p–ISSN: 2723 - 6609 e-ISSN: 2745-5254
Vol. 5, No. 12, December 2024 http://jist.publikasiindonesia.id/
Indonesian Journal of Social Technology, Vol. 5, No. 12, December 2024 6046
Government Support, Trust, and UTAUT 2 in Willingness to
Adopt & Pay Smart Home Indonesia
Anggoro Ary Nugroho1*, Imam Salehudin2
Universitas Indonesia, Indonesia
Email: [email protected]
*Correspondence
ABSTRACT
Keywords: smart home;
willingness to adopt;
UTAUT 2; willingness to
pay
Smart home is one of the Internet of Things (IoT) currently
developing in Indonesia. The research examines the factors
determining Willingness to Adopt and Willingness to Pay to
use a Smart home in Indonesia. This study uses the Unified
Theory of Acceptance and Use of Technology 2 (UTAUT
2), Government Support, and Trust to examine the
relationship of influencing factors. Respondents from the
study consisted of 353 people who already owned a
house/place to live and did not have a smart home device,
which was obtained from an online survey. Analysis of
research data using Structural Equation Modeling (SEM)
with the help of SMART PLS software. The research results
show that Facilitating Conditions, Habit, Government
Support, and Trust positively affect Willingness to Adopt
Smart homes. Social Influence, Facilitating Conditions,
Price Value, and Willingness to Adopt positively affect the
Willingness to Pay for a Smart home. This research can
provide insight into smart home development in Indonesia.
Introduction
The internet has become one of the most essential elements in modern life.
Research on internet use's characteristics, behaviour, and impact is becoming increasingly
relevant in this information era. Indonesia is presently undergoing a surge in the number
of individuals utilising the Internet, along with advancements in providing associated
services and products (Seifert, 2016; Leguna, 2021). The number of internet users in
January 2023 reached 212.9 million, indicating a growth rate of 5.2% compared to the
previous year (Statista, 2023). The term "Internet of Things" (IoT) refers to a network of
interconnected electronic gadgets that possess unique identities and have the ability to
exchange data information through the Internet (Al-Ameen, Chauhan, Ahsan, & Kocabas,
2021). The advent of the Internet of Things (IoT) has enabled consumers to engage with
services autonomously, establish interconnections, and access and utilise them at any
given time and location (Chouk & Mani, 2019). The use of IoT in advanced community
life is to use it in the integration of urban facilities and infrastructure in the form of a
Smart City (Shafiullah et al., 2023).
Government Support, Trust, and UTAUT 2 in Willingness to Adopt & Pay Smart Home
Indonesia
Indonesian Journal of Social Technology, Vol. 5, No. 12, December 2024 6047
Smart Cities are now becoming the newest issue in the Southeast Asia region
following campaigns on energy efficiency, the use of environmentally friendly energy
and efforts to improve community services which have made the government need to
implement the Smart City concept (Rohmah et al., 2023; Shafiullah et al., 2023). Smart
City is a concept where facilities, transportation, infrastructure and residences in the city
are integrated and connected through integrated communications and control systems
(Shafiullah et al., 2023). One of the components of the Smart City concept is the Smart
home (Balta-Ozkan et al., 2014). Smart homes play a role in supporting energy efficiency
and providing convenience in carrying out activities at home (Elian, 2022; Marikyan et
al., 2023).
A smart house is a dwelling with an integrated automation system that utilises
sensors and telecommunications technologies to connect various electronic equipment
(Azis et al., 2023; Mainardi et al., n.d.; Shin et al., 2018). This connectivity is facilitated
through user interfaces such as buttons, touch screens, keyboards, and voice and gesture
recognition mechanisms. The concept of a smart home encompasses various equipment
categories, including smart home appliances, control and connectivity devices, security
devices, entertainment devices, comfort and lighting systems, and energy management
solutions (Mainardi et al., n.d.; Marikyan et al., 2023; Shin et al., 2018). The Indonesian
market presently offers a range of smart home appliances across various categories,
including smart refrigerators and washing machines (classified as smart appliances),
Google Home Assistant (categorised as a smart control and connectivity device), smart
door locks, and integrated CCTV systems (classified as smart security devices), as well
as smart TVs and associated equipment (classified as entertainment devices) (Alifah &
Kusumawati, 2022; Elian, 2022).
