Prioritization of drivers of corporate social responsibility in the footwear industry in an emerging economy: A fuzzy AHP approach

Accepted Manuscript Prioritization of Drivers of Corporate Social Responsibility in The Footwear Industry in an Emerging Economy: A Fuzzy AHP Approach

Abdul Moktadir, Towfique Rahman, Charbel Jose Chiappetta Jabbour, Syed Mithun Ali, Golam Kabir PII:

S0959-6526(18)32321-7

DOI:

10.1016/j.jclepro.2018.07.326

Reference:

JCLP 13780

To appear in:

Journal of Cleaner Production

Received Date:

06 March 2018

Accepted Date:

31 July 2018

Please cite this article as: Abdul Moktadir, Towfique Rahman, Charbel Jose Chiappetta Jabbour, Syed Mithun Ali, Golam Kabir, Prioritization of Drivers of Corporate Social Responsibility in The Footwear Industry in an Emerging Economy: A Fuzzy AHP Approach, Journal of Cleaner Production (2018), doi: 10.1016/j.jclepro.2018.07.326

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Prioritization of Drivers of Corporate Social Responsibility in The Footwear Industry in an Emerging Economy: A Fuzzy AHP Approach

Md. Abdul Moktadir Institute of Leather Engineering and Technology University of Dhaka Dhaka-1209, Bangladesh. E-mail: [email protected] Towfique Rahman Department of Industrial and Production Engineering Bangladesh University of Engineering and Technology (BUET) Dhaka-1000, Bangladesh. E-mail: [email protected] Dr. Charbel Jose Chiappetta Jabbour Associate Professor Montpellier Research in Management Montpellier Business School 2300, Avenue des Moulins, 34185 Montpellier, Cédex 4, France. E-mail: [email protected] Dr. Syed Mithun Ali Associate Professor Department of Industrial and Production Engineering Bangladesh University of Engineering and Technology (BUET) Dhaka-1000, Bangladesh. E-mail: [email protected] Dr. Golam Kabir* Assistant Professor Department of Mechanical, Automotive & Materials Engineering University of Windsor 401 Sunset Avenue, Windsor, ON Canada N9B 3P4 E-mail: [email protected] *Corresponding Author

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Prioritization of Drivers of Corporate Social Responsibility in The Footwear Industry in an Emerging Economy: A Fuzzy AHP Approach Abstract: Corporate social responsibility (CSR) is gaining popularity among researchers and practitioners due to its strong influence on the global market. Recently, the decision-makers of footwear companies have given special attention on CSR issues due to increased stakeholders’ awareness on social and environmental issues. In this study, the fuzzy analytical hierarchy process (FAHP) has been used to identify and evaluate drivers to CSR-based sourcing in the context of the footwear industry of Bangladesh. A total of 20 drivers are identified through a literature review and experts’ opinions. The results indicate that financial drivers are paramount toward CSR-based sourcing into existing supply chains followed by environmental drivers. This study offers some managerial implications that may assist companies to incorporate CSR-based sourcing into existing supply chains. The identified drivers may guide footwear companies in strategic planning to create a sustainable business structure in the competitive market. Keywords: Corporate social responsibility; Drivers; Delphi; F-AHP; Footwear industry; Sourcing; Sustainability. 1. INTRODUCTION The modern trend of today’s business world is to adopt contemporary business strategies to sustain their business in the global market (Diddi and Niehm, 2017; Wang and Sarkis, 2017; Haleem et al., 2017). Corporate social responsibility (CSR), sustainable manufacturing, green manufacturing are some of the famous business strategies that are widely adopted in the business arena (Hasan and Habib, 2017; Shibin et al., 2017). Among all the business strategies, CSR is attracting significant attention among academics and practitioners. CSR is beneficial to society, the environment, and the economy of the country. This strategy can help the manufacturer to increase the sales revenue and market share, to earn more profit, to improve customer satisfaction, and to enhance overall business performances. Thus, CSR can contribute to the business world as a driving force by sustaining the global business market. In the footwear industry, CSR practices can force and encourage management to ensure social and environmental responsibility across the supply chain. Environment, health, and safety are the 1

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major focuses in the CSR domain. Sustainable environment, proper occupational health and safety issues for workers and employees should be examined in the footwear companies of Bangladesh. Such CSR-based practices can attract global buyers and can help maintain a long-term business relationship with them with fame, esteem, and efficiency (Moon, 2004). The reason behind the choice of the footwear industry to evaluate drivers to CSR-based sourcing is that the footwear industry is playing a vital role in shaping the economy of Bangladesh. Foreign investors show their interest in investing in this sector. Many famous footwear companies are getting foreign orders, which are directly contributing to the economy of this country. To attract foreign buyers, CSR activities must be incorporated and well maintained in the manufacturing plants. The possibility of any kind of accident, unsafe, and unhealthy environment in the manufacturing plants may discourage foreign buyers from investing in this sector (Pohle and Hittner, 2008; Székely and Knirsch, 2005). Therefore, it is important to address influencing drivers in implementing CSR-based sourcing in the footwear supply chain of Bangladesh. This industrial sector requires best practices in the supply chains to assist new and old companies in reshaping the global image by incorporating CSR-based sourcing (Govindan et al., 2014b). Over the years, researchers’ have explored the importance of CSR implementation in the supply chain activities. However, the exact definition and importance of CSR research for footwear companies are under the recent stage of research. To identify the gaps in research on CSR topics in the context of emerging economy, several most recent type of research on CSR issues have been justified. As an example, Barrena-Martinez et al., (2018) has examined drivers and barriers to socially responsible human resource management (HRM). The findings of this research mentioned that working environment may act as significant drivers in the implementation of action in HRM. Halkos and Skouloudis, (2018) explored the importance of critical driver such as innovation which helps to set the CSR agenda of business entities. Borghesi, (2018) showed the role of director and CEO to enhance the CSR activities within the firm’s activities whereas Crifo et al., (2018) showed the corporate governance as a key driver of CSR. A recent study by Arena et al., (2018) investigated the drivers that help to develop CSR strategies. Anusree, (2018) reviewed the articles on drivers of CSR in the context of India. Jerónimo Silvestre et al., (2018) developed a framework 2

