ISO 21500 and the Sustainability Focused ANP-BOCR Framework for Subcontractor Selection in Megaprojects

Abstract

The aim of this article is to present a novel framework that integrates the strategic dimensions of sustainability and the ISO 21500 standard to evaluate and select subcontractors for megaprojects. ISO 21500 processes were utilized to develop benefits, opportunities, costs, and risks subnets, whereas triple-bottom line sustainability dimensions were used as strategic criteria. Subsequently, the Analytic Network Process was used to examine the proposed framework for the Qatar Rail megaproject. The proposed framework supports organizations dealing with megaprojects to align their subcontractor selection with ISO 21500 and achieve ecological and social objectives alongside the project’s stipulated economic benefits.

Keywords

ISO 21500 Analytic Network Process subcontractor selection megaprojects Qatar

Introduction

Megaprojects are complex and massive projects with significant social and often far-reaching environmental impacts (Guo et al., 2020; Denicol et al., 2020) and characterized by large scale, long durations, high investments, and increased complexity (Flyvbjerg, 2014; Brookes & Locatelli, 2015). The megaproject’s complexity requires project contractors to break down the whole project into components that subcontractors could preferably manage based on their expertise. The subcontractor chosen for the job must be capable of delivering the project on time and meeting safety and quality standards (Putra & Wang, 2020; de Araújo et al., 2017). Subcontractors who are unable to handle the demanding megaproject conditions could cause delays, quality problems, cost overruns, and disagreements (Dowlatshahi et al., 2015; Safa et al., 2014). Therefore, selection of subcontractors that support megaproject’s objectives is of prime importance.

Choosing the right subcontractor has always been challenging for contractors (Cooke & Williams, 2013; Arditi & Chotibhongs, 2005; Brunet, 2021). The selection criteria may be arbitrary and qualitative (Okoroh & Torrance, 1999) and they are occasionally unclear (Cooke & Williams, 2013). In reality, general contractors frequently select subcontractors based on the proposal with the lowest price (Putra & Wang, 2020) and previous experience (Choudhry et al., 2012; Bingol & Polat, 2020). Subcontractors who offer the lowest bid may compromise on quality, while previous subcontractors could lead to control issues, influencing the project’s performance (Bingal & Polat, 2020). It is challenging to evaluate subcontractors before beginning the project (Trinkūnienė et al., 2017); therefore, a methodical and comprehensive method is required to measure, assess, and analyze subcontractors on specific standards and select those who are most qualified for the megaproject.

Our study tries to fill the gap in the existing literature on subcontractor selection by providing a practical methodology for the selection process. It is evident from the literature that subcontractor selection is mainly related to project-specific requirements such as construction projects. The majority of researchers have taken up construction projects as complex megaprojects. In addition, there is an empirical void for a universal methodology to be used in finalizing the criteria for subcontractor selection; therefore it posits a need to use and develop a model based on industry standards. Consequently, an integrated multicriteria decision aid model was developed to fill these gaps, integrating the ISO 21500 standard of project governance. The developed model was applied to an ongoing megaproject in Qatar, to select subcontractors based on benefits, opportunities, costs, and risks related to the megaproject. The major focus of the present research is to effectively integrate ISO 21500 and sustainability in subcontractor selection considering the critical role that subcontractors play in the successful completion and implementation of megaprojects.

This article is structured as follows: After the Introduction, the next section discusses the review of the existing literature on subcontractor selection in megaprojects, followed by the development of the Analytic Network Process-Benefits Opportunities Costs Risks model in the third section. In the fourth section, a case application was developed for Qatar Rail as a complex megaproject, and the results were discussed in the fifth section. The sixth section deliberates the conclusion, implications to theory and industry, followed by limitations of the study and future research directions.

Literature Review

Subcontractor Selection in Megaprojects

Mbachu (2008) indicates that up to 85% of megaprojects usually are subcontracted, and subcontractors are mainly responsible for solving problems such as financial limitations, shortage of resources, and any particular need for expertise (Demirkesen & Bayhan, 2019). Subcontracting, also referred to as “project outsourcing” by Mahmoudi et al. (2020, p. 99), can be beneficial to the main contractor, keeping in mind economic factors, competitive advantage, lower operating costs, diversification against risk (Tserng & Lin, 2002), resource efficiency (e.g., equipment), economic aspects (Eskerod & Ang, 2017; Arditi & Chotibongs, 2005), competitive advantage, and meeting the challenges of the local situation (Adnan et al., 2008). In addition, the impact of poor work execution by subcontractors will affect the project’s overall performance in terms of relationship, quality, cost, and time (Wang et al., 2021; Ma et al., 2021). In addition to impeding the subcontractor’s performance, it also holds contractors accountable for the disaster (Nassar & Hosny, 2013; Stegen & Palovic, 2014); as a result, choosing the best subcontractor for a project’s successful completion puts the contractor’s reputation and performance at risk (Ma et al., 2021).

The subcontractor selection in megaprojects is essentially a multicriteria decision-making process where several incommensurable and conflicting dimensions are involved (Hartmann et al., 2009; Arslan et al., 2008; Elazouni & Metwally, 2000). Elazouni and Metwally (2000) proposed a framework based on a decision support system to subcontract the best portion of the work. Cost is frequently the main factor used by many contractors when choosing a subcontractor (Demirkesen & Bayhan, 2019). However, choosing the option with the lowest price frequently leads to problems such as time extensions, decreased confidence between the contractor and the subcontractor, more operating costs, and perhaps lower quality of work (Li & Wan, 2014; Dowlatshahi et al., 2015; Nassar & Hosny, 2013). Few studies have examined a prequalification or screening procedure to confirm the subcontractors’ proficiency in quality standards, budget, and schedule (Ergönül & Yilmaz, 2011; Hadidi & Khater, 2015; Hosseini-Nasab & Ghamsarian, 2015). Others have emphasized the importance of resource capacity and project fit when choosing a suitable subcontractor (Ergönül & Yilmaz, 2011; El-Sawalhi et al., 2007; Hadidi & Khater, 2015).

