A model for assessing the impact of sustainable supplier selection on the performance of service supply chains

Abstract

This study intends to develop and validate a model for assessing the impact of supplier sustainability on the performance of a service supply chain. Supplier sustainability is assessed based on five main criteria derived from literature and validated by industry experts (i.e. Environment Management, Social Responsibility, Green Products, Technology Standards and Health and Safety Management). Each criterion is further characterised using three sub-criteria (indicators). The impact of these sustainability aspects on the performance of service supply chains is assessed based on two sets of economic and competitiveness measures. To validate the model, empirical data were collected from a large sample of supply chain experts at major service firms in the United Arab Emirates (UAE). Structural Equation Modelling was then used to analyse the data and derive conclusions. Results indicated a valid structure of the proposed model based on the selected criteria (latent variables) and confirmed their positive impact on the performance of service supply chains. This has demonstrated the model fitness to assess supplier sustainability in service supply chains. However, the criteria related to the supplier’s Environmental Management and Technology Standards were found to be of a higher impact on performance while the competitiveness aspects of performance were found to be of higher importance compared to economic aspects. The implications of study findings on service supply chains were discussed along with study limitations and directions for future research.

Keywords:

• Sustainability
• green sourcing
• supplier selection
• service supply chain
• Structural Equation Modelling

1. Introduction

Supplier assessment and selection is a key function in the sourcing process. Its substantial operational and financial implications have made it a challenge to many purchasing, procurement and supply chain managers (Swink et al. 2013). As outlined by Chen et al. (2007), it contributes to a business maintaining a strategically competitive advantage, building partnerships with suppliers, and supporting sustainable performance. This is particularly important with the increased level of outsourcing in local and global firms and the growing number of suppliers. As discussed in Sarkis, Zhu, and Lai (2011), companies nowadays have more options to choose from and the emphasis of the sourcing decision has shifted to ensure quality, variety and fast delivery of supplies at low cost.

Furthermore, companies are currently under an increasing pressure to integrate sustainability into their supply chains including upstream tiers (i.e. the suppliers) (Ageron, Gunaskaran, and Sapalnzani 2012; Handfield et al. 2002). This is mainly motivated by measures of economics and cost-effectiveness (e.g. reduced energy and wastes), environmental regulations (e.g. reduced emissions rates and pollutants) and social responsibility (e.g. better impact on stakeholders and communities). These three sustainability aspects are typically referred to the commonly used Triple Bottom Line (TBL) model (Elkington 1997), which is a key initiative to support the Sustainable Supply Chain Management (SSCM) within an organisation.

Krause, Vachon, and Klassen (2009) emphasised that the sustainability across a supply chain is dependent on the sustainability of each and every chain link including upstream suppliers and downstream distributors and retailers. As noted by Leppelt et al. (2013), businesses nowadays tend to go beyond their organisational boundaries to manage their sustainability standards. Consequently, sustainable supplier selection has become a central component in sourcing decisions and the management of a sustainable supply chain in general. As discussed in Faisal et al. (2013), this is mainly driven by legal obligations, competitiveness, corporate responsibility and brand image.

Key manufacturing organisations have started implementing sustainability standards across their upstream tiers (e.g. Ford, Nike, Sony and Adidas). Example studies on incorporating sustainability into supplier evaluation and selection include chemical industry (Foerstl et al. 2010), automotive industry (Koplin, Seuring, and Mesterharm 2007), aerospace industry (Gopalakrishnan et al. 2012) and steel industry (Faisal et al. 2013). However, this is still not the case with service industries such as hospitality, health care and banking where less attention has been paid to supplier sustainability. This is mainly due to the nature of service systems (Sengupta, Heiser, and Koll 2006). As noted by Sampson (2000), the sustainability interaction with services in general is different from that with manufacturing. For example, in manufacturing, green practices typically have a direct interaction with products and processes while in services, green practices focus more on environmental management, health and safety management (HSM) and social responsibility (Fu, Zhu, and Sarkis 2012). This reflects on the sustainability practices across the service supply chain.

Reviewed literature shows a scarcity of studies that assess supplier sustainability and its impact on the performance of supply chains in service firms. Good body of literature has been published on supplier sustainability (e.g. Brandenburg et al. 2014; Gimenez and Tachizawa 2012; Sarkis and Dhavale 2015) but few publications have focused on service supply chains (e.g. Hussain, Khan, and Al-Aomar 2016; Song et al. 2016). The focus of the study on service supply chains stems from its growing and pivotal role in economic development (Brechbühl 2004). The reviewed previous work on supplier selection has addressed the major aspects of sustainability (see e.g. Foerstl et al. 2015; Malik, Abudullah, and Hussain 2016; Meixell and Luoma 2015; Seuring 2011). However, the majority of this research is directed to manufacturing firms with emphasis on environmental and social aspects of sustainability. Furthermore, there is a lack of empirical studies that address the impact of supplier sustainability on service supply chains. Previous work has emphasised the identification of sustainability criteria with less attention being paid to assess the impact of selected criteria on the performance of supply chain (e.g. Schaltegger and Burritt 2014).

This paper contributes to these two research gaps in the body of literature (i.e. developing an assessment model for sustainable supplier selection and investigating the impact of model variables on the performance of service supply chains). To this end, a structured literature review was conducted and five sustainability criteria or latent variables (i.e. Environmen Management, Social Responsibility, Green Products, Technology Standards and Health and Safety Management) were identified to assess supplier sustainability. The details and explanations of deriving these particular sustainability aspects from literature are discussed in Section 2.2 and summarised in Table 1. The relevancy of the literature-based criteria to service supply chains was also validated through the expert opinion of managers and senior managers of supply chains from health care, airline and hospitality industries. The model also links the identified criteria to the competitiveness and economic performance of service supply chains. An empirical study of a large sample of service companies in the UAE was conducted to collect data and validate the fitness of the proposed model to service supply chains. Structural Equation Modelling (SEM) was used to analyse the empirical data and derive conclusions. The practical and theoretical implications of the results were also discussed.

Table 1. Elements of sustainable supplier selection framework.