The majority of smart home users in Indonesia are consumers who already own a
place of residence or private property. Several motives or reasons for consumers to use
smart homes are convenience, social media content, business, or entertainment while at
home (Arradian, 2021). The characteristics of an individual who already owns a home or
private residence tend to have emotional maturity, both personally and financially, and
always consider what decisions will be advantageous or disadvantageous when
implementing smart home technology in their dwelling. The integration of novel
technology is intricately intertwined with several aspects that influence financial,
personal, and external circumstances (Viswanath Venkatesh, 2013). Consumer interest in
a particular technology is likely influenced by the perceived positive values associated
with its benefits, hazards, and ease of acquisition (Shi et al., 2022). Multiple theories have
been proposed to explain the technology acceptance and adoption process to influence
consumers' attitudes and motivations to incorporate such technology into their daily lives
(Viswanath Venkatesh, 2013). This idea posits various factors to be considered while
embracing technological advancements. These factors encompass individual, societal,
security, and external variables impacting the acceptance process.
The existing market of smart home users in Indonesia is relatively small compared
to the potential revenue that can be realised. The lack of activity and intense competition
Anggoro Ary Nugroho, Imam Salehudin
Indonesian Journal of Social Technology, Vol. 5, No. 12, December 2024 6048
within the smart home market in Indonesia can be attributed to inadequate knowledge
regarding smart homes and a prevailing dissatisfaction with the existing smart home
gadgets in use (Alifah & Kusumawati, 2022). This finding indicates that individuals who
utilise smart home technology can be classified as innovators and early adopters since
they are willing and able to purchase and embrace novel technologies that have not yet
achieved widespread adoption (Rogers, 2003). The adoption of smart home technology
in the urban area of Jabodetabek (Jakarta Bogor Depok Tanggerang Bekasi) is affected
by its usage's perceived functionality and benefits (Gulton & Asvial, 2020). However, the
primary concern hindering its widespread adoption is the potential security threats
associated with such systems. The adoption of smart homes in Indonesia is driven by its
advantages in promoting environmentally sustainable energy consumption and reducing
operational expenses (Elian, 2022). This is achieved through the smart home's ability to
monitor the energy usage of electronic devices and facilitate environmental conservation
efforts (Elian, 2022).
The primary aim of this study is to investigate the determinants that influence
consumers' adoption and utilisation of smart home technology, as well as their propensity
to allocate financial resources towards its acquisition. Achieving this primary objective
also facilitates organisations in comprehending the essential variables that necessitate
consideration before market entry, as well as the preparations that engender consumer
willingness to pay for smart home services.
Method
This research employed a descriptive quantitative methodology using a survey
method. Descriptive research is a study that examines explicit hypotheses with a
structured approach to observe phenomena or characteristics associated with the subject
population and estimates the relationships among variables within the population
concerning the proportion that possesses specific characteristics (Cooper & Schindler,
2014).
The research sample was selected using a non-random purposive sampling
technique, as the respondents were chosen based on criteria established by the researchers
(Hair et al., 2019). The respondent criteria include individuals who own a home/private
residence and do not yet have smart home devices. The questionnaire was created using
Google Forms and contains 45 question items developed from previous research. The
questionnaire was carried out in three parts: the first was screening questions to screen
respondents, the second was questions related to research, and the last (Shi et al., 2022;
Tamilmani et al., 2019) was questions about respondent demographics. This study uses a
7-level Likert scale from "strongly disagree" to "strongly agree".
