Green supply chain management practices in India: a confirmatory empirical study

Figures & data

Table 1. Critical practices for GSCM implementation emphasized by selected authors.

Variable No.	Critical practices	Representative reference
VAR01	Processes to comply with emission standards	Handfield et al. (2005), Zhu et al. (2005), and Rao (2007)
VAR02	Processes to reduce solid wastes	Handfield et al. (2005), Zhu et al. (2005), and Rao (2007)
VAR03	Processes to reduce water use	Handfield et al. (2005), Zhu et al. (2005), and Rao (2007)
VAR04	Processes to reduce air emissions	Handfield et al. (2005), Zhu et al. (2005), and Rao (2007)
VAR05	Processes to reduce noise	Handfield et al. (2005), Zhu et al. (2005), and Rao (2007)
VAR06	Use of environmental friendly raw materials	Rao (2002) and Yuang and Kielkiewicz-Yuang (2001)
VAR07	Cleaner technology processes to make savings	Rao (2002), Yuang and Kielkiewicz-Yuang (2001), and Rao (2007)
VAR08	Use of waste of other companies	Rao (2002) and Rao (2007)
VAR09	Recycling of materials internal to the company	Handfield et al. (2005) and Rao (2007)
VAR10	Use of remanufacturing	Handfield et al. (2005), Rao (2002), Yuang and Kielkiewicz-Yuang (2001), and Rao (2007)
VAR11	Choosing suppliers by environmental criteria	Handfield et al. (2005), Yuang and Kielkiewicz-Yuang (2001), and Vachon (2007)
VAR12	Recovery of the end-of-life products	Handfield et al. (2005) and Rao (2007)
VAR13	Urging supplier(s) to take environmental actions	Handfield et al. (2005), Yuang and Kielkiewicz-Yuang (2001), and Rao (2007)
VAR14	Environmental improvement of packaging	Handfield et al. (2005) and Rao (2007)
VAR15	Informing customers on environmental friendly products	Rao (2002) and Yuang and Kielkiewicz-Yuang (2001)

Table 2. Factor analysis of environmental actions statements.

	Factor loading	Percent variance explained	Cronbach’s alpha
[F1] Factor 1: REVERSE LOGISTICS		14.316	.625
1. [VAR10] Use of re-manufacturing	.819
2. [VAR12] Recovery of the end-of-life products	.664
[F2] Factor 2: INBOUND GREENING		14.139	.612
1. [VAR11] Choosing suppliers by environmental criteria	.842
2. [VAR08] Use of waste of other companies	.732
3. [VAR13] Urging supplier(s) to take environmental actions	.650
[F3] Factor 3: COMPLIANCE GREENING		13.298	.613
1. [VAR05] Processes to reduce noise	.691
2. [VAR01] Processes to comply with emission standards	.688
3. [VAR06] Use of environmental friendly raw materials	.828
[F4] Factor 4: ECOLOGICAL GREENING		12.568	.629
1. [VAR02] Processes to reduce solid wastes	.886
2. [VAR03] Processes to reduce water use	.820
3. [VAR04] Processes to reduce air emissions	.517
[F5] Factor 5: OUTBOUND GREENING		11.352	.619
1. [VAR09] Recycling of waste materials internal to the company	.875
2. [VAR15] Informing consumers on environmental friendly products	.836
[F6] Factor 6: TECHNOLOGY GREENING		10.731	.645
1. [VAR14] Environmental improvement of packaging	.804
2. [VAR07] Cleaner technology processes to make savings	.809

Table 3. Scale purification.

Scale/subscale	Final loading
Factor 1: [F1] REVERSE LOGISTICS
1. [VAR10] Use of re-manufacturing	.698
2. [VAR12] Recovery of the end-of-life products	.698
Factor 2: [F2] INBOUND GREENING
1. [VAR11] Choice of suppliers by environmental criteria	.884
2. [VAR08] Use of waste of other companies	.319
3. [VAR13] Urging supplier(s) to take environmental actions	.630
Factor 3: [F3] COMPLIANCE GREENING
1. [VAR05] Processes to reduce noise	.609
2. [VAR01] Processes to comply with emission standards	.682
3. [VAR06] Use of environmental friendly raw materials	.535
Factor 4: [F4] ECOLOGICAL GREENING
1. [VAR02] Processes to reduce solid wastes	.827
2. [VAR03] Processes to reduce water use	.812
3. [VAR04] Processes to reduce air emissions	.292
Factor 5: [F5] OUTBOUND GREENING
1. [VAR09] Recycling of waste materials internal to the company	.679
2. [VAR15] Informing consumers on environmental friendly products	.679
Factor 6: [F6] TECHNOLOGY GREENING
1. [VAR14] Environmental improvement of packaging	.691
2. [VAR07] Cleaner technology processes to make savings	.691

Table 4. Reliability analysis.