Assessment criteria	Supporting literature	Measurement indicator	Relevancy to service supply chain
Sustainable supplier selection
Environment Management (ENM)	Seuring et al. (2008), Carter and Rogers (2008), Lieb and Lieb (2010), Youn et al. (2013)	Pollution control Resource consumption Environmental certification	Reduced supply chain emissions and consumption wastes Regulatory Conformance
Social Responsibility (SR)	Handfield et al. (2002), Valiente, Ayerbe, and Figueras (2012), Ching and Moreira (2014), Carter and Jennings (2004), Hutchins and Sutherland (2008)	Employment practices Local communities influence Stakeholders influence	Improved work environment Positive social impact Successful collaboration an partnerships
Green Products (GP)	Carter and Easton (2011), Rao and Holt (2005), and Malik, Abudullah, and Hussain (2016)	Recycling policy Green packaging Green transportation	Higher recycling rates Less material wastes Less transportation costs
Technology Standards (TS)	Lu, Wu, and Kuo (2007), Carter and Rogers (2008), Valiente, Ayerbe, and Figueras (2012), Akamp and Müller (2013), Govindan et al. (2015)	R&D capability Technology level Design capability	Innovative products and services High-Tech processing Green technology
Health and Safety Management (HSM)	Shrivastava (1995), Handfield et al. (2002), Carter and Rogers (2008), Weingarten et al. (2016)	Health and safety practices Health and safety certification Health and safety incidents	Improved health and safety practices Safety and health assurance and regulatory conformance Less health and safety incidents
Performance of service supply chain
Performance aspect	Supporting literature	Performance measures	Relevancy to service supply chain
Competitiveness Performance (CMP)	Rao and Holt (2005), Reuter et al. (2010)	Sales and capital investments Operating expenditures New market opportunities	Improved sales and healthier financial status Reduced operating cost Expansion and growth
Economic Performance (ECP)	Orlitzky, Schmidt, and Rynes (2003), Inoue and Lee (2011), Wu and Shen (2013), Cornett, Erhemjamts, and Tehranian (2014)	Efficiency Quality Customer Satisfaction	Effective operations Quality products and services Higher retention rates and loyalty

The remainder of this paper is structured as follows; Section 2 provides a literature review, Section 3 presents the proposed assessment model, Section 4 presents the research plan of the empirical study, Section 5 presents the results of data analyses, and Section 6 offers a discussion of results and practical implications with directions for future research.

2. Literature review

Current literature (theoretical and empirical) confirms that companies have progressively expanded the scope of their supply chain management approach to include the sustainability aspects of the TBL model (i.e. environmental, economic and social) (Elkington 1997; Gimenez and Tachizawa 2012; Hacking and Guthrie 2008). A good body of this research is focused on explaining, modelling, and analysing the structure, the criteria and the impacts of SSCM (Carter and Easton 2011). The research also outlined the initiatives taken by the firms in order to meet the supplier sustainability challenges, such as green sourcing (Turner and Houston 2009), environmental purchasing (Carter, Kale, and Grimm 2000) and socially sustainable supplier selection (Ehrgott et al. 2011).

Researchers also agree that the supplier sustainability is a key driver of the overall supply chain sustainability. For example, Foerstl et al. (2015) outlined the different drivers of sustainability and highlighted the importance of supplier sustainability. It is also agreed on that the impacts of supplier sustainability practices across the supply chain are heavily dependent on the supplier selection process (sourcing decisions). However, as outlined by Lu et al. (2007), there are relatively fewer theoretical or empirical studies that consider environmental criteria in the supplier selection process than those focused on the traditional criteria of supplier selection such as cost, quality, delivery reliability, and financial stability. The gap is more evident in the context of service supply chains. This section summarises the current research on supply chain sustainability and sustainable supplier selection (criticality, methods and impact on performance).

2.1. Sustainability in service supply chains

The significance of the service sector is growing rapidly within the emerging and developing economies. Such growth has motivated researchers to pay more attention to the effectiveness and the sustainability of service supply chains. However, and as discussed in Sengupta, Heiser, and Koll (2006), service supply chains possess different characteristics from manufacturing supply chains. Similarly, He et al. (2016) analysed the forward and the corresponding reverse network of service supply chains and confirmed that the service supply chains are heterogeneous and the interest in the reverse service supply chain is more recent than manufacturing. Ellram, Wendy, and Corey (2004) also highlighted that the value chain processes of service firms are much less standardised compared to those of typical manufacturing firms and service value chains display significant variations between and across sectors.

Recently, Song et al. (2016) confirmed through an empirical study of Chinese service industry that strategic interaction among different parties is indeed critical to create value in a service supply chain (sustainability being one form of strategic interaction). As discussed in Malik, Abudullah, and Hussain (2016), the theory and sustainability criteria of TBL model also fit the service supply chain. Indeed, sustainable sourcing decisions may be of a higher impact in service supply chains. This is mainly due to the higher involvement of customers (buyers) in service supply chains than that in manufacturing (Vargo and Lusch 2008). Further details on the characteristics and sustainability practices in a service supply chain can be found in Høgevold, Svensson, and Padin (2015).

2.2. Sustainable supplier selection

Supplier selection is an important operational, financial and strategic challenge for incorporating sustainability into service supply chains. In addition to its environmental and social impacts, it has a high financial leverage as it impacts the firm’s economical and operational performance objectives in terms of profitability, reliability and flexibility. It is also the foundation of any subsequent supplier development and buyer–supplier collaboration (Gold and Awasthi 2015; Sancha, Longoni, and Gimenez 2015). Part of supplier selection involves supplier evaluation and ranking across multiple dimensions (Sarkis and Talluri 2015). These dimensions tend to increase geometrically when sustainability is to be considered (Bai and Sarkis 2010a).

As outlined by Keskin, İlhan, and Özkan (2010), supplier selection decisions are in most cases complex multi-criteria problems. Thus, several researches in this field have applied multi-criteria decision-making methods, such as analytic hierarchy process (AHP), analytic network process (ANP), data envelopment analysis (DEA) and mathematical programming. For example, Bai and Sarkis (2010b) analytically evaluated green supplier selection using rough set theory. Faisal et al. (2013) proposed an ANP model to integrate sustainability into the supplier selection for a steel producer. Punniyamoorty, Mathiyalagan, and Lakshmi (2012) developed a composite model using SEM and AHP for the selection of suppliers. Dai and Blackhurst (2012) presented a four-phase AHP-QFD approach for sustainable supplier assessment. Sharma and Balan (2013) presented an integrative supplier selection model using Taguchi loss function, TOPSIS and goal programming. In such models, and as pointed out by De Boer, Labro, and Morlacchi (2001), the supplier selection process includes a qualitative phase for structuring the overall supplier selection problem and defining the selection criteria and a quantitative phase for preselecting suitable candidates and making a final decision. Neumüller, Lasch, and Kellner (2016) presented a summary of several methodologies and tools used for integrating sustainability into the supplier selection process.