In this research there are eight variables from UTAUT 2, namely Performance
Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic
Motivations, Price Value, Habit and Willingness to Adopt as well as additional variables
namely Government Support, Trust and Willingness to Pay for Smart Home. The research
model of the UTAUT 2 framework can be seen in Figure 1. Each variable is measured
Government Support, Trust, and UTAUT 2 in Willingness to Adopt & Pay Smart Home
Indonesia
Indonesian Journal of Social Technology, Vol. 5, No. 12, December 2024 6049
using questionnaire items that have been modified from previous research. Changes to
questionnaire items are required to adapt to the Smart home context (Table 1). The
UTAUT 2 variable measurement, Government Support, Trust and Willingness to Pay has
four items, apart from Social Influence which has five measurement items
(Pienwisetkaew et al., 2023; Shi et al., 2022).
Table 1
Variable Operational Definitions
Variable Items Adapted sources
Performance
Expectancy (PE)
1. Smart Home will help me in operating electronic
equipment at home
Pienwisetkaew,
2023
2. Smart Home will help me in monitoring electronic use at
home
3. Smart Home will help me in managing electronic
equipment at home remotely
4. Smart Home will help me in improving the efficiency of
electronic equipment at home
Effort
Expectancy
(EE)
1. I will easily learn how to operate Smart Home
Pienwisetkaew,
2023
2. I will master the Smart Home operation quickly
3. I will be able to use Smart Home with the knowledge I
have
4. I will be able to use Smart Home without excessive
business/draining energy
Social Influence
(SI)
1. My family has recommended the use of smart home
Pienwisetkaew,
2023
2. Someone I respect will be happy if I use Smart Home
3. I will get a better social status if I use Smart Home
4. Friends/colleagues recommend me using Smart Home
5. social media Friends will like if I use Smart Home
Facilitating
Condition (FC)
1. To use Smart Home, I have the required
equipment/facilities (internet, mobile phone/tablet,
electricity)
Pienwisetkaew,
2023
2. To use Smart Home, I have the knowledge needed (how
to connect, operation and maintenance)
3. If I use smart home and face problems, I know the
contact of assistance to be contacted
4. If I use smart home and face problems, I know the
place/location to solve it
Hedonic
Motivation
(HM)
1. The use of smart home will be fun
Pienwisetkaew,
2023
2. The use of smart home will entertain
3. The use of smart home will make me calm
4. The use of smart home will make me feel happy
Price Value
(PV)
1. Smart Home has a reasonable price
Pienwisetkaew,
2023
2. Smart Home has a price commensurate on the features
offered
3. Smart Home has a good feature at the current price
4. Smart Home has a reasonable operational cost
Habit (HB) 1. I feel that the use of Smart Home will be my habit
Pienwisetkaew,
2023
2. I feel that the use of smart home will be my basic need
3. I feel the use of smart home will be my daily life
Anggoro Ary Nugroho, Imam Salehudin
Indonesian Journal of Social Technology, Vol. 5, No. 12, December 2024 6050
4. I feel that the use of smart home will be a new
trend/social habit
Trust (TR) 1. In my opinion, the use of Smart Home will be trusted
Pienwisetkaew,
2023
2. In my opinion, the use of Smart Home will be able to
fulfill its duties
3. In my opinion, the use of Smart Home will be able to
guarantee the safety of home/residence
4. In my opinion, the use of smart home can be relied upon
in carrying out its duties
Willingness to
Adopt (WTA)
1. I am interested in using Smart Home
Pienwisetkaew,
2023
2. I will try using Smart Home
3. I plan to use Smart Home
4. I will definitely use Smart Home in the future
Government
Support (GS)
1. In my opinion, government support related to promotion
and information is needed to increase the use of smart
home
Pienwisetkaew,
2023
2. In my opinion, government support related to marketing
and sales licensing is needed to increase the use of smart
home
3. In my opinion, government support related to consumer
protection regulations and policies is an important
consideration in the use of Smart Home
4. In my opinion, government support in the form of
subsidies/discounts/financing assistance can help increase
smart home users
Willingness to
Pay (WTP)
1. I am willing to buy a smart home even though the price
offered is quite expensive
Pienwisetkaew,
2023
2. I am willing to pay more for smart home with better
features
3. I am willing to buy additional equipment needed to use
Smart Home (Internet, Mobile Phone/Tablet, Electricity)
4. I am willing to pay premium prices for the use of smart
home
Results and Discussion
Respondent Characteristics
Six hundred eighty-nine respondents took part in the survey. Where valid responses
that met the research criteria were 353 people with a rate of 51.2%. The number of
respondents has met the minimum required by multiplying the number of indicators by
five (Hair et al., 2019). This research uses 45 indicators, so the minimum number of
respondents is 225. Table 2 will show the demographic profile of the respondents. The
majority of respondents were men (189; 53.9 percent), aged 37-46 years (156; 40.4
percent), domiciled in Jabodetabek (187; 56 percent), had private sector professions (102;
30.5 percent), the amount of expenditure per month is between 2.5 m and 5 m (141;40.4
percent) and for electricity needs at 500 thousand and 1 m (146;41 percent). In terms of
education, the majority are undergraduate graduates (175; 44.9 percent).
