This study aims to analyze the influence of
Environmental Concern and Perceived Value on
Purchase Intention, mediated by Subjective Norm,
Attitude, and Perceived Behavioral Control. The
study population consists of Generation Y
individuals in Jakarta, with a total sample of 385
respondents determined using Cochran's formula.
The sampling technique used was purposive
sampling mixed with Snowball Sampling, and data
were collected through an online questionnaire
distributed via Google Form. The results show that
Environmental Concern has a positive and
significant effect on Subjective Norm, Attitude,
Perceived Behavioral Control, and Purchase
Intention. All three mediating variables partially
mediate the relationship between Environmental
Concern and Purchase Intention. In addition,
Subjective Norm, Attitude, and Perceived
Behavioral Control each have a positive and
significant effect on Purchase Intention. Perceived
Value also positively affects Attitude and Purchase
Intention, with Attitude acting as a partial
mediator. All forms of mediation identified in this
study are classified as partial mediation. These
findings indicate that Generation Y’s intention to
purchase electric cars in Jakarta is influenced not
only by environmental concern and perceived
product value, but also significantly shaped by
Subjective Norm, Attitude, and Perceived
Behavioral Control in the Purchase Intention to
electric cars.
Enviromental ConcernPerceived ValueSubjective NormAttitudePerceived Behavioral ControlPurchase IntentionFile created by JATS EditorJATS Editorissue-created-year2025INTRODUCTION
The SDGs, adopted by the UN in 2015, emphasize sustainable
development with 17 goals covering social, economic, and environmental
aspects (Aji & Kartono, 2022). In the context of transportation,
electric vehicles are a strategic solution to support SDGs 11, 12, and
13 because they can reduce air pollution, reduce dependence on fossil
fuels, and reduce carbon emissions by up to 50% (Sudjoko, 2021; Zola
et al., 2023). In Indonesia, the government is encouraging the
accelerated adoption of electric cars through incentives,
infrastructure development, and collaboration with the private sector
to accelerate the transition to clean energy.
The Indonesian government targeted 8,500 electric car sales in
2022, with sales reaching 10,327 units. In 2023, sales increased to
17,062 units, despite no specific target. However, the 2024 target of
50,000 units fell short, with only 11,940 units sold as of
September.
Image
Source: databoks.katadata.co.id, 2024 processed.
Wholesale sales of BEV electric vehicles in Indonesia have been
trending upward, although they fluctuate from month to month. This
pattern indicates that sales consistency remains a challenge. Support
from manufacturers' policies and marketing strategies is crucial to
maintaining the growth trend (Gaikindo, 2024).
Image
Source:
www.jakarta.bps.go.id/
2025 processed.
According to data from the Central Statistics Agency (BPS) of
Jakarta, the majority of Jakarta's population is of productive age,
with Generation Y making up 26.66 percent and Generation Z accounting
for 25.35 percent. Both groups are known to be more environmentally
conscious and familiar with new technologies. This potential makes
Jakarta a strategic market for electric vehicles (Mittal et al.,
2023).
Image
Source: databoks.katadata.co.id, 2024.
The interest in purchasing electric vehicles has declined
from 2022 to 2024.
Although the government has provided various fiscal incentives
and developed public electric vehicle charging stations (SPKLU),
their effectiveness in increasing public interest remains limited.
This indicates the need for a more comprehensive educational
approach (Mittal et al., 2023).
The selection of the purchase intention
variable is relevant because it reflects the consumer’s initial intention, which can develop into an
actual purchasing decision. Generation Y and Z in Jakarta, with
their high purchasing power and environmental awareness, hold
significant potential in driving the adoption of electric vehicles.
Therefore, research on the factors influencing purchase intention is
crucial to strengthen marketing strategies and sustainable energy
transition policies.
Previous studies have shown that attitude,
environmental concern, perceived
behavioral control, perceived value, and
subjective norm influence purchase intention
(Gunawan et al., 2022; Huang & Ge, 2019). However, some
studies have found inconsistent results, revealing a research gap (Supriadi et al.,
2024; Zhao, 2024). Referring to the Theory of Planned
Behavior, this study examines the influence of
environmental concern and perceived value on electric vehicle
purchase intention, with the mediating role of TPB variables among
Generation Y in Jakarta.
THEORETICAL REVIEWSustainable Development Goals (SDGs)
The Sustainable Development Goals (SDGs) are a global agenda set
by the United Nations (UN) to achieve sustainable development in
social, economic, and environmental aspects by 2030. Of the 17 SDGs,
this study specifically focuses on SDG 11 (Sustainable
Cities and Communities), SDG 12 (Responsible
Consumption and Production), and SDG 13 (Climate
Action) due to their high relevance to the issue of clean
energy transition and the use of environmentally
friendly vehicles. SDG 11 emphasizes the importance of building
sustainable cities by reducing pollution and congestion through
clean transportation systems such as electric vehicles.