In recent research, several papers have explored and modelled sustainability across upstream tiers. In terms of model structuring, many of these models are minor extensions of conventional supplier selection models. Igarashi, de Boer, and Fet (2013) noted that a minor extension to the conventional supplier selection aimed at including some of the sustainable criteria is not likely to be effective. Instead, the organisation needs to actively cooperate with its suppliers for environmental objectives. Gimenez and Tachizawa (2012) provided a systematic literature review on the governance structures used to extend sustainability to suppliers. They concluded that both supplier assessment and collaboration are effective in improving sustainability. On the other hand, Akamp and Müller (2013) found that cooperative activities such as supplier development and supplier integration were effective but the supplier monitoring by quality, environmental or social audits did not have a positive influence on suppliers’ performance. Consequently, Lieb and Lieb (2010) suggested that third party logistics (3PL) providers need to be encouraged to develop and maintain an Environmental Management System (EMS) for green supplies and operations.

Researchers also highlighted the impact of supplier sustainability on sourcing decisions. For example, Thomas et al. (2016) confirmed that environmental and social sustainability practices of suppliers are indeed relevant sourcing considerations that influence the buying decision and ultimate supplier selection in a purchasing organisation. Jabbour and Jabbour (2009) analysed the green selection criteria of suppliers in Brazilian companies and concluded that a company with more advanced environmental management tends to adopt more formal procedures for selecting environmentally appropriate suppliers. Busse et al. (2016) explored several contextual barriers to supplier development for sustainability in global supply chains and outlined the managerial remedies to mitigate such barriers.

In terms of sustainability assessment framework, most researchers agree that the environmental, social and economic supplier characteristics of the TBL model are necessary considerations for effective sustainable supplier evaluation and selection (e.g. Malik, Abudullah, and Hussain 2016). Examples of TBL-based sustainable supplier selection models include Öztürk and Özçelik (2014) and Amindoust et al. (2012). Brandenburg et al. (2014) presented a brief on several other quantitative models used for sustainable supplier selection.

In terms of assessing the impact of supply chain sustainability on performance, there is a good body of research on that with limited studies dedicated to the performance impacts of sustainable supplier selection. For example, Ortas, Moneva, and Álvarez (2014) investigated the link between sustainable supply chain of a company and its financial performance. The study recommended to improve/develop the sustainability of suppliers in order to improve performance. Neumüller, Lasch, and Kellner (2016) presented a hybrid model of ANP and GP to align strategic supplier portfolio selection with corporate sustainability targets. They concluded that integrating economic, environmental and social targets into strategic supplier portfolio affects performance in terms of reducing supply risks and promoting the achievement of the sustainability goals of the purchasing company. Closs, Speier, and Meacham (2011) confirmed that managing suppliers in a sustainable manner through a rigorous sourcing and purchasing function can address economic, social and environmental aspects of firm performance.

However, there is a lack of studies targeting performance assessment of sustainable supplier selection in terms of competitiveness and economic effectiveness, especially within the context of service supply chains. This research gap is addressed in this paper by developing a supplier sustainability assessment model that integrates the key sustainability aspects of most relevancy to service supply chains. These categories/criteria are widely used in the supplier sustainability assessment frameworks and models in the reviewed literature. They are also likely to contribute to the performance of service supply chains. The following sections discuss the identified five criteria for sustainable supplier selection with a review of supportive literature.

2.2.1. Environment management

In business today, companies, including suppliers and purchasers, cannot ignore environmental issues across their supply chains. Increasing government regulation and stronger public mandates for environmental accountability have brought these issues into the executive suite, and onto strategic planning agendas. Consequently, a host of environmental measures to save energy, water and reduce wastes across the supply chain are being adopted by different service firms (Hussain and Malik 2016). For some companies, the environmental sustainability has become a business imperative because of the soaring energy and commodities costs. This applies to the supply chains of both manufacturing a service industries.

However, the adoption of green practices by suppliers (i.e. green sourcing) is a key driver for the sustainability of the supply chain in order for the firm to reduce costs and stay competitive. As highlighted by Turner and Houston (2009), green sourcing takes into account the environmental impact of a particular choice such as transportation, materials, energy source, or packaging design on the ecological footprint made by a product or services. Several other environmental management initiatives and certifications can qualify suppliers in green sourcing. The reviewed literature has also emphasised the need for the suppliers to adopt effective systems for pollution control and resource consumption and to attain an environment-related certification (e.g. Carter and Rogers 2008; Lieb and Lieb 2010; Seuring et al. 2008; Youn et al. 2013).

2.2.2. Social responsibility

Social responsibility has been also considered as an important pillar of the TBL model that applies to all partners of service supply chains including suppliers. However, the socially responsible activities may vary across industrial sectors. For example, in an empirical study to measure Spanish firms’ corporate social performance, Valiente, Ayerbe, and Figueras (2012) concluded that the stakeholder pressure on the business to adopt a formal social responsibility strategy was linked to a firm’s size which ultimately contributed to their corporate social responsibility performance. The study found this to be particularly valid for the service sector such as the Spanish catering and commerce businesses.

Consequently, sustainable supply chains tend to require their suppliers to adhere to high Corporate Social Responsibility (CSR) standards. Some have started auditing the social responsibility initiatives of their key suppliers. Apart from a CSR strategy, Seuring and Müller (2008) highlighted the importance of management systems to facilitate the implementation of corporate sustainable initiatives. Based on Ching and Moreira (2014), the inclusion of social measures such as the codes of conduct that condemn work discrimination, freedom of association, diversity, health and safety, child labour, fair treatment and working hours in management systems are likely to ensure adherence to high CSR standards. Based on the reviewed literature, it was evident that the employment practices of suppliers, attention towards local communities and other stakeholders are deemed as the socially responsible practice that may influence the sustainable performance of a supply chain (e.g. Brown and Duguid 1991; Carter and Jennings 2004; Hutchins and Sutherland 2008).

2.2.3. Green products

Green products is a key green sourcing criterion that involves many challenging issues, starting from sourcing raw materials until even beyond delivering the final product to the end consumer. Instead of focusing on material prices, organisations keen on green products would focus more on other aspects such as sourcing sustainable materials with least environmental impacts, packaging them using recyclable materials with minimal waste and transporting them with low pollution effects. Such practice does not stop at delivering the final product to end consumers, it continues beyond that through recycling and clean disposal.