Price, quality, technical expertise, and cooperation were highlighted by Hartmann et al. (2009) as the four key criteria for choosing a subcontractor. Price was shown to be the most important selection factor in their investigation. El-Sawalhi et al. (2007) discussed eight factors that are important to consider when choosing a subcontractor, including safety considerations, experience resources, performance, quality control, technical proficiency, and financial stability. Twenty-six subfactors within the elements specified by El-Sawalhi et al. (2007) were compiled by Thomas and Flynn (2011) for examining the subcontractor during the prequalification stage. According to Mbachu (2008), prequalification and tender pricing are the two most crucial considerations for selecting subcontractors in South Africa. To choose quality subcontractors, Yin et al. (2009) presented a two-step (primary and secondary) procedure. Their study assessed subcontractors’ competence to deliver the project as required, using several indices. de Araújo et al. (2017) conducted a review of 14 types of projects. They found 28 categories, including 41 subcategories used in literature to help select subcontractors.

Cost, time, and service quality are often the three main criteria for choosing a subcontractor (Ergönül & Yilmaz, 2011). Five project scenarios were utilized by Lavelle et al. (2007), which utilized 14 weighted factors to test the hypothesis that the selection of subcontractors is based on price. They came to the conclusion that pricing is not always more significant than prior performance, safety, and insurance coverage. According to Hartmann et al. (2009), contractors in Singapore adopt a multicriteria selection technique, favoring price over quality, technical proficiency, and subcontractor cooperation. Despite the significance and scope of subcontracting in megaprojects, it is frequently overlooked and requires significant improvement (Putra & Wang, 2020; de Arajo et al., 2017; Bingal & Polat, 2020).

Decision Models for Subcontractor Selection

A significant problem with the selection process for subcontractors is an adoption of a proper selection procedure (Chen et al., 2006; Karabayir et al., 2019). Multicriteria decision-making (MCDM) methods are frequently employed in the literature. The use of techniques, such as OPA (Ordinal Priority Approach), MOORA (Multi-Objective Optimization on the basis of Ratio Analysis), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (VIekriterijumsko KOmpromisno Rangiranje), AHP (Analytic Hierarchy Process), ANP (Analytic Network Process), along with mathematical programming techniques like linear programming, genetic algorithm, neural network, grey system theory, natural language processing (NLP), as well as a combination of these methods are used in the literature (Chai et al., 2013; Simić et al., 2017; Keshavarz-Ghorabaee et al., 2017). Fuzzy set theory is one of the most widely used ways of dealing with information uncertainty; hence, the literature also suggests fuzzy MCDM techniques (Keshavarz-Ghorabaee et al., 2017). Different MCDM techniques have been developed depending on the level of uncertainty, precision, and preference aggregation. Mahmoudi and Javed (2022) used the OPA approach to evaluate the performance of the subcontractors for the construction industry and developed a Relative Performance Index attempting to create a standard system of evaluation.

Adhikari et al. (2015b) compared alternatives for optimum solutions using distance-based approaches (TOPSIS and VIKOR). Multimethods have also been employed by several writers to address MCDM issues concerning subcontractor/supplier selection. Sarkis et al. (2012) employed both ANP and AHP, whereas Rodriguez et al. (2013) used “COmplex PRoportional Assessment of alternatives to Grey relations” with AHP. Zolfani et al. (2012) also applied “COmplex PRoportional Assessment of alternatives to Grey relations” with AHP. Arslan et al. (2008) suggested WEBSES, a web-based assessment system to rate subcontractors according to several criteria. As one can compare the benefits of each approach, combining methodologies appears to be a viable strategy when more than one technique is sought (de Arajo et al., 2017). The best way to choose would depend on the level of precision required, the available information, and the type of criteria, even though it is best to utilize a variety of methods to avoid making the situation more complex. Although various new techniques have been used in the extant literature for supplier selection, subcontractor selection has not received much attention.

In addition to MCDM techniques, authors have suggested several other strategies to better comprehend and clarify the subcontractor selection process. To choose subcontractors, Hatush and Skitmore (1997) employed the Delphi interview approach; Mahdi et al. (2002a) integrated the Delphi method with AHP as an MCDM method. El-Mashaleh (2009) created a data envelopment analysis (DEA) methodology and employed 11 criteria to choose a subcontractor. The 46 variables in an empirical study conducted by Marzouk et al. (2013) identified the most critical variables in the subcontractor selection. When generally conflicting criteria were utilized to make decisions, classification (Nassar & Hosny, 2013) and ranking procedures were applied (Adhikary et al., 2015b; Yang et al., 2010).

The Standard—ISO 21500

Although choosing a subcontractor is a common issue in project management, there are no widely accepted guidelines that can be used uniformly across all industrial sectors, especially for megaprojects. Any internationally recognized standard fills the void in this context. This is where ISO 21500—the first project management and governance standard produced by ISO—might be helpful. Thirty-nine processes are included in ISO 21500 and are categorized into five areas. It offers project management advice that any business may use, regardless of the project’s complexity, duration, or scale (iso.org). It also provides direction for the project management procedures and theories that affect how well the projects turn out. It lists 10 elements—communication, money, quality, time, resource, risk, procurement, stakeholder, integration, and scope—that can be used to organize project processes (Stellingwerf & Zandhuis 2013).

Which methodology is the most appropriate to be utilized for a multidisciplinary project? This question is addressed by ISO 21500. How can we establish a line of communication among the many stakeholders? Several initiatives, such as the Global Project Management Forum (1994), standards by Project Management Institute (PMI, 1996), Global Alliance for Project Performance Standards (2002), and ISO 10006, were undertaken in the past to create a set of standards for complex and megaprojects (2003); the requirements for preserving quality in the projects are described in ISO 10006. None of these standards was able to gain widespread support. According to Stellingwerf and Zandhuis (2013) and Varajão et al. (2017), the project management guidance standard ISO 21500 is a step in the right direction.