Theoretical Framework Consumer Behavior
Consumer behavior is the study of how individuals, groups, and
organizations search for, evaluate, purchase, and use products,
services, or experiences to fulfill their needs and wants (Kotler
& Keller, 2016; Schiffman & Wisenblit, 2015).
Theory of Planned Behavior (TPB)
The Theory of Planned Behavior, developed by Icek Ajzen in 1991,
is one of the most widely used theoretical frameworks for
understanding and predicting human behavior. TPB states that an
individual’s intention to perform a behavior is the primary
predictor of that behavior itself (Ajzen, 1991).
Purchase Intention
Purchase intention is an individual’s psychological tendency to
purchase a product, influenced by attitudes, past behaviors,
perceived benefits, as well as attitude toward
behavior, subjective norm, and
perceived behavioral control as direct predictors
of purchasing actions (Ajzen, 1991; Kotler & Keller, 2016;
Schiffman & Kanuk, 2007).
Attitude
Attitude refers to a person's tendency to evaluate a product or
action based on beliefs about its consequences, playing a key role
in shaping purchasing decisions (Banerjee & Solomon, 2017;
Patiro & Budiyanti, 2016).
Environmental Concern
Environmental concern refers to the level of awareness, care, and
willingness of individuals to support and contribute to addressing
environmental issues (Dunlap & Jones, 2002; Ozaki &
Sevastyanova, 2011).
Perceived Value
Perceived value is the consumer’s assessment comparing the benefits, risks,
and usefulness of a product based on perceptions of what is given
and received relative to alternatives (Kotler & Keller, 2016;
Zeithaml, 1998).
Image
Conceptual Framework
Source: Processed by the Researcher
H1: Environmental Concern has a positive effect on Subjective
Norm. H2: Environmental Concern has a positive effect on
Attitude.
H3: Environmental Concern has a positive effect on Perceived
Behavioral Control.
H4: Perceived Value has a positive effect on Attitude.
H5: Environmental Concern has a positive effect on Purchase Intention.
H6: Perceived Value has a positive effect on Purchase Intention.
H7: Subjective Norm has a positive effect on Purchase Intention.
H8: Attitude has a positive effect on Purchase Intention.
H9: Perceived Behavioral Control has a positive effect on Purchase Intention.
H10: Subjective Norm mediates the relationship between Environmental Concern and Purchase Intention.
H11: Attitude mediates the relationship between Environmental Concern and Purchase Intention.
H12: Perceived Behavioral Control mediates the relationship between Environmental Concern and Purchase Intention.
H13: Attitude mediates the relationship between Perceived Value and Purchase Intention.
METHODOLOGY
This study employs a causal-associative design to explain the
influence of Environmental Concern and Perceived Value on Purchase
Intention for electric vehicles, with the mediation of Theory of
Planned Behavior (TPB) variables among Generation Y in Jakarta. The
study population comprises Generation Y (born between 1981–1996) who
reside in the Special Capital Region of Jakarta, have income, and plan
to purchase an electric vehicle. The sampling techniques used are
purposive sampling and snowball sampling. Based on Cochran’s formula,
the sample size was determined to be 385 respondents.
RESULTS
Variable Description
Variable
Indicator Code
Statement
Mean
Environmental Concern (Buhmann et al., 2024; S. Wang et al., 2016)
EC1
I believe every individual has a responsibility to participate in environmental protection.
3.60
EC2
I care deeply about environmental sustainability.
3.93
EC3
I feel that environmental issues have become increasingly serious in recent years.
3.97
EC4
I consider environmental impacts when deciding to own a car.
3.96
EC5
I believe everyone should live in harmony with the environment through sustainable development.
4.01
Average
3.89
Variable
Indicator Code
Statement
Mean
Perceived Value (Loudiyi et al., 2022; Philipsen et al., 2016)
PV1
In my opinion, buying an electric vehicle is worth it.
3.57
PV2
I find buying and using an electric vehicle more appealing than using a gasoline-powered car.
3.22
PV3
I believe that buying and using an electric vehicle makes more sense.
3.90
PV4
I think buying and using an electric vehicle is a wise decision.
3.89
Average
3.65
Variable
Indicator Code
Statement
Mean
Subjective Norm (Gunawan et al., 2022; Huang & Ge, 2019)
SN1
I may purchase and use an electric vehicle if my family and relatives already own and use one.
4.08
SN2
I might be interested in buying and using an electric vehicle if a close friend recommends it.