Sourcing environmentally friendly materials and parts from sustainable suppliers has multiple implications on the supply chain. For example, Carter and Easton (2011) and Al-Aomar and Hussain (2017) emphasised the importance of reduced packaging and effective design for reuse and recycling in sustainable suppliers. Furthermore, Miemczyk, Johnsen, and Macquet (2012) highlighted transportation-related activities including choice of means of transportation, fuel selections, vehicle mapping and reduced carbon emissions. This may also require the suppliers to develop a network for reverse logistics to take back products from point of consumption to the point of origin for the purpose of recapturing value or proper disposal (Kumar, Teichman, and Timpernagel 2012). The majority of literature has assessed the green products of suppliers in terms of having an effective recycling policy, a green packaging system and a green transportation system (e.g. Carter and Easton 2011; Malik, Abudullah, and Hussain 2016; Rao and Holt 2005).

2.2.4. Technology standards

Supplier’s technology standards are an important criterion for supplier selection, especially those related to green technology. A firm needs competent technical support from its suppliers to provide a consistently high-quality products or services. This is particularly important when the firm’s supply and technology strategy includes development of a new product or technology or access to proprietary technology. Technical criteria may motivate a firm to move into the global marketplace (Kahraman, Cebeci, and Ulukan 2003). Furthermore, suppliers are more likely to assume greater responsibility for outsourced design, engineering service, prototype development and research and development (Nazario et al. 2013).

The supplier’s technology standards and ability to provide the necessary technical assistance also affect the sustainability practices of the entire supply chain for both manufacturing and services (Malik, Abudullah, and Hussain 2016). Thus, the literature assesses the technology standards in terms of R&D capability of suppliers, the technology level of suppliers and the design capability of suppliers (see e.g. Akamp and Müller 2013; Carter and Rogers 2008; Govindan et al. 2015; Lu, Wu, and Kuo 2007; Valiente, Ayerbe, and Figueras 2012).

2.2.5. Health and safety management

While the TBL view is a widely accepted academic description of sustainability, there have been studies that identify the concept of HSM as crucial to sustainable operations (in terms of reduced hazards and risks across the supply chain caused by supplies and suppliers). Zsidisin, Panelli, and Upton (2000) extended the view of suppliers’ risks to include any potential occurrence of an inbound supply incident that might lead to the inability to meet customer demand. Carter and Rogers (2008) integrated the risks associated with the typical supply chain activities such as the operations, sourcing and distribution activities with the risks associated with the workers, work place, public safety and environmental hazards have to be managed for a sustainable supply chain. The majority of reviewed literature has emphasised the HSM of suppliers in terms of the supplier’s health and safety practices, health and safety certification and health and safety incidents (e.g. Carter and Rogers 2008; Handfield et al. 2002; Shrivastava 1995).

2.3. The performance a service supply chain

The ultimate goal of incorporating sustainability into supply chains is to positively impact performance. However, the literature shows a considerable variation in the performance measures chosen to assess the impact of sustainable supply chain practices on the success of a business. For example, Horváthová (2010) investigated the inconsistencies in the studies that reported the relationship between environmental practices and financial performance. Similarly, Fujii, Iwata, and Kaneko (2013) observed that the practices leading to improved environmental performance are significantly and positively related to the financial performance of the Japanese manufacturing firms. Golicic and Smith (2013) found that only 46 of the 77 empirical studies have reported a significant positive relationship between the environmental supply chain practices and a firm’s performance, whereas the remaining 33 studies showed a negative or non-significant association. Rao and Holt (2005) investigated the linkages between green supply chain management and the economic performance and competitiveness of supply chains amongst a sample of companies in South East Asia. They confirmed that greening the different phases of the supply chain leads to an integrated green supply chain, which ultimately leads to competitiveness and economic performance. Studies such as Orlitzky, Schmidt, and Rynes (2003), Inoue and Lee (2011), Wu and Shen (2013) and Cornett, Erhemjamts, and Tehranian (2014) have focused on the financial performance of a firm while considering environmentally and socially responsible practices. Other measures such as market-based and operational performance indicators were also used (Cho et al. 2012).

Based on the reviewed literature, it was evident that the majority of the studies have only focused, directly or indirectly, on the economic dimension of the sustainable supply chain (see e.g. Klassen and McLaughlin 1996; Seuring 2011; Seuring and Müller 2008; Wu, Chuang, and Hsu 2014). A comprehensive performance measurement system is, therefore, needed to assess the sustainable performance of a supply chain. Schaltegger and Burritt (2014) provided a structured overview of sustainability performance measurement and management literature and discussed its frameworks and approaches. Taticchi, Tonelli, and Pasqualino (2013) also provided a critical literature review of sustainable supply chain performance measurement. Soosay, Fahimnia, and Sarkis (2014) provided and approach with multidimensional indicators to frame the supply chain performance in terms of the three TBL aspects.

The primary question in this regard is ‘whether sustainable supplier selection increases the competitiveness of the service supply chains’? This study contributes to existing literature by linking the supplier sustainability to the service supply chain performance in terms of economic and competiveness. The three measures selected to assess the supply chain’s economic performance include sales and capital investments, operating expenditures and new market opportunities. For supply chain competitiveness, selected measures include efficiency, quality, and customer satisfaction. These measures are commonly used in the reviewed literature. Consequently, the supplier selection decisions within the context of service supply chains will not be only based on the supplier sustainability practices but it will be extended to the impact of such sustainability practices on the supply chain performance.

3. Model variables and hypothesis

The main criteria of supplier sustainability and the corresponding performance measures in the proposed model were derived from the reviewed literature and validated by industry experts. At each sustainability criterion, a specific set of supplier requirements were identified. Table 1 summarises these criteria, the supporting literature, the selected supplier requirements and the relevancy to the service supply chain. Table 1 also presents the selected corresponding performance measures at each performance aspect along with the supporting literature and the relevancy to the service supply chain.

In addition to the reviewed literature in Table 1, the constructs of sustainability assessment criteria of Environment Management (ENM), Technology Standards, and Green Products have been emphasised in the work of Lee et al. (2009). The constructs of Social Responsibility and HSM have been adapted from the work of Bai and Sarkis (2010a). The selected measures for economic performance and competiveness have been emphasised in the work of Rao and Holt (2005). Furthermore, the content validity of the selected constructs and measures were verified through face-to-face interviews with supply chain managers of three service companies. Consequently, some of items were removed and adjusted to fit the scope of this study. Figure 1 presents the final construct of the proposed conceptual model in this research. The model includes seven latent variables and each latent variable comprises of a number of constructs. Five latent variables are related to the selected assessment criteria of supplier sustainability and two are related to the performance of service supply chain. Arrows represent the hypothesised impact of one variable on another.

