Understanding Behavioral Intention to Use of Air Quality Monitoring Solutions with Emphasis on Technology Readiness

Figures & data

Figure 1. AQMS mobile user.

Table 1. Dataset AQMS test.

No	Document Name	Size (kb)	Request Processing Time (ms)	Analysis Process Time (ms)	Total Time (ms)	Average Times (ms)
1	AQ#1	812	43.72	377.85	4749.85	4749.85
2	AQ#2	731	32.73	237.54	3510.54	3510.54
3	AQ#3	513	52.73	243.39	5516.39	6662.04
4	AQ#4	876	63.82	280.04	6662.04	2290.00
5	AQ#5	467	11.10	1180	2290	144,70.00
6	AQ#6	961	127.70	1700	14,470	2548.50
7	AQ#7	523	22.73	275.5	2548.5	3825.96
8	AQ#8	659	32.16	609.96	3825.96	3314.83
9	AQ#9	891	22.31	1083.83	3314.83	4844.286
10	AQ#10	814	12.32	322.75	1554.75	1554.75

Table 2. Study of the art TR.

Title	Year & Author	Novelty	Method	Limitation
Environmental awareness and air quality: The mediating role of environmental protective behaviors	2022, Kou, Afzal, Ahmed, Bojnec	Examines the relationship between environmental awareness, individual behavior, and air quality.	Uses research design to tackle the research problem.	Absence of demographic and psychological factors.
Creating Supportive Environments and Enhancing Personal Perception The Digital Divide	2019, Han, Nam	Explores the impact of supportive environments and enhancing personal perceptions.	Mixed-methods design, surveying older adults to assess digital skills and environmental factors.	Changing technology and digital landscapes may challenge longevity.
A Reliable IoT Edge Computing Trust Management Mechanism for Smart Cities	2020, Wang, Li, Jin, Guo	Focuses on improving the service quality of smart city’s IoT ecosystem.	IoT-based methods.	Effectiveness may vary with specific factors in the smart city.
Awareness, Understanding, and Action of User Experiences and Expectations about Indoor Air Quality	2020, Kim, Sunyoung, Mu Yang Li	Understanding IAQ and empowering users to take meaningful actions.	Survey and conceptual framework.	Dynamic nature of technology and user preferences.

Figure 2. Conceptual framework.

Figure 3. The demographic data of the respondents (n = 371).

Table 3. Data reliability.

Variable	Item	Range Factor loading	AVE	CR	a	Status
EE	3	0.874–0.889	0.776	0.912	0.857	Reliable
PE	3	0.855–0.915	0.790	0.919	0.869	Reliable
SI	2	0.857–0.879	0.762	0.906	0.869	Reliable
FC	3	0.928–0.931	0.776	0.927	0.843	Reliable
HM	3	0.793–0.861	0.684	0.867	0.776	Reliable
HB	2	0.820–0.850	0.704	0.877	0.792	Reliable
PV	3	0.639–0.886	0.630	0.834	0.785	Reliable
BIU	2	0.904–0.910	0.823	0.903	0.785	Reliable

Table 4: Diagonal values represent the square root of AVE of constructs (√AVE), while the off-diagonal values are the correlation among the constructs.

Table 4. Fornell-Larcker.

	BIU	EE	FC	H	HM	PE	PV	SI	TR
BI	0.907
EE	0.767	0.881
FC	0.719	0.787	0.929
HB	0.742	0.688	0.654	0.8 39
HM	0.797	0.757	0.786	0.715	0.8 27
PE	0.781	0.753	0.730	0.769	0.768	0.8 90
PV	0.714	0.792	0.747	0.669	0.771	0.676	0.793
SI	0.799	0.799	0.759	0.752	0.817	0.763	0.873	0.873
TR	0.785	0.784	0.801	0.688	0.778	0.774	0.792	0.705	0.810

Table 5. Results of R² value.

Variable	R-Square	R-Square Adjusted
Behavioral Intention_(BI)	0.759	0.753

Table 6. GOF Index.

Average Community	Average R^2	Indeks GOF
0.854	0.759	0.830

Figure 4. Hypothesis result. Note: ** p < 0.01, *** p < 0.001 (Ko & Lee, 2017).

