How are the barriers of social sustainability perceived in a multi-tier supply chain? A case of textile and clothing industry

Abstract

Social sustainability plays a significant role in achieving sustainable development goals (SDGs), especially in emerging economies. Textile and clothing is a labour-intensive industry and presents serious challenges to adopting social sustainability at various stages of the supply chain. The objective of this research is to analyse the perspectives of brands, clothing manufacturers (tier-1) and textile manufacturers (tier-2) on the barriers to social sustainability in multitier textile and clothing supply chains operating in India. Twenty barriers have been shortlisted through an exhaustive search of extant literature followed by a survey among textile and clothing supply chain practitioners. Seven barriers related to consumers, regulation and norms, and financial aspects are found to be common cause-group barriers for all three stages of the supply chain. Lack of top management commitment, lack of competitive pressure and lack of policy on social sustainability are omnipresent as effect-group barriers. However, the relational barriers between the partners (low price offered and pressure for further cost reduction from the brands) reveal the existence of conflicting perceptions at different stages of the textile and clothing supply chain. This implies a lack of synergy and symbiotic dependence, which has been analysed through the lenses of three different organisational theories. This article contributes by underpinning the tire-wise perspectives of barriers to social sustainability in the textile and clothing supply chain. The policymakers and supply chain managers would find the results helpful for making strategies to adopt and implement social sustainability leading to achieving SDGs.

Appendix

1.1 DEMATEL method

1.1.1 Formation of average direct relation matrix A

The first step is to gather experts’ opinion regarding the influence of one barrier over the other in a pair on the scale of 0–4, where 0 represents ‘no influence’, 1 ‘low influence’, 2 ‘medium influence’, 3 ‘high influence’ and 4 ‘very high influence’. The response of each expert is captured in the form of n×n positive matrix with diagonal elements set to 0 as the barriers do not influence themselves. For H number of experts, the average matrix A is computed by averaging their scores using equation A1. The average matrix A = [a_ij]_{n × n} is known as initial direct relation matrix.

$$a_{ij}=\frac1H{\textstyle\sum_{k=1}^H}b_{ij}^{(k)}$$

(A1)

where ${b}_{ij}^{\left(k\right)}$ is the response of k^th expert while comparing barrier i with barrier j.

1.1.2 Calculation of normalised direct relation matrix D

The normalised direct relation matrix is obtained by diving each element of the initial direct relation matrix by largest value between the row sums and column sums using equations A2 and A3. After noramalising, the value of each element of the normalised direct relation matrix D lies between 0 and 1.

$$D=\frac As$$

(A2)

$$s=max\lbrack max_{1\leq i\leq n}{\textstyle\sum_{j=1}^n}a_{ij},\;max_{1\leq j\leq n}{\textstyle\sum_{i=1}^n}a_{ij}\;\rbrack$$

(A3)

1.1.3 Calculation of total relation matrix T

The total relation matrix is obtained using the equation A4.

$$T=D{(I-D)}^{-1}$$

(A4)

where I is the identity matrix having n×n dimension.

1.1.4 Calculation of prominence and relation values for each barrier

The sums of rows and columns of the total relation matrix T are represented by vector [r_i]_{n × 1} and [c_j]_{1 × n} respectively. Then the sum (r_i + c_j) and difference (r_i – c_j) are calculated. The value of (r_i + c_j), called ‘prominence’, indicates the overall effect the variable i experiences and contributes to the system. The barriers for which the difference (r_i – c_j) is positive, are clubbed into cause-group. On the other hand, the barriers with negative values of (r_i – c_j) are put under the effect-group.