The ISO 21500 standard is essential for complex projects. Due to the length and complexity of large projects, there is a considerable risk associated with funding and stakeholder involvement. It is also apparent that managing the expectations of significant (external) stakeholders, such as subcontractors, is crucial because they can make or ruin a project. Project governance must be adequately created for complicated megaprojects. The assignment roles required for complicated projects are expressly defined by ISO 21500. ISO 21500 is a practical reference for managing complex projects (Varajão et al., 2017; Takagi & Varajão, 2021). With ISO 21500, the contractors can have an established and applied approach toward subcontractor selection. The components of ISO 21500 were analyzed using the ANP-BOCR model, which deals with the bidirectional nature and interdependency of subcontractor selection criteria. In addition to helping academicians fill the vacuum in the literature regarding the methodology of subcontractor selection, it will also assist practitioners in developing a comprehensive framework for subcontractor selection regardless of the industry.

Methodology

The framework proposed in this study is based on standards that have been mentioned in both literature and practice. In this study, we split the ISO 21500 components into four BOCR model subnets. Short-term results have benefits and risks that are quite evident. Whereas benefits are perceived as a favorable aspect, costs are considered as a negative feature of a project. Long-term, unpredictable opportunities or dangers could have a favorable or negative impact on accomplishing the goals depending on their contribution (Shang et al., 2004).

Using the Delphi Technique, ISO 21500 processes were categorized in a BOCR model. We have contacted consultants and company project managers involved in executing megaprojects, such as construction and railways, or implementing ISO 21500 in their organizations or both. Project managers at the general manager level or higher with at least five years of experience working with subcontractors were invited to participate in the Delphi study. Ten managers and five consultants were approached from the national capitol region of New Delhi, India. Finally, four managers and two consultants agreed to participate in the Delphi study. An online meeting moderated by authors was convened; due to the ongoing COVID-19 pandemic, face-to-face meetings were ruled out. The discussion covered each element of ISO 21500. The participants were asked to provide their opinion on whether the given component falls into the opportunities, benefits, risk, or cost subnets. Those ISO 21500 components that did not align with any of the given subnets of BOCR were excluded from further analysis, and the selected ISO 21500 components were finally used to design the ANP-BOCR model (see Figure 1). The panel came to an agreement on 27 of the 39 ISO 21500 components, which were included in the final study. As shown in Table 1, the selected 27 components were arranged in BOCR subnets.

Figure 1.

Hierarchy-based ANP network.

Table 1.

BOCR Criteria Development Using ISO 21500

S. No	ISO 21500 component	Criteria for Subcontractors	Available Literature	BOCR Model Component
1.	Stakeholders 1. Identify 2. Manage	It is the coordination and cooperation provided with other parties, such as subcontractors, searching for a partnership. It entails interactions with rival businesses, insurance providers, clients and suppliers, teams, local government officials, and subcontractors.	Hosseini-Nasab & Ghamsarian (2015), Safa et al. (2015), Dowlatshahi et al. (2015), Safa et al. (2014), El-Abbasy et al. (2013)	Opportunities
2.	Scope 3. Define scope 4. Create work breakdown structure 5. Define activities 6. Control scope	It includes the time needed for completion, delivery, bidding, execution, etc. It also includes subcontractor-related operations, project duration, transit time, and control over tasks such as maintenance.	Sarkis et al. (2012), Chen et al. (2013), El-Abbasy et al. (2013), Cheng & Kang (2012), Arslan (2012), Alhumaidi (2015)	Benefits
3.	Time 7. Sequence activities 8. Estimate activity durations 9. Develop schedule 10. Control schedule	It is described as the subcontractor’s capacity to assist the principal contractor in completing the megaproject on schedule. Estimating activity durations, developing a schedule, and contract-compliant control are all included.	Alhumaidi (2015), Marzouk et al. (2013), Dowlatshahi et al. (2015), El-Abbasy et al. (2013), Stegen & Palovic (2014)	Benefits
4.	Quality 11. Plan quality 12. Perform quality assurance 13. Perform quality control	It discusses the subcontractor’s quality offerings in detail. It consists of QA/QC programs, auditor evaluations, quality assurance and control procedures, and quality certifications that subcontractors accept. The main contractor’s quality evaluation process includes checking for compliance with quality terms and conditions.	Hosseini-Nasab & Ghamsarian (2015), Dowlatshahi et al. (2015), Yang et al. (2010), Alhumaidi (2015), Li & Wan (2014)	Benefits
5.	Risk 14. Identify risks 15. Assess risks 16. Treat risks 17. Control risks	The subcontractor is capable of identifying and evaluating the project’s hazards. One of the key components of large-scale and complex projects is risk management and activity control. Geopolitical, political, economic, and delivery-related risks may all be present.	Nassar & Hosny (2013), Stegen & Palovic (2014), Dowlatshahi et al. (2015)	Risks
6.	Cost 18. Estimate cost 19. Develop budget 20. Control costs	This component discusses the subcontractors’ capacity to calculate the project’s cost. Creating budgets and maintaining control over project expenditures include estimation. For the subcontractors, the cost factor is crucial, and good supervision is required at every project stage. It is necessary to implement suitable reporting systems so that they may be compared to the client’s estimates. Past project management costs, procurement costs, the dollar amount of performance incentives, cost risk, cost overruns, transportation expenses, performance level, etc., are all included in this.	Safa et al. (2015), Alhumaidi (2015), Chen et al. (2013), Li & Wan (2014), Zolfani et al. (2012), Dowlatshahi et al. (2015), Yang et al. (2010).	Costs
7.	Resources 21. Develop Project Team	The subcontractor can create technical personnel, a strong team, labor resources, and other manpower requirements.	Hadidi & Khater (2015), Arslan (2012), Dowlatshahi et al. (2015).	Benefits
8.	Communication 22. Plan communications 23. Distribute information 24. Manage communications	Management is a crucial trait for a subcontractor because it is one of the key factors in team communication, management, and stakeholder relations. For the project to be successful, subcontractors’ distribution, management, and communication planning are crucial.	Dowlatshahi et al. (2015), Sarkis et al. (2012), Wang et al. (2021)	Opportunities
9.	Procurement 25. Plan procurements 26. Select suppliers 27. Administer contracts	This relates to the subcontractor’s capacity to choose appropriate suppliers and organize purchases. Formal contracts that specify the time line and terms and conditions of the contract are necessary for the subcontractor when choosing suppliers.	Alhumaidi (2015), Cheng et al. (2012), El-Abbasy et al. (2013), Yang et al. (2012), Stegen & Palovic (2014), Rodríguez et al. (2013).	Opportunities

