| Literature DB >> 35756717 |
Angelo Arcuri1, Lorenzo Giolli1.
Abstract
Luxury fashion firms increasingly incorporate the principles of environmental sustainability into their supply chain strategies in response to a growing demand for sustainable products. Starting from this premise, the present study explores the relationship between upstream vertical integration and environmental sustainability in the luxury fashion industry. Based on data from a survey and a set of interviews, this paper examines the link between firm size and the importance of environmental sustainability as a driver of upstream integration. In addition, it investigates the connection between the degree of upstream integration and manufacturers' environmental sustainability performance. According to the research results, large corporations attach greater importance than SMEs to environmental sustainability when considering an upstream integration process. Also, higher levels of vertical integration are positively associated with better sustainability performances.Entities:
Keywords: Fashion; Luxury; Manufacturing; Sustainability; Upstream vertical integration
Year: 2022 PMID: 35756717 PMCID: PMC9213213 DOI: 10.1007/s43546-022-00252-z
Source DB: PubMed Journal: SN Bus Econ ISSN: 2662-9399
Frequency data
| Level of importance of environmental sustainability | Firm size | Total | |
|---|---|---|---|
| SMEs | Large corporations | ||
| 1 Not important | 5 8.06 | 0 0.00 | 5 8.06 |
| 2 Slightly important | 9 14.52 | 0 0.00 | 9 14.52 |
| 3 Moderately important | 14 22.58 | 3 4.84 | 17 27.42 |
| 4 Important | 3 4.84 | 14 22.58 | 17 27.42 |
| 5 Very important | 0 0.00 | 14 22.58 | 14 22.58 |
| Total | 31 | 31 | 62 |
| 50.00 | 50.00 | 100.00 | |
Measures of association
| Pearson chi2(4) = | 42.235 | Pr = 0.000 |
| Likelihood-ratio chi2(4) = | 54.262 | Pr = 0.000 |
| Cramér's | 0.825 | |
| Gamma = | 0.979 | ASE = 0.017 |
| Kendall's tau- | 0.719 | ASE = 0.035 |
| Fisher's exact = | 0.000 |
Ordered logistic regression
| Coef | Std err | [95% conf. interval] | ||||
|---|---|---|---|---|---|---|
| Firm size | 4.664 | 0.856 | 5.450 | 0.000 | 2.986 | 6.342 |
Log likelihood = − 68.224
Number of Obs = 62
LR chi2(1) = 53.120
Prob > chi2 = 0.000
Pseudo R2 = 0.280
Linear regression
| Coef | Std. Err | [95% conf. interval] | ||||
|---|---|---|---|---|---|---|
| Degree of VI | 21.880 | 0.628 | 34.790 | 0.000 | 20.622 | 23.138 |
| _Cons | − 0.572 | 0.372 | − 1.540 | 0.130 | − 1.317 | 0.172 |
Number of Obs = 62
F1(1,60) = 1210.190
Prob > F = 0.000
R-squared = 0.952
Adj R-squared = 0.952
Root MSE = 0.976