio-economic condition (X2)

Pre-disaster transport infrastructure condition (X3)

Disaster recovery performance (Y)

shown in Table 3. Cronbach's alpha and composite reliability threshold values, based on Churchill (1979) and Nunnally et al. (1967) suggestions, indicated that all the constructs have high levels of internal consistency reliability and the measurement indicators were appropriate for their respective constructs. The AVE values generated by SmartPLS 2.0 software are well above the required minimum level of 0.5 (Table 3). Hence, the measures of reflective constructs have high levels of convergent validity. The results in Table 3 indicate there was convergent validity and good internal consistency in the measurement model which implies that the measurement indicators of each construct measured them well and were not measuring another construct. Cross loadings is an indicator's correlation with other constructs in the model and an analysis of cross-loadings indicates that an indicator's outer loading on the associated construct should be greater than all of its loadings on the other constructs (Chin, 1998). An analysis of AVE or the Fornell–Larcker criterion (Fornell and Larcker, 1981) is a second and more conservative approach to analysing the discriminant validity because it compares the square root of the AVE values with the latent variable correlations. The square root of each construct's AVE should be greater than its highest correlation with any other construct (Hair et al., 2014a) because this indicates that more variance is shared between the construct and its indicators than with another construct representing different sets of indicators (Hulland, 1999). A cross-loading assessment was carried out using Smart-PLS 2.0 software; the results are illustrated in Table 4 and show that all the indicators loaded higher on the construct and they were theoretically specified to measure any other construct in the measurement models. This result indicates that all 18 indicators loaded distinctly on the specified construct they measured, and therefore demonstrate a discriminant validity of the constructs. Table 5 presents the correlation matrix for the constructs. There was no correlation identified between any two latent constructs that were larger than or even equal to the square root

of these two constructs. This shows that the discriminant validity test did not display any serious predicament and indicated that all the constructs differed from each other. Based on the results in Tables 3 to 5, the measurement model presents acceptable indicator reliability, convergent validity, and discriminant validity. Thus, the measurement model demonstrates the sufficient robustness needed to test the relationship between the constructs. 6.2. Structural model path coefficients The significance of t-values associated with each path was tested using the Bootstrap procedure of the SmartPLS 2.0 software with 36 cases and 500 resamples. Table 6 summarises the path results and the corresponding t-values. Due to the exploratory nature of this study, hypotheses were considered

Table 4 Analysis of cross-loading of the latent variables. Code

X1

X2

X3

Y

X11 X12 X13 X21 X22 X25 X26 X32 X33 X34 X35 Y1 Y2 Y3 Y5 Y6 Y8

0.950 0.939 0.914 0.350 0.296 0.299 0.315 0.357 0.485 0.418 0.325 0.403 0.456 0.311 0.486 0.498 0.460

0.115 0.218 0.379 0.969 0.804 0.954 0.848 0.273 0.394 0.258 0.146 0.429 0.135 0.245 0.344 0.454 0.165

0.341 0.411 0.413 0.216 0.292 0.471 0.449 0.910 0.837 0.862 0.750 0.341 0.415 0.351 0.402 0.344 0.293

0.471 0.483 0.449 0.285 0.462 0.368 0.273 0.251 0.444 0.494 0.377 0.702 0.840 0.741 0.877 0.820 0.719

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Table 5 Fornell–Larcker criterion.

7. Discussion

Constructs

AVE

X1

X2

X3

Y

X1 X2 X3 Y

0.873 0.815 0.650 0.752

0.934 0.114 0.057 0.500

0.903 0.073 0.086

0.806 0.005

0.867

Note: The diagonal elements (in bold) are the square root of the AVEs; non-diagonal elements are latent variable correlations.

to be supported based on a significant level of 10% (1.65) (Hair et al., 2014a). Table 6 illustrates that all of the hypotheses were fully supported.