The Analytic Network Process (ANP)

To deal with bidirectional nature and interdependency among criteria and subcriteria in an MCDM problem, the Analytic Network Process (ANP) method was introduced by Saaty (1996). The ANP model is made up of a decision network with clusters, their constituent parts, and connections among those parts. Some or all of the elements of any other cluster may be impacted by the components of one cluster (Faisal et al., 2011). When using arcs to depict relationships, the arc’s orientations show the relationship’s directional dependence. The four major steps of the ANP model are described in the following subsections.

Formulation of the Model

For the ANP model to provide the decision maker with a clear image, the problem must be structured as a network. To obtain the network architecture, approaches such as brainstorming should be applied (Molinos-Senante et al., 2015). The criteria and clusters in an ANP model are linked to one another using a variety of connections like one-way, two-way, and loop connections. If there is a one-way dependency between two clusters, then there is just a one-way link, which is represented by the directional rows. Bidirectional arrows are also utilized for two-way interdependence between two clusters. Loop connections are employed for internal dependencies and comparisons within a cluster (Onut et al., 2011).

Pair-Wise Comparison Matrices

As suggested by Saaty (1996), pair-wise comparison is made consistently for all combinations, using a comparison scale of 1 to 9. An eigenvector (local priority vector) w is computed once the pair-wise comparisons are completed. This eigenvector w is an indicator of the relative importance accorded to the elements in a pair-wise comparison. Since personal judgments are involved in making comparisons, to ensure reliability, a consistency ratio (CR) is used. The value of CR should be less than 0.1 for matrices that are larger than 3 × 3 (Khan & Faisal, 2008). The decision maker must update their conclusions for higher CR values. The inputs obtained from pair-wise comparisons are utilized to obtain the weighted priority vector.

Formation of Super Matrix

Each submatrix of the partitioned matrix contains a set of relationships both within and between the levels. An unweighted super matrix comprising all the eigenvectors is derived from the pair-wise comparison matrices of the model. This super matrix typically has columns that are not equal to one or normalized, which results in an unweighted matrix. The unweighted super matrix should be synthesized and normalized to make it column = stochastic (sum of entries in each column equals 1). The outcome is referred to as weighted super matrix M (Lin & Huang, 2015; Al-Naimi et al., 2020).

Alternatives Prioritization

The final step of the ANP is to calculate the global priority vector. By increasing the weighted super matrix’s large powers, this can be found. The weighted super matrix is multiplied by 2x (x is a random large number). The result of this technique is the limit super matrix (Saaty, 1996). The global priority vector is demonstrated via the limit super matrix, a converged version of the weighted super matrix. Here, convergent form means that when the super matrix’s values are multiplied together, they remain unchanged. This results in a set of consistent weights that are stable over the long term. The fundamental concept underlying increasing the super matrix’s power is the synthesis of the transitive connections between factors and clusters within the ANP network.

The final step is the execution of sensitivity analysis, which is based on replicating multiple situations. Depending on the criteria, this step may be highly desired even when not required. As a result, the consistency of the different rankings and results is assessed. The ANP model’s sensitivity analysis considers how the elements of the decision issue affect the alternatives while also considering how the elements of the weighted matrix affect the alternatives. Therefore, to perform a sensitivity analysis, the factor with the largest weight should be chosen first. The emphasis should then be on changing the weights of those factors most affected by the most significant element.

The BOCR Network Method

Factor choice in an ANP analysis is a complex and challenging issue to handle. The complexity is aggravated when the focus is on a novel concept and there is a dearth of similar models. In such cases, it is suggested to conduct an exhaustive survey to select the issues that need to be examined. When survey participants cannot agree, the ANP with BOCR is a useful technique (Yi et al., 2011). The BOCR network’s criteria are broken down into four subnets: B for benefits, O for opportunities, C for costs, and R for risks. Cost and benefits generally represent the immediate and visible outcomes. Benefits are positive, whereas costs are negative results. Opportunities and risks are associated with long-term, ambiguous, or possible criteria depending on their contribution to the goal (positive or negative) (Liang & Li, 2008). The integrated ANP-BOCR method is used in studies on national energy policy modeling (Vaidya & Kumar, 2006), sustainable forest management (Ghajar & Najafi, 2012), and alternative fuels for residential home heating (Gencer & Gürpinar, 2007).

Subnetworks of benefits (B) and opportunities (O), which are organized into hierarchies, pair-wise comparison questions in the BOCR network ask about the most advantageous or the best opportunity choice (O). However, the pair-wise comparison questions seek to determine which alternative, according to each specific criterion or subcriterion in the hierarchy for risks (R) and costs (C), is the riskiest or most expensive (subnets). The weights of the alternatives are first combined based on the weights of the subcriteria and specific criteria for each hierarchy (subnet). To obtain a single numerical number for each alternative, their weights under the categories of benefits, opportunities, cost, and hazards are further combined. These values are developed using two formulas, additive and multiplicative, as suggested by Ghajar and Najafi (2012) and Erdogmus et al. (2005). The additive and multiplicative formulas can be given as bB_i+ oO_i - cC_i- rR_i and B_i^b+ O_i^o + [(1/C_i)Normalized]^c + [(1/R_i)_Normalized]^r, respectively. In these formulas, b, o, c, and r represent the normalized weights of BOCR, and B_i, O_i, C_i, and R_i signify the synthesized results of alternative i under merits B, O, C, and R, respectively.

