COVID-19 as a trigger for dynamic capability development and supply chain resilience improvement

Figures & data

Figure 1. The conceptual model.

The conceptual model showing the hypotheses where COVID-19 upstream and downstream impacts lead to sensing, seizing, and responding capabilities, which further lead to supply chain resilience.

Table 1. Summary of the characteristics of the respondents.

Features of the respondents	n	%
Company size
Micro	17	15.5
Small	24	21.8
Medium	24	21.8
Large	13	11.8
N/A	32	29.1
Total	110	100
Industry
Medical devices	22	20
Biomedical technology	4	3.6
Laboratory testing and in vitro diagnostics	13	11.8
Services and software	7	6.4
Other	32	29.1
N/A	32	29.1
Total	110	100
Respondents’ position
Top management	33	30
Middle management	25	22.7
Operative task	17	15.5
Expert	1	0.9
Other	1	0.9
N/A	33	30
Total	110	100

Table 2. Measurement reliabilities.

	Loading	t-value	p-value	Mean	SD	CR	AVE
CovUI (COVID-19 Upstream Impact)						0.790	0.563
SUI1	0.900	9.410	****	3.02	1.498
SUI2	0.683	3.268	***	1.862	2.121
SUI3	0.641	4.360	****	2.532	1.628
CovDI (COVID-19 Downstream Impact)						0.844	0.645
SDI1	0.795	4.616	****	0.473	1.128
SDI2	0.706	2.954	***	2.042	2.056
SDI3	0.898	5.050	****	1.432	1.946
SEN (Sensing)						0.805	0.587
SEN1	0.600	2.683	***	3.49	1.551
SEN2	0.924	4.539	****	3.44	1.227
SEN3	0.741	3.036	***	3.29	1.806
SEIZ (Seizing)						0.848	0.653
SEIZ1	0.742	5.449	****	3.61	1.582
SEIZ2	0.915	23.313	****	3.66	1.589
SEIZ3	0.755	10.755	****	3.18	2.151
RECO (Reconfiguring)						0.834	0.636
RECO1	0.541	3.285	***	2.63	1.939
RECO2	0.888	13.961	****	3.13	1.867
RECO3	0.910	19.811	****	3.47	2.159
RESL (Resilience)						0.888	0.726
RESL1	0.799	7.213	****	3.88	2.054
RESL2	0.881	27.990	****	3.52	1.636
RESL3	0.873	16.369	****	3.22	2.241

Note: n: not significant; *statistically significant at p < 0.1; **statistically significant at p < 0.05; ***statistically significant at p < 0.01; ****statistically significant at p < 0.001.

Table 3. Main model to test the hypotheses and results of the post hoc analysis.

Hypothesis	Path	β	T Statistics	P Values
Main model
H1a	SEN → SEIZ	0.367	3.142	**
H1b	SEIZ → RECON	0.307	2.913	**
H2a	CovUI → SEN	−0.181	1.051	n
H2b	CovUI → SEIZ	−0.368	4.074	***
H2c	CovUI → RECON	−0.205	1.535	n
H3a	CovDI → SEN	0.063	0.407	n
H3b	CovDI → SEIZ	0.104	0.911	n
H3c	CovDI → RECON	0.421	2.948	**
H4a	SEN → RESL	0.102	0.786	n
H4b	SEIZ → RESL	0.344	2.450	*
H4c	RECON → RESL	0.402	3.607	***
Post hoc tests for total indirect effects
	SEN →→ RECON	.059	1.913	n
	CovUI →→ RESL	−.304	4.421	****
	CovDI →→ RESL	.235	2.469	*

Notes: n: not significant; *statistically significant at p < 0.05; **statistically significant at p < 0.01; ***statistically significant at p < 0.001.

