INTENTION TO ADOPT BIG DATA IN SUPPLY CHAIN MANAGEMENT: A BRAZILIAN PERSPECTIVE

QUEIROZ, MACIEL M; PEREIRA, SUSANA CARLA FARIAS

doi:10.1590/S0034-759020190605

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Revista de Administração de Empresas

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FORUM • Rev. adm. empres. 59 (6) • Nov-Dec 2019 • https://doi.org/10.1590/S0034-759020190605 copy

INTENTION TO ADOPT BIG DATA IN SUPPLY CHAIN MANAGEMENT: A BRAZILIAN PERSPECTIVE

Intención de adopción de big data en la cadena de suministros: Una perspectiva brasileña

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Big data applications have been remodeling several business models and provoking strong radical transformations in supply chain management (SCM). Supported by the literature on big data, supply chain management, and the unified theory of acceptance and use of technology (UTAUT), this study aims to evaluate the variables that influence the intention of Brazilian SCM professionals to adopt big data. To this end, we adapted and validated a previously developed UTAUT model. A survey of 152 supply chain respondents revealed that facilitating conditions (e.g., IT infrastructure) have a high influence on their intention to adopt big data. However, social influence and performance expectancy showed no significant effect. This study contributes to the practical field, offering valuable insights for decision-makers considering big data projects. It also contributes to the literature by helping minimize the research gap in big data in the Brazilian context.

KEYWORDS:
Big data; supply chain management; adoption; survey; partial least squares structural equation modeling

Construct	Label	Items	Sources
Performance expectancy (PE)	PE1	I find big data useful in my daily life.	(Alalwan et al., 2017Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99-110. doi:10.1016/j.ijinfomgt.2017.01.002 https://doi.org/10.1016/j.ijinfomgt.2017... ; Venkatesh et al., 2003Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. doi:10.2307/30036540 https://doi.org/10.2307/30036540... , 2012Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. doi:10.2307/41410412 https://doi.org/10.2307/41410412... ; Queiroz & Wamba, 2019Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70-82. doi:10.1016/j.ijinfomgt.2018.11.021 https://doi.org/10.1016/j.ijinfomgt.2018... )
	PE2	Using big data increases my chances of completing tasks that are important to me.
	PE3	Using big data helps me accomplish tasks more quickly.
	PE4	Using big data increases my productivity.
Effort expectancy (EE)	EE1	Learning how to use big data is easy for me.	(Alalwan et al., 2017Ahmad, S. Z., & Khalid, K. (2017). The adoption of M-government services from the user's perspectives: Empirical evidence from the United Arab Emirates. International Journal of Information Management, 37(5), 367-379. doi:10.1016/j.ijinfomgt.2017.03.008 https://doi.org/10.1016/j.ijinfomgt.2017... ; Venkatesh et al., 2003Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. doi:10.2307/30036540 https://doi.org/10.2307/30036540... , 2012Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. doi:10.2307/41410412 https://doi.org/10.2307/41410412... ; Queiroz & Wamba, 2019Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70-82. doi:10.1016/j.ijinfomgt.2018.11.021 https://doi.org/10.1016/j.ijinfomgt.2018... )
	EE2	My interaction with big data is clear and understandable.
	EE3	I find big data easy to use.
	EE4	It is easy for me to become skilful at using big data.
Social influence (SI)	SI1	People who are important to me think I should use big data.	(Alalwan et al., 2017Ahmad, S. Z., & Khalid, K. (2017). The adoption of M-government services from the user's perspectives: Empirical evidence from the United Arab Emirates. International Journal of Information Management, 37(5), 367-379. doi:10.1016/j.ijinfomgt.2017.03.008 https://doi.org/10.1016/j.ijinfomgt.2017... ; Venkatesh et al., 2003Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. doi:10.2307/30036540 https://doi.org/10.2307/30036540... , 2012Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. doi:10.2307/41410412 https://doi.org/10.2307/41410412... ; Queiroz & Wamba, 2019Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70-82. doi:10.1016/j.ijinfomgt.2018.11.021 https://doi.org/10.1016/j.ijinfomgt.2018... )
	SI2	People who influence my behaviour think I should use big data.
	SI3	People whose opinions that I value prefer I use big data.
Facilitating conditions (FC)	FC1	I have the resources necessary to use big data.	(Alalwan et al., 2017Ahmad, S. Z., & Khalid, K. (2017). The adoption of M-government services from the user's perspectives: Empirical evidence from the United Arab Emirates. International Journal of Information Management, 37(5), 367-379. doi:10.1016/j.ijinfomgt.2017.03.008 https://doi.org/10.1016/j.ijinfomgt.2017... ; Venkatesh et al., 2003Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. doi:10.2307/30036540 https://doi.org/10.2307/30036540... , 2012Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. doi:10.2307/41410412 https://doi.org/10.2307/41410412... ; Queiroz & Wamba, 2019Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70-82. doi:10.1016/j.ijinfomgt.2018.11.021 https://doi.org/10.1016/j.ijinfomgt.2018... )
	FC2	I have the knowledge necessary to use big data.
	FC3	Big data is compatible with other technologies I use.
	FC4	I can get help from others when I have difficulties using big data.
Behavioral intention to use (BI)	BI1	I intend to use big data in the future.	(Alalwan et al., 2017Ahmad, S. Z., & Khalid, K. (2017). The adoption of M-government services from the user's perspectives: Empirical evidence from the United Arab Emirates. International Journal of Information Management, 37(5), 367-379. doi:10.1016/j.ijinfomgt.2017.03.008 https://doi.org/10.1016/j.ijinfomgt.2017... ; Venkatesh et al., 2003Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. doi:10.2307/30036540 https://doi.org/10.2307/30036540... , 2012Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. doi:10.2307/41410412 https://doi.org/10.2307/41410412... ; Queiroz & Wamba, 2019Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70-82. doi:10.1016/j.ijinfomgt.2018.11.021 https://doi.org/10.1016/j.ijinfomgt.2018... )
	BI2	I expect to use big data in the future
	BI3	I plan to use big data in future.
Trust (TR)	TR1	I believe that big data is trustworthy.	(Alalwan et al., 2017Ahmad, S. Z., & Khalid, K. (2017). The adoption of M-government services from the user's perspectives: Empirical evidence from the United Arab Emirates. International Journal of Information Management, 37(5), 367-379. doi:10.1016/j.ijinfomgt.2017.03.008 https://doi.org/10.1016/j.ijinfomgt.2017... ; Gefen et al., 2003Gefen, Karahanna, & Straub. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90. doi:10.2307/30036519 https://doi.org/10.2307/30036519... )
	TR2	I have trust in big data.
	TR3	I do not doubt the honesty of big data.
	TR4	I feel assured that legal and technological structures adequately protect me from problems on big data.
	TR5	Big data has the ability to fulfil its task.