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topology by analyzing drivers in the context of sustainability for corporate sustainability. AgudoValiente et al., (2017) investigated drivers and barriers of CSR according to managers' perception in the context of Spanish firms. Akin and Yilmaz, (2016) investigated drivers of CSR in the context of Turkish Banking Sector. Zhu and Zhang, (2015) evaluated drivers of CSR in the context of Chinese. Govindan et al., (2018) constructed a framework for supplier selection in the era of CSR practices. Dočekalová, (2013) studied on the indicator of corporate governance performance measurement in the Czech Republic. Beschorner and Müller, (2007) investigated the social standards for ethical involvement in the business of developing countries. Sadeghi et al., (2016) investigated the corporate social performance on the financial performance of manufacturing companies in Tehran Stock Exchange. To the best of our knowledge, the literature of supply chain lacks examining drivers to CSR-based sourcing in the context of an emerging economy, particularly for the footwear industry. Thus, this research fills this research gap and contributes to the state-of-the-art literature as follows: 1. What are the drivers to implementing CSR-based sourcing in the supply chain in the context of an emerging economy? 2. How can the drivers of CSR-based sourcing be examined and evaluated? The above research questions are explored using an example supply chain from Bangladesh. The specific objectives of the present study are as follows: 1. To identify different key drivers that may help in implementing CSR-based sourcing in the footwear industry of Bangladesh. 2. To rank the identified drivers to CSR-based sourcing for the footwear industry supply chain. 3. To provide managerial implications of this research. To achieve the above-mentioned objectives, a Delphi-based fuzzy AHP approach was used in this research. The Delphi method is used to identify drivers and the fuzzy Analytical Hierarchy Process (AHP) method is presented to rank the drivers to CSR-based sourcing. The Delphi method is an effective technique to assess multiple data in which a group of experts gives their feedback until they reach a consensus on a given research question. AHP can evaluate multi-criteria decision3

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making (MCDM) problems (Bouzon et al., 2016), but it sometimes gives unbalanced results due to an unbalanced scale of judgments. To avoid this limitation, FAHP is used in this research. 2. THEORETICAL BACKGROUND 2.1 Emerging Economy The term emerging economies refers to the economic condition of developing countries that are investing in more productive capacity (Hoskisson et al., 2000). These countries are trying to move forward with the rapid advancement of economic condition and growing industrialization. Emerging markets are important for developed and developing countries because they drive the global economy simultaneously (Frynas, 2006). According to the World Bank, those countries are considered as developing countries whose per capita income is less than $4035 (World Bank Report, 2017). Based on the report of World Bank, Bangladesh is a developing country which has socio-political instability, higher unemployment, and lower level of business activity compared to the U.S. but has much higher economic growth rates. This economic growth rates may help to move forward in competition with the developed countries. 2.2 Corporate Social Responsibility (CSR) The term CSR was first used by two professors of Harvard University in 1930 (Russo and Perrini, 2010). Since then, the idea of CSR is attracting the business world. It contributes to the economic, social, and environmental development and benefits for all stakeholders (Hou et al., 2017). Some of the important components of CSR belong to human rights, corporate governance, health, safety, environmental effect, working conditions, and their contributions to economic development. In addition, Windsor, (2013) mentioned that CSR is a broad concept includes corporate citizenship, business

ethics,

corporate

environmental

sustainability/responsibility,

corporate

social

performance, corporate social responsibility/sustainability, corporate financial performance, sustainable development and grasping the tipple bottom line (TBL). Sustainability is a major concern of CSR. Even, CSR covers three pillars of sustainability that are social, economic and environmental. Many scholarly articles indicate CSR as a socially responsible business strategy (Barrena-Martinez et al., 2018; Crifo et al., 2018; Beschorner and Müller, 2007). These articles mentioned that adopting CSR practices in the manufacturing firms may help to make a positive 4

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impact on the stakeholders (including consumers, employees, investors, communities, and others) and environment. A recent study documented by Śmiechowski and Lament, (2017) presented the impact of CSR practices on pro-ecological actions of tanneries. This research indicated that the environmental sustainability can be achieved through CSR practices in the tanneries industry. Many of the manufacturing and business firms consider CSR as a business strategy to achieve sustainability via strongly integrating environmental responsibility. As an example, Cazeri et al., (2018) investigated the integration between CSR with management to achieve sustainability in Brazilian enterprises. The authors urged that sustainability can be achieved by integrating CSR with the management system. The authors believed that CSR is a business strategy which may act as driving force for achieving sustainability. A recent review conducted by Xia et al., (2018) showed that construction industry may achieve the sustainability through CSR practices. The authors pointed out that this industry may be benefited via strongly integrating social and environmental issues. For instance, CSR is the idea of companies’ involvement with the environment around itself. Nonprofit works, bettering the environment and connecting with people are important parts of CSR activities. The biggest organizations in the world have adopted CSR activities. Google Corporation named its CSR activity as ‘Google Green’ which aims to use resources efficiently and support renewable power. BMW initiated a CSR project named ‘The school’s environmental education development project’ to create awareness of the social and environmental issues (Ait Sidhoum and Serra, 2017). This modern trend is comparatively new in a developing country like Bangladesh. The footwear industry of Bangladesh needs to initiate CSR activities to enhance social, economic, and environmental development and increase their reputation among the business world to attract buyers from home and abroad. 2.3 CSR in the Footwear Industry CSR is a key to sustainable business in today’s competitive world. Nowadays, customers and stakeholders are conscious of CSR activities. Previously, footwear manufacturers in Bangladesh were not conscious about CSR due to lack of pressure from stakeholders, lack of knowledge on CSR, fear of financial burden to implement CSR, and so on.

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Today’s business fields are aware of CSR because it helps in achieving success in the global market, increasing sales value and earning brand image (Jabbour et al., 2017). Because of global pressure from stakeholders, footwear manufacturers are giving much importance to incorporating CSR activities. After the Rana Plaza collapse in the year 2013 with the death toll of 1134, foreign buyers are giving much importance to CSR-related activities in Bangladesh (Perry et al., 2015). 2.4 Drivers of CSR CSR is a broad term used to take initiatives to participate in improving society by adopting various responsibility-based activities (Murmura et al., 2017). These initiatives include various non-profit activities and to implement environmentally friendly policies in the workplace (Hasan and Habib, 2017). CSR activities are important for organizations to increase their public image by active media coverage. CSR activities help employees engage in societal activities (Lozano, 2015). These activities help organizations attract and retain investors. CSR promotes personal philanthropic attitude, which encourages professional and personal growth (Khan et al., 2016; Muthuri et al., 2009). Social drivers are those drivers thatreduce the destructive activities of society, nature, and environment and improve the bio-ecological bonding of nature (Hutchins and Sutherland, 2008; Mani et al., 2016). Social drivers ensure the companies’ sustainable development through CSRbased sourcing. Financial drivers are those drivers thatcan force industries to introduceCSR practices by ensuring promising profit throughout business activities. In the globalization era, financial drivers are the most important drivers as manufacturers want to get more profit in a sustainable condition. Environmental drivers are those drivers thatare generally involved in development and improvement of green activities through supply chain network by introducing CSR activities. Greening the activities through reducing pollution, improving worker facility, minimizing risk are those significant factors to improve the CSR activities in manufacturing plants (Allevi et al., 2017). 6