Figure 1. Conceptual model of sustainable supplier selection.

Based on Figure 1, five research hypotheses have been developed to verify the fitness of the proposed conceptual model to assess the performance of service supply chains. The first hypothesis indicates that ENM practices of suppliers have positive influence on Service Supply Chain (SSC). The hypothesis is stated as follows:

H1: ENM practices of suppliers positively impact SSC

The three indicators (constructs) that are used to assess the supplier’s ENM are:

• ENM1: Pollution control system of suppliers

• ENM2: Resource consumption system of suppliers

• ENM3: Environment-related certification of suppliers

The second hypothesis explores the relationship between the suppliers’ Social Responsibility (SR) and SSC. The hypothesis is stated as follows:

H2: SR initiatives of suppliers positively impact SSC

The three indicators (constructs) that are used to assess the supplier’s SR are:

• SR1: Employment practices of suppliers

• SR2: Local communities influence of suppliers

• SR3: Stakeholders influence of suppliers

The third hypothesis explores the relationship between the suppliers’ Green products (GP) (SR) and SSC. The hypothesis is stated as follows:

H3: GP of suppliers positively impact SSC

The three indicators (constructs) that are used to assess the supplier’s GP are:

• GP1: Recycling policy of suppliers

• GP2: Green packaging system of suppliers

• GP3: Green transportation system of suppliers

The fourth hypothesis explores the relationship between the suppliers’ Technology Standards (TS) and SSC. The hypothesis is stated as follows:

H4: TS of suppliers positively impact SSC

The three indicators (constructs) that are used to assess the supplier’s TS are:

• TS1: R&D capability of suppliers

• TS2: Technology level of suppliers

• TS3: Design capability of suppliers

Finally, the fifth hypothesis explores the relationship between the suppliers’ HSM systems and SSC. The hypothesis is stated as follows:

H5: HSM practices of suppliers positively impact SSC

The three indicators that are used to assess the supplier’s HSM are:

• HSM1: Health and safety practices of suppliers

• HSM2: Health and safety certification of suppliers

• HSM3: Health and safety incidents of suppliers

The second element of the developed model is related to the impact of supplier sustainability on the performance of service supply chain. As shown in Figure 1, the service supply chain performance is assessed using two main categories (aspects); Economic Performance (ECP) and Competitiveness Performance (CMP). The impact of the five stated hypotheses on the performance of service supply chains is tested based on both ECP and CMP.

The three measures that are used to assess the service supply chain’s ECP are:

• ECP1: Sales and capital investments

• ECP2: Operating expenditures

• ECP3: New market opportunities

The three measures that are used to assess the service supply chain’s CMP are:

• CMP1: Efficiency

• CMP2: Quality

• CMP3: Customer Satisfaction

4. Study plan

The key objective of this research is to model and assess the impact of sustainable supplier selection on the performance of service supply chains. To this end, a study plan of three main stages was adopted including conceptual model development (Figure 1), empirical data collection from service companies and analysis of collected data using SEM. Primary data of the empirical study have been collected through a survey distributed to a large sample of service companies in the UAE. Secondary data used to structure the conceptual model was derived from literature review of sustainable supplier selection. This section presents the details of the study plan.

4.1. Survey instrument

The survey instrument is divided into 8 sections with a total of 21 indicators covering the 7 latent variables shown in Figure 1. Section 1 of the survey deals with the demographic questions and the remaining seven sections are related to the seven latent variables of the study. Specific items are used in the survey to assess the seven latent variables. The survey instrument is designed so that assessments are made by the survey administers based on a 1–5 Likert scale. Before finalising the questionnaire, content validity, face validity and pilot testing were performed for all the items contained in the questionnaire. The initial version of the survey instrument was experimented through interviews and face-to-face discussions with the senior managers of operations, supply chain, procurement and strategic management departments of five service firms. As a result, several items were rephrased, included and excluded from the questionnaire. The validity and reliability of the proposed survey instrument as well as the unidemensionality of the items in the Likert scale have been tested using different statistical techniques. The distributed survey tool questionnaire is included in Appendix A.

4.2. Sample selection

Service supply chains (e.g. Banks, Hospitals, Airlines, etc.) in UAE were the target population. Purposive sampling technique was used for the collection of data. The population comprised of individuals at middle and top management levels and was limited to those working in operations, supply chain, procurement and strategic management departments of these organisations. A total of 350 questionnaires were floated to individuals from the target population at 15 service supply chains. Initially, one week time was allocated for the replies, however, response rate was lower and a gentle reminder was sent after one week. Overall, it took two and half weeks to collect all the responses. Most of the questionnaires were self-administered, in order to attain high response rate. Out of 350 questionnaires, 82 were not returned and 13 were not valid due to incomplete data and, therefore, were excluded from the analysis. Hence, a total of 255 respondents were considered as the sample for the study. A descriptive summary of the study respondents is depicted in Table 2.

Table 2. Descriptive Statistics of the Respondents.

	Frequency	Percentage
Gender
Male	161	64.4
Female	94	36.6
Qualification
Bachelor degree	96	37.6
Master degree	132	51.8
Other	27	10.6
Department
Operations	59	23.1
Supply Chain	48	18.9
Procurement	117	45.9
Strategic Management	31	12.1
Nature of organisation
Bank	83	32.5
Hospitals	97	38.0
Airlines	75	29.5
Experience
0–5	47	18.4
6–10	137	53.7
Above 10	66	25.9
Job title
Top	91	35.7
Middle	164	64.3

4.3. SEM analyses

SEM refers to a diverse set of mathematical models and statistical methods that fit networks of constructs to data. SEM mainly includes Confirmatory Factor Analysis (CFA), path analysis, and multiple other types of analytical methods. CFA is typically used to validate the fitness of a conceptual model and path analysis is used to test model hypotheses. In this research, CFA was used to validate the proposed supplier sustainability assessment model (Figure 1) and path analysis was used to assess the scales and the relationship among different latent variables in the model (five sustainable supplier selection criteria and two performance aspects). Details and examples of SEM applications can be found in Westland (2015), Tabachnick and Fidell (2012), and Brandenburg et al. (2014). A software tool (AMOS 16) has been used for data analysis. Figure 2 presents a diagram of data analysis and SEM techniques used in this research.

Figure 2. Flowchart of data analysis and SEM techniques.