Case Application

The planned structure for the massive Qatar Rail project was assessed. To finish various phases of the Doha, Qatar, metro project, joint ventures (JV) have been given multiple megaprojects. Each JV utilizes the services of subcontractors; although JVs propose their subcontractors, the decision to award the contract to a subcontractor requires the approval from Qatar Rail. Specific standards are used by the Qatar Rail team, and a team of professionals creates ratings for the subcontractors. This subcontractor approval process is straightforward, but it has several drawbacks such as a failure to consider how the various criteria interact. The suggested framework offers a thorough assessment of subcontractors because it incorporates sustainability, dimensions, and ISO 21500. Following are the steps of the suggested framework.

Experts’ Team

The suggested model structure needed considerable feedback from professionals. As a result, the senior management of Qatar Rail assisted in the meticulous selection of experts. Twelve experts who participated in the subcontractor assessments were first introduced to the authors individually. Following conversations with these specialists, a group of seven experts were chosen to move forward with the modeling process. The range of projects handled, the number of years of experience, and the position within the company all played roles in the selection process. Inputs from experts guided each stage of the model development, including analysis, the finalization of each strategic criterion’s subcriteria, the consideration of ISO 21500 classifications for the subcriteria under each element of the BOCR subnetworks and, finally, the determination of the dependencies among subnetworks’ risks, costs, opportunities, and benefits. To avoid the drawbacks of utilizing mean values, consensus values were used in the model computations, which required extensive expert consultations.

Determining Control Hierarchy and Strategic Criteria

Subnetworks and the control hierarchy make up the suggested framework. In Figure 1, the control hierarchy is displayed. The major objective of the framework is subcontractor prioritization. Regarding the objective, the proposed strategic criteria are the sustainability dimensions, including economic, ecological, and social. The decision to choose a subcontractor involves both positive and negative aspects that must be taken into account. According to Ghajar and Najafi (2012), the current approach labels some favorable characteristics as benefits while labeling the unfavorable ones as costs. These categories, however, show immediate and observable results. While the unclear negative aspects would be known as risks, the uncertain good factors may be called opportunities. These categories describe long-term and unclear criteria in contrast to benefits and costs (Liang & Li, 2008).

Determining the Subnetworks of BOCR

There are four subnetworks in the proposed model: benefits, opportunities, costs, and risks. Every cluster contains nodes with one-way and two-way relationships. One-way relationships are shown by unidirectional arrows, and two-way links are shown by bidirectional arrows. The creation of subnetworks utilizing the ISO 21500 framework is a unique feature of the suggested framework. In a later section, a thorough explanation of how ISO 21500 can be used to create subnetworks is presented. To illustrate the subnetwork, Figure 2 represents the case of benefits subnet.

Figure 2.

Network for benefits subnet.

Obtaining Pair-Wise Evaluation Matrices for Strategic Criteria

The Delphi technique was used to carry out the pair-wise evaluations. By planning a series of workshops with the seven experts chosen in Step 1, pair-wise comparisons were conducted. The purpose of the first workshop was to explain the suggested framework and comprehend the current procedure. Experts were encouraged to share their thoughts on the advantages and disadvantages of the suggested framework. A few components in the framework needed in-depth explanations, and there were some minor changes in the framework based on inputs. Pair-wise evaluations were done in the second and third workshops, and each expert was instructed to use the Saaty and Sagir (2015) scale, which ranges from 1 to 9. On this scale, equal importance, moderate importance, strong importance, extremely strong or demonstrated importance, and extreme importance have values 1, 3, 5, 7, and 9, respectively. Additionally, even-numbered values signify intermediate importance levels (Erdogmus et al., 2005). To establish the weights of strategic criteria, initial pair-wise comparisons were conducted. Although earlier researchers calculated expert scores using geometric means, in our case, experts agreed to discuss pair-wise evaluations together, which allowed us to reach consensus for the scores. Table 2 represents the pair-wise evaluation matrix for strategic criteria.

Similar calculations are used to determine the weights of each strategic criterion, as indicated in Tables 3 –5.

Table 2.

Matrix for Pair-Wise Evaluation with Respect to Strategic Criteria

	Social	Ecological	Economical	Weights
Social	1	3	7	0.67
Ecological	1/3	1	3	0.24
Economical	1/7	1/3	1	0.09

CR: 0.0067

Table 3.

Matrix for Pair-Wise Evaluation with Respect to Economical Subcriteria

	Cost	Time	Quality	Weights
Cost	1	1/8	1/6	0.065
Time	8	1	2	0.595
Quality	6	1/2	1	0.340

CR: 0.0176

Table 4.

Matrix for Pair-Wise Evaluation with Respect to Ecological Subcriteria

	Waste Management	Resource Use	Environmental Impact	Weights
Waste Management	1	3	1/3	0.258
Resource Use	1/3	1	1/5	0.105
Environmental Impact	3	5	1	0.637

CR: 0.0370

Table 5.

Matrix for Pair-Wise Evaluation with Respect to Social Subcriteria

	Stakeholders Satisfaction	Meeting User Requirements	Achieving Intended Purpose	Weights
Stakeholders Satisfaction	1	3	2	0.540
Meeting User Requirements	1/3	1	1/2	0.163
Intended Purpose	1/2	2	1	0.297

CR: 0.0088

Weights Assessment for BOCR Based on Strategic Criteria

Benefits, opportunities, costs, and risks are not weighted equally by Qatar Rail’s management. Based on the linguistic values listed in Table 6 for each strategic subcriterion, BOCR was assessed. The weights of the strategic criteria found in Table 2 are shown in Table 7. The normalized weights of BOCR are shown in the last column in Table 7. These weights are calculated by multiplying each value in a row by the weight of the strategic subcriteria under the column in which the BOCR value is found.

Table 6.

Linguistic Values and Average Numbers

Linguistic Values	Average Numbers
Very High (VH)	1
High (H)	0.75
Medium (M)	0.5
Low (L)	0.25
Very Low (VL)	0

(Source: Kabak & Dağdeviren, 2014)

Table 7.

