Is it practical to uphold both resilience and sustainability of supply chains using closed-loop supply chain models considering circular economy? An empirical investigation from Kenya

Figures & data

Table 1. Supply chain resilience measures and meanings.

Resilience Measure	Resilience Dimension Meaning	References
Adaptability	The capability to plan for uncertain occurrences, act in response to risk disruptions and recuperate by upholding sustainable operations.	Wieland & Wallenburg, 2012 Singh et al., 2019
Robustness; Redundancy is a major aspect of robustness	Ability to calculate and careful selection of capacity and inventory space usage for managing crises to deal with demand surges and shortages of supplies.	Christopher & Peck, 2004 Tukamuhabwa et al. 2015 Wieland & Wallenburg, 2012 Singh et al., 2019
Agility; Flexibility is a primary component of agility	The ability to respond fast to volatile disruptions in supply and demand. It’s measured through visibility and velocity where visibility is the capacity to understand the status of a supply chain environment and assets while velocity concentrates on the speed of flexibility on the capacity to adapt and hence a determinant of the resurgence speed of a supply chain from a disturbance. Flexibility is the capacity of a system to absorb change of whichever kind with minimum attempt to time.	Christopher & Peck, 2004 Tukamuhabwa et al., 2015 Pettit et al., 2013 Erol et al., 2010 Wieland & Wallenburg, 2012 Singh et al., 2019
Risk Management Culture	The ability of a system to cope with foreseeable occurrences that can disturb operations.	Christopher & Peck, 2004 Gunasekaran et al., 2015 Erol et al., 2010 Singh et al., 2019

Table 2. Measurement items.

Measure and Indicator	References
Green Design/Design for the Environment (9) Dematerialization, Design for disassembly, Design for recyclability, Design for recoverability, Design for regulatory compliance, Design for no or less hazardous ingredients in materials, Design for easier, ISO Certified and Substitutability	Saeed et al., 2018 Khan & Pasha, 2022, Galati et al., 2022
Green Procurement (7) Procuring materials with greater energy and water efficiency, Procuring materials that utilize clean energy and technology, Procuring materials with controlled use or an absence of toxic substances, Procuring materials that improve recycling ability, Procuring materials with minimal packaging needs, Procuring materials with extended durability and Supplier audits.	Govindan et al., 2014, Khan & Pasha, 2022 Galati et al., 2022
Green Manufacturing/Cleaner Production (17) Reduction of water/energy use, Use of recyclable and biodegradable materials, Use of less toxic materials, Just-in-time manufacturing, Employee/stakeholder safety measures, Employment of work schedules of shifts, Provision of protective gear for safety, Recycling/reuse of water, working towards zero defects, Cleaning Equipment and Servicing Equipment. 5S methodology (6); the existence of a maintenance department, sorting, setting in order, shine, standardize and sustain	Chin et al., 2015a Khan & Pasha, 2022, Qalati et al., 2022 Sangode, 2018 (5S), Chen et al. 2019 (TPM)
Green Logistics (22) Green Transportation (6) Direct deliveries, Well-planned vehicle routes, Energy efficient vehicles, Grouping orders for the same destination together, Well-temperature-controlled systems and Keen on good disposal of vehicles.	Govindan et al., 2014, Saeed et al.; 2018 Qalati et al., 2022
Green Warehousing (7) Reusing older facilities, Efficient space usage, Energy/water saving and Improved indoor air quality, Usage of sensor bulbs and doors, Well-coordinated labelling of inventory, Training of workforce and Warehouse waste management.	Saeed et al. 2018
Eco-Packaging and Labelling (9) Downsizing packaging, Recycling/reuse of packaging, Standardized packaging, Materials with natural texture for its packaging, Packaging that is easier to open and store and uses less space, Keen on well labelling, Use of natural colors for packaging, Use packaging free of excess layers and Second life packaging.	Chin et al., 2015b
Reverse Logistics (8) Reusing, Reduction, Recycling, Refurbishing, Remanufacturing, Product return collection centers, Product return awareness platform for customers and Support to customers for product return.	Souza, 2013, Govindan et al., 2014, 2015 Qalati et al., 2022
Adaptability (3) Your supply chain plans for uncertainties by developing a risk register; your supply chain reacts to risk disruptions through environmental supply chains and adapts quickly to a disruption.	Wieland & Wallenburg, 2012
Robustness (1) Your supply chain carefully selects environmental supply chains for managing risk disruptions.	Christopher & Peck, 2004 Tukamuhabwa et al., 2015; Wieland and Wallenburg, 2012
Agility (2) Your supply chain responds very quickly to disruptions and understands the status of its supply chain environment and assets.	Christopher & Peck, 2004; Tukamuhabwa et al., 2015; Pettit et al., 2013; Juttner & Maklan, 2011 Erol et al., 2010; Wieland and Wallenburg, 2012
Risk Management Culture (3) Your supply chain identifies areas of risk exposure, Your supply chain prepares on how to cope with foreseeable uncertainties and Has a formal risk management plan	Gunasekaran et al., 2015 Erol et al., 2010
Economic Sustainability (2) Keen on achieving the lowest total cost of ownership across the supply chain (reduction in costs), Keen on efficient resource util	Emas, 2015
Environmental Sustainability (3) Keen on achieving near zero carbon emissions (reduction of carbon emissions), Keen on achieving near zero waste (reduction of waste) and Keen on Climate Res	Emas, 2015
Social Sustainability (3) Keen on the safety of its employees, Keen on the health of its employees and customers and Keen on fair selection of employe	Emas, 2015
Internal Environmental Management (3) Building Trust, Information Sharing and Transparency	Beamon, 2005 Mafini & Loury Okoumba, 2018