Government Support, Trust, and UTAUT 2 in Willingness to Adopt & Pay Smart Home
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Indonesian Journal of Social Technology, Vol. 5, No. 12, December 2024 6051
Table 2
Respondent Profile
Measure Item N Percentage (%)
Gender Male 189 53.90%
Female 164 46.10%
Age 27-36 140 35.90%
37-46 156 40.40%
47-56 57 21.90%
Domicile Jabodetabek 187 56.00%
Outside Jabodetabek
(Java Island)
143 37.10%
Outside Java Island 23 6.90%
Profession
Government
Employees
42 12.60%
Private Sector
Employees
102 30.50%
State-owned
Entreprise
Employees
91 24.30%
Enterpreneur 44 10.20%
Others 74 22.50%
Education High School or below 21 9.90%
Diploma 141 40.40%
Bachelor 175 44.90%
Master 13 3.90%
Doctoral 3 0.90%
Monthly Spending
Rp 1.000.000 - < Rp
2.500.000
71 19.20%
Rp 2.500.000 - < Rp
5.000.000
141 39.50%
Rp 5.000.000 - < Rp
10.000.000
138 38.30%
> Rp 10.000.000 3 0.90%
Electricity Related
Monthly Spending
Rp 100.000 - < Rp
500.000
134 37.40%
Rp 500.000 - < Rp
1.000.000
146 41.00%
Rp 1.000.000 - < Rp
1.500.000
52 12.60%
> Rp 1.500.000 21 6.30%
Convergent Validity & Reliability
Structural Equation Modeling (SEM) was carried out to analyze the measurement
and structural models using SMART PLS 3.2.9 software. The stages that are followed in
using the application are the measurement model and the structural model. The
measurement model describes the relationship between variables and the measurement
items that measure them. The structural model describes the relationship of influence
between research variables or research hypotheses that are built (Hair et al.,2017). The
Anggoro Ary Nugroho, Imam Salehudin
Indonesian Journal of Social Technology, Vol. 5, No. 12, December 2024 6052
results of the validity test (convergent and discriminant validity) as well as the reliability
test of the measurement model can be seen in Table 3.