BOCR Ranking With Regard to Strategic Criteria

Criteria	Economical0.09			Ecological0.24			Social0.67
Subcriteria	Quality	Time	Cost	Impact	Resource Use	Waste Management	Intended Purpose	User Requirement	Stakeholder Satisfaction	Total Rate	NormalizedRate
Weights	0.340	0.595	0.065	0.637	0.105	0.258	0.297	0.163	0.540
Global Weights	0.030	0.052	0.006	0.155	0.025	0.063	0.199	0.109	0.361
Benefits	Very High (1)	Very High (1)	Very High (1)	Very High (1)	High (0.75)	Very High (1)	Very High Author(1)	Very High (1)	High (0.75)	0.90	0.37
Opportunities	High (0.75)	Medium (0.5)	Medium (0.5)	High (0.75)	High (0.75)	High (0.75)	High (0.75)	Medium (0.5)	High (0.75)	0.71	0.29
Costs	Medium (0.5)	Medium (0.5)	Very High (1)	Low (0.25)	Very High (1)	Very High (1)	High (0.75)	Very Low (0.0)	Very Low (0.0)	0.32	0.13
Risks	Low (0.25)	Very High (1)	Very High (1)	Very High (1)	Medium (0.75)	Low (0.25)	Medium (0.5)	Medium (0.5)	Low (0.25)	0.49	0.21

BOCR Criteria Weights Assessment

The weights of the criteria listed under each BOCR element are determined in this stage. To do this, paired matrices must be created, and each element, benefit, opportunity, cost, and risk must be viewed as a subnetwork. With the aid of the program SuperDecisions Version 2.10, a super matrix is created for each subnetwork. Table 8 lists the final relative weights as determined by the software. The most crucial criterion for the benefits subnetwork, as shown in Table 8, is time, whereas for opportunities, it is stakeholder satisfaction, cost estimation, and risk identification.

Table 8.

Final Relative Weights of Criteria in BOCR Subnetworks

BOCR Component		Criteria Description	Final Relative Weights
Benefits (B)	B1	Scope	0.258
	B2	Time	0.452
	B3	Quality	0.290
Opportunities (O)	O1	Stakeholder Satisfaction	0.491
	O2	Communications	0.277
	O3	Procurement	0.232
Costs (C)	C1	Cost Estimation	0.377
	C2	Budget Development	0.313
	C3	Cost Control	0.311
Risks (R)	R1	Risk Identification	0.399
	R2	Risk Assessment	0.293
	R3	Risk Control	0.308

Determining Priorities of Subcontractors

The relative relevance ratings for the alternatives are determined in the analysis’s concluding stage using an additive formula that subtracts the sum of the weighted bads from the weighted goods (Erdogmus et al., 2005). In this context, goods refers to advantages and chances, whereas bads refers to dangers and expenses. The final importance or priority of the subcontractors are displayed in Table 9 using the additive formula.

Table 9.

Subcontractors (Alternatives) Rank According to Final Importance

	B (0.371)	O (0.291)	C (0.133)	R (0.205)	1/C	N_1/C	1/R	N_1/R	Additive	Multiplicative
Subcontractor A	0.181	0.274	0.173	0.214	3.654	0.243	4.684	0.287	0.080	0.233
Subcontractor B	0.193	0.293	0.272	0.301	3.683	0.245	3.320	0.203	0.059	0.227
Subcontractor C	0.278	0.314	0.348	0.270	2.878	0.191	3.709	0.227	0.093	0.263
Subcontractor D	0.348	0.119	0.208	0.216	4.817	0.320	4.636	0.284	0.092	0.241
Total					15.031		16.349

Sensitivity Analysis

An important requirement for quantitative models is their robustness, which is established using sensitivity analysis (Shang et al., 2004). Sensitivity analysis helps to predict the changes in results due to changes in key variables of the model. In our study, before sensitivity analysis is conducted, an easy and intuitive analysis based on Table 9, that is, the final importance ranking of subcontractors, was done.

From Table 9, we find that the advantage of subcontractor C is significant. Subcontractor C’s excellent performance on opportunities and cost-effectiveness (0.314 and 0.348, respectively), and the high priorities for benefits (0.278), provide the most advantage. Although subcontractor C’s performance on the benefits is slightly lower than subcontractor D, the other parameters favor the former. In addition, the best performance in terms of risk factors is for subcontractor B, but its limitations in terms of benefits and opportunities do not favor its selection. Although subcontractor C performs poorest in terms of cost at a lower priority of cost (0.133), it prevents it from losing the advantage of benefits and opportunities. The gap between subcontractors A and B (worst performers) with respect to subcontractors C and D (best performers) can be appreciated from the additive score.

A sensitivity analysis was further conducted by changing the range of BOCR priorities one at a time from 0.05 to 0.95 (Liang & Li, 2008). Figures 3 through 6 show the sensitivity analysis results concerning benefits, opportunities, cost, and risk. The transition points also indicate the change in alternatives from the original corresponding points.

Figure 3.

Sensitivity analysis results concerning b.

From Figure 3, it can be concluded that although the performance of subcontractor D is best at the original point (b = 0.371) and improves subsequently, the advantage is lost at lower levels of b. Within the range of 0.25 to 0.35, subcontractor C performs best, and below 0.25 values of b, subcontractor A performs well. However, when it comes to opportunities, cost, and risk (Figures 4 through 6), subcontractor C has the advantage over all of the others. The opportunity level of b from the original point (0.291) onwards favors subcontractor C. In addition, the significant advantage that subcontractor C has over subcontractor D is in the area of cost and risks (Figures 5 and 6).

Figure 4.

Sensitivity analysis results concerning o.

Figure 5.

Sensitivity analysis results concerning c.

Figure 6.

Sensitivity analysis results concerning r.

Similarly, when we increase the importance of cost, subcontractor C performs well in comparison to D (after 0.15) and A (after 0.20). There are more chances of growing cost priorities when considered on a larger scale. Therefore, subcontractor C has a clear advantage in terms of costs as compared to others. At a higher range of risk, the results favor subcontractor C as compared to subcontractors D and A. Although subcontractor B is performing best in risk priority, its performance in other priority segments (b, o, and c) is very poor compared to C, A, and D. It is expected that the risk in megaprojects is high, and most likely it would increase as the megaproject enters into later stages thereby favoring option C.