Table 3. PLS-SEM measurement model interpretation.

Reflective measurement model	Meaning	Interpretation (acceptable range)
Indicator reliability	Analysis of the degree to which an indicator’s variance is explained by its construct.	Indicator loadings = > 0.0708 are preferred as the threshold shows the capability of the construct to explain more than 50% of its indicator variance.
Internal consistency reliability	Analysis of the degree to which indicators measuring the same construct are related.	Measured through; Composite reliability (Maximum is 0.95), Cronbach alpha (0.7 or 0.6 in ER) and Reliability coefficient rho (0.8-0.9)
Convergent Validity	The degree to which a construct converges to explain an indicator variance.	Measured through; Average variance extracted (> 0.5)
Discriminant Validity	The degree to which a construct is empirically distinct from other constructs in the structural model.	Measured through heterotrait-monotrait (HTMT) ratio (< 0.9)

Figure 1. Conceptual model.

Figure 2. G power estimates.

Figure 3. Confirmatory tetrad analysis interpretation (Wong, 2019).

Figure 4. Research model- PLS-SEM graphical output (with moderator).

Table 4. PLS-SEM measurement model, indicator loadings.

	Outer Loadings
No	Indicators	Res	EcoSus	Nevus	SocSus	GP& DFE	GM/CP	GL	RL	IEM
1	Adap 1	0.723
2	Adap 2	0.659
3	Adap 3	0.587
4	Agil 1	0.746
5	Agil 2	0.736
6	Risk 2	0.797
7	Risk 3	0.707
8	Robustness	0.752
9	Econ 1		0.881
10	Econ 2		0.887
11	Env 1			0.912
12	Env 2			0.826
13	Env 3			0.863
14	Soc 1				0.931
15	Soc 2				0.76
16	Soc 3				0.934
17	GP1					0.734
18	GP6					0.747
19	GP7					0.636
20	GPDF1					0.733
21	GPDF2					0.585
22	GPDF4					0.825
23	GPDF5					0.693
24	GPDF6					0.842
25	GPDF9					0.645
26	CP2						0.693
27	CP4						0.689
28	CP6						0.62
29	CP7						0.727
30	CP8S2						0.798
31	CP9S3						0.721
32	CP10S4						0.78
33	LL4							0.636
34	LL5							0.814
35	LL6							0.789
36	LP1							0.573
37	LP4							0.81
38	LP6							0.716
38	LW1							0.707

Table 5. PLS-SEM measurement model-internal consistency reliability.

	Cronbach’s Alpha	(rho-a)	(rho-c)	Validity
RES	0.848	0.854	0.879	Supported
Econ Sus	0.721	0.721	0.878	Supported
Env Sus	0.835	0.838	0.901	Supported
Soc Sus	0.851	0.891	0.910	Supported
GP & DFE	0.882	0.893	0.905	Supported
CP	0.844	0.848	0.883	Supported
GL	0.877	0.892	0.901	Supported
RL	0.872	0.881	0.901	Supported
IEMm	0.723	0.746	0.841	Supported

Table 6. PLS-SEM measurement model-convergent validity, AVE.

Indicator	AVE Values	Supported
RES	0.511	Supported
Econ Sus	0.782	Supported
Env Sus	0.752	Supported
Soc Sus	0.772	Supported
GP & DFE	0.519	Supported
CP	0.519	Supported
GL	0.506	Supported
RL	0.566	Supported
IEM	0.638	Supported

Table 7. PLS-SEM measurement model, discriminant validity.