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Reputation and support-related drivers are those drivers that are responsible in improving the business performance directly. These driver directly assist the companies to stay and survive in the competitive global market (Andersen and Skjoett‐Larsen, 2009; Welford and Frost, 2006). The literature on drivers to CSR practices is shown in Table 1. 2.5 CSR-Based Research Gap in Bangladeshi Context CSR-based sourcing is not a common phenomenon in the footwear industry of Bangladesh. Some footwear companies are trying to practise but most of them are not practising properly. The literature on CSR is not well established in the context of the footwear industry of Bangladesh. In the context of Bangladesh, it is a new approach. Research on CSR will help the companies improve the business performance in the global market. This research builds a framework of CSR activities that may help practitioners and industrial decision-makers to adopt CSR activities in their business domain. The existing works on relevant fields are presented in Table 2. 3. RESEARCH METHODOLOGY 3.1 Research Design To complete this research, first we reviewed related papers on CSR in the context of different countries and collected the drivers under different major categories. Then, we found out the research gap in an emerging economy context and identify the objective of the research. After that, we selected the most common drivers from the relevant literature review with the help of the relevant industrial experts (from the case company). Sixteen industrial experts from the case company were participated for the proper assessment of the drivers. In the process of evaluation of the major drivers and sub-drivers of CSR-based sourcing, the FAHP tool is employed. The research design is shown in Figure 1. 3.2 The Delphi Method The Delphi method is adopted to identify drivers toward CSR-based sourcing. In this method, a group of experts is taken for getting better results by supplying several questionnaires. The Delphi

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method is a systematic method for prioritizing drivers. In addition, specialists from relevant fields share their ideas until they come to a final and mutual decision (Lummus et al., 2005). In the Delphi method, generally, there is no exact rule of taking experts to assess data. Moreover, different authors suggest different rules to consider the number of experts for evaluating criteria (Bouzon et al., 2016). Some authors suggested that 10 experts are enough to get reliable results. Okoli and Pawlowski (2004) advised that 10 to 18 experts’ opinions can be considered for getting the best result, whereas Murry and Hammons (1995) suggested that 10 to 30 experts’ opinions are required to ensure the best results. In this research, a total of 16 industrial experts were assigned. The experts assigned for this data evaluation had substantial experience (at least 15 years) in supply chain management, operations management, and logistics management. A three-round Delphi procedure was carried out with the assigned experts. 3.3 Fuzzy AHP Fuzzy set (FS) theory was first introduced by Zadeh (1965). The fuzzy-based AHP methodology has been developed from FS theory. It can deal the vague data in an informative form. In addition, FS theory was taken into account to settle the ambiguity and imprecision of human judgment by using linguistics scale (Chang, 1996). FS theory is more powerful to handle real-world problems whereas classic set theory does not handle such problems. A triangular fuzzy number (TFN) M is given in Figure 2. A TFN is indicated as (p, q, r) where r>q>p. A TFN is simply written as (p, q, r). p shows the smallest conceivable value, while q is the middle value, and r is the biggest conceivable value. Each TFN has linear portrayals to its left side and right side with the end goal that its membership function can be composed as Eq. (1):

{

0, 𝑧 < 𝑙 𝑧 ‒ 𝑝) ( (𝑞 ‒ 𝑝), 𝑝 ≤ 𝑧 ≤ 𝑞 𝜇(𝑧 𝑀) = (𝑟 ‒ 𝑧) (𝑟 ‒ 𝑞), 𝑞 ≤ 𝑧 ≤ 𝑟 0, 𝑧 > 𝑟

(1)

Many positioning techniques are accessible in the existing literature. In this research work, the authors utilized an attribute-based TFN scale of importance as given in Table 3 (Lin and Yeh, 2012), and Figure 3 gives the TFN.

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Various methods have been proposed in the literature for assigning the weights in the FAHP method. In this study, we utilize an extent FAHP framework originally proposed by Chang (1996) employing extent analysisfor assessing and prioritizing the drivers of CSR-based sourcing. The method is described below: Assuming Z   z1 , z2 , z3 ,..........., zn  is a set of an object and V  v1 , v2 , v3 , v4 ..........., vn  is a goal set wherein each object is taken and extent analysis for each goal 𝑔𝑖 is performed. Thus, 𝑚 extent analysis

values

for

each

object

can

be

obtained,

M 1gi , M 2 gi , M 3 gi , M 4 gi ,..........M m gi ; i  1, 2,3,......., n ; and M

j gi

using

the

given

signs:

( j  1, 2,3........, m) .

Chang’s extent methodology is interpreted below (Chang, 1996): Step1: Fuzzy synthetic extent value with respect to the ith object is prescribed below:

n m j  Si   M g i    M g i  j 1  i 1 j 1  m

1

j

(2) m

Fuzzy addition operation is first conducted for a particular matrix to get

M j 1

j gi

values and

addition operation is given below: m m m  M  l , m , u j 1 gi  j 1 i j 1 i j 1 i    m

j

 n m To acquire   M  i 1 j 1

(3)

1

j

 the fuzzy addition operation is initially carried out of gi   , n

M

j gi

( j  1, 2,3........, m) values, such that

 i 1

m



m

m

m



j 1



j 1

j 1

j 1



    li ,  mi ,  ui 

(4)

In addition, the inverse of the vector above is assessed as presented in Equation (5).

 n m   M  i 1 j 1

1

j

  1 1 1  gi      n u , n m , n l    i 1 i  i 1 i  i 1 i 

(5)

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Step2: In this step, the degree of possibility of M 2   l , m, u   M 1   p, q, r  is defined as V  M 2  M 1  sup  min   M 1  x  ,  M 2  y   

(6)

y x

Therefore, Equation (6) can be equivalently presented as follows: 1, 𝑖𝑓𝑞 ≥ 𝑚 V  M 2  M 1   hgt  M 1  M 2   0, 𝑖𝑓𝑙 ≥ 𝑟 𝑉(𝑀2 ≥ 𝑀1) = 𝑙‒𝑟 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (𝑞 ‒ 𝑟) ‒ (𝑚 ‒ 𝑙)

{

(7)

Equation (7) can be represented by Figure 4, where, 𝑑 represents the ordinate of the highest intersection point of 𝐷 between  M 1 and  M 2 (see Figure 4). To assess M 1 and M 2 values, we should know both the values of (M1≥M2) and V(M2≥M1). Step 3: In this step, the degree of possibility for a convex fuzzy number to be greater than 𝑘 convey fuzzy number 𝑀𝑖; 𝑖 = 1,2,……..,𝑘 can be explained as follows: V  M  M 1 , M 2 ,...M k   V  M  M 1  and  M  M 2  and .............and ( M  M k )   min V ( M  M i )