5. Results of data analysis

A total of 21 items of instrument measures (indicators) were used to assess the seven constructs (latent variables) in the model analysis. Typically, a minimum of two indicators is required for measuring each latent variable in the construct (Hussain, Khan, and Al-Aomar 2016). The study used three measures for each latent variable. Each indicator represents the mean of survey responses of the items pertinent to that indicator. The following sections summarise the results of data analyses.

5.1. CFA results

CFA is a validation tool of the proposed model. As shown in Figure 2, CFA steps includes model reliability, validity (unidimensionality, convergent and discriminant) and model fitness.

5.1.1. Measures reliability

The degree of consistency of a measure is referred to as its reliability. The reliability coefficient, Cronbach’s, α is generally used to test the reliability of a scale. α values of 0.70 or greater are deemed to be indicative of good scale reliability (O’Leary-Kelly and Vokurka 1998). As it can be seen from the results in Table 3, the Cronbach’s α for the seven latent variables of the model range from 0.75 to 0.95. These results reflect a reliable and consistent scaling by respondents and suggest that the theoretical constructs exhibit good psychometric properties.

Table 3. Results of the Reliability Test.

Variables	No. of Items	Cronbach’s alpha
Environment management	3	0.81
Social responsibility	3	0.95
Green product	3	0.76
Technology standards	3	0.87
Risk management	3	0.79
Economic performance	3	0.82
Competitiveness	3	0.75

5.1.2. Unidimensionality

The assessment of the observed variables, i.e. measurement items, often reflects the unobserved (latent) variables in the studied construct. The proposed model has a total of 7 latent variables (constructs) and CFA was used to determine whether the 21 indicators of these variables have adequately measured their constructs. Measuring the degree to which items in the survey questionnaire (the 3 indicators of each construct) measure the same construct is referred to as unidimensionality. Empirical evidence of unidimensionality in CFA is generally assessed using criteria such as the comparative fit index (CFI), squared multiple correlations (R²), the significance of parameter estimates (factor loading), and goodness-of-fit statistic (GFI). Table 4 summarises the results of these tests.

Table 4. Assessment of Goodness-of-Fit Statistics for CFA.

Factors and indicators	CFI	Factor loading	R^2
A. Environment management	1.00
ENM1. Management considers Pollution controls system of suppliers		0.94	0.89
ENM 2. Management considers Environmental management system of suppliers		0.90	0.81
ENM3. Management considers Environment-related certificates of suppliers		0.89	0.79
B. Social responsibility	1.00
SR1. Management considers Employment practices of suppliers		0.90	0.83
SR2. Management considers Local communities influence of suppliers		0.92	0.84
SR3. Management considers Other stakeholder influence of suppliers		0.91	0.81
C. Green product	1.00
GP1. Management considers Recycling policy of suppliers		0.85	0.72
GP2. Management considers Green packaging system of suppliers		0.87	0.75
GP3. Management considers Ability to alter process and product of supplier for reducing the impact on natural resources		0.88	0.76
D. Technology standards	1.00
TS1. Management considers Capability of R&D of suppliers		0.89	0.81
TS2. Management considers Technology level of suppliers		0.90	0.82
TS3. Management considers Capability of design of suppliers		0.91	0.78
E. Health and Safety Management	1.00
RM1. Management considers Health and safety practices of suppliers		0.70	0.49
RM2. Management considers Health and safety incidents of suppliers		0.79	0.61
RM3.Management considers Risk management certifications of suppliers		0.63	0.51
F. Economic performance	1.00
ECP1.Sustainable practices significantly improve sales and market share		0.92	0.85
ECP2. Sustainable practices significantly decrease Operating expenditures		0.94	0.87
ECP3. Sustainable practices significantly increase new market opportunities		0.80	0.65
G. Competitiveness	1.00
CMP1. Sustainable practices significantly increase Efficiency		0.88	0.78
CMP2. Sustainable practices significantly increase Quality		0.93	0.90
CMP3. Sustainable practices significantly increase Customer satisfaction		0.95	0.87

The CFI is the most commonly applied technique to compare proposed model with the base (null) model assuming lack of relationships between the measures. A CFI value that is greater than 0.90 indicates an acceptable fit to the data (Bentler 1995). It can be seen from the results in Table 4 that CFI values for the seven latent variables are 1.00, which suggests excellent model fitness. Squared multiple correlations (analogous to R²) is used in CFA to judge communality of the variables and indicate the percentage of variance in an indicator that is explained by a factor. As shown in Table 4, the standardised regression weights range from 0.49 to 0.90 (the majority are above 0.7), which can be considered as significant. Table 4 also confirms that the factor loadings, which are the standardised regression weights, are satisfactorily high. (Values range from 0.63 to 0.95 and the majority are above 0.80). This confirms that a significant degree of calculated variable’s variance is provided by its latent construct. Hence, all of the seven constructs of the latent variables have good fit and thus are unidimensional.

5.1.3. Convergent validity

Convergent validity is a measure of construct validity as it scrutinises the degree to which measures of the construct are related to each other. It is commonly evaluated by the Bentler–Bonett’s Normed Fit Index (NFI). Bentler (1995) found that NFI values of 0.90 or above are considered statistically fit index. An examination of Table 5 shows that the values of NFI are greater than 0.95 which reveals that all seven model variables are related to the same proposed model for assessing the impact of sustainable supplier selection on the performance of service supply chains.

Table 5. Scale validity analysis.

Variables	Convergent validity	Discriminant validity
Environment management	0.97	0.42
Social responsibility	0.95	0.58
Green product	1.00	0.51
Technology standards	1.00	0.47
Health and safety management	0.98	0.53
Economic performance	0.95	0.57
Competitiveness	0.98	0.48

5.1.4. Discriminant validity

Discriminant validity tests whether different variables and their indicators are distinct and unrelated to each other (Bagozzi, Yi, and Phillips 1991). There are different methods for calculating discriminant validity. In this paper, it is calculated by comparing the Cronbach’s alpha of a latent construct to its mean correlations with other model latent variables. If the alpha value of a latent construct is adequately higher than the mean of its correlations with other variables then there is a signal of discriminant validity (Ghiselli, Campbell, and Zedeck 1981). Table 5 shows that the difference between the Cronbach’s alpha value of every latent scale and the mean correlation of each latent scale with the other scales is sufficiently high and this provides an evidence that the scale does not correlate with other conceptually distinct constructs.

5.2. Assessment of model fit

Finally, the conceptual model was tested to investigate the relationships among the latent variables. Six model fit indices (χ²/df, GFI, AGFI, NFI, CFI and RMSEA) were used to test the fitness of the proposed model (Fotopoulos and Psomas 2010). The values of these indices of model fitness are itemised in Table 6. Results show that all the goodness-of-fit statistics are in the acceptable range.