Variables	CP	Econ Sus	Env Sus	GL	GP & DFE	Moderator = IEM	RL	RES	Soc Sus	Validity
CP
Econ Sus	0.841									Supported
Env Sus	0.874	0.474								Supported
GL	0.656	0.391	0.582							Supported
GP & DFE	0.878	0.578	0.873	0.785						Supported
Moderator = IEM	0.825	0.437	0.685	0.745	0.816					Supported
RL	0.575	0.303	0.450	0.636	0.603	0.586				Supported
RES	0.519	0.583	0.429	0.443	0.494	0.477	0.641			Supported
Soc Sus	0.845	0.820	0.519	0.494	0.609	0.485	0.401	0.305		Supported

Table 8. Demographic characteristics.

Demographic Data A: Industry Type	Frequency	Percentage
Food & Beverage	38	38
Chemical & Allied	17	17
Pharmaceuticals	17	17
Building, Mining and Construction	10	10
Energy, Electricals & Electronics	9	9
Cosmetics	8	8
Unresponsive	1	1
Demographic Data B: Gender Orientation	Frequency	Percentage
Male	67	67
Female	32	32
Prefer not to say	1	1
Demographic Data C: Affiliate Department	Frequency	Percentage
Procurement, Imports, Purchasing, Planning & Logistics	45	45
Production & Quality Assurance	20	20
General Supply Chain	16	16
General Operations like Managing Directors Office	9	9
Accounts, Finance & Administration	8	8
Unresponsive	2	2
Demographic Data D: Affiliate Position	Frequency	Percentage
Procurement (Senior Manager, Head, In charge, Officer & Analyst), Logistics (Assistant &Officer), Stores Manager, Export Dispatch Manager, Marketing Manager, Purchasing Manager and Managing Director (Finance & Administration)	45	45
Supply Chain (Head, Manager (Tenders & Contracts), Group Manager, Manager (Logistics & Supply Chain), Procurement & Supplies Expert, Assistant Store & Stock Controller, Procurement Officer, Project Manager & Procurer	20	20
Production Manager, Quality Officer, Compliance Officer, safety and environment manager & Assistant Lab Manager	16	16
Managing Director, Chief Executive Officer &General Manager	9	9
Accountant, Administrator & Human Resource Officer	8	8

Table

Unresponsive	2	2
Demographic Data E: Years of Experience	Frequency	Percentage
0-5	51	51
5-10	20	20
10-15	12	12
>20	7	7
15-20	5	5
Unresponsive	5	5

Table 9. PLS-SEM structural model, collinearity assessment using outer VIF.

No	Indicator	Outer VIF	Validity
1	CP*IEM	1.000	Supported
2	CP2	2.100	Supported
3	CP4	1.940	Supported
4	CP6	1.395	Supported
5	CP7	1.828	Supported
6	CP8S2	2.831	Supported
7	CP9S3	1.766	Supported
8	CP10S4	2.174	Supported
9	GPDF1	1.871	Supported
10	GPDF5	3.213	Supported
11	GPDF6	3.657	Supported
12	GPDF9	4.242	Supported
13	GP1	2.018	Supported
14	GP6	2.067	Supported
15	GP7	1.783	Supported
16	GPDF4	2.813	Supported
17	Econ 1	1.466	Supported
18	Econ 2	1.466	Supported
19	Env 1	2.908	Supported
20	Env 2	1.582	Supported
21	Env 3	2.495	Supported

Table 10. PLS-SEM structural model, R square and adjusted R square values.

Indicator	Original Sample (O)	Sample Mean (M)	Standard Deviation (STADEV)	T Statistic (O/STADEV)	P Values	Validity
R Square Values
Res	0.507	0.550	0.061	8.292	0.000	Supported
sss	0.477	0.501	0.102	4.660	0.000	Supported
EnvSus	0.645	0.667	0.074	8.703	0.000	Supported
SocSus	0.542	0.559	0.094	5.774	0.000	Supported
Adjusted R Square Values
Res	0.470	0.516	0.066	7.136	0.000	Supported
EconSus	0.449	0.474	0.108	4.167	0.000	Supported
EnvSus	0.626	0.649	0.078	8.022	0.000	Supported
SocSus	0.523	0.541	0.098	5.343	0.000	Supported

Table 11. PLS-SEM structural model, F square values.

