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An Agent-Based Model to Simulate the Diffusion of New Energy Vehicles


Abstract and Figures

This paper demonstrates the use of an agent-based model (ABM) to study the mechanism of social influence in the diffusion of new energy vehicles. We introduce the “consumat” cognition model so that agents with different need satisfaction thresholds have different cognitive processes. In addition, supported by survey data, our study considers more characteristics of opinion leaders, such as innovative behavior, lower sensitivity to price influence, and a better ability to judge the product quality. Through the primary group and control group experiments, the simulations demonstrated that the opinion leaders play a significant role in the spread of information and the percentage of product adoption. The results indicate that targeting opinion leaders will be a valuable marketing strategy for new energy vehicles. It also provides some advice for assessing policies that promote sustainable behaviors.
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Research Article
An Agent-Based Model to Simulate the Diffusion of New
Energy Vehicles
Hao Zhang ,
Peifeng Zhu ,
and Zhichao Yao
College of Economics & Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
School of Health Economics & Management, Nanjing University of Chinese Medicine, Nanjing 210023, China
Correspondence should be addressed to Peifeng Zhu;
Received 9 September 2022; Revised 9 February 2023; Accepted 20 February 2023; Published 4 March 2023
Academic Editor: Qingling Wang
Copyright ©2023 Hao Zhang et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
is paper demonstrates the use of an agent-based model (ABM) to study the mechanism of social inuence in the diusion of
new energy vehicles. We introduce the “consumat” cognition model so that agents with dierent need satisfaction thresholds have
dierent cognitive processes. In addition, supported by survey data, our study considers more characteristics of opinion leaders,
such as innovative behavior, lower sensitivity to price inuence, and a better ability to judge the product quality. rough the
primary group and control group experiments, the simulations demonstrated that the opinion leaders play a signicant role in the
spread of information and the percentage of product adoption. e results indicate that targeting opinion leaders will be a valuable
marketing strategy for new energy vehicles. It also provides some advice for assessing policies that promote sustainable behaviors.
1. Introduction
Wired Travelhas asked many Tesla vehicle owners, “Why
did you buy Tesla?” there is always a similar answer: “because
Musk is a person who is building cars, still building rockets
and going to Mars. Such an articially manufactured car
must also be cool.” According to Tencent Technology,
Musk has posted 74 microblog searches since 3
2020. Musk said on his social media, Twitter, that “he is
considering quitting his job and becoming an online ce-
lebrity full-time.” It attracted the attention of netizens
worldwide for a while, and almost 20,000 responses were
received within an hour of its release, showing its signicant
social inuence.
In today’s booming new energy vehicle market,
reexamining the role of stars in the technology diusion
process will signicantly help the popularization of new
energy vehicles in China. e Chinese vehicle brands need
to learn not only from Tesla’s strong product competi-
tiveness but also from its unique marketing ability.
However, more research studies need to be conducted on
the impact of key opinion leaders on new energy vehicles.
erefore, this paper aims to discuss the impact of key
opinion leaders based on agent-based simulation mod-
eling (ABM). Based on the theory of complex adaptive
systems and innovation diusion, we have established
a simulation model of the diusion of new energy vehicles.
rough the simulation of the consumer decision-making
process, interaction process, and the market, the real
society can be restored to a maximum reasonable extent,
and the mechanism of the diusion process of new energy
vehicles and key inuencing factors can be explored. In
addition, we distinguish opinion leaders from general
consumers according to their characteristics, so that we
can observe the experimental results more easily. At the
same time, in order to make the simulation results closer
to the real situation of Chinese consumers, we collected
empirical data from the Chinese automobile market
through a questionnaire survey. Finally, according to the
experimental results, we put forward some practical
marketing suggestions for the promotion of new energy
vehicles in China.
In the following sections, we rst discuss cognition and
parameter settings; subsequently, we describe the simulation
model and experimental results. Finally, we draw conclu-
sions and have some discussions.