Now assume that, d ’  Ai   min V  Si  S k 

(8) (9)

Here, k = 1, 2,…,n; k ≠ i. Therefore, the weight vector is defined as follows: W’ = (d’(A1), d’(A2),…, d’(An))T

(10)

In the above equation, Ai(i = 1,2,…,n) has n elements. Step 4: In this step, the normalized weight vectors are determined with the assistance of the following equation W’ = (d(A1), d(A2),…, d(An))T

(11)

Where, W’ is a non-fuzzy number. 4. CASE STUDY The research framework was investigated in a footwear company ‘AFT’ from Bangladesh. The company ‘AFT’ started business in 1990. This company is one of the leading footwear manufacturers and exporters in Bangladesh. The products produced by ‘AFT’ footwear company 10

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are exported to different countries, like the USA, China, Japan, and Western Europe. This footwear company has received revenues of 185 million US dollars in the fiscal year 2016 (EPB Report, 2016). This company expects to reduce environmental pollution, green the supply chain, enhance the brand value, and has the desire to implement CSR-based sourcing of materials. Therefore, ‘AFT’ Footwear Company wanted to assess the drivers of CSR for viable execution of CSR-based sourcing in their existing supply chains. The basic reason behind this, first they sought the importance of drivers; second, they want to control their production according to buyer requirement by employing CSR practices. After listing the drivers of CSR-based sourcing under four major categories from a field survey and literature review, we sent the list to professionals to evaluate the importance of the drivers. After receiving feedback from the expert via the Delphi study, we assessed the drivers using the FAHP method. The three-step procedure is explained below in detail: Step 1: Identifying the drivers to CSR-based sourcing with the help of assigned experts We gathered drivers of CSR-based sourcing from existing literature survey, sorted them into four noteworthy classes, which are given in Table 2. Step 2: Constructing the AHP frame of drivers and their sub-drivers With the help of experts’ feedback, we construct a hierarchy of drivers (Figure 5). The established hierarchical diagram carries four levels: 1) the goal present research work, 2) Major CSR drivers, 3) CSR sub-drivers, and 4) Evaluating drivers. Step 3: Evaluation of the major drivers and sub-drivers with the assistance of the FAHP method In this step, construct a pairwise comparison matrix among major drivers and sub-drivers using a fuzzy linguistic scale with the assistance of assigned experts. This linguistic scale was taken from the previous study with the help of Lin and Yeh (2012) (Figure 3 and Table 3). With the help of Equation (2), fuzzy synthetic extent value with respect to each major driver is assessed. Fuzzy synthetic extent value with respect to each major driver is demonstrated by S1, S2, S3, and S4. The fuzzy synthetic extent values are given below: S1   2.5000, 3.6667, 5.0000    0.0432, 0.0588, 0.0785    0.1079, 0.2157, 0.3927  11

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Therefore, the degree of possibility of Si over Sj (i≠j) is examined by Equation (7). V  S1  S 2   0.5911, V  S1  S3   0.9299, V  S1  S 4   1 .

Therefore, Equation (8) helps to evaluate the minimum degree of possibility and it can be stated as follows: Min V  S1  S 2 , S3 , S 4 , S5   min  0.5911, 0.9299, 1  0.5911 .

Thus, with the assistance of Equation (10) the weight vector W = (0.5911, 1, 0.6807, 0.5585)T is computed. After that, the normalized weight vector is evaluated using Equation (11) with respect to major drivers D1, D2, D3, and D4 and it is given below: WG = (0.2089, 0.3533, 0.2405, 0.1973)T The complete result is shown in Table 4. The comparison matrices of sub-drivers are performed in a similar way. The complete results and the ranking of all major drivers and specific drivers are given in Table 5. 5. RESULTS AND DISCUSSION 5.1 Major Drivers Ranking Using the FAHP Approach Based on Table 5, the ranking of drivers is as follows: D2>D3>D1>D4. It is concluded that the ‘financial drivers (D2)’ get the highest weight in the priority ranking, thus indicating that the influence of financial drivers during CSR-based sourcing is more important than other listed drivers. A recent study conducted by Cantele and Zardini, (2018) argued that financial issue is the major issue for incorporating CSR practices in the firm’s supply chain. Authors showed that the dimensions of sustainability (i.e., social, environmental and economic) help to achieve the financial performance around the supply chain. Contrary to our findings, Chowdhury et al., (2018) reported that social activities like employee well-being and community development are those key value driver for CSR practices rather than economic activities in the domain of Oil & Gas companies. A study by Nollet et al., (2016) showed the corporate social performance-corporate financial performance relationship and reported that governance is the prime driver affecting the relationship. The above-indicated scholarly articles confirm that financial driver may act as a pivotal driving factor for CSR implementation. 12

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Then, the ‘environmental drivers (D3)’ hold the second position in the priority ranking and this is also an important driver for CSR-based sourcing. Basically, CSR covers three pillars of sustainability and the environmental driver has significant contribution in CSR implementation. Our finding also aligns with the recent study conducted by Bini et al., (2018). Authors reported that environmental drivers may help to develop a sustainable business model by integrating social and economic issues. Now a day, environmental issue is the prominent factor in the competitive business model. Business organizations are trying to integrating three pillars of sustainability in the existing supply networks (Agudo-Valiente et al., 2017). Crifo et al., (2018) reported that environmental driver may contribute to developing the strategic policy of business firms. Literature comparison ensures that environmental drivers for CSR implementation is not negligible and the footwear company may be benefited by incorporating CSR practices in their supply chains. ‘Social drivers (D1)’ take the third position. However, socially responsible supply chains are getting popularity day by day among the researchers and practitioners. Whatever CSR mainly focuses on the social responsibility. Our finding aligns with the recent study by Barrena-Martinez et al., (2018). The authors have examined drivers and barriers to socially responsible human resource management (HRM) and claimed that social drivers may help to develop the sustainable business model. Contrary our finding with Bernhardt and Pollak, (2016) that authors conducted a comparative study to measure the social and economic upgrading dynamic in the context of the global value chain and mentioned that social upgrading received lesser attention. Govindan et al., (2018) showed the importance of social drivers for supplier selection for implementing CSR practices within the firm’s operations. The importance of social drivers found in several scholarly researches (Govindan et al., 2018; Jerónimo Silvestre et al., 2018; Govindan et al., 2014). Those reviewed confirm that social drivers may act as a significant driving factor for CSR implementation. At the last, in the priority ranking, ‘Reputation & support-related drivers (D4)’ gets the last position during CSR-based sourcing. Reputation and support may motivate the firms to the adoption of CSR activities. A study conducted by Borghesi, (2018) confirmed that support form director and CEO may help to enhance the CSR activities within the firm’s activities. Crifo et al., (2018) argued that corporate governance may drive the companies to adopt CSR practices. Dočekalová, (2013) reported that corporate governance is the key indicator for achieving economic performance in the 13