3.72
SN3
Advertisements about electric vehicles in various media could encourage me to buy and use one.
3.86
SN4
If my work environment uses electric vehicles, I might be inclined to buy and use one as well.
3.75
Average
3.85
Variable
Indicator Code
Statement
Mean
Attitude (Gunawan et al., 2022; Lee, 2009)
AT1
I think using and buying an electric vehicle is a good idea.
3.35
AT2
By buying and using an electric vehicle, I can actively support the government’s EV acceleration program.
4.11
AT3
I believe the government’s electric vehicle acceleration program is positive and beneficial.
3.91
AT4
I would be happy if the electric vehicle I eventually buy and use can reduce pollution.
3.82
Average
3.80
Variable
Indicator Code
Statement
Mean
Perceived Behavioral Control (Gunawan et al., 2022; Huang & Ge, 2019)
PBC1
I have the freedom to decide whether or not to buy and use an electric vehicle.
3.09
PBC2
I have the financial ability to buy and use an electric vehicle.
3.48
PBC3
If I want to, I am confident that I can buy and use an electric vehicle.
3.71
PBC4
I have the knowledge of how to operate an electric vehicle.
3.81
Average
3.52
Variable
Indicator Code
Statement
Mean
Purchase Intention (Gunawan et al., 2022; Huang & Ge, 2019)
PI1
I will consider buying and using an electric vehicle.
3.90
PI2
I plan to try buying and using an electric vehicle.
4.00
PI3
I will buy and recommend electric vehicles to colleagues, friends, or family.
4.21
PI4
I will buy and use an electric vehicle while waiting for the launch of various EV brands in the market.
3.47
Average
3.90
Source: Processed Primary Data by the Researcher
Descriptive Analysis Summary
The descriptive analysis results indicate that the variables
environmental concern, perceived value, subjective norm, attitude, and
perceived behavioral control fall into the high rating category
(3,41-4,20), with the average respondent answers leaning toward
neutral to agree. Meanwhile, the purchase intention variable is in the
very high category (4,21-5,00), with most respondents selecting agree
to strongly agree. These findings suggest that consumers have a
positive attitude toward electric vehicles and show a strong tendency
to buy and use them.
Indicator Reliability - Outer Loading
Test Results Table Indicator Reliability - Outer Loading
Variable
Indicator
AT
EC
PBC
PI
PV
SN
Attitude
AT1
0.722
AT2
0.819
AT3
0.838
AT4
0.801
Environmental Concern
EC1
0.752
EC2
0.821
EC3
0.880
EC4
0.862
EC5
0.758
Perceived Behavioral Control
PBC1
0.756
PBC2
0.769
PBC3
0.749
PBC4
0.747
Purchase Intention
PI1
0.834
PI2
0.809
PI3
0.743
PI4
0.741
Perceived Value
PV1
0.719
PV2
0.726
PV3
0.777
PV4
0.799
Subjective Norm
SN1
0.771
SN2
0.757
SN3
0.806
SN4
0.746
Source: SmartPLS 4.0 Processing Results, 2025
Based on the results of the reliability indicator test, all outer
loading values were above 0.70, in accordance with Hair Jr. et al.'s
(2017) criteria that a value ≥ 0.70 indicates a high correlation with the construct being
measured. Thus, all indicators were declared individually valid and
had good reliability.0.70 indicates a high correlation with the
construct being measured. Thus, all indicators were declared
individually valid and had good reliability.indicates a high
correlation with the construct being measured. Thus, all indicators
were declared individually valid and had good reliability.high
correlation with the construct being measured. Thus, all indicators were declared
individually valid and had good reliability.
high correlation with the construct being measured. Thus, all
indicators were declared individually valid and had good
reliability.
Cronbach Alpha and Composite Reliability Test Results Table
Variabel
Cronbach's alpha
Composite reliability (rho_c)
Attitude
0,806
0,873
Enviromental
Concern
0,873
0,909
Perceived
Behavioral
Control
0,749
0,842
Purchase Intention
0,788
0,863
Perceived Value
0,749
0,842
Subjective Norm
0,771
0,854
Source: SmartPLS 4.0 Processing Results, 2025.
Based on the results of the internal reliability test, all
constructs had Cronbach's Alpha and Composite Reliability (CR)
values above 0.70, indicating adequate internal consistency. Thus,
all constructs met the reliability criteria according to Hair Jr. et
al. (2017) and were reliable for further structural model
analysis.Convergent Validity – AVE
Test Results Table Convergent Validity – AVE
Variabel
Average variance extracted (AVE)
Attitude
0,634
Enviromental Concern
0,666
Perceived Behavioral Control
0,571
Purchase Intention
0,613
Perceived Value
0,571
Subjective Norm
0,593
Source: SmartPLS 4.0 Processing Results, 2025.
