Adoption of Big Data Analytics (BDA) Technologies in Disaster Management: A Decomposed Theory of Planned Behavior (DTPB) Approach

Figures & data

Figure 1. Conceptual framework of Intention to Adopt Big Data

Figure 2. Demographic results of the respondents. (a) Respondents’ Gender (b) Respondents’ Age (c) Respondents’ Education Level (d) Respondents’ Job Level (e) Control Question 1

Table 1. Construct Reliability and Validity

	rho-A	Composite Reliability (CR)			Average Variance Extracted (AVE)
ATT	0.80161		0.88205			0.71392
Comp.	0.7585		0.84605			0.57884
EE	0.77338		0.83886			0.56714
FC	0.72569		0.87328			0.77516
ITABD	0.85565		0.89647			0.63672
MMI	0.81945		0.81621			0.6061
PBC	0.82165		0.89289			0.73553
PE	0.73508		0.82874			0.54854
SE	0.82466		0.84162			0.57395
SN	0.77668		0.85343			0.59637

Table 2. “Discriminant validity (heterotrait-monotrait ratio, HTMT)”

ATT COM.			EE	FC	ITABD		MMI	PBC	PE	SE	SN
ATT
COMP.	0.59
EE	0.4	0.43
FC	0.2	0.13	0.32
ITABD	0.62	0.47	0.52	0.3
MMI	0.63	0.38	0.4	0.47		0.636
PBC	0.09	0.08	0.28	0.63		0.338	0.47
PE	0.55	0.55	0.87	0.32		0.581	0.51	0.28
SE	0.23	0.15	0.24	0.23		0.169	0.19	0.26	0.17
SN	0.55	0.57	0.35	0.27		0.506	0.42	0.08	0.42	0.23

Figure 3. Direct path coefficient of the structural model (PLS algorithm)

Figure 4. Structural model

Table 3. Result of Structural equation modeling (SEM) and hypothesis testing

Hypotheses	Relationship		Path		Std.	t	p-  Suppor ted		R2	Q2	F2
			Co efficient		Error	Value	Value
H1	ATT >>		0.407		0.041	9.884	0.000	Yes	0.279	0.186	0.205
	ITABD
H2	SN >>		0.226		0.043	5.250	0.000	Yes	0.107	0.057	0.064
H3	ITABD
	PBC >>		0.194		0.045	4.320	0.000	Yes	0.255	0.174	0.057
	ITABD
H4	PE >> ATT		0.254		0.055	4.626	0.000	Yes			0.048
H5	EE >> ATT		0.044		0.049	0.897	0.185	No			0.002
H6	Comp. >>		0.341		0.038	9.038	0.000	Yes			0.132
	ATT
H7	MMI >> SN		0.327		0.037	8.778	0.000	Yes			0.120
H8	SE >> PBC		0.141		0.036	3.922	0.000	Yes			0.026
H9	FC >> PBC		0.459		0.047	9.828	0.000	Yes			0.272

Table

APPENDIX A
Demographic Category	Results	Frequency	Percentage
Gender	Male	329	83%
	Female	67	17%
Age	≤ 25	12	3%
	26–35	47	12%
	36-45	147	37%
	46-55	131	33%
	56-60	59	15%
	60+	0	0
Education	Bachelors	210	53%
	Masters/	166	42%
	MPhil.
	PhD	20	5%
Job Level	Beginner/	87	22%
	Entry
	Level
	Mid- level	171	43%
	Senior-	79	20%
	level
	Top -level	59	15%
Do you have an understanding of the	Yes	297	75%
role of big data analytics in disaster	No	99	25%
management?

Table

Intention (INT.) BI1- In disaster management settings, I intend to use big data analytics in the future BI2-I will use big data analytics in disaster management settings in the future BI3-Given the chance, I predict I will use big data analytics in the future in disaster management BI4-It is likely that I will use big data analytics in disaster management in the future. BI5-I expect to use big data analytics in disaster management in the future.	Attitude (ATT) ATT1- Using big data analytics in disaster management would be a good idea. ATT2- Using big data analytics in disaster management would be a wise idea. ATT3- I like the idea of using big data analytics in disaster management. ATT4- Using big data analytics in disaster management would be pleasant.
Effort expectancy (EE) EE1- Learning to operate big data analytics in disaster management would be easy for me. EE2- I would find it easy to get the big data analytics in disaster management to do what I want to do EE3- My interaction with big data analytics in disaster management would be clear and understandable. EE4- I would find the big data analytics in disaster management easy to use.	Compatibility (COM) COM1- I think big data analytics services are compatible in disaster management settings. COM2- I think big data analytics services are compatible with the way I like to do disaster management services. COM3- I think using big data analytics fits with my disaster management preferences COP4- I think big data analytics services fit well with all aspects of my disaster management activities.
Performance expectancy (PE) PE1- Using big data analytics in disaster management would improve my overall performance PE2- Using big data analytics in disaster management would increase my productivity PE3- Using big data analytics in disaster management enhance my effectiveness PE4- Using big data analytics in disaster management would enable me to accomplish tasks quicker	Subjective norm (SN) SN1- People who influence my behavior think that I should use big data analytics in disaster management SN2- People who are important to me think I should use big data analytics in disaster management SN3- People whose opinion I value think I should use big data analytics in disaster management SN4- People who influence my decision think that I should use big data analytics in disaster management.
Mass media influence (MMI) MMI1- The mass media suggest that I should use big data analytics in disaster management MMI2- The mass media urge me to use big data analytics in disaster management MMI3- Mass media is full of reports, articles, TV, radio, newspapers and internet suggest that I should use big data analytics in disaster management MMI4- Mass media and advertising consistently recommend that I should use big data analytics in disaster management.	Perceived behavioral control (PBC) PBC1- I would be able to use big data analytics in disaster management. PBC2- I have the resources to use big data analytics in disaster management PBC3- I have the knowledge to use big data analytics in disaster management PBC4- I have the ability to use big data analytics in disaster management
Self-efficacy (SE) SE1- Whether or not I use big data analytics in disaster management is entirely up to me SE2- I am confident that I can use big data analytics in disaster management regularly SE3- I am very sure that I would be able to use big data analytics in disaster management next week. SE4- I am certain that I will be able to refrain myself from use big data analytics in disaster management that are not used SE5- If I wanted to, it would be very easy for me to use big data analytics in disaster management regularly.	Facilitating condition (FC) FC1- I have the necessary resources to use big data analytics in disaster management facilities FC2- I have the necessary knowledge to use big data analytics in disaster management facilities. FC3- I have enough internet experience to use big data analytics in disaster management