Volume 2023, Article ID 6773087, 9 pages
2. Literature Review
Agent-based models (ABMs) have often been used to study
the diusion of new products and technologies because of
their exible architecture and ability to operate in various
environments. Among them are many studies on the sim-
ulation research of new energy vehicles [1–5]. However,
most focus on the macrolevel, such as charging in-
frastructure, nancial subsidies, market share, and other
factors, and pay less attention to the microlevel of social
interaction, such as opinion leaders. erefore, modeling
new energy vehicles is still a new area of research.
2.1. Social Inuence in ABM. Scholars have long found the
importance of social inuence in innovation communica-
tion and applied it to the traditional equation model of
innovation diusion (Bass) [6]. However, the limitation of
the Bass model is that it does not introduce social interaction
factors. Because the agent modeling method can accurately
simulate interpersonal interaction at the microlevel, it is
more prevalent among scholars when analyzing social
inuencing factors. Janssen and Jager analyzed social in-
uence by dening two kinds of utility functions (social
utility and personal utility) of consumer agents. e utility
value of consumers is divided into two parts which are
aected by norms and information, respectively. In-
formation inuence refers to the satisfaction of the learned
commodity-related information to the consumer’s utility
brought about by the consumer’s physiological needs and
personal preferences. e normative inuence aects the
satisfaction of consumer products to social needs such as
consumers’ social status and sense of belonging through
social norms and group atmosphere. Consumers can decide
to accept goods based on the quality of the goods. However,
the social inuence caused by neighboring consumers will
also bring social pressure on them, thereby strengthening or
changing their initial purchase intention [7]. Kiesling et al.
dierentiate between microlevel, mesolevel, and macrolevel
social inuence. Microlevel social inuence refers to the
impact between two subjects through pairwise communi-
cation links, such as word of mouth (WOM). Mesolevel
social inuence refers to the inuence of the surrounding
environment of the subject, such as neighbor inuence and
group inuence (herd behavior, conspicuous consumption,
etc.). Macrolevel inuence refers to the inuence of the
social level on the subject, such as fashion and trends [8].
In many of the reviewed articles, the term“social inu-
ence”is used in the sense of mesolevel social inuence. e
research of relevant scholars shows that in agent-based
simulation modeling, a suitable network structure signi-
cantly impacts the path and speed of diusion of innovation
[9]. e two most common types in the agent model are
small-world networks and scale-free networks [10, 11].
Barabasi and Bonabeau both believe that a scale-free network
is the closest to a social, interpersonal network in the real
world [12, 13]. e cognitive model of the subject is also an
essential factor in determining the success of ABM. A large
amount of literature builds the cognitive model based on the
social psychology theory. Ajzen’s theory of planned behavior
(TPB) is one of the most widely used theories. Subject
decision-making is aected by attitude, subjective norms, and
perceived behavior control [14]. Jager et al. put forward
another theory of consumat [1517]. Consumat (consumer
agent) makes purchase decisions according to satisfaction and
perceived uncertainty. ese four decisions are deliberating,
social comparison, repeating, and imitation.
2.2. Opinion Leaders in ABM. ere is no strict denition of
opinion leaders in the academic eld. Engel et al. believed
that opinion leaders should be innovators or rst users who
can inuence other consumers through innovative behavior
and specic product knowledge [18]. Feick and Price pro-
posed that market experts who have no specic professional
knowledge but are more familiar with the market are more
likely to become opinion leaders [19]. Rogers believes that
opinion leaders are not sensitive to old social norms or are
more willing to accept new social norms, so they may be
more willing to accept innovative or rebellious products
[20]. Lyons and Henderson put forward a more compre-
hensive denition of opinion leaders. Compared to con-
sumers who solicit their opinions, opinion leaders often have
more experience or expertise, they have disclosed or ob-
tained more information about the products, they show
more exploratory and innovative behaviors, and thus show
a higher product participation. is denition distinguishes
opinion leaders and ordinary consumers from the per-
spective of individual heterogeneity and provides great
convenience for the setting, identication, and use of
opinion leaders in ABM [21]. Nielsen believes that opinion
leaders are often experts in the industry from the perspective
of marketing, and their professional evaluation is a unique
marketing method which is transmitted to mass consumers
through particular ways (such as WOM) [22].