Table 3
Measurement Model Evaluation
Variable Indicator Mean
Standard
Devitiaon
Outer
Loading
AVE
Composite
Reliability
Cronbach's
Alpha
Performance
Expectancy
PE1 5.88 1.086 0.868 0.702 0.904 0.859
PE2 5.79 1.206 0.837
PE3 5.99 1.113 0.830
PE4 5.56 1.265 0.816
Effort
Expectancy
EE1 5.45 1.217 0.853 0.665 0.888 0.864
EE2 5.40 1.219 0.858
EE3 5.56 1.162 0.757
EE4 5.66 1.182 0.790
Social Influence SI1 4.90 1.442 0.763 0.648 0.902 0.833
SI2 5.11 1.314 0.812
SI3 4.99 1.543 0.784
SI4 4.93 1.474 0.840
SI5 4.96 1.509 0.825
Facilitating
Conditions
FC1 4.91 1.563 0.780 0.637 0.875 0.810
FC2 5.43 1.275 0.760
FC3 4.82 1.505 0.843
FC4 5.03 1.450 0.808
Hedonic
Motivations
HM1 5.59 1.288 0.837 0.74 0.919 0.883
HM2 5.48 1.221 0.881
HM3 5.30 1.319 0.890
HM4 5.28 1.307 0.833
Price Value PV1 4.86 1.418 0.789 0.722 0.912 0.871
PV2 5.32 1.193 0.859
PV3 5.17 1.234 0.883
PV4 4.97 1.348 0.864
Habit HB1 5.31 1.384 0.878 0.724 0.912 0.869
HB2 4.79 1.467 0.896
HB3 5.00 1.385 0.907
HB4 5.46 1.268 0.707
Trust TR1 5.33 1.236 0.845 0.733 0.916 0.878
TR2 5.40 1.233 0.844
TR3 5.33 1.300 0.851
TR4 5.45 1.218 0.882
Willingness to
Adopt
WTA1 5.47 1.390 0.881 0.794 0.939 0.914
WTA2 5.30 1.375 0.906
WTA3 5.25 1.368 0.909
WTA4 5.16 1.412 0.868
Government
Support
GS1 5.58 1.275 0.862 0.739 0.919 0.882
GS2 5.60 1.302 0.894
Government Support, Trust, and UTAUT 2 in Willingness to Adopt & Pay Smart Home
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Indonesian Journal of Social Technology, Vol. 5, No. 12, December 2024 6053
GS3 5.55 1.320 0.861
GS4 5.46 1.414 0.820
Willingness to
Pay
WTP1 4.55 1.657 0.890 0.807 0.944 0.920
WTP2 4.93 1.521 0.899
WTP3 4.87 1.502 0.888
WTP4 4.61 1.600 0.917
Results of the validity test of the first measurement model through Outer Loading
and Average Variance Extract (AVE) statistically (Chin, 2010; Hair et al., 2021). This is
done by checking the Outer Loading which has a value of >0.70 and checking the AVE
of the observed variable, which has a value of >0.50 (Hair et al. 2021). Next, the
Composite Reliability or Construct Reliability (CR) criteria >0.70 and Cronbach's Alpha
>0.70 were evaluated (Hair et al., 2019). The results from table 3 show that the model has
good convergent validity and good reliability with Composite Reliability values between
0.8 and 0.93.
Discriminant Validity
Discriminant validity analysis was carried out to ensure that each concept from each
latent model was different from the other variables. Discriminant validity can be seen
from the Fornell-Lacker Test and Heterotrait Monotrait (HTMT). Based on the Fornell
Lacker Criterion, the AVE root for each variable is higher than the correlation of other
variables so that the discriminant validity evaluation based on the Fornell and Lacker
criteria is acceptable. The next thing is the Heterotrait-monotrait Ratio (HTMT) with a
value <0.90 (Hair et al. 2021). Based on research by Henseler and Sarstedt (2014) which
evaluates the discriminant validity method, HTMT has a level of higher sensitivity than
the Fornell Lacker Criterion method. Based on table 4, it is concluded that all variables
contained in the model meet the standard requirements with Heterotrait-monotrait Ratio
(HTMT) <0.90.
Table 8 encapsulates the outcomes of hypothesized relationships between
constructs such as Performance Expectancy, Effort Expectancy, Social Influence, and
others with the dependent variables: Willingness to Adopt and Willingness to Pay. The T
Statistics column, calculated by dividing the original sample's path coefficients by their
standard deviation, enables the evaluation of the hypotheses' statistical significance. In
this analysis, path coefficients with associated p-values below the threshold of 0.05 are
considered statistically significant, indicating that the independent variables have a
substantial impact on the dependent variables.