Based on the convergent validity test results, all constructs had
Average Variance Extracted (AVE) values above 0.50, meaning they
were able to explain more than 50 percent of the variance in their indicators. Thus,
the constructs met the convergent validity criteria as outlined by
Hair Jr. et al. (2017), which states that an AVE value ≥ 0.50
indicates adequate internal consistency.
Discriminate Validity – Cross Loading
Table of Discriminant Validity – Cross Loading Test Results
Variabel
Indikator
AT
EC
PBC
PI
PV
SN
Attitude
AT1
0,722
0,326
0,380
0,492
0,371
0,531
AT2
0,819
0,348
0,376
0,460
0,293
0,473
AT3
0,838
0,316
0,359
0,449
0,320
0,358
AT4
0,801
0,358
0,381
0,456
0,268
0,349
Enviromental
Concern
EC1
0,279
0,752
0,380
0,463
0,262
0,376
EC2
0,387
0,821
0,406
0,507
0,281
0,429
EC3
0,356
0,880
0,433
0,530
0,270
0,382
EC4
0,394
0,862
0,459
0,546
0,302
0,426
EC5
0,305
0,758
0,393
0,489
0,255
0,344
Perceived Behavioral
Control
PBC1
0,363
0,388
0,756
0,635
0,425
0,432
PBC2
0,397
0,406
0,769
0,597
0,407
0,436
PBC3
0,347
0,381
0,749
0,592
0,339
0,458
PBC4
0,316
0,361
0,747
0,573
0,355
0,418
Purchase Intention
PI1
0,507
0,499
0,718
0,834
0,524
0,609
PI2
0,482
0,483
0,612
0,809
0,513
0,564
PI3
0,404
0,488
0,539
0,743
0,473
0,493
PI4
0,435
0,481
0,602
0,741
0,489
0,601
Perceived Value
PV1
0,296
0,239
0,339
0,473
0,719
0,417
PV2
0,304
0,241
0,367
0,459
0,726
0,353
PV3
0,256
0,272
0,427
0,488
0,777
0,365
PV4
0,337
0,263
0,397
0,512
0,799
0,402
Subjective Norm
SN1
0,350
0,329
0,451
0,550
0,386
0,771
SN2
0,363
0,351
0,422
0,522
0,394
0,757
SN3
0,347
0,384
0,405
0,558
0,366
0,806
SN4
0,590
0,410
0,494
0,600
0,418
0,746
Source: SmartPLS 4.0 Processing Results, 2025.
Based on the results of the discriminant validity test using the
cross- loading method, all indicators had the highest loading values
on the constructs they measured compared to other constructs. Thus,
each indicator is able to differentiate its original construct, thus
meeting the discriminant validity (cross- loading) criteria as
determined by Hair Jr. et al. (2017).
Discriminant Validity – Fornell-Larcker
Table of Discriminant Validity Test Results – Fornell-Larcker
AT
EC
PBC
PI
PV
SN
AT
0,796
EC
0,425
0,816
PBC
0,472
0,509
0,804
PI
0,586
0,622
0,752
0,783
PV
0,396
0,336
0,506
0,640
0,756
SN
0,542
0,481
0,577
0,726
0,509
0,770
Source: SmartPLS 4.0 Processing Results, 2025.
All constructs had a square root mean squared correlation (AVE)
value greater than their correlations with other constructs, thus
meeting the Fornell- Larcker discriminant validity criteria. Thus,
each construct was able to adequately differentiate itself from the
other constructs in this research model.
Discriminate Validity – HTMT
Discriminate Validity Test Results Table – HTMT
AT
EC
PBC
PI
PV
SN
AT
EC
0,503
PBC
0,604
0,628
PI
0,73
0,751
0,867
PV
0,506
0,415
0,674
0,832
SN
0,699
0,605
0,787
0,854
0,696
Source: SmartPLS 4.0 Processing Results, 2025.
Based on the discriminant validity test using the HTMT method in
Table 4.18, all values are below the threshold of 0.90. This
indicates that the constructs in the model have adequate empirical
differences so that discriminant validity is met according to the
criteria of Hair Jr. et al. (2017).