Valente and Davis rst studied the role of opinion
leaders in the agent model. ey dened homogeneous
consumer subjects. If 15% of the neighbors adopt the in-
novative product, they will also make consumption de-
cisions. Moreover, if the network model that randomly
assigns a relationship to each agent is applied, then there will
be no very closed social network structure similar to the real
world [23]. e simulation results show that when opinion
leaders initiate the diusion, it will diuse faster and that
purposeful opinion leaders can speed up the diusion
process. Van Eck et al. dened heterogeneous consumer
subjects, considering the pursuit of quality by opinion
leaders and the impact of knowledge level on consumers
[24]. Like Janssen and Jager, the subject’s decision to accept
the product is based on a utility threshold function that
includes personal preferences and social inuence. However,
they did not simulate social inuence as a xed value but as
a constant value. For example, if more neighbors accept the
commodity, the normative inuence of the commodity will
increase. e scale-free network is used for simulation.
Finally, it is concluded that opinion leaders increase the
transmission speed of information, the acceptance process,
and the maximum number of recipients ratio.
3. The Simulation Experiment
3.1. Agent and Cognitive Model. We use NetLogo software to
develop an agent-based model of the diusion of new energy
vehicles. To simplify the model, we divide consumer agents
into two categories, ordinary consumers (OC) and opinion
leaders (OL). e heterogeneity of consumers is shown by
using the dierent attribute values between consumer
agents, which distinguishes opinion leaders from ordinary
consumers. Specically, agents identied as opinion leaders
will have the ability to know the actual quality of goods
through the mass media (better quality judgment), the
higher weight of personal utility in the total utility (not easily
aected by norms), and the tremendous enthusiasm to try
out new things (innovative and price-insensitive).
e cognitive model proposed by Jager et al. is a good
description of social inuence [15], but subsequent studies
have been applied to analyzing small-world networks. We
further introduce the“consumat”into the scale-free network
and modify the original practical function of the consumer
agent. Each consumer agent will receive a commodity sat-
isfaction threshold S(i,min)and an uncertainty threshold
U(i,min). e commodity satisfaction threshold is the lowest
utility value the utility brought about when choosing
a commodity that can satisfy consumers. When the utility
value obtained by the consumers from the selected com-
modity is lower than the satisfaction threshold, then the
consumer agent is in a state of dissatisfaction or low sat-
isfaction; otherwise, it indicates that consumers are satised
with the selected commodity. When the uncertainty is
higher than the uncertainty threshold, it indicates that the
consumers are skeptical about the goods they currently buy
and will refer to the use of others to decide what goods they
will buy next. erefore, the model in this paper sets the
cognitive process of consumers as follows (see Figure 1):
(1) Repeat: when SiS(i,min)and UiU(i,min), consumat
will continue to purchase the products purchased in
the last time step.
(2) Deliberation: when Si<S(i,min)and UiU(i,min), con-
sumat will estimate the utility value of each product and
will nally choose the product with the highest score.
With the same utility value, the choice will be at random.
(3) Imitation: when SiS(i,min)and UiU(i,min), con-
sumat will evaluate the product that is consumed
most in its social network. at which is consumed
the most in the previous time step will be chosen for
current consumption. With the same holding rate,
the choice will be made at random.
(4) Social comparison: when SiS(i,min)and UiU(i,min),
the consumer agent calculates the model with the
highest purchases in its social network. e candidate
product with the highest market share will be chosen
for consumption.
3.2. Parameters and Assumptions. We collected many con-
sumer data from China’s auto market through questionnaires and
interviews. Finally, we selected price, fuel consumption, and
subsidy attributes to build the initial agent assignment. Two in-
dicators measured the promotion of new energy vehicles: the
diusion of information related to new energy vehicles and the
adoption of new energy vehicles. at is, the diusion of new
energy vehicles is observed by the awareness rate of consumers
about new energy vehicles and the purchase rate of new energy
vehicles. is model divides the process into three stages for the
diusion of new energy vehicles in the network: mass commu-
nication, word of mouth, and strategic decision. ese three stages
constitute the diusion mechanism of new energy vehicles in this
model, which is continually repeated during the operation of the
model. e microlevel decision-making and consumption be-
havior of consumer agents continue to evolve through this, and
nally, the actual trend of the automobile market emerges at the
Combined with the abovementioned collected data, the
initial data of the three attributes of the two types of au-
tomotive agents are shown in Table 1.