Therefore, from the sensitivity analysis, it can be concluded with confidence that the results of our research are stable, the decision model is robust, and subcontractor C is the best possible choice among the available options for the Qatar Rail megaproject.

Discussion

This article aims to advance our understanding of the subcontractor selection problem in megaprojects by developing a comprehensive model. The proposed model is based on a well-accepted ANP methodology and ISO 21500, an emerging project management standard. This approach captures the best results when decision criteria are dependent on one another. Furthermore, using BOCR analysis, the proposed method complements traditional MCDM approaches as it highlights negative and positive aspects of subcontractor selection in megaprojects. The proposed methodology complements previous studies as this approach is more structured than other MCDM methods used in supplier selection like AHP, which does not capture the interdependencies among criteria.

By enabling decision makers to assess diverse possibilities and comprehend the influence of numerous interdependencies among criteria, the ANP-BOCR method enhances human decision-making. The multicriteria decision model based on the ANP-BOCR network is proposed in the current study to enhance the subcontractor selection in megaprojects. Additionally, the criteria considered for each BOCR category were extracted from the ISO 21500 standard. This standard is frequently applied to certify contractors engaged in project work. The suggested framework is assessed using suggestions from professionals involved in subcontractor evaluation in a company managing many megaprojects.

Comparing the ANP-BOCR method to the conventional method of ranking subcontractors according to a list of criteria, there are two key benefits. First, it is beneficial to take into account both qualitative and quantitative standards while judging subcontractors. Second, it takes into account the relationships among the criteria under each category of benefits, risks, opportunities, and costs. Other ranking systems do not have the ability to take dependencies into account when evaluating alternatives. It aids the decision maker in taking into account how one criterion may benefit or harm another.

The consistency of the experts’ opinions has a significant impact on the outcomes of an ANP-BOCR model (Erdogmus et al., 2005). As suggested by earlier researchers, the consistency ratio in the current study is consistently lower than the acceptable value of 0.1. The social component is given top attention in the pair-wise evaluation of strategic goal-related criteria. This was unexpected because, in the majority of preceding models, the economic factors were given priority. The nature of Qatar’s rail megaprojects, which the government has mandated to offer citizens of the country accessible and inexpensive means of transportation with a reduction in total pollution from the usage of cars, may be a contributing factor.

It should be noted that the practical application of this model requires a panel of experts to provide their critical view on the list of selection criteria, performance judgments, and pair-wise comparisons. Therefore, a more careful definition of the problem is desired. In addition, with the application of the case study, the proposed model can be replicated. However, conversely, in different contexts, managers of megaprojects need to select the appropriate experts and the proper criteria and subcriteria to ensure accurate and objective selection of subcontractors.

The last two columns in Table 9 display the final ranking of subcontractors. Subcontractor C is the best option among the four subcontractors being examined for the contract according to the findings of the additive and multiplicative formulas, and subcontractor B is the least desired. Additionally, it offers a comparative evaluation of all four subcontractors based on the factors of advantages, opportunities, prices, and risks. The model’s robustness is proven because the sensitivity analysis infers the same conclusions.

Conclusions, Implications, and Future Research Directions

The fact that sustainability dimensions are taken into account as a strategic criterion in the current research is a considerable advantage. Typically, in subcontractor evaluation, sustainability is merely one of the factors. However, under the suggested paradigm, sustainability dominates the entire subcontractor evaluation procedure. Due to the involvement of subject matter experts at every stage of decision-making and evaluation, the suggested model is not a theoretical framework. Additionally, it gave specialists a deeper comprehension of how to include sustainability at the highest level of decision-making. Additionally, each criterion’s subcriteria included the megaproject success variables that were taken into account in the theoretical model that was provided.

To achieve a competitive advantage and for the success of megaprojects, managers agree that subcontractor selection is crucial (Bottani et al., 2018; Demirkesen & Bayhan, 2019). The proposed methodology is flexible as it is easy to include additional criteria in the model and therefore, it can be applied to any subcontractor selection problem. In addition, the model can be used and implemented in different contexts and provides managers/organizations with a guide to select the most suitable subcontractor that supports the organization’s objectives. The results from strategic criteria showed resultant ranking from ecological, economic, and social perspectives. In conclusion, the proposed methodology is effective and robust in identifying subcontractors for a megaproject. It can be deployed by organizations as a supporting tool for decision-making and managing the portfolio of subcontractors.

For megaprojects with high uncertainty and complexity, subcontractor relationships are essential to ensure megaproject success and reduction in transaction costs (Wang et al., 2019; Ninan & Sergeeva, 2022). Therefore, this study offers an actionable understanding and a mechanism for subcontractor selection for megaprojects. Organizations dealing in megaprojects can ask subcontractors to be ISO 21500 certified in advance, in order to be eligible to bid for the subcontracting part of the megaproject. This would help the organization to reduce transaction costs, and the subcontractors’ shortlisting efforts will be more aligned with organizations broader goals. The coordination and control will be more with the contractors looking to subcontract the task(s) for a megaproject’s successful execution. When a subcontractor is ISO 21500 certified, the capabilities are known, and uncertainties can be reduced.

Theoretical and Practical Implications

Megaprojects characterized by a high level of complexity and uncertainty have created a burden for managers in achieving a balance between flexibility and control without affecting the performance of the megaproject (Wang et al., 2021). The subcontractor selection process can lead to escalating managerial tensions for contract management of the project and may increase time line and transaction costs (Zhang et al., 2018). Thus, handling such contingencies requires a systematic and practical approach to subcontractor selection. Although the literature suggests a few techniques and mechanisms for the selection, the majority of them, however, are context specific and no standard practice has been used for developing a generalized approach. Further, in cases of dysfunction of a governance system, relationship tensions arise (Lobo & Abid, 2020) and influence the delivery of megaprojects (Wu et al., 2020a). Our study has integrated the governance standard ISO 21500, an upcoming international standard for project governance and management, in the subcontractor selection process. Further, using the ANP-BOCR model, we have developed a comprehensive framework for subcontractor selection. Prior studies have indicated that subcontractor selection and relationships play a crucial role in the success of megaprojects (Wu et al., 2020b; Brunet, 2021). The present study further elaborates the significance of subcontractor selection and its functional importance in the success of megaprojects, thus laying a foundation for utilizing standards for project governance in literature.

