Big data analytics in supply chain management and its impact on business performance

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KMO and Bartlett Test of Big Data Analysis Capability

KMO and Bartlett sphericity test
KMO measures adequacy of sampling		0.861
Bartlett sphericity test	Approximate chi-square	785.342
	Df	135
	Sig.	0.000

Results of enterprise performance reliability analysis

Variable	Dimensionality	Question number	CITC	After deletion Cronbach’s ɑ	Cronbach’s ɑ
Enterprise performance (EP)	Market performance (EP1)	EP11	0.5537	0.8092	0.8508	0.8446
		EP12	0.5229	0.8165
		EP13	0.6237	0.8017
		EP14	0.4804	0.8205
	Operational performance (EP2)	EP21	0.6018	0.8026	0.8513
		EP22	0.5921	0.8368
		EP23	0.6206	0.8007
		EP24	0.4498	0.8223

Total Variance Explained of Supply Chain Flexibility

Factor	Initial eigenvalue			Extract sum of squares and load
	Total	Variation%	Cumulative %	Total	Variation%	Cumulative %
1	7.8453	36.7231	36.7231	7.8453	36.7231	36.7231
2	4.3710	20.0144	56.7375	4.3710	20.0144	56.7375
3	1.8433	8.3794	65.1169	1.8433	8.3794	65.1169
4	1.2137	5.7011	70.8180	1.2137	5.7011	70.8180
5	1.0580	4.6118	75.4298	1.0580	4.6118	75.4298
6	0.7835	3.7012	79.1310
7	0.5294	2.4963	81.6273
8	0.4905	2.4440	84.0713
9	0.4826	2.1544	86.2257
10	0.3701	1.9600	88.1857
11	0.3553	1.8825	90.0682
12	0.3132	1.5074	91.5756
13	0.2756	1.3818	92.9574
14	0.2224	1.3044	94.2618
15	0.1979	1.0637	95.3255
16	0.2907	1.0399	96.3654
17	0.1806	0.9884	97.3538
18	0.1326	0.7926	98.1464
19	0.0836	0.5862	98.7326
20	0.0793	0.5364	99.2690
21	0.0678	0.3854	99.6544
22	0.0515	0.3456	100.0000

Enterprise performance measurement scale

Variable	Dimensionality	Question number	Item
Enterprise performance (EP)	Market performance (EP1)	EP11	Firms enter new markets faster than their competitors.
		EP12	Companies bring new products or services to market faster than their competitors.
		EP13	The success rate of a company’s new product or service is consistently higher than that of its competitors.
		EP14	The company’s market share has surpassed that of its competitors.
	Operational performance (EP2)	EP21	The productivity of the company has surpassed that of its competitors
		EP22	The company’s profit margin exceeds that of its competitors.
		EP23	The company’s return on investment exceeds that of its competitors.
		EP24	The company’s sales revenue exceeded that of its competitors.

Total Variance Explained of Firm Performance

Factor	Initial eigenvalue			Extract sum of squares and load
	Total	Variation%	Cumulative %	Total	Variation%	Cumulative %
1	5.2155	58.6864	58.6864	5.2155	58.6864	58.6864
2	1.1438	14.9662	73.6526	1.1438	14.9662	73.6526
3	0.6628	8.1228	81.7754
4	0.4656	5.2589	87.0343
5	0.3335	4.3841	91.4184
6	0.2958	3.7094	95.1278
7	0.2378	2.8466	97.9744
8	0.1616	2.0256	100.0000

Reliability analysis results of big data analysis capability

Variable	Dimensionality	Question number	CITC	After deletion Cronbach’s ɑ	Cronbach’s ɑ
Big data analysis ability (BDAC)	Resource acquisition ability (BDAC1)	BDAC11	0.4337	0.8567	0.8437	0.8618
		BDAC12	0.6128	0.8443
		BDAC13	0.5436	0.8592
		BDAC14	0.5208	0.8560
	Integration and analytical ability (BDAC2)	BDAC21	0.4783	0.8546	0.8532
		BDAC22	0.6254	0.8433
		BDAC23	0.4759	0.8583
		BDAC24	0.6001	0.8472
	Insight and prediction ability (BDAC3)	BDAC31	0.5225	0.8406	0.8442
		BDAC32	0.6237	0.8428
		BDAC33	0.4332	0.8555
		BDAC34	0.6108	0.8443