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Czech Republic. These literatures confirm that reputation & support-related drivers may potentially help footwear companies to introduce CSR based sourcing. 5.1.1 Sub-Drivers Rankings Using FAHP In this section, the ranking of sub-drivers is presented for understanding the importance of subdrivers for CSR-based footwear sourcing in Bangladesh. 5.1.1.1 Social Drivers Social drivers are relevant to social activities. The importance of social drivers in the footwear companies is not negligible. This driver gets the third position in the priority ranking. Some authors give the importance of social drivers for CSR practices in manufacturing companies. Govindan et al. (2014b) show that social drivers are influencing group’s driver for the mining industry, which indicates the importance of social drivers for CSR practices. In this analysis, it also earns significant driving power by indicatingthe third position in the priority rank and that could drive the current situation. In addition, the five sub-drivers have been considered and ranked accordingly. The ranking of listed sub-drivers is as follows, D11>D14>D13>D12>D15. ‘Prohibition of child labour (D11)’ dimensions of driver hold the first priority. For CSR-based sourcing ‘prohibition of child labour (D11)’ can act as the most significant driver for the case industry. Every footwear company should maintain rules regarding child labour prohibition. Many countries have strictly enforced rules and regulations regarding child labour (Liu et al., 2017). ‘Support from top management to facilitate women labour (D14)’ has secured the second position. In the context of the footwear industry, the contribution of women labour is significant. Therefore, top management should formulate some rules to facilitate women labour to work safely and efficiently in the workplace. ‘Proper facility for workers (D13)’ gets the third rank. The worker is a major part of footwear manufacturing. In addition, proper law and care of workers may influence the implementation of CSR-based sourcing greatly. ‘Create job opportunity for unemployed people (D12)’ cannot be neglected and thus gets the fourth position in the group of social drivers. To gain social sustainability, this driver may act as a driving force by creating more job facilitiesfor unemployed people. Finally, ‘Business ethics (D15)’ secures the last position in the priority rank under the category of social driver. It can ensure a flexible and ethical working environment for the workers.

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5.1.1.2 Financial Drivers A financial driver is a type of profit-based strategic driver that may help the footwear company earn more profit and contribute to the national economy. Moreover, it is the main driving force for CSR-based sourcing in the context of Bangladesh. Financial drivers hold the top rank and ensures that this driver can enhance the CSR practices greatly. The ranking of sub-drivers is as follows, D21>D22>D24>D23. ‘Long-term economic benefits (D21)’ has got the first rank in this category. Long-term economic benefits can play an important driver for CSR-based sourcing. Studies on CSR show that the economic benefit can act as asignificant driver (Kanji and Chopra, 2010; Lv et al., 2010). The following driver ‘Strategic advantages (D22)’ holds the next position in the priority rank. Strategic drivers may also help footwear companies enhance the overall CSR-based performance. In the context of Bangladesh, strategic advantages can force the companies to introduce CSR-based practices. The next one is ‘Maximize net return on investments (D24)’ which holds the third rank in the priority ranking. Therefore, implementation of CSR-based sourcing in the footwear company may help to maximize the net return on investment. ‘Competitive advantages in a global market (D23)’ has got the last position in the priority ranking. This driver indicates that to get more competitive advantages from the global market, it should be mandatory for footwear companies to implement CSR practices. 5.1.1.3 Environmental Drivers Environmental drivers obtained the second position in the priority ranking. Environmental drivers may help footwear companies implement CSR-based sourcing. Authors from different countries have given special attention to environmental issues due to energy deficiency, reputational issues, and for the brand images (Robinson, 2011; Scott, 2011; Zailani, 2006). In this category of driver, five sub-drivers have beenconsidered. The ranking of sub-drivers can be shown asfollows, D35>D33>D32>D31>D34. ‘Protection of workers from chemicals (D35)’ comes first in the ranking. Some finishing chemicals are directly involved in footwear manufacturing. Therefore, protection of workers should be mandatory. ‘Minimize accidents (D33)’ holds the second place in the ranking as an accident can hamper production, which can impose a bad image to the buyers. ‘Minimize waste (D32)’ gets the third rank. In footwear manufacturing, multiple types of waste are produced like waste leather, scarp, thread, chemicals, packaging materials. This waste should be minimized and thus can be 15

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minimized by CSR practices (Kanji and Chopra, 2010). The ‘protection of the environment (D31)’ comes in the next priority rank. Protection of the environment is an important driver for CSRbased sourcing of footwear. ‘Employee enrolment in environmental rules and regulations (D34)’ comes at the last of the ranking. Companies should introduce some mandatory rules regarding environmental protection in their existing business policy. 5.1.1.4 Reputation &Support-Related Drivers The last main category of the driver is reputation & support-related drivers. In this category, the ranking of drivers is as follows; D42>D41>D45>D43>D44>D46. ‘Long-term opportunity (D42)’ occupies the first position in the ranking. The driver long-term opportunity inspires the companies to adopt CSR-based sourcing. ‘Enhance company reputation in the global platform (D41)’ comes in the second position of the ranking. Nowadays, reputation is one of the major issues for sustaining business in the global market. Therefore, companies should be aware of this reputational issue (Maloni and Brown, 2006; Welford and Frost, 2006). ‘NGS’s activities (D45)’ holds the third position. NGS’s activities belong to social awareness program, training regarding social rights, human rights, etc. Therefore, NGS’s activities in Bangladesh may act as important drivers for successful implementation of CSR-based sourcing. ‘Customer loyalty (D43)’ comes in the next position of the rank.The long-term business relationship is based on customer loyalty. Companies should focus on this driver for the smooth implementation of CSR-based activities. ‘Government’s rules and regulations toward CSR practices (D44)’ holds the fifth position in the ranking. Governmental rules and regulation regarding CSR is a great driver for implementing CSR-based activities. The government can force companies to adopt CSR-based sourcing. Bangladeshi industries are in the early stage of adoption of CSR-based sourcing. Hence, strict governmental rules can force the companies to adopt CSR-based activities promptly.‘Support from media (D46)’ comes last in the rank. Media is a source of protecting violence. Media can act as a significant contributor to CSR issues. It can promote abetter workplace and better environment for workers by focusing on recent illegal issues that occurred in manufacturing industries. 5.2 Sensitivity Analysis The data evaluation in MCDM suffers from a lack of certainty, accuracy, and exactness of data (Govindan et al., 2014a). It is necessary to scrutinize the stability of ranking because a small 16