Structural Test Results (Inner Model) R-Square
R-Square Test Results Table
Variabel
R-square
Attitude (Z)
0,253
Perceived Behavioral Control
(Z)
0,259
Purchase Intention
(Y)
0,807
Subjective Norm (Z)
0,231
Source: SmartPLS 4.0 Processing Results, 2025
The R-square value of 0.807 for Purchase Intention indicates a
strong categorization, where 80.7% of the variability in electric
car purchase intention is explained by Attitude, Environmental
Concern, Perceived Behavioral Control, Perceived Value, and
Subjective Norm, while 19.3% is influenced by factors outside the
model. The R-square values for Attitude (0.253), Perceived
Behavioral Control (0.259), and Subjective Norm (0.231) are
considered weak, indicating that only a small portion of the
variability in these three constructs is influenced by exogenous constructs, while the remainder is influenced by factors outside the study.
Q-Square
Q-Square Test Results Table
Variabel
Q²predict
Attitude (Z)
0,244
Perceived Behavioral Control
(Z)
0,254
Purchase Intention
(Y)
0,536
Subjective Norm (Z)
0,225
Source: SmartPLS 4.0 Processing Results, 2025.
The Q-square test results show a Q² value of 0.536 for Purchase
Intention (PI), which falls into the large predictive relevance
category. Meanwhile, Attitude (0.244), Perceived Behavioral Control
(0.254), and Subjective Norm (0.225) fall into the medium predictive
relevance category. Thus, this model has strong predictive
relevance, particularly in explaining purchase intention, the
primary research variable.
f-Square
f-Square Test Results Table
f-square
AT -> PI
0.035
EC -> AT
0,089
EC -> PBC
0,243
EC -> PI
0,084
EC -> SN
0,209
PBC -> PI
0,321
PV -> AT
0.097
PV -> PI
0,103
SN -> PI
0,108
Source: SmartPLS 4.0 Processing Results, 2025
The f-square test results show that the relationship between
Perceived Behavioral Control (PBC) and Purchase Intention (PI) has
the highest f² value of 0.321 (moderate category), followed by the relationship between
Environmental Concern (EC) and PBC (0.243) and EC and Subjective
Norm (SN) (0.209) which are also moderate. Meanwhile, other pathways
such as Attitude (AT) and PI, EC and AT, and Perceived Value (PV)
and PI are in the small category (0.02 ≤ f² < 0.15), but still
have a significant influence on the objective variable.
Model Fit
Tabel Hasil Pengujian Model Fit
Saturated model
Estimated model
SRMR
0,068
0,126
d_ULS
1,492
5,153
d_G
0,547
0,698
Chi-square
1.185,635
1.364,662
NFI
0,758
0,721
Source: SmartPLS 4.0 Processing Results, 2025
The results of the model fit test on the saturated model showed
an SRMR value of 0.068 (<0.09), a low Chi-Square of 1,185.635,
and an NFI of 0.758 (>0.5), indicating an acceptable fit.
Therefore, the model in this study meets the fit criteria
established by Hu & Bentler (1999).
VIF
Table 4.23 VIF Test Results
VIF
AT -> PI
1,552
EC -> AT
1,127
EC -> PBC
1,000
EC -> PI
1,491
EC -> SN
1,000
PBC -> PI
1,853
PV -> AT
1,127
PV -> PI
1,501
SN -> PI
1,932
Source: SmartPLS 4.0 Processing Results, 2025
Multicollinearity test results using Variance Inflation Factor
(VIF) values indicate that all independent variables have VIFs
between 1.000 and 1.932, below the threshold of 5.0 (Hair Jr. et
al., 2017). Thus, the model is free from multicollinearity, and the
path coefficient estimates in PLS-SEM are declared valid for further
analysis.
Hypothesis Test Results (Path Coefficient Estimation)
Hypothesis Test Results Table
Source: SmartPLS 4.0 Processing Results, 2025
Original sample (O)
Sample mean (M)
Standard deviation (STDEV)
T statistics (|O/STDEV|)
P values
Result
AT -> PI
0,102
0,101
0,027
3,723
0,000
Accepted
EC -> AT
0,329
0,329
0,051
6,393
0,000
Accepted
EC -> PBC
0,509
0,511
0,042
11,983
0,000
Accepted
EC -> PI
0,186
0,186
0,032
5,914
0,000
Accepted
EC -> SN
0,481
0,483
0,048
9,978
0,000
Accepted
PBC -> PI
0,407
0,406
0,034
12,035
0,000
Accepted
PV -> AT
0,286
0,287
0,046
6,166
0,000
Accepted
PV -> PI
0,208
0,208
0,032
6,532
0,000
Accepted
SN -> PI
0,241
0,241
0,035
6,951
0,000
Accepted
EC -> PBC -> PI
0,207
0,207
0,022
9,427
0,000
Accepted
EC -> AT -> PI
0,034
0,034
0,011
2,977
0,003
Accepted
PV -> AT -> PI
0,029
0,029
0,008
3,526
0,000
Accepted
EC -> SN -> PI
0,116
0,116
0,020
5,827
0,000
Accepted
Source: SmartPLS 4.0 Processing Results, 2025
Source: SmartPLS 4.0 Processing Results, 2025
DISCUSSIONThe Influence of Environmental Concern on Subjective Norm
The results show that environmental concern has a positive and
significant effect on subjective norm, with the dominant indicator
EC3 having the highest T-statistic value of 54.868. This means
that the more concerned Generation Y in Jakarta is about
environmental issues, the greater their perception of social
support from family, friends, colleagues, and media in taking
pro-environmental actions such as purchasing electric vehicles.