6.3. Analysis of mediating effects The extent to which the variance of the dependent variable (performance of disaster recovery project) was directly explained by the independent variable (local council stakeholder attributes) and how much of the target construct's variance (performance of disaster recovery project) was explained by the indirect relationship via the mediator variables (socio-economic and transport infrastructure conditions) could be determined. Table 7 shows that local council attributes had a high and significant effect on the socio-economic and transport infrastructure conditions, which in turn had a robust and significant relationship with the performance of disaster recovery project. The indirect effect of stakeholder attributes (i.e., 0.724, p b 0.01) via the mediator construct – socio-economic– was significant, whereas the direct relationship between stakeholder attributes and performance of disaster recovery project remained significant (path coefficient of 0.104, p b 0. 10). Thus, the socio-economic condition fully mediated the relationship between stakeholder attributes and performance of disaster recovery project, which provided empirical evidence for H4a. Similarly, the indirect effect of stakeholder attributes (i.e., 0.433, p b 0.01) via the mediator construct – transport infrastructure– was also significant because the direct relationship between stakeholder attributes and performance of disaster recovery project also remained significant (path coefficient of 0.372, p b 0. 10). Thus, the transport infrastructure condition partially mediated the relationship between stakeholder attributes and performance of disaster recovery project and provided empirical evidence for H4b. The results of the structural model and hypotheses tests generated by SmartPLS 2.0 are depicted in Fig. 2.

7.1. The effect of stakeholder attributes on performance of disaster recovery projects The findings indicate that local councils with more power, legitimacy and urgency have managed disaster recovery projects with better performance. Interestingly, the positive coefficient of 0.500 between local councils' stakeholder attributes and recovery activities respectively, imply that increasing local councils' stakeholder attributes (power, legitimacy and urgency) is highly justified in enhancing the performance of disaster recovery projects. Finally, enhancing stakeholder attributes not only reduces the disaster damage, but also enriches the performance of disaster recovery projects executed by stakeholders. Therefore, the results are consistent with some of previous studies which argued that stakeholder attributes – power, legitimacy and urgency – have been playing essential role in firm's performance (Olander, 2007, Phillips et al., 2003, Freeman, 1984) and stakeholder theory has supported this claim (Mitchell et al., 1997). 7.2. Stakeholder attributes and mediating roles of socio-economic and transport infrastructure conditions It is found that socio-economic condition (X2), as measured by population density (X21), GRP per capita (X22), population at risk due to disasters (X23), and the income level (X26) play mediating effects on the direct relationship between local council attributes (X1)-power (X11), legitimacy (X12), urgency (X13), and performance of disaster recovery project (Y). The indirect effect of stakeholder attributes (i.e., 0.724, p b 0.01) via socio-economic condition (mediator) was significant while simultaneously, the direct relationship between stakeholder attributes and performance of disaster recovery project remained significant as well (path coefficient of 0.104, p b 0. 10). Thus, the socio-economic condition fully mediated the relationship between stakeholder attributes and performance of disaster recovery project, and provided empirical evidence for Hypothesis 4a (H4a: the socio-economic condition mediates the relationship between stakeholder attributes and performance of disaster recovery project). Similarly, the exposure and vulnerability of transport infrastructure (X3) which is measured by local non-urban sealed roads (X32), local non-urban unsealed roads (X33), the total length of bridges and culverts on local roads (X34), the roads and bridges at risk of flood (X35) have mediating effects on the direct relationship between Local Council attributes (X1)

Table 6 Results of hypotheses testing. Relation (hypothesis)

Path coefficient

t-Value

Inference

H1: Stakeholder attributes → Project performance H2: Socio-economic condition → Project performance H3: Transport infrastructure condition → Project performance

+ 0.500 + 0.559 + 0.433

3.747 5.795 4.469

Supported Supported Supported

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M. Mojtahedi, B.L. Oo / International Journal of Project Management xx (2017) xxx–xxx

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Table 7 Analysis of mediating effects. Constructs/indicators

Direct effect

Indirect effect

Total effect

Bootstrap t-statistic

VAF

H4a: Local council attributes → recovery (via socio-economic condition) H4b: Local council attributes → recovery (via transport infrastructure condition)

0.104 ⁎⁎ 0.372 ⁎

0.724 ⁎⁎⁎ 0.433 ⁎⁎⁎

0.828 ⁎⁎⁎ 0.805 ⁎⁎⁎

3.07 2.25

87.44% (full mediation) 53.78% (partial mediation)

⁎ p b 0.10. ⁎⁎ p b 0.05. ⁎⁎⁎ p b 0.01.