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variation of relative weights of drivers may lead to large variations in the obtained ranking (Mangla et al., 2017). Moreover, the sensitivity analysis can ensure the changes of final ranking by the small changes in relative weights of drivers. In this research, ‘Financial drivers (D2)’ was ranked top among the four major drivers (see Table 5). Therefore, ‘Financial drivers (D2)’ may influence the other drivers for CSR-based sourcing. Moreover, the financial driver (D2) was taken into account for sensitivity analysis. In this research, the weight of financial driver (D2) has been changed from 0.1 to 0.9 with 0.1 as an increment. At the same time, the changes of weights of other drivers were investigated. The findings reveal that maximum relative changes of weight occurred in environmental drivers (D3)-related drivers (see Table A1). Because of variations in weights of the driver, in the fundamental driver list, at 0.1 and 0.2 weights, D3 keep up the best rank. However, from 0.3 to 0.9 weights variation, D2 keeps up the best rank and D4 is positioned last. Therefore, it is clear that D2 is the most significant driver to CSR-base sourcing. Major Driver weights acquired by increasing weights of the D2-related driver from 0.1 to 0.9 are presented in Figure 6 and Table A1. In addition, the weights and ranking of sub-drivers also changed with changing of main drivers’ weights. In this analysis, at point 0.1 and 0.2, D35 holds the first rank in the priority rank whereas D23 holds the last position in priority ranking. Because of the variation of weights, at the point 0.3 to 0.9, D21 takes the first rank and D46 holds the last position. Table A2 shows the global weights for sub-drivers using sensitivity analysis when D2related driver values were increased from 0.1 to 0.9. Figure 7 and Table A3 show the ranking of sub-drivers. After conducting sensitivity analysis, it can be clarified that financial driver is the most significant driver. 6. MANAGERIAL IMPLICATIONS CSR-based sourcing is getting popular in the fields of manufacturing activities. This research can help the industrial manager by giving a clear idea about the actual nature of several drivers toward CSR-based sourcing. This analysis also helps to formulate tactical and strategic decisions 17

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regarding implementing CSR practices in the existing supply chains. Moreover, some important practical implications are formulated by this study, which are summarized below:  Formulating strategic policy for implementing CSR-based sourcing: It is important to implement CSR-based practices in the footwear manufacturing companies to earn a reputation from the global market. This research can help companies formulate astrategic policy for global image building.  Establishing supportive policy and arranging training program: CSR-based sourcing is not an easy practice for the manufacturing industry. Therefore, training on CSR issues can help industrial managers increase social value as well as brand value. Government and top management can take initiatives for increasing CSR practices in local industries. Hence, this study helps manager know the insights of formulating some training program and supporting policy by proper understanding of the drivers.  Proper understanding the global market and use this study as the benchmark for CSRbased practices: Global competition infootwear products is huge. Therefore, Bangladeshi industries should include CSR-based practices as soon as possible for sustaining the business. Therefore, this study finds out the actual nature of several drivers of CSR-based sourcing in the context of Bangladesh. Other industrial managers can also use this study for improving their manufacturing activities in future. 7. CONCLUSIONS The present research aimed to investigate the drivers toward CSR-based sourcing in the context of footwear companies in Bangladesh. A Delphi-based fuzzy AHP framework was proposed for the analysis of drivers. In this study, 20 drivers were taken under four major categories by literature review and experts’ inputs and fuzzy-based AHP was used to rank the drivers. The main drivers to CSR-based sourcing have been recognized and ranked consecutively as follows: financial drivers, environmental drivers, social drivers, and reputation & support-related drivers. The financial drivers ranked in the first position can force the companies to implement CSR-based sourcing in the existing supply chains. Therefore, the role of financial drivers is more than any other drivers. Hence, the decision-maker should payspecial attention to this driver for 18

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improving the CSR-based practices. In addition, sub-drivers to CSR-based sourcing have been also prioritized. In this study, long-term economic benefits under the category of financial driver got the first position in the priority ranking. Thus, long-term economic benefits encourage industrial managers to adopt CSR-based sourcing in their business policy. The driver strategic advantages are identified as the second most important driver. Strategic advantages may force the companies to implement CSR-based practices. Protection of workers from chemicals got the third position because in footwear companies there are some chemical operations thatmay create harmful disease. Therefore, protection of workers from chemicals can play avital role in CSRbased sourcing. Ranking of the other drivers has also been performed to identify the importance of each driver. Further, a sensitivity analysis has been carried out to monitor the stability of the ranking. A case example was also introduced to justify the validity of the current work. The findings can help decision-makers realize the importance of each driver for the successful implementation of CSR practices in the manufacturing supply chains of the footwear industry. 7.1 Implications for Mature and Emerging Economies This study will help existing companies and new entrepreneurs understand the importance of financial drivers of CSR activities for mature and emerging economies. The financial driver has received paramount importance in the priority ranking and thus indicates that the role of financial drivers in an emerging economy of Bangladesh is more significant than any other drivers for smooth implementation of CSR activities. The proper understanding of financial drivers may help the practitioners and decision-makers adopt CSR-based activities. The footwear market of Bangladesh is growing very fast and significantly contributing to the economy of the country. The footwear companies of Bangladesh are exporting footwear-related products to foreign countries. The footwear companies of Bangladesh can attract world-famous footwear brand companies to invest in this country and get their footwear products produced by the experts of this country. This will immensely contribute to the emerging economy of Bangladesh. Adopting CSR-based activities will enhance the brand image of the footwear sector of this country and will contribute significantly to the emerging economy of Bangladesh. Thus, the footwear companies of Bangladesh will be motivated by understanding the financial drivers of CSR activities and will 19

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take initiatives to adopt CSR-based activities. 7.2 Limitations and Directions of Future Research This work has some limitations. In this study, only 20 drivers were undertaken for ranking. In addition, the Delphi-based fuzzy AHP method was used to rank the drivers and the method is much dependent on human judgments. In this work, we only considered one case company, which cannot ensure the rank of other manufacturing industries. The above limitations may help to indicate some advantages for future research. In future, more drivers could be addressed for analysis with considering multiple industrial perspectives. In future, other multi-criteria decision analysis tools like the TISM and fuzzy-based DEMATEL may be introduced to find the rank as well as the interrelationships among them.