After referring to the previous literature and by compre-
hensively considering the eect of NetLogo operation and ef-
ciency, this paper sets the initial number of agents in the model
to 500 people. is value can be adjusted by using the slider,
which can be set according to the needs of the subsequent
experiments. Among them, 50% are fuel vehicle consumers,
while the remaining 50% are potential vehicle consumers who
intend to buy a vehicle but have not yet bought it. According to
the theory described previously, we dene consumers with the
highest score on the questionnaire as opinion leaders. Among
these 500 people, about 30% of the agents will be set as opinion
leaders, and their characteristic values are only partially con-
sistent with those of ordinary consumers. e settings of the
various attributes of the consumer subjects are shown in Table 2.
In this model, the opinion leaders’ innovation will be
reected in the following aspects: opinion leaders have
higher fuel consumption and price thresholds for buying
cars and can aord higher prices than ordinary consumers.
e fuel consumption and price thresholds of each opinion
leader are evenly distributed between 0.2 and 1, while the
fuel consumption and price thresholds of ordinary con-
sumers are evenly distributed between 0 and 1. e 0.2 gaps
can not only reect the dierences between opinion leaders
and ordinary consumers but also control the dierences to
avoid the situation that opinion leaders are too innovative. If
an opinion leader is too innovative, he will likely accept
those innovative products that prove unsuccessful and then
lose his position in the eld [16].
For ordinary consumers, we have three assumptions.
First, each consumer has dierent characteristic values.
Second, the threshold of ordinary consumers for fuel con-
sumption and the price is lower than that of opinion leaders.
ird, ordinary consumers cannot accurately judge the
quality of goods based on information.
3.3. Experimental Manipulation. To accurately judge the
dierences in the results of the simulation under dierent
initial conditions, rst, we complete the experiment of the
experimental group, that is, simulate the diusion of new
energy vehicles under the initial conditions given above, to
Complexity 3
provide a basis for the subsequent treatment of the results of
the control group experiment. Next, we compare dierent
simulation results with the data obtained from control group
experiments to judge the role of opinion leaders in the
diusion of new energy vehicles and what factors will aect
the role of opinion leaders in the group. e setting of the
opinion leaders and general consumers attributes are shown
in Table 3.
4. Results and Analysis
4.1. Number of Opinion Leaders. We conduct two control
group experiments to test whether opinion leaders play
a role in the diusion of new energy vehicles. In D1, the
Accept information?
Cognitive behavior Update memory map
Sufficient satisfaction?
High uncertainty?
High uncertainty?
DeliberationSocial comparisonRepeatImitation
Purchase decision
Arrival time step?
NWord of mouth
Figure 1: Decision logic for a consumat.
Table 1: Basic parameter settings.
Type Variables Price Subsidy Oil consumption
New energy vehicles Cost and price 23 20% 5.0L/100km
Weights 0.46 0.1 0.2
Fuel vehicles Cost and price 21.2 0 7.39L/100km
Weights 0.3 0 0.3
Other parameters Number of agents 500
Number of runs/experiments 500
Table 2: Model parameter settings.