and performance of disaster recovery project (Y). The indirect effect of stakeholder attributes (i.e., 0.433, p b 0.01) via the mediator construct – transport infrastructure– was also significant, as was the direct relationship between stakeholder attributes and performance of disaster recovery project (path coefficient of 0.372, p b 0.10). Thus, the condition of transport infrastructure partially mediated the relationship between stakeholder attributes and performance of disaster recovery project and provided empirical evidence for Hypothesis 4b (H4b: the transport infrastructure condition mediates the relationship between stakeholder attributes and performance of disaster recovery project). These findings agree with some previous research findings (e.g., IPCC, 2012, Nicholls and Tol, 2006, O'Brien and Leichenko, 2000) which showed that increases in exposure and vulnerability of the socio-economic and transport infrastructure conditions resulted in higher direct economic losses from disasters. Furthermore, the findings indicate that a region with higher local council's stakeholder attributes (powerful, legitimate, and urgent local council) is more likely to have better performance in managing disaster recovery projects, although the socio-economic

and transport infrastructure conditions of a region would change the strength of this relationship. In other words, increasing local councils' stakeholder attributes and decreasing exposure and vulnerability of socio-economic and transport infrastructure conditions of a region should be practised at the same time as improving the performance of disaster recovery projects. This claim is also consistent with Olander (2007) findings which indicated that stakeholder attributes are not the only factors to predict the overall performance of an organisation, the external environmental factors should also be considered. 7.3. Theoretical implications This research bridges the theoretical gaps in previous studies of disaster risk management in the built environment by explaining how three stakeholder attributes of power, legitimacy and urgency from a new theoretical perspective developed from stakeholder management literature (Mitchell et al., 1997, Donaldson and Preston, 1995) and how they affect performance of disaster recovery projects. The study provides empirical evidence to support the claim that stakeholder attributes to performance of disaster recovery projects are important because stakeholder theory amalgamates power, legitimacy and urgency to propose dynamism in a systematic identification of stakeholders' approaches (Olander, 2007). This study also contributes to knowledge by discovering the mediating role played by the socio-economic and transport infrastructure conditions on the relationship between stakeholder attributes and performance of disaster recovery projects. These findings may suggest that stakeholder theory can no longer explain why local council areas have faced devastating disaster damage and low performance in managing disaster recovery projects despite the local councils having a high level of power, legitimacy and urgency in disaster risk management. This phenomenon may be partly explained in relation to the socio-economic and transport infrastructure of a particular local council area in terms of its exposure and vulnerability to disasters. 7.4. Practical implications and limitations

Fig. 2. Results of research model and hypotheses testing.

The empirical findings of this study have implications for the key stakeholders who are involved in disaster recovery projects such as local councils, contractors, project managers and policy makers, particularly in disaster recovery project management of transport infrastructure. Stakeholders and policy makers who seek to manage disaster recovery projects should understand that careful management of

Please cite this article as: M. Mojtahedi, B.L. Oo, 2017. The impact of stakeholder attributes on performance of disaster recovery projects: The case of transport infrastructure, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.02.006

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stakeholder attributes, such as strengthening power, legitimacy and urgency, is essential for reducing disasters' devastating impacts while also enhancing the performance of disaster recovery project delivery to disasters. Power enables stakeholders to use social and political forces and benefit from disaster recovery project management resources from their respective organisations. Legitimacy enables local councils to abide by beneficial or harmful risks pertinent to disaster recovery project management because legitimacy is a generalised assumption that a local council will behave properly within socially constructed systems of norms, mandates and procedures. Finally, urgency enables local councils to coordinate immediate response and recovery activities in disaster recovery project management. The observed or modelled relationship between socio-economic condition and disaster recovery project performance indicated that a wealthier local council is better equipped to manage the disaster recovery projects. This is due to higher GRP per capita, higher income levels and lower population density. Roads and bridges with a higher exposure and vulnerability exacerbate the negative relationship between local councils' attributes and project performance in disaster recovery stage because more non-urban unsealed roads and bridges are located in some local areas, which makes those regions more vulnerable to low performance in project delivery. It was found that some local councils did not respond to disasters quickly, particularly in recovery activities such as post-flood reconstruction. Hence, local councils need to implement lean post-disaster reconstruction practices to reduce the reconstruction time. This study presented empirical evidence that contributes to knowledge about managing disaster recovery projects, but these research findings must be interpreted within the limitations of this study, which is exploratory in nature. In particular, most of the measurement indicators for the constructs were borrowed from cross-discipline studies and then re-contextualised into disaster risk management in the transport infrastructure context. The measurement models developed in this study considered complex constructs that are intangible such as stakeholders' attributes and dynamic such as socio-economic condition and soft disaster risk management activities such as disaster recovery project management. Although the results showed an acceptable level of constructs that were reliable and valid, measurement indicators for the constructs should be updated continuously to improve our understanding of how to enhance performance of disaster recovery projects for the respective stakeholders in disaster risk management in transport infrastructure. In addition, a further empirical study by focusing on the relationship between individual variables (indicators) and the management performance of disaster recovery projects is highly recommended. The form and strength of the proposed relationships between constructs were likely to differ in different states and territories and different countries. 8. Conclusion Scholars and practitioners have acknowledged the significance of the more stakeholder engagement to achieve better project performance by increasing stakeholder attributes in managing projects. However, results of disaster risk management studies