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List of Tables Table 1: Selected existing works on CSR Table 2: Drivers with identification code Table 3: Fuzzy linguistic scale (Lin and Yeh, 2012) Table 4: The fuzzy evaluation matrix with respect to the goal Table 5: Local and Global weights of all major drivers and sub-driver of CSR based sourcing

28

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Table 1: Selected existing works on CSR Reference

Contribution

Methodology

(Cheong et al., 2017)

Authors investigated the impact of CSR activities on market and opinion of the investors. (Akin and Yilmaz, This research finds out the relationship between CSR 2016) disclosure scores and corporate governance related bank characteristics by considering five dimensions of CSR (Bhandari and Relationship between CSR activities and firm-level Javakhadze, 2017) capital allocation efficiency is identified in this study. (Liu et al., 2017) This study investigates the impact of CSR activities on ownership, management & governance of a business. (Badri Ahmadi et al., This study assesses the social sustainability of supply 2017) chains using Best Worst Method (Su et al., 2017) The impact of CSR activities on consumers’ attitudinal and behavioral responses is identified in this study. (Theodoulidis et al., This study investigates the relationship between 2017) stakeholder management, expressed as CSR activities. (Zhu and Zhang, A CSR concept for Chinese SOCs by using both the international ISO26000 framework is developed in 2015) this research work. (Govindan et al., This study made a systematic assessment of drivers to 2014b) CSR responsibilities in the mining industry with MCDA approach.

29

Empirical analysis Empirical analysis

Empirical analysis Empirical analysis Best Worst Method Empirical framework Statistical analysis Statistical analysis Fuzzy-DEMATEL

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Table 2: Drivers with identification code Main Drivers Social drivers (D1)

1. 2. 3. 4.

Sub-Drivers Prohibition of child labor (D11) Create job opportunity to unemployed people (D12) Proper facility for workers (D13) Support from top management to facilitate women labor (D14) Business ethics (D15)

Relevant work (Majumdar and Nishant, 2008) (Lombart and Louis, 2014)

Financial drivers (D2)

1. Long term economic benefits (D21) 2. Strategic benefits (D22)

Environmental drivers (D3)

3. Competitive advantages in global market (D23) 4. Maximize net return on investments (D24) 1. Protection of environment (D31) 2. Minimize waste (D32)

(Moktadir et al., 2018) (Foote et al., 2010; Jenkins and Yakovleva, 2006) (Cornelius et al., 2007; Somerville and Wood, 2001) (Moktadir et al., 2017) (Andersen and Skjoett‐Larsen, 2009; Spence and Bourlakis, 2009) (Bhattacharya et al., 2009; O’Riordan and Fairbrass, 2008) ( Cruz and Wakolbinger, 2008; Cruz and Matsypura, 2009) (Moon, 2004) (Moon, 2004;Govindan et al., 2014b)

3. Minimize accidents (D33)

(Govindan et al., 2014b)

4. Employee enrollment in environmental rules and regulations (D34) 5. Protection of workers from chemicals (D35) 1. Enhance company reputation in global platform (D41) 2. Long term opportunity (D42)

(Kuo et al., 2010)

5.

Reputation & support related drivers (D4)

Authors contributions

3. Customer loyalty (D43)

(Badri Ahmadi et al., 2016;Govindan et al., 2014b) (Maloni and Brown, 2006; Welford and Frost, 2006) (Govindan et al., 2014b)

4. Governments rules and regulations toward CSR practices (D44) 5. NGS’s activities (D45)

(Badri Ahmadi et al., 2016; Govindan et al., 2014b) (Govindan et al., 2014b)

6. Support from media (D46)

(Badri Ahmadi et al., 2016; Govindan et al., 2014b)

Table 3: Fuzzy linguistic scale (Lin and Yeh, 2012) Linguistic variables Just equal Medium important A little important Important Very important Extremely important

Triangular fuzzy scale (1,1,1) (1/2,1,3/2) (1,3/2,2) (3/2,2,5/2) (2,5/2,3) (5/2,3,7/2) 30

Triangular fuzzy reciprocal scale (1,1,1) (2/3,1,2) (1/2,2/3,1) (2/5,1/2,2/3) (1/3,2/5,1/2) (2/7,1/3,2/5)

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Table 4: The fuzzy evaluation matrix with respect to the goal Main Driver D1 D2 D3 D4

D1 (1,1,1) (1,1.5,2) (0.67,1,2) (0.67,1,2)

D2 (0.5,0.67,1) (1,1,1) (0.4,0.5,0.67) (0.5,0.67,1)

D3 (0.5,1,1.5) (1.5,2,2.5) (1,1,1) (0.5,0.67,1)

D4 (0.5,1,1.5) (1,1.5,2) (1,1.5,2) (1,1,1)

W

WG

0.5911 1.0000 0.6807 0.5585

0.2089 0.3533 0.2405 0.1973

Table 5: Local and Global weights of all major drivers and sub-driver of CSR based sourcing Driver

D1

D2

D3

D4

Weight

0.2089

0.3533

0.2405

0.1973

Rank

3

1

2

4

Sub-driver

Weight

Rank

Global weight

Rank

D11

0.2865

1

0.0599

4

D12

0.1854

4

0.0387

11

D13

0.2025

3

0.0423

10

D14

0.2169

2

0.0453

8

D15

0.1087

5

0.0227

19

D21

0.4037

1

0.1426

1

D22

0.3453

2

0.1220

2

D23

0.1010

4

0.0357

12

D24

0.1500

3

0.0530

6

D31

0.1459

4

0.0351

14

D32

0.2146

3

0.0516

7

D33

0.2290

2

0.0551

5

D34

0.1046

5

0.0252

18

D35

0.3059

1

0.0736

3

D41

0.1782

2

0.0352

13

D42

0.2253

1

0.0444

9

D43

0.1657

4

0.0327

16

D44

0.1613

5

0.0318

17

D45

0.1686

3

0.0333

15

D46

0.1009

6

0.0199

20

31

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List of Figures Figure 1: Research design for present work Figure 2: TFN, 𝑀 (Modified from Ashrafzadeh et al., 2012) Figure 3: Fuzzy set scale represented graphically (Adapted from Lin and Yeh, 2012) Figure 4: Intersection point of M1 and M2 Figure 5: Hierarchical level of drivers of CSR based sourcing Figure 6: Sensitivity analysis of sub-drivers to CSR based sourcing in the footwear industry of Bangladesh (by global weights). Figure 7: Sensitivity analysis of sub-drivers to CSR based sourcing in the footwear industry of Bangladesh (by rank).