Tabel 4
Discriminant Validity
Fornell-Larcker Criterion
Construct EE FC GS HB HM PE PV SI TR WTA WTP
EE 0.816
FC 0.625 0.798
GS 0.478 0.588 0.859
HB 0.479 0.619 0.669 0.851
HM 0.647 0.667 0.639 0.613 0.860
PE 0.634 0.470 0.484 0.387 0.605 0.838
Anggoro Ary Nugroho, Imam Salehudin
Indonesian Journal of Social Technology, Vol. 5, No. 12, December 2024 6054
PV 0.572 0.703 0.586 0.630 0.649 0.474 0.850
SI 0.564 0.718 0.593 0.668 0.705 0.516 0.657 0.805
TR 0.550 0.646 0.695 0.710 0.674 0.504 0.677 0.622 0.856
WTA 0.566 0.680 0.695 0.732 0.668 0.477 0.650 0.644 0.721 0.891
WTP 0.393 0.634 0.534 0.713 0.489 0.254 0.645 0.581 0.599 0.666 0.899
HTMT Ratio Approach
Construct EE FC GS HB HM PE PV SI TR WTA WTP
EE
FC 0.765
GS 0.557 0.693
HB 0.557 0.736 0.766
HM 0.751 0.791 0.723 0.699
PE 0.756 0.566 0.546 0.444 0.691
PV 0.663 0.832 0.663 0.722 0.738 0.541
SI 0.655 0.852 0.677 0.771 0.802 0.593 0.75
TR 0.635 0.765 0.789 0.814 0.765 0.576 0.768 0.711
WTA 0.643 0.788 0.773 0.822 0.743 0.532 0.724 0.72 0.805
WTP 0.437 0.731 0.591 0.795 0.54 0.275 0.716 0.65 0.665 0.724
Table 5 encapsulates the outcomes of hypothesized relationships between
constructs such as Performance Expectancy, Effort Expectancy, Social Influence, and
others with the dependent variables: Willingness to Adopt and Willingness to Pay. The T
Statistics column, calculated by dividing the original sample's path coefficients by their
standard deviation, enables the evaluation of the hypotheses' statistical significance. In
this analysis, path coefficients with associated p-values below the threshold of 0.05 are
considered statistically significant, indicating that the independent variables have a
substantial impact on the dependent variables. For instance, the relationship 'Facilitating
Conditions -> Willingness to Adopt' with a p-value of 0.006 suggests a statistically
significant positive influence of facilitating conditions on the willingness to adopt.
Conversely, relationships with p-values above the 0.05 threshold, such as
'Performance Expectancy -> Willingness to Pay,' are not considered statistically
significant, implying insufficient evidence to support the proposed hypothesis of a
positive impact of performance expectancy on the willingness to pay. The table also
includes a moderation analysis, as indicated by the 'Moderating FC - PE -> Willingness
to Adopt' row, which examines the interactive effect of Facilitating Conditions and
Performance Expectancy on the willingness to adopt. However, the p-value suggests a
non-significant moderating effect.
The results show that H7, H12, H13, and H16 are supported hypotheses with a p-
value smaller than 0.05 on the willingness to Adopt a relationship. Apart from that, H6,
H8, H11, and H16 have a significant and positive relationship with Willingness to Pay
Smart Home.
Government Support, Trust, and UTAUT 2 in Willingness to Adopt & Pay Smart Home
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Indonesian Journal of Social Technology, Vol. 5, No. 12, December 2024 6055
Figure 1. Hyphotesis test results
The results of the hypothesis test show that the relationship between Social
Influence and Willingness to Pay has a T Value of 1.750 which is greater than the limit
of 1.65. This makes H6 accepted and shows that Social Influence does not have a positive
influence on the Willingness to Pay smart home. The results of the hypothesis test show
that the relationship between Facilitating Conditions and Willingness to Adopt has a T
value of 2.484 which is greater than the limit of 1.65. This makes H7 accepted and shows
that Facilitating Conditions positively influence Willingness to Adopt smart homes.
The results of the hypothesis test show that the relationship between Facilitating
Conditions and Willingness to Pay has a T Value of 2.923 which is greater than the limit
of 1.65. This makes H8 accepted and shows that Facilitating Conditions positively
influence Willingness to Pay for smart homes. The results of the hypothesis test show that
the relationship between Price Value and Willingness to Pay has a T Value of 3.802 which
is greater than the limit of 1.65. This makes H11 accepted and shows that Price Value
positively influences Willingness to Pay smart home. The results of the hypothesis test
show that the relationship between Habit and Willingness to Adopt has a T value of 3.669
which is greater than the limit of 1.65. This makes H12 accepted and shows that Habit
has a positive influence on Willingness to Adopt Smart home.