The proposed model is a novel attempt to generalize the subcontractor selection procedure through which a manager can make trade-offs among structural and relational requirements to ensure megaproject success. Compared to individual approaches, this integrated approach has a few advantages. First, the requirement of the managers, as well as the organization, can be considered simultaneously to evaluate the available subcontractors, thus ensuring the selection of the best company. Second, highlighting the positive and negative aspects of the subcontractors, the BOCR network helps divide the original criteria into four sets, thereby allowing the organization to make informed strategic decisions. Lastly, the proposed ANP-BOCR and ISO 21500 methodology is customizable and flexible and based on the organization’s requirements; it can be modified and extended further by including/excluding additional criteria. The results of this article will assist managers in identifying the most suitable subcontractors concerning strategic requirements and improve the relationships with the organization. Managers are advised to link the proposed model results with their shortlisting strategy, taking into account the nature of the megaproject, prevailing market situations, and industry expectations.

Limitations and Future Research

The present study is focused on subcontractor selection using ISO 21500 standards for project governance and the management of projects. The model developed in this research was evaluated for a Qatar Rail megaproject, and a hierarchy of subcontractors for selection was suggested. For other megaprojects, the importance of criteria for selection may vary depending upon the types of megaprojects and the availability of subcontractors. Although experts were involved in selecting criteria and subdividing them into benefits, opportunities, cost, and risk factors, there is a chance that some criteria might have been overlooked, which can be included in future extensions of the proposed model. Selection of experts plays a critical role, as a wrong choice of experts may result in inconsistencies in the implementation of the model. In such cases, the proposed model may require some corrections or amendments during the implementation phase of the model (Kou et al., 2014).

The ANP method used in the present work also suffers from some limitations; first, the calculation process for ANP is time-consuming due to the creation of pair-wise matrices. This may become more cumbersome if the numbers of criteria and subcriteria increase. Second, the ANP method requires converting the linguistic variables into numbers, which further adds to the complexity of the model. In future research, the Ordinal Priority Approach (OPA), suggested by Ataei et al. (2020), can be applied to minimize the constraints of the ANP model. The OPA provides several advantages compared to traditional methods, such as AHP or BWM, in solving multicriteria decision-making problems. In the future, the proposed model can be further extended to consider other strategic perspectives, such as technological and political, among others.

Footnotes

Acknowledgments

The authors would like to express their deepest gratitude to the editor, Professor Shazia Nauman, and the esteemed reviewers whose insightful comments and constructive critiques have resulted in significant improvements to the article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Mohd. Nishat Faisal

Author Biographies

Abdulla Abdulaziz Al Subaie is currently the minister of municipality in Qatar. He received his PhD in leadership in megaprojects from the College of Business & Economics, Qatar University. He also serves as the managing director and chief executive officer of Qatar Rail Company, which is responsible for all the projects related to Doha Metro in Qatar. Under his leadership, Qatar Rail has successfully completed and begun operating three Metro lines. He is also the chairman of the board for Al Meera Consumer Goods Company, Qatar. Formerly, he served as the group chief executive officer of Barwa, Qatar’s leading real estate development and investment holding group, chief executive officer of Smeet (an affiliate of Qatari Diar Group), and held various leadership and senior project management positions with the Qatar General Electricity and Water Corporation (Kahramaa), managing multibillion-dollar infrastructure projects. He can be contacted at abdullas.cbe@gmail.com

Mohd. Nishat Faisal is a professor at the Department of Management and Marketing, College of Business and Economics, Qatar University. He received his PhD in supply chain management as the first National Doctoral Fellow in Management from IIT Delhi, India. His name has appeared in the List of Top 2% Researchers/Scientists of the World in a Stanford study and Elsevier report in 2020, 2021, and 2022. His articles on supply chain risk management are among the most highly cited articles in this area. He is the recipient of Emerald’s Highly Commended Award 2008, Winner of Outstanding Paper 2018, and CBE, QU-Excellence in Research Award, 2011-2012. He can be contacted at nishat786@qu.edu.qa

Belaid Aouni is the associate dean for Research and Graduate Studies at the College of Business and Economics, Qatar University. He received a BS in production and operations management from Tlemcen University in Algeria, and an MBA and a PhD in management sciences from Laval University in Canada. His teaching and research interests are related to the areas of multiple objective programming and goal programming, multiple criteria decision aid, mathematical programming, and supply chain and logistics. His publications have appeared in several top-tier journals. He served as president of the Canadian Operational Research Society. Prior to joining Qatar University, Dr. Aouni was a professor of quantitative methods and operations management at Laurentian University and acting dean for faculty of graduate studies at Laurentian University, Canada. Dr. Aouni has extensive professional experience with the government of Quebec, Canada. He can be contacted at belaid.aouni@qu.edu.qa

Lamay Bin Sabir is an assistant professor of operations management in the Department of Business Administration at Aligarh Muslim University, Aligarh, India. He holds a PhD in operations management, MBA, and a bachelor’s degree in mechanical engineering. He received the UGC-Maulana Azad National Fellowship in 2011 and qualified for the UGC-National Eligibility Test for Lectureship in Management in 2013. He was awarded a Special Mention Paper Award at PAN IIM World Management International Conference in 2019. He is a certified data science and big data professional. His research interests are in the areas of supply chain management, information technology, business analytics, quality management, and sustainability. He has presented papers at national and international conferences and published articles in numerous professional journals. He can be contacted at lamaybinsabir@gmail.com

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