Table 6. Summary Statistics of the Model Fitness Indices.

Index	Suggested value	Observed value
Chi-square/degrees of freedom	≤3.00	2.19
GFI	≥0.90	0.92
AGFI	≥0.80	0.85
NFI	≥0.90	0.91
CFI	≥0.90	0.95
RMSEA	≤0.08	0.06

Notes: GFI = goodness-of-fit index; AGFI = adjusted goodness-of-fit index; NFI = normed fit index; CFI = comparative fit index; RMSEA = root mean square error of approximation.

5.3. Hypothesis testing

Hypothesis testing was performed through SEM path analysis to quantify the relationships amongst model variables. Figure 3 shows the results of the standardised regression weights on the paths of the conceptual model. As shown in Figure 3, the five latent variables for were found to be valid supplier sustainability assessment criteria with positive impact on supply chain performance. The results for the first two hypotheses (H1 and H2) showed that ENM and Social Responsibility can be confirmed to have positive impact on the performance of service supply chains. These two criteria are commonly used for assessing supplier sustainability since they are directly related to the two main aspects of the TBL model (Environmental and Social). The other three assessment criteria (sustainable supply chain antecedents) were also verified based on hypotheses H3, H4 and H5, respectively. Consequently, Green Products, Technology Standards and HSM were also confirmed to have positive impact on the performance of service supply chains. The factor loadings weights in Figure 3 also show that ENM has the highest impact (γ = 0.52) on the performance of the service supply chain followed by Technology Standards (γ = 0.43). Both criteria had a higher significance with p < 0.01. Green products (γ = 0.35), HSM (γ = 0.23), and Social Responsibility (γ = 0.21) criteria had a lower significance with p < 0.05.

Figure 3. SEM model results.

Notes: **p< 0.01, *p < 0.05.

In terms of performance, and as shown in Figure 3, the five latent variables directly impact the performance of service supply chain at various levels. The performance of service supply chain was treated as a second order construct consisting of two dimensions (Economic ECP and Competitiveness CMP). However, results indicated that Competitiveness (γ = 0.36) was found by the respondents to be more important as a performance aspect than Economic Performance (γ = 0.24). This implies that supply chain managers in the surveyed service companies were more concerned with the three competitiveness measures (Efficiency, Quality, and Customer Satisfaction) when assessing the sustainability of suppliers. The p values obtained for both ECP and CMP reveal that both performance aspects have significant impact. Finally, and although the competitiveness measures are more operational, they can be evidently linked to supply chain revenues and economics.

6. Results discussion and implications

This study has addressed the issue related to the assessment of supplier sustainability in service supply chains and its impact on the supply chain performance. This issue is receiving a growing attention as researchers and practitioners emphasise the need to extend sustainability practices to suppliers and capitalise on their supply chain performance. It also contributes to a research gap in integrating the assessment criteria of supplier sustainability and measuring their impact on service supply chain competiveness and economic performance. To this end, an assessment model was developed based on the reviewed literature and the model was validated through an empirical study of a large sample of service supply chains. The model has linked supplier sustainability to the competitiveness and economic performance of the service supply chains.

The proposed model includes five latent variables for sustainable supplier selection and two latent variables for performance impacts. Three indicators were used for measuring each model variable resulting in a total of 21 indicators. Empirical data were collected using a survey distributed to a large sample of respondents from major service companies in the UAE. The validity and reliability of the survey instrument have been measured and confirmed and SEM was used analyse the empirical data (CFA was used to validate the fitness of the conceptual model and path analysis was used to test model hypotheses). The CFA results confirmed the validity of the proposed assessment model (the constructs of the five latent variables for supplier sustainability assessment and the two latent variables for supply chain performance). Results of path analyses showed that the five latent variables in the construct have positive impact on the performance of service supply chain in terms of competitiveness and economic performance.

A thorough analysis of study results has revealed several implications on service supply chains and the role of supplier sustainability. These implications add to existing literature and open new directions for researchers. First, the fact that all the selected sustainability criteria (ENM, Technology Standards, Green Products, HSM, and Social Responsibility) were confirmed by CFA has two important implications. It first confirms the relevance of these criteria to the sustainability of service supply chains (i.e. the theoretical framework) and the validity of the proposed model for assessing supplier sustainability (i.e. the empirical assessments of service supply chains experts). These criteria were selected from the literature on sustainability in service supply chains, as outlined in Table 1, validated through experts’ opinion, and confirmed by CFA results in Table 4. Secondly, the positive impact of the selected criteria on the performance of service supply chain highlights the importance of extending sustainability practices to suppliers and encourages practitioners to use the model for assessing supplier sustainability.

Based on SEM results in Figure 3, the ENM of suppliers was confirmed with the highest correlation weight. This is in accordance with the findings of Malik, Abudullah, and Hussain (2016), where it has been found that the compliance to environmental regulations had the highest overall priority in health care organisations. The three indicators of this latent variable had a strong positive impact on the performance of service supply chains. Pollution control was the indicator with highest value of factor loading. This has reemphasised the environment protection and green operations as critical elements of suppliers’ sustainability measures (Horváthová 2010). Practically, the supplier’s environmental protection practices are increasingly receiving more attention in the industry. Green supplier selection criteria can consequently help lessen the environmental and legal risks across the supply chain in addition to increasing the competitiveness of supply chains.

Technology Standards and Green Products had the second level of correlation weights. The indicators of these two criteria have close values of factor loading. This finding is expected as service supply chains have been under an increasing pressure over the last decade to adopt green technology and standards and to supply green products and services (Ageron, Gunaskaran, and Sapalnzani 2012; Handfield et al. 2002). As such, several supply chains have turned to their suppliers and customers to find innovative solutions to green technology and environmental issues. This finding also shows that ‘Green Products’ has become an important driver for the sustainability of service supply chain similar to manufacturing firms. The finding should receive more attention from researchers as the impact of green products in service supply chains is not fully explored.

The third level of correlation weights was for Social Responsibility and the Health and Safety Management practices of suppliers. Respondents found these two criteria to be of a relatively low impact compared to the other aspects of supplier sustainability. Typically, these criteria will have a higher impact on the social aspects of the TBL model in line with Öztürk and Özçelik (2014) and Hacking and Guthrie (2008). Still, the industry experts have confirmed their positive impact on the competitiveness and economic aspects of the service supply chain but at a lower level. They have basically confirmed that a socially responsible supplier with excellent health and safety records can positively impact the overall supply chain performance. For example, as discussed in Closs, Speier, and Meacham (2011), socially responsible purchasing has proven implications on all elements of the service supply chain as service companies could be held responsible for social standards of their suppliers.