Dimension	Cronbach alpha
Reverse logistics greening	.621
Inbound greening	.711
Compliance greening	.628
Ecological greening	.804
Outbound greening	.618
Technology greening	.645
External pressures	.657
Internal pressures	.687

Figure 1. Default model of GSCM.

Table 5. Results of fit indices.

Goodness-of-fit statistics	Symbol	Model 1	Model 2	Model 3	Model 4
Chi-square test	χ^2	2113.984	539.829	2238.463	2333.913
Degree of freedom	df	45	18	63	95
Chi-square/degree of freedom ratio	χ^2/df	46.977	29.991	35.531	24.568
Root mean square error of approximation	RMSEA	.352	.289	.310	.190
Turker-Lewis index	TLI	.064	.302	.042	.733
Normed fit index	NFI	.361	.479	.425	.720
Comparative fit index	CFI	.362	.488	.425	.722
Parsimonious normed fit index	PNFI	.246	.351	.255	.587

Table 6. Assessment of normality.

Variable	Min	Max	Skew	C.R.	Kurtosis	C.R.
TYPE	1.000	2.000	.566	4.769	−1.680	−7.077
INDUSTRY	1.000	5.000	.605	5.098	−.787	−3.314
EDUCATION	1.000	5.000	.284	2.397	−.638	−2.689
TSUPPORT	1.000	3.000	−.221	−1.864	−.739	−3.114
TRAINING	1.000	5.000	−.520	−4.384	−.995	−4.192
COMPETITOR	1.000	5.000	−.026	−.219	−.261	−1.101
COMMUNITY	1.000	5.000	.373	3.145	−.359	−1.511
DCLIENT	1.000	5.000	.725	6.108	.513	2.163
FCLIENT	2.000	5.000	.551	4.644	.216	.910
REGULATORY	1.000	5.000	−.581	−4.892	.297	1.251
GSCM	2.000	4.000	−.142	−1.194	.903	3.805
F6	1.000	5.000	.493	4.158	−.725	−3.055
F5	1.000	5.000	.820	6.912	−.444	−1.870
F4	2.000	5.000	.705	5.939	.062	.261
F3	2.000	5.000	−.177	−1.491	−.993	−4.182
F2	1.000	5.000	.131	1.105	−.958	−4.038
F1	1.000	5.000	.255	2.149	−.341	−1.436
Multivariate					−1.525	−.619

Table 7. Regression weights of the default model.

			Estimate	S.E.	C.R.	p
GSCM	< –	REGULATORY	.231	.020	11.331	***
GSCM	< –	FCLIENT	−.067	.025	−2.736	.006
GSCM	< –	DCLIENT	.323	.028	11.375	***
GSCM	< –	COMMUNITY	−.207	.063	−3.309	***
GSCM	< –	COMPETITOR	.077	.027	2.835	.005
GSCM	< –	EDUCATION	.035	.014	2.564	.010
GSCM	< –	TSUPPORT	.196	.037	5.249	***
GSCM	< –	TRAINING	.116	.013	8.791	***
GSCM	< –	INDUSTRY	.085	.037	2.274	.023
GSCM	< –	TYPE	−.016	.031	−.496	.620
F1	< –	GSCM	.872	.090	9.702	***
F2	< –	GSCM	.534	.108	4.955	***
F3	< –	GSCM	.949	.084	11.259	***
F4	< –	GSCM	.916	.070	12.999	***
F5	< –	GSCM	−.107	.101	−1.055	.291
F6	< –	GSCM	.658	.117	5.625	***

*** Significant at p > 0.001.

Table 8. Intercepts of the default model.

	Estimate	S.E.	C.R.	p
GSCM	.659	.124	5.333	***
F1	.556	.267	2.084	.037
F2	1.754	.320	5.479	***
F3	.803	.251	3.204	.001
F4	.383	.209	1.829	.067
F5	2.969	.300	9.897	***
F6	.562	.348	1.615	.106

*** Significant at p > 0.001.

Table 9. Results showing some significant relationships in the default model.