	Original Sample (O)	Sample Meas (M)	Standard Deviation (STADEV)	T Statistics (O/STADEV)	P Values	Validity
CP -> Econ Sus	0.496	0.531	0.267	1.858	0.032	Supported
CP -> Env Sus	0.130	0.150	0.119	1.095	0.137	Unsupported
CP -> RES	0.039	0.055	0.053	0.737	0.231	Unsupported
CP -> Soc Sus	0.547	0.560	0.271	2.014	0.022	Supported
GL -> Econ Sus	0.000	0.011	0.015	0.009	0.497	Unsupported
GL -> Env Sus	0.000	0.017	0.025	0.016	0.493	Unsupported
GL -> RES	0.002	0.017	0.025	0.080	0.468	Unsupported
GL -> Soc Sus	0.007	0.017	0.020	0.359	0.360	Unsupported
GP &DF -> Econ Sus	0.000	0.013	0.020	0.003	0.499	Unsupported
GP &DF -> Env Sus	0.272	0.277	0.153	1.777	0.038	Supported
GP &DF -> RES	0.002	0.017	0.025	0.060	0.476	Unsupported
GP &DF -> Soc Sus	0.008	0.017	0.023	0.362	0.359	Unsupported
IEM -> Econ Sus	0.022	0.036	0.041	0.530	0.298	Unsupported
IEM -> Env Sus	0.000	0.019	0.027	0.002	0.499	Unsupported
IEM -> RES	0.022	0.031	0.037	0.597	0.275	Unsupported
Moderating 1-> RES	0.055	0.055	0.047	1.162	0.123	Unsupported
Moderating 2-> RES	0.067	0.080	0.068	0.976	0.165	Unsupported
RL-> Econ Sus	0.005	0.013	0.017	0.304	0.380	Unsupported
RL-> Env Sus	0.006	0.030	0.042	0.151	0.440	Unsupported
RL-> RES	0.257	0.267	0.137	1.880	0.030	Supported
RL-> Soc Sus	0.000	0.009	0.014	0.005	0.498	Unsupported

Table 12. PLS-SEM structural model, path coefficient values.

Indicators	Original Sample (O)	Sample Mean (M)	Standard deviation (STDEV)	T statistics (|O/STDEV|)	P values	Validity
CP -> Econ Sus	0.822	0.817	0.132	6.238	0.000	Supported
CP -> Env Sus	0.346	0.342	0.140	2.476	0.007	Supported
CP -> RES	0.236	0.247	0.140	1.679	0.047	Unsupported
CP -> SocSus	0.772	0.751	0.128	6.033	0.000	Supported
GL -> Econ Sus	−0.013	−0.000	0.119	0.105	0.458	Unsupported
GL -> Env Sus	−0.018	−0.009	0.123	0.147	0.442	Unsupported
GL -> RES	−0.050	−0.029	0.151	0.331	0.370	Unsupported
GL -> SocSus	0.085	0.093	0.094	0.904	0.183	Unsupported
GP &DF -> Econ Sus	−0.010	−0.007	0.154	0.063	0.475	Unsupported
GP &DF -> Env Sus	0.560	0.548	0.147	3.798	0.000	Supported
GP &DF -> RES	0.050	0.033	0.171	0.293	0.385	Unsupported
GP &DF -> Soc Sus	−0.109	−0.096	0.133	0.822	0.206	Unsupported
IEM-> Econ Sus	−0.153	−0.161	0.121	1.267	0.103	Unsupported
IEM -> Env Sus	−0.006	0.012	0.119	0.047	0.481	Unsupported
IEM-> RES	0.156	0.141	0.124	1.256	0.229	Unsupported
RL -> Econ Sus	−0.068	−0.066	0.091	0.743	0.260	Unsupported
RL -> Env Sus	−0.062	−0.093	0.096	0.644	0.258	Unsupported
RL -> RES	0.471	0.471	0.104	4.532	0.000	Supported
RL -> SocSus	−0.007	−0.002	0.087	0.085	0.466	Unsupported

Table 13. PLS-SEM structural model, Q square (PLS-Predict, 10 folds, 10 repetitions): LV prediction summary.

	RMSE	MAE	Q2_Predict	Validity
Econ Sus	0.800	0.593	0.405	Supported
Env Sus	0.686	0.517	0.572	Supported
RES	0.797	0.625	0.391	Supported
Soc Sus	0.730	0.560	0.498	Supported

Figure 5. Integrated resilient sustainability framework based CLSMs (transitioning to the quadruple bottom line).

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Data availability statement

The corresponding authors data supporting the findings of this study are available upon reasonable request.