Variable Parameter
Distribution of
Satisfaction thresholds S(i,min)U(0,1)U(0,1)
Uncertainty thresholds U(i,min)U(0,0.2)U(0,0.2)
e fuel consumption
thresholds PQiU(0.2,1)U(0,1)
e price thresholds CQiU(0.2,1)U(0,1)
Weight of price inuence αiU(0.5,0.2)U(0.54,0.2)
Weight of normative inuence βiU(0.49,0.2)U(0.58,0.2)
Quality of the product judgment NA Yes No
number of opinion leaders in the network is only half of that
in normal circumstances. At the same time, in D2, there is no
distinction between opinion leaders and ordinary con-
sumers, and all consumer subjects have the same attributes
and characteristics.
e experiment Ais the primary experiment; on an
average, 170 opinion leaders account for 30% of the total
leaders. e results are reected in the diamond-shaped
dot line. Experiment D1has an average of 83.3% of
opinion leaders; the experimental data are shown in
a straight line of the gure. D2removes opinion leaders
from the consumption circle; there are only ordinary
consumers in the model. e data in this model are
represented in the gure by triangular segments. It can be
seen that the number of opinion leaders has a signicant
impact on the diusion of information and products for
new energy vehicles. When opinion leaders do not reach
the average proportion in the crowd, the diusion of
vehicle-related information is signicantly constrained,
which can only reach 85% coverage. Its diusion speed has
also been signicantly decreased compared to the 2.72
time steps in the preliminary experiment. Although the
diusion of information in D1has also increased signif-
icantly in the initial stage, it still takes an average of 22.15
time steps to reach the maximum cognitive rate of in-
formation from new energy vehicles, and the buyers of
new energy vehicles only reach about 1/5 of that under
normal circumstances (see Figure 2).
Among consumer groups without opinion leaders, the
diusion process of new energy vehicle information is slow
and extremely limited. After 25 time steps, only about 110
consumers can learn about the new energy vehicles. Con-
sidering that the probability of diusion of new energy
vehicles through the mass media is about 1%, it can be
considered that in the consumer market without opinion
leaders, the ability to communicate is almost zero, and the
weak ability of information diusion also leads to the result
of a purchase of 0 in the D2.
e t-test analysis of the three groups of experimental
data can yield a more actual and intuitive conclusion. At the
signicance level (when αis 0.05), there are signicant
dierences in the cognitive rate and the purchase rate of
consumers in these three groups of experiments. It can be
seen that dierent numbers of opinion leaders make dif-
ferent diusion of new energy vehicles. We can conclude
that opinion leaders play an essential role in the diusion of
new energy vehicles, and the speed of information and
product diusion depends on the number of opinion
4.2. Degrees of Innovation. To reect the innovation of
opinion leaders, we raised the thresholds of opinion leaders
for fuel consumption and selling price; that is, they need to
pay more attention to the price of the products. Figure 3
shows the diusion of new energy vehicles in the network
with dierent innovation capabilities of opinion leaders.
e solid line represents the data of the preliminary
experiment. In contrast, the dotted line represents the dif-
fusion of new energy vehicles in the environment of opinion
leaders after the decline of innovation (that is, opinion
leaders have the same fuel consumption and price threshold
as general consumers). It can be seen from the two gures
that the decline in the innovation of opinion leaders has
helped the diusion of new energy vehicles. In the experi-
ment B, new energy vehicles can reach the peak of in-
formation dissemination in an average of 2.5 time steps,
covering all the 500 consumer subjects. e number of
people who nally buy new energy vehicles is nearly two
more than that in experiment A. In both experiments,
opinion leaders account for about half of the consumers who
buy new energy vehicles. By comparing these data, opinion
leaders’degree of innovation plays a minor role in the dif-
fusion of new energy vehicles in this model (see Figure 3).
is result can be explained by the fact that the creative
expression of opinion leaders in the model needs to be more
accurate, and the dierence in threshold cannot bring ad-
vantages to the nal decision-making of choice. e second
is the error caused by randomness in the model. erefore, it
needs to be further discussed in subsequent experiments
whether opinion leaders’ innovation plays a positive role in
4.3. Weights of Normative Inuence. We conducted exper-
iments C1and C2, respectively, which simulated the role of
opinion leaders with dierent weights of regulatory inu-
ence in the diusion process of new energy vehicles. e data
analysis is shown in Figure 4.