Results of mediation analysis

	Path	SE	Effect value	Bias-corrected 95% CI		P	Ratio of total effect
				Lower	Upper
H8	BDAC → SCF → EP	0.081	0.237	0.105	0.449	0.003	100%
H9	BDAC → SCF1 → EP	0.041	0.083	0.015	0.142	0.021	35.02%
H10	BDAC → SCF2 → EP	0.083	0.154	0.031	0.385	0.031	64.98%

Test results of the path coefficients affecting the relationship

	Path	β	S.E.	C.R.	P	Result
H1	BDAC → SCF	0.441	0.081	5.735	0.000	Support
H2	BDAC → SCF1	0.448	0.098	5.551	0.000	Support
H3	BDAC → SCF2	0.637	0.075	6.637	0.000	Support
H4	BDAC → EP	0.481	0.105	4.388	0.000	Support
H5	SCF → EP	0.432	0.114	3.251	0.008	Support
H6	SCF1 → EP	0.386	0.045	2.667	0.004	Support
H7	SCF2 → EP	0.503	0.091	2.735	0.009	Support

Descriptive statistics of the samples

Information	Type	Frequency	%	Cumulative %
Gender	Male	121	45.83	45.83
	Female	143	54.17	100
Position	Senior manager	31	11.74	11.74
	Middle manager	99	37.50	49.24
	Grass-roots manager	130	49.24	98.48
	Other personnel	4	1.52	100.00
Establishment period	Less than 3 years	9	3.41	3.41
	3-5 years	24	9.09	12.50
	5-10 years	67	25.38	37.88
	10-20 years	101	38.26	76.14
	More than 20 years	63	23.86	100.00
Nature of company	State-owned enterprise	48	18.18	18.18
	Joint venture	31	11.74	29.92
	Private enterprise	171	64.77	94.70
	Foreign-funded enterprise	10	3.79	98.48
	Other	4	1.52	100.00
Industry category	Manufacturing industry	121	45.83	45.83
	Service industry	61	23.11	68.94
	High-tech industry	59	22.35	91.29
	Other	23	8.71	100.00
Personnel size	Less than 100	63	23.86	23.86
	101-500	141	53.41	77.27
	501-1000	33	12.50	89.77
	1001-500	21	7.95	97.73
	More than 5000	6	2.27	100.00
Corporate capital	Less than 10 million	101	38.26	38.26
	10.1-50 million	67	25.38	63.64
	50.01-100 million	71	26.89	90.53
	100.01- 500 million	23	8.71	99.24
	More than 500 million	2	0.76	100.00

Reliability test results of supply chain flexibility

Variable	Index	Question number	CITC	After deletion Cronbach’s ɑ	Cronbach’s ɑ
Supply chain flexibility (SCF)	New product flexibility (SCF1)	SCF11	0.6073	0.8823	0.8864	0.8687
		SCF12	0.6445	0.9355
		SCF13	0.6302	0.8852
		SCF14	0.5203	0.8673
	Procurement flexibility (SCF2)	SCF21	0.6288	0.8718	0.8646
		SCF22	0.7889	0.8969
		SCF23	0.6625	0.9115
		SCF24	0.8032	0.9332
		SCF25	0.7155	0.8995
	Product flexibility (SCF3)	SCF31	0.7489	0.9329	0.8247
		SCF32	0.6441	0.8791
		SCF33	0.7799	0.9149
		SCF34	0.7236	0.8756
	Delivery flexibility (SCF4)	SCF41	0.7124	0.9174	0.8306
		SCF42	0.7781	0.9301
		SCF43	0.7875	0.9085
		SCF44	0.8420	0.9410
	Information system flexibility (SCF5)	SCF51	0.5991	0.9091	0.8583
		SCF52	0.8380	0.9170
		SCF53	0.7736	0.8816
		SCF54	0.6873	0.8753
		SCF55	0.5390	0.8820

KMO and Bartlett Test of Firm Performance

KMO and Bartlett sphericity test
KMO measures adequacy of sampling		0.858
Bartlett sphericity test	Approximate chi-square	422.805
	Df	39
	Sig.	0.000