Anggoro Ary Nugroho, Imam Salehudin
Indonesian Journal of Social Technology, Vol. 5, No. 12, December 2024 6056
This research was conducted to determine what factors influence the willingness to
adopt and pay for a smart home. This research uses the UTAUT 2 model framework with
the addition of Trust and Government Support factors. Several previous studies used
UTAUT 2 to look for factors that influence the adoption of new technology. The proposed
model is explained using the PLS-SEM statistical analysis method.
The results of the PLS-SEM method show that Facilitating Conditions (FC), Habit
(HB), Government Support (GS), and Trust (TR) have a positive relationship with
Willingness to Adopt Smart Homes. This is in line with other research which reveals that
FC, GS and TR influence technology adoption (Gu & Liu, 2019; Shi et al., 2022). This
shows that if there are facilities and government support, it will encourage someone to
want to adopt this new technology. New habits in carrying out daily activities can also
encourage someone to use technology to make their work easier and trust in this
technology to carry out their duties (He et al. 2020).
The relationship between Willingness to Adopt and Willingness to Pay Smart
home is significant and positive. This aligns with research by Shi et al. (2022), which
states that someone willing to use new technology will be willing to spend money to get
it. Apart from that, the results also show that this relationship is dominated by potential
users who are in urban areas. In line with Willingness to Adopt which has a significant
and positive relationship, namely price value. This shows that someone is willing to pay
for a new technology if it is felt to have value commensurate with what customers think.
This is in line with research by Zhang et al. (2020), who found that consumers of energy-
saving electronic devices have a lot of interest if they feel it is appropriate to the
reciprocity they get.
Table 5. Hypothesis Results
Hypothesis Path
Path
Coefficients
T Values P Values Result
H1 PE → PV 0.475 9.331 0.000 Supported
H2 PE → WTP 0.019 0.370 0.352 Rejected
H3 PE → WTA -0.196 3.892 0.000 Rejected
H4 EE → WTP 0.067 1.326 0.091 Rejected
H5 EE → WTA -0.081 1.473 0.068 Rejected
H6 SI → WTP 0.000 0.001 0.500 Rejected
H7 SI → WTA 0.122 1.750 0.039 Supported
H8 FC → WTP 0.154 2.484 0.006 Supported
H9 FC → WTA 0.199 2.923 0.002 Supported
H10 HM → WTA 0.086 1.080 0.144 Rejected
H11 PV → WTP 0.048 0.792 0.208 Rejected
H12 PV → WTA 0.271 3.802 0.000 Supported
H13 HB → WTA 0.276 3.669 0.000 Supported
H14 GS → WTA 0.185 3.225 0.001 Supported
H15 TR → WTP 0.164 2.289 0.010 Supported
H16 TR → WTA 0.115 1.613 0.055 Rejected
H17 WTA → WTP 0.330 4.494 0.000 Supported
Government Support, Trust, and UTAUT 2 in Willingness to Adopt & Pay Smart Home
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Indonesian Journal of Social Technology, Vol. 5, No. 12, December 2024 6057
Facilitating Conditions and Social Influence also positively affect Willingness to
Pay for Smart Homes. A person is willing to adopt a technology in his life and pay for
the technology if the facilities and equipment needed are available and affordable. Apart
from that, the influence of someone who is respected or close to someone also influences
the willingness to pay for a Smart home. This aligns with research conducted by Gu &
Liue (2019) and Go & Heo (2020).
Conclusion
A recent study shows that in the context of technology adoption, willingness to pay
has different factors, although there is one factor that can influence both. First, the
relationship between facilitating conditions, Habit, Government Support, and Trust
factors can influence the Willingness to Adopt a Smart home. Second, facilitating
conditions, price value, social influence, and willingness to adopt can significantly and
positively influence willingness to pay for a smart home. Finally, facilitating conditions
can influence willingness to adopt and pay for a smart home.
Anggoro Ary Nugroho, Imam Salehudin
Indonesian Journal of Social Technology, Vol. 5, No. 12, December 2024 6058
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