Table

Supply Chain Resilience	Ambulkar, Blackhurst, and Grawe (2015)
Assess how well your company has adapted to the supply chain disruptions caused by COVID-19. Please rate the following items using the following scale: 1 = strongly disagree, 5 = strongly agree.
RESL1: We are able to cope with changes brought by the supply chain disruption caused by COVID-19.
RESL2: We are able to provide a quick response to the supply chain disruption caused by COVID-19.
RESL3: We are able to maintain high situational awareness at the time of COVID-19.
Sensing Capability	Lee and Rha (2016)
Assess how well your company maintains awareness of the business environment during COVID-19. Please rate the following items using the following scale: 1 = strongly disagree, 5 = strongly agree.
SEN1: We can feel the major potential opportunities and threats in our supply chain.
SEN2: We have good observation and judgment abilities in our supply chain.
SEN3: We have frequent interactions throughout the organisation to acquire new knowledge related to product development, process innovation, or logistics and distribution practices.
Seizing Capability	Lee and Rha (2016)
Assess how well strategic decision-making is performed in your company during COVID-19. Please rate the following items using the following scale: 1 = strongly disagree, 5 = strongly agree.
SEIZ1: We can quickly deal with conflicts in strategic decision-making in our supply chain.
SEIZ2: We can make timely decisions to deal with supply chain problems.
SEIZ3: We can reconfigure resources in time to address environmental change.
Reconfiguring Capability	Lee and Rha (2016)
Assess how well your company aligns operations with the changed business environment during COVID-19. Please rate the following items using the following scale: 1 = strongly disagree, 5 = strongly agree.
RECO1: We can successfully reconfigure supply chain resources to come up with new productive assets.
RECO2: We are able to align (or re-distribute) skills to meet the current needs of the whole supply chain.
RECO3: We can effectively integrate and combine existing resources into novel combinations in the supply chain.
COVID-19 Upstream Impact	Authors modified based on Pettit, Fiksel, and Croxton (2010)
How much has COVID-19 directly or indirectly affected the following areas in your company? Please rate the following items using the following scale: 1 = not at all, 5 = very much.
SUI1: Overall supply chain
SUI2: Procurement costs/prices for the purchased items
SUI3: Delivery reliability (on-time delivery, order accuracy)
COVID-19 Downstream Impact	Authors modified based on Pettit, Fiksel, and Croxton (2010)
How much has COVID-19 directly or indirectly affected the following areas in your company? Please rate the following items using the following scale 1 = not at all, 5 = very much.
SDI1: Product quality of our final product(s)
SDI2: Responsiveness to customer demands
SDI3: Customer satisfaction

Table

	CovUI	CovDI	SEN	SEIZ	RECO	RESL
SUI1	0.9	0.12	−0.12	−0.4	−0.28	−0.51
SUI2	0.68	0.58	−0.24	−0.25	0.08	0.03
SUI3	0.64	0.2	0.01	−0.16	−0.1	−0.15
SDN1	0.23	0.79	−0.02	0.05	0.27	0.19
SDN2	0.46	0.71	0.03	−0.14	0.07	0.06
SDN3	0.28	0.9	0.01	−0.04	0.35	0.29
SENS1	0.05	0.22	0.6	0.23	0.21	0.24
SENS2	−0.2	−0.09	0.92	0.43	0.17	0.25
SENS3	−0.16	−0.05	0.74	0.29	−0.02	0.23
SEIZ1	−0.36	−0.1	0.3	0.74	0.42	0.38
SEIZ2	−0.24	−0.05	0.34	0.92	0.26	0.39
SEIZ3	−0.33	0.09	0.38	0.76	0.23	0.51
RECO1	−0.32	−0.01	0.08	0.04	0.54	0.18
RECO2	−0.16	0.24	−0.05	0.32	0.89	0.41
RECO3	−0.11	0.42	0.27	0.41	0.91	0.58
RESL1	−0.14	0.37	0.16	0.14	0.43	0.8
RESL2	−0.26	0.28	0.42	0.6	0.51	0.88
RESL3	−0.48	0.08	0.15	0.51	0.45	0.87

Ambulkar, S., J. Blackhurst, and S. Grawe. 2015. “Firm’s Resilience to Supply Chain Disruptions: Scale Development and Empirical Examination.” Journal of Operations Management 33-34 (1): 111–122.

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Lee, S. M., and J. S. Rha. 2016. “Ambidextrous Supply Chain as a Dynamic Capability: Building a Resilient Supply Chain.” Management Decision 54 (1): 2–23.

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Pettit, T. J., J. Fiksel, and K. L. Croxton. 2010. “Ensuring Supply Chain Resilience: Development of a Conceptual Framework.” Journal of Business Logistics 31 (1): 1–21.

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Data availability statement

Data are not available due to industry-specific restrictions. Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data are not available.