Table 7. Path coefficients and results of hypothesis testing.

Hypothesis	Path	Original Sample	Sample Mean (M)	Standard Deviation (STD EV)	T Statistics	p values	Result
H1	EE ->BIU	0.097	0.100	0.069	1.114	0.253	Not Suppoted
H2	PE ->BIU	0.167	0.169	0.062	2.751	0.006	Supported
H3	SI ->BIU	0.149	0.152	0.070	2.606	0.009	Supported
H4	FC ->BIU	−0.042	−0.045	0.064	0.945	0.345	Not Suppoted
H5	HM ->BIU	0.217	0.216	0.064	3.019	0.003	Supported
H6	PV ->BIU	−0.011	−0.009	0.056	1.262	0.207	Not Suppoted
H7	HB ->BIU	0.186	0.185	0.0 56	2.455	0.014	Supported
H8	TR ->BIU	0.199	0.1 98	0.0 74	2.670	0.008	Supported

Table 8. Technology readiness result.

Indicator	Standard Deviation (STDEV)	T statistics (—O/STD EV—)	p values
Discomfort	0.073	2.703	0.000
Innovativeness	0.032	1.478	0.000
Optimism	1.093	0.397	0.000
Insecurity	0.068	1.703	0.000

Table 9. Comparison of groups of paths towards gender.

Path	Gender	Sample	Standard Deviation (STDEV)	p values
EE ->BIU	Male	192	0.082	0.053
	Female	179	0.151	0.967
FC ->BIU	Male	192	0.082	0.512
	Female	179	0.162	0.885
HB ->BIU	Male	192	0.057	0.129
	Female	179	0.081	0.008
HM ->BIU	Male	192	0.076	0.509
	Female	179	0.092	0.003
PE ->BIU	Male	192	0.064	0.117
	Female	179	0.155	0.094
PV ->BIU	Male	192	0.091	0.041
	Female	179	0.157	0.479
SI ->BIU	Male	192	0.082	0.141
	Female	179	0.082	0.114
TR ->BIU	Male	192	0.130	0.030
	Female	179	0.194	0.141

Table 10. Comparison of groups of paths towards age.

Path	Age	Sample	Standard Deviation (STDEV)	p values
EE ->BIU	15–20	113	0.190	0.023
	21–25	89	0.183	0.017
	26–30	40	0.071	0.023
	31–35	38	0.145	0.940
	36–40	26	0.057	0.043
	41–45	65	0.082	0.942
FC ->BIU	15–20	113	0.934	0.350
	21–25	89	1.040	0.235
	26–30	40	1.006	0.079
	31–35	38	0.161	0.460
	36–40	26	0.057	0.200
	41–45	65	0.134	0.746
HB ->BIU	15–20	113	1.251	0.321
	21–25	89	0.167	0.647
	26–30	40	0.007	0.646
	31–35	38	0.168	0.965
	36–40	26	0.021	0.318
	41–45	65	0.145	0.079
HM ->BIU	15–20	113	1.251	0.211
	21–25	89	0.151	0.268
	26–30	40	0.615	0.042
	31–35	38	0.560	0.550
	36-40	26	0.133	0.010
	41–45	65	0.123	0.382
PE ->BIU	15–20	113	1.193	0.046
	21–25	89	1.994	0.052
	26–30	40	1.156	0.202
	31–35	38	0.162	0.302
	36–40	26	0.144	0.233
	41–45	65	0.082	0.003
PV ->BIU	15–20	113	0.912	0.362
	21–25	89	1.180	0.359
	26–30	40	0.321	0.191
	31–35	38	0.087	0.826
	36–40	26	0.057	0.794
	41–45	65	0.035	0.268
SI ->BIU	15–20	113	0.420	0.472
	21–25	89	0.116	0.710
	26–30	40	0.282	0.888
	31–35	38	0.164	0.090
	36–40	26	0.057	0.301
	41–45	65	0.178	0.028
TR ->BIU	15–20	113	0.791	0.031
	21–25	89	1.724	0.343
	26–30	40	0.161	0.061
	31–35	38	0.168	0.702
	36–40	26	0.142	0.122
	41–45	65	0.161	0.491

Table 11. Comparison Of groups of paths towards location.