These findings are supported by Buhmann et al. (2024) and Dutta
& Hwang (2021), who also found a significant positive
relationship between environmental concern and subjective
norm.
The Influence of Environmental Concern on Attitude
The study found that environmental concern positively and
significantly affects attitude, with indicator EC3 showing a
T-statistic of 54.868. The higher the environmental awareness
among Generation Y in Jakarta, the more positive their evaluation of purchasing electric vehicles.
This suggests that environmental concern motivates individuals to
perceive EVs as ethical, beneficial, and relevant choices for
reducing pollution. These results align with Buhmann et al. (2024)
and Dutta & Hwang (2021).
The Influence of Environmental Concern on Perceived Behavioral Control
Environmental concern has a positive and significant influence
on perceived behavioral control, with indicator EC3 also showing a
T-statistic of 54.868. Greater concern for environmental issues
boosts Generation Y’s confidence in their ability to make
decisions about purchasing EVs. This reflects that environmental
awareness strengthens individuals' self- efficacy and readiness to
take pro-environmental actions. These findings are consistent with
Buhmann et al. (2024) and Dutta & Hwang (2021).
The Influence of Perceived Value on Attitude
The study shows that perceived value has a positive and
significant effect on attitude, with PV4 being the dominant
indicator (T-statistic: 30.766). Generation Y in Jakarta who
perceive EV purchases as wise decisions tend to have positive
attitudes toward them. This reflects the belief that EVs provide
both environmental and cost-saving benefits. This finding is
supported by Asadi et al. (2021) and Pai et al. (2023).
The Influence of Environmental Concern on Purchase
Intention
Results indicate that environmental concern positively and
significantly affects purchase intention, with EC3 again showing a
T-statistic of 54.868. The more Generation Y is concerned with
environmental issues, the stronger their intention to purchase
electric vehicles as an eco-friendly alternative. This aligns with
the TPB framework, where background factors can influence
attitude, social norms, and perceived control. Supported by Yegin
& Ikram (2022) and Wang et al. (2021).
The Influence of Perceived Value on Purchase
Intention
Perceived value was found to positively and significantly
influence purchase intention, with PV4 being the dominant
indicator (T-statistic: 30.766). Generation Y in Jakarta who see
EV purchases as wise decisions tend to have higher buying
intentions. This shows that perceptions of benefit and value
encourage individuals to commit to eco-friendly products.
Supported by Loudiyi et al. (2022) and Ninh (2021).
The Influence of Subjective Norm on Purchase Intention
Subjective norm has a positive and significant impact on
purchase intention, with indicator SN3 showing a T-statistic of
34.893. Social support from family, friends, and media exposure
plays a vital role in encouraging Generation Y to intend to buy
EVs. In the TPB framework, social norms shape intention by
aligning behavior with social expectations. These findings are
supported by He et al. (2023) and Gunawan et al. (2022).
The Influence of Attitude on Purchase Intention
Attitude positively and significantly affects purchase
intention, with AT3 being the strongest indicator (T-statistic:
40.266). The more positive Generation Y’s perception of EV
policies and benefits, the stronger their purchase intention. In
the TPB model, positive attitudes formed by perceived benefits
strengthen purchase intentions. This finding is supported by
Clarita & Chalid (2024) and Supriadi et al. (2024).
The Influence of Perceived Behavioral Control on Purchase Intention
The study shows that perceived behavioral control positively and
significantly influences purchase intention, with PBC1 having a T-statistic
of 34.024. Generation Y individuals who feel greater personal control—
both in freedom of choice and decision-making—are more likely to intend
to buy EVs. In TPB, stronger perceived control increases the likelihood of
actual behavior. This is consistent with Zhao (2024) and Maso & Balqiah
(2022).
Mediating Role of Subjective Norm in the Relationship Between Environmental Concern and Purchase Intention
Attitude significantly mediates the influence of environmental concern on
purchase intention (T-statistic: 2.977). This is also a partial mediation,
indicating that environmental concern strengthens positive attitudes
toward EVs, thereby increasing purchase intention. Supported by
Buhmann et al. (2024) and Dutta & Hwang (2021).