Questionnaire of Supply Chain Flexibility

Variable	Index	Question number	Item
Supply chain flexibility (SCF)	New product flexibility (SCF1)	SCF11	The ability of the company to introduce new products every year
		SCF12	The degree of consumer involvement in the development of new products.
		SCF13	Use computer technology to assist in the design and production of new products.
		SCF14	Effectively plan development cost and time during new product development.
	Procurement flexibility (SCF2)	SCF21	The ability of enterprises to maintain supplier relationships in a changing environment.
		SCF22	The ability of suppliers to respond to changes in raw material types and demand.
		SCF23	The ability of enterprises to quickly meet the diversification of raw material demand.
		SCF24	Enterprise’s ability to change suppliers.
		SCF25	The company’s ability to adapt to changes in supplier delivery cycles.
	Product flexibility (SCF3)	SCF31	Ability to offer different product combinations according to consumer needs.
		SCF32	Change the ability of existing product design quickly and accurately according to consumer demand.
		SCF33	Enterprise production equipment can rapidly transform functions to produce the ability of different products.
		SCF34	The time and cost required to produce non-standard products.
	Delivery flexibility (SCF4)	SCF41	The enterprise can provide a variety of distribution modes for each product.
		SCF42	The ability to adjust the distribution mode to meet the urgent needs of customers.
		SCF43	Coordinate warehouse, distribution channel and factory to complete user order.
		SCF44	Cost and time can be effectively controlled when changing the quantity and type of products shipped.
	Information system flexibility (SCF5)	SCF51	The ability to communicate timely information between supply chain partners.
		SCF52	The ability to meet different information needs through existing information systems.
		SCF53	Quality and accuracy of information transfer between supply chain enterprises.
		SCF54	Supply chain information system can be reused and reconfigurable.
		SCF55	The scalability of supply chain information system according to business needs.

Total Variance Explained of Big Data Analysis Capability

Factor	Initial eigenvalue			Extract sum of squares and load
	Total	Variation%	Cumulative %	Total	Variation%	Cumulative %
1	7.6703	49.0995	49.0995	7.6703	49.0995	49.0995
2	2.3125	14.7197	63.8192	2.3125	14.7197	63.8192
3	1.2198	7.6946	71.5138	1.2198	7.6946	71.5138
4	0.8230	5.5841	77.0979
5	0.6382	4.1689	81.2668
6	0.4696	3.0857	84.3525
7	0.4316	2.8568	87.2093
8	0.4001	2.4866	89.6959
9	0.2917	2.006	91.7019
10	0.2690	1.9935	93.6954
11	0.2530	1.6704	95.3658
12	0.2129	1.4801	96.8459
13	0.2070	1.2864	98.1323
14	0.1005	0.974	99.1063
15	0.0907	0.8937	100.0000

KMO and Bartlett Test of Supply Chain Flexibility

KMO and Bartlett sphericity test
KMO measures adequacy of sampling		0.837
Bartlett sphericity test	Approximate chi-square	1080.533
	Df	234
	Sig.	0.000

Questionnaire of Big Data Capability

Variable	Dimensionality	Question number	Item
Big data analysis ability (BDAC)	Resource acquisition ability (BDAC1)	BDAC11	The enterprise can obtain internal and external data resources to support the business.
		BDAC12	Companies can get enough data to analyze the required professionals.
		BDAC13	Enterprises can obtain sufficient technical equipment and skills needed for data analysis.
		BDAC14	Companies can update the data, talent, and technical resources they need in a timely manner.
	Integration and analytical ability (BDAC2)	BDAC21	Enterprise big data analysts have the basic skills to complete big data analysis.
		BDAC22	Companies can integrate internal data with external data to facilitate analysis of the business environment.
		BDAC23	Organizations can analyze very large amounts of unstructured or highly dynamic data.
		BDAC24	Enterprises can identify and screen out commercially valuable information from massive data.
	Insight and prediction ability (BDAC3)	BDAC31	Companies base their decisions on big data analytics rather than intuition.
		BDAC32	Big data analytics can support business activities.
		BDAC33	Enterprises can achieve real-time insights into the market based on big data analysis.
		BDAC34	Enterprises can discover the potential needs of customers through data analysis.

Descriptive statistics of variables

Variable			N	Minimum	Maximum	Mean	SD
Dependent variable	Enterprise performance	EP1	264	3.2505	7.0000	5.7819	0.7206
		EP2	264	2.7510	7.0000	5.7858	0.6347
Independent variable	Big data analysis ability	BDAC1	264	3.8234	7.0000	5.7305	0.6782
		BDAC2	264	3.6761	7.0000	5.7912	0.6833
		BDAC3	264	3.5681	6.9342	5.7548	0.6359
Mediation variable	Supply chain flexibility	SCF1	264	4.0000	7.0000	5.9595	0.7225
		SCF2	264	4.0000	7.0000	5.7814	0.7019
		SCF3	264	4.0000	7.0000	5.6533	0.8912
		SCF4	264	4.0000	7.0000	5.7316	0.7437
		SCF5	264	4.0000	7.0000	5.5051	0.7553