Path	Age	Sample	Standard deviation (STDEV)	p values
EE ->BIU	Tangerang	208	0.189	0.760
	Jakarta	109	0.212	0.229
	Bandung	12	0.137	0.080
	Palembang	11	0.120	0.377
	Salatiga	7	0.122	0.502
FC ->BIU	Tangerang	208	0.172	0.339
	Jakarta	109	0.151	0.270
	Bandung	12	0.165	0.853
	Palembang	11	0.158	0.170
	Salatiga	7	0.168	0.579
HB ->BIU	Tangerang	208	0.615	0.042
	Jakarta	109	0.934	0.350
	Bandung	12	1.040	0.235
	Palembang	11	1.006	0.079
	Salatiga	7	0.161	0.460
HM ->BIU	Tangerang	208	0.057	0.200
	Jakarta	109	0.190	0.023
	Bandung	12	0.145	0.079
	Palembang	11	1.251	0.211
	Salatiga	7	0.151	0.268
PE ->BIU	Tangerang	208	0.133	0.010
	Jakarta	109	0.560	0.550
	Bandung	12	0.145	0.079
	Palembang	11	0.123	0.382
	Salatiga	7	1.193	0.046
PV ->BIU	Tangerang	208	0.912	0.362
	Jakarta	109	0.151	0.268
	Bandung	12	0.158	0.170
	Palembang	11	0.120	0.377
	Salatiga	7	0.057	0.794
SI ->BIU	Tangerang	208	0.035	0.268
	Jakarta	109	0.420	0.472
	Bandung	12	0.116	0.710
	Palembang	11	0.282	0.888
	Salatiga	7	0.164	0.090
TR ->BIU	Tangerang	208	0.791	0.031
	Jakarta	109	0.178	0.028
	Bandung	12	0.172	0.339
	Palembang	11	1.724	0.343
	Salatiga	7	0.161	0.061

Table

Questionaire AQMS
8	22	10
Variable	Questionnaire	References
Effort Expectancy (EE)	It is easy for me to become an expert at using the AQMS mobile application	Fleury et.al
	I expect the AQMS mobile application would be easy to use
	My interaction with AQMS would be clear and understandable.
Performance Expectancy (PE)	Using the AQMS mobile application helps me achieve and prepare for health protection from PM 2.5 easier.	Ong et.al
	Using the AQMS mobile application helps me prepare and understand protection from PM 2.5 daily.	Genser et.al
	I found the AQMS mobile application is helpful to see air quality
Social Influence (SI)	The important persons in my life think I should use the AQMS mobile application.	Genser et.al
	The important person in my life who uses the AQMS mobile application makes my interested to use it.

Table

Questionaire AQMS - TR Constuct
4	12
Factor	Questionnaire
Optimism	I believe that using the AQMS will significantly contribute to improving air quality awareness.
	I am optimistic about the positive impact that AQMS will have on my understanding of environmental conditions.
	I feel confident that AQMS will provide valuable insights for better decision-making.
Innovativeness	I am open to trying out new features or updates introduced in the AQMS
	I consider myself an early adopter of technological advancements, including those related to environmental monitoring
	I am willing to explore and use innovative feature that the AQMS may introduce in the future.
Discomfort	I feel some discomfort or hesitation in using the AQMS to monitor environmental conditions.
	The complexity of features in the AQMS contributes to my discomfort or unease while using the application.
	There are specific aspects of the AQMS that make me feel uneasy or uncertain.
Insecurity	I am concerned about the security and privacy of my data when using the AQMS.
	I worry about potential risks or vulnerabilities associated with the integration of technology in environmental monitoring applications.
	Specific security-related aspects of the AQMS raise concerns for me.

Ko, M. S., & Lee, W. H. (2017). Analysis of the relationships among perceived service encounter quality, service value, satisfaction and behavioral intention for physical therapy patients. Journal of Physical Therapy Science, 29(11), 2000–2003. https://doi.org/10.1589/jpts.29.2000

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