Mediating Role of Attitude in the Relationship Between Environmental Concern and Purchase Intention
Attitude significantly mediates the influence of environmental
concern on purchase intention (T-statistic: 2.977). This is also a
partial mediation, indicating that environmental concern
strengthens positive attitudes toward EVs, thereby increasing
purchase intention. Supported by Buhmann et al. (2024) and Dutta
& Hwang (2021).
Mediating Role of Perceived Behavioral Control in theRelationship Between Environmental Concern and Purchase Intention
Perceived behavioral control mediates the relationship between
environmental concern and purchase intention significantly
(T-statistic: 9.427). Again, a partial mediation, showing that
environmental concern enhances the perception of control, boosting
Generation Y’s confidence in purchasing EVs. Supported by Buhmann
et al. (2024) and Dutta & Hwang (2021).
Mediating Role of Attitude in the Relationship Between Perceived Value and Purchase Intention
The study shows that attitude significantly mediates the
relationship between perceived value and purchase intention
(T-statistic: 2.977). This partial mediation indicates that high
perceived value strengthens positive attitudes, which in turn
increase purchase intention. Supported by Pai et al. (2023) and
Loudiyi et al. (2022).
CONCLUSION
This study concludes that both Environmental Concern and Perceived
Value have positive and significant effects on Purchase Intention
toward electric vehicles among Generation Y in Jakarta. Additionally,
Attitude, Subjective Norm, and Perceived Behavioral Control are also
found to directly and significantly influence purchase intention. The
mediation analysis reveals that the three TPB variables partially
mediate the relationships between the independent variables and
purchase intention.
Thus, environmental concern, perceived value, attitude, subjective
norm, and perceived behavioral control are collectively critical
factors that drive electric vehicle purchase intention. These findings
provide theoretical contributions to the development of the Theory of
Planned Behavior (TPB) and practical implications for electric vehicle
marketing strategies in Indonesia.
RECOMMENDATIONS
Strengthen sustainability campaigns, highlight financial
rationality, and emphasize contributions to national EV
programs.
Implement community-based marketing strategies, interactive
educational approaches, and digital campaigns aligned with
Generation Y’s lifestyle.
Focus on customer experiences and user testimonials to
reinforce purchase intention and encourage voluntary
recommendations.
Integrate additional theories such as Diffusion of Innovation
(Rogers, 2003) and Value-Belief-Norm Theory (Stern et al., 1999)
to explore new variables.
Expand the respondent base across generations (Gen X, Y, Z) and
across different regions or countries.
Broaden the research scope for more comprehensive and relevant
insights to support marketing strategies and public policy.
ReferencesAjiS. P.KartonoD. T.2022Kebermanfaat Adanya Sustainable Development Goals (SDGs)Journal of Social Research1650751210.55324/josr.v1i6.110AjzenI.1991The theory of planned behaviorOrganizational Behavior and Human Decision Processes50217921110.1016/0749-5978(91)90020-TAsadiS.NilashiM.SamadS.AbdullahR.MahmoudM.AlkinaniM. H.YadegaridehkordiE.2021Factors impacting consumers’ intention toward adoption of electric vehicles in MalaysiaJournal of Cleaner Production28212447410.1016/j.jclepro.2020.124474BanerjeeA.SolomonB. D.2017Eco-labeling for energy efficiency and sustainability: A meta-evaluation of US programsLabelling Strategies in Environmental Policy3143945310.4324/9781351154529-35BuhmannK. M.Rialp-CriadoJ.Rialp-CriadoA.2024Predicting Consumer Intention to Adopt Battery Electric Vehicles: Extending the Theory of Planned BehaviorSustainability (Switzerland)16310.3390/su16031284ClaritaA.ChalidD. A.2024Analysis Of Factors Influencing Consumer Purchase Intention For Electric Cars: A Case Study In Greater Jakarta[Journal name not provided]12441554170DunlapR. E.JonesR. E.2002Environmental Concern: Conceptual and Measurement Issues[Unpublished report]October 2002DuttaB.HwangH. G.2021Consumers purchase intentions of green electric vehicles: The influence of consumers technological and environmental considerationsSustainability (Switzerland)132110.3390/su132112025GunawanI.RediA. A. N. P.SantosaA. A.MaghfirohM. F. N.PandyaswargoA. H.KurniawanA. C.2022Determinants of Customer Intentions to Use Electric Vehicle in Indonesia: An Integrated Model AnalysisSustainability (Switzerland)14412210.3390/su14041972Hair Jr.J. F.HultG. T. M.RingleC. M.SarstedtM. A.2017A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)2Sage PublicationsHeZ.ZhouY.WangJ.ShenW.LiW.LuW.2023Influence of emotion on purchase intention of electric vehicles: a comparative study of consumers with different income levelsCurrent Psychology4225217042171910.1007/s12144-022-03253-1HuL.BentlerP. M.1999Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternativesStructural Equation Modeling: A Multidisciplinary Journal6115510.1080/10705519909540118HuangX.GeJ.2019Electric vehicle development in Beijing: An analysis of consumer purchase intentionJournal of Cleaner Production21636137210.1016/j.jclepro.2019.01.231KotlerP.KellerK. L.2016Marketing ManagementPearson Education LimitedLeeM.-C.2009Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefitElectronic Commerce Research and Applications8313014110.1016/j.elerap.2008.11.006LoudiyiH.ChetiouiY.LebdaouiH.2022Economics of Electric Vehicle Adoption: An Integrated Framework for Investigating the Antecedents of Perceived Value and Purchase IntentInternational Journal of Economics and Financial Issues125293810.32479/ijefi.13328MasoR. A.BalqiahT. E.2022Analyzing Factors Affecting Purchase Intention of Electric Vehicle in Indonesia; Moderation Role of Personal Innovativeness on Those FactorsProceedings of International Conference on Economics Business and Government Challenges5135036110.33005/ic-ebgc.v1i1.47MittalS. K.KordeP.PalanirajaS.OmaseN.GuchhaitP.MundraP.2023A Systematic Review on Recent Advancement in Electric Vehicle Technologies[Journal name not provided]11December3744NinhN. G.2021Resistance to change and purchase intention of electric vehicles: Empirical evidence from VietnamAsian Journal of Business Research1128310110.14707/ajbr.210108OzakiR.SevastyanovaK.2011Going hybrid: An analysis of consumer purchase motivationsEnergy Policy3952217222710.1016/j.enpol.2010.04.024PaiF. Y.ShihY. J.ChuangY. C.YehT. M.2023Supporting Environment Sustainability: Purchasing Intentions Relating to Battery Electric Vehicles in TaiwanSustainability (Switzerland)152411910.3390/su152416786PatiroS. P. S.BudiyantiH.2016The Theory of Planned Behavior, Past Behavior, Situational Factors, and Self-Identity Factors Drive Indonesian Enterpreneurs to Be IndebtednessDeReMa (Development Research of Management): Jurnal Manajemen1114610.19166/derema.v11i1.188PhilipsenR.SchmidtT.van HeekJ.ZiefleM.2016Fast-charging station here, please! User criteria for electric vehicle fast-charging locationsTransportation Research Part F: Traffic Psychology and Behaviour4011912910.1016/j.trf.2016.04.013RogersE. M.2003Diffusion of Innovations5th ed.Free PressSchiffmanL. G.KanukL. L.2007Consumer BehaviorPearson Prentice HallSchiffmanL. G.WisenblitJ.2015Consumer behavior11th ed.PearsonSternP. C.DietzT.AbelT.GuagnanoG. A.KalofL.1999A value-belief-norm theory of support for social movements: The case of environmentalismHuman Ecology Review628197SudjokoC.2021Strategi Pemanfaatan Kendaraan Listrik Berkelanjutan Sebagai Solusi Untuk Mengurangi Emisi KarbonJurnal Paradigma: Jurnal Multidisipliner Mahasiswa Pascasarjana Indonesia225468SupriadiD.ImanA. H.SaputraY.2024Studi Intensi Pembelian Kendaraan Listrik pada Generasi-Z: Pendekatan Teori Perilaku Terencana yang diperluasEKOMABIS: Jurnal Ekonomi Manajemen Bisnis501839810.37366/ekomabis.v5i01.1444WangX. W.CaoY. M.ZhangN.2021The influences of incentive policy perceptions and consumer social attributes on battery electric vehicle purchase intentionsEnergy Policy151February10.1016/j.enpol.2021.112163YeginT.IkramM.2022Analysis of consumers’ electric vehicle purchase intentions: An expansion of the TPBSustainabilityZeithamlV. A.1998Consumer Perceptions of Price, Quality and Value: A Means-End Model and Synthesis of EvidenceJournal of Marketing52322210.1177/002224298805200302ZhaoB.2024Determining Intention of Consumers to Purchase Electric Vehicles in Chongqing, ChinaSustainability162162172ZolaG.SiskaNugraheniD.AndhienRosianaA.DzamarPambudyA.AgustantaN.2023Inovasi kendaraan listrik sebagai upaya meningkatkan kelestarian lingkungan dan mendorong pertumbuhan ekonomi hijau di IndonesiaEkonomi Sumberdaya Dan Lingkungan11323031220