Sr. No.	Regression relations	C.R.	Supported (Yes/No)^a
H1	External pressures of government regulations lead to GSCM practices	11.331	Yes*
H2	External pressures of domestic clients lead to GSCM practices	−2.736	Yes**
H3	External pressures of foreign clients lead to GSCM practices	11.375	Yes***
H4	External pressures of neighbouring communities lead to GSCM practices	−3.309	Yes***
H5	External pressures of competitors lead to GSCM practices	2.835	Yes**
H6	Internal pressures of top managers’ support lead to GSCM practices	2.564	Yes**
H7	Internal pressures of on-the-job training lead to GSCM practices	5.249	Yes***
H8	Internal pressures of professional education of employees lead to GSCM practices	8.791	Yes***
H9	There is a significant difference in the mean greening scores for different nature of MSMEs industry in respect of different factors affecting GSCM	2.274	Yes **
H10	There is a significant difference in the mean greening scores for different types of MSMEs in respect of different factors affecting GSCM	−.496	No ***
H11	GSCM practices of MSMEs lead to Reverse Logistics Greening	9.702	Yes***
H12	GSCM practices of MSMEs lead to Inbound Greening	4.955	Yes***
H13	GSCM practices of MSMEs lead to Compliance Greening	11.259	Yes***
H14	GSCM practices of MSMEs lead to Ecological Greening	12.999	Yes***
H15	GSCM practices of MSMEs lead to Outbound Greening	−1.055	No***
H16	GSCM practices of MSMEs lead to Technology Greening	5.625	Yes***
H17	There is a significant difference in mean greening scores on pressures from foreign clients for adopting GSCM practices	85.963	Yes***
H18	There is a significant difference in mean greening scores on pressures from domestic clients for adopting GSCM practices	49.533	Yes***
H19	There is a significant difference in mean greening scores on pressures from neighbouring communities for adopting GSCM practices	66.409	Yes***
H20	There is a significant difference in mean greening scores on pressures from competitors for adopting GSCM practices	60.044	Yes***
H21	There is a significant difference in mean greening scores on pressures from governmental regulations for adopting GSCM practices	79.474	Yes***
H22	There is a significant difference in mean greening scores on pressures from on-the-job training for adopting GSCM practices	62.090	Yes***
H23	There is a significant difference in mean greening scores on pressures from top-managers’ support for adopting GSCM practices	68.776	Yes***
H24	There is a significant difference in mean greening scores on pressures from professional education of employees for adopting GSCM practices	44.393	Yes***
H25	There is a significant difference in mean greening scores on pressures from different nature of industries for adopting GSCM practices	43.745	Yes***
H26	There is a significant difference in mean greening scores on pressures from different types of industries for adopting GSCM practices	58.441	Yes***

^a Based on t-tests for null hypothesis, t-value greater than 1.96.

*** are significant at p > .05; t-value greater than 2.567.

** are significant at p > .01; and t-value greater than 3.29.

* are significant at p > .001 (Hatcher, 1994).

Table 10. Direct effects of the default model.

	Type	Industry	Education	Tsupport	Training	Competitor	Community	Dclient	Fclient	Regulatory	GSCM
GSCM	−.016	.085	.035	.196	.116	.077	−.207	.323	−.067	.231	.000
F6	.000	.000	.000	.000	.000	.000	.000	.000	.000	.000	.658
F5	.000	.000	.000	.000	.000	.000	.000	.000	.000	.000	−.107
F4	.000	.000	.000	.000	.000	.000	.000	.000	.000	.000	.916
F3	.000	.000	.000	.000	.000	.000	.000	.000	.000	.000	.949
F2	.000	.000	.000	.000	.000	.000	.000	.000	.000	.000	.534
F1	.000	.000	.000	.000	.000	.000	.000	.000	.000	.000	.872

Table 11. Indirect effects of the default model.

	Type	Industry	Education	Tsupport	Training	Competitor	Community	Dclient	Fclient	Regulatory	GSCM
GSCM	.000	.000	.000	.000	.000	.000	.000	.000	.000	.000	.000
F6	−.010	.056	.023	.129	.076	.051	−.136	.213	−.044	.152	.000
F5	.002	−.009	−.004	−.021	−.012	−.008	.022	−.034	.007	−.025	.000
F4	−.014	.077	.032	.180	.106	.071	−.190	.296	−.062	.212	.000
F3	−.015	.080	.034	.187	.110	.074	−.197	.307	−.064	.219	.000
F2	−.008	.045	.019	.105	.062	.041	−.111	.172	−.036	.123	.000
F1	−.014	.074	.031	.171	.101	.068	−.181	.282	−.059	.201	.000

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