Existing literature review

32

Experts’ opinion via Delphi study

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

M

33

1

M k(y) Mq(y)

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Figure 2: TFN, 𝑀(Modified from Ashrafzadeh et al., 2012)

Medium Importance

Importance A little Importance

Very Importance

Extremely Importance

u(x) 1

0 1/2

1

3/2

2

5/2

3

7/2

Figure 3: Fuzzy set scale represented graphically (Adapted from Lin and Yeh, 2012)

34

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m

M2

M1

D V(M2>M1)

p

q

l

d

r

m

Figure 4: Intersection point of M1 and M2

35

u

M

Le ve l-4

Le vel -3

Le vel -2

Le vel -1

36 1 5

D 1 3 D 1 4 D

D 12

D1 1

Social driver (D1)

D2 4

D2 3

D 22

D2 1

Financia l drivers (D2)

Ev al ua tio n of m os t inf lu en tia l dr iv er s fo

on dri ver s for the CS R bas ed gar me nts sou rcin g

D 46

D4 5

D4 4

D3 4 D3 5

D4 3

D4 2

D 41

Reputation & support related driver (D4)

D3 3

D3 2

D 31

Environ mental drivers (D3)

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Figure 5: Hierarchical level of drivers of CSR based sourcing

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0.40 0.35

Global weight

0.30 0.25 0.20 0.15 0.10 0.05 0.00 D11 D12 D13 D14 D15 D21 D22 D23 D24 D31 D32 D33 D34 D35 D41 D42 D43 D44 D45 D46 Normal (0.3533) 0.4 0.8

0.1 0.5 0.9

0.2 0.6

0.3 0.7

Figure 6: Sensitivity analysis of sub-drivers to CSR based sourcing in the footwear industry of Bangladesh (by global weights).

20 18 16

Global rank

14 12 10 8 6 4 2 0 D11 D12 D13 D14 D15 D21 D22 D23 D24 D31 D32 D33 D34 D35 D41 D42 D43 D44 D45 D46 Normal (0.3533) 0.4 0.8

0.1 0.5 0.9

0.2 0.6

0.3 0.7

Figure 7: Sensitivity analysis of sub-drivers to CSR based sourcing in the footwear industry of Bangladesh (by rank). 37

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Appendix Table A1: Values of preference weights for listed drivers when sensitivity analysis is conducted Listed Driver Values of preference weights for listed driver Category Normal 0.1 0.2 0.3 0.4 0.5 (0.3533) D1 0.2089 0.2907 0.2584 0.2261 0.1938 0.1615

0.6

0.7

0.8

0.9

0.1292

0.0969

0.0646

0.0323

D2

0.3533

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

D3

0.2405

0.3347

0.2975

0.2603

0.2231

0.1860

0.1488

0.1116

0.0744

0.0372

D4

0.1973

0.2746

0.2441

0.2136

0.1831

0.1525

0.1220

0.0915

0.0610

0.0305

Total

1

1

1

1

1

1

1

1

1

1

Table A2: Global weights for sub-drivers by sensitivity analysis when “Financial Driver (D2)” value increases from 0.1 to 0.9.

D11

Normal (0.3533) 0.05985

D12

0.03873

0.05390

0.04791

0.04193

0.03593

0.02993

0.02394

0.01798

0.01197

0.00599

D13

0.04229

0.05886

0.05232

0.04578

0.03924

0.03270

0.02616

0.01962

0.01308

0.00654

D14

0.04532

0.06307

0.05606

0.04905

0.04205

0.03504

0.02803

0.02102

0.01402

0.00701

D15

0.02271

0.03160

0.02809

0.02458

0.02107

0.01756

0.01405

0.01053

0.00702

0.00351

D21

0.14262

0.04037

0.08073

0.12110

0.16147

0.20184

0.24220

0.28257

0.32294

0.36331

D22

0.12198

0.03453

0.06905

0.10358

0.13811

0.17263

0.20716

0.24169

0.27621

0.31074

D23

0.03569

0.01010

0.02021

0.03031

0.04041

0.05052

0.06062

0.07072

0.08083

0.09093

D24

0.05301

0.01500

0.03001

0.04501

0.06001

0.07501

0.09002

0.10502

0.12002

0.13503

D31

0.03510

0.04884

0.04342

0.03800

0.03256

0.02714

0.02171

0.01628

0.01085

0.00543

D32

0.05160

0.07181

0.06383

0.05586

0.04787

0.03990

0.03192

0.02395

0.01596

0.00798

D33

0.05508

0.07666

0.06814

0.05962

0.05110

0.04259

0.03407

0.02555

0.01703

0.00852

D34

0.02516

0.03501

0.03112

0.02723

0.02334

0.01945

0.01556

0.01167

0.00778

0.00389

D35

0.07356

0.10237

0.09100

0.07962

0.06825

0.05687

0.04550

0.03412

0.02275

0.01137

D41

0.03517

0.04895

0.04350

0.03806

0.03263

0.02719

0.02175

0.01631

0.01088

0.00544

D42

0.04445

0.06186

0.05499

0.04811

0.04124

0.03437

0.02749

0.02062

0.01375

0.00687

D43

0.03268

0.04549

0.04043

0.03538

0.03032

0.02527

0.02022

0.01516

0.01011

0.00505

D44

0.03183

0.04429

0.03937

0.03445

0.02953

0.02461

0.01969

0.01476

0.00984

0.00492

D45

0.03327

0.04630

0.04116

0.03601

0.03087

0.02572

0.02058

0.01543

0.01029

0.00514

D46

0.01990

0.02770

0.02462

0.02154

0.01847

0.01539

0.01231

0.00923

0.00616

0.00308

Total

1.00000

1.00000

1.00000

1.00000

1.00000

1.00000

1.00000

1.00000

1.00000

1.00000

Drivers

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.08329

0.07404

0.06478

0.05553

0.04627

0.03702

0.02777

0.01851

0.00925

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Table A3: Global rank for sub drivers by sensitivity analysis when “Financial Driver (D2)” value increases from 0.1 to 0.9 Drivers

Normal (0.3533) 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

D11

4

2

3

4

5

6

6

6

6

6

D12

11

8

10

11

12

12

12

12

12

12

D13

10

7

9

9

11

11

11

11

11

11

D14

8

5

7

7

8

9

9

9

9

9

D15

19

17

18

19

19

19

19

19

19

19

D21

1

14

2

1

1

1

1

1

1

1

D22

2

16

4

2

2

2

2

2

2

2

D23

12

20

20

17

10

5

4

4

4

4

D24

6

19

17

10

4

3

3

3

3

3

D31

14

10

12

13

14

14

14

14

14

14

D32

7

4

6

6

7

8

8

8

8

8

D33

5

3

5

5

6

7

7

7

7

7

D34

18

15

16

18

18

18

18

18

18

18

D35

3

1

1

3

3

4

5

5

5

5

D41

13

9

11

12

13

13

13

13

13

13

D42

9

6

8

8

9

10

10

10

10

10

D43

16

12

14

15

16

16

16

16

16

16

D44

17

13

15

16

17

17

17

17

17

17

D45

15

11

13

14

15

15

15

15

15

15

D46

20

18

19

20

20

20

20

20

20

20

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Highlights 1. Identification of key drivers and sub drivers for implementing CSR-based sourcing in the footwear industry 2. Model framework development using Delphi method and fuzzy AHP 3. Prioritization of the key drivers and sub drivers for implementing CSR based sourcing in the footwear industry 4. Discussion of practical implications of the model frameworks