suggest the need for research on intermediate mechanisms linking the impact of stakeholder attributes to project performance. This study applies a mediation model in which socio-economic and transport infrastructure conditions mediate the effect of stakeholder attributes on the performance of disaster recovery projects. The structural equation model shows that the greater the stakeholder attributes such as power, legitimacy and urgency, the better disaster recovery project performance. The results show that the performance of disaster recovery projects depends on both socio-economic and transport infrastructure conditions. This means that more resilient society with more resilient built environment experience better project performance in disaster recovery. The results also support the notion that socio-economic and transport infrastructure of a local council have mediating effects on the direct relationship between their stakeholder attributes and performance of disaster recovery projects. Furthermore, organisations who are involved in disaster risk management should undertake strategic managerial approaches to strengthen power, legitimacy and urgency attributes of engaged stakeholders for improving the performance of disaster recovery projects. Classifying stakeholders and studying the influence of the stakeholder classification on the performance of disaster recovery projects in the context of the built environment would be a rewarding research stream. Furthermore, it would be useful to explore stakeholder attributes in more detail, such as how to identify and increase power, legitimacy and urgency in stakeholders with limited or minimum resources to realise the full potential advantages of stakeholder attributes to the management of disaster recovery projects. Conflict of interest The authors declare that there is no conflict of interest. References Aibinu, A.A., Al-Lawati, A.M., 2010. Using PLS-SEM technique to model construction organizations' willingness to participate in e-bidding. Autom. Constr. 19, 714–724. Almoradie, A., Cortes, V., Jonoski, A., 2013. Web-based stakeholder collaboration in flood risk management. J. Flood Risk Manage. Altay, N., Green, W.G., 2006. OR/MS research in disaster operations management. Eur. J. Oper. Res. 175, 475–493. Amaratunga, D., Haigh, R., 2011. Post-Disaster Reconstruction of the Built Environment: Rebuilding for Resilience. John Wiley & Sons. Barakat, S., 2003. Housing reconstruction after conflict and disaster. Netw. Pap. 43, 1–40. BITRE, 2012. Bureau of Infrastructure Transport and Regional Economics (BITRE), Australian Infrastructure Statistics Yearbook 2011 (Canberra ACT). Blong, R., 2004. Residential building damage and natural perils: Australian examples and issues. Build. Res. Inf. 32, 37–41. Bharosa, N., Lee, J., Janssen, M., 2010. Challenges and obstacles in sharing and coordinating information during multi-agency disaster response: Propositions from field exercises. Information Systems Frontiers 12, 49–65. Bolin, R., Stanford, L., 1991. housing and recovery: a comparison of U.S. Disasters. Disasters 15, 24–34. Bosher, L., Dainty, A., Carrillo, P., Glass, J., 2007. Built-in resilience to disasters: a pre-emptive approach. Eng. Constr. Archit. Manag. 14, 434–446. Bosher, L., Dainty, A., Carrillo, P., Glass, J., Price, A., 2009. Attaining improved resilience to floods: a proactive multi-stakeholder approach. Disaster Prev Manag 18, 9–22.

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Please cite this article as: M. Mojtahedi, B.L. Oo, 2017. The impact of stakeholder attributes on performance of disaster recovery projects: The case of transport infrastructure, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2017.02.006