The performance of service supply chain was treated as a second order construct consisting of two dimensions (economic and competitiveness measures). SEM results showed that both performance aspects were positively impacted by the five assessment criteria in the proposed model. However, the Competitiveness of service supply chain was found by respondents to be more important than the Economic performance. This was quiet intuitive since the three competitiveness measures (Efficiency, Quality and Customer Satisfaction) assess the operational effectiveness of the service supply chain. These operational measures were emphasised by supply chain managers and specialists in the surveyed service supply chains as means to improve competitiveness. This explains why service supply chains, including hospitals, banks, airlines, etc., were keen on developing their suppliers to adopt sustainable practices (Song et al. 2016). The economic measures (sales, expenditure, and growth) were viewed by respondents as macro-level indicators that typically concern the top management of service organisations (Ortas, Moneva, and Álvarez (2014). Researchers can further investigate the link between of the operational measures of competitiveness and the economic health of service supply chains.

6.1. Managerial implications

The supply management is a critical managerial function to an organisation’s success. In addition to the suppliers’ impact on overall business performance, the cost of supplied items or services accounts for a high proportion of the total cost structure. The amount an organisation spends on the purchased materials and services is typically 50–70% of the value of sales which further establishes the significance of supplier selection decision and the overall supply management process (Van Weele 2009). Therefore, effective management of sourcing decisions including sustainable supplier selection is nowadays essential to assist organisations (manufacturing and service) in meeting their strategic objectives.

A key driver of effective sourcing that was addressed in this paper is related to the impact of supplier sustainability on the performance of service supply chains in terms of economic and competitiveness measures. This is particularly important to engineering, practitioners, and supply chain managers as most current managerial practices for supplier selection either do not take sustainability into account. This is mainly due to the lack of valid models for sustainability assessment. This could result in economic losses and efficiency problems across the supply chain along with negative environmental and social impacts.

This paper has, therefore, proposed an empirically validated model that can be used by supply chain managers to assess supplier sustainability based on a set of criteria that are most relevant to services and have direct impact on the performance of service supply chains. Since the model was validated using data from a large sample of service firms, practitioners and managers can utilise the 21 indicators of model to assess suppliers’ sustainability and measure its economic and operational impacts. The developed model can be also used by managers to build a sustainable supply base using specific supplier practices and data.

Still, however, sustainable supplier selection procedures need to be complemented by developing long-term collaborative business relationships with the suppliers of service firms. In some cases, supply chain managers need to work with their suppliers to develop their sustainability awareness and practices. Also, supply chain managers need to be aware of the performance impacts of supplier sustainability on key economic and competiveness measures such as price, quality, delivery speed, availability, innovation and customer service. Such competiveness drives the long-term organisational strategy in maintaining economic benefits along with positive environmental and social impact. Managers can, therefore, use the model design performance evaluation system for different supply chain partners.

Finally, the developed model is expected to be more acceptable to managers who strive to reduce the multiples risks of supplier selection and improve the sustainability measures for their service supply chains. Thus, the proposed model may be enhanced by combining previous performance measures and new observational data to implement a systematic data acquisition process or a decision-support system. Ultimately, a clear support and buy-in by management will be needed to achieve effective implementation of the proposed model.

6.2. Limitations and directions for future research

Supply chains are increasingly realising the need to identify sustainability drivers in order to satisfy regulatory requirements and remain competitive. Sustainable supplier selection is a key driver of sustainability across the supply chain. However, the literature on sustainable supplier selection within the context of service supply chains was found to be relatively limited. In addition, the literature has two main gaps; the majority of supplier sustainability assessment models/frameworks were not validated for construct and relevancy and the impact of assessment criteria on supply chain performance (in terms of both economic and competitiveness measures) was not empirically confirmed. The gap is more evident in the supply chains of service industries in the UAE market where the service sector accounts for approximately three-quarters of the economy’s output. This study has contributed to existing literature by addressing these two gaps through an empirical study in the UAE service sector.

However, there are some limitations related to the structure of the proposed model and the study context. The model was developed by integrating five main assessment criteria based on an intensive literature review and the opinion of a sample of service industry experts in UAE. One may argue that the selected criteria may not be comprehensive for assessing supplier sustainability across the wide spectrum of service sector. Furthermore, the selected criteria (i.e. their construct and performance impacts) were validated through SEM analyses. However, the data used in SEM was collected from a sample of service companies in the UAE market. One may, therefore, challenge the value and generality of study findings based on the context and representativeness of collected data.

Despite these limitations, the methodical contribution of the study and the structure of the proposed model can be valuable to researchers and practitioners. The proposed methodology can be used to include other sustainability criteria and to expand the model structure and broaden its applications. Model criteria and performance measures can be also adapted to consider the requirements of particular service industries. The indicators within each criterion can be also expanded to increase the breadth of assessment for supplier sustainability. Comparison studies can be then conducted to compare the model performance when applied to assess supplier sustainability in various industries. Similarly, the supply chain performance in the study was only focused on economic and competitiveness aspects. For example, it did not measure social and environmental aspects as expected in the TBL model. Both aspects were included as independent variables (supplier selection criteria) in the model. Future studies can, therefore, incorporate social and environmental aspects as performance measures of the supply chain as they apply to both suppliers and buyers. In addition, the proposed model can be further expanded and investigated using higher order or multilevel methods.

In terms of study context and representativeness, the selected sample was relatively large with a broad coverage of service companies in UAE (banks, hospitals, hotels, etc.). Collecting data from different contexts may potentially lead to different results of correlation weights and tested hypotheses. However, as the majority of selected service companies were entities of international firms, the relationships established using SEM can be still tested for other regions around the world. Consequently, the methodical contribution of this paper in terms of the proposed model and the SEM analyses can be still used by researchers and practitioners in different contexts to support sustainable supplier selection globally.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Matloub Hussain is an associate professor of operations management in the College of Business Administration at Abu Dhabi University. He has more than 20 publications in the area of sustainable operations and supply chain management.

Raid Al-Aomar is a professor of Industrial Engineering and the director of Master of Engineering Management (MEM) program at Abu Dhabi University in the UAE. His research interests include simulation-based optimization and supply chain management. He has over 50 publications in the field of Industrial Engineering.

Appendix A. The survey tool