From the abovementioned data analysis, whether
opinion leaders value the impact of norms has nothing to do
with the diusion of new energy vehicle information. All
three experiments can let consumers know about the rele-
vant information of new energy vehicles in a relatively short
time (the average time of Ais 2.71, C1is 2.82 time steps, and
C2only needs 2.84 time steps). However, the weight of the
normative inuence of opinion leaders has produced un-
expected results in purchasing new energy vehicles. rough
comparison and analysis, the following reasons may cause it:
Although car ownership in China has increased signicantly
in recent years, it has yet to be entirely popularized. For most
Chinese consumers, car consumption may still be in the
Table 3: Experimental parameter settings.
A U(0.2,1)U(0.49,0.2)30% Yes U(0.5,0.2)Yes
B U(0,1)U(0.49,0.2)30% Yes U(0.5,0.2)Yes
C1U(0.2,1)U(0.58,0.2)30% Yes U(0.5,0.2)Yes
C2U(0.2,1)U(0.2,0.2)30% Yes U(0.5,0.2)Yes
D1U(0.2,1)U(0.49,0.2)15% Yes U(0.5,0.2)Yes
D2U(0.2,1)U(0.49,0.2)NA Yes U(0.5,0.2)Yes
E U(0.2,1)U(0.49,0.2)30% NA U(0.5,0.2)Yes
F U(0.2,1)U(0.49,0.2)30% Yes U(0.54,0.2)Yes
G U(0.2,1)U(0.49,0.2)30% Yes U(0.5,0.2)NA
Note. IOL , innovativeness of opinion leader; WNIOL, weight of normative
inuence opinion leader; NOL, number of opinion leaders; QPJ, quality of
the product judgment; WPIOL, weight of price inuence opinion leader;
OCT, ordinary consumers’ trust in opinion leaders.
Complexity 5
initial stage. Consumers are too closely connected with their
identity, status, and strength when buying a car. ey also
hope to gain honor and reputation through car consumption
(see Figure 4).
e weight of social utility (compared to personal utility)
for Chinese consumers is too large compared to western
consumers. Does this need to be adjusted accordingly in the
initialization of the model? It may need further discussion in
future experiments.
4.4. Judgment of Product Quality. Because opinion leaders
have a more in-depth understanding and involvement in
a particular eld, they have more professional knowledge
than ordinary consumers. ey can more accurately judge
the quality of relevant goods. In the experiment E, the ability
of opinion leaders has been stripped away; that is, opinion
leaders can only make a general judgment on the quality of
new energy vehicles. e data analysis is shown in Figure 5.
e judgment ability of opinion leaders on product quality is
not the main reason they can eectively promote the in-
formation and product diusion of new energy vehicles (see
Figure 5).
4.5. Weights of Price. Previous empirical studies have shown
that, unlike ordinary consumers, opinion leaders do not
care too much about the price of goods when buying. To test
whether this characteristic is the factor that promotes the
diusion of new energy vehicles, we carry out the following
data analysis for experiments Aand E. It is not dicult to see
that the price weight does not play a decisive role in the new
energy vehicle market. e result may also be explained by
the fact that the gap between ordinary consumers and
opinion leaders is not very prominent, coupled with the
randomness of the simulation system itself (see Figure 6).
4.6. Degrees of Trust. Ordinary consumers only believe in
commodity information from opinion leaders and those
who have used experience, reecting ordinary consumers’
trust and dependence on opinion leaders in a specic eld.
To test whether the change in ordinary consumers’ trust in
opinion leaders will aect the diusion of new energy ve-
hicles, the data analysis of the basic experiment Aand
control group experiment Gis shown in Figure 7.
e graph obtained from the data analysis is similar to
the graph of the number of dierent opinion leaders in
Figure 3. It can be seen that the degrees of trust play an
essential role in the diusion of new energy vehicles. It is due
to the great trust that they will recognize the information
recommended by the opinion leaders, which makes the
information related to new energy vehicles spread smoothly
and quickly (see Figure 7).
357 9 11 13 15 17 19 21 23 251
3 5 7 9 11 13 15 17 19 21 23 251
Figure 2: Comparison of the diusion of NEV with dierent numbers of opinion leaders: (a) diusion of information and (b) diusion of
357 232113 15 17 19 251911
Figure 3: Comparison of the diusion of NEV with the dierent innovation capabilities of opinion leaders: (a) diusion of information and
(b) diusion of products.