Gender	n	%
Male	136	89.5
Female	16	10.5
Age
26-33	40	26.32
34-41	80	52.63
42-49	12	7.89
50+	20	13.16
Highest education level
Bachelor degree	60	39.47
Postgraduate/MBA	84	55.26
Master of Science (MSc)	8	5.26
Number of years spent working in the organization
Less than one year	28	18.42
2-5 years	76	50.00
6-10 years	32	21.05
11-15 years	16	10.53
Occupation
Logistics analyst	70	46.05
Operations manager	28	18.42
Transportation manager	40	26.32
Supply chain manager	14	9.21

	BI	EE	FC	PE	SI	TR
BI1	0.887
BI2	0.916
BI3	0.900
EE1		0.945
EE2		0.906
EE3		0.947
EE4		0.900
FC1			0.789
FC2			0.884
FC3			0.719
FC4			0.800
PE1				0.764
PE2				0.803
PE3				0.914
PE4				0.899
SI1					0.953
SI2					0.983
SI3					0.967
TR1						0.964
TR2						0.944
TR3						0.918
TR4						0.937
TR5						0.913

Construct	Cronbach's alpha	Composite reliability	Average variance extracted
BI	0.881	0.926	0.808
EE	0.942	0.959	0.853
FC	0.806	0.873	0.637
PE	0.864	0.908	0.715
SI	0.965	0.977	0.934
TR	0.963	0.971	0.871

Construct	BI	EE	FC	PE	SI	TR
BI	0.901
EE	0.527	0.925
FC	0.614	0.577	0.800
PE	0.314	0.238	0.620	0.847
SI	0.399	0.390	0.508	0.421	0.968
TR	0.511	0.456	0.580	0.640	0.715	0.935

Path	Beta	Standard deviation	t-statistics	p-value	Result
FC -> EE	0.578	0.053	10.921^* * p < 0.001;	0.000	Supported
FC -> PE	0.380	0.064	5.875^* * p < 0.001;	0.000	Supported
FC -> BI	0.490	0.097	5.016^* * p < 0.001;	0.000	Supported
TR -> PE	0.413	0.080	5.301^* * p < 0.001;	0.000	Supported
TR -> BI	0.327	0.112	2.987^ p < 0.05.	0.003	Supported
SI -> TR	0.710	0.047	15.17^* * p < 0.001;	0.000	Supported
SI -> BI	-0.073	0.097	0.646	0.519	Rejected
EE -> BI	0.166	0.090	1.86	0.063	Rejected
PE -> BI	-0.214	0.099	2.184^ p < 0.05.	0.029	Rejected

Construct	R²	R² adjusted
BI	0.477	0.460
EE	0.337	0.333
PE	0.504	0.498
R	0.506	0.503

Fundação Getulio Vargas, Escola de Administração de Empresas de S.Paulo Av 9 de Julho, 2029, 01313-902 S. Paulo - SP Brasil, Tel.: (55 11) 3799-7999, Fax: (55 11) 3799-7871 - São Paulo - SP - Brazil
E-mail: rae@fgv.br

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[1] maciel.queiroz@docente.unip.br