3 5 7 9 11 13 15 17 19 21 23 251
3 5 7 9 11 13 15 17 19 21 23 251
Figure 5: Comparison of the diusion of NEV with dierent judgment abilities of opinion leaders: (a) diusion of information and (b)
diusion of products (the blue line represents experiment Aand the red line represents experiment E).
3 5 7 9 11131517192123251
3 5 7 9 11 13 15 17 19 21 23 251
Figure 6: Comparison of the diusion of NEV with a dierent price: (a) diusion of information and (b) diusion of products (the blue line
represents experiment Aand the red line represents experiment F).
3 5 7 9 11 13 15 17 19 21 23 251
Figure 7: Comparison of the diusion of NEV with dierent trust: (a) diusion of information and (b) diusion of products (e blue line
represents experiment Aand the red line represents experiment G).
3 5 7 9 11131517192123251
3 5 7 9 11 13 15 17 19 21 23 251
Figure 4: Comparison of the diusion of NEV with the dierent normative inuences of opinion leaders: (a) diusion of information and
(b) diusion of products (the blue line represents experiment A, the red line represents group C1, and the green line represents C2).
Complexity 7
5. Conclusions and Future Research
In this study, we explore the role of opinion leaders in the
diusion of new energy vehicles using an agent-based
simulation model based on the NetLogo platform. is
study comes to the following conclusions. First, opinion
leaders play a signicant role in the diusion of new energy
vehicles. It increases the diusion speed of new energy
vehicle information and promotes the sales of new energy
vehicles to a certain extent. Second, the study also found that
the critical factor causing this impact is the trust of ordinary
consumers in opinion leaders. In particular, the role of
normative inuence on the diusion of new energy vehicles
is more signicant than that attained by the conclusions of
the relevant literature.
rough the simulation experiments, we understand the
mechanisms that may inuence the adoption rate of new
energy vehicles. is model aims to provide practical in-
formation to automobile manufacturers and governmental
policymakers on how consumers may be motivated to
change their preferences more favorably toward the new
energy vehicles. From the perspective of automobile man-
ufacturers, they should fully use the role of opinion leaders
in the group. Especially at the initial stage, they can target
opinion leaders in the automotive consumer market, who
may be auto enthusiasts, professionals in automotive in-
dustries, or other elds. From the perspective of government
policymakers, we should deliberately cultivate opinion
leaders to become propagandists and leaders of new social
norms. Living in a society and feeling pressure from all
aspects of society all the time, the consumers will comply
with the opinions of mainstream ideas to a certain extent to
maintain their status and interpersonal relationships in
the group.
Our ndings have some limitations that can be
addressed in future research studies. First, the research
object of the model designed in this paper is limited only to
two types of agents. e promotion and diusion of new
energy vehicles involve a more comprehensive range of
objects, such as automobile manufacturers and government
agencies. In addition, to simplify the model, the attribute
settings of the two entity types are relatively small, which
cannot fully reect all the characteristics of their actual
market. Second, we expressed the heterogeneity between
agents by using the characteristics of ABM during initiali-
zation; the characteristics of the whole social relationship
group have yet to be indeed reproduced, such as the strength
of the relationship between various subjects.
Future research may introduce new subjects, such as the
government and automobile manufacturers, into the au-
thenticity and complexity so as to bring them closer to the
actual situation. At the same time, the model could include
more complex network structures, such as dynamic net-
works, and be closer to the Chinese market.
Data Availability
e data used to support the ndings of the study can be
obtained from the corresponding author upon request.
Additional Points
Note: 1. WeChat Ocial Accounts: https://news.pedaily.
cn/202112/483292.shtml. 2. Tencent Technology: https://
Conflicts of Interest
e authors declare that they have no conicts of interest.
is work was supported by the Fundamental Research
Funds for the Central Universities (Grant No. ND2020001)
and the Humanities and Social Sciences Project of the
Ministry of Education (Grant No. 21YJA630088).
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